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Record W4376114923 · doi:10.1242/jeb.245982

Streamlining allows fan worms to make a speedy retreat

2023· article· en· W4376114923 on OpenAlexaboutno aff
Kathryn Knight

Bibliographic record

VenueJournal of Experimental Biology · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsnot available
Fundersnot available
KeywordsCreaturesChinaTube (container)ArtHistoryEngineeringArchaeologyMechanical engineering

Abstract

fetched live from OpenAlex

Home building is an honourable tradition. Termites construct mounds, birds weave nests and fan worms secrete tubes in which they reside, protruding their feathery tentacles from the top to breathe and dine. Yet, when startled, the diaphanous worms retract almost instantaneously into the security of their homes. Impressed by the worm's high-speed manoeuvre, Zhao Pan (University of Waterloo, Canada), Zhigang Wu and Jianing Wu (Sun Yat-Sen University, China) were curious how the frilly creatures retreat so fast without damaging their delicate tentacles, but first they had to figure out how to see the enclosed worms in action.‘The fast speed and the fact that the tube is not see-through make it hard to understand what happens’, says Jianing Wu. However, Michael Bok (University of Lund, Sweden) knew that the worms can relocate if their home is no longer adequate; could the team rehome the worms in transparent glass tubes to get a better view? Collecting six species of fan worm from an aquarium shop in Zhangzhou, Fujian, China, Wei Jiang and Yu Sun (Sun Yat-Sen University) snipped the worms’ tubes off at the base and gently squeezed the animals out before offering them a glass tube to crawl into. Once the worms had settled in, Jiang and Sun filmed the animals as they extended their bodies 2–3 times their usual length, contentedly unfurling their tentacles. Then, they startled the worms with a sudden squirt of water.Impressively, the worms pulled their tentacles in at speeds of up to ∼400 mm s−1 – faster for their size than the speediest fish in the sea – retracting entirely within 76 ms. But how did the worms power their extraordinary withdrawals? Jiang and Sun measured the quantity of the muscles running along the length of the animals’ bodies, which contract when pulling the tentacles to safety, and realised that on average the muscles comprise 43% of the worm's trunk, compared with just 29% for a regular worm, producing contraction forces 36 times stronger than their own body weight. So, fan worms have the muscle mass and power to pull their tentacles to safety in the blink of an eye, but how do the animals protect their fluffy tentacles from destruction when yanked so forcibly?Filming the tentacles with a high-speed camera (3200 frames s−1), Jiang and Sun saw the barbule-like structures (pinnules), which give the tentacles their feathery appearance when fluffed out, collapse in toward the tentacle until it looked like a stripped feather. Only then did the worms begin to pull their limbs to safety. The pair then calculated how the pinnules’ collapse affected how strongly the water dragged on a tentacle, realising that the drag forces were reduced by almost 50% when the pinnules lay flat. In addition, the team discovered that the smooth tentacles would carry 75% less water than when fluffed out, allowing the worm to retract them more efficiently.The team also noticed pronounced circular ridges running around the circumference of the worm's body when fully extended, which squashed flat as the worm retreated inside its glass tube. Calculating the impact of losing the ridges on the worm's friction while withdrawing into the tube, the team found that the friction was decreased by 89% within the first 7 ms of the manoeuvre, allowing the body to slide almost effortlessly past the tube walls.Pan and JianingWu say, ‘These strategies allow fan worms to execute rapid escape responses’, protecting their frond-like tentacles from hungry fish – and they are optimistic that streamlined fan worms could inspire the next generation of robots designed to clean pipes.

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 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 score0.035
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.023
GPT teacher head0.321
Teacher spread0.298 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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