Lower statistical support with larger datasets: insights from the Ochrophyta radiation
Bibliographic record
Abstract
Abstract It is commonly assumed that increasing the number of characters has the potential to resolving radiations. We studied photosynthetic stramenopiles (Ochrophyta) using alignments of heterogeneous size and origin (6,762 sites for mitochondrion, 21,692 sites for plastid and 209,105 sites for nucleus). While statistical support for the relationships between the six major Ochrophyta lineages increases when comparing the mitochondrion and plastid trees, it decreases in the nuclear tree. Statistical support is not simply related to the dataset size but also to the quantity of phylogenetic signal available at each position and our ability to extract it. Here, we show that proper signal extraction is difficult to attain, as demonstrated by conflicting results obtained when varying taxon sampling. Even though the use of a better fitting model improved signal extraction and reduced the observed conflicts, the plastid dataset provided higher statistical support for the ochrophyte radiation than the larger nucleus dataset. We propose that the higher support observed in the plastid tree is due to an acceleration of the evolutionary rate in one short deep internal branch, implying that more phylogenetic signal per position is available to resolve the Ochrophyta radiation in the plastid than in the nuclear dataset. Our work therefore suggests that, in order to resolve radiations, beyond the obvious use of datasets with more positions, we need to continue developing models of sequence evolution that better extract the phylogenetic signal and design methods to search for genes/characters that contain more signal specifically for short internal branches.
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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 | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".