SISTEMA DE TRAZABILIDAD EN LA CADENA DE SUMINISTRO DE MALANGA EN VERACRUZ, MÉXICO: PROSPECTIVA
Bibliographic record
Abstract
Background. Taro (Colocasia esculenta 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. Objective. To propose a traceability system in the supply chain of taro produced in Veracruz. Methodology. 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. Results and discussion. 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 (Apodemus sylvaticus). 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. Implications. The traceability model for the Mexican taro could be adapted to the traditional way of production in Mexico to increase competitiveness. Conclusions. 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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".