Design and experimental study of the self-adaptive splitting technology of lotus seeds
Bibliographic record
Abstract
The lotus plumule has high medicinal value and is an important part of the lotus seed. Usually, the lotus seed must be split symmetrically into two halves through a splitting process to obtain an intact lotus plumule. However, this process is difficult to mechanize and automate, as different lotus seeds are of different sizes. In this study, a novel automatic self-adaptive splitting technology (SAST) is proposed for lotus seeds, based on a specially designed combined linkage mechanism and a roller pair centering mechanism. The technology can automatically adjust the position of the splitting point taper punch according to the size of the lotus seed and ensure that the tip of the punch is on the axis of the lotus seed. First, the centering deviation of the centering mechanism was analyzed. A mathematical model for the SAST was developed, and the key parameters were optimized using the firefly algorithm. An automatic splitting machine and a test bench were designed for centering deviation measurements, and both centering and splitting experiments were conducted. The generated maximum centering deviation of the SAST was <0.176 mm; the highest accurate splitting rates of 95% and 93.05% were achieved for unclassified and graded lotus seeds, respectively.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".