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Record W4389786093 · doi:10.4006/0836-1398-36.4.385

A physical mechanism of the generation of stable positive kinetic energy systems and a qualitative explanation of the proportions of the four ingredients in the universe

2023· article· en· W4389786093 on OpenAlexvenueno aff
Huai‐Yu Wang

Bibliographic record

VenuePhysics Essays · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsPhysicsDark matterDark energyUniverseTheoretical physicsKinetic energyAstrophysicsQuantum mechanicsCosmology

Abstract

fetched live from OpenAlex

The author’s opinion is that the negative energy solutions of the Dirac equation mean that a particle can be of negative kinetic energy (NKE) besides positive kinetic energy (PKE). We think that NKE particles are dark ones, and NKE matter is dark matter. In our previous works, the dark matter theory of the NKE version and the dark energy theory that matched the dark matter theory were put forth. In this work, we investigate the topics related to the metamorphosis of objects between PKE and NKE. We first evaluate the collisions between a PKE and a NKE particles. A scenario of accelerating PKE particles is raised. We put forth the cosmic dark radiation background and gravity potential background. In the universe, negative energy is predominating. In the observable universe, substances constitute stable PKE systems. The total energy of every such system is negative. We propose a mechanism that NKE substances combine into stable PKE systems. Macroscopically, NKE objects can constitute stable PKE astrophysical systems by means of gravity between them. Microscopically, NKE particles can combine into stable PKE systems by means of attractive interactions between them, say, Coulomb attraction. Currently, people think that there are four ingredients in the universe: Photons Ω R0 , matter Ω M0 , dark matter Ω DM , and dark energy Ω Λ0 . We analyze the order of the appearance of the four ingredients and conclude that qualitatively, their proportions should be <mml:math display="inline"> <mml:mrow> <mml:msub> <mml:mi>Ω</mml:mi> <mml:mrow> <mml:mi>Λ</mml:mi> <mml:mn>0</mml:mn> </mml:mrow> </mml:msub> <mml:mo>></mml:mo> <mml:msub> <mml:mi>Ω</mml:mi> <mml:mrow> <mml:mtext>DM</mml:mtext> <mml:mn>0</mml:mn> </mml:mrow> </mml:msub> <mml:mo>></mml:mo> <mml:msub> <mml:mi>Ω</mml:mi> <mml:mrow> <mml:mi mathvariant="normal">M</mml:mi> <mml:mn>0</mml:mn> </mml:mrow> </mml:msub> <mml:mo>></mml:mo> <mml:msub> <mml:mi>Ω</mml:mi> <mml:mrow> <mml:mi mathvariant="normal">R</mml:mi> <mml:mn>0</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> .

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 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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.151

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.031
GPT teacher head0.260
Teacher spread0.229 · 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 teacher head, 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

Citations1
Published2023
Admission routes1
Has abstractyes

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