PARTITIONING THE WHEAT GRAINS BY THE UNIFORMITY OF PROTEIN QUALITY BASED ON REMOTE SENSING
Bibliographic record
Abstract
Separating the wheat grains by its protein quality (PQ) uniformity can add the values of the wheat.This paper proposed an approach to predict the PQ uniformity of wheat when they are growing on field and help partitioning them on PQ uniformity by region.We first partitioned the experimental plot as three scales of 10 m × 10 m, 15 m × 15 m, 20 m × 20 m and used three different methods to ascertain that the best scale to partition the wheat grains by the PQ uniformity was the 10 m × 10 m, and the best method was the M5 model tree.Secondly, we used the M5 model tree on the 10 m × 10 m scale, and applied the spatial information to reinforce the partition performance.Compared with the traditional approach based on Kriging, the method proposed in the paper improved the stability performance of the traditional approach without losing accuracy.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".