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Record W2987662829 · doi:10.1029/2019gl085116

The Dynamic Response of Sea Ice to Warming in the Canadian Arctic Archipelago

2019· article· en· W2987662829 on OpenAlexaffabout
Stephen Howell, Mike Brady

Bibliographic record

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsArctic ice packSea iceArcticArchipelagoArctic sea ice declineArctic dipole anomalyArctic geoengineeringAntarctic sea iceOceanographyDrift iceGeologyClimatologyEnvironmental scienceSea ice thicknessIce-albedo feedbackFlux (metallurgy)Global warmingClimate changeChemistry

Abstract

fetched live from OpenAlex

Abstract Ice arches in the Canadian Arctic Archipelago (CAA) block the inflow of Arctic Ocean ice for the majority of the year. A 22 year record (1997–2018) of Arctic Ocean‐CAA ice exchange was used to investigate the effect of warming on CAA sea ice dynamics. Larger ice area flux values were associated with longer flow duration and faster ice speed facilitated by increased open water leeway from the CAA's transition to a younger and thinner ice regime, which together have contributed to a significant ice area flux increase (10 3 km 2 /year) from Arctic Ocean into the northern CAA from 1997 to 2018. Remarkably, the 2016 Arctic Ocean ice area flux into the CAA (161 × 10 3 km 2 ) was 7 times greater than the 1997–2018 average (23 × 10 3 km 2 ) and almost double the 2007 ice area flux into Nares Strait (87 × 10 3 km 2 ). Continued warming may result in the CAA becoming a larger outlet for Arctic Ocean ice area loss.

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)0.002
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 score0.023
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.017
GPT teacher head0.271
Teacher spread0.254 · 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

Citations46
Published2019
Admission routes2
Has abstractyes

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