Flurprimidol Foliar Sprays and Substrate Drenches Control Growth of `Pacino' Pot Sunflowers
Bibliographic record
Abstract
Flurprimidol substrate drenches at 2 mg a.i. per 15.3 cm (6 inch) pot were more effective on `Pacino' pot sunflowers ( Helianthus annuus ) than flurprimidol foliar sprays of ≥30 mg. L –1 (ppm), but both treatments resulted in significantly smaller plant height and diameter than the control (28,350 mg = 1 oz). Flurprimidol drenches of 2 mg were comparable in controlling plant height and diameter to the commercial drench recommendations of 2 mg paclobutrazol. The commercial recommendation of daminozide foliar sprays at 4000 mg. L –1 had greater efficacy in controlling plant height than the most effective flurprimidol foliar sprays of ≥30 mg. L –1 . Daminozide had no effect on plant diameter, while flurprimidol resulted in narrower plants. Flurprimidol and paclobutrazol drenches of 2 mg offer the economic advantage to producers of increased plant density on greenhouse benches, while plants treated with daminozide would require a greater amount of bench area. Producers should evaluate the trade-offs between the added costs of a drench vs. the higher cost-per-square-foot-week of production space required for a daminozide foliar spray. With these options, producers can select a plant growth regulator (PGR) that best fits their production and market requirements.
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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".