Análisis de la volatilidad de precios al productor de limón en la costa del Pacífico mexicano
Bibliographic record
Abstract
Objetivo: evaluar y comparar el riesgo de precios que enfrentan los productores primarios de limón mexicano (Citrus aurantifolia, S.) de Colima, Michoacán, Guerrero y Oaxaca. La hipótesis general es que una alta volatilidad tiende a reducir la inversión de la cadena agroalimentaria del estado que la presenta. Materiales y métodos: se estiman diferentes modelos simétricos y asimétricos de media y varianza condicionada, tipo ARIMA-GARCH, para los precios diarios pagados al productor por el periodo 2015 a 2018. Resultados: estos modelos indican que la persistencia de la volatilidad es alta en Oaxaca, Colima y Guerrero, y menor para Michoacán. El impacto de las malas noticias en la volatilidad tiene magnitudes similares en los precios en los cuatro estados, pero la influencia de las buenas noticias es considerablemente más alta en Michoacán que en el resto de los estados. Conclusión: La relativamente menor volatilidad de largo plazo en Michoacán contribuye a mejorar las condiciones para la inversión en el sector.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".