Alternative forage feeds for beef cattle in Northwestern Alberta, Canada: forage yield and nutritive value of forage brassicas and forbs
Bibliographic record
Abstract
Forbs and forage brassicas are non-traditional forage crops in northwestern Alberta, Canada. Ten forage brassicas (barkant turnips, bayou kale hybrid, daikon radish, collard, inka brand marrowstem kale, malwira turnip rape, purple top turnips, tillage radish, vivant hybrid cross and winfred) and four forbs (buckwheat, chicory, plantain and phacelia) were seeded on 25 May 2018 and 23 May 2019. The above-ground parts of plants were harvested for forage yield and nutritive quality on 15 August 2018 and 29 August 2019. Forage dry matter (DM) yield, crude protein (CP) and total digestible nutrients (TDN) respectively varied from 2953 to 10740 kg DM ha−1, 12.2–23.5% CP and 58.8–77.9% TDN. Some crops had no nitrate (ant-nutritional factor) detected, while six crops (bayou kale cross, chicory, phacelia, plantain, purple top turnips and tillage radish) had nitrate concentrations ranging 0.26–0.56%, considered toxic for beef cattle. In general, the brassicas and forbs investigated produced forage with high nutritional quality. Based on forage DM yield and nitrate level in the forage, the crops with the most attractive forage options that can provide alternative forage feed for beef cattle production from this study would be buckwheat, daikon radish, inka brand marrowstem kale and forage collards.
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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 | 0.001 | 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".