Development of Damage Assessment Method of Rice Crop for Agricultural Insurance Using Satellite Data
Bibliographic record
Abstract
Goal is to develop new method utilizing satellite data for assessment of damage in paddy field which can contribute toward substantial reduction of the damage assessment time and costs in framework of agricultural insurance. For the damage assessment, estimation of yield in each paddy plot is a key, so the research on the estimation of rice yeild was carried out using satellite data in Hokkaido, Japan. Both multiple linear regression analysis and the projection pursuit regression analysis were conducted for the estimation of yeild using data from 3 different satellites about 3 different rice varieties. As the result, the projection pursuit regression analysis showed smaller value of predictive error than that of the multiple linear regression analysis, and the lowest predictive error was indicated when SPOT5 data with 10 m resolution was used. Moreover, for reducing the predictive error, it was found that a yield estimation formula should be created for each of different rice varieties. The results out of this research suggest that satellite data can be effectively used for estimation of yield and also assessment of damage at lower costs to calculate indemnity in agricultural insurance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".