Bibliographic record
Abstract
Abstract Approximately 31 million tons of animal by-products are generated by livestock and poultry farming, meat processing, and retailing in the U.S. and Canada each year. Utilizing rather than discarding these resources prevents the release of large amounts of GHGs into the atmosphere. Renderers reclaim the carbon from these by-products and transform them into 6.3 million tons of refined animal fats and 8.9 million tons of rendered protein meals for use as animal food ingredients annually. In 2019, 1.5 million tons of rendered protein meals and 289 thousand tons of animal fats were used in pet food, and another 1.83 million tons of fresh and frozen meat and poultry by-products and organ meats were used in pet food without first being rendered. Rendering cookers break down tissues so that fats can be pressed out of muscle, bone, and connective tissues. Rendered products are safe from biological hazards as cooking is done at higher temperatures for longer times than necessary for food safety. The Prioritization of food safety in both rendering and pet food industries in recent years have delayed emphasis on some quality factors. Many citations in literature document that excessive heat damages proteins and decreases their quality and digestibility. Research is planned to provide guidance to renderers on cooking procedures that would meet the needs for processing and food safety without sacrificing quality and digestibility. Other factors that impact the availability and usability of rendered products include parts available for rendering after fresh parts are selected for pet food, upstream human food safety interventions, time and temperature conditions between slaughter and rendering, cooling, drying, storage, transport, and the use of antioxidants, antimicrobials, and other processing aids. Opportunities exist to modernize plants and processes to provide high quality ingredients. Renderers have invested with the pet food sector to collaborate on strategies to improve safety, quality, and sustainability of pet food ingredients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".