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Record W2005477098 · doi:10.1139/f07-090

Evidence for greater reproductive output per unit area in areas protected from fishing

2007· article· en· W2005477098 on OpenAlexvenueno aff
Michel J. Kaiser, ROBERT E. BLYTH‐SKYRME, Paul J. B. Hart, Gareth Edwards‐Jones, David C. Palmer

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
FundersDepartment for Environment, Food and Rural Affairs, UK GovernmentCentre for Environment, Fisheries and Aquaculture Science
KeywordsFishingOverfishingFisheryMarine protected areaFisheries managementMarine reserveBiologyGeographyEcologyHabitat

Abstract

fetched live from OpenAlex

Marine protected areas are advocated as an essential management tool to ensure the sustainable use of marine resources by providing insurance against over-exploitation and through the provision of refuge for a large biomass of sexually mature adults. Using a unique fishing gear-restriction, voluntary management system as a large-scale experiment, we found that adult scallops (Pecten maximus) within areas protected from towed bottom-fishing gear had heavier adductor muscle tissue and gonads that were 19%–24% heavier than those of scallops in fished areas, while other body and age characteristics were similar in both areas. The scallops within the protected area also occurred at a much higher abundance than adjacent, chronically fished (× 12.83) and wider commercially exploited (× 2.18) areas. These results provide evidence that the use of towed bottom-fishing gear can further exacerbate the effects of overfishing through the suppression of the reproductive potential of individuals of similar body size. These findings underline the utility of using closed areas as tools for fisheries conservation of sedentary species of commercial importance.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.099
GPT teacher head0.281
Teacher spread0.182 · 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

Citations41
Published2007
Admission routes1
Has abstractyes

Explore more

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