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Record W4320880544 · doi:10.21203/rs.3.rs-2256932/v1

Models of Motion in Abstract Linear Algebra

2023· preprint· en· W4320880544 on OpenAlexaff
Bianca Drumea

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldMathematics
TopicAdvanced Topics in Algebra
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMotion (physics)Algebra over a fieldLinear algebraMathematicsComputer sciencePure mathematicsGeometryArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract This paper will solve problems using numerical tools from various branches of math, including linear algebra, analytical topology, and calculus. The main question is of how one can define an abstract algebraic group within a Hilbert space to describe an individual performing a specific movement. Several other questions arise from this, including the modeling of an axis of rotation. These are best resolved by an abstract representation of a motion of a body in space, then applying it to a specific material case. The abstract representation will be in the form of an algebraic group with a defined topology. The model maps the possible singular points in space the individual could occupy. The method of arriving at a solution for each step of the problem was the creation of a series of conclusions, linked by reasoning, expanding from an initial material example. This is known as divergent reasoning. Existent theories related to the concept at the root of the computational result were analyzed to identify any contradictions in lines of reasoning. The model was developed by means of an example case of a sport with elements of martial arts and gymnastics, called tricking. This was accomplished using four theorems: the Hilbert Basis theorem, the Nullstellensatz theorem, the weak Nullstellensatz theorem, and a new theorem, which describes the axis of rotation. The intended outcome is achieved by the end of the paper. The model was proved with a physical example, in which the algebraic group was translated into a vector function in R 3 between a limiting interval of time.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
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 score0.040
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.003
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 head0.329
GPT teacher head0.494
Teacher spread0.164 · 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.

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
Published2023
Admission routes1
Has abstractyes

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