PSVI-28 Palatability and Scent Detection of Oxidation Levels in Poultry Meal
Bibliographic record
Abstract
Abstract Pet food made from fresh and rendered high quality meat products are considered safe and nutritious products. Currently the main assessment of meat freshness and fat products is based on peroxide values (PV), quantifying secondary oxidation products such as aldehydes, ketones, and alcohols. Research on how rancidity or peroxidation affects the health/safety of pets has not been adequately investigated. Exploring how Labrador retrievers interact with PV associated aromas, the goal was to observe any correlations in canine aromatic preference to differing poultry meal PV levels. A pilot study was conducted to gather preliminary data and screen 60 Labrador Retrievers (30 male/30 female) for those best suited for this novel aromatic palatability approach. 10 Labrador Retrievers (5 male/5 female) were hand selected from the original group of 60, according to their willingness to interact repeatedly with the aromatic boxes designed to prevent consumption while allowing interaction with varied PV poultry meal aromas. Many dogs lost interest quickly when they learned they could not get to the inside contents of the boxes, making the pilot study a crucial step in the preliminary selection process. First approach was recorded for both trials as well as time spent interacting. Time spent at each box was converted to ratios and both were statistically analyzed. Data falling outside 2 standard deviations from the mean were deemed outliers and excluded from analysis. Ratio analysis examined over both trials pointed to a higher peroxide value (PV) preference, when paired with sample 1, especially sample 5. PV levels 2, 4, and 5 showed significantly higher (p=< 0.05) interaction times and 6 neared significance (P = 0.08), compared to PV level 1. Further exploration could compare all PV levels to one another, determining if a specific threshold or range of preference exists within the 6 levels we examined in this study.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".