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Record W4409439093 · doi:10.3390/wild2020012

First Characterization of Nesting Behaviors of Leatherback Turtles (Dermochelys coriacea) and Hawksbill Turtles (Eretmochelys imbricata) in Martinique and Inter-Species Comparison

2025· article· en· W4409439093 on OpenAlexaff
M. S. Rossi, Morjane Safi, Benjamin de Montgolfier

Bibliographic record

VenueWild · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsFisheryBiologyNesting (process)ZoologyMartiniqueEcologyWest indiesEngineeringHistory

Abstract

fetched live from OpenAlex

Sea turtles use specific nesting strategies to maximize the survival of their offspring. Few studies have investigated the nesting behavior of leatherback (Dermochelys coriacea) and hawksbill (Eretmochelys imbricata) turtles. The aim of this study was to characterize the nesting behavior of these two species, taking into account anthropogenic and natural impacts, and to compare behavior between species. Nocturnal monitoring was conducted from 2020 to 2024 on three beaches in Martinique: Madiana, Diamant, and Salines. The average duration of the nesting sequence for leatherback turtles was 101.50 min, with camouflage being the dominant behavior for 27.06% of the total time. For hawksbill turtles, nesting duration was 109.49 min, with digging as the predominant activity (31.68% of total time). No effect of anthropogenic disturbances and obstacles on the behavior of leatherback turtles was observed, while that of hawksbill turtles was significantly influenced by light and the presence of roots. The results revealed behavioral differences between these two species, as well as between two other species: loggerhead (Caretta caretta) and green (Chelonia mydas) turtles. These differences could be explained by morphological differences, nesting habitat peculiarities, beach characteristics, and different evolutionary strategies. Further research is needed to better understand these behaviors and improve conservation efforts.

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 score0.143
Threshold uncertainty score0.353

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 science0.0000.000
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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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