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Record W6991890260

Infrastructure in proximity of railways - External environmental monitoring and risk analysis // Infrastruktur i narheten av jarnvagar - Extern miljoovervakning och riskanalys : A document and comparative analysis conducted at Trafikverket; risk analysis of infrastructure in close proximity of railways

2024· article· en· W6991890260 on OpenAlexaboutno aff

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsnot available
Fundersnot available
KeywordsRisk managementResilience (materials science)Spatial planningRisk assessmentTransportation infrastructureStrategic planningRisk governanceStrategic environmental assessmentPsychological resilience
DOInot available

Abstract

fetched live from OpenAlex

This thesis investigates international railway safety regulations and spatial planning, with a focus on the spatial relationships between railways and adjacent roads. Employing a methodological framework rooted in comparative and document analyses, the study critically evaluates regulatory frameworks from five nations: Sweden, Canada, Norway, Germany, and China. Through the lens of Risk Assessment and Management Theories, Resilience Theory, and dynamic risk management frameworks, the research discuss how different countries approach risk identification, assessment, and mitigation strategies within their railway infrastructure. The findings highlight variations in regulatory approaches and underscore the importance of adaptability, transparency, and consideration of spatial dynamics in enhancing railway safety. By synthesising perceptions from international practices, this study contributes valuable perspectives to the ongoing discourse on optimal spatial relationships in railway infrastructure, with implications for policy development and strategic planning within the transportation sector.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.010
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.018
GPT teacher head0.292
Teacher spread0.274 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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