MétaCan
Menu
Back to cohort
Record W4401201142 · doi:10.48550/arxiv.2407.19059

The IBEX Knowledge-Base: Achieving more together with open science

2024· preprint· en· W4401201142 on OpenAlexafffund
Andrea J. Radtke, Ifeanyichukwu U. Anidi, Leanne Arakkal, Armando Arroyo-Mejías, Rebecca T. Beuschel, Katy Börner, Colin J. Chu, Beatrice Clark, Menna R. Clatworthy, Jake Colautti, Joshua Croteau, Saven Denha, Rose Dever, Walderez O. Dutra, Sonja Fritzsche, Spencer Fullam, Michael Y. Gerner, Anita Gola, Kenneth J. Gollob, Jonathan M. Hernandez, Jyh Liang Hor, Hiroshi Ichise, Zhixin Jing, Danny Jonigk, Evelyn Kandov, Wolfgang Kastenmueller, Joshua F. E. Koenig, Aanandita Kothurkar, Alexandra Y. Kreins, Ian T. Lamborn, Yuri Lin, Kátia L.P. Morais, Aleksandra Lunich, Jean CS Luz, Ryan B. MacDonald, Chen Makranz, Vivien I. Maltez, Ryan V Moriaty, Juan M Ocampo-Godinez, Vitoria M Olyntho, Kartika Padhan, Kirsten Remmert, Nathan Richoz, Edward C. Schrom, Wanjing Shang, Lihong Shi, Rochelle M. Shih, Emily Speranza, Salome Stierli, Sarah A. Teichmann, Tibor Z. Veres, Megan Vierhout, Brianna T. Wachter, Adam K Wade-Vallance, Margaret Williams, Nathan Zangger, Ronald N. Germain, Ziv Yaniv

Bibliographic record

VenuePubMed · 2024
Typepreprint
Languageen
FieldComputer Science
TopicAI in cancer detection
Canadian institutionsInstitute of Infection and ImmunityMcMaster UniversityMcMaster University Medical Centre
FundersNHLBI Division of Intramural ResearchNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Allergy and Infectious DiseasesNational Cancer InstituteNational Heart, Lung, and Blood InstituteNIHR Cambridge Biomedical Research CentreWellcome TrustBiotechnology and Biological Sciences Research CouncilDepartment of Health and Social CareMedical Research CouncilCommon FundConselho Nacional de Desenvolvimento Científico e TecnológicoDirectorate for Biological SciencesNational Institutes of HealthInstituto Politécnico NacionalInstitute of Infection and ImmunityFundação de Amparo à Pesquisa do Estado de São PauloNewcastle UniversityNational Institute of General Medical SciencesInstitute of Circulatory and Respiratory HealthNational Institute for Health and Care ResearchNHS Blood and TransplantFundação de Amparo à Pesquisa do Estado de Minas GeraisCanadian Allergy, Asthma and Immunology FoundationNIH Office of the DirectorInstituto Nacional de Ciência e Tecnologia de Doenças TropicaisCanadian Institute for Advanced ResearchCanadian Institutes of Health ResearchDamon Runyon Cancer Research Foundation
KeywordsResource (disambiguation)Knowledge baseComputer scienceBase (topology)Knowledge managementData scienceNanotechnologyWorld Wide WebMathematicsMaterials science

Abstract

fetched live from OpenAlex

Iterative Bleaching Extends multipleXity (IBEX) is a versatile method for highly multiplexed imaging of diverse tissues. Based on open science principles, we created the IBEX Knowledge-Base, a resource for reagents, protocols and more, to empower innovation.

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)0.018
metaresearch head score (Gemma)0.066
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.992
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.066
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0120.008
Science and technology studies0.0020.002
Scholarly communication0.0120.014
Open science0.0080.008
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0230.028

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 head0.035
GPT teacher head0.287
Teacher spread0.252 · 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.

Study designNot applicable
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

Citations1
Published2024
Admission routes2
Has abstractyes

Explore more

Same venuePubMedSame topicAI in cancer detectionFrench-language works237,207