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Record W3127199470 · doi:10.1016/j.isci.2021.102097

Moray eels are more common on coral reefs subject to higher human pressure in the greater Caribbean

2021· article· en· W3127199470 on OpenAlexaff
Gina M. Clementi, Judith Bakker, Kathryn I. Flowers, Bautisse Postaire, Elizabeth A. Babcock, Mark E. Bond, Dayne Buddo, Diego Cardeñosa, Leanne M. Currey‐Randall, Jordan S. Goetze, Euan S. Harvey, Michelle R. Heupel, Jérémy J. Kiszka, Fabian Kyne, M. Aaron MacNeil, Mark G. Meekan, Matthew J. Rees, Colin A. Simpfendorfer, Conrad W. Speed, Michael R. Heithaus, Demian D. Chapman

Bibliographic record

VenueiScience · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCoral reefCoralSubject (documents)FisheryReefEnvironmental issues with coral reefsOceanographyGeographyEcologyBiologyLibrary scienceGeologyComputer science

Abstract

fetched live from OpenAlex

Proximity and size of the nearest market ('market gravity') have been shown to have strong negative effects on coral reef fish communities that can be mitigated by the establishment of closed areas. However, moray eels are functionally unique predators that are generally not subject to targeted fishing and should therefore not directly be affected by these factors. We used baited remote underwater video systems to investigate associations between morays and anthropogenic, habitat, and ecological factors in the Caribbean region. Market gravity had a positive effect on morays, while the opposite pattern was observed in a predator group subject to exploitation (sharks). Environmental DNA analyses corroborated the positive effect of market gravity on morays. We hypothesize that the observed pattern could be the indirect result of the depletion of moray competitors and predators near humans.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.068
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.264
Teacher spread0.237 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

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