Higher Yield and Fewer Weeds in Four-Species Grass/Legume Mixtures Than in Monocultures: Results from the First Year at 20 Sites of Cost Action 852
Bibliographic record
Abstract
Utilisation of grass/legume mixtures instead of grass monocultures is a sensible alternative for low input, efficient agricultural systems that reduce production costs, promote environmental policy and maintain a living countryside. Consequently, widely adapted forage legumes will become increasingly important. Instability of simple grass / legume mixtures with only one grass and one legume species is a major problem (Wachendorf et al., 2001). An experiment was established in 39 sites in Europe, Australia and Canada within COST Action 852 to: (1) assess the benefits of grass / legume mixtures in terms of forage production, (2) test whether the combination of fast and slow-growing species improves the stability of the mixtures and (3) assess response patterns over a large environmental gradient.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".