Growth and Yield Comparison of French Bean as Influenced by Three Varieties and Nutrients
Bibliographic record
Abstract
The experiment was conducted in the Horticultural Farm of Sher-e-Bangla Agricultural University, Dhaka, Bangladesh with aim to find out the suitable variety, optimum nutrients and suitable combination variety and optimum nutrients for maximum growth, yield of French bean in Bangladesh. The experiment consisted of two factors. Factor A: different varieties as V1 = BARI French bean-1, V2= BARI French bean-2, V3= BARI French bean-3 and Factor B: different level of nutrients as N1= cowdung (5 t/ha) + vermicompost (2 t/ha), N2= cowdung (5 t/ha) + N90 P30 K45 kg/ha, N3= cowdung (5 t/ha) + vermicompost (2 t/ha) + N90 P30 K45 kg/ha. The experiment was laid out in a Randomized Complete Block Design with three replications. In case of different varieties of French bean the maximum number of flowers (27.83), the highest number of pod harvested per plant (22.14) and the highest pod yield (15.95 t/ha) were found from V2, whereas the lowest was from V3. For the different levels of nutrients1 the highest number of flowers (25.58), the highest number of pod per plant (20.60) and the highest pod yield (14.10 t/ha) were recorded from N2 treatment, while the lowest was from N1 treatment. Due to the combined effect, the maximum number of flowers (32.02), the maximum number of pod harvested per plant (26.50), the highest pod yield (18.92 t/ha) were observed from V2N2 treatment combination, while the lowest were from V3N1 treatment combination. Therefore, the V2N2 treatment combination appeared to be the best for achieving the higher growth and yield of French bean.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".