Describing population dynamics for early life stages of rainbow trout (<i>Oncorhynchus mykiss</i>) using a stock synthesis model<sup>1</sup>This article is a companion to Korman et al. 2011, published this issue.
Bibliographic record
Abstract
A stock synthesis model was used to assess effects of experimental flows on early life stages of nonnative rainbow trout ( Oncorhynchus mykiss ) in the Colorado River below Glen Canyon Dam (Arizona, USA). The model estimated time-varying survival rates while correcting for entry of new recruits to the age-0 population and changes in vulnerability to capture associated with growth and ontogenetic habitat shifts. A controlled flood, designed in part to enhance native fish habitat, led to an 11-fold increase in early survival rates (fertilization to ~1 month from emergence) of weekly cohorts of trout fertilized after the flood. Effects of increased flow fluctuations during incubation, designed to reduce trout abundance, were not apparent. Age-0 mortality between August and September was over twofold higher in years when there was a 50% reduction in the minimum flow compared with years when flow was stable. There was strong support for models that simulated an ontogenetic shift to deeper habitat in four of five study years. The integration of detailed field information in a stock synthesis model to describe early life history dynamics is a valuable approach that can be applied in a wide range of systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".