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Record W2909381086 · doi:10.5194/tc-2018-259

Brief communication: Supraglacial debris-cover changesin the Caucasus Mountains

2019· article· en· W2909381086 on OpenAlexaff
Levan Tielidze, Tobias Bolch, Roger Wheate, Stanislav Kutuzov, Ivan Lavrentiev, Michael Zemp

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Northern British Columbia
FundersRussian Science FoundationShota Rustaveli National Science FoundationNational Science Foundation
KeywordsDebrisGlacierGeologyGlacier mass balanceRock glacierPhysical geographyMoraineGlacier ice accumulationElevation (ballistics)Glacier morphologyGeomorphologyTidewater glacier cycleLithologyIce streamClimatologyCryosphereGeographyGeochemistryIce calvingOceanography

Abstract

fetched live from OpenAlex

Abstract. Debris cover on glaciers can significantly alter melt, and hence, glacier mass balance and runoff. Debris coverage typically increases with shrinking glaciers. Here, we present data on debris cover and its changes for 559 glaciers located in different regions of the Greater Caucasus mountains based on 1986, 2000 and 2014 Landsat and SPOT images. Over this time period, the total glacier area decreased from 691.5 km2 to 590.0 km2 (0.52 % yr−1). Thereby, the debris covered area increased from ~ 11 to ~ 24 % on the northern, and from ~ 4 to 10 % on the southern macro-slope between 1986 and 2014. Overall, we found 18 % debris cover for the year 2014. With the glacier shrinkage, debris-covered area and the number of debris-covered glaciers increased as a function of elevation, slope, aspect, glacier morphological type, Little Ice Age moraines, and lithology.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.219
Teacher spread0.199 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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