MétaCan
Menu
Back to cohort
Record W2156855811 · doi:10.1139/f03-151

Effects of sex, stock, and environment on the shape of known-age Atlantic cod (<i>Gadus morhua</i>) otoliths

2004· article· en· W2156855811 on OpenAlexvenueno aff
Massimiliano Cardinale, Peer Doering‐Arjes, M. Kastowsky, Henrik Mosegaard

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
FundersEuropean Commission
KeywordsGadusOtolithMorphometricsAtlantic codFisheryStock (firearms)BiologyGadidaeGeographyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

The effects of sex, stock, and environment on the shape of known-age Atlantic cod (Gadus morhua) otoliths from the Faroe Islands were investigated. Moreover, the feasibility of otolith shape analysis for stock identification was evaluated. The shape was described by using several normalized Fourier descriptors and morphometric variables. There were no consistent differences between the left and right otoliths and between sexes within different age classes, stocks, and environments. With our experimental design, we could evaluate the relative importance of genetic and environmental conditions (water temperature and diet regime) on otolith shape and morphometrics. Using otolith shape, cod individuals were significantly separated into Bank and Plateau stocks. Total classification success was between 79% and 85% between stocks and between 85% and 96% between environments for the different age classes. The significant differences in otolith shape between Faroe Bank and Faroe Plateau cod stocks provided a phenotypic basis for stock separation. Stock and environmental influences were substantial in determining the shape of cod otoliths.

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 head0.014
GPT teacher head0.209
Teacher spread0.195 · 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

Citations313
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicMarine and fisheries researchFrench-language works237,207