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Record W4210875059 · doi:10.22214/ijraset.2022.40237

Analysis of Materials Used in Hyperloop Capsule

2022· article· en· W4210875059 on OpenAlexaff
Vinod Kumar. G. S, B Adarsh., M Hemanth., B Jathin., Jayaprakash. D. V

Bibliographic record

VenueInternational Journal for Research in Applied Science and Engineering Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsCapsuleConstruction engineeringNatural materialsComputer scienceCivil engineeringForensic engineeringEnvironmental scienceMaterials scienceEngineeringPolymer scienceGeology

Abstract

fetched live from OpenAlex

Abstract: Hyperloop transportation system is the advancement of railway system. It can eliminate most of the problems which occurs when we use road as a medium of transport. It uses a capsule kind of a thing which carries upto 28 passengers. The main area of research done is on the materials used to make the capsule of the hyperloop. We have compared many materials and have come out with the best material as far as our knowledge is concerned . The capsule must be made of materials which possess very good properties and which are resistant to impact forces. We have come across 3 such materials and have included another interesting material which can be made in the near future. While selecting the material we have to do a pre feasibility study which covers aspects such as budget longetivity availability etc. The four materials which we came across are steel , concrete, carbon fibre and vibranium.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.033
GPT teacher head0.335
Teacher spread0.302 · 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 designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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