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Record W1979423408 · doi:10.3189/002214308785836995

An inventory and morphometric analysis of British Columbia glaciers, Canada

2008· article· en· W1979423408 on OpenAlexafffundabout
Erik Schiefer, Brian Menounos, Roger Wheate

Bibliographic record

VenueJournal of Glaciology · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Northern British Columbia
FundersCanadian Foundation for Climate and Atmospheric Sciences
KeywordsGlacierGeologyGlacier mass balanceAltitude (triangle)Digital elevation modelTidewater glacier cyclePhysical geographyGlacier morphologyScale (ratio)ClimatologyGeomorphologyGeographyCartographyRemote sensingCryosphereIce streamIce calving

Abstract

fetched live from OpenAlex

Abstract We describe an automated method to generate an inventory of glaciers and glacier morphometry from a digital topographic database containing glacier boundaries and a digital elevation model for British Columbia, Canada. The inventory contains over 12 000 glaciers with a total cumulative area that exceeds 25 000 km 2 , based on mapping from aerial photographs circa the mid-1980s. We use the inventory to examine dimensional characteristics among glaciers, namely the scaling relations between glacier length, width and area. Glacier length is a good predictor of glacier area, and its predictive ability improves when glaciers are stratified by the number of up-valley accumulation basins. The spatial pattern of glacier mid-range altitude parallels glaciation limits previously mapped for British Columbia and similarly reflects large-scale controls of orographic precipitation and continentality. The inventory is also used to refine models that relate glacier mid-range and terminus altitudes to regional position, aspect and, in the case of terminus altitudes, an index of glacier shape. Relations between glacier altitude limits and controlling spatial and topographic factors are used to make further climatic and mass-balance inferences from the glacier inventory.

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 score0.091
Threshold uncertainty score0.796

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)0.0010.000

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 head0.015
GPT teacher head0.197
Teacher spread0.181 · 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

Citations60
Published2008
Admission routes3
Has abstractyes

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