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Record W2156029039 · doi:10.1109/icsmc.2009.5345965

Risk management of liability uncertainties to facilitate brownfield redevelopment: Comparing the situation of Canada with the US

2009· article· en· W2156029039 on OpenAlexaffabout
Lizhong Wang, Liping Fang, Keith W. Hipel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsUniversity of WaterlooToronto Metropolitan University
Fundersnot available
KeywordsBrownfieldLiabilityRedevelopmentBusinessRisk managementObstacleRisk analysis (engineering)Environmental planningActuarial scienceFinanceEngineeringEnvironmental sciencePolitical scienceCivil engineeringLaw

Abstract

fetched live from OpenAlex

The uncertainties associated with liability in brownfield redevelopment and relevant risk management tools are discussed based on a comparative review of the situation in Canada and the United States. The changes of regulations and policies, inherent uncertainties of site assessment and remediation techniques, incidents of contaminant transport and exposure, and variations of economic and financial conditions all lead to the uncertainties of environmental liability. The fear of liability especially the associated uncertainties is the key obstacle for owners or developers to undertake cleanup and redevelopment due to the subsequent unpredictability of economic profitability. Various risk management tools have been gradually developed in the last three decades to address liability uncertainties, both within Canada and the US, among which environmental insurance and innocent owner's shelter from liability are the two most viable instruments to reduce the fear of liability. Policy making and risk management have evolved more slowly in Canada than in the US and there is a trend for Canadian provinces to adopt the successful policies and tools used in the US rather than formulate their own.

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)0.001
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 score0.350
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.031
GPT teacher head0.259
Teacher spread0.228 · 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

Citations3
Published2009
Admission routes2
Has abstractyes

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