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Record W6996686675

Standards, codes and regulations of Hydrogen Refueling Stations and Hydrogen Fuel Cell Vehicles

2014· dissertation· en· W6996686675 on OpenAlexaboutno aff

Bibliographic record

VenueRECERCAT (Consorci de Serveis Universitaris de Catalunya) · 2014
Typedissertation
Languageen
FieldChemical Engineering
TopicChemical Safety and Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsHydrogenPrincipal (computer security)NormativeFuel cellsWork (physics)Hydrogen fuelOrder (exchange)
DOInot available

Abstract

fetched live from OpenAlex

The origin of the project appears because of the necessity of the “Laboratoire de pile à combustible” of UTBM University to work on the safety and normative aspects of their project. They work on the field of hydrogen refueling station (HRS) and hydrogen fuel cell vehicles (HFCV). For that reason the objective of this project has been to create an efficient database where all the standards, codes and regulations (International, European, USA, Canadian, French and Spanish) that concerns to their project were collected.\nIn order to reach the final objective, the project begins with a study of hydrogen properties and hazards. This part is very important to understand why hydrogen is considered as a dangerous gas and what is the potential severity of the accidents that it could cause.\nThen a description of the project parts (hydrogen delivery, hydrogen refueling stations and hydrogen fuel cell vehicles) has been made. In this part, the general characteristics and the principal elements of each one are explained.\nBefore starting with efficient classification of the normative, it has been necessary to do a deep research of all the standards, codes and regulations that concern the project. With them, the first database has been created, where the main information like the title, the abstract, the restrictions and the cost of each one has been collected.\nFinally, with all this information it has been possible to start with the efficient classification. For that, the two main parts HRS and HFCV have been divided in its different components and all the normative has been organized according to them. A website has been created with the objective of containing all this classification in a useful media where the user can find easily and quickly, and, the most important, from everywhere, all the information that he needs.\nMoreover, a survey to people from different countries has been made in order to do a social and cultural study about the feelings that people have about HFCV and also to do a normative comparison between standards of different countries and regions, to understand the different perceptions of the hazards.

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)0.006
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.013
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0150.015

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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2014
Admission routes1
Has abstractyes

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