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Record W2064086548 · doi:10.1071/zo10017

Australian fur seals establish haulout sites and a breeding colony in South Australia

2010· article· en· W2064086548 on OpenAlexaff
P. D. Shaughnessy, Jane McKenzie, Melanie L. Lancaster, Simon Goldsworthy, T. E. Dennis

Bibliographic record

VenueAustralian Journal of Zoology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsShaughnessy Hospital
Fundersnot available
KeywordsFur sealElephant sealBiologyEndangered speciesBreedDingoFisheryCapeZoologyWildlifeMonotremeZoogeographySouthern elephant sealFaunaGeographyEcologyArchaeologyPredationHabitatSystematicsTaxonomy (biology)

Abstract

fetched live from OpenAlex

Australian fur seals (Arctocephalus pusillus doriferus) breed on Bass Strait islands in Victoria and Tasmania. They have been recorded in South Australia (SA) for many years as non-breeding visitors and on Kangaroo Island frequently since 1988, mostly in breeding colonies of the New Zealand fur seal (A. forsteri) which is the most numerous pinniped in SA. Australian fur seals have displaced New Zealand fur seals from sections of the Cape Gantheaume colony on Kangaroo Island. North Casuarina Island produced 29 Australian fur seal pups in February 2008. Australian fur seal pups were larger than New Zealand fur seal pups in the same colony and have been identified genetically using a 263-bp fragment of the mitochondrial DNA control region. North Casuarina Island has been an important breeding colony of New Zealand fur seals, but pup numbers there decreased since 1992–93 (contrary to trends in SA for New Zealand fur seals), while numbers of Australian fur seals there have increased. This study confirms that Australian fur seals breed in SA. The two fur seal species compete for space onshore at several sites. Australian fur seals may compete for food with endangered Australian sea lions (Neophoca cinerea) because both are bottom feeders.

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)0.001
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 score0.014
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.040
GPT teacher head0.285
Teacher spread0.245 · 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

Citations12
Published2010
Admission routes1
Has abstractyes

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