Identification of Cotton Resistance to Fusarium and Verticillium Wilts and the Influence of the Diseases on Yield
Bibliographic record
Abstract
Fifty new lines or verieties in Gossypium hirsutum L from Yangtze River drainage area were used to test the resistance to Fusarium or Verticillium wilts Ten of the lines were selected to analyze the seed cotton yield lost per plant that was caused by different grade diseases The results showed that (1) Four lines (10 0% of 40 lines) were resistant to Fusarium wilt, 27 tolerant (67 5%), 9 susceptible (22 5%) (2) One line (3 3% of 30 lines) was resistant to Verticillium wilt, 26 tolerant (86 7%), 3 susceptible (10 0%) (3) The diseases were scored by Ⅰ-Ⅳ grade, and the disease Ⅰ of Fusarium wilt caused yield reduction in seed by 32 0%, diseaseⅡ by 52 9%;disease Ⅲ by 68 7%; disease Ⅳ by 83 4% (4) The disease Ⅰ of Verticillium wilt yield lost by 30 3%, diseaseⅡ by 51 9%, disease Ⅲ by 68 4%, disease Ⅳ by 86 7%
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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".