Establishment and persistence of kura clover no-till drilled into pastures with herbicide sod suppression and nitrogen fertilization
Bibliographic record
Abstract
Kura clover was successfully established via sod-seeding in different environments. Its establishment and yields were initially inferior to that of red clover and white clover. But, its presence slowly increased in the sward and, by the first harvest of the second post seeding season, it was yielding significantly more than the legume species presently recommended for pastures. Clover establishment increased with increasing intensity of herbicide suppression. Best overall results were obtained with glyphosate at low rate (0.8 kg a.i. ha-1). Paraquat (0.9 kg a.i. ha -1) did not suppress sufficiently the grass population for clovers to establish while glyphosate applied at high rates (3.3 kg a.i. ha -1) led to excessive grass suppression, excessive legume content and temporary weed encroachment. N fertilization at seeding did not consistently increase establishment of sod-seeded Kura clover. Forage quality was positively correlated with clover content.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".