Bibliographic record
Abstract
Consumption of sugar sweetened beverage is one of its dietary causes, as sugar plays an important role in our daily life. Sucrose is highly metabolically active and result in weight gain and type-II diabetes. These side effects make companies to launch various synthetic sweetening agents known as alternative sweeteners or non-nutritive sweeteners. NNSs are ubiquitous and widely used every day in a variety of food, dietary products and beverages. Most NNSs are not metabolized by the body so, they do not contribute any energy or very low colonies, so especially advantageous for person are diets requiring calorie restriction (diabetes or overweight). Six of these agents e.g. aspartame, saccharine, neotame, acesulfame-K, sucralose and Stevia have previously received a generally recognized as safe status and approved by FDA. These are claimed to promote weight loss and avoiding other problems associated with excessive caloric intake and also deemed safe for consumption by diabetes and help them achieving good glycemic control. Glycosides present in them like stevia sides in Stevia which is extracted from natural source have anti- hyperglycemic, anti-oxidant, and anti-hyperglycemic effects. These also help preventing tooth decay. NNSs are efficacious weight management strategy and good for diabetes are may provide very low calories and much sweeter than table sugar Thus, satisfying sweet cravings is their major advantage. Recommendations about alternative sweaters used should be tailored to the specific dietary and lifestyle patterns as each of the available sweaters has certain advantages and disadvantageous in case of long term usage.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".