Ochratoxin A in wine and grape juice sold in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ochratoxin A (OTA) was determined in 251 samples of wines and grape juice collected over 3 years in Canada. In total, 25/84 samples of red wine, 22/96 samples of white wine, 3/46 red grape juices and 1/25 white grape juices contained OTA levels above the limit of quantitation (LOQ). Canadian wines, when compared with imported products, showed both a lower OTA occurrence, noted as positive (19 versus 48% above the limit of detection (LOD) for wines), and a lower level of OTA contamination (upper bound mean of 17.5 versus 163pg ml(-1) for wines). Wines from the USA contained no quantifiable levels of ochratoxin A. OTA was found in Canadian and US grape juice samples, with 12.9% above the LOD and an upper bound mean of 13.3pg ml(-1). It was extracted from a wine or grape juice sample by passing it through an immunoaffinity column. The sample matrix was washed off the column with water. OTA was eluted from the column with methanol and quantitatively determined by liquid chromatography using a fluorescence detector. The presence of OTA was confirmed by esterification with boron trifluoride-methanol. The LOQ of OTA was estimated as 20 pg ml(-1) in white wine (S/N 10:1) and 40 pg ml(-1) in red wine, white grape juice and red grape juice (S/N 20.1). The LOD was estimated as 4pgml(-1) for white wine and 8pgml(-1) for red wine and white and red grape juices (S/N 3:1).
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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 | 0.000 | 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 it