PSXI-21 Validation of a Method to Determine Dogs’ Preference for Flavors
Bibliographic record
Abstract
Abstract There is little information regarding dogs’ preference for a given flavor, especially in water. Therefore, the objective of this study was to validate the method of using a consumption test to determine the dogs’ ability to discriminate water-based flavors. There were three phases of the study to validate the model using water as a flavor carrier. First was to measure position bias, second to evaluate for preference for specific elemental flavors, and third to evaluate the dogs’ ability to district by dose. To eliminate confounding variables; salt (salty), dextrose (sweet), citric acid (sour) and MSG (umami) were mixed with tap water to create colorless and odorless test solutions (salt 0.5%, dextrose 4%, citric acid 0.1%, MSG 0.035%), with tap water as the control. For evaluating dose sensitivity, salt waters at increasing levels of concentration (0%, 0.25%, 0.5%, 1%, 2%) were evaluated. In this study, twelve adult Beagle dogs (average age one year) were individually housed. Using a randomized block design, ceramic bowls, labeled A-E, were placed along the back wall of each pen and filled with 400g of the control or test solutions. Bowl position was randomized daily, to account for any position bias; and water disappearance was measured for five days. Data were analyzed using a mixed model (SAS version 9.4, SAS Institute, Inc., Cary, NC) with treatment as a fixed effect and the dog and day as random effects. Of the elemental flavors, sweet was most preferred (average 250.05g/day) with sour and salty (average 96.45g/day and 85.77g /day) least preferred (p <0.05). Regarding dose distinction, a linear decrease (p<0.05) in water disappearance was observed as salt concentration increased. These results suggest that dogs do have the ability to distinct between flavors. It also suggests that dogs do have the ability to discriminate between different doses.
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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 | 0.007 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".