Identification of recruitment regime shifts with a hidden Markov stock-recruitment model
Bibliographic record
Abstract
Abstract Stock-recruitment relationships (SRRs) may differ substantially among environmental regimes. We developed a methodology including a Hidden Markov Stock-recruitment Model (HMSM), the maximum likelihood approach and a model selection procedure to identify abrupt changes of stock-recruitment (SR) dynamics. This method allows us to objectively identify the unobserved regimes, estimate regime-specific parameters, and predict the transition probabilities among regimes. First, we used simulation to verify that our method could identify the correct number of regimes and estimate the model parameters well. Then, we applied the models to an Atlantic cod stock on the southern Grand Bank off Newfoundland, Canada. Results indicated that the HMSM assuming 2 regimes performed the best, and the cod stock shifted to a regime characterized with lower productivity and higher density dependence in late 1980s. Additionally, the estimated probability to return to the previous high-productivity regime was very low, suggesting the cod stock may remain at the low-productivity regime for a prolonged period. Overall, we consider the methodology proposed in this paper as a useful tool to model regime shifts of SRRs in fisheries stock assessment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".