Bibliographic record
Abstract
Kura clover (Trifolium ambiguum M.B.) is a persistent perennial forage legume that produces high-quality herbage for grazing, but its response to potassium (K), an essential plant nutrient, is unknown. Our objective was to determine the effect of four dipotassium oxide (K2O) rates on forage yields and stands of Kura clover compared to birdsfoot trefoil (Lotus corniculatus L.) and alfalfa (Medicago sativa L.). There was a significant linear response in Kura clover and alfalfa forage yields to K2O fertilization each year. Averaged over 4 years, Kura clover forage yield increased by 0.024 Mg ha–1 per kg of K2O applied, and the yield response was similar for alfalfa. Birdsfoot trefoil forage yield response to K2O fertilization was less than those of alfalfa and Kura clover. In the first 2 years after seeding, Kura clover forage yields were consistently less than for alfalfa at all rates of K2O fertilizer, but in subsequent years Kura clover yields were similar to or exceeded those of alfalfa. Final groundcover values, an estimate of stand density, of unfertilized (0 K2O rate) Kura clover, birdsfoot trefoil, and alfalfa were 86, 35, and 21%, respectively. Final groundcover of Kura clover and alfalfa was increased by K2O fertilization. We conclude that Kura clover has good persistence with low soil K fertility but stands and yields can be increased by fertilization.
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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 | 0.000 | 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".