A Simple Mixed-Effects Model to Smooth and Extrapolate Weights-at-Age for 3Ps Cod
Bibliographic record
Abstract
Good estimates of weight-at-age for fishery catches and the stock are necessary for more reliable stock assessment and projections. I use a simple model to filter out “noise” in the 3Ps cod weight-at-age estimates, and to fill in missing values, especially for older ages and the age 14+ group that will be used in assessment models for the stock. The weight model is applied to estimated weight-at-age from fishery monitoring activities and also weight-at-age from the Fisheries and Oceans Canada (DFO) Spring research vessel (RV) survey which are assumed to represent weight-at-age in the stock. The other important model inputs are information about the precision of sampling estimates of weight-at-age which the model uses to help distinguish between population variability and measurement error. The model fits the fishery weights-at-age closely for those ages with low measurement error coefficients of variation (CVs). The survey weights-at-age have more between-year variability and presumably higher CVs and therefore the model did not fit these data as well but did capture the overall trends in the weights-at-age over time.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.009 |
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".