Reproducibility of enzyme‐linked immunosorbent assay and immunofluorescence for detecting <i>Clavibacter michiganensis</i> subsp. <i>sepedonicus</i> in multiple laboratories*
Bibliographic record
Abstract
Results from enzyme‐linked immunosorbent assays (ELISA) and immunofluorescence (IF) tests for the detection of Clavibacter michiganensis subsp. sepedonicus in potato tissue were analysed to determine the variation that occurs when different analysts perform the test. Data generated in accredited laboratories from sets of proficiency panel samples were used for the analysis. Sensitivity and specificity for both the ELISA and IF tests were very high as very few false positive and false negative results occurred. Analysis of z‐scores for the positive samples in the proficiency panel sets showed, for both serological tests, that about 90% of the results were within the acceptable range around the assigned values for the samples. Rescaled sum of scores for individual analysts who had false positive or negative results in ELISA were generally outside the acceptable range, although most analysts with high or low rescaled sums of z‐scores still identified each sample correctly as being positive or negative for the ring‐rot pathogen. For IF there were no false positive results, and the false negative results were not associated with aberrant rescaled sums of z‐score for the analysts, suggesting that perhaps an error in testing occurred rather than a problem with quantifying cell numbers.
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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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".