Phylogenomic Analysis of Deep-Branching Telonemid
Bibliographic record
Abstract
The evolutionary history of eukaryotic supergroups has been investigated primarily by large-scale phylogenomics, but one major hindrance to continued progress is that some major eukaryotic groups have extremely sparse sampling. The phylum Telonemia is one such group. Environmental sampling shows two major subgroups of Telonemia, but there are only multigene phylogenomic data from three closely related species belonging to one of the subgroups, TEL1. Here, a single cell was isolated from the pelagic Pacific Ocean, which SSU phylogenetic analysis reveals to be a telonemid of the TEL2 subgroup and distantly related to telonemids with multigene sequence data. Through single-cell transcriptome sequencing and phylogenomic analysis, we investigate the impact of this new telonemid on the relation of Telonemia to the Stramenopila-Alveolata-Rhizaria supergroup (SAR) and other sparsely sampled or historically unstable supergroups, namely, Hemimastigophora, Provora, and Haptista. Our maximum-likelihood (ML) analysis supports Telonemia as sister to Hemimastigophora, together as sister to SAR, with Haptista and Provora forming a clade and sister to all three. However, our Bayesian analysis failed to converge on a topology. Throughout different Telonemia sampling, gene sampling, alignment trimming, and site removal schemes, the sisterhood of Telonemia and Hemimastigophora remained largely supported in ML trees, even when their sisterhood to SAR dissolved, as was the sisterhood of Haptista and Provora. Inclusion of our TEL2 telonemid largely did not influence these relationships. However, our results also highlight the unstable placements of aforementioned groups throughout variations of our data, of which subsets give results consistent with other previously published analyses of this scale.
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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 | 0.000 |
| 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 | 0.000 | 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 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".