Characteristics of North American meat & bone meal relevant to the development of non-feed applications
Bibliographic record
Abstract
ABSTRACT. Unmarketable animal tissues are typically processed by rendering plants, which transform them into meat and bone meal (MBM) or similar products. MBM’s traditional use as animal feed has become increasingly threatened, but MBM has potential for non-feed applications. Development of new products and processes is hindered by lack of reliable data on many of MBM’s chemical and physical properties. MBM samples, as well as data on raw material and process, were collected from 19 rendering facilities in the United States and Canada. A large majority of the raw material was tissue from cattle, swine, and poultry. All facilities surveyed practiced continuous dry rendering; 89 % of the facilities use continuous cookers and 11% use falling film evaporators. MBM is high in protein (44.6-62.8%, mfb), but this protein is poorly soluble; at pH 7 solubility ranged from 2.20 % to 7.22%. Among all samples, the particles ’ median geometric mean diameter was 387 m, and the size distribution was broad. The median density of MBM particles was 1.41 (g/mL); median density of MBM in bulk ranged from 0.50 g/mL when loose-filled to 0.68 g/mL when packed. pH values of the samples ranged from 5.89 to 7.19, and samples containing the most cattle tissue had the highest pH. Thermal diffusivity and thermal conductivity values for both loose-filled and packed MBM are reported, as well as CIE L*a*b * color values.
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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 | 0.001 | 0.002 |
| 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) | 0.006 | 0.002 |
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".