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Record W3092298872 · doi:10.1242/jeb.214544

A mucous house built for feeding

2020· article· en· W3092298872 on OpenAlexaff
Andy J. Turko

Bibliographic record

VenueJournal of Experimental Biology · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsMcMaster UniversityUniversity of Windsor
Fundersnot available
KeywordsCreaturesDeep seaShoreBiologyPaleontologyGeologyOceanographyNatural (archaeology)

Abstract

fetched live from OpenAlex

Some of the world's weirdest and most fascinating creatures live in the deep sea, but frustratingly for marine biologists, these creatures are also some of the hardest to study. The remoteness, darkness, and high pressures of the deep make human observation difficult, and many deep-sea animals are too fragile to study in captivity. This means most people have never heard of these marvelous creatures, let alone understand how they make their living. Giant larvaceans are one of these amazing, but largely unknown, animals. The worm-like invertebrates float around the deep sea encased in a pair of large (up to 1 m diameter) mucous houses, with one tucked within the other. The outer house is thought to be a protective structure, while the smaller inner house is used for filter feeding on tiny plankton. However, the details of the larvacean filtering mechanism have been only superficially understood because of the immense technical limitations of studying a large and fragile ball of mucus drifting through the deep ocean.A new study, led by Kakani Katija at the Monterey Bay Aquarium Research Institute, USA, used a sophisticated laser imaging system attached to a remotely operated vehicle to study wild giant larvaceans and their mucous homes at depths up to 400 m. By shining a laser at the animals while the robot carefully maneuvered around them, the edges, folds and other intricacies of the mucous house could be illuminated and photographed. Then, back on shore, the team used these images to generate a complete 3D model of the filter feeding mechanism. At sea, the team also used their underwater robot to release small amounts of fluorescent dye into the water, which allowed them to observe flow patterns throughout the mucous houses without otherwise disturbing the animals.The team discovered that the larvacean house is an amazingly complex structure, especially considering it's made entirely from mucus and may be discarded and replaced daily. Sea water, pumped by the beating tail of the larvacean, enters the inner house via two long tubes that extend to the periphery of the outer house, passing through a protective pre-filter on the way. The flowing water is then divided into a symmetrical pair of food-concentrating filters, which allow water to exit while food particles are trapped and then passed directly to the larvacean's mouth. A complex series of valves, extra chambers and connecting threads further regulate water flow and internal pressure, enabling the mucous house to remain properly inflated.Thanks to this cutting-edge imaging technology, the structure and function of the inner mucous house is now well understood. Getting good images of the thin mucous walls of the larger, outer larvacean house remains a challenge, however, and so the details of its structure and function remain unknown. One possibility is that the mucus serves as a coarse filter that prevents large particles from clogging the delicate inner feeding apparatus. The outer house may also deter predators by acting as a physical barrier, or even by functioning as a cloaking device that muffles the turbulence produced within the inner house during filter feeding. Finding the answer will require further advances in deep-sea robotics, laser-assisted video recording, and other technologies, but given the recent achievements of Katija and her crew, finding answers to these sorts of questions finally seems within the realm of possibility.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0350.017

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 head0.029
GPT teacher head0.291
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), 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
Published2020
Admission routes1
Has abstractyes

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