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Record W2323883476 · doi:10.3189/2014jog13j039

Volume loss from lower Peyto Glacier, Alberta, Canada, between 1966 and 2010

2013· article· en· W2323883476 on OpenAlexfundaboutno aff
L. M. Kehrl, R. L. Hawley, E. C. Osterberg, Dominic Winski, Alexander Lee

Bibliographic record

VenueJournal of Glaciology · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
FundersParks CanadaDartmouth College
KeywordsGlacierGeologyGlacier mass balanceTidewater glacier cyclePhysical geographySnoutAccumulation zoneCirque glacierHydrology (agriculture)GeomorphologyClimatologyIce streamGeographyCryosphereEcologyIce calving

Abstract

fetched live from OpenAlex

Abstract Mass loss from mountain glaciers contributes to sea-level rise and reduces freshwater availability in glacier-fed river basins, with negative effects on hydropower generation, agriculture and the health of aquatic ecosystems. In this study, we determine the volume of lower Peyto Glacier, Alberta, Canada, from ground-penetrating radar surveys in 2008–10, and compare our volume estimate with previous estimates from 1966 and 1984. The long-term record of mass-balance estimates on Peyto Glacier highlights Peyto’s importance as an ‘index’ glacier for the region. We calculate a mean volume of (3.39 ± 0.30) × 107 m3 for the glacier snout for the period 2008–10. Glacier volume decreased linearly from 1966 to 2010. If this trend persists, the glacier snout will disappear by ~2019 and Peyto Glacier will have retreated by ~1 km. Our results agree with modelling studies, which suggest that Peyto Glacier and other nearby glaciers along the eastern slopes of the Canadian Rocky Mountains will likely lose 80–90% of their present-day volume by 2100.

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 score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.187
Teacher spread0.177 · 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

Citations21
Published2013
Admission routes2
Has abstractyes

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