Comparison of the cognitive palatability assessment protocol and the two-pan test for use in assessing palatability of two similar foods in dogs
Bibliographic record
Abstract
OBJECTIVE: To compare preferences of dogs for 2 similar foods by use of 2 distinct methods (the cognitive palatability assessment protocol [CPAP] and the 2-pan test). ANIMALS: 13 Beagles. PROCEDURE: 6 dogs were trained in a 3-choice object-discrimination-learning task in which their nonpreferred objects were associated with a reward of a lamb-based or chicken-based food. The number of choices for each object was used to determine food preferences. Preference of the same foods was also assessed by use of a 2-pan test in which all 13 dogs were provided the 2 foods in identical bowls. The amount of each food consumed in 10 minutes was used to determine food preference. RESULTS: All dogs had a noticeable preference for the chicken-based food during the CPAP. Once established, preferences remained consistent and were not affected by satiety. The 2-pan test identified a preference for the chicken-based food in dogs with previous exposure to the food but only a weak and nonsignificant preference for the same food in dogs without previous exposure. Food preferences in the 2-pan test varied considerably. Total food consumption and the ability to detect a preference were reduced when dogs were fed prior to testing. CONCLUSIONS AND CLINICAL RELEVANCE: The CPAP provides a reliable measure of food preference that requires few test subjects. The 2-pan test reveals similar preferences but with variability in data that requires larger numbers of subjects and is susceptible to effects from prior exposure and feeding of the test foods to the subjects.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.001 | 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".