Modelling net energy of commercial cat diets
Bibliographic record
Abstract
Net energy accounts for the proportion of energy expenditure attributed to the digestion, metabolism, and absorption of ingested food. Currently, there are no models available to predict net energy density of food for domestic cats. Therefore, the objectives of this study were to measure the heat increment of feeding in cats, and to model the net energy of commercial diets. Metabolizable energy and calorimetry data from two previous studies was reanalyzed to create net energy models in the present study. Energy expenditure was calculated using measurements of CO2 production and O2 consumption. Net energy was determined as the metabolizable energy of the diets minus the heat increment of feeding. The heat increment of feeding was determined as the area under the energy expenditure curve above the resting fed metabolic rate. Eight net energy models were developed using metabolizable energy, 1 of 4 dietary parameters (crude protein, fat, fiber, and starch), and heat increment of feeding values from 0-2 h or 0-21 h. Two hours postprandial, and over the full calorimetry period, the heat increment of feeding amounted for 1.74, and 20.9% of the metabolizable energy, respectively. Of the models tested, the models using crude protein in combination with metabolizable energy as dietary parameters best fit the observed data, thus providing a more accurate estimate of dietary energy availability for cats.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".