Vehicle and Equipment Decontamination During Outbreaks of Notifiable Animal Diseases in Cold Weather
Bibliographic record
Abstract
The objective of this study was to evaluate various procedures for decontamination of vehicles and equipment during outbreaks of notifiable animal diseases in cold weather. The evaluation was done in 4 field trials held in outdoor operational settings in Canada, at ambient temperatures from –2°C to 11°C. Procedures included various combinations of dry cleaning, wet cleaning, disinfection, and final rinsing. Geobacillus stearothermophilus spores and infectious bursal disease virus (IBDV) were used as surrogates for bacterial and viral pathogens, particularly Bacillus anthracis spores and foot-and-mouth disease virus. Spores and viruses were suspended in a light organic soil preparation, inoculated onto stainless-steel disks, and covered with a heavy soil preparation. Inoculated disks were attached to various surfaces of farm vehicles and equipment. In all field trials, spore and IBDV reduction was greater (P < .05) on disks where the soil was completely removed, as compared with only partially removed. Greater (P < .05) spore and IBDV reduction was seen when disinfection and final rinse steps were included than when not included after dry and wet cleaning in 3 of 3 and 1 of 3 field trials, respectively. A wet-cleaning step before application of a disinfectant increased (P < .05) spore and IBDV reduction versus no wet cleaning. The results provide evidence that vehicle and equipment decontamination in cold weather could be significantly improved by thorough removal of organic matter to enhance disinfection and elimination of disease agents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".