Tadpole Locomotion: Axial Movement and Tail Functions in a Largely Vertebraeless Vertebrate1
Bibliographic record
Abstract
Synopsis.Tadpoles are exceptional among vertebrates in lacking vertebrae along most of their body axis. Their caudal myotomes are also unusually simple for free-living vertebrates. This overall morphological simplicity, in theory, makes tadpoles good models for exploring how vertebrates control undulatory movements. We used electromyography (EMG), high speed ciné, computational fluid dynamics (CFD), and mechanical tissue testing to understand how Rana tadpoles regulate their locomotion.Bullfrog (Rana catesbeiana) tadpoles have several patterns of muscle activity, each specific to a particular swimming behavior. Ipsilateral muscles in the tail were active either in series or simultaneously, depending on the tadpole's velocity, and linear and angular acceleration. When R. catesbeiana larvae swam at their natural preferred tail beat frequency, muscles at the caudal end of their tail were inactive. Mechanical tests of tissue further suggest that the preferred tail beat frequency closely matches the resonance frequency of the tail thus minimizing the energetic cost of locomotion.CFD modeling has demonstrated that the characteristically high amplitude oscillations at a tadpole's snout during normal rectilinear locomotion do not add to drag, as might be supposed, but rather help generate thrust. Mechanical testing of the tadpole tail fin has revealed that the fin is viscoelastic and stiffer in small rather than large deformations. This property (among others) allows the tail to be light and flexible, yet stiff enough to generate thrust in the absence of a bony or cartilaginous skeleton.Many recent studies have documented predator-induced polyphenism in tadpole tail shape. We suggest that this developmental plasticity in locomotor structures is more common in tadpoles than in other vertebrates because tadpoles do not need to reform skeletal tissue to change overall caudal shape.Tadpole tail fins and tip, in the absence of any skeleton, are fragile and often scarred by predators. Based on the high incidence of tail fin injury seen in tadpoles in the wild, we suggest that the tadpole tail fin and tip may play an ecological role that goes beyond serving as a propeller to help tadpoles stay beyond predators' reach. Those soft tissue axial structures, by failing under very small tensile loads, may also allow tadpoles to tear free of a predator's grasp.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".