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Record W2042147086 · doi:10.1139/z99-208

Seasonal distribution and diving behaviour of male sperm whales off Kaikoura: foraging implications

2000· article· en· W2042147086 on OpenAlexvenueno aff
Nathalie Jaquet, Steve Dawson, Elisabeth Slooten

Bibliographic record

VenueCanadian Journal of Zoology · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCephalopods and Marine Biology
Canadian institutionsnot available
Fundersnot available
KeywordsBiologySpermPredationForagingEcologyWhalingSperm competitionFisherySperm whaleZoology

Abstract

fetched live from OpenAlex

Male sperm whales (Physeter macrocephalus) were the preferred target of the whaling industry between 1950 and 1985, but despite hundreds of thousands of kills, very little is known about their ecology. To partially redress this, we present data on residency, seasonal distribution, and diving behaviour of individually identified sperm whales off Kaikoura, South Island, New Zealand, gathered during 15 field seasons over 8 years. One hundred and thirty-six sperm whales were identified within the study area. A lack of statistically significant differences in the abundance of sperm whales between summer and winter, and among the 15 seasons of fieldwork, suggests an adequate food supply year-round. Significant differences in distribution between summer and winter suggest that off Kaikoura, male sperm whales may change their diet in response to fluctuations in prey biomass. Diving behaviour was also significantly different between summer and winter: sperm whales dived for longer, stayed longer at the surface, and travelled farther between consecutive fluke-ups in summer than in winter. Unlike female sperm whales, males at Kaikoura spent little time at the surface; they spent about 83% of their total time under water. This paper represent the most extensive non-invasive study of male sperm whales and provides new insights into their ecology.

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 score0.233
Threshold uncertainty score1.000

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)0.0010.000

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 head0.012
GPT teacher head0.209
Teacher spread0.197 · 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

Citations98
Published2000
Admission routes1
Has abstractyes

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