86 Evaluation of ensiled triticale varieties (‘Taza’ and ‘Bunker’ Triticosecale) and barley (Hordeum vulgare) on performance of backgrounding beef steers
Bibliographic record
Abstract
Abstract A 3-year study compared two triticale (Triticosecale) varieties, Bunker and Taza, with a conventional barley (Hordeum vulgare) fed as silage to backgrounding beef steers on the basis of crop dry matter (DM) yield, nutritive value, steer performance, and total daily feeding costs. Each year, 240 fall weaned beef steers were stratified by BW (308 ± 4.9 kg) and allocated to 1 of 3 replicated (n = 4) dietary treatments containing Bunker triticale, Taza triticale, or barley silage. Steers were fed a total mixed ration (TMR) consisting of silage (55–68%), barley grain (20–34%), supplement (5%), and (0.4–6%) canola meal (DM basis). Diets were formulated to be isonitrogenous and isocaloric to meet or exceed NRC (2001) requirements for TDN and CP. Crop DM yield of Taza (6750 kg DM ha-1) and Bunker triticale (6592 kg DM ha-1) was similar (P > 0.05), and both were greater (P = 0.01) than barley (6008 kg DM ha-1). Over 2 years, steer ADG, DMI, G:F, and the calculated NEm, and NEg did not differ (P > 0.05) among treatments. Cost per head per day was lowest for Taza fed steers at $1.26 hd-1 d-1, followed by Bunker silage at $1.33 hd-1 d-1, and Barley silage at $1.34 hd-1 d-1. Total cost of gain for Taza, Bunker, and barley fed steers was $0.84, $0.91, and $0.93, per kg of gain, respectively. Study results suggest that Taza and Bunker triticale silage can be used as an alternative to barley silage when fed to backgrounding beef steers.
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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".