Genetics of alcohol and tobacco use in humans
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
The field of genetics holds great promise for furthering our understanding of the etiology of drug dependence and for identifying novel targets for treatment. Genetic studies utilizing twins and families have demonstrated a considerable role for genetics in nicotine and/or alcohol dependence. Risk for alcoholism or nicotine dependence is likely to be the result of a large number of genes, each contributing a small fraction of the overall risk. While this review will focus on studies in humans, many of the candidate genes for human nicotine and alcohol dependence listed here were originally postulated to be important, based on data from animal studies. The review will briefly summarize the results from twin and adoption studies that provide estimations of heritability, the results from chromosomal linkage studies that identify regions of chromosomes that may contain relevant genes, and the results of candidate gene studies. For each topic the data will be presented for nicotine dependence, alcohol dependence, and for nicotine and alcohol dependence together. In addition, each section will review briefly some of the confounding issues in the specific type of approach utilized.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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