Individual differences in the neurobiology of responses to alcohol in humans
Bibliographic record
Abstract
Background …….. 1.1.1Alcohol Dependence …….. 1.1.1.1Definition …….. 1.1.1.2Prevalence and Consequences …….. 1.1.1.3Typologies …….. 1.1.2Vulnerability to Alcohol Dependence …….. 1.1.2.1 Hazardous Drinking …….. 1.1.2.2 Subjective Responses to Alcohol …….. 1.1.2.3 Personality Traits …….. 1.1.3Rodent Models of Alcohol Dependence …….. 1.1.3.1 Criteria and the Alcohol-Preferring P rat …….. 1.1.3.2Vulnerability Traits …….. 1.1.4The Mesolimbic Dopamine System …….. 1.1.4.1 Anatomy, Connectivity and Transmission …….. 1.1.4.2The Role of Dopamine in Reward and Addiction …….. 1.1.5Alcohol and Dopamine …….. 1.1.5.1 Effects of Ethanol on Dopamine Neurons and Transmission …….. 1.1.5.2 Dopamine, Ethanol and Behaviour: Studies in Animals …….. 1.1.5.3 Dopamine, Ethanol and Behaviour: Studies in Humans …….. 1.1.5.4 Dopamine and Vulnerability to AUDs …….. 1.1.6The Opioid System …….. 1.1.6.1 Endogenous Opioid System …….. 1.1.6.2Opioids and Alcohol Reinforcement and Reward: Studies in Animals …….. 1.1.6.3Opioids and Alcohol Reinforcement and Reward: Studies in Humans …….. 1.1.6.4Naltrexone for the Treatment of Alcohol Dependence …….. 1.1.6.5 A118G Polymorphism of the OPRM1 gene …….. 1.3 Objectives …….. Chapter 2: Differential Striatal Dopamine Responses Following Oral Alcohol in Individuals at Varying Risk for Dependence 2.0 Preface …….. 2.1 Abstract …….. 2.2 Introduction …….. 2.3 Methods and Materials …….. 2.3.1 Subjects …….. 2.3.1.1 High vs. Low Risk Groups: Low Level Chapter 4: General Discussion 4.0 Summary …….. 4.1 Opioids vs. Dopamine: "Liking" vs. "Wanting" …….. 4.2 Conditioned vs. Pharmacological Effects of Ethanol …….. 4.3 Measuring Subjective Effects of Alcohol ……..
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".