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Record W2949601561 · doi:10.1029/2019jc014940

Deepening of the Winter Mixed Layer in the Canada Basin, Arctic Ocean Over 2006–2017

2019· article· en· W2949601561 on OpenAlexaboutno aff
Sylvia T. Cole, James Stadler

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersNational Science Foundation of Sri LankaWoods Hole Oceanographic InstitutionNational Science Foundation
KeywordsHaloclineMixed layerCanada BasinOceanographyStratification (seeds)SalinityArcticGeologyTemperature salinity diagramsEnvironmental science

Abstract

fetched live from OpenAlex

Abstract The Arctic Ocean mixed layer interacts with the ice cover above and warmer, nutrient‐rich waters below. Ice‐Tethered Profiler observations in the Canada Basin of the Arctic Ocean over 2006–2017 are used to investigate changes in mixed layer properties. In contrast to decades of shoaling since at least the 1980s, the mixed layer deepened by 9 m from 2006–2012 to 2013–2017. Deepening resulted from an increase in mixed layer salinity that also weakened stratification at the base of the mixed layer. Vertical mixing alone can explain less than half of the observed change in mixed layer salinity, and so the observed increase in salinity is inferred to result from changes in freshwater accumulation via changes to ice‐ocean circulation or ice melt/growth and river runoff. Even though salinity increased, the shallowest density surfaces deepened by 5 m on average suggesting that Ekman pumping over this time period remained downward. A deeper mixed layer with weaker stratification has implications for the accessibility of heat and nutrients stored in the upper halocline. The extent to which the mixed layer will continue to deepen appears to depend primarily on the complex set of processes influencing freshwater accumulation.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.004
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.267
Teacher spread0.246 · 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.

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

Citations29
Published2019
Admission routes1
Has abstractyes

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