The Analysis of a Feed Component Imported into South Africa for Aflatoxin in Relation to Fungal and Mycotoxin Contamination
Bibliographic record
Abstract
Abstract: Currently there is concern with respect to the occurrence of mycotoxins in feed commodities, which could result in the loss of animal production and danger to consumers. Recent legislation to control the trading of such contaminated materials has been initiated with the result that it is imperative to be able to analyse for mycotoxins in feed commodities, rapidly and with sufficient accuracy to ensure that bulk cargoes of such materials are within set safety limits. To this end a large batch (800 tonnes) of cotton-seed meal was consigned to a South African feed miller and was sampled according to a protocol devised under the European Union Framework 6 Biotracer programme. These were split and analysed for aflatoxins (AFs) by two laboratories using the VICAM fluorimetry aflatoxin method (VF) and by an high performance liquid chromatography (HPLC) method (HPLC) as part of another study to determine the statistical variation of using composite samples derived from a large bulk cargo (Reiter et al., 2011). The results from the HPLC method showed that all the composites were contaminated with aflatoxins (AF) ranging from 24 – 93µg/kg. A comparison of the two analytical methods used,
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".