Study on Phenotypic Diversity of Fruits Character in Catalpa fargesii f.Duclouxii
Bibliographic record
Abstract
The phenotypes variation of 9 populations of Catalpa fargesii f.duclouxii,8 traits were investigated and the phenotype diversities within and inter-popula-tions were analyzed by adopting the methods of nested analysis of variance,coefficient of variation,correlation analysis and cluster analysis.The results showed that fruit traits among populations had reached a very significant level(p0.01) differences within populations.The CV of phenotypic traits of the average was 31.03%,with a change in the range of 24.53% to 39.28%.Phenotypic differentiation coefficient between populations(10.91% to 36.34%) was 22.81%.The intra-population phenotypic differentiation(77.19%) was significantly higher than the inter-population's(22.81%).According to the UPGMA cluster analysis of the populations of C.fargesii f.duclouxii could be divided into three groups.These cluster results were not due to geographic distancesa and Phenotypic variation between groups showed discontinuous.
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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) | 0.000 | 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".