Utilization and application of wet potato processing coproducts for finishing cattle1
Bibliographic record
Abstract
Wet coproducts fed to beef cattle include processing coproducts of the fruit, vegetable, juice, and brewing industries. Considerations for their utilization in beef cattle diets include quantity available, feeding value, quality of animal products produced, economics (e.g., transportation of water), storage and preservation, consumer perception, nuisance concerns, contaminants, and interactions with other diet ingredients. Potato (Solanum tuberosum) coproducts from processing for frozen food products may be quantitatively most important because the 11.3 million t of potatoes (fresh weight) processed in the United States and Canada in 2008 resulted in an estimated 4.3 million t (as-is basis) of coproduct. Chemical composition and feeding value of potato coproducts depends on the coproduct type. The names of coproducts vary among potato processors and some processors combine the different coproducts into one product commonly called slurry. The 4 main potato coproducts are 1) potato peels; 2) screen solids (small potatoes and pieces); 3) fried product (fries, hash browns, batter, crumbles); and 4) material from the water recovery systems (oxidation ditch, belt solids, filter cake). The coproducts, except the fried products, ensile rapidly, reaching pH 5 in 7 d or less. Dry matter content varies from 10 to 30% and on a DM basis varies in CP (5 to 27%), starch (3 to 56%), NDF (4 to 41%), and ether extract (3 to 37%) content among potato coproducts. Type of coproduct and frying greatly affect the energy value (0.6 to 1.6 Mcal of NE(g)/kg of DM). Composition, quality, and shelf life of beef was not affected by potato coproduct feeding in contrast to perceptions of some purveyors and chefs. Potato coproducts are quantitatively important energy sources in beef cattle diets, which, in turn, solve a potentially massive disposal problem for the food processing 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 | 0.001 | 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 | 0.001 | 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".