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
The aim was to predict which group of people would have blood pressure problems. A random sample of 1000 people were used to investigate hypertension problems according to logistic regression. This project is to use logistic regression prediction technology to predict and analyze which group of people will have blood pressure problems by analyzing historical data such as age and weight. The independent variables are age and weight, and the dependent variables are 0 or 1 whether you have a blood pressure problem. Based on the Kaggle data set provided by a medical insurance company and the logistic regression model, we established an instance classifier of object logistic regression for calculation, and finally obtained the weight of independent variables, to roughly understand which factors are risk factors of hypertension. This weight can also be used to predict a person's likelihood of hypertension based on risk factors. We show that older and younger adults with high body weight are more likely to develop high blood pressure than the rest of the population. Excess body weight was strongly correlated with hypertension.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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