Effect of sod-seeding bloat-free legumes on pasture productivity, steer performance, and production economics
Bibliographic record
Abstract
A 5 yr experiment evaluated the effects of sod-seeding sainfoin and cicer milkvetch into monoculture grass (Lanigan, SK) or legume (Lethbridge, AB) stands on pasture productivity, steer performance, and economics. At Lanigan, sainfoin decreased (treatment × year P = 0.01) from 13% in year 1 to 2% in year 2 (% plant population) and did not differ thereafter, whereas cicer milkvetch maintained a proportion of 16% in the stand. Forage yield was greater (treatment × year; P < 0.01) in year 1 in the sainfoin and cicer milkvetch treatments compared with control. Dry matter intake of steers was greater only in year 5 and average daily gain was greater (P < 0.01) in sainfoin and cicer milkvetch treatments compared with control. At Lethbridge, sainfoin decreased (treatment × year; P = 0.01) from 46% to 17% (% dry matter yield), whereas cicer milkvetch maintained its proportion at 11%. Forage yield increased (treatment × year; P < 0.01) only in years 2 and 3 of sainfoin, compared with cicer milkvetch or control. Average daily gain gain was not affected by treatment. At Lanigan, sainfoin and cicer milkvetch generated greater gross returns compared with control; however, once establishment costs were applied, there were no differences in the present value of net returns.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".