1440 Chemical and energy profiles of value added pellet products based on combination of new coproducts from biofuel/bio-oil processing, low grade of peas, and lignosulfonate chemical compound at different levels for ruminants
Bibliographic record
Abstract
The aim of this project was to test and develop eight high-value-added pellet products based on combination of coproducts from biofuel/bio-oil processing, low grade of peas, and lignosulfonate at different levels for ruminants. Statistical analyses were performed using PROC MIXED of SAS 9.3 with significance declared at P < 0.05. The results showed that BPP3 (high level of carinata meal, low level of peas, and no lignosulfonate), BPP4 (high level of carinata meal and low level of peas and lignosulfonate), and BPP7 (high level of canola meal, low level of peas, and no lignosulfonate) had the higher CP (P < 0.05), whereas both BPP3 and BPP4 also had the higher neutral detergent insoluble CP (NDICP; P < 0.05) and BPP6 (low level of canola meal and high level of peas and lignosulfonate) and BPP7 and BPP8 (high level of canola meal and low level of peas and lignosulfonate) had the higher acid detergent insoluble CP (ADICP; P < 0.05). BPP7 and BPP8 had the higher NDF, ADF, and ADL compared with the other blend pellet products (P < 0.05). Energy values using the NRC summative approach indicated that BPP1 (low level of carinata meal, high level of peas, and no lignosulfonate) and BPP6 (low level of canola meal and high level of peas and lignosulfonate) had the higher truly digestible nonfiber carbohydrate (tdNFC; P < 0.05); BPP3 and BPP4 had higher truly digestible CP (tdCP; P < 0.05); BPP1 was higher in truly digestible NDF (tdNDF; P < 0.05); and BPP7 and BPP5 (low level of canola meal, high level of peas, and no lignosulfonate) had the higher truly digestible fatty acids (tdFA; P < 0.05). However, BPP1 showed the higher level of total digestible nutrient (TDN; P < 0.05) and BPP1, BPP3, and BPP4 had the higher NEL, NEm, and NEg (P < 0.05). In conclusion, carinata meal–based pellet products have more available protein (higher NDICP but lower ADICP) than canola meal–based blend pellet products. Canola meal–based blend pellet products have higher levels of NDF, ADF, and ADL than carinata meal–based pellet products. Pellet products based on carinata meal combined with peas has potential to be used as a good energy and good protein source compared with pellet products based on canola meal combined with peas.
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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 | 0.001 | 0.001 |
| 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".