Effects of an increase in Mexican strawberry exports to Canada on the profitability of pro-ducers in Mexico
Bibliographic record
Abstract
Objective: determine the viability of increasing the exported quantity of Mexican strawberries to the Canadian market. Design/methodology/approach: Likewise, a simulated scenario was developed with the purpose of carrying out a forecast on the conditions that may occur to have a more accurate knowledge of the operation of the international strawberry trade between Mexico and Canada. To perform this analysis, the international market was represented in a partial equilibrium model. Results: According to the calculated price flexibility, an increase in the exported quantity of Mexican strawberries to Canada of 50% in one year would cause a positive final effect. With this estimate, it can be established that an increase in the exported quantity of Mexican strawberries to Canada of 50% in one year would be viable in the economic sense. In this simulated scenario, the Benefit/Cost Ratio (B/C R) calculated for the producers of Michoacan, Baja California and Guanajuato would be 1.0865, 1.196 and 0.6856 respectively. Limitations on study/implications: not all products and all states of Mexico are examined. Findings/conclusions: The results showed that an increase in strawberry production to export to Canada in Michoacan and Baja California would be profitable for the producer, while an increase in strawberry production for export to the Canadian market in Guanajuato would further decrease profitability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".