A Hedonic Analysis of Apple Prices and Product Quality Characteristics in British Columbia
Bibliographic record
Abstract
The quality and market characteristics of apples have important implications for the merchandising strategy of packers and marketers. A hedonic price function relating apple prices to product and market quality characteristics is estimated for British Columbia over three marketing years (1994–96). The results indicate that grade, cultivar, storage and marketing season are the most significant variables influencing apple prices. The results show that price discounts and premiums for quality characteristics are relatively larger for the linear model than for the log‐linear or power‐transformed models. Les caractères qualitatifs et commerciaux des pommes ont des répercussions importantes sur les stratégies de vente des emballeurs et des vendeurs. L'auteur examine une fonction hédonique des prix qui relie le prix des pommes aux caractères du produit et à sa qualité commerciale au cours de trois campagnes de mise en marche (1994–1996) en Colombie‐Britannique. À partir des résultats, il découle que le classement, le cultivar, la conservation et la période de mise en marché sont les variables les plus significatives du prix des pommes. Les résultats montrent que les rabais et les primes à la qualité sont relativement plus importants selon le modèle linéaire que dans les modèles log linéaires ou dans les modèles à transformée exponentielle.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".