How important is phylogenetic history in explaining character states in pleurocarpous mosses?
Bibliographic record
Abstract
A redundancy analysis (RDA) with following forward selection and variance partitioning was performed to evaluate whether character state variation in pleurocarpous mosses is best explained by higher level taxonomic position or a selected set of environmental parameters. The studied explanatory parameters explain ca. 30% of the total character state variation. Taxonomic position, reflecting the phylogenetic component, is the most important among the studied explanatory parameters, and the phylogenetic component on its own is relatively more important in explaining the variation in the gametophyte than in the sporophyte. Among the environmental parameters, the general habitat parameter was the most important, followed by the climatic zone, and the wetland to nonwetland gradient. In the RDA, gradients of the sporophyte character states are more important than those of the gametophyte. Those of the sporophyte relate to the degree of sporophyte specialization, whereas those of the gametophyte relate to characters associated with water conduction. Phylogenetic time lags are likely to account for the strong influence of the phylogenetic component in most cases, because correlations between taxonomic position and environmental parameters were very few, whereas convergent or parallel evolution is likely to explain the similar states found in strongly specialized sporophytes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".