Sanitary and Phytosanitary measures for the export of Gherkins
Bibliographic record
Abstract
Gherkins (Cucumis sativus), the small, crunchy cucumbers renowned for their use in pickling and condiments, have emerged as a significant agricultural commodity in India. This study is based on secondary data collected from FAOand analysed using ratio analysis. The results reflect the regulatory standards designed toensuring food safety and consumerprotection by specifying permissible levels for each category of food additive. Maximum levels often align with internationalstandards or Codex Alimentarius guidelines to facilitate trade and ensure consistency in food safety regulations worldwide. Tin (Sn) is a metal that can be present in food due to various processing and packaging methods. The MRL of 250 mg/kg,calculated as Sn, indicates a threshold beyond which tin contamination in food is considered unacceptable. Lead is a toxicheavy metal that can contaminate food through environmental pollution, water, soil and food processing. MRL of lead infood is set at one mg/kg to protect consumers, particularly vulnerable populations such as infants and pregnant women,from the harmful effects of lead exposure. The Codex, set at 72, serves as a baseline for comparison across countries. Deviation from Codex values gives insight into how countries compare to the global average (Codex). For instance, the EUsignificantly surpasses the Codex benchmark with a deviation of 2.81. Conversely, Australia (58) falls below the Codexbenchmark, with a deviation of 0.81, implying fewer AI entities relative to the global average. Using India as a referencepoint (1.00), Canada and the USA have a deviation of 1.04, suggesting a slightly higher number of AI entities compared to India. Malaysia, Brazil and Chile all show deviations close to 0.97, indicating a similar AI presence to that of India. Thegherkins growers need to be educated on the pre-harvest interval of sprays, permissible maximum residue levels, judicioususe of pesticides, insecticides and herbicides.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".