Evolutionary studies of ectomycorrhizal fungi: recent advances and future directions
Bibliographic record
Abstract
The three biggest advances in fungal molecular phylogenetics in the last few years have been (1) the huge expansion in data sets, (2) the development of nonribosomal loci for phylogenetic analysis, and (3) the use of increasingly sophisticated types of analyses. In addition, advances in parallel computing hold great promise for dramatic increases in speed of analysis. These changes have had, or will have, a direct impact on mycorrhizal ecology through the use of sequence-based identification and an indirect impact through the conclusions drawn from such studies. One problem in the field has been the accidental addition of erroneous sequences to the public databases through a variety of means, including polymerase change reaction contamination. We discuss several examples, suggest ways to identify errors, and argue the case for third-party annotations of sequences. Multiple studies have produced compelling evidence that the ectomycorrhizal habit has developed convergently in multiple lineages of fungi and plants. We reexamine the case for loss of the ectomycorrhizal habit in fungi and show that the results are model dependent.Key words: internal transcribed spacer (ITS) region, peroxidase genes, likelihood models, erroneous data, ectomycorrhizal habit.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".