Assessing population sustainability and response to fishing in terms of aggregation structure for greenlip abalone (<i>Haliotis laevigata</i>) fishery management
Bibliographic record
Abstract
The greenlip abalone (Haliotis laevigata) population in Waterloo Bay, South Australia, has undergone collapse and fishery closure twice since 1978. A rich data set, including survey measures of degree of spatial aggregation, has been gathered over that time and provides a unique opportunity to identify factors accounting for persistence or collapse, an issue that is of significance to abalone fisheries worldwide. Statistical analyses of fishery and survey data were undertaken to infer functional relationships between catch, effort, recruitment, adult density, and extent of aggregation. Catch rates were hyperstable, an observation consistent with the targeting of large aggregations. Statistical analysis of recruitment showed a significant year effect, implying an environmental signal, and suggested that aggregation size impacts fertilization success. Aggregation size grew under closure, suggesting an impact of fishing on this demographic feature. Aggregative behaviour appears to be critical for subpopulation sustainability. Fishery collapse may be triggered by depletion of larger abalone aggregations by heavy fishing, resulting in declines in fertilized egg production. When heavy fishing coincides with unfavourable environmental conditions, recruitment may be insufficient to sustain the subpopulation. If aggregation is similarly critical for other subpopulations, management strategies could be adapted according to the extent of aggregation in each subpopulation.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".