Reconstruction of Biogeographic and Evolutionary Networks Using Reticulograms
Bibliographic record
Abstract
A reticulogram is a general network capable of representing a reticulate evolutionary structure. It is particularly useful for portraying relationships among organisms that may be related in a nonunique way to their common ancestor - relationships that cannot be represented by a dendrogram or a phylogenetic tree. We propose a new method for constructing reticulograms that represent a given distance matrix. Reticulate evolution applies first to phylogenetic problems; it has been found in nature, for example, in the within-species microevolution of eukaryotes and in lateral gene transfer in bacteria. In this paper, we propose a new method for reconstructing reticulation networks and we develop applications of the reticulate evolution model to ecological biogeographic, population microevolutionary, and hybridization problems. The first example considers a spatially constrained reticulogram representing the postglacial dispersal of freshwater fishes in the Québec peninsula; the reticulogram provides a better model of postglacial dispersal than does a tree model. The second example depicts the morphological similarities among local populations of muskrats in a river valley in Belgium; adding supplementary branches to a tree depicting the river network leads to a better representation of the morphological distances among local populations of muskrats than does a tree structure. A third example involves hybrids between plants of the genus Aphelandra.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".