Adolescent Vulnerabilities to Chronic Alcohol or Nicotine Exposure: Findings From Rodent Models
Bibliographic record
Abstract
This article presents an overview of the proceedings from a symposium entitled "Is adolescence special? Possible age-related vulnerabilities to chronic alcohol or nicotine exposure," organized by Susan Barron and Linda Spear and held at the 2004 Research Society on Alcoholism Meeting in Vancouver, British Columbia. This symposium, co-sponsored by the Fetal Alcohol Syndrome Study Group and the Neurobehavioral Teratology Society, focused on our current knowledge regarding the long-term consequences of ethanol and/or nicotine exposure during adolescence with the emphasis on data from rodent models. The support from these two societies represents the understanding by these research groups that adolescence represents a unique developmental stage for the effects of chronic drug exposure and also marks an age in which many risky behaviors including alcohol consumption and smoking typically begin. The speakers included (1) Aaron White, who presented data on the effects of adolescent ethanol exposure on subsequent motor or cognitive response to an ethanol challenge in adulthood; (2) Richard Bell, who presented data suggesting that genetic differences could play a role in adolescent vulnerability to ethanol; (3) Craig Slawecki, who presented data looking at the effects of chronic exposure to alcohol or nicotine on neurophysiologic and behavioral end points; and (4) Ed Levin, who presented data on acute and long-term consequences of adolescent nicotine exposure. Finally, Linda Spear provided some summary points and recommendations regarding unresolved issues and future directions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".