The Effect of Suckercide Application Timing and Cultivar Maturity on Chemical Topping of Burley Tobacco
Bibliographic record
Abstract
Experiments were initiated in 2015 to evaluate the efficacy of chemical topping for burley tobacco (Nicotiana tabacum L.). The major objectives for this study were to determine the optimum timing of suckercide application and appropriate cultivar maturity for effective chemical topping. Burley tobacco cultivars TN 90 (medium maturity), KT 210, and KT 215 (late maturity) were chemically topped at the 10% button, 50% button, and 10% bloom growth stages. The 10% button and 50% button application timings were best suited for chemical topping practices. Treatments that targeted the 10% bloom stage did not completely halt inflorescence growth; however, all application timings resulted in excellent sucker control. Both medium and late maturing burley cultivars proved to be acceptable for chemical topping methods; however, timing the suckercide application may be less difficult with later maturing cultivars. Chemically topped treatments generally resulted in shorter, narrower tip leaves than manually topped treatments. There were no significant differences in total yield of TN 90 when comparing tobacco that was manually topped at 10% bloom to tobacco that was chemically topped at 10% button, 50% button, or 10% bloom across all environments. In 4 out of 6 environments, total yield was not significantly different between manual topping and any chemically topped application timing in the late maturing burley cultivars; however, at least 1 chemically topped application timing had equivalent yield to manually topped tobacco in all environments.
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 | 0.001 | 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".