Contraction of the banana prawn (Penaeus merguiensis) fishery of Albatross Bay in the Gulf of Carpentaria, Australia
Bibliographic record
Abstract
When the biomass and area occupied by a stock decline together, catch rates can remain high (hyperstability) and management with effort controls may be ineffectual. Banana prawn (Penaeus merguiensis) catches declined from 2000 until 2005 in the Albatross Bay area in the Gulf of Carpentaria (GOC), Australia. Data from commercial logbooks were used to investigate historical changes in the banana prawn fishery in this and other regions of the Northern Prawn Fishery to infer the potential causes of this decline. Data since 1970 were analysed using: (1) the mapping of catch and effort; and (2) normalised rank order catch curves, to determine the distribution of catches across fishing areas. These analyses show that there has been a marked contraction of the Albatross Bay fishery over 33 years of fishing into the centre of a stable ‘hotspot’, suggesting a potential mechanism for the observed negative relationship between catchability and biomass. We believe this is the first observation of a Peneaus prawn fishing ground contracting as biomass declines, supporting the view that the contraction of an area occupied by a stock as biomass declines, is a generalised phenomena observed widely across fisheries resources and not a dynamic confined to certain finfish and molluscs.
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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".