MétaCan
Menu
Back to cohort

Synoptic controls of extreme ice area flux events along Nares Strait

2025· preprint· en· W4411628930 on OpenAlexaffabout
Kaitlin McNeil, G. W. K. Moore, Stephen Howell, Mike Brady

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicMarine and environmental studies
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Toronto
Fundersnot available
KeywordsGeologyClimatologyIcebergOceanographyFlux (metallurgy)Sea iceGeography

Abstract

fetched live from OpenAlex

Nares Strait, the channel between Ellesmere Island and northwest Greenland, connects the Arctic Ocean’s Lincoln Sea to northern Baffin Bay and onwards to the subpolar North Atlantic Ocean. The transport of thick, multi-year sea ice southward down the Strait contributes to the overall loss of this important ice class from the Arctic as well as contributing to the salinity budget of the subpolar North Atlantic Ocean. Using sea ice motion derived from synthetic aperture radar imagery, we characterize the variability and extremes in ice transport across a flux gate situated at its northern terminus. Although the mean transport is approximately 300 km2/day southwards, we observe frequent, large, and short-lived reversals during which the ice is transported northwards into the Lincoln Sea with extreme events exceeding 590 km2/day. Here, we characterize the synoptic conditions associated with extreme ice transport events along the Strait. Extreme southward transport events exceeding 1070 km2/day are associated with a synoptic-scale environment characterized by higher sea-level pressures over the Arctic Ocean and lower sea-level pressures over Davis Strait and the Labrador Sea. The newly identified northward transport events are associated with a reversal in the pressure gradient as well as a pan-Arctic reversal in ice motion.

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 score0.031
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.031
GPT teacher head0.212
Teacher spread0.181 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same topicMarine and environmental studiesFrench-language works237,207