Searching for consistent postemergence weed control in progressively inconsistent weather
Bibliographic record
Abstract
Abstract Foliar-applied postemergence herbicides are a critical component of corn ( Zea mays L.) and soybean [ Glycine max (L.) Merr.] weed management programs in North America. Rainfall and air temperature around the time of application may affect the efficacy of herbicides applied postemergence in corn or soybean production fields. However, previous research utilized a limited number of site-years and may not capture the range of rainfall and air temperatures that these herbicides are exposed to throughout North America. The objective of this research was to model the probability of achieving successful weed control (≥85%) with commonly applied postemergence herbicides across a broad range of environments. A large database of more than 10,000 individual herbicide evaluation field trials conducted throughout North America was used in this study. The database was filtered to include only trials with a single postemergence application of fomesafen, glyphosate, mesotrione, or fomesafen + glyphosate. Waterhemp [ Amaranthus tuberculatus (Moq.) Sauer], morningglory species ( Ipomoea spp.), and giant foxtail ( Setaria faberi Herrm.) were the weeds of focus. Separate random forest models were created for each weed species by herbicide combination. The probability of successful weed control deteriorated when the average air temperature within the first 10 d after application was <19 or >25 C for most of the herbicide by weed species models. Additionally, drier conditions before postemergence herbicide application reduced the probability of successful control for several of the herbicide by weed species models. As air temperatures increase and rainfall becomes more variable, weed control with many of the commonly used postemergence herbicides is likely to become less reliable.
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 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 | 0.000 |
| 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".