Genetic identification of 43 elite clonal accessions of <i>Populus deltoides</i> by SSR fingerprinting
Bibliographic record
Abstract
In this study, 43 elite clones of Populus deltoides were fingerprinted with ABI 3730 capillary electrophoresis by using six SSR primer pairs. Based on the fingerprinting profiles, 62 polymorphic bands were generated with a mean number of 10 alleles per locus, and allele numbers amplified by each primer in these clones ranged from 5 (NJFUP-poly10) to 16 (NJFUP-poly07). The power of discrimination values for these primer pairs ranged from 0.80 to 0.94, with an average value of 0.89. Among the six primer pairs, the most efficient primer pair for genetic discrimination of these elite clones was NJFUP-poly02, which could identify 22 of the 43 elite clones directly. In conclusion, all the 43 elite clones of P. deltoides could be discriminated unambiguously based on their genotypes at the six SSR loci. The combination of all six loci gave considerable reliability and accuracy for genetic identification of these clonal accessions. Subsequently, genetic relationship of these clones was plotted by the UPGMA clustering and principle component analysis. Results of both analyses indicated that clone “C7-1” had the largest genetic distance from the other clones, followed by clone “C51-1” and a subgroup comprising clone “C100-3” and “C62-3”. These clones are proposed to be possibly more affected by the inter-specific gene introgression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".