Local Influence Diagnostics for Quasi‐Likelihood and Lognormal Estimates of a Biological Reference Point from Some Fish Stock and Recruitment Models
Bibliographic record
Abstract
We present local influence diagnostics to measure the sensitivity of a biological limit reference point (LRP) estimated from fitting a model to stock and recruitment data. LRPs are low levels of stock size that the management of commercial fisheries should avoid with high probability. The LRP we examine is the stock size at which recruitment is 50% of the maximum (S(50%)). We derive analytic equations to describe the effects on S(50%) of changing the weight that observations are given in estimation. We derive equations for the Ricker, Beverton-Holt, and hockey-stick stock-recruit models, and four estimation methods including the error sums of squares method on log responses and three quasi-likelihood methods. We conclude from case studies that the hockey-stick model produces the most robust estimates.
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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) | 0.000 | 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".