Investigaciones actuales de bebidas energizantes y sus efectos en el consumidor
Bibliographic record
Abstract
Currently, the use of energy drinks is daily and is increasing, the popularity that these products generate a rapid cure for exhaustion and the easy access to these products makes the consumer can easily acquire them, without knowing the adverse effects caused by these products. This research aims to know the current information on studies regarding energy drinks and the effect it produces on the consumer. At present there is no evidence of studies that recommend the use of energy drinks; However, there are studies and evidences of the harmful effect of some of its components used in the manufacture of these products, therefore, it is recommended that before consuming them, the nutritional information of the content and the effects that it causes in the body such as headache is reviewed. , diuresis, dehydration, arrhythmias, abdominal pain, vomiting, insomnia, anxiety, sweating and even dependence, among other effects. On the other hand, it is recommended to conduct more studies on the safety of these drinks, and duly inform the consumer. In Peru there is no legal system for the control of energy drinks for proper use, as they do in other developed countries: United Kingdom, Lithuania and Europe, prohibit the sale to minors, restrict their sale in events, prohibit advertising in media where minors have access, as well as countries such as Argentina, Uruguay, Mexico, Canada, France, Denmark, Sweden, laws have been enacted to regulate the sale of energy drinks as well as their components.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".