Prioritization of knowledge‐needs to achieve best practices for bottom trawling in relation to seabed habitats
Bibliographic record
Abstract
Abstract Management and technical approaches that achieve a sustainable level of fish production while at the same time minimizing or limiting the wider ecological effects caused through fishing gear contact with the seabed might be considered to be ‘best practice’. To identify future knowledge‐needs that would help to support a transition towards the adoption of best practices for trawling, a prioritization exercise was undertaken with a group of 39 practitioners from the seafood industry and management, and 13 research scientists who have an active research interest in bottom‐trawl and dredge fisheries. A list of 108 knowledge‐needs related to trawl and dredge fisheries was developed in conjunction with an ‘expert task force’. The long list was further refined through a three stage process of voting and scoring, including discussions of each knowledge‐need. The top 25 knowledge‐needs are presented, as scored separately by practitioners and scientists. There was considerable consistency in the priorities identified by these two groups. The top priority knowledge‐need to improve current understanding on the distribution and extent of different habitat types also reinforced the concomitant need for the provision and access to data on the spatial and temporal distribution of all forms of towed bottom‐fishing activities. Many of the other top 25 knowledge‐needs concerned the evaluation of different management approaches or implementation of different fishing practices, particularly those that explore trade‐offs between effects of bottom trawling on biodiversity and ecosystem services and the benefits of fish production as food.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".