Bibliographic record
Abstract
Dieting is not an easy task especially for people who love to eat and try out different types of meal everyday. It is very general human behavior that we don't like the idea of restrictions. Now even if maintaining a healthy diet is not particularly liked by everyone. For some people it is something they must adopt in their daily life. Psychology plays a vital role in making it beneficial and easier. If a person thinks that restraining from eating a whole lot of meals everyday and being super conscious and aware of what is going in our stomach is a very different task. It will be no surprise that he or she will find it difficult to achieve a healthy diet. Similarly, if you are extra sensitive and over conscious about your diet it means you are panicking about your weight gain as a result you might end up eating more than usual. Many people have a habit of stress eating. Other times people stop eating at all because their weight psychologically disturbs them to a great extent. Since psychology is so important in weight change journey it should be observed that a person is psychologically focused and attentive while eating their meals. Munching on random snacks while watching a movie or whole talking to someone is a big no. A balance between over conscious and not being conscious at all should be maintained. In a nutshell, psychologically strong people or the people who don't worry a lot about weight changes tend to lose or gain more weight depending on their desires.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".