Bibliographic record
Abstract
The picture featured on the cover of this issue depicts light being transmitted by fibre optics. Fibre optic technology is used to link computers within local area networks, is the basis of endoscopy, and has virtually replaced copper wire in long-distance telephone lines. Fibre optic cable consists of hair-thin glass fibres (typically 0.125 mm diameter). Currently, the purity of silica glass fibres is such that infrared light in the wavelength ranges of 0.8 to 0.9 m or 1.3 to 1.6 m can travel for 100 km or more without the need for boosting by repeaters. These wavelengths are efficiently generated by lightemitting diodes or semiconductor lasers and suffer the least signal attenuation in glass fibres. As pharmacists, we depend on the efficient transmission and dissemination not of light, but of information. Having accurate, up-to-date information is critical to our work. Ensuring its availability is particularly tough in some instances, most notably for disease caused by the human immunodeficiency virus, where changes in therapy occur so rapidly that keeping up is often difficult. But keeping up and knowing what is right and true become impossible when information is withheld. The 1990s brought dramatic changes to the pharmaceutical industry, mergers being the most obvious. However, during this period there was also a change in
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.070 | 0.035 |
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".