Does complete plastid genome sequencing improve species discrimination and phylogenetic resolution in <i>Araucaria</i>?
Bibliographic record
Abstract
Obtaining accurate phylogenies and effective species discrimination using a small standardized set of plastid genes is challenging in evolutionarily young lineages. Complete plastid genome sequencing offers an increasingly easy-to-access source of characters that helps address this. The usefulness of this approach, however, depends on the extent to which plastid haplotypes track morphological species boundaries. We have tested the power of complete plastid genomes to discriminate among multiple accessions of 11 of 13 New Caledonian Araucaria species, an evolutionarily young lineage where the standard DNA barcoding approach has so far failed and phylogenetic relationships have remained elusive. Additionally, 11 nuclear gene regions were Sanger sequenced for all accessions to ascertain the success of species discrimination using a moderate number of nuclear genes. Overall, fewer than half of the New Caledonian Araucaria species with multiple accessions were monophyletic in the plastid or nuclear trees. However, the plastid data retrieved a phylogeny with a higher resolution compared to any previously published tree of this clade and supported the monophyly of about twice as many species and nodes compared to the nuclear data set. Modest gains in discrimination thus are possible, but using complete plastid genomes or a small number of nuclear genes in DNA barcoding may not substantially raise species discriminatory power in many evolutionarily young lineages. The big challenge therefore remains to develop techniques that allow routine access to large numbers of nuclear markers scaleable to thousands of individuals from phylogenetically disparate sample sets.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".