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Record W2570917570 · doi:10.1139/cjfas-2016-0200

Simulating future climate impacts on tropical fisheries: are contemporary spatial fishery management strategies sufficient?

2017· article· en· W2570917570 on OpenAlexvenueno aff
Maia R. Kapur, Erik C. Franklin

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsnot available
FundersNational Marine Fisheries ServicePacific Islands Fisheries Science CenterU.S. Geological SurveyNational Oceanic and Atmospheric Administration
KeywordsCoral reefFisheryReefCoral reef fishMarine protected areaCoral bleachingMarine reserveHerbivoreBiomass (ecology)Climate changeHabitatCoralEcologyGeographyEnvironmental scienceBiologyFishing

Abstract

fetched live from OpenAlex

We demonstrated a possible future wherein coral reefs shift to an algae-dominated state that retains low coral cover and a functional biomass of herbivorous fishes that sustains a reef fish fishery. We evaluate the effect of no-take marine protected areas (MPAs) and increased coastal nutrients under two Intergovernmental Panel on Climate Change climate scenarios for years 2000–2100, which are implemented as coral bleaching events. Coral mortality from bleaching events drove a lagged increase in herbivorous fish populations via a shift from coral-dominated to algae-dominated habitats. Biomass and catch of piscivorous fish declined significantly, with the fishery shifting to the harvest of herbivorous fish. No-take MPAs for 20% of reef areas represented a threshold that had a positive effect on herbivorous fishes but no influence on the steep declines of corals and piscivorous fishes. Contemporary no-take MPAs protect less than 1% of coral reef areas around the Hawaiian Islands; substantial management action would be required to approach the 20% area threshold.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.229

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.026
GPT teacher head0.234
Teacher spread0.209 · 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 designSimulation or modeling
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

Citations10
Published2017
Admission routes1
Has abstractyes

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