The Power-Law-Tail in the Distribution of the Nucleotides of Genomes Was Related to the Complexity of Organism: New Classification of Organisms
Bibliographic record
Abstract
We proposed a new index of the classification of organisms (cells) based on the appearance frequency of four nucleotides (bases) of various genomes. In double logarithmic plot of L (distance of a base to the next base, x-axis) vs F (frequencies of a base at L, y-axis), each value of four bases was expressed in y = ae-bx at L = 1 ~ 15, and y = Ux + W (power-law-tail) at L = more than 16 bases, respectively, in a single-strand of DNA. The a-, b- and U-values (slope) of four bases were resulted from the GC-content (%) and the size (nt) of the genome. Moreover, each value was identical as A to T, and as G to C, respectively, in one organism. The power-law-tail should be unique to the genomes of the same species, the eukaryotes, the prokaryotes. The eukaryotic genomes were essentially composed of great number of bases with plural long power-law-tail regions when compared with those of the prokaryotes. In the prokaryotes, the base-distribution was partitioned at L = 20, and the U-values (base-distribution in power-law-tail region) of the archaea were similar to the eukaryotes compared with those of the eubacteria. Thus, the power-law-tail of the genomic DNA should be come from the structural features of the cells, i.e., the size, the GC-content and other characteristics of the genomic DNA. These results indicated that the power-law-tail would be specific for the complexity of organisms in individual genome, and might be a new index for cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".