Getting back in the black: an interactive decision-support tool to aid timely management decisions associated with Alaska black cod (sablefish) discarding
Bibliographic record
Abstract
Fisheries management decisions often depend on complex modeling, but conveying results in non-technical terms can be challenging for scientists lacking formal communication training. As co-management gains traction, it is imperative that scientists develop less esoteric methods to convey results in broadly accessible formats. Online tools are appealing because interactive applications align with many stakeholders' digital comfort zone and programming in R is a common skillset among fisheries scientists. We developed an online decision support tool to address a time-sensitive management issue (allowing discards of small, low-value fish) within the Alaska sablefish fixed-gear fishery. Based on catch projection models, discarding due to the proposed de facto minimum size limit (22 in.) was shown to have minimal impacts on the resource or revenue. Pairing a technical document with an interactive application helped convey results and may have improved understanding and transparency of a complex analysis. The sablefish example underscores the need for a paradigm shift from technocratic approaches to more interactive and accessible methods for conveying scientific results at the science-policy interface.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".