Assessment of genetic susceptibility to ethanol intoxication in mice
Bibliographic record
Abstract
Increased use of gene manipulation in mice (e.g., targeted or random mutagenesis) has been accompanied by increased reliance on a very few rapid and simple behavioral assays, each of which aspires to model a human behavioral domain. Yet, each assay comprises multiple traits, influenced by multiple genetic factors. Motor incoordination (ataxia), a common characteristic of many neurological disorders, may reflect disordered balance, muscle strength, proprioception, and/or patterned gait. Impaired motor performance can confound interpretation of behavioral assays of learning and memory, exploration, motivation, and sensory competence. The rotarod is one of the most commonly used tests to measure coordination in mice. We show here that exactly how the rotarod test is performed can markedly alter the apparent patterns of genetic influence both in undrugged performance and sensitivity to ethanol intoxication. However, when tested with well chosen parameters, the accelerating rotarod test showed very high inter- and intralaboratory reliability. Depending on test conditions, ethanol can either disrupt or enhance performance in some strains. Genetic contribution to performance on the accelerating versus the fixed-speed rotarod assay can be completely dissociated under some test conditions, and multiple test parameters are needed to assess the range of genetic influence adequately.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".