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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 | 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 teacher head, 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".