MétaCan
Menu
Back to cohort
Record W4289257858 · doi:10.1109/biocas.2015.7348277

Welcome message from the general chair

2015· article· en· W4289257858 on OpenAlexfundno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsnot available
FundersLeibniz-GemeinschaftUniversity of TorontoMedizinischen Hochschule HannoverUniversity of California, San DiegoUniversity of California, IrvineHospital for Sick ChildrenNational University of SingaporeGottfried Wilhelm Leibniz Universität HannoverHarvard UniversityMasdar Institute of Science and TechnologyImperial College LondonUniversidad de SevillaCase Western Reserve UniversityEmory UniversityCleveland ClinicMichigan State UniversityCarnegie Mellon UniversityBrown UniversityArizona State UniversityCollege of Engineering, Michigan State UniversityGeorgia Institute of TechnologyNewcastle UniversityUniversity of PennsylvaniaUniversity of Southern CaliforniaUniversity of California, San FranciscoUniversity of Maryland, Baltimore County
KeywordsComputer science

Abstract

fetched live from OpenAlex

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

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)0.005
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.761
Threshold uncertainty score0.800

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0060.003
Open science0.0020.004
Research integrity0.0120.014
Insufficient payload (model declined to judge)0.2390.194

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 head0.049
GPT teacher head0.264
Teacher spread0.215 · 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
GenreEditorial

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
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicHealthcare Systems and TechnologyFrench-language works237,207