SISTEMA DE TRAZABILIDAD EN LA CADENA DE SUMINISTRO DE MALANGA EN VERACRUZ, MÉXICO: PROSPECTIVA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p><strong>Background.</strong> Taro (<em>Colocasia esculenta</em> L. Schott) is an economical import crop in Actopan, Veracruz, since it is exported to the United States of America (USA) and Canada. In Mexico, there is no regulation to implement traceability systems in agricultural products<strong>. Objective</strong>. To propose a traceability system in the supply chain of taro produced in Veracruz. <strong>Methodology</strong>. A questionnaire was designed to obtain information related to the production process; a second questionnaire asked about the packaging information; twenty-four producers and eight packinghouse managers were interviewed. <strong>Results and discussion.</strong> Producers have been growing taro for 10 years, they do not have any specific planting season and grow the variety Coco. No soil analysis is performed, irrigation is by flooding and the main pest is the mouse (<em>Apodemus sylvaticus</em>). Each producer makes an agreement with a packing house; the company performs the harvesting and then the packaging process. Wooden pallets are used for packaging; each pallet piles up 60 sacks of 18 kg of taro. <strong>Implications.</strong> The traceability model for the Mexican taro could be adapted to the traditional way of production in Mexico to increase competitiveness. <strong>Conclusions.</strong> The shipping label contains scarce information about the origin of the product. Links of the supply chain were identified and a traceability model for taro is proposed for first time.</p>
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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) | 0.000 | 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) | 0.001 | 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 it