Research on Agricultural Food Supply Chain Prediction and Control Based on Microbial Forecast
Bibliographic record
Abstract
The literature review on applying Predictive Microbiology into the field of agricultural food safety is presented in this paper at first. Then based on related risk analysis theory and considering various stages of agricultural food supply chain, it is calculated the shelf life of agricultural food and predicted microbial quantity correspondingly. On this basis, using probability statistics theory on detection of agricultural food, Monte Carlo simulation is implemented. At last, we discussed the quality security control model of agricultural food supply chain on the basis of Prediction Model, and it can came up with the conclusions that Microbial Predictive Technology plays a good part on quality and safety protection of agricultural food supply chain. Key words: Agricultural Food Supply Chain; Quality and safety of agricultural food; Predictive Microbiology; Warning Management; Monte Carlo Simulation Resume: La revue de l’application de la microbiologie predictive dans la securite de l’alimentation agriculturelle est d’abord presentee dans l’article present. Et puis, basee sur de la theorie de l’analyse de risque concernee et la prise en compte de differentes phases de la chaine d’approvisionnement des aliments agricols, la duree de conservation des aliments agricols est calculee et la quantite microbienne correspondante est predite. Sur cette base, utilisant la theorie de probabilites et de statistique dans la detection des aliments agricols, l’article effectue la simulation Monte Carlo. Finalement, nous discutons le modele de controle de la secutite de qualite de la chaine d’approvisionnement des aliments agricols en fondant sur le Modele de Prediction, et arrivons a la conclusion que la technologie predictive microbienne joue un role important dans la protection de qualite et de securite de la chaine d’approvisionnement des aliments agricols. Mots-Cles: chaine d’approvisionnement des aliments agricols, qualite et securite des aliments agricols, microbiologie predictive, management d’avertissement, simulation Monte Carlo
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".