Effect of Quality Characteristics on Consumers' Willingness to Pay for Gala Apples
Bibliographic record
Abstract
This paper uses individual apple‐level data that include consumer sensory assessments and instrumental measurements of internal quality to analyze willingness to pay for Washington State Gala apples. Three distinct models are estimated: a model that includes destructive internal quality measures of apple characteristics, a model that utilizes only non‐destructive internal quality measures, and a consumer model that includes subjective consumer sensory evaluations and consumer socio‐demographic characteristics. The objective is to identify instrumental measures of internal quality that can be used to inform the apple industry of consumer preferences. The consumer model serves as a benchmark. Finally, we evaluate whether non‐destructive measures of internal quality can substitute for destructive measures. We find that firmness and soluble solids content are significant and can be measured effectively using non‐destructive measures. Implications of the findings for the apple industry in terms of marketing and possible “elite” standards for apples are discussed. Le présent article utilise des données individuelles, notamment des évaluations sensorielles et des mesures instrumentales, pour analyser la volonté de payer des consommateurs pour des pommes Gala de l'État de Washington. Nous avons estimé trois modèles : un modèle comprenant des mesures instrumentales qui affectent la qualité interne des pommes; un modèle qui utilise des mesures instrumentales qui n'affectent pas la qualité interne; un modèle du consommateur qui comprend des évaluations sensorielles subjectives et des caractéristiques sociodémographiques. L'objectif consistait à identifier les mesures instrumentales qui pourraient être utilisées pour renseigner l'industrie pomicole sur les préférences des consommateurs. Le modèle du consommateur a servi de point de référence. Finalement, nous avons examiné si les techniques non destructives de mesure de la qualité interne pouvaient ou non remplacer les techniques destructives. Nous avons trouvé que la fermeté et la teneur en solides solubles sont des caractéristiques importantes et qu'elles peuvent être mesurées efficacement au moyen de techniques non destructives. Nous discutons des répercussions de ces résultats sur l'industrie pomicole sur le plan du marketing et de l'établissement possible de normes ≪élite≫.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".