Grain processing differences between barley varieties for cattle
Bibliographic record
Abstract
Barley is fed to cattle as a concentrated energy source. Before feeding, the grain is cracked (processed) to expose the endosperm to rumen fermentation. Processing disrupts the endosperm starch-protein matrix and produces fine particles (fines). Fines may lead to acidosis and liver abscesses in cattle. In 2004, nine Western Canadian barley varieties, including seven feed and two malt varieties, were analyzed for fines produced after three processing treatments: dry with minimal processing, dry with excessive processing, and tempered with excessive processing. Fines were measured as the percentage of processed sample falling through a 1.40 mm brass sieve. Grain hardness, using Single Kernel Characterization System (SKCS), and protein content, using Near Infrared Transmittance (NIT) were analyzed to identify their relationship with fines production. Varieties differed in % fines produced after rolling with variety by processing interaction being present (P<0.05). However, Xena and CDC Dolly produced significantly less fines for all processing methods and CDC Trey and CDC Bold produced more (P<0.05). Varieties with more protein produced fewer fines when minimally dry rolled, with the exception of Xena (P<0.05). Grain hardness and protein content appear related to processing characteristics; however, correlations were not significant. Grain hardness was significantly correlated with protein (R=0.77, P<0.05).
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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 | 0.001 | 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) | 0.001 | 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".