Science-to-Management Pathways for Collaborative Herring Stock Survey Data: Using network analysis to track information flow and potential influence in fisheries management
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Herring in the Northwest Atlantic are not overfished and overfishing is not occurring. However concerns for the potential of localized depletion and negative impacts on other fisheries and economic sectors have led to a sequence of management plans and amendments in the U.S. in recent years. Stock assessments have been vital in these management deliberations and there are several sources of herring stock survey data in the Gulf of Maine and Georges Bank, including a collaborative industry-science acoustic survey and government-administered trawl surveys. A joint U.S.-Canadian technical committee of scientists conducts the stock assessment from these data. We first describe the stock survey approaches, including the outcome of a 2005 external peer review of the collaborative acoustic survey, and examine their use in the assessment process. Second, we use a network analysis methodology to map the communication patterns among participants in the development of a fisheries management plan (FMP). Individuals (nodes) and their connections (links) are spatially arranged in a network map based upon the communicative relationship among all individuals. We track the pathways through which the collaborativelyderived stock survey data flow into the stock assessment (science) and the FMP decision-making (management) process. We compare pathways for their communication efficacy in feeding stock survey information into science and management. The resulting map shows participants in the collaborative survey well connected to the stock assessment and fisheries management process, although not institutionalized and dependent upon key individual participants serving as bridgers between informational resources.
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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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".