Evaluation of a chinook salmon (<i>Oncorhynchus tshawytscha</i>) bioenergetics model
Bibliographic record
Abstract
We evaluated the Wisconsin bioenergetics model for chinook salmon (Oncorhynchus tshawytscha) in both the laboratory and the field. Chinook salmon in laboratory tanks were fed alewife (Alosa pseudoharengus), the predominant food of chinook salmon in Lake Michigan. Food consumption and growth by chinook salmon during the experiment were measured. To estimate the efficiency with which chinook salmon retain polychlorinated biphenyls (PCBs) from their food in the laboratory, PCB concentrations of the alewife and of the chinook salmon at both the beginning and end of the experiment were determined. Based on our laboratory evaluation, the bioenergetics model was furnishing unbiased estimates of food consumption by chinook salmon. Additionally, from the laboratory experiment, we calculated that chinook salmon retained 75% of the PCBs contained within their food. In an earlier study, assimilation rate of PCBs to chinook salmon from their food in Lake Michigan was estimated at 53%, thereby suggesting that the model was substantially overestimating food consumption by chinook salmon in Lake Michigan. However, we concluded that field performance of the model could not be accurately assessed because PCB assimilation efficiency is dependent on feeding rate, and feeding rate of chinook salmon was likely much lower in our laboratory tanks than in Lake Michigan.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".