Integrating multiple sources of data on migratory timing and catchability to estimate escapement for steelhead trout (<i>Oncorhynchus mykiss</i>)
Bibliographic record
Abstract
We evaluated the influence of biological, physical, and year effects on catchability, survey life, and departure timing for a winter-run steelhead (Oncorhynchus mykiss) population and compared the performance of alternate escapement models. Date of entry and gender explained 65% of the variability in survey life, and there was no evidence for differences in survey life among years. The median date of departure for male spawners occurred 2 weeks later relative to females, and a gender-based departure model was strongly supported. Departure timing was significantly different among years (p < 0.05), but the maximum difference in median departure dates was only 11 days. The ratio of horizontal visibility to discharge explained 50% of the variation in catchability, and there was weak support for a model that accounted for effects associated with courtship and spawning behaviour. There was strong support for an escapement model that assumed survey life and catchability relationships were common among years. Joint use of departure timing and survey life data reduced uncertainty in escapement estimates by an average of 40%. The major advantage of our escapement model is that it increases the precision of estimates while avoiding the use of overly simplistic assumptions about run timing and catchability.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".