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Record W3017103204 · doi:10.1126/science.aax9412

Meeting fisheries, ecosystem function, and biodiversity goals in a human-dominated world

2020· article· en· W3017103204 on OpenAlexafffund
Joshua E. Cinner, Jessica Zamborain‐Mason, Georgina G. Gurney, Nicholas A. J. Graham, M. Aaron MacNeil, Andrew S. Hoey, Camilo Mora, Sébastien Villéger, Eva Maire, Tim R. McClanahan, Joseph Maina, John N. Kittinger, Christina C. Hicks, Stéphanie D’Agata, Cindy Huchery, Michele L. Barnes, David A. Feary, Ivor D. Williams, Michel Kulbicki, Laurent Vigliola, Laurent Wantiez, Graham J. Edgar, Rick D. Stuart‐Smith, Stuart A. Sandin, Alison L. Green, Maria Beger, Alan M. Friedlander, Shaun K. Wilson, Eran Brokovich, Andrew J. Brooks, Juan J. Cruz‐Motta, David J. Booth, Pascale Chabanet, Mark Tupper, Sebastian C. A. Ferse, U. Rashid Sumaila, Marah J. Hardt, David Mouillot

Bibliographic record

VenueScience · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsUniversity of British ColumbiaDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaConsortium of International Agricultural Research CentersAustralian Research CouncilUniversité de MontpellierDeutsche ForschungsgemeinschaftRoyal SocietyTotal FoundationBiodiversa+Agence Nationale de la RechercheCentre of Excellence for Coral Reef Studies, Australian Research CouncilNational Science Foundation
KeywordsCoral reefBiodiversityLivelihoodContext (archaeology)ReefEnvironmental resource managementFisheryEcosystemEcosystem servicesFunctional ecologyFunction (biology)Fisheries managementBusinessGeographyEnvironmental planningEcologyFishingEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

The worldwide decline of coral reefs necessitates targeting management solutions that can sustain reefs and the livelihoods of the people who depend on them. However, little is known about the context in which different reef management tools can help to achieve multiple social and ecological goals. Because of nonlinearities in the likelihood of achieving combined fisheries, ecological function, and biodiversity goals along a gradient of human pressure, relatively small changes in the context in which management is implemented could have substantial impacts on whether these goals are likely to be met. Critically, management can provide substantial conservation benefits to most reefs for fisheries and ecological function, but not biodiversity goals, given their degraded state and the levels of human pressure they face.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.017
GPT teacher head0.196
Teacher spread0.180 · 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

Citations162
Published2020
Admission routes2
Has abstractyes

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