A review of data collection methods used to monitor the associations of wild species with marine aquaculture sites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it