Grazing of spring and winter cereals in southwest Saskatchewan
Bibliographic record
Abstract
A pasture system that combines the early high productivity of a spring cereal and the late-season growth ability of a winter cereal vegetative tillers may provide an important forage/pasture resource in southwestern Saskatchewan. At the Semiarid Agricultural Research Centre-Agriculture and Agri-Food Canada, SK, two annual cereals, a spring barley, cultivar AC Lacombe, and a winter rye, Prima, were seeded in early May of 2001 into four pastures each 1.3 ha. Two pastures utilized steers that were implanted (Component™ E-S) and had received CRC rumensin while the other two pastures utilized steers with no implant or CRC rumensin. Results found that the implanted and CRC rumensin treated steers were more efficient in converting the cereal forage to gains than the control steers. Average daily gains of treated steers were higher than the control group and were 1.2 kg d-1 vs 0.7 kg d-1, respectively. Grazing days and total kg of livestock production per ha for treated verses control treatments were 133 verses 131 and 12.1 verses 5.5, respectively. It is possible that a synergistic and/or additive effect may have occurred through the use of the implant and rumensin treatments and resulted in a higher than expected improvement in animal performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".