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Record W1862473302 · doi:10.5539/mer.v5n2p9

System Hazard Platform: Case Study NASA Field Joint Failure

2015· article· en· W1862473302 on OpenAlexvenueno aff
Kouroush Jenab, Josh Herrin, Saeid Moslehpour, Sam Khoury

Bibliographic record

VenueMechanical Engineering Research · 2015
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsSpace ShuttleAeronauticsBooster (rocketry)Rocket (weapon)Joint (building)Aerospace engineeringSolid-fuel rocketHazardSpace launchEnvironmental scienceComputer scienceEngineeringLaunch vehiclePropellant

Abstract

fetched live from OpenAlex

<p class="1Body">NASA became overconfident with consecutive successful flights with no major failures leading up to Flight 25 of the Space Shuttle Challenger and failed to correctly apply quality assurance to reanalyze the possibilities of failure when extreme cold weather was present for what would become the last Challenger launch. System Hazard Analysis applied correctly to analyze the failure rate patterns of the NASA Space Shuttle Challenger Solid Rocket Booster field joints may have prevented the launch of the tragic Flight 25, where there was a total loss of aircraft and seven astronauts were killed in the accident. The steps of System Hazard Analysis will be explained that if followed may have provided the data necessary for NASA to correct the field joint error prior to instead of after the Challenger explosion.</p>

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.087
GPT teacher head0.323
Teacher spread0.236 · 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.

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

Citations1
Published2015
Admission routes1
Has abstractyes

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