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Record W2001843910 · doi:10.1002/2014gl062960

Rapid disappearance of perennial ice on Canada's most northern lake

2015· article· en· W2001843910 on OpenAlexaffabout
Michel Paquette, Daniel Fortier, Derek Mueller, Denis Sarrazin, Warwick F. Vincent

Bibliographic record

VenueGeophysical Research Letters · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsCarleton UniversityUniversité de MontréalUniversité LavalCenter for Northern Studies
Fundersnot available
KeywordsPerennial plantGeologyAdvectionMelt pondShelf iceArctic ice packCryospherePhysical geographyClimatologySea iceIce shelfOceanographyGeography

Abstract

fetched live from OpenAlex

Abstract Field records, aerial photographs, and satellite imagery show that the perennial ice cover on Ward Hunt Lake at Canada's northern coast experienced rapid contraction and thinning after at least 50 years of relative stability. On all dates of sampling from 1953 to 2007, 3.5 to 4.3 m of perennial ice covered 65–85% of the lake surface in summer. The ice cover thinned from 2008 onward, and the lake became ice free in 2011, an event followed by 26 days of open water conditions in 2012. This rapid ice loss corresponded to a significant increase in melting degree days (MDD), from a mean (±SD) of 80.4 (±36.5) MDD (1996–2007) to 136.2 (±16.4) MDD (2008–2012). The shallow bathymetry combined with heat advection by warm inflows caused feedback effects that accelerated the ice decay. These observations show how changes across a critical threshold can result in the rapid disappearance of thick perennial ice.

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 score0.068
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
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 head0.030
GPT teacher head0.257
Teacher spread0.227 · 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

Citations41
Published2015
Admission routes2
Has abstractyes

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