A Seasonal Inverse Almost Ideal Demand System for North American Fresh Tomatoes
Bibliographic record
Abstract
Increased fresh tomato trade has prompted a number of trade disputes between the United States, Canada, and Mexico. One precondition of an antidumping dispute is meeting the “likeness of product” criterion. However, fresh tomato shipments and imports are highly seasonal, suggesting that the degree of substitutability (or product likeness) may depend fundamentally on whether fresh tomato varieties are in‐ or out‐of‐season. We develop a seasonally adjusted inverse demand system using Canadian and Mexican monthly import data along with U.S. state shipping data to address both seasonality and product substitutability in the fresh tomato market. We find that market equilibrium and the degree of product substitution are affected by seasonality and product availability in the consumer choice set. Le commerce accru de la tomate fraîche suscite des différends commerciaux entre les États‐Unis, le Canada et le Mexique. L'une des préconditions d'un différend en matière d'antidumping est liée au respect du critère de la « similarité du produit ». Toutefois, les expéditions et les importations de tomate fraîche sont très saisonnières, ce qui laisse supposer que le degré de substituabilité (ou de similarité du produit) puisse reposer fondamentalement sur le fait qu'il s'agisse ou non de variétés de tomate fraîche pleine saison ou hors‐saison. Nous avons mis au point un système désaisonnalisé de la demande inverse utilisant des données mensuelles sur les importations canadiennes et mexicaines et des données sur les expéditions des États‐Unis pour étudier la saisonnalité et la substituabilité dans le marché de la tomate fraîche. Nous sommes arrivés à la conclusion que la saisonnalité et la disponibilité des produits offerts au consommateur influent sur l'équilibre du marché et le degré de substitution du produit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".