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Record W4399684079 · doi:10.1111/jfb.15829

Unsupervised estimation of the age at sea departure of Atlantic salmon using magnesium, manganese, and zinc otolith chemistry

2024· article· en· W4399684079 on OpenAlexfundno aff
Anthony Fontaine, M.R. Vignon, Hélène Tabouret, Christophe Pécheyran, Gilles Bareille

Bibliographic record

VenueJournal of Fish Biology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
FundersFondation Pour La Conservation Du Saumon AtlantiqueOffice Français de la BiodiversitéUniversité de Pau et des Pays de l'AdourAgence de l'Eau Adour-GaronneConseil Régional AquitaineCentre National de la Recherche Scientifique
KeywordsOtolithFish migrationManganeseBayZincBiologyFisheryFish <Actinopterygii>MagnesiumOceanographyEcologyChemistryGeology

Abstract

fetched live from OpenAlex

Investigating the age at sea departure of returning Atlantic salmon is critical to better understand the role of emigration timing in survival. Among the methods available, the use of otolith chemistry can be challenging in anadromous fish, as the elements frequently used (i.e., magnesium, manganese, and zinc) can jointly reflect individual metabolism and environmental conditions. Here, we present a new unsupervised and robust method for successfully estimating the outmigration age of Atlantic salmon in the Bay of Biscay, based on otolith zinc concentrations.

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)0.001
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 score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.014
GPT teacher head0.256
Teacher spread0.242 · 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
Published2024
Admission routes1
Has abstractyes

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