High-Throughput Behavioral Screens: the First Step towards Finding Genes Involved in Vertebrate Brain Function Using Zebrafish
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The zebrafish has been in the forefront of developmental biology for three decades and has become a favorite of geneticists. Due to the accumulated genetic knowledge and tools developed for the zebrafish it is gaining popularity in other disciplines, including neuroscience. The zebrafish offers a compromise between system complexity (it is a vertebrate similar in many ways to our own species) and practical simplicity (it is small, easy to keep, and prolific). Such features make zebrafish an excellent choice for high throughput mutation and drug screening. For the identification of mutation or drug induced alteration of brain function arguably the best methods are behavioral test paradigms. This review does not present experimental examples for the identification of particular genes or drugs. Instead it describes how behavioral screening methods may enable one to find functional alterations in the vertebrate brain. Furthermore, the review is not comprehensive. The behavioral test examples presented are biased according to the personal interests of the author. They will cover research areas including learning and memory, fear and anxiety, and social behavior. Nevertheless, the general principles will apply to other functional domains and should represent a snapshot of the rapidly evolving behavioral screening field with zebrafish.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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