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Record W2308324630 · doi:10.1021/acs.est.5b03066

Vanishing High Mountain Glacial Archives: Challenges and Perspectives

2015· article· en· W2308324630 on OpenAlexaffabout
Qianggong Zhang, Shichang Kang, Paolo Gabrielli, Mark Loewen, Margit Schwikowski

Bibliographic record

VenueEnvironmental Science & Technology · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Manitoba
FundersNational Natural Science Foundation of ChinaChinese Academy of SciencesNational Science Foundation
KeywordsChinaChinese academy of sciencesBeijingLibrary scienceGlacierGeographyGlacial periodPhysical geographyArchaeologyGeologyComputer science

Abstract

fetched live from OpenAlex

ADVERTISEMENT RETURN TO ISSUEPREVViewpointNEXTVanishing High Mountain Glacial Archives: Challenges and PerspectivesQianggong Zhang*†‡∇, Shichang Kang*‡§, Paolo Gabrielli∥⊥, Mark Loewen#, and Margit Schwikowski∇View Author Information† Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, P.R. China‡ CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, P.R. China§ State Key Laboratory of Cryospheric Sciences, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, 730000, P.R. China∥ School of Earth Sciences, 275 Mendenhall Laboratory, The Ohio State University, 125 South Oval Mall, Columbus, Ohio 43210, United States⊥ Byrd Polar and Climate Research Center, The Ohio State University, 108 Scott Hall, 1090 Carmack Road, Columbus, Ohio 43210-1002, United States# Department of Chemistry, University of Manitoba, Winnipeg, MB R3T 2N2, Canada∇ PSI, Paul Scherrer Institute, CH-5232 Villigen PSI, Switzerland*E-mail: [email protected]*E-mail: [email protected]Cite this: Environ. Sci. Technol. 2015, 49, 16, 9499–9500Publication Date (Web):July 31, 2015Publication History Received30 June 2015Published online31 July 2015Published inissue 18 August 2015https://doi.org/10.1021/acs.est.5b03066Copyright © 2015 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views1170Altmetric-Citations15LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (802 KB) Get e-AlertsSUBJECTS:Ablation,Atmospheric chemistry,Deposition,Ice,Impurities Get e-Alerts

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)0.012
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.051
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0040.005
Scholarly communication0.0130.023
Open science0.0040.010
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.0510.012

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 head0.023
GPT teacher head0.203
Teacher spread0.180 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations23
Published2015
Admission routes2
Has abstractyes

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