The Pattern of Stomatal Opening through the Exposure of High-Frequency Sound Wave with the Different Duration and Age of Soybeans (Glycine max (L.) Merril)
Bibliographic record
Abstract
Since the productivity of soybean in Indonesia is still low, a new innovation is needed to improve it. The objectives of this study are to determine a pattern of the stomatal opening of soybean’s leaves which exposed by high-frequency sound waves with different duration time and age of soybean plants. This study was carried out at the Experimental Farm Agriculture Faculty and Biology Laboratory of Science Faculty of the Islamic University of Malang, East Java in April to August 2013.The first factor was duration time of the exposure which consist of three levels: 20 minutes (D1), 40 minutes (D2) and 60 minute (D3).The second factor was the age of the soybean which consist of three levels: 15 days after planting (dap) (A1), 25 dap (A2) and 35 dap (A3). The soybean plants were exposure by the 5000 hertz frequency. The variables measured consisted of stomatal opening width, plant height, leaf area, fresh weight of pods, fresh weight of seed, oven dry weight of beans and harvest index. Increase of the duration time of exposure of high frequency sound waves by 20 to 60 minutes, tends to decrease the width of stomatal opening. The treatment of duration of exposure by 40 minutes at the age of 15 dap had the highest soybean grain yield by 24.10 g.plant-1, equivalent to 3.93 ton.ha-1. The relationship between the widths of the stomatal opening with soybean production
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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".