The Body Size of Stimulus Conspecifics Affects Social Preference in a Binary Choice Task in Wild-Type, But Not in <i>dyrk1aa</i> Mutant, Zebrafish
Bibliographic record
Abstract
Abstract The zebrafish has become an appropriate animal model in the analysis of numerous human brain disorders. A variety of neuropsychiatric conditions and neurodevelopmental disorders are comorbid with abnormal social behavior. Given the translational relevance of zebrafish, multidisciplinary studies employing behavioral, neurobiological, and molecular methods with this species may provide insights into human central nervous system (CNS) disorders. Many of these studies impinge upon our ability to properly induce and quantify the behavior of zebrafish, a relatively understudied aspect of this species. In this study, we investigate how the body size of conspecifics relative to that of the test subject influences social (shoaling) responses in zebrafish. We found a robust preference by wild-type (WT) test zebrafish toward big conspecifics, but not toward smaller conspecifics. Additionally, we tested an autism-relevant zebrafish knockout (KO) model. The dyrk1aa KO zebrafish showed impaired social preference compared with WT in the social behavior test. Our results confirm the effect of relative body size on social preference and that the social preference task developed for zebrafish may uncover the function of genes and biological mechanisms potentially associated with human CNS disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".