Impact of Soybean Leaf Interference and Row Spacing on Preharvest Glyphosate Application<sup>1</sup>
Bibliographic record
Abstract
Preharvest applications of glyphosate can be useful in controlling perennial weeds. Experiments were conducted from 1996 to 1999 to determine whether preharvest glyphosate applications are affected by differences in the amount of soybean canopy present at the time of application by measuring spray deposition and subsequently horsenettle or Canada thistle control. Soybean leaf interference levels were achieved by use of three soybean cultivars with different maturity groups to achieve no leaf interference, moderate leaf interference, and maximum leaf interference, and soybean was planted in three row spacings ranging from 19 to 76 cm. As soybean leaf interference increased, spray coverage of spray deposition cards decreased. There was a similar trend for relative spray volume, determined by intensity of the color change with water-sensitive cards. Row spacing did not influence spray coverage or relative spray volume. Percent change in horsenettle or Canada thistle stems from fall to spring counts was inconsistent. Differences detected in spray coverage did not influence weed control or weed stem density the following spring.Nomenclature: Glyphosate; Canada thistle, Cirsium arvense (L.) Scop. #3 CIRAR; horsenettle, Solanum carolinense L. # SOLCA; soybean, Glycine max (L.) Merr.Additional index words: CIRAR, Cirsium arvense, cultural practices, integrated weed management, perennial weed control, Solanum carolinense, SOLCA, spray deposition.
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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".