<i>Mycobacterium avium</i>in the Postgenomic Era
Bibliographic record
Abstract
The past several years have witnessed an upsurge of genomic data pertaining to the Mycobacterium avium complex (MAC). Despite clear advances, problems with the detection of MAC persist, spanning the tests that can be used, samples required for their validation, and the use of appropriate nomenclature. Additionally, the amount of genomic variability documented to date greatly outstrips the functional understanding of epidemiologically different subsets of the organism. In this review, we discuss how postgenomic insights into the MAC have helped to clarify the relationships between MAC organisms, highlighting the distinction between environmental and pathogenic subsets of M. avium. We discuss the availability of various genetic targets for accurate classification of organisms and how these results provide a framework for future studies of MAC variability. The results of postgenomic M. avium study provide optimism that a functional understanding of these organisms will soon emerge, with genomically defined subsets that are epidemiologically distinct and possess different survival mechanisms for their various niches. Although the status quo has largely been to study different M. avium subsets in isolation, it is expected that attention to the similarities and differences between M. avium organisms will provide greater insight into their fundamental differences, including their propensity to cause disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".