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G super unification vector presentation

2018· article· en· W2883931755 on OpenAlexaff
Yiying Guan, Liying Liu, Tianyu Guan, Yang Zhang, Feisi Yong

Bibliographic record

VenueJournal of Physics Conference Series · 2018
Typearticle
Languageen
FieldComputer Science
TopicScientific Research and Philosophical Inquiry
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhysicsClassical mechanicsCharge (physics)Projection (relational algebra)Rotation (mathematics)Quantum mechanicsGeometryMathematicsAlgorithm

Abstract

fetched live from OpenAlex

The velocity vector generated by the rotation of the particles in the complex space distracts the time dimension, the vector forms the weak charge in the space projection; its rotary angular velocity vector distracts the energy dimension, the vector forms the mass charge in the space projection; Its rotary centripetal acceleration vector distracts the space dimension to form the electric charge; the acceleration jerk (variation of acceleration) generated by the rotation of the vector distracts the color dimension, the vector forms the color charge in the space projection; the projections of the electric charge, mass charge, color charge and weak charge into the 3D space form the three-nature (positive, negative and neutral electrodes), three-generation, three color and the past, present and future of particles; The direct product group U (1) XSU (2) XSU (3) XU (4) or simple group U (20) constitute the super unification group of the physical world. The universe is holographic, bounded and boundless. All information in the universe can be stored on the visual interface in the form of holographic images formed by the interference of the imaginary part of the material wave. This is the physical mechanism of quantum entanglement.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.051
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0510.013

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 head0.100
GPT teacher head0.336
Teacher spread0.236 · 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 designTheoretical or conceptual
Domainnot available
GenreEmpirical

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
Published2018
Admission routes1
Has abstractyes

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