Palmer amaranth (<i>Amaranthus palmeri</i> S. Wats.) and waterhemp (<i>Amaranthus tuberculatus</i> (Moq.) J.D. Sauer) biovigilance in Canadian Agro-ecosystems
Bibliographic record
Abstract
The evolution and spread of herbicide-resistant weeds threatens long-term sustainability of Canadian agro-ecosystems. Herbicide-resistant weeds increase management inputs and costs, increase off-target and environmental exposure to pesticides, reduce yield quality and quantity, and impede harvest efficiency. Amaranthus species including Palmer amaranth ( Amaranthus palmeri S. Wats.) and waterhemp ( Amaranthus tuberculatus (Moq.) J.D. Sauer) are particularly concerning due to both their propensity towards herbicide resistance evolution, their history of invasion, and their spread in agro-ecosystems. A biovigilance approach is taken to build awareness of these pigweeds' initial invasion and spread in the USA. Characteristics of their identification, potential hybridization, and known herbicide resistance evolution are reviewed. Fourteen species of Amaranthus are found in Canada, nine of which (including waterhemp) possess herbicide-resistant biotypes. A total of 45 hybrids between various Canadian Amaranthus species with each other or Palmer amaranth have been noted. Hybrids have been experimentally produced or observed from herbarium specimens, with three cases of herbicide resistance transfer notably with Palmer amaranth or waterhemp. Mitigation strategies will depend on successful species identification and herbicide resistance status determination. Common pathways for Palmer amaranth introductions in the northern USA include both animal feed systems with grain screenings and crop production systems including seed and equipment contamination. Regional awareness campaigns will be critical to support Canadian farmers in identifying and quickly mitigating invasions of Palmer amaranth and waterhemp to prevent establishment and spread of infestations into new areas.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".