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Record W2750251492 · doi:10.1071/am17001

Fur seals and sea lions (family Otariidae) on the breakwaters at Adelaide’s Outer Harbor, South Australia

2017· article· en· W2750251492 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAustralian Mammalogy · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsShaughnessy Hospital
Fundersnot available
KeywordsBreakwaterSea lionFisheryRookeryGreat barrier reefEstuaryFur sealOceanographyGeographyBiologyReefGeology

Abstract

fetched live from OpenAlex

Long-nosed fur seals (Arctocephalus forsteri) and Australian sea lions (Neophoca cinerea) on the breakwaters at the mouth of the Port River estuary at Adelaide’s Outer Harbor were counted from 2004 to 2015. Observed counts were modelled using a generalised linear model. Fur seal numbers have been increasing since 2011; for sea lions there was a small discernible annual trend in counts. Counts of fur seals varied seasonally; most annual maxima were in August or September with modelled peak numbers around 9–11 September. The maximum count of fur seals was 79 in September 2015. For sea lions, the model predicts annual peaks in the period 28 August to 19 September. The maximum count of sea lions was nine in September 2009. The haulout sites on the Outer Harbor breakwaters are easily accessible by boats, including pleasure craft. In particular, the seaward end of the outer breakwater is a popular spot with recreational anglers whose lines are often within a few metres of the seals. We propose that a management plan should be developed involving a study of the effect of boat approaches on seals utilising the Outer Harbor area followed by education coupled with enforcement.

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.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.996

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.005

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 head0.065
GPT teacher head0.276
Teacher spread0.211 · 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