Old Lineages in a New Ecosystem: Diversification of Arcellinid Amoebae (Amoebozoa) and Peatland Mosses
Bibliographic record
Abstract
Arcellinid testate amoebae (Amoebozoa) form a group of free-living microbial eukaryotes with one of the oldest fossil records known, yet several aspects of their evolutionary history remain poorly understood. Arcellinids occur in a range of terrestrial, freshwater and even brackish habitats; however, many arcellinid morphospecies such as Hyalosphenia papilio are particularly abundant in Sphagnum-dominated peatlands, a relatively new ecosystem that appeared during the diversification of Sphagnum species in the Miocene (5-20 Myr ago). Here, we reconstruct divergence times in arcellinid testate amoebae after selecting several fossils for clock calibrations and then infer whether or not arcellinids followed a pattern of diversification that parallels the pattern described for Sphagnum. We found that the diversification of core arcellinids occurred during the Phanerozoic, which is congruent with most arcellinid fossils but not with the oldest known amoebozoan fossil (i.e. at ca. 662 or ca. 750 Myr). Overall, Sphagnum and the Hyalospheniidae exhibit different patterns of diversification. However, an extensive molecular phylogenetic analysis of distinct clades within H. papilio species complex demonstrated a correlation between the recent diversification of H. papilio, the recent diversification of Sphagnum mosses, and the establishment of peatlands.
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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 | 0.001 |
| 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 | 0.001 |
| 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".