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Record W2936891675 · doi:10.1101/614966

A How-To guide to: Quantitative high-speed video profiling to discriminate between variants of primary ciliary dyskinesia

2019· preprint· en· W2936891675 on OpenAlexafffund
Maurizio Chioccioli, Luigi Feriani, Quynh Nguyen, Jurij Kotar, Sharon Dell, Vito Mennella, Israel Amirav, Pietro Cicuta

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Kidney Cyst Diseases
Canadian institutionsUniversity of AlbertaSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Lung Association
KeywordsPrimary ciliary dyskinesiaCiliumProfiling (computer programming)Computer scienceMotile ciliumComplement (music)Video microscopyArtificial intelligenceComputational biologyNeuroscienceBiologyMedicineGeneticsPhenotypeCell biologyLungInternal medicine

Abstract

fetched live from OpenAlex

Abstract This document presents detailed steps to analysing the waveforms of beating cilia, measured in high speed video microscopy. We show that in the case of PCD caused by mutations in DNAH11 and HYDIN the classification by a handful of parameters describing ciliary dynamics allows to distinguish the genotype, as well as (much more easily) distinguishing healthy from PCD samples. This document is intended to complement the brief highlight (1) and presents the details of the datasets used in that letter.

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)0.002
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.198
Threshold uncertainty score0.663

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.1980.249

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 head0.015
GPT teacher head0.247
Teacher spread0.232 · 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
GenreMethods

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

Citations4
Published2019
Admission routes2
Has abstractyes

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