THE EARLY DIVERSIFICATION HISTORY OF DIDELPHID MARSUPIALS: A WINDOW INTO SOUTH AMERICA'S “SPLENDID ISOLATION”
Bibliographic record
Abstract
The geological record of South American mammals is spatially biased because productive fossil sites are concentrated at high latitudes. As a result, the history of mammalian diversification in Amazonia and other tropical biomes is largely unknown. Here we report diversification analyses based on a time-calibrated molecular phylogeny of opossums (Didelphidae), a species-rich clade of mostly tropical marsupials descended from a Late Oligocene common ancestor. Optimizations of habitat and geography on this phylogeny suggest that (1) basal didelphid lineages inhabited South American moist forests; (2) didelphids did not diversify in dry-forest habitats until the Late Miocene; and (3) most didelphid lineages did not enter North America until the Pliocene. We also summarize evidence for an Early- to Middle-Miocene mass extinction event, for which alternative causal explanations are discussed. To the best of our knowledge, this study provides the first published molecular-phylogenetic evidence for mass extinction in any animal clade, and it is the first time that evidence for such an event (in any plant or animal taxon) has been tested for statistical significance. Potentially falsifying observations that could help discriminate between the proposed alternative explanations for didelphid mass extinction may be obtainable from diversification analyses of other sympatric mammalian groups.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".