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Record W4383723909 · doi:10.1142/9781800613775_0009

VIEWS, VARIETY, AND CELESTIAL SPHERES

2023· book-chapter· en· W4383723909 on OpenAlexaff
Lee Smolin

Bibliographic record

VenueWORLD SCIENTIFIC (EUROPE) eBooks · 2023
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsPerimeter InstituteUniversity of Waterloo
Fundersnot available
KeywordsVariety (cybernetics)SPHERESAstronomyAstrobiologyPhysicsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This paper describes a continuation of the program of causal views, in which the world consists of nothing but a vast number of partial views of its past. Each view is associated to an event, and is a representation of the immediate causal past of that event. These consists mainly of processes that transfer energy, momentum and other charges to it from its past events. There is fundamentally no space or spacetime, just a large number of events, which are the causes of events to come. This is a development of energetic causal set theories, developed with Marina Cortes. Momentum and energy are fundamental, and are conserved under their transformation from present events to future events. As a result Minkowski spacetime emerges, in a way that preserves causal relations. The locality of events as constructed in the emergent spacetime is a consequence of the conservation of energy-momentum fundamentally. In this paper we propose that the views of events can be represented in terms of degrees of freedom on punctured two surfaces-each puncture corresponding to an immediate past event. This opens up to the use of mathematical tools from twistor theory and the nascent area of celestial spheres. We show that This makes possible versions of the theory that are relativistically invariant.

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)0.001
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.009
Scholarly communication0.0120.005
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.002

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 head0.082
GPT teacher head0.263
Teacher spread0.181 · 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
GenreOther

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

Citations3
Published2023
Admission routes1
Has abstractno

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Same venueWORLD SCIENTIFIC (EUROPE) eBooks→Same topicHorticultural and Viticultural Research→French-language works237,207→