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Osteological differences between two sympatric forms of bottlenose dolphins (genus <i>Tursiops</i>) in Chinese waters

2000· article· en· W2155775962 on OpenAlexaff
J. Y. Wang, Lien‐Siang Chou, Bradley N. White

Bibliographic record

VenueJournal of Zoology · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOsteologyBiologySympatric speciationZoologyMeristicsBottlenose dolphinGenusNomenclatureKey (lock)Taxonomy (biology)Ecology

Abstract

fetched live from OpenAlex

Abstract Although there has been extensive research on bottlenose dolphins (genus Tursiops ), much controversy over the classification within this genus still exists. Even though many morphological variants occur, the prevailing view is for a single species, Tursiops truncatus (Montagu, 1821). To evaluate this view, two sympatric forms of bottlenose dolphins inhabiting Chinese waters were examined using multivariate statistical analyses of osteological data for two meristic and 18 cranial morphometric characters from 71 specimens. Cluster and principal components analyses revealed clear osteological separation of the two forms. Furthermore, the distributions of the total number of vertebrae and several proportions of cranial characters were non‐overlapping between the two forms. These results provided strong evidence that the two sympatric forms of Tursiops in Chinese waters are isolated reproductively and do not support the current view of a monotypic genus. Classification functions of a discriminant analysis and a key of several characters were developed to help researchers identify unassigned specimens. Provisional names ( T. truncatus and T. aduncus ) were suggested, but a formal taxonomic revision of this genus is still required. However, regardless of the nomenclature, immediate amendments to all present wildlife conservation legislation are strongly urged.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0030.000

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 head0.013
GPT teacher head0.251
Teacher spread0.238 · 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.

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

Citations95
Published2000
Admission routes1
Has abstractyes

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