Using highest density intervals can reduce perceived uncertainty in stock assessments
Bibliographic record
Abstract
Sustainably managed fisheries provide conservation and socio-economic benefits. To set catch quotas, decision makers are guided by stock assessments that use mathematical models to make quantitative predictions of how populations will change under different management scenarios. Stock assessments need to communicate uncertainty of estimated quantities, which is often done through figures and tables depicting credible or confidence intervals. We show that computing such intervals with the usual equal-tailed approach has undesirable consequences, such as excluding highly probable values yet including relatively improbable values. This can give an overly optimistic impression of the health of fish stocks, with potential unexpected management implications. Instead, using highest density intervals can resolve these problems, resulting in narrower intervals that reduce perceived uncertainty (by > 3 billion fish for a recent cohort of Pacific Hake, Merluccius productus , for example). Therefore, we recommend consideration of highest density intervals in fisheries applications and other fields to better characterize uncertainty and improve conservation advice. We introduce our new R package, hdiAnalysis, which enhances previous methods to encourage and facilitate uptake by practitioners.
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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 | 0.022 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".