MétaCan
Menu
Back to cohort
Record W2124394718 · doi:10.2307/25470711

Upside-Down Gliding of<i>Lymnaea</i>

2008· article· en· W2124394718 on OpenAlexaff
Kanako Aono, Ayachika Fusada, Yorichika Fusada, Wataru Ishii, Yuji Kanaya, Mami Komuro, Kanae Matsui, Satoru Meguro, Ayumi Miyamae, Yurie Miyamae, Aya Murata, Shizuka Narita, Hiroe Nozaka, Wakana Saito, Ayumi Watanabe, Kaori Nishikata, Akira Kanazawa, Yutaka Fujito, Miki Yamagishi, Takashi Abe, Masafumi Nagayama, Tsutomu Uchida, Kazutoshi Gohara, Ken Lukowiak, Etsuro Ito

Bibliographic record

VenueBiological Bulletin · 2008
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsUniversity of Calgary
FundersJapan Society for the Promotion of Science
KeywordsLymnaea stagnalisBiologySnailLymnaeaCiliumBeat (acoustics)UnderwaterAnatomyBiophysicsEcologyPhysicsOceanographyCell biologyOptics

Abstract

fetched live from OpenAlex

The pond snail Lymnaea stagnalis can often be observed moving upside down on its back just below the surface of the water. We have termed this form of movement "upside-down gliding." To elucidate the mechanism of this locomotion, we performed a series of experiments involving behavioral analyses and microscopic observations. These experiments were designed (1) to measure the speed of this locomotion; (2) to determine whether the mucus secreted from the foot of Lymnaea repels water, thereby allowing the snail to exploit the surface tension of the water for upside-down gliding; and (3) to observe the beating of foot cilia in this behavior. The beating of these cilia is thought to be the primary driving force for upside-down gliding. Our results demonstrate that upside-down gliding is an efficient active process involving the secretion of mucus that floats up to the water surface to serve as a substrate upon which cilia beat to cause locomotion at the underside of the water surface.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.102
GPT teacher head0.290
Teacher spread0.188 · 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 designObservational
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

Citations12
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueBiological BulletinSame topicNeurobiology and Insect Physiology ResearchFrench-language works237,207