Ivyleaf morningglory (<i>Ipomoea hederacea</i> Jacq.) competition is not intensified by drought in silage corn in central New York State, USA
Bibliographic record
Abstract
Ivyleaf morningglory (IMG, Ipomoea hederacea Jacq.) is a summer annual vine that is native to the Americas and invasive globally. This species decreases field crop yields through competition and interference with harvesting. Here, we explore the potential of IMG to compete with silage corn ( Zea mays L.) in New York State, USA. In a 2-year field study, we measured silage yield at five IMG planting densities (0–8 plants m−2) under no-drought conditions and a drought treatment established with rainout shelters. Volumetric water content was 26%–28% lower in the drought treatment than the no-drought treatment. Drought reduced fresh corn silage yield ( P = 0.003). Fresh silage yield was 21 600 ± 700 kg ha−1 in the no-drought treatment in 2016, 19 100 ± 900 kg ha−1 in the drought treatment in 2016, 30 500 ± 900 kg ha−1 in the no-drought treatment in 2017, and 28 200 ± 700 kg ha−1 in the drought treatment in 2017. Silage yield was not strongly responsive to IMG density, regardless of drought. These data suggest that the risk of corn silage yield losses due to IMG is relatively low in New York State and unlikely to be affected by drought. The risk posed by IMG may differ in other cropping systems or regions and under climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".