Organisation of Multi-Mycotoxin Proficiency Tests: Evaluation of the Performances of the Laboratories Using the Triple A Rating Approach
Bibliographic record
Abstract
; ochratoxin A, the T-2 toxin; and the HT-2 toxin were conducted in 2019 and 2020 using cornflakes and rusk flours that were prepared in house. The homogeneity and the stability of these materials were verified according to the criteria laid down in ISO 13528 using randomly selected samples. Most of the targeted toxins were found to be homogenously distributed in both materials with no significant changes during the timescale of the PTs. Next, the materials were distributed to approximately 25 participating laboratories from Europe, Canada, and the United States. The obtained datasets were computed using robust statistics. The outliers were checked and removed, and the toxin concentrations were assigned as the consensus value of the results of the participants at Horwitz ratios <1.2. The z scores were generated for all mycotoxins, and the results were pooled to calculate the relative sum of squared z scores (SZ2) indexes and were clustered according to the triple A rating. Overall, at least 80% of the participating laboratories achieved good and acceptable performances. The most frequent categories assigned to good performances (SZ2 ≤ 2) were AAA (51%) and BAA (13%). Clusters of BBA + CBA (6%) included laboratories reporting acceptable z scores <90% of the total z scores for less than 90% or 50% of the mycotoxins targeted in the 2 matrices. The triple A rating seems to be appropriate in evaluating the performances of laboratories involved in multi-mycotoxin analyses. Accredited and non-accredited analytical methods achieved good and acceptable performances.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".