Effects of pre‐treatment, renovation procedure and cultivar on the growth of white clover sown into a permanent pasture under both grazing and mowing regimes
Bibliographic record
Abstract
White clover (Trifolium repens) is a valuable pasture component that is frequently present in insufficient quantity for optimal animal nutrition. Several methods of reintroducing white clover into a permanent pasture without conventional tillage were investigated. Three seeders (Hunter, Vredo and a conventional seed drill), two white clover cultivars (Sacramento, and Sonja), two pasture pre‐treatments (a hard spring grazing or grazing plus light harrowing) and two defoliation regimes (grazing or mowing) were used to determine optimal seedling establishment conditions. Defoliation treatments were used as a method of investigating pasture improvement experiments. Measurements were taken to determine proportion of white clover present and total herbage mass. Plots renovated using a Hunter drill had the highest white clover content in the months immediately after renovation. Subsequently pre‐treatment method appeared to have no significant effect on herbage mass or species composition. The proportion of white clover in plots sown with the cultivar Sacramento was frequently higher than that in plots sown with the cultivar Sonja, but, overall, herbage production of cultivars was not different. Mowed plots had higher herbage production and tended towards a greater white clover content than grazed plots. Compaction of the surface to a depth of 10 cm in the grazed plots may have been a factor in the observed difference in herbage production. Regardless of management, within two years white clover content was similar among all treatments, including controls.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".