<i>Time and temperature requirements for improved heat killing of pathogens in swine transport trailers</i>
Bibliographic record
Abstract
Abstract. Biosecurity continues to be one of the most important elements of modern swine production. Transport trailers are of particular concern for spreading pathogens between premises, and require extensive cleaning, washing and disinfection after each load. In addition, the industry is using heat-bays for disinfection of trailers, which expose trailers to hot air for varying amounts of time. Current protocols involve heating trailers to 70<sup>o</sup>C for 10-15 minutes. The objective of this study was to find the optimal time and temperature required to heat-inactivate selected swine pathogens including Porcine Epidemic Diarrhea Virus (PEDV), Porcine Reproductive and Respiratory Syndrome Virus (PRRSV), Swine influenza virus (SIV), Streptococcus suis, Salmonella spp. and Escherichia coli to name a few. We chose three different experimental settings for our analysis. First, purified pathogens were inactivated in cell culture. Secondly, fecal matter was included to resemble the insulating capacity of biological material. Thirdly, the pathogens were added directly into fecal matter to closer resemble field conditions. It was determined that viral inactivation was complete for RNA and DNA viruses at 75<sup>o</sup>C in cell culture for 20 minutes. However, we were able to confirm that remainders of fecal matter within the trailer negatively affect the effectiveness of the heat-treatment. For example, within fist-size mass of dried fecal matter, the inside never reached temperatures above 65<sup>o</sup>C, highlighting the need for proper cleaning. In summary, our data suggests that heat-treatment of a clean trailer for 75<sup>o</sup>C for 20 minutes is sufficient to inactivate both bacterial and viral pathogens relevant to the swine industry.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".