Impatiens glandulifera SNP and SilicoDArT genotyping data
Bibliographic record
Abstract
We conducted genomic characterization based on SNP and SilicoDArT markers on the invasive Himalayan balsam (Impatiens glandulifera) plants originating from the native and non-native regions of their distribution. When genetic relationships were explored by PCoA based on 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 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 the individuals into clusters resembling the patterns observed by PCoA. The Bayesian BAPS analysis 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 constructed 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 AMOVA analysis showed that 45% and 26% of genetic variation was present among the I. glandulifera groups/populations and the rest within them based on SNP and SilicoDArT markers, respectively. Overall, the Bayesian BAPS analysis and the following gene flow network were the most informative tools for resolving relationships among native and introduced plants.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.001 |
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".