Grey Correlative Degree Analysis on the Cold-Resistant Traits of Parthenocarpic Eggplant
Bibliographic record
Abstract
In order to breed new eggplant variety with parthenocarpic ability and strong cold-resistance, the six cold-resistant traits of fifteen eggplant resources with parthenocarpic ability were evaluated with grey correlative degree analysis. One non-parthenocarpic resource ‘Shenggao No. 2’ was used as control. The results indicated that ‘29’, ‘32’, ‘30’, ‘XBL’, ‘31-2-1’, ‘TXQ’ and ‘31-2-2’ had strong cold-resistance; ‘HLMQ’, ‘HQ’, ‘ZHQ’, control and ‘HXZ’ had weak cold-resistance; the other four resources had moderate cold-resistance. When suffered cold stress, the resources with strong cold-resistance had a larger increase of SOD, POD, CAT activities and a least increase of MDA content and EL. Among the strong cold-resistant resources, ‘29’, ‘30’, ‘31-2-1’, ‘TXQ’ and ‘31-2-2’ had purplish red fruits and significantly stronger cold-resistance than control, which were suitable for cultivation in south China. ‘32’ and ‘XBL’ with white fruits could be used as breeding materials.
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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 | 0.001 |
| 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".