Phylogenetics of Acari and their cousins reshuffled by ultraconserved elements (UCE’s): can beauty emerge from chaos?
Bibliographic record
Abstract
Determining the chelicerate tree of life has been the subject of much research, with deep phylogenetic divergences and high evolutionary rates complicating both morphological and molecular approaches (Noah et al., 2020; Ontano et al., 2021; Sharma et al., 2021; Ballesteros et al., 2022). Within Acari, similar challenges exist—even the basic question of whether Acari is mono- or diphyletic remains unresolved (Pepato & Klimov, 2015; Lozano-Fernandez et al., 2019; Van Dam et al., 2019; Ontano et al., 2021). Several recent molecular studies have attempted to resolve these issues using large multi-gene datasets (Pepato et al., 2022; Klimov et al., 2018), mitochondrial genomes (Ban et al., 2022), and transcriptomes (Lozano-Fernandez et al., 2019). Ultraconserved elements (Faircloth et al., 2012) have recently shown great promise for constructing phylogenies for both deeply- and shallowly-diverged taxa (Zhang et al., 2019), including for Chelicerata (Starrett et al., 2017) and for Arrenuridae within Acari (Shoop, 2019), but have not yet been used across the Acari.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".