Applying Cluster Analysis to Screen SSR Markers with High Identifying Ability from SSR Fingerprint Data of Maize Hybrids
Bibliographic record
Abstract
How to effectively use crop DNA fingerprint to identify crop varieties is urgently needed to be solved after establishing fingerprint data.In this study,supposed genetic distance between varieties at a(some)molecular marker locus(loci)was bigger than 0,then considered the molecular marker(s)could identify the varieties and with high identifying ability.Further,supposed least genetic distance between varieties at a(some)molecular marker locus(loci)was maximal,then considered the molecular marker(s)could identify the varieties and with the best identifying ability.Afterward,used cluster analysis,the molecular marker and markers combination with the best identifying ability could be screened from 20 SSR markers fingerprints of 48 maize hybrids.The results showed that this method could be intuitionistic,feasible,fast and highly efficient,and of good reference of effectively applying DNA fingerprint for crop.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".