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Record W3146043688

Variation of freshwater components in the Canada Basin during 1967-2010

2014· article· en· W3146043688 on OpenAlexaboutno aff
Pan, Hong Hong, Chen, Мин, Jinlu, Qiu, Yusheng, Minfang, 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 runoffSeawaterArcticMeltwaterHydrology (agriculture)Environmental scienceWater massStructural basinOceanographySalinityDrainage basinStreamflowOcean gyreGeologySnowFisheryGeographyEcology
DOInot available

Abstract

fetched live from OpenAlex

As a conservative tracer, oxygen isotopes in seawater are widely used for water mass analysis, along with temperature and salinity. In this study, seawater oxygen-18 datasets in the Canada Basin during 1967–2010 were obtained from the four cruises of the Chinese National Arctic Research Expedition(1999, 2003, 2008, and 2010) and the NASA database. Fractions of sea ice meltwater and river runoff were determined from the salinity-18O system. Our results showed that the river runoff decreased from the south to the north in the Canada Basin. The enhanced amount of river runoff observed in the southern Canada Basin may originate from the Mackenzie River, transported by the Beaufort Gyre. The river runoff component showed maximum fractions during 1967–1969, 1978–1979, 1984–1985, 1993–1994, and 2008–2010, indicating the refresh time of the river runoff was 5.0–16.0 a in the Canada Basin. The temporal variation of the river runoff was related to the change of the Arctic Oscillation(AO) index, suggesting the freshwater stored in the Canada Basin was affected by surface sea ice drift and water mass movement driven by atmospheric circulation.

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
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.165
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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