Bibliographic record
Abstract
Why have numbers of reported chlamydia cases been going up for at least a decade in many developed countries? In this issue, Rekart and Brunham ( see page 87 ) and Miller ( see page 82 ) debate whether or not the observed trend means that public health measures to control chlamydia are failing.1 2 Their opinions are “no” and “we don’t know”. Rekart and Brunham argue that “arrested immunity” is the main explanation for the increasing trend.1 The starting point for this debate is their hypothesis that widespread early treatment has impaired the development of immune responses that would protect against reinfection, resulting in a paradoxical increase in population susceptibility to chlamydia.3 At a population level, they argue that this phenomenon would fit observed trends in reported cases from British Columbia, Canada and some European countries, where declines in the late 1980s and early 1990s have reversed and increased continuously since. Miller argues, however, that trends …
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
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; both teacher heads agree on what is shown here.
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".