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Record W4388480877 · doi:10.4000/rga.12203

Transferring Cryosphere Knowledge between Mountains Globally: A Case Study of Western Canadian Mountains, the European Alps and the Scandes

2023· article· en· W4388480877 on OpenAlexafffundabout
Emilie Stewart-Jones, Stephan Gruber

Bibliographic record

VenueRevue de géographie alpine · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPermafrostCryospherePhysical geographyHomogeneousClimate changeClimatologyGeologyGeographyOceanography

Abstract

fetched live from OpenAlex

Permafrost is present in mountains globally, yet most research has been focussed in the small area covered by the European Alps. This paper presents a method for comparing regional climates at a coarse scale to highlight similarities and differences between the European Alps and the Scandes to western Canadian mountain regions with permafrost. Climate variables from the ERA5 reanalysis relevant to mountain permafrost are averaged over the 1986–2005 period and compared. This helps to understand where permafrost conditions can be compared and where new research is needed. In this application, we conclude that a direct transfer of knowledge about ground temperature regimes and spatial patterns from the Scandes and Alps to western Canada is inappropriate because (1) the areas in western Canada receive more radiation than those in the Scandes, and less than in the Alps, (2) the areas in western Canada are more continental than the Scandes and the Alps, (3) the areas in western Canada extend into much colder conditions that the Scandes and the Alps, and (4) overlap in climatic variables is concentrated in small areas. Further research is needed to understand permafrost in mountains of western Canada.Despite the imperfections of reanalysis products, they present a unique and homogeneous data source for the remote and sparsely measured cryosphere regions. As such, this method can better inform the transfer of cryosphere knowledge between mountains globally.

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)0.001
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 score0.403
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
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 head0.028
GPT teacher head0.241
Teacher spread0.213 · 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

Citations4
Published2023
Admission routes3
Has abstractyes

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