Assessment of genetic relationships among native and introduced Himalayan balsam (<i>Impatiens glandulifera</i>) plants based on genome profiling
Bibliographic record
Abstract
Abstract We conducted genomic characterization based on SNP and SilicoDArT markers on the invasive Himalayan balsam (Impatiens glandulifera) plants originating from native and non‐native regions of their distribution. When genetic relationships were explored by PCoA using SNP and SilicoDArT marker data, the first, second, and third principal coordinates explained altogether 37.4% and 31.0% of the variability, respectively. Samples from the UK, Canada, and Pakistan were grouped together, while Indian plants were clearly distinct based on SNP markers but relatively close to the UK–Canada–Pakistan group based on SilicoDArT markers. Constructed trees differentiated individuals into clusters resembling the PCoA patterns. The Bayesian BAPS analysis performed for the SNP data revealed that the individuals were distributed in seven clusters, representing samples from each of the four Finnish populations, India, Pakistan, and the combination of the UK and Canada. Similar clustering was visible in the UPGMA tree. The Indian cluster did not display any ancestral gene flow with the others, while the Pakistani cluster showed ancestral gene flow only with the combined UK and Canada cluster. Furthermore, the latter cluster displayed ancestral gene flow with the Finnish populations varying from 0% to 3.1%. The BAPS analyses conducted for the SilicoDArT data differ slightly: The individuals were distributed in nine clusters, and the Indian cluster exhibited ancestral gene flow with the mixed cluster including Canadian, Pakistani, and UK samples, and one Finnish sample. The AMOVA showed that 45% and 26% of variation was present among the I. glandulifera groups/populations and the rest within them based on SNP and SilicoDArT markers, respectively. The Bayesian BAPS analyses and the gene flow networks were the most informative tools for resolving relationships among native and introduced plants. It is notable that the small sample sizes for non‐Finnish plant materials may affect the accuracy of the gene flow and other estimates.
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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".