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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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.142
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
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 teacher head, not a consensus.

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
Published2014
Admission routes1
Has abstractyes

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