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Record W4401978080 · doi:10.1071/mf23182

First insights into the vertical habitat use of young porbeagles in the north-western Atlantic with implications for bycatch reduction strategies

2024· article· en· W4401978080 on OpenAlexaff
Brooke N. Anderson, Heather D. Bowlby, Steven Saul, Yun Hee Kang, Neil Hammerschlag, Lisa J. Natanson, James A. Sulikowski

Bibliographic record

VenueMarine and Freshwater Research · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsAtlantic Industries (Canada)Bedford Institute of Oceanography
FundersUniversity of New England
KeywordsBycatchFisheryOceanographyEstuaryHabitatReduction (mathematics)BiologyGeographyEcologyFishingGeology

Abstract

fetched live from OpenAlex

Context For threatened marine species, data on their vertical habitat use patterns can reveal risk of interactions with fishing gear and can inform bycatch avoidance strategies. Such data are lacking for young porbeagles (Lamna nasus), which are captured as bycatch in north-western Atlantic fisheries. Aims We aimed to examine temporal patterns in diving and characterise vertical habitat use of young porbeagles during summer and autumn. Methods We used data from short-term (28-day), high-resolution (5-min interval) pop-off satellite tags attached to 14 young (young-of-the-year and 1-year-old) porbeagles to model depth use. Key results Occupied depths ranged from the sea surface to 679 m, with ambient water temperatures of −0.2 to 26°C. Diel period and season were factors related to depth use. Conclusions Sharks exhibited a diel activity pattern characterised by more extensive use of the water column during the day while remaining primarily at the surface at night. Depth use differed between seasons, with summer characterised by greater affinity for surface waters (0–10 m) compared to autumn. Implications Young porbeagles are at risk of interaction with active fisheries on the continental shelf, but interactions may be reduced by setting gear deeper at night or during summer.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.384
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.054
GPT teacher head0.308
Teacher spread0.253 · 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.

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

Citations2
Published2024
Admission routes1
Has abstractyes

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