Typing and classification of non-tuberculous mycobacteria isolates
Bibliographic record
Abstract
<ns3:p> <ns3:bold>Background:</ns3:bold> There are a large and growing number of non-tuberculous mycobacteria (NTM) species that have been isolated, identified, and described in the literature, yet there are many clinical isolates which are not assignable to known species even when the genome has been sequenced. Additionally, a recent manuscript has proposed the reclassification of the <ns3:italic>Mycobacterium</ns3:italic> genus into five distinct genera. </ns3:p> <ns3:p> <ns3:bold>Methods:</ns3:bold> We describe using a fast average nucleotide identity (ANI) approximation method, MASH, for classifying NTM genomes by comparison to a resource of type strain genomes and proxy genomes. We evaluate the genus reclassification proposal in light of our ANI, MLST, and pan-genome work. </ns3:p> <ns3:p> <ns3:bold>Results:</ns3:bold> We describe here a sequencing study of hundreds of clinical NTM isolates. To aid in characterizing these isolates we defined a multi-locus sequence typing (MLST) schema for NTMs which can differentiate strains at the species and subspecies level using eight ribosomal protein genes. We determined and deposited the allele profiles for 2,802 NTM and <ns3:italic>Mycobacterium tuberculosis</ns3:italic> complex strains in PubMLST. </ns3:p> <ns3:p> <ns3:bold>Conclusions:</ns3:bold> The MLST schema and our pan-genome analysis of Mycobacteria can help inform the design of marker-gene diagnostics. The ANI comparisons likewise can assist in the classification of unknown genomes, even from previously unknown species. </ns3:p>
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".