ESSENTIAL RENDERING All About The Animal By-Products Industry Edited by
Bibliographic record
Abstract
The first book written about the rendering industry was produced by the National Renderers Association in 1978 and was titled The Invisible Industry. In 1996, a second book entitled The Original Recyclers was published to tell everyone in government, academia, and the public what renderers are—environmentally aware producers of safe products—the original recyclers. That book was to move us into the twenty-first century, but with the pace of change, we find ourselves already in need of a new book on the rendering industry. So much has happened in the past decade that it has become necessary to publish this book, Essential Rendering. This book documents the technologies, manufacturing procedures, capabilities, research, and infrastructure that make the industry so important to the United States and Canada. Two cases of indigenous bovine spongiform encephalopathy discovered in the United States and eight in Canada, as well as high pathogenic avian influenza around the world, challenge renderers today. Thus, society needs to know how renderers handle, in a biosecure manner, over 59 billion pounds of the by-products from animal food production every year in the United States and Canada. Government, which promulgates rules to answer today’s diverse challenges, academia, which influences users of rendered products, and the public, which uses the products of the industry’s operations, all need to know about rendering in today’s world. They need to know how rendering prevents both animal and human diseases and what the ramifications are of not having rendering. Society should not take renderers ’ services for granted or forget that they operate in a free enterprise system.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.106 | 0.062 |
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".