Mathematical modeling of the technology of gentle machine harvesting of cabbage
Bibliographic record
Abstract
Relevance. In recent years, in many countries of the world, in particular, in Denmark, Belgium, Canada, China, Japan, Belarus, Kazakhstan, as well as in our country, an increased interest in the mechanization of the cabbage harvesting process was shown. Harvesting using machines reduces labor costs by more than 3 times. At the same time, due to the introduction of traditional mechanized technologies for harvesting cabbage, there was a problem associated with maintaining the original quality of products, since when the heads are shipped to the body of the vehicle in bulk, as well as in the process of laying for storage, their mechanical damage occurs. Methods. In this regard, the machine cleaning of cabbages with manual careful laying of cabbages in containers installed in the body of the accompanying vehicle using a belt conveying device is proposed and justified. The process of mechanized harvesting of cabbage according to the proposed technology will be stable while ensuring the necessary intensity of shifting the heads from the web of the belt conveying device into containers. In this regard, in order to optimize the technological parameters, the workflow of the proposed cabbage harvesting method is modeled using elements of the queuing theory. Results. As a result, the number of maintenance personnel necessary for the smooth execution of the cabbage harvesting workflow according to the described technology has been established.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".