Bibliographic record
Abstract
Three types of regressive evolution are reviewed: loss, reversal, and regain after loss. Loss refers to the loss of a physical entity, either a structure or an organ, whereas reversals apply to character states returning to plesiomorphic from apomorphic conditions. The regain of characters after their loss represents a third type of evolutionary character change. The reconstruction of multiple losses and gains of characters by mapping on phylogenies is often problematic because of a lack of information about the relative likelihood of losses and gains. A developmental genetic approach using morphological, developmental, and molecular analysis is therefore an extremely important adjunct to phylogenetic approaches in interpreting losses, reversals, and regains. The molecular developmental basis of character loss and reversal is gradually becoming better understood. Loss of organs can occur by gain-of-function mutations (suppression) and loss-of-function mutations (that often leave a vestigial structure). The regain of characters after loss may occur by regulatory capture (a gain-of-function mutation) or by loss of function in suppressor genes. Reversals may occur by cryptic innovation (the formation of a new structure that mimics the old structure by gain-of-function mutations) or by loss of gene function associated with the apomorphic state (although this may have pleitropic or neomorphic effects). The genetic landscape of reversal is illustrated by the reversal to polysymmetry from monosymmetry in flowers. The range of observed phenotypes, loss with vestige, cryptic innovation, and loss with neomorphism matches the range of changes predicted."In plants with separated sexes, the male flowers often have a rudiment of a pistil; and Kolreuter found that by crossing such male plants with an hermaphrodite species, the rudiment of the pistil in the hybrid offspring was much increased in size; and this shows that the rudiment and the perfect pistil are essentially alike in nature" (Darwin 1859).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".