Bibliographic record
Abstract
Abstract In systematic biology homology hypotheses are typically based on points of similarity and tested using congruence, of which the two stages have come to be distinguished as “primary” versus “secondary” homology. Primary homology is often regarded as prior to logical test, being a kind of background assumption or prior knowledge. Similarity can, however, be tested by more detailed studies that corroborate or weaken previous homology hypotheses before the test of congruence is applied. Indeed testing similarity is the only way to test the homology of characters, as congruence only tests their states. Traditional homology criteria include topology, special similarity, function, ontogeny and step‐counting (for example, transformation in one step versus two via loss and gain). Here we present a method to compare quantitatively the ability of such criteria, and competing homology schema, to explain morphological observations. We apply the method to a classic and difficult problem in the homology of male spider genital sclerites. For this test case topology performed better than special similarity or function. Primary homologies founded on topology resulted in hypotheses that were globally more parsimonious than those based on other criteria, and therefore yielded a more coherent and congruent nomenclature of palpal sclerites in theridiid spiders than prior attempts. Finally, we question whether primary homology should be insulated as “prior knowledge” from the usual issues and demands that quantitative phylogenetic analyses pose, such as weighting and global versus local optima. © The Willi Hennig Society 2007.
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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 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".