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Record W2162994596

Best Practice in Alcohol Ignition Interlock Schemes

2013· article· en· W2162994596 on OpenAlexaboutno aff
T J Bailey, V. L. Lindsay, Jaime Royals

Bibliographic record

VenueAdelaide Research & Scholarship (AR&S) (University of Adelaide) · 2013
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsnot available
Fundersnot available
KeywordsInterlockBest practiceSoftware deploymentPoison controlOccupational safety and healthBusinessComputer securityAeronauticsOperations managementComputer scienceEngineeringMedicineMedical emergencyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Australia’s National Road Safety Strategy 2011-2020 proposes greater use of alcohol ignition interlocks. To inform a potential expansion of interlock use, an international literature review examined the influence of mandatory versus voluntary alcohol ignition interlock schemes (AIS) in offenders’ subsequent driving and broader rehabilitation, and interlocks as preventative measures in occupational driving contexts. Additionally, the review documented AIS operational effectiveness in relation to first offenders versus repeat offenders, timing of program admittance and exit, program monitoring, participant support programs and problems experienced in AIS implementation. Evaluations of road safety effectiveness for AIS in Canada, USA, Sweden and Australia were also studied. The literature review yielded a substantial list of considered best practice components of effective AIS, ranging from the various broad contexts where interlock use can be usefully encouraged or mandated, down to specific operational considerations. Identifying best practice components affords assistance to any efforts to progress the National Strategy’s vision for the future deployment of interlocks.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.004
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.002

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 head0.047
GPT teacher head0.304
Teacher spread0.257 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations1
Published2013
Admission routes1
Has abstractyes

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