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Record W7035958466

ALBACORE (Thunnus alalunga) REPRODUCTIVE BIOLOGY STUDY FOR THE NORTH ATLANTIC STOCK:YEARS 2020 AND 2021

2022· other· en· W7035958466 on OpenAlexaboutno aff

Bibliographic record

VenueDIGITAL.CSIC (Spanish National Research Council (CSIC)) · 2022
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Toxicity and Pharmacological Properties
Canadian institutionsnot available
Fundersnot available
KeywordsAlbacoreReproductive biologyFecundityTunaReproductionMaturity (psychological)
DOInot available

Abstract

fetched live from OpenAlex

The ICCAT North Atlantic Albacore (Thunnus alalunga) Research Program was established to \nenhance knowledge on albacore to provide more accurate scientific advice to the Commission. \nFunds are provided to the Albacore WG to develop research activities to accomplish several \nobjectives. One of the research objectives is to increase knowledge on reproductive biology for \nthe northern Atlantic stock, maturity schedules (L50) and egg production (size/age related \nfecundity. In March 2021, Terms of Reference were published by ICCAT. A Consortium \nintegrated by Canada, Venezuela, Chinese-Taipei and Spain presented an offer to collect \ngonad samples and spines throughout the year and carry out the study of reproductive biology \nfor North Atlantic albacore stock. \nResults of histological analysis: maturity stages, batch fecundity and seasonal area of \nspawners are presented as well as the age determined of partially collection of albacore \nspines. Analysis were done with the total albacore gonads samples collected in 2020 and \n2021 for the reproductive biology study of northern albacore.

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 score0.100
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.140
GPT teacher head0.353
Teacher spread0.214 · 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
Published2022
Admission routes1
Has abstractyes

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