313 Does quantity equal quality? Source of protein influences American dog owner purchasing decisions more than quantity of protein in the absence of marketing claims.
Bibliographic record
Abstract
Abstract Protein is the most expensive macronutrient worldwide, yet protein-related claims like “high protein” or “protein #1 ingredient” strongly influence dog food purchases. Little is known about how dog owners define protein quality (PQ) and how this knowledge shapes purchasing decisions. This study investigated how perceptions of dietary protein translate from personal to dog feeding habits, the level of PQ knowledge among dog owners, and whether protein source or amount has a greater influence on dog food choice. A 60-question survey was distributed to dog owners across the United States using Qualtrics (Utah, USA) (n = 691). Each respondent answered 12 choice experiment questions in which dog food options varied by protein amount (20%, 35%), protein source (peas, chicken, chicken meal), and price ($80, $95, $110). Descriptive statistics were analyzed in SPSS Statistics (Version 29, IBM Corp.), and a multinomial logit model in STATA (Version BE) was used for choice experiment analysis, with significance set at P < 0.05. Chicken had the greatest influence on purchasing choice, followed by chicken meal, and 35% protein, compared to a baseline diet of 20% protein and peas (P < 0.001). The majority (30%) of respondents believed PQ was parallel to quantity, while only 18% could correctly define PQ as the ability of an ingredient to meet the indispensable amino acid requirements of an individual. For respondents who correctly defined PQ, all protein sources positively influenced choice, while greater protein quantity negatively influenced choice (P < 0.001). Ultimately, protein source, not amount, drives purchasing behavior in the absence of protein-related claims on dog food.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".