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Record W4411895391 · doi:10.5539/jmr.v17n2p69

Relationalism: Geometric Model of Ceti f Orbital Duration

2025· article· en· W4411895391 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Mathematics Research · 2025
Typearticle
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsnot available
Fundersnot available
KeywordsMathematicsDuration (music)LiteratureArt

Abstract

fetched live from OpenAlex

This work advances a novel relationalistic proposal, a real-world signal constrained Euclidean modeling system that departs from conventional spacetime ontology, physics, or prior relationalistic proposals. Primative geometric elements of time—durations and temporal points—and of space—extensions and spatial points—are defined as relational magnitudes (one-dimensional) and locations (zero-dimensional) respectively. In rishta relationalism, primitive elements form from discrete relations—separate from measurement methods used to predict or quantify them. Using static symmetry, a dynamic system model uses static states of here and now for object and relational geometric element(s), without relying on spacetime, coordinates, or inertial frames. This article focuses on modeling a canonical object-oriented duration (orbital period of Ceti f) using a known measurant duration (Earth’s orbital period) by scaling selected length elements and pairing with temporal elements quantified by modern physics to generate novel spatiotemporal elements. The resulting model is measurable with standard units. Unique spatiotemporal elements, partitioned into units, introduces a novel application for Euclidean translation in relationalism. Rishta relationalism captures primitive metrics in the shared “now,” using a synchronized, omniscient view of an omnipresent universe.

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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.005
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.060
GPT teacher head0.336
Teacher spread0.276 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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