JAPANESE MILLERS' PREFERENCES FOR WHEAT AND FLOUR: A STATED PREFERENCE ANALYSIS
Bibliographic record
Abstract
Japan imports 6.3 MMT of wheat annually and consumes almost 35 percent of this in the form of noodles. The purpose of this paper is to report on a study that evaluates the preferences of Japanese millers for the various characteristics of wheat and flour that are used in noodle making in Japan. The study used stated preference methodologies (SPM), that were developed and pre-tested through initial interviews with Japanese flour millers. In total, 57 purchase and quality managers for 22 Japanese milling companies were surveyed by means of direct interviews and 41 respondents completed the full SPM survey. Multinomial logit models of millers' preferences were developed and tested and the parameter estimates of these are reported in the paper. This elicited their choices of wheat and flour with alternative combinations of characteristics, at specified levels, for various wheat classes and noodle flours. Data were also collected on stated choices for wheat sourced from different origins. Millers prefer wheat with test weights of minimum 80, dockage below 0.4 percent and falling numbers above 250. Preferences for protein, ash and color were specific for different wheat classes and for use in different noodle flours. Millers also display a preference for amylograph at minimum of 400 BU for noodle flour. For hard wheat millers preferred wheat of U.S. and Canadian origin, but for semi-hard and medium wheat, they preferred Australian origin wheat. These results may assist wheat breeders and traders in exporting nations in marketing their products and positioning these in this important and premium wheat market.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".