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Record W2598386753 · doi:10.1109/ram.2017.7889726

Carbon monoxide risks and implications on maintenance-intensive fuel-burning appliances — A regulatory perspective

2017· article· en· W2598386753 on OpenAlexaff
Supraja Sridharan, Srikanth Mangalam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsPublic Safety Canada
Fundersnot available
KeywordsPerspective (graphical)Carbon monoxideEnvironmental scienceRisk analysis (engineering)BusinessNatural resource economicsWaste managementComputer scienceChemistryEngineeringEconomics

Abstract

fetched live from OpenAlex

The harmful consequences of carbon monoxide (CO) within indoor environments is globally regarded as a safety issue. In the context of fuel-burning appliances, routine maintenance is vital to ensuring the safe equipment performance, and avoiding exposure to unacceptable levels of CO formed due to improper fuel combustion. However, the enforcement of maintenance requirements in certain locations, such as residences, could be hindered by factors including a lack of jurisdiction to enforce, or restrictions imposed by regulations, thus adding to the complexities faced by regulators in managing this risk to the public. Additionally, maintenance processes can be intensive and challenging to follow due to such considerations as cost, as well as a lack of awareness of obligations. This paper focuses on the complexities around managing the risk of failure in a regulatory context, associated with fuel-burning appliances across various locations given the aforementioned complexities. A special emphasis will be given towards the risk of CO release in residences. The paper will use actual evidence, innovative risk measurements to contextualise the unique challenges with the management of residential CO risk.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.037
GPT teacher head0.296
Teacher spread0.258 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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