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Record W7017900269

Climate and Surging of Donjek Glacier, Yukon, Canada

2020· article· en· W7017900269 on OpenAlexfundaboutno aff

Bibliographic record

VenueScholar Works (Boise State University) · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaDirektoratet for internasjonalisering og kvalitetsutvikling i høgare utdanningUniversitetet i OsloUniversity of OttawaDartmouth CollegeNational Science Foundation
KeywordsSurgeSnowIce fieldGlacierClimate changeStorm surge
DOInot available

Abstract

fetched live from OpenAlex

Links between climate and glacier surges are poorly understood but are required to enable prediction of surges and mitigation of associated hazards. Here, we investigate the role of snow accumulation, rain, and temperature on surge periodicity, area changes, and timing of surge initiation since the 1930s at Donjek Glacier, Yukon, Canada. Snow accumulation measured in three ice cores collected at Eclipse Icefield indicates that a cumulative accumulation of 15.5 ± 1.46 or 16.6 ± 2.0 m w.e. occurred in the ten to twelve years between each of its last eight surges, depending on ice motion spatiotemporal offset corrections. Although we find consistent snow accumulation between surges, the transient snow line has risen 10.3 m decade−1 vertically since the 1950s, and Burwash Landing weather station records indicate a 0.5°C decade−1 increase in mean annual air temperature since the 1960s. Changes in surface mass balance are accompanied by a consistent surge interval but decreasing surge extent. The three recent surge events initiated in years with the rainiest summers on record. These findings highlight a complex interplay between external (i.e., climate) and internal glacier processes that control surging at Donjek Glacier, with climate having a more direct influence on surge extent than on recurrence interval.

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 score0.279
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.155
Teacher spread0.145 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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