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Record W4406074366 · doi:10.1111/mms.13171

Worldwide variation in shape and size of orca (<i>Orcinus orca</i>) saddle patches

2024· article· en· W4406074366 on OpenAlexafffund
Pirjo H. Mäkeläinen, Ingrid N. Visser, Tracy E. Cooper, Mathieu Cusson

Bibliographic record

VenueMarine Mammal Science · 2024
Typearticle
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVariation (astronomy)GeographySaddleBiologyMathematics

Abstract

fetched live from OpenAlex

Abstract The global distribution of Orcinus orca (orcas/killer whales) encompasses populations which differ from each other. Saddle patch shapes and sizes were compared for nearly 4,000 individuals, in 48 geographically or ecologically divided groups/populations/ecotypes (GP/E), in four Ocean Basins. Some Antarctic GP/E had five shapes, contrary to previous studies, which found only one shape in these Southern GP/E. Pacific Resident ecotypes had the highest variation in saddle patch shapes. Globally, the most common shape was the ‘Smooth’ category. Saddle patch sizes were measured using a ratio of the width of the saddle patch compared to the width of the dorsal fin base and averaged within each GP/E. The narrowest saddle patches were observed in New Zealand waters. The widest saddle patches were observed at the Crozet Islands and the Falkland Islands (Islas Malvinas). Globally, we found that the shape and size of saddle patches helped to define various GP/E, reinforcing earlier predictions that this pigmentation may be indicative of population divisions. Our findings may help with describing poorly defined or undescribed ecotypes. Such results may therefore aid assessments by management authorities/policy makers and provide levels of guidance in the creation of conservation or recovery plans.

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 score0.202
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.206
Teacher spread0.199 · 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
Published2024
Admission routes2
Has abstractyes

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