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Record W2141111613 · doi:10.14430/arctic4079

Hydrographic Changes in Nares Strait (Canadian Arctic Archipelago) in Recent Decades Based on δ18O Profiles of Bivalve Shells

2011· article· en· W2141111613 on OpenAlexaffvenueabout
Marta E. Torres, Daniela Zima, Kelly K. Falkner, Robie W. Macdonald, Mary O'Brien, Bernd R. Schöne, Tim Siferd

Bibliographic record

VenueARCTIC · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of ManitobaFisheries and Oceans Canada
FundersOffice of Polar ProgramsNational Science Foundation
KeywordsArchipelagoOceanographyHydrographyBayArcticGeologyWater massSea iceSeawaterArctic dipole anomalyArctic ice packAntarctic sea ice

Abstract

fetched live from OpenAlex

<span style="font-family: 'Times New Roman';">Nares Strait is one of three main passages of the Canadian Archipelago that channel relatively fresh seawater from the Arctic Ocean through Baffin Bay to the Labrador Sea. Oxygen isotopic profiles along the growth axis of bivalve shells, collected live over the 5 – 30 m depth range from the Greenland and Ellesmere Island sides of the strait, were used to reconstruct changes in the hydrography of the region over the past century. The variability in oxygen isotope ratios is mainly attributed to variations in salinity and suggests that the northern end of Nares Strait has been experiencing an increase in freshwater runoff since the mid 1980s. The recent changes are most pronounced at the northern end of the strait and diminish toward the south, a pattern consistent with proximity to the apparently freshening Arctic Ocean source in the north and mixing with Baffin Bay waters as the water progresses southward. This increasing freshwater signal may reflect changes in circulation and ice formation that favor an increased flow of relatively fresh waters from the Arctic Ocean into Nares Strait. </span>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0030.000

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 head0.023
GPT teacher head0.209
Teacher spread0.186 · 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 teacher head, not a consensus.

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

Citations15
Published2011
Admission routes3
Has abstractyes

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