Testing the variability of chloroplast sequences for plant phylogeography
Bibliographic record
Abstract
Phylogeography in plants is hampered by lack of DNA-sequence regions that detect sufficient variation in intra-specific lineages to reveal historical patterns. We tested 13 putatively highly variable non-coding chloroplast regions in six species complexes, from four different angiosperm families, where phylogeographic patterns have previously been identified using restriction fragment length polymorphism analysis of the chloroplast genome. All regions tested amplified in most of the species. The intergenic spacer regions trnQ–rps16, trnS–trnG, psbA–trnH, psbD–trnT and ndhC–trnV were the five most promising regions for phylogeographic analysis in terms of variability, and petB and rpl16 were variable, given the utility of being amplified in a single reaction. The trnQ–rps16 and psbA–trnH intergenic spacer regions and the rpl16 D4-loop intron showed variation between known lineages in all species. The psbA–trnH intergenic spacer that has been suggested as a suitable barcoding gene for plants, generally showed a level of variation similar to that in other variable regions in the species investigated here, suggesting that some caution is required in the use of this region for barcoding applications. The present analysis identified a set of seven chloroplast regions that are a useful basis for informed selection of sequences for assessment of phylogeographic structure in 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".