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Record W2226027321 · doi:10.14419/ijet.v5i1.4673

Application of scenario-driven hazard analysis in the solid rocket booster

2016· article· en· W2226027321 on OpenAlexfundaboutno aff
Kouroush Jenab, Nikita Ottosen, Saeid Moslehpour

Bibliographic record

VenueInternational Journal of Engineering & Technology · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsnot available
FundersNational Research Council CanadaEmbry-Riddle Aeronautical UniversityUniversity of HartfordBoeing
KeywordsEngineeringManagementOfficerAeronauticsLibrary scienceStrategic studiesPolitical science

Abstract

fetched live from OpenAlex

Bibliographical Notes: Kouroush Jenab is a senior member of IEEE, received the B.Sc. degree from the IE Department at Isfahan University of Technology (1989), the M.Sc. degree from the IE Department at Tehran Polytechnic (1992), and the Ph.D. degree from the Department of Mechanical Engineering at the University of Ottawa (2005). He served as a senior engineer/manager in auto, and high-tech industries for 18 years. He joined the National Research Council Canada as a research officer where he participated in several international research projects. In 2006, he joined the Department of Mechanical and Industrial Engineering at Ryerson University, Toronto as assistant professor. Currently, Dr. Jenab is Faculty of the College of Aeronautics at Embry-riddle Aeronautical University, FL, USA. He has published over 110 papers in international scientific journals based on his experiences in industries.Nikita Ottosen is a current Systems Engineering graduate student at Embry-Riddle Aeronautical University. She received her undergraduate degree from ERAU in Aeronautics and is currently working as an Assistant Campus Director at the Crestview, FL campus. She gained valuable knowledge previously working for the Boeing Company and Seattle-Tacoma International Airport, known as The Port of Seattle, in Seattle, Washington. During her time at The Port of Seattle she worked alongside the Wildlife Management department to conduct a study on effective wildlife management strategies. Her study will become a part of a future Airport Cooperative Research Program (ACRP) publication, sponsored by the FAA’s Transportation Research Board of the National Academies.Saeid Moslehpour is a full professor and department chair in the Electrical and Computer Engineering Department in the College of Engineering, Technology, and Architecture at the University of Hartford. He holds Ph.D. (1993) from Iowa State University and Bachelor of Science (1989) and Master of Science (1990) degrees from University of Central Missouri. His research interests include failure analysis, logic design, CPLDs, FPGAs, Embedded electronic system testing and distance learning.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.018
GPT teacher head0.329
Teacher spread0.311 · 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 designSimulation or modeling
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
Published2016
Admission routes2
Has abstractyes

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