Heat- and Lignosulfonate-Treated Canola Meal as a Source of Ruminal Undegradable Protein for Lactating Dairy Cows
Bibliographic record
Abstract
This experiment used 18 lactating Holstein cows in a 3 x 3 Latin square replicated 6 times to determine the effectiveness of processing with moist heat or moist heat combined with lignosulfonate (LSO3) for increasing the ruminal undegradable fraction of canola meal for use as a protein supplement for lactating dairy cows. Diets were formulated to be isonitrogenous and contained one of 3 forms of canola meal; untreated canola meal (UCM), heat-treated canola meal (HTCM) or heat-and LSO3-treated canola meal (LSO3CM). Total collection of urine and feces was taken from each cow during the last 5 d of each 42-d experimental period. Milk production was greater for cows fed the LSO3CM diet (36.6 kg/d) than for cows fed the UCM diet (34.8 kg/d) but did not differ from cows fed the HTCM diet (35.3 kg/d). Digestibility of crude protein was lower for cows supplemented with LSO3CM and they had reduced concentrations of ruminal ammonia N, blood urea N, and milk urea N compared with cows supplemented with UCM or HTCM. Dry matter intake and apparent digestibilities of neutral and acid detergent fiber were increased in cows fed the LSO3CM diet. Urinary N excretion (as % of N intake) was reduced in cows fed the LSO3CM diet. These results indicate that moist heat combined with LSO3 treatment of canola meal was effective in increasing the proportion of crude protein digested in the lower digestive tract of lactating cows and was therefore used more effectively as a source of protein than UCM or HTCM.
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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".