Bibliographic record
Abstract
<div> This study investigates the factors influencing the age of menarche in various Indian states over a quarter century from 1992 to 2019, with the aim of understanding how climate change and demographic factors have shaped menarche timing. Data from the Indian Demographic and Health Survey (DHS) for 1992–93 and 2019–21, along with climate data from NASA’s POWER project, were analyzed using a cross-sectional study design, including 23,083 respondents from 1992 and 45,329 from 2019. Across most states, a slight decrease in age at menarche was observed, with the exception of Maharashtra, which showed an increase. Higher specific humidity was associated with earlier onset of menarche, whereas higher temperatures correlated with delayed onset. Improvements in educational attainment, particularly higher education levels, were strongly linked to earlier menarche, indicating that demographic changes had a significant influence. The findings highlight the need for public health interventions that improve nutrition, healthcare access, and educational programs to promote health awareness. Ongoing monitoring of climatic impacts on health is essential for understanding and mitigating the effects of environmental changes on menarche timing. </div>
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 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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.482 | 0.026 |
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".