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Record W2337680413 · doi:10.1139/cjfas-2015-0205

Lack of recreational fishing compliance may compromise effectiveness of Rockfish Conservation Areas in British Columbia

2016· article· en· W2337680413 on OpenAlexafffundvenueabout
Dana Haggarty, Steve J.D. Martell, Jonathan B. Shurin

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsUniversity of British Columbia HospitalUniversity of British Columbia
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsFishingRockfishFisheryRecreational fishingEnforcementMarine protected areaGeographyEcologyFish <Actinopterygii>HabitatBiology

Abstract

fetched live from OpenAlex

Compliance with spatial fishing regulations (e.g., marine protected areas, fishing closures) is one of the most important, yet rarely measured, determinants of ecological recovery. We used aerial observations of recreational fishing events from creel surveys before, during, and after 77 Rockfish Conservation Areas (RCAs) were established in British Columbia, Canada. There was no evidence of a change in fishing effort in 83% of the RCAs, and effort in five RCAs increased after establishment. Fishing effort in open areas adjacent to the RCAs declined with time and was higher than effort in the RCAs in all 3 years. Next, we used compliance data for 105 RCAs around Vancouver Island to model the drivers of compliance. Compliance was related to the level of fishing effort around the RCA, the size and perimeter-to-area ratio of RCAs, proximity to fishing lodges, and the level of enforcement. Noncompliance in RCAs may be hampering their effectiveness and impeding rockfish recovery. Education and enforcement efforts to reduce fishing effort inside protected areas are critical to the recovery of depleted fish 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 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)0.001
metaresearch head score (Gemma)0.006
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 score0.028
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.237
Teacher spread0.196 · 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

Citations38
Published2016
Admission routes4
Has abstractyes

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