Bibliographic record
Abstract
Venus Factor is a digital weight loss program for women. It contains several eBooks (PDFs), videos and audio files, and a community forum exclusively for its members only. If you are a woman, you might have noticed that it is more difficult to lose weight. Maybe you have noticed your boyfriend, husband or male friends losing weight and building muscle quickly- but you struggle to shift that extra flab around your belly, thighs and hips? It turns out that there are reasons why females find it more difficult to lose weight – and The Venus Factor is the solution. Women store fat differently than men and even with exercise and dieting this fat can be difficult to shift. This is why a one-size-fits-all fitness program might not work as well for women. This is a program that takes into consideration the way that women store fat and what exercises they can do to shift it. The Venus Factor is a 12 week nutrition plan that is designed to help women lose weight as efficiently as possible. It is a quick and easy solution to getting a slim body. The system has been finely tuned over time and it has been proven to work on real women. So why do women need a different diet and exercise plan? Barban explains that women should be treated differently when it comes to diet and exercise because they have different hormones. The hormone leptin is responsible for fat burning and it speeds up the metabolism and signals for your body to burn fat. The Venus Factor was written by John Barban, who is an expert in health and fitness. He has studied his masters degree in Human Biology and Nutrition at the University of Guelph and he then went on to do a number of other graduate studies at the University of Florida. He also taught at the University of Florida in the department of Health and Human performance.
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 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.562 | 0.475 |
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".