コンバイン収穫と協調したロボットトラクタによる早期稲わら鋤き込み技術 : 労働時間削減効果と翌年産水稲の収量向上効果
Bibliographic record
Abstract
Incorporation of rice straw in the paddy soil immediately after the rice has been harvested is expected to increase the content of inorganic nitrogen in the soil by the following spring. In practice, however, rice straw is often incorporated during the winter off-season due to labor shortages during the harves. In the present study, the smart system that an unmanned robot-tractor is collaborate-operated for the incorporation of rice straw simultaneously with a combine harvester worked in nearby paddy field plot was examined. The goal of this smart system was the incorporation of rice straw as early as possible while minimizing labor needs. Using this smart system, we showed that the labor needs could be reduced by 13% compared to the local conventional system that rice straw was incorporated by man-operated tractor after rice harvest in autumn or winter. The work efficiency using this smart system was reduced because of unexpected stops made by the robot-tractor due to a malfunctioning obstacle sensor. The yields of rice produced by this smart system was 6-11% higher than that obtained by local conventional system in three rice cultivars. The inorganic nitrogen concentration in the paddy soil applied smart system was a little higher than that with local conventional system. However, there were no significant differences in both rice yield and inorganic nitrogen concentration in soil between 2 systems. These findings demonstrate that incorporation of rice straw using an unmanned robot-tractor on the same day as the rice is harvested could contribute to a reduction in labor and improvement in yield.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".