Characterization of MCPA resistance in Palmer amaranth (<i>Amaranthus palmeri</i>)
Bibliographic record
Abstract
Abstract Phenoxy herbicides (2,4-D and MCPA) are widely used to manage broadleaf weeds including Palmer amaranth (Amaranthus palmeri S. Watson), one of the most troublesome weeds in U.S. cropping systems. Previously, we documented resistance to 2,4-D and MCPA in an A. palmeri population (KCTR) from Kansas. Our recent research suggested rapid metabolism of 2,4-D bestows resistance in KCTR A. palmeri; nonetheless, the mechanism of MCPA resistance in this population is still unknown. The objectives of this research were to (1) evaluate the level of resistance to MCPA in KCTR compared with two known susceptible populations of A. palmeri, MSS and KSS; (2) study the absorption and translocation of [14C]MCPA in KCTR and MSS plants: (3) investigate the metabolic profile of [14C]MCPA in KCTR and MSS and compare those with MCPA-tolerant wheat (Triticum aestivum L.) plants; and (4) assess the possible role of cytochrome P450 enzymes (P450s) in MCPA metabolism. Experiments were conducted to assess the level of resistance in KCTR. Using [14C]MCPA, the absorption, translocation, and metabolic profiles were assessed in A. palmeri. Involvement of P450s was confirmed using malathion, a known P450 inhibitor. Regression analyses indicate that KCTR population exhibits an $\sim $ 3-fold resistance to MCPA. No difference in absorption of [14C]MCPA was found between MSS and KCTR. However, the KCTR plants translocated less [14C]MCPA at 48 h after treatment (HAT) and metabolized MCPA more rapidly than MSS plants at 12 and 24 HAT. MCPA resistance in KCTR was reversed upon treatment with malathion, indicating the involvement of P450s in metabolism of this herbicide. This is the first report of characterization of MCPA resistance in A. palmeri.
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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".