Bibliographic record
Abstract
Mol Syst Biol. 3: 142 Antibiotics are arguably the most successful medicine on the planet, but the one under huge threat from antibiotic resistance in the face of diminishing new antimicrobial discovery efforts (Hancock, 2007). One of the great hopes for discovering new antibiotics arose when whole‐genome sequencing came of age in 1995 with the decoding of the Haemophilus influenzae genome, followed rapidly by those of many other pathogens. Although this offered antibiotics researchers a window into every possible antibiotic target and stimulated massive efforts in Pharma and Biotech to uncover and exploit these targets, we have not seen a single new antibiotic arising from such studies. The reason is elusive, but could relate to the concept that antibiotics have much more complex mechanisms and targets than previously hypothesized (see Brazas and Hancock, 2005a for discussion). Indeed, a plethora of microarray studies have indicated that all studied antibiotics induce or repress dozens to hundreds of genes at or below their minimal inhibitory concentrations (MIC), and these patterns of expressed genes (signatures) appear to relate to the general mechanism of action of a particular antibiotic, with signatures for cell wall synthesis inhibition, …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.026 | 0.027 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".