Development of a Bioenergetics Model for Humpback Chub and Evaluation of Water Temperature Changes in the Grand Canyon, Colorado River
Bibliographic record
Abstract
Abstract The construction of Glen Canyon Dam above the Grand Canyon (Arizona) has reduced the water temperature in the Colorado River and altered the growth rate and feeding patterns of the federally endangered humpback chub Gila cypha. A bioenergetics model for humpback chub was developed and used to examine how warmer water temperatures in the lower Colorado River (achieved through a temperature control device [TCD] at Glen Canyon Dam) might influence their growth rate and food requirements. Parameter values for humpback chub were developed by Monte Carlo filtering and fitting to laboratory growth. Parameter bounds were established from the literature for Gila species, random parameter sets were selected within these bounds, and the growth of modeled humpback chub was compared with criteria from a laboratory growth experiment at 24°C. This method of parameter estimation could be applied to other imperiled fishes where physiological studies are impractical. Final parameter values were corroborated by comparison with the growth rates of humpback chub from independent field and laboratory studies. Simulations indicated that increasing water temperatures from approximately 9°C to 16°C during summer and fall, the change expected from the TCD, may have a minimal effect on humpback chub growth rate unless food availability also increases with temperature. To evaluate the effects of increased temperatures on humpback chub in the lower Colorado River, it will be essential to monitor their growth rate, the invertebrate community, and the predators of humpback chub, which are also influenced by temperature changes. Bioenergetics models for humpback chub and their predators should be helpful tools for identifying potential scenarios and evaluating the complex interactions resulting from a TCD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".