Bibliographic record
Abstract
Madam, Caffeine is widely consumed as part of many all over the world. The Food and Drug Administration of USA started to doubt the safety of caffeinated first in 2010. Four Loko which was the first drink that combined caffeine with alcohol and received opposition.1 In 2013 Health Canada limited quantity content of caffeine to 180mg per regular serving bottle. As compared to other like tea, coffee and soft drinks, caffeine content contained by energy drinks exceeds this limit which is associated with more adverse effects.2,3 The common one of these being electrolyte imbalances such as hypokalaemia leading to arrhythmias and endothelial cell dysfunction which impairs coronary blood flow during exercise hence leading to ischaemia and cardiac arrest.2 Energy have also been shown to alter neural functions mediated by acetylcholine. Neurological effects include increased attention, agility and anxiety on acute use but withdrawal after chronic use causes inattention, irritability and depression.3 Coadministration of ingredients like Taurine and Ginseng which are found in can cause stomach upset and bowel irritability.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".