Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Consistent with the diversity of Latin America, there is profound variability in asthma burden among and within countries in this region. Regional variation in asthma prevalence is likely multifactorial and due to genetics, perinatal exposures, diet, obesity, tobacco use, indoor and outdoor pollutants, psychosocial stress and microbial or parasitic infections. Similarly, non-uniform progress in asthma management leads to regional variability in disease morbidity. Future studies of distinct asthma phenotypes should follow-up well-characterised Latin American subgroups and examine risk factors that are unique or common in Latin America (eg, stress and violence, parasitic infections and use of biomass fuels for cooking). Because most Latin American countries share the same barriers to asthma management, concerted and multifaceted public health and research efforts are needed, including approaches to curtail tobacco use, campaigns to improve asthma treatment, broadening access to care and clinical trials of non-pharmacological interventions (eg, replacing biomass fuels with gas or electric stoves).
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it