Selection for glyphosate resistance in <i>Conyza</i> spp. occurring in the railway network of southern Spain
Bibliographic record
Abstract
Conyza spp. are broadleaf weeds that occur in many crops but are also common in non-crop systems such as roadsides and railways. Conyza have selected for glyphosate resistance along railway tracks in southern Spain due to the misuse of this herbicide and the high seed dispersal rate of these species. Twenty-three samples of the genus Conyza (11 Conyza canadensis and 12 Conyza bonariensis) were collected from the margins of railways in different routes of the Andalusia railway network running adjacent to nearby crop fields. The glyphosate resistance level of Conyza populations was evaluated through GR50 (herbicide rate causing 50% growth reduction) and resistance factor (RF) values in every population collected. The highest GR50 were 1851.2 g a.e. ha−1 (RF = 52.53) in C. canadensis (Malaga–Cordoba route) and 1972.4 g a.e. ha−1 (RF = 35.20) in C. bonariensis (Seville–Cordoba route), and the lowest were 46.9 g a.e. ha−1 (RF = 1.33) in C. canadensis (Seville–Cordoba route) and 23.2 g a.e. ha−1 (RF = 0.41) in C. bonariensis (Seville–Cordoba route). The results showed that, among all the C. canadensis populations collected, 18.2% were glyphosate-resistant (RF > 10), 45.5% showed a tendency to develop resistance (RF = 2.5–5), and 36.4% were susceptible (RF < 2.5). Of the 25% of C. bonariensis populations that had resistance to glyphosate, 16.7% had moderate resistance (RF = 5–10) and 58.3% were susceptible. This study found that there are already glyphosate-resistant Conyza spp. along the railway network in southern Spain. This could lead to possible seed exchange between the railway and adjacent places. Therefore, it is vital to consider the railway network when planning control measures against resistance.
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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 | 0.001 | 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 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".