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Record W2007914817 · doi:10.1029/2009gl038775

Sea‐ice production over the Laptev Sea shelf inferred from historical summer‐to‐winter hydrographic observations of 1960s–1990s

2009· article· en· W2007914817 on OpenAlexafffund
Igor Dmitrenko, Sergey Kirillov, Bruno Tremblay, Dorothea Bauch, Sascha Willmes

Bibliographic record

VenueGeophysical Research Letters · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsMcGill University
FundersDivision of Arctic SciencesOffice of Polar ProgramsNatural Sciences and Engineering Research Council of CanadaBundesministerium für Bildung und ForschungDeutsche ForschungsgemeinschaftNational Science Foundation
KeywordsHydrographyOceanographyClimatologyGeologySea iceEnvironmental science

Abstract

fetched live from OpenAlex

The winter net sea‐ice production (NSIP) over the Laptev Sea shelf is inferred from continuous summer‐to‐winter historical salinity records of 1960s–1990s. While the NSIP strongly depends on the assumed salinity of newly formed ice, the NSIP quasi‐decadal variability can be linked to the wind‐driven circulation anomalies in the Laptev Sea region. The increased wind‐driven advection of ice away from the Laptev Sea coast when the Arctic Oscillation (AO) is positive implies enhanced coastal polynya sea‐ice production and brine release in the shelf water. When the AO is negative, the NSIP and seasonal salinity amplitude tends to weaken. These results are in reasonable agreement with sea‐ice observations and modeling.

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 score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.047
GPT teacher head0.278
Teacher spread0.231 · 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
Published2009
Admission routes2
Has abstractyes

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