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Record W3017697635 · doi:10.5539/cis.v13n2p43

Elemental Design Base of Chinese Supercomputer Technologies

2020· article· en· W3017697635 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueComputer and Information Science · 2020
Typearticle
Languageen
FieldComputer Science
TopicCognitive Computing and Networks
Canadian institutionsnot available
Fundersnot available
KeywordsSupercomputerMicroprocessorComputer scienceGovernment (linguistics)ChinaState (computer science)Chinese academy of sciencesProduction (economics)ServerOperating systemEmbedded systemPolitical scienceEconomicsAlgorithmLaw

Abstract

fetched live from OpenAlex

China intends to close the technological gap from Western countries in microprocessor production by 2021. 46 new projects worth billions of dollars have already been launched in the country. Most of these projects receive direct or indirect state support, which creates a huge demand for domestic semiconductors. By a government decree, all servers in government offices and state-owned enterprises will be preferentially equipped with processors of domestic production. As a result, their sales are expected to grow at a rate of 20% annually. First of all, we are talking about “Loongson”, “Shenwei” and “Phytium” chips (the latter are the development of the Chinese military-industrial complex). The Shenwei processors, which have proved their effectiveness, are promising: they are the ones that run the world's most powerful supercomputer, Sunway Taihu Light, which consists of 10.65 million cores and performs 93 quadrillion operations per second.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.005
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.237
Teacher spread0.217 · 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