Genetic variability in the Physconia muscigena group (Physciaceae, Ascomycota) in the Northern Hemisphere
Bibliographic record
Abstract
The principal goal of our study was to test whether ecologically and chemically different populations of lichens in the Physconia muscigena (Ach.) Poelt group belong to multiple species or one. We used sequence data from three markers (ITS rDNA, mtSSU rDNA and TEF1-α) for the reconstruction of phylogenetic trees based on a sampling of mostly European and Canadian populations of P. muscigena (Ach.) Poelt, P. muscigena var. bayeri (Nádv.) Poelt, and P. isidiomuscigena Essl. Further, we sought any possible geographical or ecological trends among chemotypes and haplotypes. Results show that: 1) Sequence data of ITS rDNA and TEF1-α shows large genetic variation in the Physconia muscigena group. This genetic variability does not correlate with geographical distribution or thallus chemistry; 2) Physconia muscigena var. bayeri and P. isidiomuscigena appear undifferentiated in our phylogenetic trees with P. muscigena. These three species cannot be distinguished on the basis of ITS rDNA, mtSSU rDNA and TEF1-α sequences. 3) We synonymized Physconia muscigena var. bayeri with P. muscigena and we recombine P. isidiomuscigena as a variety of P. muscigena.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".