Bayesian inference of cetacean phylogeny based on mitochondrial genomes
Bibliographic record
Abstract
The phylogeny of Cetacea (whales, dolphins, porpoises) has long attracted the interests of biologists and has been investigated by many researchers based on different datasets. However, some phylogenetic relationships within Cetacea still remain controversial. In this study, Bayesian analyses were performed to infer the phylogeny of 25 representative species within Cetacea based on their mitochondrial genomes for the first time. The analyses recovered the clades resolved by the previous studies and strongly supported most of the current cetacean classifications, such as the monophyly of Odontoceti (toothed whales) and Mysticeti (baleen whales). The analyses provided a reliable and comprehensive phylogeny of Cetacea which can provide a foundation for further exploration of cetacean ecology, conservation and biology. The results also showed that: (i) the mitochondrial genomes were very informative for inferring phylogeny of Cetacea; and (ii) the Bayesian analyses outperformed other phylogenetic methods on inferring mitochondrial genome-based phylogeny of Cetacea.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".