Effects of resting perennial pastures during the sensitive pre-dormancy period in western Manitoba: Pasture productivity and beef cattle performance
Bibliographic record
Abstract
Durunna, O. N., Baron, V., Scott, S. L., Robins, C., Khakbazan, M. and Block, H. C. 2015. Effects of resting perennial pastures during the sensitive pre-dormancy period in western Manitoba: Pasture productivity and beef cattle performance. Can. J. Anim. Sci. 95: 129–141. The objective of this experiment was to determine whether avoiding grazing during the sensitive pre-dormancy period (ca. 6 wk prior to a dormancy-inducing frost) would improve forage production, stand quality, alfalfa persistence and animal productivity in perennial pastures. There were two pasture species (PS), alfalfa–grass (AG) or grass (G), and three grazing phases. Phase I was conventional rotational grazing of all AG and G sections. In Phase II, one half of AG and G was rotationally grazed (conventional treatment, CT) while the other half was not (rested treatment, RT). Resting AG and G in Phase II required transferring RT animals to swath-graze early-seeded cereals. In Phase III, RT animals that swath-grazed in Phase II were moved to graze the rested sections of the pastures while those that grazed the unrested sections (CT animals) were transferred to swath-graze late-seeded cereals. There was no PS (P>0.05) or rest period (P>0.13) effect on total forage yield, carrying capacity, forage disappearance and forage residues. There was no effect (P>0.13) of resting on botanical composition or yield in AG. The current study did not observe significant benefits of resting on pasture yield, botanical composition or animal performance.
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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 | 0.000 | 0.000 |
| 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".