Birds in a bush: Toward an avian phylogenetic network
Bibliographic record
Abstract
ABSTRACT Reconstructing the avian tree of life has become one of the major goals in ornithology. The use of genomic tools seemed a promising approach to reach this goal, but, instead, phylogenetic analyses of large numbers of genes uncovered high levels of incongruence between the resulting gene trees. This incongruence can be caused by several biological processes, such as recombination, hybridization, and rapid speciation (which can lead to incomplete lineage sorting). These processes directly or indirectly amount to deviations from tree-like patterns, thereby thwarting the use of phylogenetic trees. Phylogenetic networks provide an ideal tool to deal with these difficulties. We illustrate the usefulness of phylogenetic networks to capture the complexity and subtleties of diversification processes by discussing several recent genomic analyses of birds in general and the well-known radiation of Darwin's finches. With the increasing amount of genomic data in avian phylogenetic studies, capturing the evolutionary history of a set of taxa in a phylogenetic tree will become increasingly difficult. Moreover, given the widespread occurrence of hybridization and the numerous adaptive radiations in birds, phylogenetic networks provide a powerful tool to display and analyse the evolutionary history of many bird groups. The genomic era might thus result in a paradigm shift in avian phylogenetics from trees to bushes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".