MétaCan
Menu
Back to cohort
Record W3159035322 · doi:10.26434/chemrxiv.14454645.v1

Vesicle Viewer: Online Analysis of Small Angle Scattering from Lipid Vesicles

2021· preprint· en· W3159035322 on OpenAlexafffund
Aislyn Lewis-Laurent, Milka Doktorova, Frederick A. Heberle, Drew Marquardt

Bibliographic record

VenueChemRxiv · 2021
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsPython (programming language)VesicleComputer scienceLipid vesicleThe InternetSmall-angle scatteringLipid bilayerScatteringWorld Wide WebNanotechnologyChemistryMaterials sciencePhysicsOpticsProgramming languageMembrane

Abstract

fetched live from OpenAlex

In this project, we developed an internet-based application, called Vesicle Viewer, to visualize and analyze small angle scattering data generated in the study of lipid bilayers. Vesicle Viewer models SAS data using the EZ-SDP model. In this way, key bilayer structural parameters, such as area per lipid and bilayer thickness, can be easily determined. This application primarily uses Django, a python package specialized for the development of robust web applications. In addition, several other libraries are used to support the more technical aspects of the project – notable examples are MatPlotLib (for graphs) and NumPy (for calculations). Without the barrier of downloading and installing software, the development of this web-based application will allow scientists all over the world to take advantage of this solution, regardless of their preferred operating system.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.010

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 head0.020
GPT teacher head0.260
Teacher spread0.240 · 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 designNot applicable
Domainnot available
GenreSoftware

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

Citations2
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueChemRxivSame topicLipid Membrane Structure and BehaviorFrench-language works237,207