Effect of tillage, cover crop and crop rotation on the composition of weed flora in a sandy soil
Bibliographic record
Abstract
The development of integrated weed management strategies requires knowledge of mechanisms that influence compositional changes in weed flora. A 9‐year study was initiated in 1988 at Delhi, Canada, on a loamy sand soil to evaluate the effect of tillage systems [conventional (CT) and no‐till (NT)] and cover crops (only in NT) on weed density, species composition and associations, and crop yield in a winter wheat (Triticum aestivum L.)/bean/winter wheat rotation. Three bean types: soyabean (Glycine max L. Merr.), white bean (Phaseolus vulgaris L.) and kidney bean (P. vulgaris L.) were included. The NT system included variations: rye (Secale cereale L.) or maize (Zea mays L.) cover crop, volunteer wheat disked after harvest and wheat stubble. Data were collected in 1994, 1995 and 1996. Tillage systems, cover crops and crop type had differential effects on weed densities, species composition and associations. Weed densities were not affected by tillage or cover crops in wheat but, in the beans, densities were greater in the CT than in the NT systems. Various associations of weed species with tillage system, cover crop and crop type were observed. Crop yields were not affected by tillage type or cover crop, except that soyabean yields were highest in plots with cover crops.
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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".