Bibliographic record
Abstract
Experience is a valuable asset in scientific discovery. It enables the researcher to understand what has and has not worked in the past, and the way that problems can or should be addressed. However sometimes experience becomes enmired in dogma, and we must become careful as scientists not to think that what has been done previously is the only meaningful route forward. A strong object lesson is provided by the antibiotic discovery paradigm. With few exceptions, since the very first investigations we have considered that the gold standard for discovery of a useful antibiotic is its ability to kill or prevent the growth of serious bacterial pathogens using standard laboratory protocols that have been enshrined as the Clinical and Laboratory Standards Institute (CLSI) guidelines (Wiegand et al, 2008).
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 | 0.114 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.008 | 0.071 |
| Scholarly communication | 0.029 | 0.065 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.018 | 0.048 |
| Insufficient payload (model declined to judge) | 0.005 | 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".