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Record W2341196206 · doi:10.1139/cjz-2015-0240

Large-scale investment in the excavation and “camouflaging” phases by nesting Leatherback Turtles (<i>Dermochelys coriacea</i>)

2016· article· en· W2341196206 on OpenAlexvenueno aff
Thomas J. Burns, Hannah Davidson, Malcolm W. Kennedy

Bibliographic record

VenueCanadian Journal of Zoology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsnot available
FundersUniversity of GlasgowCarnegie Trust for the Universities of Scotland
KeywordsNest (protein structural motif)PredationBiologyNesting (process)EcologyFisheryAvian clutch sizeReproduction

Abstract

fetched live from OpenAlex

Nesting female Leatherback Turtles (Dermochelys coriacea (Vandelli, 1761)) are ungainly and vulnerable to predation, stranding, and physiological stress, dangers encountered repeatedly in a nesting season. Minimising these risks must be balanced against optimising investment in nest-site selection, excavation, refilling, and concealment of their egg clutches. We investigated the relative investment in the different phases of nesting in Leatherback Turtles by recording the duration of, and respiratory frequency during, each phase. Nest excavation and the final sand scattering (usually termed “camouflage” or “disguise”) phases were the longest in duration. By combining the relative durations of nesting phases with literature estimates of oxygen consumption, we found that these may be the most expensive phases energetically, sand scattering in particular. Leatherback Turtles follow convoluted paths during the sand scattering phase, which were mapped and measured. They disturb large areas (mean (±SD) 30.3 ± 11.1 m 2 , maximum 57 m 2 ), the centroids of which were always offset from the nest itself. Interestingly, in none of 26 nestings observed did the centre line of females move over the nest itself. Nesting Leatherbacks therefore invest heavily in obscuring the egg clutch, and the pattern of their movements in doing so suggests decoy behaviour.

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 score0.041
Threshold uncertainty score0.975

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 head0.009
GPT teacher head0.200
Teacher spread0.191 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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