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Record W7033905054

Spatial analysis of the environmental risks of rail lines and industrial sites in Point Douglas and South St. Boniface, Winnipeg, Canada

2024· dissertation· en· W7033905054 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2024
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicInsurance and Financial Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsLand usePoint (geometry)Industrial areaRisk assessmentWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

Rail and industrial operations pose significant environmental and safety risks. This study conducted a spatial analysis of these risks in Point Douglas and South St. Boniface, Winnipeg, Canada. Using ArcGIS, the rail network, industrial sites, and land uses in these areas were mapped, with a focus on the proximity of rail lines and industrial sites to vulnerable land uses. The analysis identified populations at risk from potential hazards such as fires, spills, or train derailments involving dangerous goods, which may require evacuation or isolation. The findings underscore the need for mitigation strategies and policy interventions to reduce environmental and safety risks in these communities.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.706

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 head0.024
GPT teacher head0.187
Teacher spread0.163 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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