MétaCan
Menu
Back to cohort
Record W2807993176

“The Fastest Man Alive” – but how?

2018· article· en· W2807993176 on OpenAlexaff
Dilshan Pieris

Bibliographic record

VenueJournal of Interdisciplinary Science Topics · 2018
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsMcMaster University
Fundersnot available
KeywordsFlash (photography)SprintNerve conduction velocityBiomechanicsNeurophysiologyAxonPhysicsMechanicsEngineeringAnatomyNeuroscienceOpticsMedicineBiology
DOInot available

Abstract

fetched live from OpenAlex

Although the Flash is considered the fastest man alive, the nature of his speed is poorly understood. This paper explores the biomechanics and neurophysiology of the Flash’s speed during a 100 m sprint. The results show that the Flash must apply 13.9 MN of force in a single step from his starting position in order to accelerate to his maximum velocity of 4472.44 ms -1 in a 100 m dash. Moreover, exerting such forces requires a substantially high nerve conduction velocity, which can be achieved by increasing myelin thickness and axon diameter. Future studies should quantify this conduction velocity, as well as its accompanying myelin thickness and axon diameter.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0040.011
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.006

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 head0.031
GPT teacher head0.325
Teacher spread0.293 · 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 designObservational
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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Interdisciplinary Science TopicsSame topicEEG and Brain-Computer InterfacesFrench-language works237,207