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Record W2902266489 · doi:10.5194/tc-2018-250

Changes in glacier facies zonation on Devon Ice Cap, Nunavut, detected from SAR imagery and field observations

2018· article· en· W2902266489 on OpenAlexafffundabout
Tyler de Jong, Luke Copland, David Burgess

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsNatural Resources CanadaUniversity of Ottawa
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaUniversity of Ottawa
KeywordsGeologyIce streamGlacierGlacier morphologyGlacier ice accumulationAccumulation zoneMeltwaterFirnSnowAblation zoneAntarctic sea iceGlacier mass balanceIce sheetGeomorphologyArctic ice packSea iceCryosphereClimatology

Abstract

fetched live from OpenAlex

Abstract. Envisat ASAR WS images, verified against mass balance, ice core, ground-penetrating radar and air temperature measurements, are used to map changes in the distribution of glacier facies zones across Devon Ice Cap between 2004 and 2011. Glacier ice, saturation/percolation and pseudo dry snow zones are readily distinguishable in the satellite imagery, and the superimposed ice zone can be mapped after comparison with ground measurements. Over the study period there has been a clear upglacier migration of glacier facies, resulting in regions close to the firn line switching from being part of the accumulation area with high backscatter to being part of the ablation area with relatively low backscatter. This has coincided with a rapid increase in positive degree days near the ice cap summit, and an increase in the glacier ice zone from 71 % of the ice cap in 2005 to 92 % of the ice cap in 2011. This has significant implications for the area of the ice cap subject to meltwater runoff.

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)0.000
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 score0.280
Threshold uncertainty score0.564

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.034
GPT teacher head0.225
Teacher spread0.191 · 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

Citations5
Published2018
Admission routes3
Has abstractyes

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