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Record W2225526831

Smoke Alarms Work, But Not Forever: posing the challenge of adopting multifaceted, sustained, interagency responses to ensuring the presence of a functioning smoke alarm.

2012· article· en· W2225526831 on OpenAlexaff
Len Garis, Joseph Clare

Bibliographic record

VenueUWA Profiles and Research Repository (University of Western Australia) · 2012
Typearticle
Languageen
FieldEngineering
TopicFire Detection and Safety Systems
Canadian institutionsUniversity of the Fraser Valley
Fundersnot available
KeywordsSmokeALARMWork (physics)Computer securityPsychologyComputer scienceEngineeringWaste management
DOInot available

Abstract

fetched live from OpenAlex

The Purpose of this ResearchThis report explains why a comprehensive, whole-of-government commitment should be pursued to ensure every dwelling in Canada posses a present, functioning smoke alarm.Initially, three sets of findings with respect to smoke alarms are outlined:• The presence of functioning smoke alarms saves lives.• The functionality of smoke alarms deteriorates with time, meaning that they need to be tested on a regular basis.• It is possible to increase the likelihood of a present, functioning smoke alarm in the event of a fire.However, this third point has the caveat that typical approaches that have positively influenced the presence of functioning smoke alarms have suffered from a lack of an unconditional, systematic, ongoing commitment, meaning that positive impacts typically diminish with time and the problem re-emerges.With these three findings in mind, the research poses the challenge of ensuring that smoke alarm presence and functionality is monitored in a comprehensive, consistent, ongoing manner, with some potential mechanisms for achieving this outcome proposed.Two key challenges to achieving this objective, along with some potential solutions to these issues are discussed:1. Making an ongoing commitment to ensuring present, functioning smoke alarms in every residence.A range of strategies exist to meet this challenge, including the use of positive and negative incentives, interagency approaches, and leveraging existing resources; and 2. Applying a consistent approach that can be evaluated for impact, taking into account future 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.063
metaresearch head score (Gemma)0.129
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.063
Threshold uncertainty score0.331

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0630.129
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0130.007
Scholarly communication0.0160.012
Open science0.0050.013
Research integrity0.0160.015
Insufficient payload (model declined to judge)0.0060.003

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 head0.087
GPT teacher head0.292
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2012
Admission routes1
Has abstractno

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