Roadsides and neighboring field edges harbor different weed compositions
Bibliographic record
Abstract
Roadsides are vectors of spread for invasive and other non-native plants. Therefore, fields located along roadsides could harbour more weeds and less native species compared to more isolated fields. To determine if field edges that are close to roadsides have different floras compared to more isolated fields, we surveyed 26 field pairs (52 fields) located in the province of Québec, Canada. For each pair, one field could be directly accessed by a major paved road (AD fields) while the other field, located on the same farm, was more isolated (IS fields) and could only be accessed via a secondary farm road. Two borders of these fields (IS) were sampled as well as the parallel (AD-pa) and the perpendicular border (AD-pe) of A fields and the adjacent roadside (RO). Plant species present along these field borders were recorded and classified (e.g. non-native, native, monocot, dicot, annual, perennial) in 0.5 m2 quadrats located every 20 m. The number of common ragweed (Ambrosia artemisiifolia) plants was also noted. Analyses included linear mixed models, generalized estimating equations models and multiple correspondence analyses. All border types had equivalent species richness. Roadsides had higher densities of common ragweed and lower occurrences of native and perennial species compared to field edges. All analyses indicate roadside floras are different from field edge floras but field edges close to roadsides were similar to those of more isolated fields. Results do not support a simple diffusive spread of roadside plants into field crop edges since field edges located along roadsides did not harbour more common ragweed plants or more roadside species.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".