AFLP analysis of genetic polymorphism and evolutionary relationships among cultivated and wild<i>Nicotiana</i>species
Bibliographic record
Abstract
Amplified fragment length polymorphism (AFLP) analysis was used to determine the degree of intra- and inter-specific genetic variation in the genus Nicotiana. Forty-six lines of cultivated tobacco (Nicotiana tabacum L.) and seven wild Nicotiana species, including N. sylvestris, N. tomentosiformis, N. otophora, N. glutinosa, N. suaveolens, N. rustica, and N. longiflora, were analyzed, using at least eight different oligonucleotide primer combinations capable of detecting a minimum of 50 polymorphic bands per primer pair. The amount of genetic polymorphism present among cultivated tobacco lines (N. tabacum) was limited, as evidenced by the high degree of similarity in the AFLP profiles of cultivars collected worldwide. Six major clusters were found within cultivated tobacco that were primarily based upon geographic origin and manufacturing quality traits. A greater amount of genetic polymorphism exists among wild species of Nicotiana than among cultivated forms. Pairwise comparisons of the AFLP profiles of wild and cultivated Nicotiana species show that polymorphic bands present in N. tabacum can be found in at least one of three proposed wild progenitor species (i.e., N. sylvestris, N. tomentosiformis, and N. otophora). This observation provides additional support for these species contributing to the origin of N. tabacum.
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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 | 0.001 |
| 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) | 0.000 | 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".