Estimation of Bioenergetics Parameters for Rainbow Trout (Oncorhynchus mykiss) Using Capture-Recapture Data with Comparison to Estimates from a Laboratory-Based Model
Bibliographic record
Abstract
Bioenergetics models provide estimates of growth and consumption in fish and other animals.These estimates can then be used to infer metabolic and population-level consequences of various natural or human-induced environmental perturbations to fish populations.Most existing models utilize parameter values and functions derived from laboratory experiments on similar, closely related populations or species.However, the use of parameters from other species has long been criticized and recent work suggests that certain metabolic rates can vary substantially between closely related species and geographically separated populations of the same species.We evaluate a new model framework (termed the general bioenergetics model) which estimates bioenergetics parameters from length-increment and length-at-age data taken from the same population being modelled.Estimates of growth and consumption from this general model are compared with the commonly used "Wisconsin" bioenergetics model in terms of model fit and predictions resulting from simulated climate warming.Growth estimates using the general bioenergetics model were slightly higher than that of the Wisconsin model but consumption estimates were similar.Both models made similar predictions about effect of climate warming, although there was a consistent difference between model estimates of growth.The findings of this study add weight to the notion that metabolic information through bioenergetics models can be estimated from the population, although further validation should be conducted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".