Design and deployment of an affordable and long-lasting deep-water subsurface fish aggregation device
Bibliographic record
Abstract
Abstract Fish aggregation devices (FADs) are used worldwide to enhance the efficiency of various fisheries. Devices consist of a floating or subsurface component designed to exploit natural fish behavior, using species’ attraction to structure (e.g., Sargassum spp.) to aggregate fish and increase capture success in open ocean environments. Concerns have arisen regarding the scale and management of FAD-associated fisheries, however, the efficiency of FADs to aggregate fish also introduces the possibility for FADs to be used as conservation tools to study pelagic species ecology. Building on two successful and several failed deployments of anchored deep-water (>500 m) subsurface (10 m) FADs over three years in The Bahamas, and observations from the subsequent FAD monitoring program, the objectives of the paper are to: 1) provide details and considerations for the design, construction, and deployment of an affordable and durable deep-water subsurface FAD that can be deployed using small boats; and 2) highlight the potential for a long-lasting moored FAD to be used as a sustainable and reliable scientific platform for pelagic species research and conservation, lending specifically to several research applications. This information will be useful for assessing the impacts that FADs and other anthropogenic marine infrastructure have on wild marine species, and their efficacy for conserving pelagic fish through increased encounters for study.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".