Bibliographic record
Abstract
Various buyout or retirement schemes are used, primarily to reduce fishing effort and capacity. Buyouts are used to provide economic aid in cases of natural disaster and to reduce the numbers of vessels, licenses, and gear in a fishery, i.e., capacity reduction. This paper is part of a larger internship report conducted at the National Audubon Society's Living Oceans Program, Islip, New York. The report was the final requirement for an M.A. in Marine Affairs and Policy at the University of Miami, Florida. In this paper I briefly outline two case studies, The Texas shrimp license buyout and the Florida net ban. I also illustrate the complexities when discussing multiple buyout programs such as New England and Canada Atlantic groundfish. The impetus of this research project w as to examine the case study of Atlantic swordfish and its pelagic longline fishery including the pros and cons of the management tech- niques of conservation and buyouts. In conclusion, I discuss the salient points of buyouts and provide recommendations for fut ure buyouts.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".