Bibliographic record
Abstract
Wine consumption has increased worldwide by 5.6% since 1994. All the major wine consuming regions have reported increases in consumption: Asia (China, Japan, South Korea, Singapore and Taiwan), Northern Europe (Denmark, Sweden, Finland and Norway) and North America (USA and Canada) have experienced the largest increases of 68%, 29.9% and 23.6% respectively. This study investigates the wine drinking patterns of people in Hong Kong. The findings indicate that nearly half of all local adults have drunk wine over the past year. There was an almost equal distribution between male and female wine‐drinking respondents. Wine‐drinkers in general were found to have higher education levels, better jobs and to earn more money. Half of the wine consumption was found to occur in both Western and Chinese restaurants and surprisingly 40% of the wine was consumed at home. Half of the wine was purchased in local supermarkets. Red wine was much more popular than both white and sparkling wine and the preferred country of origin was France. Hong Kong wine drinkers were, however, found to be infrequent consumers of the product.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".