Fur seals and sea lions (family Otariidae) on the breakwaters at Adelaide’s Outer Harbor, South Australia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".