Self-starting cumulative sum harvest control rule (SS-CUSUM-HCR) for status-quo management of data-limited fisheries
Bibliographic record
Abstract
We demonstrate a harvest control rule based on the self-starting cumulative sum (SS-CUSUM) control chart that can maintain a fish stock at its starting (status-quo) level. The SS-CUSUM is an indicator monitoring tool commonly used in quality control engineering and does not require a long time series or predefined reference point for detecting temporal trends. The reference points in SS-CUSUM are calibrated in the form of running means that are updated on an ongoing basis when new observations become available. The SS-CUSUM can be initiated with as few as two observations in the time series and can be applied long before many other methods, soon after initial data become available. A wide range of stock indicators can be monitored, but in this study, we demonstrate the method using an equally weighted sum of two indicators: a recruitment indicator and a large fish indicator from a simulated fishery. We assume that no life history data are available other than 2 years of both indicator data and current harvest levels when the SS-CUSUM initiates. The signals generated from SS-CUSUM trigger a harvest control rule (SS-CUSUM-HCR), where the shift that occurs in the indicator time series is computed and is used as an adjustment factor for updating the total allowable catch. Our study shows that the SS-CUSUM-HCR can maintain the fish stock at its starting status-quo level (even for overfished initial states) but has limited scope if the fishery is already in an undesirable state such as a stock collapse. We discuss how the SS-CUSUM approach could be adapted to move beyond a status-quo management strategy, if additional information on the desirable state of the fishery is available.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".