OPTIMIZATION OF THE TECHNOLOGICAL PROCESS OF FLAX SEED GERMINATION
Bibliographic record
Abstract
In the world, demand for flax seeds and its processed volumes are increasing. Flax seeds are classified as natural functional food products. This is confirmed by the Ministry of Health of many countries, in particular Canada and the United States of America. Flax germination makes flax seed components biologically available. Each type of plant has its own set of germination requirements consisting of both internal and external factors. This research was aimed at studying the effect of various external factors (temperature, humidity.etc.) affecting flax seed germination energy. The temperature varied in the range 16°C to 30°C, in increments of 2°C, as further increasing the temperature requires additional equipment and, consequently, additional energy consumption. The ambient humidity was maintained at 40, 60, 70, and 95%. The flax seeds were germinated for 36 hours till seedlings, up to 3 mm long, appeared. The germination energy was determined for each combination of the controlled factors. A mathematical model of the flaxseed germination process was constructed using the regression and correlation analysis methods. The model obtained determines the optimum germination modes. In the course of the experimental research, we applied experimental design techniques and mathematical processing of the experimental data. Using the computer programmes MathCad and Microsoft Excel optimized the flax seed germination and set its optimum modes. The constructed mathematical model makes it clear that the maximum germination energy 99.64% is achieved at the temperature 27.5°C and humidity 95%. The experimental and statistical models of germination of flax seeds have been obtained, describing the process with the correlation coefficient R = 0.96–0.99. The data obtained can be used to predict the quality parameters of flax seedlings and the energy consumption to obtain them.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".