Design and Testing of a Pneumatic Oscillating Chinese Wolfberry Harvester
Bibliographic record
Abstract
A pneumatic oscillating Chinese wolfberry harvester was designed to improve the efficiency of manual harvesting and minimize fruit damage by mechanical harvesting. The shedding model of Chinese wolfberry under pneumatic forces was established based on kinetic analysis, and the flow field characteristics were investigated using computational fluid dynamics (CFD). The position and shape of the fan outlet were determined by comparing the effects of a linear reduction outlet and a Laval reduction outlet on the outlet airflow rate. The size of the oscillating mechanism was optimized with nonlinear constraints to achieve uniform oscillation of the airflow. A three-factor, three-level orthogonal test was conducted on the NingQi 7 wolfberry variety. The flow rate at the outlet, the oscillation frequency, and the outlet distance were the test factors, and the net harvesting rate of ripe fruits, the harvesting rate of green fruits, and the damage rate of ripe fruits were the test indices. The results showed that the net harvesting rate of ripe fruits was 85.21% at an outlet distance of 10 cm, an outlet flow rate of 70 m·s−1, and an oscillation frequency of 2 Hz. This study provides references for the design of pneumatic harvesters for Chinese wolfberry.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".