Toward a Better Understanding of the Infrageneric Relationships in Cortinarius (Agaricales, Basidiomycota)
Bibliographic record
Abstract
Research on the molecular systematics of Cortinarius, a species-rich mushroom genus with nearly global distribution, is just beginning. The present study explores infrageneric relationships using rDNA ITS and LSU sequence data. One large dataset of 132 rDNA ITS sequences and one combined da-taset with 54 rDNA ITS and LSU sequences were generated. Hebeloma was used as outgroup. Bayesian analyses and maximum-likelihood (ML) analyses were carried out. Bayesian phylogenetic inference performed equally well or better than ML, especially in large datasets. The phylogenetic analysis of the combined dataset with species representing all currently recognized subgenera recovered seven well-supported clades (Bayesian posterior probabilities BPP > 90%). These major clades are: /Myxacium s.l., /subg. Cortinarius, the /phlegmacioid clade (including the subclades /Phlegmacium and /Delibuti), the /calochroid clade (/Calochroi, /Ochroleuci and /Allutus), the /telamonioid clade (/Telamonia, /Orellani, /Anomali), /Dermocybe s.l. and /Myxotelamonia. Our results show that Cortinarius consists of many lineages, but the relationships among these clades could not be elucidated. On one hand, the low divergence in rDNA sequences can be held responsible for this; on the other hand, taxon sampling is problematic in Cortinarius phylogeny. Because of the incredibly high diversity (~2000 Cortinarius species), our sampling included <5% of the known species. By choosing type species of subgenera and sections, our sampling is strongly biased toward Northern Hemisphere taxa. More extensive taxon sampling, especially of species from the Southern Hemisphere, is essential to resolve the phylogeny of this important genus of ectomycorrhizal fungi.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".