Roadsides and neighboring field edges harbor different weed compositions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 m 2 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it