The replacement of fishing vessels in South Africa :\na case study of West Coast Rock Lobster nearshore fishery.
Bibliographic record
Abstract
The changing of vessels in the West Coast Rock Lobster nearshore fishery is one of the important issues which need attention within the South African fisheries management, that is, by fisheries authorities and industry (fishing right holders). This is due to the increasing problems regarding frequent vessel changes in the South African fisheries and the consequences in terms of increased fishing capacity. The thesis seeks to find major causes of vessels changes and how often the right holders change their fishing vessels. It further seeks to relate the policies of other fishing nations to gain measures to curb the problem of fishing capacity through the vessel replacement. The data were collected from primary and secondary sources and analyzed by both qualitative and quantitative methods. Various theories of capacity management were used in the study to explain the findings. The findings of this study reveal that transformation in South African fisheries has progressed, and that the fishers have shown development of their enterprise. Fishing nations like Canada and Australia have been used as cases for how to curb the problems. Some of the principles under laying their replacement policies may also be employed in the South African setting. A new and more precise replacement policy is strongly recommended for the South African WCRL fishery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".