ATP bioluminescence method as a rapid tool for assessment of cleanliness of commercial animal transport trailers
Bibliographic record
Abstract
Animal transportation is widely recognized as a significant risk for disease transmission. At present, cleanliness of animal transport trailers is mostly assessed through subjective visual inspection (i.e., ‘white-glove’ test), which may sometimes be supplemented by microbiological testing with results obtained after at least 2-3 days. In this study, the feasibility of using adenosine triphosphate (ATP) bioluminescence method as a rapid tool for objectively assessing animal transport trailer cleanliness was investigated. Eighteen newly-cleaned transport trailers were tested using both ATP bioluminescence and microbiological techniques. In each trailer, six (6) locations including floors, walls, ramps, partitions and trailer exterior surfaces were sampled using an ATP meter, and MacConkey and R2A agar contact plates. From a total of more than 500 paired samples collected from all the sampled trailers, significant correlation was found between ATP bioluminescence method and standard microbiological technique using R2A agar (r = 0.206; p = 0.001) and MacConkey agar (r = 0.154; p = 0.002) plates. Using a threshold or ‘Pass’ limit of 390 RLU per 100 cm2 of trailer surface, ATP method was able to provide a good objective measure of the surface cleanliness, thus may be considered as an additional tool available for rapid and less costly assessment of trailer surface cleanliness.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".