The Control Model for Environmental Factor Effecting on Growth of St. John's Wort
Bibliographic record
Abstract
St. John's wort (Hypericum perforatum L.) is a perennial herb and it has been used in medicine to treat various diseases. In terms of growth, it has required environmental factors and weather to appropriate. Thus, this paper presented the control model for environmental factor effecting on growth of St. John's wort by using two approaches. Those are the designed greenhouse and using Internet of Thing (IoT) and application. This work applied IoT as using the Wireless Sensor Network (WSN) and various sensors to measure, monitoring the change and control environmental factors in the greenhouse through the mobile and web application. This paper focused on three factor i.e. temperature, air humidity, and light. The proposed system was installed and tested in real situation at PSU farm. The result showed that the design and construction greenhouse can assist to control factors for the plant. The designed greenhouse can support to suitable adjust the factors of growth of St. John's wort. Moreover, the developed IoT and application can improve and controlled the factors better.
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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 | 0.000 | 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) | 0.000 | 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".