ANNUAL AND PERENNIAL FORAGES FOR FALL/WINTER GRAZING
Bibliographic record
Abstract
Extending the grazing season by maintaining beef cattle on pasture in the fall/winter has been adopted by many producers on the Prairies as it reduces the need for mechanical harvesting and can lower labour and manure management costs relative to feeding cattle in confinement. Annual and perennial forages, alone or in combination, offer the potential for low-input grazing while maintaining animal productivity. Using a range of data sources, this paper will review the methods available for extending the grazing season of beef cattle using annual and perennial forages and discuss level of adoption and the practical implications and considerations for producers. These methods included stockpile grazing, bale grazing, swath grazing, and corn grazing, with the suitability of methods based on a range of factors including the nutritive requirements of the target class of cattle, environmental conditions, and costs of inputs. In an effort to maximise the efficiency of maintaining cattle on pasture in the fall/winter period, the ability to be flexible and adaptive to changing climatic and economic conditions within and between years is essential. Furthermore, a combination of the aforementioned methods may be employed in an integrated effort to enhance the productivity and sustainability of overwintering of beef cattle.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 0.001 |
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".