Effect of Gibberellic Acid on Konjac Seeds Germination: Evidence from Data Analytics
Bibliographic record
Abstract
This paper aims to find an optimal combination of gibberellic acid concentration (C) and soaking duration time (T) in order to speed up and to improve the productivity of konjac (Amorphophallus muelleri Blume) germination process. For this purpose, a laboratory experiment was carried out with five levels of C and four levels of T. For each combination of C and T, 300 seeds were planted in three germination trays. And by using completely randomized design, in each tray, 100 seeds were planted. On the day right after a number of days after planting (D) which consists of seven levels, the germination rate is then recorded. By using data analytics method, we conclude that the optimal combination of C, D and T is C = 200 ppm, D = 7 days and T = 6 jam. Meanwhile, for a given T and D, concentration has no effect on germination rate. It is worth noting that (i) the experiment was conducted at room temperature under controlled humidity level, and (ii) according to the knowledge of the authors, this is an unprecedented study where Gibberellic acid is applied on konjac.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".