Using Machine Learning to Inform Harvest Control Rule Design in Complex Fishery Settings
Bibliographic record
Abstract
ABSTRACT In fishery science, harvest management of size‐structured stochastic populations is a long‐standing and difficult problem. Rectilinear precautionary policies based on biomass and harvesting reference points now represent a standard approach to this problem. While these standard feedback policies are based on analytical or dynamic programming solutions assuming relatively simple ecological dynamics, they are often applied to more complicated ecological settings in the real world. In this paper, we explore the problem of designing harvest control rules for partially observed, age‐structured, spasmodic fish populations using tools from reinforcement learning (RL) and Bayesian optimisation. Our focus is on the case of Walleye fisheries in Alberta, Canada, whose populations display variable recruitment dynamics. We optimised and evaluated policies using several complementary performance metrics representing key trade‐offs in harvest management. The main questions we addressed were: (1) How do standard policies based on reference points perform relative to numerically optimised policies? (2) Can an observation of mean fish weight, in addition to stock biomass, aid in policy decisions?
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 teacher head, 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".