Gender Label-Based Analysis on the Causes of Diabetes in Internet Population
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
This paper analyzes and investigates lifestyle, life quality, etc. of male and female Internet populations to find the factors that affect prevalence of diabetes among populations. Through factor analysis, it is concluded that male and female groups differ greatly inlifestyle, among which smoking frequency is a more prominent factor. By constructing a Logistic model of different genders according to the factor scores, importance of each influencing factor is analyzed, concluding that medical history factor is a significant factor among male group, while age, lifestyle factors, and BMI index are prominent factors among the female group. The factors influencing prevalence of the male and female groups include age, lifestyle and education level. It suggests that to alleviate the disease among the population, we should first focus on health of the middle-aged and elderly people. At the same time, we need raise people’s health awareness, popularize health knowledge, advocate concept of moderate exercise and diet control.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.000 |
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 it