Comparison of flail-harvested, precision-chopped and round-bale silages for growing beef cattle
Bibliographic record
Abstract
The effects of silage conservation method on silage composition and animal performance\nwere examined in two experiments. In Experiment 1, unwilted, flail-chopped\nsilages made with or without an additive (sodium nitrite and hexamethylene tetramine)\nwere compared with wilted, round-bale silage. The dry matter (DM) concentration of\nround bale silage (460 g/kg) was higher than that of flail silage (214 g/kg) and this\nrestricted fermentation and N solublisation. When fed to growing cattle, intake\n(P<0.01), live-weight (LW) gain (P<0.001) and LW gain to feed ratio (P<0.05) were\ngreater for round-bale silage than for flail silage. In Experiment 2, flail-harvested silage\nwas compared with wilted, precision-chopped and round-bale silages conserved either\nwithout or with pre-slicing immediately before baling. The DM concentration of flail,\nprecision-chopped and round-bale silages were 163, 334 and 468 g/kg, respectively.\nFermentation in flail silage was more extensive than in precision-chopped and particularly\nround-bale silages, but insoluble-N concentration was unaffected. Round-bale\nsilage was more digestible (P<0.05) than flail or precision-chopped silages. Voluntary\nintake was higher for steers fed round-bale silages compared to flail silage (P<0.05),\nwhile intake of steers fed precision-chopped silage was intermediate (P>0.05). Steers\nfed round-bale silages had higher LW gain (1.0 kg/day) than those fed flail (0.7 kg/day)\nor precision-chopped silage (0.8 kg/day; P<0.05). Efficiency of utilization of DM for LW\ngain was similar for all silages. Pre-slicing at baling had no effect on animal performance.\nIt is concluded that the increased performance by cattle offered silages made by\nthe wilted round-bale system was largely due to higher voluntary intake.
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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".