Effects of Trade Barriers on U.S. and World Apple Markets
Bibliographic record
Abstract
Regional apple demand and supply parameters using Bayesian estimation were utilized to develop a spatial equilibrium model for the world apple market. A baseline scenario with existing tariffs was developed; a reduced tariff and free trade scenarios were simulated. Comparison of these scenarios with the baseline shows that apple trade is significantly greater under trade liberalization. China increases its exports by 174% under free trade and replaces France as the largest exporter. U.S. exports increased by about 140% to all regions except to Canada and Southeast Asia. Results confirm the importance of transportation costs and the interlinkages of prices in the world market in determining the trade flows. Nous avons utilisé des paramètres régionaux de l'offre et de la demande de pomme, déterminés à l'aide d'une estimation bayésienne, pour élaborer un modèle d'équilibre spatial du marché mondial de la pomme. Nous avons élaboré un scénario de base utilisant des tarifs douaniers existants et nous avons simulé des scénarios utilisant des tarifs douaniers réduits et le libre‐échange. Une comparaison de ces scénarios avec le scénario de base montre que le commerce de la pomme est significativement plus important dans un contexte de libéralisation des échanges. La Chine augmente ses exportations de 174 p. 100 dans un contexte de libre‐échange et détrône la France en tant que premier exportateur mondial. Les exportations des États‐Unis augmentent d'environ 140 p. 100 dans toutes les régions sauf au Canada et en Asie du Sud‐Est. Les résultats confirment l'importance des coûts de transport et des liens entre les prix sur le marché mondial dans la détermination du flux des échanges commerciaux.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".