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Record W3093510944 · doi:10.3389/fmars.2020.589549

Comparing the Effectiveness of Traditional and Alternative Baits in Prince Edward Island, Canada Lobster Fishery

2020· article· en· W3093510944 on OpenAlexafffundabout
Thitiwan Patanasatienkul, Marina K. V. C. Delphino, Krishna K. Thakur

Bibliographic record

VenueFrontiers in Marine Science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of Prince Edward Island
FundersFisheries and Oceans Canada
KeywordsHomarusAmerican lobsterFisheryFishingPelagic zoneBycatchEconomic shortageGeographyBiologyCrustacean

Abstract

fetched live from OpenAlex

The American lobster (Homarus americanus) fishery is an economically important commercial activity in Prince Edward Island (PEI), Canada. This fishery requires substantial amounts of bait, resulting in an emerging conservation challenge. To address this issue, an alternative lobster bait, manufactured using fresh and process pelagic fish, and dehydrated fish, corresponding to 75% less fresh pelagic fish than traditional bait has been developed by Bait Masters Inc. The performance of the alternative bait compared to that of the traditional bait was evaluated in a field study. This field trial was conducted in eight lobster fishing bays around PEI, during the 2019 lobster fishing seasons. Bait effectiveness was assessed based on catch-per-unit-effort (total lobsters and number of legal-sized lobsters caught per trap), and the ability to produce a catch. An average of five lobsters per trap were caught for both alternative and traditional baits. The results showed that both lobster bait types performed equally well in all PEI lobster fishing areas studied. This indicates that the alternative bait is a viable replacement for traditional bait, allowing the lobster fishery industry to address the bait-species shortage and ongoing conservation challenge.

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)0.001
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 score0.061
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.001
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 head0.015
GPT teacher head0.215
Teacher spread0.200 · 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

Citations15
Published2020
Admission routes3
Has abstractyes

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