Optimization Experiment and Analysis of Pneumatic Sorting for Multiscale Fresh Tea Leaves
Bibliographic record
Abstract
To solve the problems of low sorting rates in pneumatic sorting of multiscale fresh tea leaves and easy loss of fresh leaves in repeated experiments, a double negative pressure port noncoaxial adjacent bench was used as the research object. A 1:1 fresh tea leaf model was used to replace real fresh tea leaves. Through single-factor experiments and Box-Behnken response surface methodology, the effects of the rotation speed of the porous turntable, horizontal distance from the falling position of fresh tea leaves to the negative pressure ports, and running speed of the conveyor belt on the sorting rate were investigated. Single-factor experiments determined the effective range of each factor, and response surface methodology optimized the parameters to obtain the optimal combination. The rotation speed of negative pressure port A was 38 rpm and that of negative pressure port B was 28 rpm. The horizontal distances were as follows: L A = 48 mm and L B = 69 mm. The conveyor belt speed was 0.3 m/s. Under these parameters, the average sorting rate reached 80.6%, including 85.4% for one-bud–two-leaf leaves and 75.8% for single leaves, which were significantly higher than the initial sorting rate of 67.5%. An analysis of variance showed that the conveyor belt speed had the most significant effect on the sorting rate ( F = 378.32), and there was a significant horizontal distance × conveyor belt speed interaction. This study provides a theoretical basis and technical support for the development of automatic and precise sorting equipment for fresh tea leaves.
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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 | 0.001 | 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 | 0.000 | 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".