The Impact of Different Performance Measures on Model Selection for Fraser River Sockeye Salmon
Bibliographic record
Abstract
Abstract Uncertainties prevalent in fisheries systems result in deviations between management targets and observed outcomes. As an example of attempting to deal with such uncertainty, fishery managers of sockeye salmon Oncorhynchus nerka from the Fraser River, British Columbia, use environmentally based management adjustment (MA) models to forecast indices of in-river loss of adults as they migrate upstream to spawn. Losses forecasted by MA models are directly incorporated into estimates of total allowable catch, resulting in harvest reductions that aim to increase the probability of achieving spawning escapement targets. However, the relative forecasting success of different MA models has not been rigorously assessed. Therefore, we used a suite of forecasting and hindcasting metrics to rank the performance of numerous MA models. We found that the rank of each model varied across sockeye salmon stock aggregates (i.e., run timing groups) and depended on the performance measures chosen for evaluation. Although model selection in fisheries research is often determined solely by model-fitting criteria, such as R2 and Akaike's information criterion (corrected for small-sample bias), in our case the models with the largest mean R2 value, the smallest mean corrected Akaike's information criterion, or both often ranked poorly for measures of model forecast performance (i.e., mean raw error, mean absolute error, and root mean square error). Although no single model performed best across all run timing groups, failure to apply an MA produced the worst outcome (for 3 of the 4 run timing groups) or second-worst outcome (for the fourth group). We provide a framework for model selection based on the relative importance of different model selection criteria and their associated performance measures. We urge scientists and managers to work closely together to develop appropriate metrics for assessing model performance and for objectively selecting forecast models that will best meet management objectives. Received April 29, 2010; accepted December 6, 2010
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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 | 0.057 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".