Strong character incongruence and character choice in phylogeny of sea stars of the Asterinidae
Bibliographic record
Abstract
Abstract. Historically, characters from early animal development have been a potentially rich source of phylogenetic information, but many traits associated with the gametes and larval stages of animals with complex life cycles are widely suspected to have evolved frequent convergent similarities. Such convergences will confound true phylogenetic relationships. We compared phylogenetic inferences based on early life history traits with those from mitochondrial DNA sequences for sea stars in the genera Asterina, Cryptasterina, and Patiriella (Valvatida: Asterinidae). Analysis of these two character sets produced phylogenies that shared few clades. We quantified the degree of homoplasy in each character set when mapped onto the phylogeny inferred from the alternative characters. The incongruence between early life history and nucleotide characters implies more homoplasy in the life history character set. We suggest that the early life history traits in this case are most likely to be misleading as phylogenetic characters because simple adaptive models predict convergence in early life histories. We show that adding early life history characters may slightly improve a phylogeny based on nucleotide sequences, but adding nucleotide characters may be critically important to improving inferences from phylogenies based on early life history characters.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".