Bibliographic record
Abstract
Abstract The shark control programs of New South Wales (NSW), Queensland and KwaZulu-Natal (KZN) are compared in an attempt to determine whether the fishing effort applied in the KZN program could be reduced. The stated mechanism in all three programs is to reduce shark numbers, and thereby the probability of an encounter between a shark and a bather. Large-mesh ( 50–60 cm stretched) gill nets are used in each program, and in Queensland these are supplemented by baited drumlines. The number of standard ( 100 m net) net-days per protected (meshed) bathing area per month is about 26 in NSW, 57 in Queensland and 192 in KZN. There is a four-month closed season (i.e. no control measures) in winter in NSW, a six-week closed season at some Queensland beaches and no closed season in KZN. Despite these differences, the apparent successes of the programs in reducing the total number of shark attacks recorded at meshed beaches are impressive and comparable. The same shark species are believed to have been responsible for most of the attacks in the three regions, these being the great white shark Carcharodon carcharias , the bull shark Carcharhinus leucas and the tiger shark Galeocerdo cuvier . After comparing factors such as the nearshore physical environment and trends in shark catch and catch rate, it is concluded that there is a case for reducing the number of nets in KZN.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.988 | 0.991 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".