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Record W4214736738 · doi:10.5751/es-13112-270126

Hidden benefits and risks of partial protection for coral reef fisheries

2022· article· en· W4214736738 on OpenAlexvenueno aff
Patrick Smallhorn‐West, Philippa J. Cohen, Renato A. Morais, Fraser A. Januchowski‐Hartley, Daniela M. Ceccarelli, Siola’a Malimali, Karen Stone, R. Scott Warren, Joshua E. Cinner

Bibliographic record

VenueEcology and Society · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsnot available
FundersAustralian Centre for International Agricultural ResearchAustralian Research CouncilConsortium of International Agricultural Research CentersAustralian GovernmentNational Geographic Society
KeywordsFisherySustainabilityVulnerability (computing)BusinessFisheries managementCoral reefEnvironmental resource managementLivelihoodEcosystemNatural resource economicsFishingEnvironmental scienceEcologyEconomicsAgricultureBiology

Abstract

fetched live from OpenAlex

Partially protected areas are now the dominant global form of spatial management aimed at preserving ecosystem integrity and managing human use. However, most evaluations of their efficacy use only a narrow set of conservation indicators that reflect a fraction of ways in which protection can succeed or fail. In this paper, we examine three case studies of partially protected coral reef fishery systems to evaluate benefits and risks of their use as a management tool. We use data from community-based management arrangements in three Pacific Island countries to demonstrate three vignettes of how partial protection can boost fisheries production, enhance the ease with which fishers catch their prey, and alter the composition of fisheries yields. These changes in fisheries productivity, catchability, and vulnerability under partial protection carry substantial benefits for fishers. However, they also carry significant risks for ecosystems and fisheries livelihoods unless adaptively managed so as to confer the short to medium term benefits in resource performance without risking longer term sustainability.

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)0.004
metaresearch head score (Gemma)0.016
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 score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0010.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.241
Teacher spread0.202 · 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

Citations11
Published2022
Admission routes1
Has abstractyes

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