Bibliographic record
Abstract
In spite of the earlier discovery of penicillin, it was perhaps the discovery of streptomycin for the treatment of tuberculosis that was the most important and most dramatic event in the history of infectious diseases. The introduction of an effective treatment for the “white plague” was of paramount and worldwide importance. Antibiotic treatments for other diseases in history such as cholera, plague, and syphilis soon followed, and the golden age of antibiotics began. The problems of antibiotic resistance were minimalized at first, because laboratory studies showed that mutations associated with antibiotic resistance, while possible, were rare (streptomycin was the model antibiotic at the time) and that resistant mutants appeared at such low frequencies that they would not be expected to be an impediment to therapeutic antibiotic use. The bacterial geneticists could not have realized how wrong they were! Streptomycin-resistant Mycobacterium tuberculosis, sulfonamide-resistant Streptococcus pneumoniae, and other antibiotic-resistant pathogens appeared in the clinic and were associated with treatment failure and increased mortality. In fact, the famed writer and socialist George Orwell died when his M. tuberculosis infection no longer responded to streptomycin.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.429 | 0.339 |
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".