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Record W4388422400 · doi:10.1111/jzo.13122

Skull morphology of bottlenose dolphins worldwide and patterns of adaptation between coastal and offshore environments

2023· article· en· W4388422400 on OpenAlexaff
N. Oxford‐Smith, Marcello Ruta, Anli Gao, Karine A. Viaud‐Martinez, Richard Sabin, Jeremy S. Herman, John J. Ososky, Yuko Tajima, Tadasu K. Yamada, Antigoni Kaliontzopoulou, André E. Moura

Bibliographic record

VenueJournal of Zoology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of Guelph
FundersEuropean Social FundAgencia Estatal de InvestigaciónNarodowa Agencja Wymiany Akademickiej
KeywordsEcotypeMorphometricsBiologySubmarine pipelineHabitatRostrumAdaptation (eye)CetaceaVariation (astronomy)Morphology (biology)Bottlenose dolphinEcologySkullZoologyGenusOceanographyPaleontologyGeology

Abstract

fetched live from OpenAlex

Abstract The morphological differentiation between coastal and offshore ecotypes of bottlenose dolphins (genus Tursiops ) has been researched since the 1960s, particularly in T. truncatus (Montagu, 1821), although most morphological studies have focused on localized populations. Therefore, it is unclear how patterns observed in these individual populations integrate within the global morphological variation. Here we carry out a meta‐analysis of global morphological variation between coastal and offshore ecotypes from 532 museum specimens, using both linear measurements (LM; 282 specimens), and shape data using geometric morphometrics (GM; 290 specimens). Together these analyses show consistent differentiation in skull morphology between coastal and offshore ecotypes, and provide a detailed description of variation patterns within each ecotype. These patterns show high individual morphological variation in the coastal ecotype between locations, while the offshore ecotype appears morphologically more uniform across the areas sampled. Overall, most skull shape variation can be described by features noticeable dorsally in the structures of the rostrum, whereas more limited change was found in ventral traits. Our results suggest that individual coastal populations may vary according to local environmental conditions, while those corresponding to the offshore ecotype appear to share similar morphological characteristics that might increase fitness in offshore habitats.

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 score0.017
Threshold uncertainty score0.310

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 head0.024
GPT teacher head0.248
Teacher spread0.225 · 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
Published2023
Admission routes1
Has abstractyes

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