Editorial: Data-limited research in stock assessment to increase the understanding of fisheries resources and inform and improve management efforts
Bibliographic record
Abstract
Editorial on the Research TopicData-limited research in stock assessment to increase the under- standing of fisheries resources and inform and improve manage- ment efforts Management thinker Peter Drucker is often quoted as saying "You can't manage what you can't measure."Drucker means that you cannot know whether or not you are successful unless success is defined and monitored.Such a quote is fully applicable to fishery science because only when we can estimate the status of stocks can we provide meaningful and successful management advice: that which gets measured gets managed.However, an increasing share of fishers' income is derived from fish from stocks whose status remains unassessed.In such situations, a simple rough model might be more useful than no model at all.The main reasons for the lack of assessment and associated formal harvest control rules are often associated to:lack of (quality) data to reliably inform a fully integrated stock assessment.limited capacity and funding.associated fishery characteristics, including inconsistent targeting practices, numerous unregulated operators, or profound cultural issues.the challenge of selecting from numerous possibilities and the most appropriate assessment and management options given the fishery's context.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.039 | 0.030 |
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".