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Record W2345932084 · doi:10.1111/jfb.12983

A trans‐Atlantic examination of haddock <i>Melanogrammus aeglefinus</i> food habits

2016· article· en· W2345932084 on OpenAlexaff
Jamie C. Tam, Jason S. Link, Scott I. Large, Bjarte Bogstad, Alida Bundy, Adam Cook, Gjert Endre Dingsør, Andrey V. Dolgov, Daniel Howell, Alexander Kempf, John K. Pinnegar, Anna Rindorf, S. Schückel, Anne F. Sell, Brian E. Smith

Bibliographic record

VenueJournal of Fish Biology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
Fundersnot available
KeywordsHaddockBiologyFisheryPredationMarine ecosystemEcosystemEcologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

The food habits of Melanogrammus aeglefinus were explored and contrasted across multiple north-eastern and north-western Atlantic Ocean ecosystems, using databases that span multiple decades. The results show that among all ecosystems, echinoderms are a consistent part of M. aeglefinus diet, but patterns emerge regarding where and when M. aeglefinus primarily eat fishes v. echinoderms. Melanogrammus aeglefinus does not regularly exhibit the increase in piscivory with ontogeny that other gadoids often show, and in several ecosystems there is a lower occurrence of piscivory. There is an apparent inverse relationship between the consumption of fishes and echinoderms in M. aeglefinus over time, where certain years show high levels of one prey item and low levels of the other. This apparent binary choice can be viewed as part of a gradient of prey options, contingent upon a suite of factors external to M. aeglefinus dynamics. The energetic consequences of this prey choice are discussed, noting that in some instances it may not be a choice at all.

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 score0.072
Threshold uncertainty score0.142

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 studies0.0000.000
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 head0.017
GPT teacher head0.235
Teacher spread0.219 · 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

Citations14
Published2016
Admission routes1
Has abstractyes

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