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Record W3090312103 · doi:10.3354/meps13503

Changes in ocean conditions and hurricanes affect porbeagle Lamna nasus diving behavior

2020· article· en· W3090312103 on OpenAlexaff
Zhankun Wang, Rachel M. Horwitz, HD Bowlby, F. Ding, W Joyce

Bibliographic record

VenueMarine Ecology Progress Series · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsBedford Institute of Oceanography
Fundersnot available
KeywordsUpwellingSunriseOceanographySunsetEnvironmental scienceMixed layerSea surface temperatureClimatologyGeologyAtmospheric sciences

Abstract

fetched live from OpenAlex

Recovered archival satellite tags afford a rare opportunity to evaluate diving behavior relative to ocean conditions at fine spatiotemporal scales. Ocean temperature and mixed layer depth (MLD) were strongly related to daily and seasonal patterns in diving behavior of an adult female porbeagle shark Lamna nasus. The shark remained below MLD when waters were strongly stratified in summer or in the Gulf Stream; the daily timing of dives was associated with local sunrise and sunset. In the presence of a hurricane, diving activity abruptly increased, coincident with upwelling causing an abrupt decrease in surface temperature. Our first report of the behavioral response to a hurricane as well as the seasonal patterns in depths, occupied temperatures, and vertical speeds suggest environmental preferences of porbeagle are not static and individuals may use horizontal, as well as vertical, movement to take advantage of areas with specific characteristics.

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)0.001
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.015
GPT teacher head0.261
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 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

Citations6
Published2020
Admission routes1
Has abstractyes

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