Bibliographic record
Abstract
Food production and preservation is an important social issue of increasing concern from ancient time onwards. The practice of fruit/vegetable coating was accepted long before their associated chemistries were understood, and are still practiced till date and deserves allocation of more research efforts to investigate the health effects by consumption of coated fruits and vegetables. Morpholine, O(CH2CH2)2NH is a common solvent and emulsifier used in the preparation of wax coatings for fruits and vegetables. Morpholine, by itself, in the doses that are present in fruits and vegetables probably does not constitute a health risk. However, it undergoes nitrosation during the digestion process if there are excess nitrites, formed mainly from naturally occurring nitrate in the diet to form Nnitrosomorpholine (NMOR), a genotoxic carcinogen. Although there is no direct human data on nitrosation rate of morpholine to NMOR, but according to Health Canada Health Hazard Assessment (HHA) 2008 report on Morpholine in wax coatings of apples, safe dose of morpholine in humans is 4.3ng/body weight/day. Sufficient NMOR can be produced in the human gut after ingestion of morpholine-treated fruits/vegetables to pose a health risk raising the need for its effective removal from fruits and vegetables and educating consumers about this risk.
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 | 0.000 | 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) | 0.003 | 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".