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No evidence for globally coherent warm and cold periods over the preindustrial Common Era. Nature 571, 550–554 (2019).",{"doi":421},"10.1038\u002Fs41586-019-1401-2",{"id":22,"text":423,"url":22,"identifiers":424},"PAGES 2k Consortium. A global multiproxy database for temperature reconstructions of the Common Era. Sci. Data 4, 170088 (2017).",{"doi":425},"10.1038\u002Fsdata.2017.88",{"id":22,"text":427,"url":22,"identifiers":428},"World Meteorological Organization. Global Climate in 2015-2019. https:\u002F\u002Flibrary.wmo.int\u002Fdoc_num.php?explnum_id=10251 (2020).",{},{"id":22,"text":430,"url":22,"identifiers":431},"IPCC. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. (Cambridge University Press, 2013).",{},{"id":22,"text":433,"url":22,"identifiers":434},"Berger, W. H. & Heath, G. R. Vertical mixing in pelagic sediments. J. Mar. Res. 26, 134–143 (1968).",{},{"id":22,"text":436,"url":22,"identifiers":437},"Glew, J. R., Smol, J. P. & Last, W. M. Sediment Core Collection and Extrusion. In Tracking Environmental Change Using Lake Sediments: Basin Analysis, Coring, and Chronological Techniques (eds. Last, W. M. & Smol, J. P.) 73–105 (Springer Netherlands, 2001).",{"doi":438},"10.1007\u002F0-306-47669-X_5",{"id":22,"text":440,"url":22,"identifiers":441},"Harrison, S. P. et al. Evaluation of CMIP5 palaeo-simulations to improve climate projections. Nat. Clim. Change 5, 735–743 (2015).",{"doi":442},"10.1038\u002Fnclimate2649",{"id":22,"text":444,"url":22,"identifiers":445},"Shakun, J. D. et al. Global warming preceded by increasing carbon dioxide concentrations during the last deglaciation. Nature 484, 49–54 (2012).",{"doi":446},"10.1038\u002Fnature10915",{"id":22,"text":448,"url":22,"identifiers":449},"Snyder, C. W. Evolution of global temperature over the past two million years. Nature 538, 226–228 (2016).",{"doi":450},"10.1038\u002Fnature19798",{"id":22,"text":452,"url":22,"identifiers":453},"Marsicek, J., Shuman, B. N., Bartlein, P. J., Shafer, S. L. & Brewer, S. Reconciling divergent trends and millennial variations in Holocene temperatures. Nature 554, 92 (2018).",{"doi":454},"10.1038\u002Fnature25464",{"id":22,"text":456,"url":22,"identifiers":457},"Birks, H. J. B., Heiri, O., Seppä, H. & Bjune, A. E. Strengths and weaknesses of quantitative climate reconstructions based on late-Quaternary biological proxies. Open Ecol. J. 3, 68–110 (2011).",{"doi":458},"10.2174\u002F1874213001003020068",{"id":22,"text":460,"url":22,"identifiers":461},"Juggins, S. Quantitative reconstructions in palaeolimnology: new paradigm or sick science? Quat. Sci. Rev. 64, 20–32 (2013).",{"doi":462},"10.1016\u002Fj.quascirev.2012.12.014",{"id":22,"text":464,"url":22,"identifiers":465},"Sweeney, J., Salter-Townshend, M., Edwards, T., Buck, C. E.,  Parnell, A. C. Statistical challenges in estimating past climate changes. WIREs Compt. Stat. 10, e1437 (2018).",{"doi":466},"10.1002\u002Fwics.1437",{"id":22,"text":468,"url":22,"identifiers":469},"Heikkilä, M. & Seppä, H. A 11,000 yr palaeotemperature reconstruction from the southern boreal zone in Finland. Quat. Sci. Rev. 22, 541–554 (2003).",{"doi":470},"10.1016\u002FS0277-3791(02)00189-0",{"id":22,"text":472,"url":22,"identifiers":473},"Shanahan, T. M., Hughen, K. A. & Van Mooy, B. A. S. Temperature sensitivity of branched and isoprenoid GDGTs in Arctic lakes. Org. Geochem. 64, 119–128 (2013).",{"doi":474},"10.1016\u002Fj.orggeochem.2013.09.010",{"id":22,"text":476,"url":22,"identifiers":477},"Bakker, P. et al. Temperature trends during the present and last interglacial periods – a multi-model-data comparison. Quat. Sci. Rev. 99, 224–243 (2014).",{"doi":478},"10.1016\u002Fj.quascirev.2014.06.031",{"id":22,"text":480,"url":22,"identifiers":481},"Davis, B. The pollen-climate methods intercomparison project (PC-MIP). Past Glob. Chang. Mag 25, 161–161 (2017).",{"doi":482},"10.22498\u002Fpages.25.3.161",{"id":22,"text":484,"url":22,"identifiers":485},"Poli, P. et al. ERA-20C: An atmospheric reanalysis of the Twentieth Century. J. Clim. 29, 4083–4097 (2016).",{"doi":486},"10.1175\u002FJCLI-D-15-0556.1",{"id":22,"text":488,"url":22,"identifiers":489},"Comboul, M. et al. A probabilistic model of chronological errors in layer-counted climate proxies: applications to annually banded coral archives. Clim. Past 10, 825–841 (2014).",{"doi":490},"10.5194\u002Fcp-10-825-2014",{"id":22,"text":492,"url":22,"identifiers":493},"McKay, N. P., Kaufman, D. S., Routson, C. C., Erb, M. P. & Zander, P. D. The onset and rate of Holocene Neoglacial cooling in the Arctic. Geophys. Res. Lett. 45, 12,487–12,496 (2018).",{"doi":494},"10.1029\u002F2018GL079773",{"id":22,"text":496,"url":22,"identifiers":497},"Sommer, P. S., Davis, B. A. S., Chevalier, M. & Tipton, J. R. Pyleogrid - A probabilistic approach for gridding paleo climate data. In: Sommer, P. S. Software and Numerical Tools for Paleoclimate Analysis. PhD Thesis. University of Lausanne. pp. 83–117, https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.3757356 (2020).",{"doi":498},"10.5281\u002Fzenodo.3757356",{"id":22,"text":500,"url":22,"identifiers":501},"Gelman, A. et al. Bayesian Data Analysis. (Chapman and Hall\u002FCRC, 2013).",{},{"id":22,"text":503,"url":22,"identifiers":504},"Servén, D., Brummitt, C. & Abedi, H. Dswah\u002FPygam: V0.8.0. Zenodo, https:\u002F\u002Fdoi.org\u002F10.5281\u002FZENODO.1476122 (2018).",{"doi":505},"10.5281\u002FZENODO.1476122",{"id":22,"text":507,"url":22,"identifiers":508},"von Storch, H. et al. Reconstructing past climate from noisy data. Science 306, 679–682 (2004).",{"doi":509},"10.1126\u002Fscience.1096109",{"id":22,"text":511,"url":22,"identifiers":512},"Hanhijärvi, S., Tingley, M. P. & Korhola, A. Pairwise comparisons to reconstruct mean temperature in the Arctic Atlantic region over the last 2,000 years. Clim. Dyn. 41, 2039–2060 (2013).",{"doi":513},"10.1007\u002Fs00382-013-1701-4",{"id":22,"text":515,"url":22,"identifiers":516},"Kaufman, D. S. et al. NOAA\u002FWDS Paleoclimatology - Temperature 12k Database. NOAA National Centers for Environmental Information, https:\u002F\u002Fdoi.org\u002F10.25921\u002F4RY2-G808 (2020).",{"doi":517},"10.25921\u002F4RY2-G808",{"id":22,"text":519,"url":22,"identifiers":520},"Kaufman, D. S. et al. Holocene global mean surface temperature: A multi-method reconstruction approach. figshare https:\u002F\u002Fdoi.org\u002F10.6084\u002Fm9.figshare.c.4796823 (2020).",{"doi":521},"10.6084\u002Fm9.figshare.c.4796823",{"id":22,"text":523,"url":22,"identifiers":524},"Kaufman, D.S. et al. NOAA\u002FWDS Paleoclimatology - Holocene global mean surface temperature. NOAA National Centers for Environmental Information, https:\u002F\u002Fdoi.org\u002F10.25921\u002Fvzys-1280 (2020).",{"doi":525},"10.25921\u002Fvzys-1280",{"id":22,"text":527,"url":22,"identifiers":528},"Routson, C., McKay, N., Sommer, P., Dätwyler, C. & Erb, M. Temperature12k: Analysis and plotting code for Temp12k analysis paper (Version 1.2.0). Zenodo, https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.3888590 (2020).",{"doi":529},"10.5281\u002Fzenodo.3888590",false,{"id":532,"createTime":533,"updateTime":534,"relativeEntities":535,"slug":536,"properties":537,"entityType":151,"verifyStatus":152,"verifyTime":554,"verifyNote":153,"languages":555,"translateLanguages":22,"viewCount":23,"primaryUrl":556,"fullTextUrl":22,"authors":557,"publicationType":308,"publisherRelationship":722,"citationCount":789,"citationInfo":790,"publishDate":22,"publishYear":22,"citationAnalyzeStatus":799,"lastCitationAnalyze":534,"indexDatabases":800,"openAccess":22,"references":801,"isForceReanalyzing":530},"f9d3abd3-b3df-4980-815f-b9893dc4393e","2024-12-18T08:02:07.700+00:00","2026-07-20T19:47:20.424+00:00",[],"A-lake-data-set-for-the-Tibetan-Plateau-from-the-1960s-2005-and-2014",{"mag":538,"gsPaper":540,"pmc":542,"openalex":544,"abstract":546,"title":548,"pm":550,"doi":552},{"VOID":539},"2464532968",{"VOID":541},"[]",{"VOID":543},"4915272",{"VOID":545},"W2464532968",{"EN":547},"\u003Cjats:title>Abstract\u003C\u002Fjats:title>\u003Cjats:p>Long-term datasets of number and size of lakes over the Tibetan Plateau (TP) are among the most critical components for better understanding the interactions among the cryosphere, hydrosphere, and atmosphere at regional and global scales. Due to the harsh environment and the scarcity of data over the TP, data accumulation and sharing become more valuable for scientists worldwide to make new discoveries in this region. This paper, for the first time, presents a comprehensive and freely available data set of lakes’ status (name, location, shape, area, perimeter, etc.) over the TP region dating back to the 1960s, including three time series, i.e., the 1960s, 2005, and 2014, derived from ground survey (the 1960s) or high-spatial-resolution satellite images from the China-Brazil Earth Resources Satellite (CBERS) (2005) and China’s newly launched GaoFen-1 (GF-1, which means high-resolution images in Chinese) satellite (2014). The data set could provide scientists with useful information for revealing environmental changes and mechanisms over the TP region.\u003C\u002Fjats:p>\u003Cjats:p>\u003Cjats:table-wrap>\u003Cjats:table>\u003Cjats:tbody>\n                      \u003Cjats:tr>\n                        \u003Cjats:td>Design Type(s)\u003C\u002Fjats:td>\n                        \u003Cjats:td>time series design • observation design • data integration objective\u003C\u002Fjats:td>\n                      \u003C\u002Fjats:tr>\n                      \u003Cjats:tr>\n                        \u003Cjats:td>Measurement Type(s)\u003C\u002Fjats:td>\n                        \u003Cjats:td>lake topography\u003C\u002Fjats:td>\n                      \u003C\u002Fjats:tr>\n                      \u003Cjats:tr>\n                        \u003Cjats:td>Technology Type(s)\u003C\u002Fjats:td>\n                        \u003Cjats:td>remote sensing\u003C\u002Fjats:td>\n                      \u003C\u002Fjats:tr>\n                      \u003Cjats:tr>\n                        \u003Cjats:td>Factor Type(s)\u003C\u002Fjats:td>\n                        \u003Cjats:td\u002F>\n                      \u003C\u002Fjats:tr>\n                      \u003Cjats:tr>\n                        \u003Cjats:td>Sample Characteristic(s)\u003C\u002Fjats:td>\n                        \u003Cjats:td>Tibetan Plateau • Qaidam Basin • Amu Darya • Brahmaputra River • River Ganges • Hexi District • Indus River • Mekong River • Salween River • Tarim Basin • Yangtze River • Yellow River • endorheic lake • exorheic lake\u003C\u002Fjats:td>\n                      \u003C\u002Fjats:tr>\n                    \u003C\u002Fjats:tbody>\u003C\u002Fjats:table>\u003C\u002Fjats:table-wrap>\u003C\u002Fjats:p>\u003Cjats:p>Machine-accessible metadata file describing the reported data (ISA-Tab format)\u003C\u002Fjats:p>",{"EN":549},"A lake data set for the Tibetan Plateau from the 1960s, 2005, and 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The Third Pole. Nature 454, 393–396 (2008).",{"doi":805},"10.1038\u002F454393a",{"id":22,"text":807,"url":22,"identifiers":808},"Yao, T. et al. Third Pole Environment (TPE). Environmental Development 3, 52–64 (2012).",{"doi":809},"10.1016\u002Fj.envdev.2012.04.002",{"id":22,"text":811,"url":22,"identifiers":812},"Yao, T. et al. Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings. Nature Clim. Change 2, 663–667 (2012).",{"doi":813},"10.1038\u002Fnclimate1580",{"id":22,"text":815,"url":22,"identifiers":816},"Cheng, G. & Wu, T. Responses of permafrost to climate change and their environmental significance, Qinghai-Tibet Plateau. Journal of Geophysical Research: Earth Surface 112, F02S03 (2007).",{},{"id":22,"text":818,"url":22,"identifiers":819},"Zhang, G., Xie, H., Yao, T., Liang, T. & Kang, S. Snow cover dynamics of four lake basins over Tibetan Plateau using time series MODIS data (2001–2010). Water Resources Research 48, W10529 (2012).",{"doi":820},"10.1029\u002F2011WR011396",{"id":22,"text":822,"url":22,"identifiers":823},"Korup, O. & Montgomery, D. R. Tibetan plateau river incision inhibited by glacial stabilization of the Tsangpo gorge. Nature 455, 786–789 (2008).",{"doi":824},"10.1038\u002Fnature07322",{"id":22,"text":826,"url":22,"identifiers":827},"Cui, M. et al. Warmer temperature accelerates methane emissions from the Zoige wetland on the Tibetan Plateau without changing methanogenic community composition. Scientific Reports 5, 11616 (2015).",{"doi":828},"10.1038\u002Fsrep11616",{"id":22,"text":830,"url":22,"identifiers":831},"Yang, X. & Lu, X. Drastic change in China’s lakes and reservoirs over the past decades. Scientific Reports 4, 6041 (2014).",{"doi":832},"10.1038\u002Fsrep06041",{"id":22,"text":834,"url":22,"identifiers":835},"Liu, J., Wang, S., Yu, S., Yang, D. & Zhang, L. Climate warming and growth of high-elevation inland lakes on the Tibetan Plateau. Global and Planetary Change 67, 209–217 (2009).",{"doi":836},"10.1016\u002Fj.gloplacha.2009.03.010",{"id":22,"text":838,"url":22,"identifiers":839},"Phan, V. H., Lindenbergh, R. & Menenti, M. ICESat derived elevation changes of Tibetan lakes between 2003 and 2009. International Journal of Applied Earth Observation and Geoinformation 17, 12–22 (2012).",{"doi":840},"10.1016\u002Fj.jag.2011.09.015",{"id":22,"text":842,"url":22,"identifiers":843},"Song, C., Huang, B., Ke, L. & Richards, K. S. Seasonal and abrupt changes in the water level of closed lakes on the Tibetan Plateau and implications for climate impacts. Journal of Hydrology 514, 131–144 (2014).",{"doi":844},"10.1016\u002Fj.jhydrol.2014.04.018",{"id":22,"text":846,"url":22,"identifiers":847},"Zhang, G., Xie, H., Kang, S., Yi, D. & Ackley, S. F. Monitoring lake level changes on the Tibetan Plateau using ICESat altimetry data (2003–2009). Remote Sensing of Environment 115, 1733–1742 (2011).",{"doi":848},"10.1016\u002Fj.rse.2011.03.005",{"id":22,"text":850,"url":22,"identifiers":851},"Zhang, G., Yao, T., Xie, H., Zhang, K. & Zhu, F. Lakes’ state and abundance across the Tibetan Plateau. Chinese Science Bulletin 59, 3010–3021 (2014).",{"doi":852},"10.1007\u002Fs11434-014-0258-x",{"id":22,"text":854,"url":22,"identifiers":855},"Xu, H., Hou, Z., An, Z., Liu, X. & Dong, J. Major ion chemistry of waters in Lake Qinghai catchments, NE Qinghai-Tibet plateau, China. Quaternary International 212, 35–43 (2010).",{"doi":856},"10.1016\u002Fj.quaint.2008.11.001",{"id":22,"text":858,"url":22,"identifiers":859},"Wang, M., Hou, J. & Lei, Y. Classification of Tibetan lakes based on variations in seasonal lake water temperature. Chinese Science Bulletin 59, 4847–4855 (2014).",{"doi":860},"10.1007\u002Fs11434-014-0588-8",{"id":22,"text":862,"url":22,"identifiers":863},"Lei, Y. et al. Coherent lake growth on the central Tibetan Plateau since the 1970s: Characterization and attribution. Journal of Hydrology 483, 61–67 (2013).",{"doi":864},"10.1016\u002Fj.jhydrol.2013.01.003",{"id":22,"text":866,"url":22,"identifiers":867},"Li, X., Wang, L., Chen, D., Yang, K. & Wang, A. Seasonal evapotranspiration changes (1983–2006) of four large basins on the Tibetan Plateau. Journal of Geophysical Research: Atmospheres 119 (13): 079–013,095 (2014).",{},{"id":22,"text":869,"url":22,"identifiers":870},"Song, C., Huang, B. & Ke, L. Modeling and analysis of lake water storage changes on the Tibetan Plateau using multi-mission satellite data. Remote Sensing of Environment 135, 25–35 (2013).",{"doi":871},"10.1016\u002Fj.rse.2013.03.013",{"id":22,"text":873,"url":22,"identifiers":874},"Feng, L. et al. Assessment of inundation changes of Poyang Lake using MODIS observations between 2000 and 2010. Remote Sensing of Environment 121, 80–92 (2012).",{"doi":875},"10.1016\u002Fj.rse.2012.01.014",{"id":22,"text":877,"url":22,"identifiers":878},"Verpoorter, C., Kutser, T. & Tranvik, L. Automated mapping of water bodies using Landsat multispectral data. Limnology and Oceanography: Methods 10, 1037–1050 (2012).",{},{"id":22,"text":880,"url":22,"identifiers":881},"Feyisa, G. L., Meilby, H., Fensholt, R. & Proud, S. R. Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment 140, 23–35 (2014).",{"doi":882},"10.1016\u002Fj.rse.2013.08.029",{"id":22,"text":884,"url":22,"identifiers":885},"Ma, R. et al. A half-century of changes in China's lakes: Global warming or human influence? Geophysical Research Letters 37, L24106 (2010).",{"doi":886},"10.1029\u002F2010GL045514",{"id":22,"text":888,"url":22,"identifiers":889},"Lehner, B. & Döll, P. Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology 296, 1–22 (2004).",{"doi":890},"10.1016\u002Fj.jhydrol.2004.03.028",{"id":22,"text":892,"url":22,"identifiers":893},"Verpoorter, C., Kutser, T., Seekell, D. A. & Tranvik, L. J. A global inventory of lakes based on high-resolution satellite imagery. Geophysical Research Letters 41, 6396–6402 (2014).",{"doi":894},"10.1002\u002F2014GL060641",{"id":22,"text":896,"url":22,"identifiers":897},"Sharma, S. et al. A global database of lake surface temperatures collected by in situ and satellite methods from 1985–2009. Scientific Data 2, 150008 (2015).",{"doi":898},"10.1038\u002Fsdata.2015.8",{"id":22,"text":900,"url":22,"identifiers":901},"Song, C., Huang, B., Ke, L. & Richards, K. S. Remote sensing of alpine lake water environment changes on the Tibetan Plateau and surroundings: A review. ISPRS Journal of Photogrammetry and Remote Sensing 92, 26–37 (2014).",{"doi":902},"10.1016\u002Fj.isprsjprs.2014.03.001",{"id":22,"text":904,"url":22,"identifiers":905},"Wang, S. & Dou, H . Chinese Lake Catalogue (Science Press, 1989).",{},{"id":22,"text":907,"url":22,"identifiers":908},"Ma, R. et al. China's lakes at present: Number, area and spatial distribution. Science China Earth Sciences 41, 394–401 (2010).",{},{"id":22,"text":910,"url":22,"identifiers":911},"Downing, J. A. et al. The global abundance and size distribution of lakes, ponds, and impoundments. Limnology and Oceanography 51, 2388–2397 (2006).",{"doi":912},"10.4319\u002Flo.2006.51.5.2388",{"id":22,"text":914,"url":22,"identifiers":915},"Seekell, D. A., Pace, M. L., Tranvik, L. J. & Verpoorter, C. A fractal-based approach to lake size-distributions. Geophysical Research Letters 40, 517–521 (2013).",{"doi":916},"10.1002\u002Fgrl.50139",{"id":22,"text":918,"url":22,"identifiers":919},"Balsamo, G. et al. On the contribution of lakes in predicting near-surface temperature in a global weather forecasting model. Tellus A 64, 1–12 (2012).",{"doi":920},"10.3402\u002Ftellusa.v64i0.15829",{"id":22,"text":922,"url":22,"identifiers":923},"Downing, J. A. et al. Emerging global role of small lakes and ponds: little things mean a lot. Limnetica 29, 9–24 (2010).",{"doi":924},"10.23818\u002Flimn.29.02",{"id":22,"text":926,"url":22,"identifiers":927},"Code for China Lake Name. in China Industrial Standard SL261-98 (China Water Power Press, 1998).",{},{"id":22,"text":929,"url":22,"identifiers":930},"Zhang, Y., Wan, Y., Wang, B., Kang, Y. & Xiong, J. Automatic processing of Chinese GF-1 wide field of View images. in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (36th International Symposium on Remote Sensing of Environment) Vol. XL-7\u002FW3. (Copernicus Publications, Berlin, Germany, 2015).",{},{"id":22,"text":932,"url":22,"identifiers":933},"Zeng, C., Shen, H. & Zhang, L. Recovering missing pixels for Landsat ETM+SLC-off imagery using multi-temporal regression analysis and a regularization method. Remote Sensing of Environment 131, 182–194 (2013).",{"doi":934},"10.1016\u002Fj.rse.2012.12.012",{"id":22,"text":936,"url":22,"identifiers":937},"Zhang, G., Yao, T., Xie, H., Wang, W. & Yang, W. An inventory of glacial lakes in the Third Pole region and their changes in response to global warming. Global and Planetary Change 131, 148–157 (2015).",{"doi":938},"10.1016\u002Fj.gloplacha.2015.05.013",{"id":22,"text":940,"url":22,"identifiers":941},"Wan, W. et al. Monitoring lake changes of Qinghai-Tibetan Plateau over the past 30 years using satellite remote sensing data. Chinese Science Bulletin 59, 1021–1035 (2014).",{"doi":942},"10.1007\u002Fs11434-014-0128-6",{"id":22,"text":944,"url":22,"identifiers":945},"Yao, X. et al. Spatial-temporal variations of lake area in Hoh Xil region in the past 40 years. Acta Geographica Sinica 68, 886–896 (2013).",{},{"id":22,"text":947,"url":22,"identifiers":948},"Li, J., Sheng, Y., Luo, J. & Shen, Z. Remotely sensed mapping of inland lake area changes in the Tibetan Plateau. Journal of Lake Sciences 23, 311–320 (2011).",{"doi":949},"10.18307\u002F2011.0419",{"id":22,"text":951,"url":22,"identifiers":952},"Wang, X. et al. Water-level changes in China's large lakes determined from ICESat\u002FGLAS data. Remote Sensing of Environment 132, 131–144 (2013).",{"doi":953},"10.1016\u002Fj.rse.2013.01.005",{"id":22,"text":955,"url":22,"identifiers":956},"Jacob, T., Wahr, J., Pfeffer, W. T. & Swenson, S. Recent contributions of glaciers and ice caps to sea level rise. Nature 482, 514–518 (2012).",{"doi":957},"10.1038\u002Fnature10847",{"id":22,"text":959,"url":22,"identifiers":960},"Tapley, B. D., Bettadpur, S., Watkins, M. & Reigber, C. The gravity recovery and climate experiment: Mission overview and early results. Geophysical Research Letters 31, 1–4 (2004).",{"doi":961},"10.1029\u002F2004GL019920",{"id":22,"text":963,"url":22,"identifiers":964},"Wan, W. 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Carbon release from submarine seeps at the Costa Rica fore arc: implications for the volatile cycle at the Central America convergent margin. Geochem., Geophys, Geosyst, 11 Q04S21 (2010).",{"doi":2643},"10.1029\u002F2009GC002810",{"id":22,"text":2645,"url":22,"identifiers":2646},"Schwarzenbach, E. M., Früh-Green, G. L., Bernasconi, S. M., Alt, J. C. & Plas, A. Serpentinization and carbon sequestration: A study of two ancient peridotite-hosted hydrothermal systems. Chemical Geology 351, 115–133 (2013).",{"doi":2647},"10.1016\u002Fj.chemgeo.2013.05.016",{"id":22,"text":2649,"url":22,"identifiers":2650},"McCollom, T. M. & Seewald, J. S. Serpentinites, hydrogen, and life. Elements 9, 129–134 (2013).",{"doi":2651},"10.2113\u002Fgselements.9.2.129",{"id":22,"text":2653,"url":22,"identifiers":2654},"Hilton, D. R., Fischer, T. P. & Marty, B. Noble gases and volatile recycling at subduction zones. Reviews in mineralogy and geochemistry 47, 319–370 (2002).",{"doi":2655},"10.2138\u002Frmg.2002.47.9",{"id":22,"text":2657,"url":22,"identifiers":2658},"Kulongoski, J. T. & Hilton, D. R. A quadrupole‐based mass spectrometric system for the determination of noble gas abundances in fluids. Geochemistry, Geophysics, Geosystems 3, 1–10 (2002).",{"doi":2659},"10.1029\u002F2001GC000267",{"id":22,"text":2661,"url":22,"identifiers":2662},"Füri, E. et al. Apparent decoupling of the He and Ne isotope systematics of the Icelandic mantle: the role of He depletion, melt mixing, degassing fractionation and air interaction. Geochim. Cosmochim. Acta 74, 3307–3332 (2010).",{"doi":2663},"10.1016\u002Fj.gca.2010.03.023",{"id":22,"text":2665,"url":22,"identifiers":2666},"Barry, P. H., Hilton, D. R., Fischer, T. P., De Moor, J. M., Mangasini, F. & Ramirez, C. Helium and carbon isotope systematics of cold “mazuku” CO2 vents and hydrothermal gases and fluids from Rungwe Volcanic Province, southern Tanzania. 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Geol. 127, 269–295 (1996)..",{"doi":2702},"10.1016\u002F0009-2541(95)00134-4",{"id":2704,"createTime":2705,"updateTime":2705,"relativeEntities":2706,"slug":2707,"properties":2708,"entityType":151,"verifyStatus":152,"verifyTime":2705,"verifyNote":153,"languages":2721,"translateLanguages":22,"viewCount":23,"primaryUrl":2722,"fullTextUrl":22,"authors":2723,"publicationType":308,"publisherRelationship":2743,"citationCount":2809,"citationInfo":2810,"publishDate":22,"publishYear":22,"citationAnalyzeStatus":21,"lastCitationAnalyze":22,"indexDatabases":2812,"openAccess":22,"references":2813,"isForceReanalyzing":530},"a10ddab5-fc5a-4e0b-9506-333dc25f0df5","2025-01-24T15:29:57.634+00:00",[],"Global-scale-phylogenetic-linguistic-inference-from-lexical-resources",{"openalex":2709,"mag":2711,"abstract":2713,"title":2715,"pm":2717,"doi":2719},{"VOID":2710},"W2788886867",{"VOID":2712},"2788886867",{"EN":2714},"\u003Cjats:title>Abstract\u003C\u002Fjats:title>\u003Cjats:p>Automatic phylogenetic inference plays an increasingly important role in computational historical linguistics. Most pertinent work is currently based on\u003Cjats:italic>expert cognate judgments\u003C\u002Fjats:italic>. This limits the scope of this approach to a small number of well-studied language families. We used machine learning techniques to compile data suitable for phylogenetic inference from the ASJP database, a collection of almost 7,000 phonetically transcribed word lists over 40 concepts, covering two thirds of the extant world-wide linguistic diversity. First, we estimated\u003Cjats:italic>Pointwise Mutual Information\u003C\u002Fjats:italic>scores between sound classes using weighted sequence alignment and general-purpose optimization. From this we computed a dissimilarity matrix over all ASJP word lists. This matrix is suitable for\u003Cjats:italic>distance-based\u003C\u002Fjats:italic>phylogenetic inference. Second, we applied\u003Cjats:italic>cognate clustering\u003C\u002Fjats:italic>to the ASJP data, using supervised training of an SVM classifier on expert cognacy judgments. Third, we defined two types of binary\u003Cjats:italic>characters\u003C\u002Fjats:italic>, based on automatically inferred cognate classes and on sound-class occurrences. Several tests are reported demonstrating the suitability of these characters for\u003Cjats:italic>character-based\u003C\u002Fjats:italic>phylogenetic inference.\u003C\u002Fjats:p>",{"EN":2716},"Global-scale phylogenetic linguistic inference from lexical resources",{"VOID":2718},"30299438",{"VOID":2720},"10.1038\u002Fsdata.2018.189",[155],"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fsdata2018189",[2724],{"id":2725,"sortIndex":23,"researcher":22,"roles":2726,"affiliations":2727,"properties":2736,"displayName":2740,"givenName":22,"familyName":22},"6097680b-6776-416c-a860-2c013c79a199",[],[2728],{"id":2729,"sortIndex":23,"affiliation":2730,"properties":22},"447810d1-72b8-4bd7-b5d6-f3c7065dc1ae",{"id":2729,"createTime":22,"updateTime":22,"relativeEntities":2731,"slug":22,"properties":2732,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":2735,"statistic":22},[],{"title":2733},{"EN":2734},"Tübingen University, Institute of Linguistics, Wilhelmstr. 19, Tübingen, 72074, Germany",[],{"orcid":2737,"title":2739,"openalex":2741},{"VOID":2738},"https:\u002F\u002Forcid.org\u002F0000-0002-9642-9359",{"EN":2740},"Gerhard Jäger",{"VOID":2742},"A5019358632",{"url":22,"publisher":2744,"properties":2806},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":2745,"slug":10,"properties":2746,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":2750,"manageAffiliations":2775,"indexDatabases":2786,"url":117,"thumbnailPath":22,"statistic":2801,"gsStatistic":22,"type":22,"analyzePriority":22},[],{"issn":2747,"title":2748,"country":2749},{"VOID":15},{"EN":17},{"VOID":13},[2751,2755,2759,2763,2767,2771],{"id":51,"createTime":22,"updateTime":22,"relativeEntities":2752,"label":2753,"description":2754,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":54},{},{"id":26,"createTime":22,"updateTime":22,"relativeEntities":2756,"label":2757,"description":2758,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":29,"VI":30},{},{"id":57,"createTime":22,"updateTime":22,"relativeEntities":2760,"label":2761,"description":2762,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":60},{},{"id":33,"createTime":22,"updateTime":22,"relativeEntities":2764,"label":2765,"description":2766,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":36},{},{"id":39,"createTime":22,"updateTime":22,"relativeEntities":2768,"label":2769,"description":2770,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":42},{},{"id":45,"createTime":22,"updateTime":22,"relativeEntities":2772,"label":2773,"description":2774,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":48},{},[2776,2781],{"id":64,"createTime":22,"updateTime":22,"relativeEntities":2777,"slug":22,"properties":2778,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":2780,"statistic":22},[],{"title":2779},{"EN":68},[],{"id":71,"createTime":22,"updateTime":22,"relativeEntities":2782,"slug":22,"properties":2783,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":2785,"statistic":22},[],{"title":2784},{"EN":75},[],[2787,2794],{"id":79,"indexDatabase":2788,"url":90,"indexYears":91,"academicFieldIds":2793,"indexDatabaseRanking":99},{"id":81,"createTime":22,"updateTime":22,"relativeEntities":2789,"label":2790,"description":2791,"key":87,"publicationTags":2792,"standard":22},[],{"EN":84,"VI":84},{"EN":84,"VI":86},[89],[93,94,95,96,97,98],{"id":101,"indexDatabase":2795,"url":114,"indexYears":22,"academicFieldIds":2800,"indexDatabaseRanking":22},{"id":103,"createTime":22,"updateTime":22,"relativeEntities":2796,"label":2797,"description":2798,"key":110,"publicationTags":2799,"standard":22},[],{"EN":106,"VI":106},{"EN":108,"VI":109},[112,113],[116],{"impactFactor":23,"impactFactorByYear":2802,"i10Index":120,"i10IndexLast5Year":23,"totalPublication":120,"totalPublicationByYear":2803,"totalCitation":122,"totalCitationByYear":2804,"totalCitationPerPublication":122,"totalCitationPerPublicationByYear":2805,"hindexLast5Year":120,"hindex":120},{},{},{},{},{"issue":2807,"volume":2808},{"VOID":374},{"VOID":1211},34,{"total":2809,"publishYear":22,"statisticByYear":2811},{"2018":120,"2019":234,"2020":274,"2021":792,"2022":198,"2023":274,"2024":216},[],[2814,2818,2822,2826,2830,2834,2838,2842,2846,2850,2854,2857,2861,2865,2869,2873,2877,2880,2884,2888,2891,2895,2899,2903,2907,2910,2913,2917,2920,2923,2927,2931,2934,2938,2942,2946,2950,2954,2958,2961,2965,2969,2972,2976,2980,2984,2988,2992,2995,2998,3002,3006,3009,3012,3016,3019,3023],{"id":22,"text":2815,"url":22,"identifiers":2816},"Atkinson, Q. 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Science 309, 2072–2075 (2005).",{"doi":2829},"10.1126\u002Fscience.1114615",{"id":22,"text":2831,"url":22,"identifiers":2832},"Pagel, M., Atkinson, Q. D. & Meade, A. Frequency of word-use predicts rates of lexical evolution throughout Indo-European history. Nature 449, 717–720 (2007).",{"doi":2833},"10.1038\u002Fnature06176",{"id":22,"text":2835,"url":22,"identifiers":2836},"Brown, C. H., Holman, E. W., Wichmann, S. & Velupillai, V. Automated classification of the world’s languages: A description of the method and preliminary results. STUF — Language Typology and Universals 4, 285–308 (2008).",{"doi":2837},"10.1524\u002Fstuf.2008.0026",{"id":22,"text":2839,"url":22,"identifiers":2840},"Gray, R. D., Drummond, A. J. & Greenhill, S. J. Language phylogenies reveal expansion pulses and pauses in Pacific settlement. Science 323, 479–483 (2009).",{"doi":2841},"10.1126\u002Fscience.1166858",{"id":22,"text":2843,"url":22,"identifiers":2844},"Dunn, M., Greenhill, S. J., Levinson, S. & Gray, R. 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The spatial area represented by pixel SST estimates is between 1 km\u003Cjats:sup>2\u003C\u002Fjats:sup> and 45 km\u003Cjats:sup>2\u003C\u002Fjats:sup>. The mean density of good-quality observations is 13 km\u003Cjats:sup>−2\u003C\u002Fjats:sup> yr\u003Cjats:sup>−1\u003C\u002Fjats:sup>. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr\u003Cjats:sup>−1\u003C\u002Fjats:sup> of zero with high confidence, despite maximal independence from \u003Cjats:italic>in situ\u003C\u002Fjats:italic> SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. 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