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Record W2317167018 · doi:10.3189/2014jog13j150

Disappearing semi-permanent snow in the High Arctic and its consequences

2014· article· en· W2317167018 on OpenAlexaffabout
Ming‐ko Woo, Kathy L. Young

Bibliographic record

VenueJournal of Glaciology · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsYork UniversityMcMaster University
Fundersnot available
KeywordsTundraSnowMeltwaterArcticPermafrostGlacierSnowmeltCryospherePhysical geographyArctic ice packSnow fieldVegetation (pathology)GeologyEnvironmental scienceOceanographySea iceGeographySnow coverGeomorphology

Abstract

fetched live from OpenAlex

Abstract Semi-permanent snow is part of the continuum between seasonal snow and glacier ice. Although ubiquitous in the High Arctic, most late-lying snow banks and snow beds have lost their perennial status over the past decade as the summers have become progressively warmer. The loss over the past decade is the most unprecedented since aerial photography of the Canadian Arctic islands was first undertaken over half a century ago, and it has produced observable thermal, hydrological and ecological impacts. Where the ground has become exposed beneath the perennial snow cover, seasonal ground thaw has deepened. Tundra ponds and patchy wetlands fed principally by meltwater in the summer have suffered water-level decline or desiccation. The water balance of headwater basins is also affected, losing a buffering vehicle that accumulates storage surplus from the wet cool years to support streamflow and evaporation in the dry warm years. The tundra vegetation, already sparse, undergoes changes in the long term. As an essential source of water in the polar desert environment, the widespread distribution of semi-permanent snow magnifies its Arctic-wide importance.

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)0.001
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 score0.095
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.251
Teacher spread0.217 · 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

Citations47
Published2014
Admission routes2
Has abstractyes

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