An analysis of error structure in modeling the stockrecruitment data of gadoid stocks using generalized linear models
Bibliographic record
Abstract
When modeling the stockrecruitment (SR) relationship, the Cushing, Ricker, and other SR models are fitted to the observed SR data by estimating parameters with assumptions made concerning the model error structure. Using a generalized linear model approach, we explored and identified the appropriate model error structure in modeling SR data for gadoid stocks. The SR parameter estimation was found to be influenced by the choice of error distributions assumed in the analysis. In modeling SR data for gadoid stocks, the BevertonHolt model was found to be more sensitive to the assumption of model error distribution than the Cushing and Ricker models. The lognormal and gamma distributions had higher probability of being acceptable model error distributions. Cluster analyses and summary statistics of error distributions in SR modeling did not show consistent patterns in the identification of an acceptable model error structure among species, geographic distributions, and sample sizes. A better understanding of the factors and mechanisms resulting in differences in the choice of appropriate model error distributions for different populations is needed in future research. We recommend that the generalized linear model be used to identify acceptable model error structures in quantifying SR relationships.
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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 | 0.073 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".