Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION AND AIMS: For consumers to follow drinking guidelines and limit their risk of negative consequences they need to track their ethanol consumption. This paper reviews published research on the ability of consumers to utilise information about the alcohol content of beverages when expressed in different forms, for example in standard drinks or units versus percentage alcohol content. DESIGN AND METHODS: A review of the literature on standard drink definitions and consumer understanding of these, actual drink pouring, use of standard drinks in guidelines and consumer understanding and use of these. RESULTS: Standard drink definitions vary across countries and typically contain less alcohol than actual drinks. Drinkers have difficulty defining and pouring standard drinks with over-pouring being the norm such that intake volume is typically underestimated. Drinkers have difficulty using percentage alcohol by volume and pour size information in calculating intake but can effectively utilise standard drink labelling to track intake. Standard drink labelling is an effective but little used strategy for enabling drinkers to track their alcohol intake and potentially conform to safe or low-risk drinking guidelines.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 it