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Record W2416204002 · doi:10.1145/2903150.2912887

InfiniCortex

2016· article· en· W2416204002 on OpenAlexfundno aff
Marek T. Michalewicz, Tan Geok Lian, Seng Lim, Jonathan Low, David Southwell, Jason Gunthorpe, Gabriel Noaje, Dominic Chien, Yves Poppe, Jakub Chrzęszczyk, Andrew Howard, Tan Tin Wee, Sing-Wu Liou

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsnot available
FundersNational Cancer InstituteStony Brook UniversityUniversity of TokyoCompute CanadaCanarieOak Ridge National LaboratoryNvidiaGeorge Washington UniversityUniversity of Alberta
KeywordsComputer scienceResource (disambiguation)Computer network

Abstract

fetched live from OpenAlex

Commencing in June 2014, A*STAR Computational Resource Centre (A*CRC) team in Singapore, together with dozens of partners world-wide, have been building the InfiniCortex. Four concepts are integrated together to realise InfiniCortex:

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)0.003
metaresearch head score (Gemma)0.003
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: Not applicable
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.234
Threshold uncertainty score0.783

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0050.004
Open science0.0030.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.2340.105

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 head0.270
GPT teacher head0.437
Teacher spread0.167 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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