History of Surface Longline Fishing Technology in Gouyave, Grenada
Bibliographic record
Abstract
Changes in fishing technology are important in assessing fish stocks.However, in many Fisheries Departments, it is rarely documented at the fishing community level.In the case of Gouyave, Grenada, surface longline fishers, they are constantly adapting and changing fishing technology to increase fish catch and income.The objective of this paper is to document the history of surface longline fishing technology (boat and gear), and determine how this technological knowledge, possessed by fishers could be included in fisheries management.Information was obtained from interviews with knowledgeable fishers.Traditionally, Gouyave fishers were involved in beach seine and '3line' (hand line) fishing, from non-mechanized wooden sloop canoes.By the 1980s, the Government of Grenada with assistance from the Cuban Government popularized surface longline fishing.Since then, fishers adapted and developed longline boat and gear technology to improve efficiency and effectiveness.Longline technology developed from twisted 2 x 113 kg strain monofilament mainline and droplines stored and deployed from a box, to single monofilament lines stored and deployed from reels.Boat technology developed from mechanized 5 m wooden canoes to 6 -12 m fibreglass vessels.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".