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Record W3083340410 · doi:10.1175/bams-d-20-0104.1

Global Climate

2020· article· en· W3083340410 on OpenAlexaff
Melanie Ades, R. Adler, Rob Allan, Richard P. Allan, J. Anderson, Anthony Argüez, C. Arosio, John Augustine, C. Azorin-Molina, Jonathan Barichivich, J. R. Barnes, H Beck, Andreas Becker, Nicolas Bellouin, Angela Benedetti, David I. Berry, Stephen Blenkinsop, Olivier Bock, Michael G. Bosilovich, Oliviér Boucher, Stefan A. Buehler, Laura Carrea, Hanne H. Christiansen, Fernando Chouza, John R. Christy, Eui‐Seok Chung, Melanie Coldewey‐Egbers, Gil P. Compo, Owen R. Cooper, Curt Covey, Andrew M. Crotwell, Sean Davis, Elvira de Eyto, Richard de Jeu, B.V. VanderSat, Curtis L. DeGasperi, D. A. Degenstein, Larry Di Girolamo, Martin T. Dokulil, Markus G. Donat, Wouter Dorigo, Imke Durre, G. S. Dutton, Grégory Duveiller, James W. Elkins, Vitali Fioletov, Johannes Flemming, Michael J. Foster, R. Frey, S. M. Frith, Lucien Froidevaux, J. Garforth, S. K. Gupta, Leopold Haimberger, B. D. Hall, Ian Harris, Andrew K. Heidinger, D. L. Hemming, Shu‐peng Ho, Daan Hubert, D. F. Hurst, Imke Hüser, Antje Inness, K. Isaksen, Viju O. John, P. D. Jones, J. W. Kaiser, Sean D. Kelly, Sergey Khaykin, R. Kidd, Hyungiun Kim, Zak Kipling, Benjamin M. Kraemer, D. P. Kratz, R. S. La Fuente, Xin Lan, Kathleen Lantz, Thierry Leblanc, Bailing Li, Norman G. Loeb, Craig S. Long, Diego Loyola, Włodzimierz Marszelewski, B. Martens, Linda May, Michael Mayer, Matthew F. McCabe, Tim R. McVicar, C. A. Mears, W. Paul Menzel, Christopher J. Merchant, B. R. Miller, Diego G. Miralles, S. A. Montzka, Colin Morice, Jens Mühle, R. Myneni, Julien P. Nicolas, Jeannette Noetzli, Timothy J. Osborn, Taejin Park, Adam Pasik, Andrew M. Paterson, Mauri Pelto, Sarah Perkins‐Kirkpatrick, Gabrielle Pétron, C. Phillips, Bernard Pinty, Stephen Po–Chedley, Lorenzo M. Polvani, W. Preimesberger, Merja Pulkkanen, William J. Randel, Samuel Rémy, Lucrezia Ricciardulli, Andrew D. Richardson, Landon Rieger, David A. Robinson, Matthew Rodell, Karen H. Rosenlof, Chris Roth, A. Rozanov, James A. Rusak, Оlga O. Rusanovskaya, This Rutishauser, Ahira Sánchez-Lugo, P. Sawaengphokhai, T. Scanlon, Verena Schenzinger, S. Geoffey Schladow, Robert W. Schlegel, Martin Eawag Schmid, H. B. Selkirk, Sapna Sharma, Lei Shi, Svetlana V. Shimaraeva, Eugene A. Silow, A. J. Simmons, Catherine A. Smith, Sharon L. Smith, Brian J. Soden, Viktoria Sofieva, T. H. Sparks, Paul W. Stackhouse, Wolfgang Steinbrecht, D. A. Streletskiy, G. Taha, Hagen Telg, Stephen J. Thackeray, Maxim Timofeyev, Kleareti Tourpali, Mari R. Tye, Ronald van der A, VanderSat B.V. van der Schalie Robin, Gerard van der SchrierW. Paul, Guido R. van der Werf, Piet Verburg, Jean‐Paul Vernier, Holger Vömel, Russell S. Vose, Ray H. J. Wang, Shohei Watanabe, Mark Weber, Gesa A. Weyhenmeyer, D. N. Wiese, Anne C. Wilber, Jeanette D. Wild, Takmeng Wong, R. Iestyn Woolway, Xungang Yin, Lin Zhao, Guanguo Zhao, Xinjia Zhou, J. R. Ziemke, Markus Ziese

Bibliographic record

VenueBulletin of the American Meteorological Society · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsMinistry of the Environment, Conservation and ParksUniversity of SaskatchewanNatural Resources CanadaYork UniversityGeological Survey of CanadaEnvironment and Climate Change Canada
FundersLaboratory Directed Research and DevelopmentLawrence Livermore National LaboratoryU.S. Department of Energy
KeywordsMeteorologyClimatologyEnvironmental scienceGlobal climateGeographyClimate changeGeologyOceanography

Abstract

fetched live from OpenAlex

Albedo is the fraction of shortwave radiation reflected by the surface and depends on a number of surface attributes such as snow cover, vegetation cover, and soil moisture among others. The year 2019 exhibited large regional contrasts in terms of land surface albedo in the visible part of the spectrum (see Plate 2.1ac), with the NH overall darker and the SH brighter that the baseline period 2003–10. Strong negative anomalies in visible albedo (on the order of −30%) were especially noticeable in central and eastern Europe. Similarly, high negative values were observed in the Caucasus and the Middle East; in eastern Asia (western China and Mongolia, Korea, and Japan); northwestern America (Alaska, Yukon, and Northwest Territories); eastern United States; and parts of Argentina and Chile.Large positive anomalies (up to +30%) were found in North America, in the Himalayas and eastern China, in eastern Australia, southern Africa, and sporadically in southern America.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.082
Threshold uncertainty score0.273

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0820.069

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.303
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations109
Published2020
Admission routes1
Has abstractyes

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Same venueBulletin of the American Meteorological Society→Same topicArctic and Russian Policy Studies→French-language works237,207→