Comparing factors which affect Visceral Fat Area (VFA) for male and female weight management X participants with chow method and meta regression
Bibliographic record
Abstract
Abstract The spread of fat in human’s body divided into two parts. The first is subcutaneous fat area and the second is visceral fat area (VFA). The largest fat deposit in human’s body is in the subcutaneous area. This fat is called body fat, while the remains of fat in human’s body is located in visceral area inside abdominal cavity and chest cavity. VFA is a dangerous fat, so this study proposes multiple linear regression models to know how to control VFA level more precisely based on body mass index (BMI), basal metabolic rate (BMR), chronological age, biological age, body fat, and skeletal muscle variables. There is presumption that VFA level and other variables that are considered in weight management are different between male and female, so the regression models for male and female groups are built separately. The Chow test is performed to test the similarity of both regression models for male and female groups. If both regression models for male and female groups are same, the combined regression model will be built for male and female groups which can explain the control of VFA level to relate variables in both male and female.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.027 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".