MétaCan
Menu
Back to cohort
Record W2004050658 · doi:10.1080/03014223.2004.9517771

Observations of live Gray's beaked whales <i>(Mesoplodon grayi)</i> in Mahurangi Harbour, North Island, New Zealand, with a summary of at‐sea sightings

2004· article· en· W2004050658 on OpenAlexfundno aff
Merel L. Dalebout, Kirsty Russell, Murray J. Little, Paul Ensor

Bibliographic record

VenueJournal of the Royal Society of New Zealand · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersKillam TrustsDalhousie UniversityInternational Fund for Animal Welfare
KeywordsBeaked whaleWhalingMinke whaleFisheryWhaleGeographyCircumpolar starHarbourOceanographyDorsal finBiologyBalaenopteraGeology

Abstract

fetched live from OpenAlex

Abstract Apair of free‐swimming Gray's beaked whales, Mesoplodon grayi , an adult female and a calf, were observed in Mahurangi Harbour, near Warkworth, on the North Island of New Zealand, over 5 consecutive days in June 2001. Beaked whales (family Ziphiidae) are seldom seen at sea due to their oceanic distribution, deep diving ability, elusive behaviour, and possible low abundance. Gray's beaked whale is the most common beaked whale species to strand in New Zealand but observations of live animals in these waters are rare. Colour pattern and behaviour of these little known cetaceans are described. Although both animals appeared to be in good condition, the adult female had a series of deep corrugated scars behind her dorsal fin, likely the result of a ship strike. Other at‐sea sightings of this species from International Whaling Commission (IWC)—International Decade of Cetacean Research (IDCR) minke whale assessment cruises and IWC—Southern Ocean Whale and Ecosystem Research (SOWER) circumpolar cruises are summarised. A cluster of sightings to the south‐west of the Chatham Islands may indicate the existence of a “hotspot” for M. grayi in the New Zealand region.

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 score0.075
Threshold uncertainty score0.967

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 science0.0010.000
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 head0.015
GPT teacher head0.207
Teacher spread0.192 · 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

Citations16
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Royal Society of New ZealandSame topicMarine animal studies overviewFrench-language works237,207