Bibliographic record
Abstract
Ensemble modelling for fisheries analyses is increasing and may improve on single-model approaches through better representation of uncertainty, reduced potential for bias, and greater stability in results. Stability, defined here as deviations from model estimates as each year of data are added, may be due to the use of multiple models (rather than periodic changes to a single base-case model) and from the buffering effect of characterizing the central tendency with a set of models. However, stability against the addition of new data, although logically appealing, has not been explored for fisheries stock assessment. We use the Pacific halibut (Hippoglossus stenolepis) ensemble as an example and provide a simple simulation to explore the general behavior of results from an ensemble of models. Counterintuitively, we found the models in the halibut example showed high temporal correlations among deviations. However, we found that a small number of models with low among-model deviation correlations could show a stability benefit. Among-model deviation correlations may be a useful diagnostic for analysts developing ensembles or for those performing sensitivity testing of single-model assessments.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".