Mechanisms of glyphosate resistance in common ragweed (<i>Ambrosia artemisiifolia</i>): patterns of absorption, translocation, and metabolism
Bibliographic record
Abstract
Abstract Glyphosate’s efficacy is influenced by the amount absorbed and translocated throughout the plant to inhibit 5-enolpyruvyl shikimate-3-phosphate synthase (EPSPS). Glyphosate resistance can be due to target-site (TS) or non–target site (NTS) resistance mechanisms. TS resistance includes an altered target site and gene overexpression, while NTS resistance includes reduced absorption, reduced translocation, enhanced metabolism, and exclusion/sequestration. The goal of this research was to elucidate the mechanism(s) of glyphosate resistance in common ragweed (Ambrosia artemisiifolia L.) from Ontario, Canada. The resistance factor for this glyphosate-resistant (GR) A. artemisiifolia biotype is 5.1. No amino acid substitutions were found at positions 102 or 106 of the EPSPS enzyme in this A. artemisiifolia biotype. Based on [14C]glyphosate studies, there was no difference in glyphosate absorption or translocation between glyphosate-susceptible (GS) and GR A. artemisiifolia biotypes. Radio-labeled glyphosate metabolites were similar for GS and GR A. artemisiifolia 96 h after application. Glyphosate resistance in this A. artemisiifolia biotype is not due to an altered target site due to amino acid substitutions at positions 102 and 106 in the EPSPS and is not due to the NTS mechanisms of reduced absorption, reduced translocation, or enhanced metabolism.
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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 | 0.000 | 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) | 0.001 | 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".