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Record W4385286387 · doi:10.1080/00085030.2023.2230012

Evaluation of the Intoxilyzer® 9000 evidential breath alcohol testing instrument

2023· article· en· W4385286387 on OpenAlexaffvenueabout
I.M. Bugyra, Brian Cahill, T.L. Martin

Bibliographic record

VenueCanadian Society of Forensic Science Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsHealth Sciences Centre
Fundersnot available
KeywordsBreath testEngineeringEnvironmental scienceMedicine

Abstract

fetched live from OpenAlex

The Alcohol Test Committee (ATC) of the Canadian Society of Forensic Science sets out equipment standards and evaluation procedures for the evaluation of breath testing equipment in Canada. The Intoxilyzer® 9000 was evaluated according to the 2014 Equipment Standards and Evaluation Procedures with results demonstrating that the instrument met, or exceeded, the requirements of the ATC on all criteria including accuracy, linearity, precision, and acetone interference. Human subject testing provided a comparison of blood alcohol concentrations (BACs) determined by the Intoxilyzer® 9000 with near-simultaneous breath test results from an Intoxilyzer® 8000C, an Approved Instrument in Canada since 2007. The results of 50 paired breath samples on 10 drinking subjects were well-correlated (R2 = 0.966) and described by a linear model, y = 0.95x + 0.84. The validation of the Intoxilyzer® 9000 according to the standards of the ATC demonstrates the instrument to be an accurate and reliable means of determining breath alcohol concentrations over a range of forensically relevant BACs. The Intoxilyzer® 9000 was added to the Approved Breath Analysis Instruments Order in 2019 identifying it as suitable for the purposes of Section 320.14 of the Criminal Code of Canada.

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)0.007
metaresearch head score (Gemma)0.001
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 score0.703
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
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 head0.215
GPT teacher head0.404
Teacher spread0.189 · 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

Citations1
Published2023
Admission routes3
Has abstractyes

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