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Record W2979872905 · doi:10.4006/0836-1398-32.3.272

A new understanding of superposition, quantum objects and the universe, the discreteness of space and time and finally, some relief for Schrödinger's cat

2019· article· en· W2979872905 on OpenAlexvenueno aff
Michael Prost

Bibliographic record

VenuePhysics Essays · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum Mechanics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsTheoretical physicsQuantum superpositionQuantumSuperposition principleOpen quantum systemQuantum probabilityQuantum processSchrödinger's catUncertainty principleQuantum mechanicsClassical mechanicsQuantum dynamics

Abstract

fetched live from OpenAlex

Quantum theory is extremely successful in describing the micro cosmos. The caveat is that nobody knows what quantum objects really are. Consequently, the intention of this article is not to introduce any new formalism but to develop some ideas about the ontological meaning of quantum theory. The centerpiece of quantum theory, the Schrödinger equation, requires a deterministic development of quantum systems; measurements however show an indeterministic distribution of measurement values. All previous interpretations of quantum theory are dubious. Here we will present a new understanding of one of the key features of quantum theory, namely, of superposition. Contrary to the previous understanding of the concept, we assume that superposition represents an oscillation between different states. This will lead to a completely new understanding of the universe and of quantum objects. Quantum objects are no longer considered to be elements in space, rather they are considered to be properties of space, namely, possibilities for interaction. The new understanding also leads to the conclusion that space and time are discrete. This entirely new concept will finally provide some relief for Schrödinger's cat.

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 score0.189
Threshold uncertainty score0.263

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 head0.013
GPT teacher head0.222
Teacher spread0.209 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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