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Record W4313388815 · doi:10.3390/quat6010001

Impacts of Loss of Cryosphere in the High Mountains of Northwest North America

2023· article· en· W4313388815 on OpenAlexafffund
John J. Clague, Dan H. Shugar

Bibliographic record

VenueQuaternary · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of CalgarySimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGlacierCryosphereClimate changePermafrostHolocenePhysical geographyEnvironmental scienceGlobal warmingEcosystemGreenhouse gasClimatologyOceanographyEarth scienceGeologyGeographyEcologySea ice

Abstract

fetched live from OpenAlex

Global atmospheric warming is causing physical and biotic changes in Earth’s high mountains at a rate that is likely unprecedented in the Holocene. We summarize changes in the presently glacierized mountains of northwest North America, including a rapid and large reduction in glacier ice and permafrost, a related increase in slope instability and landslides, river re-routing and other hydrological changes, and changing aquatic ecosystems. Atmospheric greenhouse gas concentrations continue to rise and will likely do so for at least the next several decades, if not longer, and mountains will continue to warm, perhaps reaching temperatures up to several degrees Celsius warmer than present over the remainder of this century. As a result, the rate of physical and biotic changes documented in this paper is very likely to dramatically increase and transform high-mountain environments.

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 score0.887
Threshold uncertainty score0.226

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.231
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 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

Citations6
Published2023
Admission routes2
Has abstractyes

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