A review of data collection methods used to monitor the associations of wild species with marine aquaculture sites
Bibliographic record
Abstract
Abstract Aquaculture contributes a significant portion of the global aquatic biomass destined for human consumption. Bivalve and marine finfish aquaculture operations require sea‐based farm sites that result in considerable interactions with the natural environment. The addition of feed waste and physical structures (e.g., net pens and longline mussel culture) can provide an attractive artificial reef for many species and studies have shown both positive and negative effects on the surrounding ecosystem due to wild species interactions with aquaculture sites. Assessing these interactions can be complex, depending on the local ecosystem, and several monitoring techniques have been used to accurately determine associations of wild finfish and decapods to marine farms. In this review, we assessed the main methods used to monitor aquaculture‐ecosystem interactions. The advantages and disadvantages of each technique are discussed and suggestions to mitigate shortfalls for future studies are outlined. It was evident that combining methodologies should be prioritised to lessen the impact of identified weaknesses of any given approach. Designing studies with complementary approaches may help attain robust data that can be used to further understand aquaculture‐ecosystem interactions and the underlying proximate mechanisms.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".