Bibliographic record
Abstract
This paper examines the prevention of the exponential growth of aflatoxin occurring in multi-month, postharvest storage in tropical countries, with examples from field experience and scientific data. Four approaches to modern, safe, postharvest storage methods are described, the most successful being the use of flexible, UltraHermetic™ airtight structures that create an unbreatheable atmosphere (low oxygen, high carbon dioxide) through insect and microorganism respiration alone, without use of chemicals, fumigants, vacuum, or refrigeration.The increase in aflatoxin levels during multi-month, postharvest storage is a serious health hazard affecting several major crops. During postharvest crop storage by conventional methods in tropical conditions, molds existing within crops can produce aflatoxin levels many times greater than at harvest, often vastly exceeding the international safety standards of 20 ppb (parts per billion). For example, field data from Mali documents that during just two months of conventional peanut storage, average aflatoxin levels rose 200%. In Uganda, aflatoxin levels in conventionally stored maize rose 300% in three months. By contrast, laboratory and field data from Mali and Uganda show that the organically modified atmosphere created using various forms of sufficiently hermetic (airtight) storage containers (ranging from 25kg to 1000-tonne capacity) prevents the exponential growth of aflatoxin-producing molds in various grains, peanuts, and seeds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".