PSI-2 Evaluation of feed by-products on in vitro DMD, methane production and ruminal fermentation parameters
Bibliographic record
Abstract
Abstract In-vitro batch culture assessment was used to examine DMD, ruminal fermentation parameters and CH4 production associated with a range of by-products feeds including pea hulls, pea screenings, soybean, soybean meal, wheat bran, wheat screenings, wheat DDGS, wheat corn DDGS, corn DDGS, coarse hemp powder, hemp hulls, flax screenings, nexera canola, commodity canola, sunflower screenings, quinoa and dogwood in a randomized complete block design. By-products were obtained from various locations across Manitoba at 3 monthly timepoints to account for sample variability. By-products (0.5g DM) were weighed individually into jars and incubated at 39°C in jars containing buffered medium and rumen fluid for 48 hrs. Gas samples were collected at 3,6,9,12,18,24,36 and 48 hr and CH4, DM disappearance, pH, VFA and ammonia concentrations were measured. Data were analysed using the MIXED procedure of SAS including treatment as a fixed effect and run, block, month and location as random effects. Methane output was regressed on CP and NDF to determine the effect of these constituents. Treatment had a significant effect (P < 0.0001) on CH4 (ml/g DMD) produced, with dogwood having the lowest cumulative CH4 (11.13 ml/g DMD) compared to all other treatments. Methane (ml/g DMD) decreased (P < 0.0005) as pH decreased. An inverse relationship between protein and CH4 was apparent, which explained 31% (R2= 0.31) of the CH4 (ml/g DMD) produced. Further, NDF (%) had a significant effect (P < 0.0001) on CH4 and explained 25% (R2=0.25) of the CH4 (ml/g DMD) produced. Overall, by-products higher in crude protein and lower in NDF produced lower (P < 0.001) CH4 ml/g DMD. Although the addition of dogwood to cattle diets is novel, it does show potential CH4 mitigation potential using in vitro assessment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".