Bibliographic record
Abstract
Obesity is an important health issue in the current day and age. As it has been reported by many studies on the COVID-19 pandemic, individuals with excess weight and people with poor health are at a higher risk for COVID-19 severity. The purpose of this research is to give a model for losing weight by selecting a plant-based diet and by the progress measuring every day. The methodology is a case study with observations over three years. A case study is used to analyze the model and to teach in many disciplines in universities such as medicine, management, chiropractic, dentistry, psychology. Based on the findings of prior empirical studies, the subject made dietary and behavioral changes and was able to lose 45 kg (95 pounds) in approximately 18 months and maintain the proper weight for two years. The main conclusion is that by following this model including nutrition changes and regular measuring, people can reduce their weight to the normal level and keep it for a long period, hopefully, for the rest of life. This study shows how to change to plant-based food, avoid processed food, and measure the progress every day to adjust the behavior. Furthermore, the study shows that it is not a special diet but a long-term lifestyle. This study has limitations because a case study is not the same as a study with a randomized large sample. However, it contributes to the field of nutrition by showing that with a plant-based diet and a minor behavioral change, one can greatly improve his or her health by staying at a normal weight. This type of nutrition has a major social impact especially at this time of the COVID-19 pandemic because it affects particularly people in poor health conditions.
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 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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".