Weed emergence patterns as affected by topsoil movement within an eroded landform
Bibliographic record
Abstract
Abstract Soil erosion significantly affects within‐field soil properties, crop productivity, weed emergence patterns, and weed growth. Spatial variability in weed densities, emergence timing, weed seed production, seed viability, herbicide bioavailability, and other factors complicate weed management strategies in eroded landscapes. Reversing soil erosion by replacing translocated topsoil (soil‐landscape rehabilitation) is one method to improve crop productivity of severely eroded land, but bulk soil movement changes soil properties and weed seedbanks that influence weed spatial distribution patterns, emergence, and growth. We evaluated weed community responses to soil movement within a hilly landform. Soil‐landscape rehabilitation was performed by moving 15–20 cm of accumulated topsoil from the lower slope and adding it to the upper slope positions (areas of net soil loss by erosion). Adjacent plots were left in their eroded condition. Weed density and species richness were monitored for 4 years. Annual grasses dominated the weed community in the upper slope, and weed abundance was highest in the most eroded landscape positions. Soil addition improved soil characteristics for crop growth but did not increase weed densities. The density of weeds was lower where soil was removed in the first 2 years after soil movement, likely through removal of weed seeds with soil. Within 3 years of soil movement, weed abundance and species richness were the same or lower in areas of soil removal and no soil removal. In these experiments, movement of soil (and entrained weed seeds) within an eroded landform to improve productivity did not exacerbate weed issues.
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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".