Simulations of random fishing behaviour as an independent validation for the effect of active targeting of greenlip abalone (<i>Haliotis laevigata</i>) aggregations
Bibliographic record
Abstract
Although active targeting of abalone aggregations is documented for various species, its impact on large aggregations is poorly understood. As large aggregations make the greatest contribution to reproductive success, yet are vulnerable to exploitation, it is important to understand how targeted fishing impacts aggregation structure. If observed postfishing patterns are equally likely to have occurred in response to more random, nontargeted fishing, then changes in aggregation patterns cannot be directly attributed to aggregation-based targeting behaviour. The effect of targeted fishing on greenlip abalone (Haliotis laevigata) aggregations was verified by simulating three different levels of "random" fishing behaviour to generate postfishing aggregation frequency distributions. Comparison of the output with postfishing survey distributions suggested that observed aggregation patterns could not have resulted from random search behaviour. The aggregation survey data can therefore be used as a valid basis on which to quantify both fishing behaviour in terms of aggregation-specific catch patterns and the response of aggregations to fishing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".