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Record W4311569772 · doi:10.5539/mer.v10n1p13

Tools and Computational Machinery for Movement

2022· article· en· W4311569772 on OpenAlexvenueno aff
H. Hemami

Bibliographic record

VenueMechanical Engineering Research · 2022
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsSensory systemRedundancy (engineering)Computer scienceHuman–computer interactionBasketballMotor systemNeuroscienceArtificial intelligencePsychologyGeography

Abstract

fetched live from OpenAlex

This paper is a discussion of computational tools and circuits that deal with the internal and external worlds of the human body. The information processing avenues in the central nervous system (CNS) are an important part of the development. All sensory channels and the vision system are involved. It clarifies the role of the sensory system in internal coordinate systems and for all activities of the CNS. It includes the inherent redundancy aspects of the motor system as well. The visual system is involved in sports such as soccer and basketball and for being aware of and paying attention to both the internal world and the external reality surrounding the athlete.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.921
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.001
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 head0.066
GPT teacher head0.330
Teacher spread0.263 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2022
Admission routes1
Has abstractyes

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