Bio-Inspired Dual-Anchor Burrowing: Effect of Vertical Curvature of the Shell
Bibliographic record
Abstract
Many organisms rely on dual-anchor strategy to burrow. A prominent biological role model is the Atlantic razor clam. By concerting the shape changing of various body parts: opening/closing of a rigid shell, extension/contraction of the muscular foot, and inflation/relief of the distal pedal, razor clams can burrow very effectively and efficiently. Using 3D discrete element method modeling, the interactions between two clam inspired dual-anchor penetrators and the surrounding granular media were captured at multiscale. A penetrator includes two major parts: a slender “shell” with time-varying diameter, and a conical “foot”. Two different penetrators were considered: one with a uniform cylindrical shell and the other with a fusiform shell. The granular material consists of spherical particles with an upscaled particle size distribution of Ottawa F65. Microscale parameters are calibrated and validated with experimental triaxial test data. The impact of shell morphology is studied. It is found that opening of the shells compresses the soil around the shell to form anchorage, and at the same time releases the stress around the foot. A fusiform shell morphology is found to have limited influence on the penetration resistance and the shell anchorage during the foot penetration process. A systematic parametric study is still needed to test the hypothesis that a streamlined shell improves the burrowing performance of razor clams.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".