Association networks in the Dutch offshore beam trawl fleet: their predictors and relationship to vessel performance
Bibliographic record
Abstract
Networks play a key role in the functioning of socioecological fishery systems. Most network studies among fish harvesters examining fishing success utilize interviews and questionnaires. Though insightful, such studies are resource and time-intensive and thus unlikely to be replicated frequently through time. Alternatively, commercial landings records and vessel monitoring systems (VMS) provide continuous sources of information that can be used to examine variation in vessel networks through time. We used VMS data to define association networks among vessels. Relationships were found between common network metrics and annual performance based on landings data. Associations between vessels were more closely examined as a function of annual activity, performance, favoured species, and landing port using temporal exponential random graph models. We examined network dynamics across 4 consecutive years. Changes in vessel associations were clearly related to performance, landing port, and species targeted. Network structure could affect the relationship between catch and nominal effort, influencing stock assessments and responses to management actions. Our methodology provides a means to follow network change, identifying situations where more detailed study is warranted.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".