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Record W3148808521

fContrasting patterns of river runoff and sea-ice melted water in the Canada Basin

2014· article· en· W3148808521 on OpenAlexaboutno aff
Jinlu, Chen, Мин, Qiu, Yusheng, LI -, Yanping, Cao, Jianping

Bibliographic record

Venue海洋学报:英文版 · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSurface runoffSea iceArctic ice packGeologyOceanographyAntarctic sea iceHydrology (agriculture)Melt pondArcticEnvironmental science
DOInot available

Abstract

fetched live from OpenAlex

The fractions of river runoff and sea-ice melted water in the Canada Basin in summer 2003 were determined by the salinity-18O system. The fraction of river runoff(fR) was high in the upper 50 m of the water column and decreased with depth and latitude. The signals of the river runoff were confined to water depths above 200 m. The total amount of river runoff in the Canada Basin was higher than that in other arctic seas, indicating that the Canada Basin is a main storage region for river runoff. The penetration depth of the sea-ice melted water was less than 50 m to the south of 78°N, while it was about 150 m to the north of 78°N. The total amount of sea-ice melted water was much higher to the north of 78°N than to the south of 78°N, indicating the sea-ice melted waters accumulated on the ice edge. The abundant sea-ice melted water on the ice edge was attributed to the earlier melted water in the southern Canada Basin and transported by the Beaufort Gyre or the reinforced melting of sea ice by solar radiation in the polynya.

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 score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.161
Teacher spread0.156 · 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
Published2014
Admission routes1
Has abstractyes

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