Widespread consumption-dependent systematic error in fish bioenergetics models and its implications
Bibliographic record
Abstract
Data from laboratory evaluations of seven fish bioenergetics models (BEMs) were used to investigate possible associations between BEM prediction error in relative growth rate (RGRerror) and levels of model input variables: mean daily food-consumption rate and fish body weight. Correlation between RGRerror and fish body weight was found in three BEMs applied under submaintenance feeding conditions. A strong correlation between RGRerror and mean daily consumption level was observed in all models over full consumption ranges; consumption level explained 70%96% of variation in RGRerror. All BEMs underestimated (by 2- to 5-fold) growth at lower consumption levels and overestimated (by 2- to 3-fold) growth at higher consumption levels. RGRerror values associated with higher consumption levels were greater (up to 22 cal·g1·day1) than those at lower consumption levels (up to 10 cal·g1·day1). Correlation between consumption rate and RGRerror in all seven models indicates widespread systematic error among BEMs that likely arises from deficiencies in consumption-dependent model parameters. Results indicate that many BEMs are substantially inaccurate when predicting fish growth from higher feeding rates or estimating consumption from higher growth rates, even when higher consumption levels or growth episodes are of short duration. Findings obtained under submaintenance feeding conditions indicate that additional body-weight- and consumption-dependent terms should be added to BEM subequations for routine metabolism to account for metabolic reduction.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".