Investigation of avian influenza viral ribonucleic acid destruction in poultry co-products under rendering conditions
Bibliographic record
Abstract
This study was conducted to determine the time and temperature requirements needed to destroy avian influenza viral RNA in high-fat poultry tissues and to determine whether those conditions are met by rendering cooking processes. Because rendered poultry products are used worldwide for feed ingredients, it is imperative to validate the destruction of avian influenza viral RNA to ensure the safety of rendering co-products and avoid cyclic reinfection and disease in poultry, livestock, and potentially humans. Typical high-fat poultry offal was spiked with a known quantity of chemically inactivated, intact H5N9 low-pathogenicity avian influenza viral RNA. After subjecting the material to a variety of thermal doses, RNA was extracted from the material and assayed for the presence of the virus by using real-time reverse-transcription PCR. With thermal treatment at 100°C for 30 s or longer or at 110°C and above for 15 s or longer, the RNA of low-pathogenicity avian influenza virus A/Turkey/Wisconsin/68 H5N9, equivalent to 6 log10 of viable virus, was destroyed within the poultry rendering materials. These thermal treatment conditions are well below the range of temperatures and times used for rendering poultry carcasses and offal in the United States and Canada, where rendered materials are subjected to a heat treatment for a minimum of 30 min with a cooker exit temperature of not less than 118°C.
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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".