Classification of Non-Animals and Invertebrates Based on Amino Acid Composition of Complete Mitochondrial Genomes
Bibliographic record
Abstract
Amino acid compositions were predicted from data sets of 47 non-animal and 58 invertebrate animal complete mitochondrial genomes, which were chosen alphabetically based on scientific names without considering biological characteristics. Using Ward’s clustering method with amino acid composition or nucleotide content as traits, non-animals were classified into Plantae, Chromalveolata, and Fungi, and invertebrates were classified into Animalia and primitive groups, Amoebozoa, Excavata, Protista, and Choanozoa. A combined sample set of primitive eukaryotes was also examined by cluster analysis using amino acid composition and nucleotide content. Some Amoebozoa comprised a single cluster, whereas other Amoebozoa were grouped with other organisms (Excavata, Prosista, Chromaleolata, Fungi and Plantae), indicating their close relationships. Choanozoa (choanoflagellates; Monosiga brevicollis), considered the closest living relatives of animals, were found to be instead closely related to Fungi (Smittium culisetae, Pleurotus ostreatus, and Epidermophyton floccosum) and Excavata (Malawimonas jakobiformis). Our results demonstrate that amino acid composition and nucleotide content are useful indices for characterizing non-animal and invertebrate complete mitochondrial genomes.
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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 | 0.002 | 0.001 |
| 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 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".