<i>Mycobacterium avium</i>in the Postgenomic Era
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.009 |
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 it