Effects of the fungal protein Nep1 and<i>Pseudomonas syringae</i>on growth of Canada thistle (<i>Cirsium arvense</i>), common ragweed (<i>Ambrosia artemisiifolia</i>), and common dandelion (<i>Taraxacum officinale</i>)
Bibliographic record
Abstract
The effects of the fungal protein Nep1 andPseudomonas syringaepv.tagetis(Pst) applied separately or in combination on Canada thistle, common ragweed, and common dandelion were examined in growth chamber experiments. Experiments examined five treatments: (1) untreated control, (2) Silwet L-77 (0.3%, v/v) control, (3) Nep1 (5 μg ml−1) plus Silwet L-77 (0.3%, v/v), (4) Pst (109colony-forming units [cfu] ml−1) plus Silwet L-77 (0.3%, v/v), and (5) Pst (109cfu ml−1) and Nep1 (5 μg ml−1) plus Silwet L-77 (0.3%, v/v). Foliar treatments were applied at 28, 26, and 21 d after planting for Canada thistle, common dandelion, and common ragweed, respectively. For all three species, foliar application of Nep1 alone or in combination with Pst caused rapid desiccation and necrosis of leaves, with the greatest effect on recent, fully expanded (RFE) leaves. Within 4 to 8 h after treatment (HAT), 60 to 80% of RFE leaves of all three species were necrotic. Measured 72 HAT, Pst populations in Canada thistle leaves treated with Nep1 plus Pst were approximately 105cfu cm−2compared with 107cfu cm−2for leaves treated with Pst alone. Measured 2 wk after treatment, foliar application of Nep1 reduced shoot dry weight of the three weeds by 30 to 41%. Treatment with Pst reduced shoot growth of common ragweed, Canada thistle, and common dandelion by 82, 31, and 41%, respectively. The large suppression of common ragweed shoot growth caused by Pst treatment was associated with a high percentage (60%) of leaf area exhibiting chlorosis. Treatment with Pst plus Nep1 did not result in significant decreases in shoot dry weight for Canada thistle and common dandelion compared with either treatment alone. For common ragweed, shoot growth reduction caused by applying Pst and Nep1 together was not greater than that caused by Pst alone.
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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) | 0.001 | 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 | 0.001 |
| 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".