Correlation between median and mean irregular particle sizes and degradation kinetics in barley genotypes
Bibliographic record
Abstract
Barleys, a major source of feed, are all coarsely dry-rolled before feeding beef and dairy cattle in North America. The shapes of coarsely dry-rolled barley particles are not round but very irregular. The model for geometric mean is not suitable for determining irregular barley particle size. In a previous study, we tested four different published models to determine the mean and median particle sizes of coarsely dry-rolled barley with irregular particle size shapes and found that the Pond's model with 0 mm=100% was the best model to compute mean and median particle sizes of the coarsely dry-rolled barley samples expressed as per cent cumulative weight oversize. However, we still did not know which parameter, median or mean particle size, was the best indicator to predict rumen nutrient degradation and availability. This information is badly needed. The objective of this study was to study the relationship between the mean and median irregular particle sizes and nutrient availability of various barley varieties which were coarsely dry-rolled. The results revealed a stronger correlation between the median irregular particle size (not mean particle size) and the rate (K(d) ) and extent (ED) of rumen degradation of dry matter (K(d) : R= -0.78, p= 0.065; EDDM: R= -0.89, p= 0.016), starch (K(d) : R= -0.96, p= 0.003; EDST: R= -0.95, p= 0.003) and crude protein (K(d) : R= -0.59, p= 0.215; EDCP: R= -0.89, p= 0.019). In conclusion, it is the median but not mean particle size that has stronger correlation with rumen degradation kinetics. The 79.9%, 78.3% and 91.0% of variation of effective degradability of dry matter, crude protein and starch, respectively, could be explained by the median irregular particle size.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".