Effects of crop management practices on <i>Echinochloa crus‐galli</i> and <i>Chenopodium album</i> seed production in a maize/soyabean rotation
Bibliographic record
Abstract
Seed production of residual weed populations needs to be taken into account when estimating the long‐term impact of low‐input agronomic practices. The objective of this study was to measure the effects and interactions of crop, weed control, tillage practice and nutrient source on the seed production of the dominant residual weed species in a maize/soyabean rotation at two sites: Echinochloa crus‐galli (L.) Beauv. on a Sainte‐Rosalie clay and Chenopodium album L. on a Duravin clay loam. Seed production per unit area was estimated in each experimental unit. Weed seed production was greater under mechanical weed control compared with chemical weed control. In 1997, E. crus‐galli seed production reached over 326 000 seeds m–2 in mechanical weed control treatments, but averaged less than 500 seeds m–2 in the chemical weed control treatments. Chenopodium album produced in the range of 766 000 and 73 000 seeds m–2 in mechanical and chemical weed control treatments respectively. Very few or no weed seeds were produced in soyabean under chemical control. Tillage intensity and nutrient source did not affect seed production of either weed species, with the exception that E. crus‐galli produced more seeds in chisel than in mouldboard plough tillage in soyabean. Weed control method had more impact on seed production than tillage intensity and nutrient source in a maize/soyabean rotation.
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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) | 0.000 | 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".