Log-correlation Distance And Fourier Transform With Kullback-Leibler Divergence Distance For Construction Of Vertebrate Phylogeny Using Complete Mitochondrial Genomes
Bibliographic record
Abstract
For vertebrate mitochondrial genomes, some phylogenies have been built by various methods with or without sequence alignment. These methods are important for the problem of classification and evolution. In this paper, we propose two approaches to analyze the phylogenetic relationship of 64 vertebrates using complete mitochondrial genomes without sequence alignment. The first approach combines discrete Fourier transform (DFT) with Kullback-Leibler divergence (KLD) distance. The second one directly uses a log-correlation distance. Both methods are based on compositional vectors of DNA sequences or protein sequences from the complete genome. The phylogenetic trees show that the mitochondrial genomes are separated into three major groups. One group corresponds to mammals; one group corresponds to fish; and the other one is Archosauria (including birds and reptiles). In particular, the structure of the tree based on log-correlation distance are roughly in agreement in topology with the current known phylogenies of vertebrates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".