Something for nothing? Reconstruction of ancestral character states in asterinid sea star development
Bibliographic record
Abstract
Traits from early development mapped onto phylogenetic trees can potentially offer insight into the evolutionary history of development by inferring the states of those characters among ancestors at nodes in the phylogeny. A key and often-overlooked aspect of such mapping is the underlying model of character evolution. Without a well-supported and realistic model ("nothing"), character mapping of ancestral traits onto phylogenetic trees might often return results ("something") that lack a sound basis. Here we reconsider a challenging case study in this area of evolutionary developmental biology: the inference of ancestral states for ecological and morphological characters in the reproduction and larval development of asterinid sea stars. We apply improved analytical methods to an expanded set of asterinid phylogenetic data and developmental character states. This analysis shows that the new methods might generally offer some independent insight into choice of a model of character evolution, but that in the specific case of asterinid sea stars the quantitative features of the model (especially the relative probabilities of different directions of change) have an important effect on the results. We suggest caution in applying ancestral state reconstructions in the absence of an independently corroborated model of character evolution, and highlight the need for such modeling in evolutionary developmental biology.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".