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Record W4414295719 · doi:10.3390/fishes10090463

Small-Scale Fisheries Are Predominant Among Human Factors Influencing Cuban Coral Reefs

2025· article· en· W4414295719 on OpenAlexfundno aff
Tamara Figueredo-Martín, Fabián Pina-Amargós, Consuelo Aguilar-Betancourt, Gaspar González-Sansón, Leonardo Espinosa–Pantoja, Dorka Cobián-Rojas, Joán I. Hernández-Albernas, Ariandy González-González, Yandy Rodríguez Cueto, Kendra Karr, Julia G. Mason, Kristin M. Kleisner, V.R. Miller

Bibliographic record

VenueFishes · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsnot available
FundersMinisterio de Ciencia, Tecnología y Medio AmbienteUniversidad de La HabanaWildlife Conservation SocietyEnvironmental Defense FundAvalon Rare MetalsJohn D. and Catherine T. MacArthur FoundationFord Foundation
KeywordsCoral reefFishingCoral reef protectionBiodiversitySustainabilityMarine protected areaReefEnvironmental issues with coral reefs

Abstract

fetched live from OpenAlex

Coral reefs provide environmental goods and services that support biodiversity and people but face diverse threats. To assess the human factors that might be influencing the status of Cuban coral reefs, we collected and analyzed data from three sources: observations made on a research cruise that circumnavigated Cuba’s waters, expert knowledge, and updated published information. Our results show that small-scale fisheries are predominant among human factors influencing Cuban coral reefs, with more than 97% of the fishing incidents detected in situ during the expedition. Many Cuban reefs are heavily fished, have low levels of contamination, and enjoy high legal protection but experience inadequate enforcement. Tourism occurs on many reefs but could be sustainably increased based on its role in supporting enforcement and compliance and reducing fishing pressure. Densities of marine debris were generally lower in Cuban waters than other Caribbean locations and even lower within protected areas. Many human factors are likely acting synergistically, making management a challenge. This is the first at-sea comprehensive visual survey of human factors in Cuban waters and evaluation of marine debris on Cuba’s reefs, establishing a baseline for future assessments. These findings highlight potential human impacts that must be addressed to safeguard the health of Cuba’s marine ecosystem.

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 score0.039
Threshold uncertainty score0.972

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)0.0010.000

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 head0.207
Teacher spread0.190 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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