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Record W3190242140 · doi:10.1126/scirobotics.abf9710

Segmentations in fins enable large morphing amplitudes combined with high flexural stiffness for fish-inspired robotic materials

2021· article· en· W3190242140 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScience Robotics · 2021
Typearticle
Languageen
FieldEngineering
TopicBiomimetic flight and propulsion mechanisms
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsMorphingFlexural rigidityFlexural strengthStiffnessFish <Actinopterygii>Structural engineeringKey (lock)Computer scienceMaterials scienceEngineeringArtificial intelligenceBiologyFishery

Abstract

fetched live from OpenAlex

Fish fins do not contain muscles, yet fish can change their shape with high precision and speed to produce large and complex hydrodynamic forces-a combination of high morphing efficiency and high flexural stiffness that is rare in modern morphing and robotic materials. These "flexo-morphing" capabilities are rare in modern morphing and robotic materials. The thin rays that stiffen the fins and transmit actuation include mineral segments, a prominent feature whose mechanics and function are not fully understood. Here, we use mechanical modeling and mechanical testing on 3D-printed ray models to show that the function of the segmentation is to provide combinations of high flexural stiffness and high morphing amplitude that are critical to the performance of the fins and would not be possible with rays made of a continuous material. Fish fin-inspired designs that combine very soft materials and very stiff segments can provide robotic materials with large morphing amplitudes and strong grasping forces.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.593

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.244
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it