Advantages of mixed effects models over traditional ANOVA models in developmental studies: A worked example in a mouse model of fetal alcohol syndrome
Why this work is in the frame
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Bibliographic record
Abstract
Developmental studies in animals often violate the assumption of statistical independence of observations due to the hierarchical nature of the data (i.e., pups cluster by litter, correlation of individual observations over time). Mixed effect modeling (MEM) provides a robust analytical approach for addressing problems associated with hierarchical data. This article compares the application of MEM to traditional ANOVA models within the context of a developmental study of prenatal ethanol exposure in mice. The results of the MEM analyses supported the ANOVA results in showing that a large proportion of the variability in both behavioral score and brain weight could be explained by ethanol. The MEM also identified that there were significant interactions between ethanol and litter size in relation to behavioral scores and brain weight. In addition, the longitudinal modeling approach using linear MEM allowed us to model for flexible weight gain over time, as well as to provide precise estimates of these effects, which would be difficult in repeated measures ANOVA.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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