Length polymorphism scanning is an efficient approach for revealing chloroplast DNA variation
Bibliographic record
Abstract
Phylogeographic and population genetic screens of chloroplast DNA (cpDNA) provide insights into seed-based gene flow in angiosperms, yet studies are frequently hampered by the low mutation rate of this genome. Detection methods for intraspecific variation can be either direct (DNA sequencing) or indirect (PCR-RFLP), although no single method incorporates the best features of both approaches. We show that screening universal chloroplast amp li cons for length polymorphism provides an accurate and efficient method for identifying cpDNA variation. By sequencing 4500 bp of cpDNA from 17 accessions of Purshia tridentata (bitterbrush), we detected 9 haplotypes, 8 of which were identifiable by unique multilocus length combinations resolvable by automated fragment analysis. In silico estimates of PCR-RFLP for these loci show that 5 haplotypes would be resolved by agarose electrophoresis. A survey of 4 intraspecific data sets from diverse angiosperms revealed that length variation in cpDNA amplicons is nearly ubiquitous, and 61 of 67 haplotypes identified by direct sequencing could be identified by screening length variation. Combined with automated fluorescent detection, length polymorphism screening of universal cpDNA regions offers a simple screen for intraspecific variation that can be used across angiosperms with minimal optimization, providing detection limits that rival direct sequencing at a fraction of the cost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".