Modeling of drying St. John's wort (Hypericum perforatum L.) leaves
Bibliographic record
Abstract
Drying of agricultural crops after harvesting is an important operation that helps in preserving product quality and quantity, particularly for medicinal plants and herbs which undergo reduction of essential oils and changes of qualitative properties such as color, both of which influence the economical value of the product. Drying of medicinal plants is a delicate operation for removing product moisture, in order to reduce enzyme activity; thus containing growth of bacteria and pathogens, and preventing product deterioration. Drying process of St. John’s wort (Hypericum perforatum L.) leaves was studied and modeled in this investigation. Independent variables included temperature at four levels (40, 50, 60 and 70°C), air velocity at three levels (0.3, 0.7 and 1 m/s), and product depth at three levels (1, 2, and 3 cm). The experiments were performed as factorial with completely randomized design in three replications. Seven drying models, namely Yagcioglu, Page, modified Page, Henderson and Pabis, Lewis, two-term and Verma, were utilized to fit the data. The Page model was found as the best model having the highest R2 and lowest χ2, RMSE and P-values. Key words: Drying, St. John’s wort, modeling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".