Estimating bioenergetics model parameters for fish with incomplete recapture histories
Bibliographic record
Abstract
Traditional methods for modeling growth of free-ranging fish are often limited by missing recapture observations that prevent individual growth estimates for a given time interval. Our purpose is to present a method for modeling growth rates of juvenile steelhead ( Oncorhynchus mykiss ) that addresses this limitation. Age-1 juvenile steelhead were individually marked with passive integrated transponder (PIT) tags, released in a Michigan, USA, watershed, and sampled monthly (May–November) with barge electrofishing. Individual growth was modeled using daily water temperature and observed fish sizes as inputs and by determining the proportion of maximum consumption parameter (P) for the bioenergetics equation that provided a minimum residual squared error. Results demonstrate that individual steelhead growth can be accurately modeled using water temperature and a temporally specific P shared by all individuals. Advantages of using this method to model fish growth include the ability to bridge data gaps where observations are lacking in individual length histories, rigorously test for differences in P across time periods, and estimate variability of P among fish within a given stream reach.
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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 | 0.002 | 0.005 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".