Identities of <i>Arthrobacter</i> spp. and <i>Arthrobacter</i> -Like Bacteria Encountered in Human Clinical Specimens
Bibliographic record
Abstract
After the initial description of Arthrobacter spp. isolated from clinical specimens in the mid-1990s, very few further reports on Arthrobacter spp. have appeared in the clinical microbiology literature. The aim of the present study was to elucidate the distribution of Arthrobacter spp. and Arthrobacter-like bacteria encountered in clinical specimens by studying 50 consecutively isolated or received strains of large-colony-forming, whiteish-grayish, non-cheese-like-smelling, nonfermentative gram-positive rods by applying phenotypic methods as well as 16S rRNA gene sequencing. We observed a very heterogenous distribution, with the 50 strains belonging to 20 different taxa and each of 13 strains as a single representative of its particular taxon. Thirty-eight strains represented true Arthrobacter strains, 7 strains belonged to the genus Brevibacterium, 2 were Microbacterium species, and each of 3 single strains was a member of the rarely encountered genera Pseudoclavibacter, Leucobacter, and Brachybacterium, respectively. A. cumminsii (n = 14) and A. oxydans (n = 11) were the most frequently found species. The present report describes the first three A. aurescens strains isolated from human clinical specimens. Comprehensive antimicrobial susceptibility data are given for the 38 Arthrobacter isolates.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".