Bibliographic record
Abstract
There have been recent reports of mass hospitalizations for alcohol intoxication following consumption of fruit-flavored, caffeinated, alcoholic drinks-especially concerning one brand in particular: Four Loko. Caffeine was quickly determined to be the culprit. In accordance with a directive by the Food and Drug Administration, caffeine was removed from Four Loko and similar beverages. However, the evidence that caffeine played a prominent role in widespread displays of intoxication is far from clear. Rather, it is likely that Four Loko-type drinks are especially effective as intoxicants because they provide alcohol in an unusual context. It has been known for many years that drug tolerance partially results from an association between drug-paired stimuli and the drug effect. When these stimuli are altered, the drug-experienced individual does not display the expected tolerant response to the drug-rather, an enhanced (i.e., nontolerant) response is seen. Four Loko and similar beverages may be especially effective intoxicants because they provide a very novel flavor context for alcohol. A recent announcement by the manufacturer of Four Loko suggests (either by design or happenstance) appreciation of the contribution of alcohol-associated cues to alcohol tolerance.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".