Swath grazing triticale and corn compared to barley and a traditional winter feeding method in central Alberta
Bibliographic record
Abstract
Baron, V. S., Doce, R. R., Basarab, J. and Dick, C. 2014. Swath-grazing triticale and corn compared to barley and a traditional winter feeding method in central Alberta. Can. J. Plant Sci. 94: 1125-1137. A 5-yr study compared swath-grazed triticale (× Triticosecale Wittmack), corn (Zea mays L.) and barley (Hordeum vulgare L.) with a traditional pen-fed, wintering diet for gestating beef cows on the basis of dry matter (DM) yield, carrying capacity, nutritive value, cow performance and total daily feeding cost. Cows (690±70 kg BW) were fed a control total mixed ration (TMR) or allocated to swath-grazed treatments in 2.5-ha paddocks. Triticale yielded 15% more than corn and corn 32% more than barley. Carrying capacity of triticale (1145 cow-d ha-1) and corn (1004 cow-d ha-1) were similar and both were greater (P<0.05) than control (516 cow-d ha-1) and barley (554 cow-d ha-1). Average utilization for triticale (83.7%) was greater (P<0.05) than corn (74.7%) and barley (71.7%). In vitro true digestibility (IVTD) for corn was highest (682 g kg-1), followed by triticale (620 g kg-1), then barley (570 g kg-1) and the control TMR (571 g kg-1). Average cow mean body condition score (BCS) was higher (P<0.05) for triticale and corn (3.0) than barley (2.9), but lower than the control (3.1). Thus, cow reproductive performance should not be compromised by swath grazing. Total daily feeding costs, averaged over years, ranked (P<0.05) triticale ($0.78 cow-d-1)<corn ($1.05 cow-d-1)<barley ($1.24 cow-d-1)
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".