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Record W2095658930 · doi:10.1002/2013gl058761

Local spring warming drives earlier river‐ice breakup in a large Arctic delta

2014· article· en· W2095658930 on OpenAlexafffund
Lance F. W. Lesack, Philip Marsh, Faye Hicks, Donald L. Forbes

Bibliographic record

VenueGeophysical Research Letters · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsMemorial University of NewfoundlandWilfrid Laurier UniversityUniversity of AlbertaSimon Fraser University
FundersNatural Resources Canada
KeywordsArcticEnvironmental scienceArctic ice packSea iceSnowBreakupCryosphereSpring (device)ClimatologyDeltaRiver deltaOceanographyGlobal warmingArctic sea ice declineClimate changeAntarctic sea iceGeology

Abstract

fetched live from OpenAlex

Abstract Pan‐Arctic rivers strongly affect the Arctic Ocean and their vast lake‐rich deltas. Their discharges may be increasing because of an intensifying hydrological cycle driven by warming climate. We show that a previously unexplained trend toward earlier ice breakup in the Mackenzie River Delta is little affected by winter warming during the period of river‐ice growth and is unaffected by river discharge, but unexpectedly is strongly related to local spring warming during the period of river‐ice melt. These results are statistically linked to declining winter snowfall that was not expected because of an intensifying Arctic hydrological cycle. Earlier ice breakup is expected to cause declining water level peaks that will reduce off‐channel flows through the lake‐rich delta before river waters enter the ocean. Thus, local spring warming with unexpected snowfall declines, rather than warmer winters, can drive earlier ice breakup in large Arctic rivers and biogeochemical changes in their river‐ocean interface.

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)0.000
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.015
GPT teacher head0.257
Teacher spread0.242 · 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

Citations54
Published2014
Admission routes2
Has abstractyes

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