Multigene Phylogenetic Analyses to Delimit New Species in Fungal Plant Pathogens
Bibliographic record
Abstract
Supporting the identification of unknown strains or specimens by sequencing a genetic marker commonly used for phylogenetics or DNA barcoding is now standard practice for mycologists and plant pathologists. Does one have a new species when a strain differs by a few base pairs when compared to reference sequences from taxonomically well-characterized species that do not differ morphologically from this new strain? If variation at the intra- and interspecific levels for the locus used for identification is already understood for all the closely related species, it is possible to make a reliable prediction of a new species status, but ultimately this question can only be properly addressed by determining the presence or absence of gene flow among a group of strains of the putative new species and strains of previously delimited species. The Phylogenetic Species Concept (PSC) and its assessment using multigene phylogeny and Genealogical Concordance Phylogenetic Species Recognition (GCPSR) are the basis for this chapter. The theoretical framework and a variety of tools to apply these concepts are explained, to assist in the assessment of whether a species is distinct or new when confronted with some sequence divergence from reference data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".