GrowChinook: an optimized multimodel and graphic user interface for predicting juvenile Chinook salmon growth in lentic ecosystems
Bibliographic record
Abstract
Linked foraging and bioenergetics models allow for increased understanding of fish growth potential and behavior by incorporating prey availability coupled to environmental conditions including temperature and prey visibility. To inform our understanding of growth and vertical migration patterns of Chinook salmon (Oncorhynchus tshawytscha) inhabiting lentic ecosystems, we linked foraging and bioenergetics models to create GrowChinook ( http://growchinook.fw.oregonstate.edu/ ). This multimodel design and optimization routine has broad applications in examining growth potential and predicting habitat use in stratified environments. We demonstrate the use of GrowChinook for the spring–summer rearing period in three Willamette River basin reservoirs, Oregon, USA. These reservoirs support juvenile spring Chinook salmon that exhibit a novel reservoir-reared life history that includes larger juvenile fish compared with nearby stream-reared subyearlings. Model outputs of predicted growth and depth use patterns based on observed prey distributions and environmental conditions were corroborated by observed empirical size and growth data from other years. Our simulations support diel vertical migration as a tactic that increases growth potential and contribute to understanding juvenile Chinook salmon growth in stratified 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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".