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Record W6947709227 · doi:10.4224/23003981

Evaluation of the communication performance of the C4 chemical, biological, radiological and nuclear mask

2018· report· en· W6947709227 on OpenAlexafffundvenueabout

Bibliographic record

VenueNPARC · 2018
Typereport
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsNational Research Council Canada
FundersNational Research Council CanadaCanadian Armed Forces
KeywordsRadiological weaponTest (biology)National standardService (business)Test method

Abstract

fetched live from OpenAlex

The Canadian Armed Forces (CAF) are using chemical, biological, radiological and nuclear (CBRN) general service respirators (GSRs) to defend against CBRN threats and hazards to protect personnel and ensure that they can safely and successfully carry out missions. The CBRN GSR currently in use by the CAF is the C4 respirator. The National Research Council of Canada assessed the communication performance of the C4 respirator. This report presents the results of the performance test. The method for assessing the communication performance used in this study was the NIOSH standard test procedure TEB-CBRN-APR-STP-0313 [1]. Other methods exist to evaluate the communication performance of respirators, but historically the NIOSH standard test procedure has been used. This report details the implementation and results of the tests conducted at the National Research Council of Canada, following the NIOSH standard test procedure.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.064
GPT teacher head0.277
Teacher spread0.213 · 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
Published2018
Admission routes4
Has abstractyes

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