Increasing Viscosity Helps Explain Locomotor Control in Swimming <i>Polypterus senegalus</i>
Bibliographic record
Abstract
Synopsis Locomotion relies on the successful integration of sensory information to adjust brain commands and basic motor rhythms created by central pattern generators. It is not clearly understood how altering the sensory environment impacts control of locomotion. In an aquatic environment, mechanical sensory feedback to the animal can be readily altered by adjusting water viscosity. Computer modeling of fish swimming systems shows that, without sensory feedback, high viscosity systems dampen kinematic output despite similar motor control input. We recorded muscle activity and kinematics of six Polypterus senegalus in four different viscosities of water from 1 cP (normal water) to 40 cP. In high viscosity, P. senegalus exhibit increased body curvature, body wave speed, and body and pectoral fin frequency during swimming. These changes are the result of increased muscle activation intensity and maintain voluntary swimming speed. Unlike the sensory-deprived model, intact sensory feedback allows fish to adjust swimming motor control and kinematic output in high viscous water but maintain typical swimming coordination.
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".