Characterizing downwind drift deposition of aerially applied glyphosate using RbCl as tracer
Bibliographic record
Abstract
Rubidium chloride (RbCl) was used as a tracer tank-mixed with active ingredients to profile downwind deposition of aerially applied crop protection and production materials to characterize off-target drift, which helps improve spray efficiency and reduce environmental contamination. Mylar sheets were placed on a holder in the field at each sampling station to collect sprayed solution. RbCl tracer was used to assess downwind drift of nozzles mounted on the booms installed and controlled on both sides of an agricultural airplane. The experiment was conducted on a field covered by Bermuda grass (Cynodon dactylon). During the experiment, the airplane was planned to fly three passes with three replications at each of three different altitudes, 3.7 m, 4.9 m, and 6.1 m for total of 27 flight runs. The results indicated that sampling station location had a significant effect on RbCl concentration. However, application release altitude was not significant to the change of RbCl. Another practical application in the same aerial application system was used to assess crop injury from the off-target drift of aerially applied glyphosate. RbCl concentrations measured from Mylar sheets were correlated with visual injury, plant height, shoot dry weight, leaf chlorophyll content, and shikimate, which were measured from the leaves and plant samples collected. Overall, RbCl is an effective tracer for monitoring spray applications from agricultural aircraft and unmanned aerial vehicles to intensify agriculture output and minimize environmental impact.
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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".