318 A 99-year journey on the development of Canadian forages for livestock production
Bibliographic record
Abstract
Abstract Perennial and annual forages are important feed ingredients in production of livestock in Canada. Cattle, bison, sheep and other ruminants play a key role in converting human inedible fiber from forages and other crops into high quality protein and energy for human consumption. Focusing on the cattle sector, this presentation will examine the development of forages in the Canadian landscape for the production of beef cattle for both grazing and feeding under confinement. This will include evaluating the implications on animal productivity and health, economic viability and environmental sustainability including greenhouse gas emissions, as well as land and water use. We will explore mechanisms by which grassland management has improved, how the breeding and development of perennial and annual forage varieties has improved nutritive value, increased yield, and lowered anti-nutritional factors to reduce the occurrence of metabolic disorders, all of which have been critical to the constant goal of improving the success and competitiveness of beef production. The paradigm of forage production has been further pushed in recent decades to extend the grazing season to maintain cattle on pasture during the fall and winter seasons during which both standing and conserved forages may be used. The use of novel forages and new ways in which to use existing forages in a bid to decrease overwintering costs has been critical to enhance the success of the cow-calf sector. Forage production has faced significant challenges with the increased pressure from production of high value annual crops and frequency of extreme weather events which presents ever growing tribulations for beef producers. Perennial forages, however, offer advantages for environmental resiliency such as carbon sequestration, water conservation, and prevention of soil erosion that may offer important opportunities to address these challenges.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.054 | 0.008 |
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".