The Research of Soybean Pod Borer Resistance about Foreign and Domestic Precocious Soybean
Bibliographic record
Abstract
336 soybean cultivars from China,Russia,Canada,Hungary,Austria,Germany,France,Poland and other 20 countries were estimated with the resistance to soybean pod borer and 58 cultivars were found to be highly resistant to soybean pod borer and 64 cultivars with satisfied resistance were screened,accounting 34. 7%. The relationships were studied between soybean pod borer resistance and agricultural characters. The results showed there is significant correlationship between nods number in main stem,pods number per plant,seeds per plant,seed weight per plant,weight of 100 seeds,pubes-cence densities,color of seed coat and soybean pod borer resistance. The conclusion was that the soybeans,which hadless nods in main stem,small seeds,non-lodging,low density of pubescence,black color of seed coat possessed poor egg deposition and less damage from soybean pod borer.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".