Bibliographic record
Abstract
Multisensory integration (MSI) is a crucial process by which organisms combine information from multiple senses to enhance their perception and adapt to the environment. This review focuses on MSI in Drosophila, an ideal model organism due to its well-characterized neural circuitry and genetic tractability. We first describe the five main sensory modalities (vision, olfaction, gustation, mechanosensation, and thermosensation) and how they contribute to the Drosophila’s behavior. Then, we discuss the basic models of MSI, including feedback, convergence, gating, parallelism, and association. The underlying neural circuits involved in MSI, such as those related to foraging, navigation, and feeding behaviors, are also explored. Additionally, we highlight the role of neuromodulators in regulating MSI and its functional significance in enhancing information acquisition and decision-making. Overall, understanding MSI in Drosophila provides valuable insights into the mechanisms underlying complex behaviors and serves as a foundation for further studies in other organisms, ultimately helping us better understand how the nervous system processes and integrates multisensory information.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".