Modelo de gestión por procesos para mejorar el desempeño en el área Agri-Food
Bibliographic record
Abstract
This research proposes improvements for the agri-food sector, which presents problems including: complaints from customers, who state that auditors and inspectors do not arrive at agreed-upon times; lack of procedures, which confuses clients since inspectors complete different operations for the same service; and finally, lack of input and output control of stored materials, so material required by all organizational personnel is frequently missing. After explaining the problems in the agri-food sector, this study aims to mitigate these directly-related problems, given that the client currently has a bad impression about the service. Likewise, the investigation was performed on a company that provides inspection, audit, testing and food certification services. The objective of this study is to determine the impact of a business process management model on the performance of the agri-food sector, with the aim of reducing complaints, standardizing activities carried out by the inspectors in the field and accounting for the materials required by all collaborators. The design of this research is time-series quasi-experimental; descriptive and inferential statistics have also been applied. Finally, the results obtained were: reduction of complaints, standardization of fieldwork and order fulfillment, all of which can be seen in the hypothesis testing.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".