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Record W7028453343

Evaluating alternative bait options for the PEI lobster fishery in Lobster Fishing Area (LFA) 25, Atlantic Canada

2018· article· en· W7028453343 on OpenAlexaboutno aff

Bibliographic record

VenueIslandScholar (University of Prince Edward Island) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical and Environmental Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFishingEconomic shortageBycatchFish <Actinopterygii>Fishing industryMackerel
DOInot available

Abstract

fetched live from OpenAlex

The American lobster (Homarus americanus) fishery in Prince Edward Island (PEI) relies on bait to capture lobster. There is unease in the industry that rising prices and pressure on specific baits, in particular, Atlantic mackerel (Scomber scombrus), will lead to bait shortages and further amplified prices. A scan of the current information on some alternative baits that are being used in the industry was conducted to ascertain their potential to alleviate some of the pressure that is currently placed on the bait sources being utilized by the industry. In addition, an economic sensitivity analysis of the species cunner (Tautogolabrus adspersus) was carried out, as it was found from the scan of potential options, to be one of the most promising species to be developed as auxiliary bait to the lobster fishery in PEI. Primary research was conducted with seven fishermen from the PEI side of Lobster Fishing Area (LFA) 25 to determine the potential catch rate of cunner. Additionally, 12 fishermen from the same LFA were surveyed to determine the potential costs of fishing and the value of cunner as auxiliary bait to that lobster fishery. In all scenarios evaluated during this study, except if cunner is valued at CAD .70 and less than five traps are fishing, cunner are economically viable and beneficial for fishers in LFA 25 to use as auxiliary bait. Further research is needed to determine if cunner is a source of sustainable bait or if auxiliary fishing activities could deplete stocks.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.041
GPT teacher head0.270
Teacher spread0.230 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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