Tillage index predicts weed seedling recruitment depth
Bibliographic record
Abstract
The influence of tillage on the depth of weed seedling recruitment has implications for species competitive ability, weed management strategy development, and the modeling of weed emergence; characterization of this relationship could aid in the creation of field-specific models of weed seedling recruitment. A tillage index based on the percentage of residue cover remaining on the soil surface after tillage events was assessed for 44 zero-tillage and 44 conventional-tillage fields across Manitoba, Canada. This index was related to in situ measurements of seedling recruitment depth from the same fields for five annual weed species. The modeled relationship was linear and unique for each species (R2 values ranged from 0.38 to 0.71). The relationship did not differ between pre- and post-seeding measurements, or between years, except for green foxtail. Slopes of the regression lines were greater for species such as volunteer wheat, which can recruit from a great range of depths. The results of this study show that recent tillage strongly influences mean weed seedling recruitment depth, and that the recruitment depth of summer annual weed seedlings is related in a continuous and reliable manner to a simple estimation of the relative level of tillage (including seeding) applied in one season. Key words: Recruitment depth, tillage index, weed emergence, weed seedlings
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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 | 0.001 |
| 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".