Weed Suppression by Seven Clover Species
Bibliographic record
Abstract
Used as cover crops, clover species may differ in their ability to suppress weed growth. Field trials were conducted in Alberta, Canada to measure the growth of brown mustard [Brassica juncea (L.) Czern.], in mowed and nonmowed production, as influenced by alsike (Trifolium hybridum L.), balansa [T. michelianum Savi var. balansae (Boiss.) Azn.], berseem (T alexandrinum L.), crimson (T. incarnatum L.), Persian (T. resupinatum L.), red (T. pratense L.), and white Dutch (T. repens L.) clover and fall rye (Secale cereale L.). In 1997, clovers reduced mustard biomass in nonmowed treatments by 29% on a high‐fertility soil (Typic Cryoboroll) at Edmonton and by 57% on a low‐fertility soil (Typic Cryoboralf) at Breton. At Edmonton, nonmowed mustard biomass was reduced by alsike and berseem clover in 1996 and by alsike, balansa, berseem, and crimson clover in 1997. At Breton, all seven clover species suppressed weed biomass. A negative correlation was noted among clover and mustard biomass at Edmonton but not at Breton. The effects of mowing varied with location, timing, and species. Mowing was beneficial to crop/weed proportion at Edmonton but not at Breton. Mowing at early flowering of mustard produced greater benefit than mowing at late flowering. With early mowing, all clover species suppressed mustard growth at Edmonton. Clovers reduced mustard regrowth (g plant−1) and the number of mustard plants producing regrowth. The characteristics of berseem clover (upright growth, long stems, high biomass, and late flowering) would support its use as a cover crop or forage in north‐central Alberta.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".