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Record W2805311211 · doi:10.3390/w10060723

Applying the Theory of Reliability to the Assessment of Hazard, Risk and Safety in a Hydrologic System: A Case Study in the Upper Sola River Catchment, Poland

2018· article· en· W2805311211 on OpenAlexaff
Bogdan Ozga-Zieliński, Jan Adamowski, M. Ciupak

Bibliographic record

VenueWater · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsFault tree analysisFlood mythReliability (semiconductor)Drainage basinHazardNatural hazardEnvironmental scienceRisk assessmentRisk managementReliability theoryRisk analysis (engineering)SafeguardWater resource managementEvent treeReliability engineeringEnvironmental resource managementComputer scienceEngineeringBusinessGeographyCartographyComputer security

Abstract

fetched live from OpenAlex

River basin safety issues and hazards arising from extreme hydrological and meteorological events pose significant risks to human life and can entail economic and financial losses. In this study, the practical aspects of reliability theory linked to reliability engineering, and the associated mathematical tools used to describe technical systems, were applied to explore the structural reliability of a quasi-natural system—a portion of the Upper Sola River catchment in Poland. As part of this study, methods such as the Fault Tree Method (FTM), Event Tree Method (ETM), Risk Matrix and Ranking Method for assessing hazard, risk and losses connected with the occurrence of such events are suggested to improve flood risk management and enhance the capacity to safeguard against such events by improving current flood protection protocols in accordance with EC Flood Directives.

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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.010
GPT teacher head0.259
Teacher spread0.249 · 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 designObservational
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

Citations7
Published2018
Admission routes1
Has abstractyes

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