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Record W2298660142 · doi:10.1111/cobi.12722

Expanding marine protected areas to include degraded coral reefs

2016· article· en· W2298660142 on OpenAlexaff
Avigdor Abelson, Peter A. Nelson, Graham J. Edgar, Nadav Shashar, Daniel C. Reed, Jonathan Belmaker, Gesche Krause, Michael W. Beck, Eran Brokovich, Robert France, Steven D. Gaines

Bibliographic record

VenueConservation Biology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCoral reefMarine protected areaReefResilience of coral reefsEcosystem servicesBiodiversityEcosystemCoral reef protectionEnvironmental scienceEnvironmental issues with coral reefsCoral reef organizationsMarine ecosystemCoralEnvironmental resource managementAquaculture of coralGeographyEcologyBiologyHabitat

Abstract

fetched live from OpenAlex

Marine protected areas (MPAs) are a commonly applied solution to coral reef degradation, yet coral reefs continue to decline worldwide. We argue that expanding the range of MPAs to include degraded reefs (DR-MPA) could help reverse this trend. This approach requires new ecological criteria for MPA design, siting, and management. Rather than focusing solely on preserving healthy reefs, our approach focuses on the potential for biodiversity recovery and renewal of ecosystem services. The new criteria would help identify sites with the highest potential for recovery and the greatest resistance to future threats (e.g., increased temperature and acidification) and sites that contribute to MPA connectivity. The DR-MPA approach is a compliment rather than a substitute for traditional MPA design approaches. We believe that the DR-MPA approach can enhance the natural, or restoration-assisted, recovery of DRs and their ecosystem services; increase total reef area available for protection; promote more resilient and better-connected MPA networks; and improve conditions for human communities dependent on MPA ecosystem services.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.028
GPT teacher head0.257
Teacher spread0.229 · 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.

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

Citations66
Published2016
Admission routes1
Has abstractyes

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