Varietal evaluation of gerbera under shade net condition in Prayagraj agroclimatic condition
Bibliographic record
Abstract
Gerberas are widely cultivated and have a significant market value due to their popularity as ornamental plants and their traditional medicinal uses. The global gerbera market includes various segments, such as cut flowers, potted plants and medicinal products. Therefore, present investigation was carried out with title at the Department of Horticulture, Naini Agricultural Institute, Sam Higginbottom University of Agriculture Technology and Sciences, Prayagraj, Uttar Pradesh during the Winter 2022-23 to determine the performance of different varieties of gerbera for its growth and flowering. Under this experiment, overall, 8 varieties were used comprising of variety Shveen, Petali, Livia, Hiami, Deepti, 17026, Alcochate and Breakdance. The current study found that variety Hiami performed better in terms of characters like plant height at 30, 60 and 90 DAP (18.13, 21.20 and 23.77 cm respectively); early for days to first flower bud emergence (39.43 DAP); days from bud to flowering (9.97 days); number of days for flowering from planting (54.53 DAP); number of days for peak flowering (58.17 DAP); maximum number of buds (10.63 buds); stalk length (64.77 cm); diameter of flower (9.33 cm) and yield per 200 m2 (11693 flowers). Variety Deepti performed better for parameters like number of leaves at 30, 60 and 90 DAP (7.53, 10.37 and 12.63 leaves respectively); plant spread at 30, 60 and 90 DAP (18.77, 26.13 and 35.30 cm respectively); Vase life (8.80 days); second highest for yield per 200 m2 (10263 flowers).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".