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Record W2147356690 · doi:10.14430/arctic4356

Contemporary Glacier Processes and Global Change: Recent Observations from Kaskawulsh Glacier and the Donjek Range, St. Elias Mountains

2014· article· en· W2147356690 on OpenAlexvenueno aff
Gwenn E. Flowers, Luke Copland, Christian Schoof

Bibliographic record

VenueARCTIC · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsGlacierGlacier morphologyGlacier mass balanceGeologyPhysical geographyCryosphereTidewater glacier cycleClimate changeClimatologyGeographyGeomorphologyIce streamOceanographySea ice

Abstract

fetched live from OpenAlex

With an extensive ice cover and rich display of glacier behaviour, the St. Elias Mountains continue to be an enviable natural laboratory for glaciological research. Recent work has been motivated in part by the magnitude and pace of observed glacier change in this area, which is so ice-rich that ice loss has a measurable impact on global sea level. Both detection and attribution of these changes, as well as investigations into fundamental glacier processes, have been central themes in projects initiated within the last decade and based at the Kluane Lake Research Station. The scientific objectives of these projects are (1) to quantify recent area and volume changes of Kaskawulsh Glacier and place them in historical perspective, (2) to investigate the regional variability of glacier response to climate and the modulating influence of ice dynamics, and (3) to characterize the hydromechanical controls on glacier sliding. A wide range of methods is being used, from ground-based manual measurements to space-based remote sensing. The observations to date show glaciers out of equilibrium, with significant ongoing changes to glacier area, volume, and dynamics. Computer models are being used to generalize these results, and to identify the processes most critical to our understanding of the coupled glacier-climate system.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.101
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

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

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 head0.054
GPT teacher head0.235
Teacher spread0.182 · 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 teacher head, 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
Published2014
Admission routes1
Has abstractyes

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Same venueARCTICSame topicCryospheric studies and observationsFrench-language works237,207