Evaluation of Pressurized Steam Disinfection of Surfaces Contaminated by <i>Mycobacterium terrae</i> : A Surrogate for <i>Mycobacterium bovis</i>
Bibliographic record
Abstract
Introduction: Decontamination of farms affected by bovine tuberculosis could be very challenging during outbreaks occurring in the winter with freezing temperatures. Steam treatment has been of practical interest, but information is needed on whether such treatment is able to inactivate the causative agent, Mycobacterium bovis . This study was to evaluate the use of pressurized steam for inactivation of Mycobacterium terrae , a surrogate for M. bovis on various surfaces. Methods: Carrier disks made of steel, wood, or rubber were inoculated with 6.32 ± 0.38 log 10 M. terrae . While being held at background temperatures of −20°C, 4°C, or 21°C, these carrier disks were treated with pressurized steam (120°C ± 5°C) for 5, 10, 15, or 20 s. Reduction in colony forming units of M. terrae and temperatures on the top and bottom surfaces of the disks were determined. Results: Complete inactivation of 6 log 10 M. terrae on steel and wood disks was achieved by 10 s of steam treatment at all three background temperatures. In comparison, 20 s of steam treatment was needed for the complete inactivation of mycobacteria on rubber disks. Corresponding to the longer treatment time required for mycobacterial inactivation, temperatures on the bottom surface of the rubber disks rose substantially slower than those of the steel and wood disks at all three background temperatures. Conclusion: The results suggested that treatment with pressurized steam has potential for efficient and effective disinfection of surfaces contaminated by mycobacteria at or below freezing temperatures in winter.
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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 | 0.000 | 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) | 0.001 | 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 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".