Comparing weed composition in natural and planted hedgerows and in herbaceous field margins adjacent to crop fields
Bibliographic record
Abstract
Hedgerows and field margins are common features of the agricultural landscape in southern Québec. In the last decade, hedgerow establishment has been encouraged primarily for the prevention of soil erosion, though many farmers view them as reservoirs of invasive weeds and pests. In this study, we compared weed assemblages of several hedgerow types and herbaceous field margins. Ranking that takes into consideration weed abundance was clearly demonstrated: planted hedgerows > herbaceous field margins > natural woody hedgerows. The latter harboured many perennial plant species of less weedy propensity than planted hedgerows, which accommodated a larger number of weedy species. In planted hedgerows close to 50% of plants were weeds, even in their central position, although planted and natural hedgerows sheltered similar weed composition in their central parts. Likewise, most species inventoried in adjacent fields were weeds. Weeds characterising the central position of hedgerows/field margins were often species of wet habitats (Lythrum salicaria L., Equisetum arvense L., Cicuta maculata L.). Management practices of hedgerows and perhaps of crop fields adjacent to hedgerows may be mostly responsible for the abundance of weeds in hedgerows and field margins. Key words: Hedgerow, field margins, weed, management practices, pesticide, fertiliser
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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 | 0.001 | 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".