An Inter-Laboratory Comparison Of Molecular Methods For The Identification Of Nosema Species In Honeybee Samples
Bibliographic record
Abstract
To evaluate the performance of the molecular methods used by national reference laboratories (NRLs) for the identification of Nosema species in bee samples, an inter-laboratory comparison (ILC) was organised in 2015. A total of 20 EU NRLs and 1 non-European NRL participated in this ILC. The specificity of the methods was tested on various Nosema species: Nosema apis, Nosema ceranae and Nosema bombi. The test panel of samples provided to the laboratories contained 17 suspensions of crushed abdomens from naturally and artificially infected honeybees and bumblebees. In addition, data on the routine methods used by the participating laboratories were collected in an online survey, covering all the steps involved in DNA extraction and PCR. Our analysis showed that the 21 NRLs use 21 different protocols, each presenting variations from the DNA extraction step to the PCR step. The results of this ILC indicate that 48% of the participating laboratories returned the expected results. Considering the 21 different methods used, 57% of participating laboratories provided satisfactory results with regard to sensitivity, and 72% with regard to specificity. The results of this ILC clearly highlight the need for improved harmonisation of molecular Nosema identification methods.
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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 | 0.069 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".