Data from: The evolution of fungal substrate specificity in a widespread group of crustose lichens
Bibliographic record
Abstract
Lichens exhibit varying degrees of specialization with regard to the surfaces they colonize, ranging from substrate generalists to strict substrate specialists. Though long recognized, the causes and consequences of substrate specialization are poorly known. Using a phylogeny of a 150-200 MYA clade of lichen fungi, we asked whether substrate niche is phylogenetically conserved, which substrates are ancestral, whether specialists arise from generalists or vice versa, and how specialization affects speciation/extinction processes. We found strong phylogenetic signal for niche conservatism. Specialists evolved into generalists and back again, but transitions from generalism to specialism were more common than the reverse. Our models suggest that for this group of fungi, “escape” from specialization for soil, rock and bark occurred, but specialization for wood foreclosed evolution away from that substrate type. In parallel, speciation models showed positive diversification rates for soil and rock dwellers but not other specialists. Patterns in the studied group suggest that fungal substrate specificity is a key determinant of evolutionary trajectory for the entire lichen symbiosis.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".