Sex, rurality and socioeconomical status in Spanish centennial population (2017)
Bibliographic record
Abstract
World's population is exponentially aging as people reaching 100 years old has increased. The number of areas with the highest centennial population rates (Blue Zones), are significantly higher. Are there any determinant factors that favor this situation in Spain? The goal of this study was to determine the possible influence of sex, rurality and socioeconomic factors (Gross Domestic Product (GDP)) on the prevalence of the centennial population of the Spanish society. The Spanish register of inhabitants was published in 2017 by the National Statistics Institute. The analysis was carried out both by Autonomous Communities and by provinces in phases: a first descriptive analysis, followed by an inferential analysis, based on statistical tests (independent T- Student test, Pearson correlation and ANOVA). There were significant interactions between: i) sex and longevity (in favor of the female population); ii) female and rural housing and iii) female, GDP and urban areas. Feminization was proven in the longevity revolution, but, in general, GDP per Capita was not a significant survival factor on its own. This study was the first step of further analysis related to extreme longevity in Spain, which will include other dependent variables such as state of health and well-being as well as social factors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".