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Record W6977344863 · doi:10.6084/m9.figshare.c.7437389

Supplementary material from "Atlantic bluefin tuna tagged off Norway show extensive annual migrations, high site-fidelity and dynamic behaviour in the Atlantic & Mediterranean Sea"

2024· other· en· W6977344863 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsThunnusTunaMediterranean climateMediterranean seaArcticNorwegian

Abstract

fetched live from OpenAlex

Atlantic bluefin tuna (ABFT; Thunnus thynnus) is a highly migratory species. To investigate the migrations and vertical behaviours of ABFT migrating to Nordic waters, we deployed pop-up satellite archival transmitting tags on 25 ABFT off Norway (Curved Fork Length: 228-292 cm). We obtained 16 full-year migrations, which differed between individuals, and physically recovered 13 tags, which provided 4,699 days of archival depth and temperature data. ABFT occupied waters from the Arctic Circle to as far south as Cabo Verde, Africa, and occupied depths down to 1190 m and temperatures from 0.5 to 27.8 °C. During their annual migrations, ABFT spent, on average, 68 days in Norwegian waters, 65 days in the Newfoundland Basin, 35 days around the Canary Islands and 33 days in the West European Basin. Most ABFT entered the Mediterranean Sea with a mean entry date of May 13th and visited known spawning grounds, staying, on average, 44 days. All ABFT with full-year deployments returned to Norwegian waters. ABFT displayed high site-fidelity and dynamic vertical diving behaviours that varied between hotspots and seasons. These spatiotemporal data provide important ecological knowledge for sustainable management and the conservation of the recently recovered eastern ABFT stock.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.803
Threshold uncertainty score0.281

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.8030.303

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 head0.022
GPT teacher head0.278
Teacher spread0.256 · 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.

Study designObservational
Domainnot available
GenreDataset

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

Citations1
Published2024
Admission routes1
Has abstractyes

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