MétaCan
Menu
Back to cohort
Record W2417470850 · doi:10.7755/fb.113.6

Behavior-dependent selectivity of yellowtail flounder (Limanda ferruginea) in the mouth of a commercial bottom trawl

2015· article· en· W2417470850 on OpenAlexaboutno aff
Melanie J. Underwood, Paul D. Winger, Anders Fernö, Arill Engås

Bibliographic record

VenueBIBSYS Brage (BIBSYS (Norway)) · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsLimandaFisheryFlounderFish <Actinopterygii>BiologyFlatfish

Abstract

fetched live from OpenAlex

To improve the efficiency of a commercial bottom trawl for catching yellowtail flounder (Limanda ferruginea), we studied the behavior of individuals in the middle of the trawl mouth. Observations were conducted with a high-definition camera attached at the center of the headline of a trawl, during the brightest time of day in June 2010 off eastern Newfoundland. Behavioral responses were quantified and analyzed to evaluate predictions related to fish behavior, orientation, and capture. Individuals showed 3 different initial responses independent of fish size, gait, and fish density: they swam close to (75%), were herded away from (19%), or moved vertically away from (6%) the seabed. Individuals primarily swam in the direction of initial orientation. No fish were oriented against the trawling direction. Fish in the center of the trawl mouth tended to swim along the bottom in the trawling direction. Only individuals that were stimulated to leave the bottom were caught. Individuals in peripheral locations within the trawl mouth more often swam inward and upward. Fish that swam inward were twice as likely to be caught. Fish size, gait, and fish density did not influence the probability of capture. A trawl that stimulates yellowtail flounder to orient inward and leave the bottom would increase the efficiency of a trawl.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.051
GPT teacher head0.281
Teacher spread0.231 · 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 teacher head, not a consensus.

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

Citations13
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueBIBSYS Brage (BIBSYS (Norway))Same topicMarine and fisheries researchFrench-language works237,207