Effect of intercropping wheat with forage legumes on wheat production and ground cover
Bibliographic record
Abstract
The use of winter legumes in southern Brazil is hindered by the slow growth of these species during establishment exposing soil surface to erosion. Introduction of these species along with spring wheat (Triticum aestivum L.) was studied as a means of increasing ground cover during their initial establishment period, without reducing wheat grain yield. Two experiments were conducted in nearby areas, one in each year. Birdsfoot trefoil (Lotus corniculatus L.), red clover (Trifolium pratense L.) cultivar Quiñequelli, white clover (T. repens L.), and arrowleaf clover (T. vesiculosum Savi) did not reduce cereal yield in either year. Wheat yield was reduced by intercropped red clover cultivar Kenland and by subclover (T. subterraneum L.) in the first year. No grain yield differences due to intercropping with any legume were detected in the second year, when rainfall was below normal. Intercropping with wheat showed to be a practical alternative to enhance ground cover at establishing forage legumes.
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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 | 0.001 |
| 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".