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Record W2170171284 · doi:10.3189/2013jog12j135

Glacier change of the Columbia Icefield, Canadian Rocky Mountains, 1919–2009

2013· article· en· W2170171284 on OpenAlexafffundabout
C. Tennant, Brian Menounos

Bibliographic record

VenueJournal of Glaciology · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Northern British Columbia
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Foundation for Climate and Atmospheric SciencesUniversity of Northern British Columbia
KeywordsGlacierIce fieldElevation (ballistics)GeologyGlacier mass balanceAccumulation zoneGlacier morphologyPhysical geographyDebrisPrecipitationThinningClimatologyGeomorphologyGeographyIce streamOceanographyCryosphereSea iceMeteorologyForestryGeometry

Abstract

fetched live from OpenAlex

Abstract We determined length, area, elevation and volume change of the Columbia Icefield using Interprovincial Boundary Commission Survey maps from 1919, eight sets of aerial photographs from 1948 to 1993, and satellite data from 1999 to 2009. Over the period 1919–2009, glaciers on average retreated 1150 ± 34 m and shrank by 2.4 ± 0.2 km 2 . Total area loss was 59.6 ± 1.2 km 2 (23 ± 5%), and mean elevation change was −49 ± 25 m w.e., resulting in a total volume loss of 14.3 ± 2.0 km 3 w.e. Large outlet glaciers experienced the greatest absolute ice loss, while small, detached glaciers lost the most relative length and area. Thinning rates of debris-covered ice were 30–60% lower than those for clean ice. All glacier changes were significantly correlated with each other ( p < 0.01), with r values ranging from 0.54 to 0.82. Temperature is correlated with length and area change over periods lagged 1–5 years ( p < 0.05), and with elevation and volume change over periods lagged 9–18 years ( p < 0.05). Precipitation is correlated with glacier change over periods lagged 1–10 years ( p < 0.05).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.997

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)0.0040.000

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 head0.198
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 teacher head, not a consensus.

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

Citations65
Published2013
Admission routes3
Has abstractyes

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