Bibliographic record
Abstract
water is when it comes to losing weight.Not only does it help to suppress appetite so you are less likely to overeat, but additionally, when you're dehydrated, your kidneys can't function properly, so the body turns to the liver for additional support.When the liver has to work so hard, the fat you consumer is stored rather than burned off.Water and fiber go hand-in-hand.Add fiber gradually and increase your water intake at the same time so you can eliminate the maximum amount of waste.Aim for about one-half your body weight in ounces every day, especially if you're exercising.You need water to regulate body temperature and to provide the means for nutrients to travel to your organs and tissues.It also helps transport oxygen to your cells, removes waste, and protects your joints and organs.Taking in too litter water or losing too much water leads to dehydration.Symptoms of mild dehydration include thirst, pains in joints and muscles, lower back pain, headaches and constipation.A good rule of thumb is if you are thirsty -it is already too late, you are dehydrated!
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.309 | 0.271 |
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".