Comparing simple and complex native forage mixtures for grazing cattle in southwestern Saskatchewan
Bibliographic record
Abstract
Diverse forage mixtures have improved resilience to drought, improved persistence, ability to adapt to changing environmental conditions, reduced fertilizer costs, improved root mass and greater soil carbon sequestration but do they improve forage and animal production. The objective was to determine if complex native forage mixtures provide superior nutritional quality throughout the grazing season as compared to simple native mixtures. Three studies were conducted in 2007 at Swift Current, SK to evaluate forage production potentials, nutritive qualities and in vitro dry matter digestibility of native and tame forage species common to or having potential in Southwestern Saskatchewan. In study one, plots were seeded in 2006 on Chernozemic Orthic Brown Swinton Loam soils and consisted of 11 native and three tame monoculture species common to southwestern Saskatchewan. Clippings at a 5 cm stubble height occurred on June 20 and every 28 days after until October 10. Forage DM production, in vitro OMD, NDF, ADF, ADL, CP, Ca and P concentrations were measured. As species matured, production and OMD declined (P¡Ü0.05) but NDF, ADF and ADL concentrations increased (P¡Ü0.05). There were harvest date by species differences (P¡Ü0.05) in forage production and nutritional qualities of C3 and C4 grass and legume species. Study two examined the in situ CP, NDF and DM disappearance of six selected species harvested in the fall. EDNDF and ADDM values did not differ (P>0.05) among C3 grasses. The C4 grasses had higher (P
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".