Communicating uncertainty in quota advice: a case for confidence interval harvest control rules (CI-HCRs) for fisheries
Bibliographic record
Abstract
Multi-annual management plans are important tactical arrangements to support upper-level marine resource policies in many countries. The newly reformed Common Fisheries Policy in the EU reiterates the role of management plans, supported by the development of harvest algorithms, commonly called harvest control rules (HCRs). Current HCRs for most commercially important fish stocks in Europe and Norway depend on point estimates of the size of the spawning stock biomass (SSB) and level of fishing mortality (F) to dictate the scientifically recommended total allowable catch (TAC). When annual TAC advice from the ICES Advisory Committee, for example, is based on a point estimate for SSB, the propagation of uncertainties (assessment models of varying complexity, variable data sources, and variable degrees and structures of random and systematic errors) and subjective expert decisions is contained, at best, in an annex of the official ICES advice document. TAC advice given as an exact number (sometimes specified to the kilogram) often occurs when clients (who commission the advice or ministerial or other government authority) expect more of science than science can deliver. We outline an alternative formulation of the HCR that reflects the knowledge base through confidence intervals (CIs) dictated by the quality of input data from multistage sample surveys and model uncertainties. Our CI-HCR determines the TAC advice given the range of SSB and F assessed and performs more robustly in the face of uncertainties than the standard HCR formulation. The advantage of CI-HCR is that the advised quota will depend on the quality of the assessments. Also, the adequate level of monitoring for advice support can be determined based on what science can actually provide.
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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 | 0.146 | 0.468 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".