Weed emergence and seedbank after three years of repetitive shallow cultivation in a muck soil field
Bibliographic record
Abstract
Frequent cultivation is often used to control weeds in crops such as lettuce. The efficacy of this technique on weed populations has been evaluated, but the effect on weed emergence and seedbanks is less documented. Studies in mineral soil indicate that soil disturbance can increase both weed emergence and seed persistence depending on where seeds are redistributed in the soil profile. Evaluations done in muck soil are scarce. This study evaluated the effect of two and four repetitive shallow (3.4 to 7.1 cm deep) cultivations on weed emergence and the weed seedbank in muck soil. Cultivation treatments (0, 2, and 4 cultivations using a inter-row rototiller) were done in lettuce plots from 2017 to 2019. Weed density was evaluated by species before each cultivation date and after crop harvest. Viable seedbanks were evaluated by collecting soil samples before and after each growing season and placing them in greenhouse flats. Statistical analyses were based on mixed models. Results showed that shallow cultivation modified the emergence patterns of weeds but did not reduce total emergence during the subsequent years or viable seedbanks. After two seasons without seed inputs, total emergence was reduced by 46.6% and the seedbank was reduced by 31.7% regardless of the cultivation treatment. However, the seedbank of the very abundant common purslane (Portulaca oleracea) remained high.
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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 | 0.001 |
| 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".