Bibliographic record
Abstract
Aspartame is one of the most extensively tested food additives, yet public confusion remains about its safety. With an increase in sedentary lifestyles and rising obesity rates, a need exists to reduce the population's caloric intake. One way to accomplish this is by reducing sugar intake in food products by substituting sugar with a non-‐caloric sweetener such as aspartame. Aspartame is approximately 180 times sweeter than sucrose. Upon ingestion, it is metabolized into three molecules – aspartic acid, phenylalanine and methanol. Health Canada claims that there is no evidence that the consumption of aspartame, along with a healthy diet, poses a health risk to consumers. One can consume up to 40 mg/kg per day over the course of a lifetime without any risk. This is approximately 16 cans (351 mL each) of a diet soft drink per day for a 70kg (154 lb) individual. No scientific evidence exists to suggest that aspartame causes brain tumours, brain damage, multiple sclerosis, or any other pathological conditions. An instance where aspartame would need to be avoided altogether is in the case of a rare condition called phenylketonuria. It has been proposed that aspartame can increase appetite and preference for sweet tastes and thus, can contribute to increases in caloric intake and the prevalence of obesity, but there have been no studies conducted to support this claim. Ultimately, Health Canada, the Joint Expert Committee on Food Additives, and the World Health Organization have proved aspartame safe for human consumption.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".