Bibliographic record
Abstract
The ecological attributes of two species may be similar through convergent evolution or common ancestry. The extent of similarity by descent can be evaluated by comparing them with their most closely‐related outgroup in a given phylogeny. I describe a method of nested sister‐group analysis for estimating ecological similarity based on landscape features or on co‐distribution. The phylogeny is dissected into triplets, each comprising two sister taxa and their outgroup. For a triplet at any phylogenetic level, the similarity of sister groups with respect to some given character can be compared with their joint similarity to the outgroup to give a single test of similarity by descent. Each comparison is independent, and the full set of triplets provides a complete accounting of phylogenetic variation at all levels. This procedure was applied to 188 moderately abundant species of dicots in two independent surveys from adjoining districts of midland England, supplemented by physical surveys of landscape attributes obtained from digitized maps of the same districts. The co‐distribution of sister species was consistently more positive than the co‐distribution of random species pairs, demonstrating the existence of a phylogenetic signal at some level. When sister species are compared with their most closely‐related outgroup, however, neither landscape attributes nor co‐distribution showed any overall similarity arising from common ancestry, in the sense that ecological attributes are not generally conserved after lineage splitting. Instead, the distribution of similarity is strikingly similar to random data. The lack of ecological similarity between closely‐related groups was attributed to rapid character change at or shortly after the splitting of lineages, coupled with a lack of correlation between successive lineage splits.
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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 | 0.009 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".