Evaluation of Ethanol’s Effects on the Biophysical Characteristics of Licking
Bibliographic record
Abstract
Alcohol use disorders are a public health issue related to adverse effects for individuals and society. A low level of response, or decreased sensitivity, to alcohol has been identified as a heritable risk factor for development of alcohol use disorders. One method for researching level of response to alcohol is through the use of rodent models, which are developed to mimic human conditions while eliminating barriers to conducting research with people. Current rodent models used to evaluate effects of ethanol on motor performance have been criticized for not being well matched to human tasks that measure level of change in body sway after alcohol consumption. This study looks at oromotor behavior as a potential alternative to gross motor performance in hopes of increasing correspondence between human and rodent measures of intoxication. To evaluate rodent oromotor performance a force transducer lickometer is used to measure several dimensions of licking behavior after administration of different concentrations of ethanol solution via gavage. Results show that force of licking is not sensitive to dose of ethanol. The total number of licks per session show dose related decreases and licking rhythm, evaluated by the length and distribution of interlick intervals, either increased or decreased for three of the four subjects. Recommendations are made for procedural modifications in order to reduce variability in data and further investigate oromotor performance and level of response to 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".