Policies, Regulations and Quality Assurance Requirements for the Agri-Food Industry
Bibliographic record
Abstract
Global agri-food industries are trying to mitigate the challenges encountered in the supply chain through various environmental agents such as pests, pathogenic diseases, locusts and their carryover residues throughout the supply chain. Fungal phytopathogens among food crops pose a severe threat of toxicosis and migration of fungal toxins into the food supply chain. Therefore, the agri-food supply chain is subject to stringent policies, regulations and quality assurance requirements to mitigate the impact of fungal phytopathogens on crop production and ensure food products’ safety. Regulatory bodies worldwide have followed stringent and magnified measures to address the challenge posed by these fungal pathogens in crops, including the usage of pesticides. Countries such as the USA, India, Canada, EU and Japan have formulated measures proper for the farm with various plant health protection measures. Joint FAO/WHO Meeting on Pesticide Residues (JMPR) convenes annually to harmonize the requirement and the risk assessment on pesticide residues, thereby recommending the acceptable level of pesticide residues. Further, the Codex Alimentarius Commission (CAC), managed by FAO and WHO, fixes the MRLs based on the recommendations of JMPR through the assessment of risk assessment data. However, every country has standards and safety measures against food pathogens, contaminants or adulterants. The quality control of foods generally happens through active inspections (surveillance and enforcement) and subsequent laboratory testing as laid down by the act and regulations.
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 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".