The apparent ineffectiveness of bicycle helmets: A case of selective citation
Bibliographic record
Abstract
Flúor en aguas de consumo público espa ñolas y prevención de la caries dental Fluoride content in tap water in Spain and prevention of dental caries Sra.Directora:La fluoración artificial del agua de consumo público ha sido la medida más eficiente para la profilaxis colectiva de la caries dental.En la actualidad, la concentración de flúor recomendada en las aguas de consumo público es de 0,7 mg/l 1 , frente a los 0,7-1,2 mg/l recomendados en 1962.Este cambio se basa en que el flúor contenido en el agua de consumo público se incorpora también a los alimentos en el curso de su elaboración, aumentando el riesgo de fluorosis dental, y a que la principal acción preventiva del flúor es posteruptiva.No obstante, se sigue aceptando que la fluoración del agua es la mejor medida de salud pública si hay una alta
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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 | 0.024 | 0.169 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".