Bibliographic record
Abstract
When parsimony ancestral character reconstruction is ambiguous, it is often resolved in favour of the more complex character state. Hence, secondary loss (secondary "absence") of a complex feature is favoured over parallel gains of that feature as this preserves the stronger hypothesis of homology. We believe that such asymmetry in character state complexity is important information for understanding character evolution in general. However, we here point out an inappropriate link that is commonly made between this approach and the accelerated transformation (ACCTRAN) algorithm. In ACCTRAN, changes are assigned along branches of a phylogenetic tree as close to the root as possible. This has been taken to imply that ACCTRAN will minimize hypotheses of parallel origins of complex traits and thus that ACCTRAN is philosophically better justified than the alternatives, such as delayed transformation (DELTRAN), where changes are assigned along branches as close to the tips as possible. We provide simple examples to show that such views are mistaken and that neither ACCTRAN nor DELTRAN consistently minimize parallel gain of complex traits. We therefore do not see theoretical grounds for favouring the popular ACCTRAN algorithm. © The Willi Hennig Society 2008.
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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 | 0.014 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".