Genome-based taxonomic classification
Bibliographic record
Abstract
Bacterial populations are routinely characterized based on microscopic examination, colony formation, and biochemical tests. However, in the recent past, bacterial identification, classification, and nomenclature have been strongly influenced by genome sequence information. Advances in bioinformatics and growth in genome databases has placed genome-based metadata analysis in the hands of researchers who will require taxonomic experience to resolve intricacies. To achieve this, different tools are now available to quantitatively measure genome relatedness within members of the same species, and genome-wide average nucleotide identity (gANI) is one such reliable tool to measure genome similarity. A genome assembly with a gANI score of <95% at the intraspecies level is generally considered indicative of a separate species. In this study, we have analysed 300 whole-genome sequences belonging to 26 different bacterial species available in the NCBI Genome database and calculated their similarity at the intraspecies level based on gANI score. At the intraspecies level, nine bacterial species showed less than 90% gANI and more than 10% of unaligned regions. We suggest the appropriate use of available bioinformatics resources after genome assembly to arrive at the proper bacterial identification, classification, and nomenclature to avoid erroneous species assignments and disparity due to diversity at the intraspecies level.
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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 | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".