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Record W2414075870 · doi:10.1177/0263617416653119

Systematic evaluation of the adsorption of organic vapors onto a miniaturized cartridge device using breakthrough tests in parallel experiment with a full size respirator cartridge

2016· article· en· W2414075870 on OpenAlexaff
Florence Janvier, Ludovic Tuduri, Daniel Cossement, Daniel Drolet, Jaime Lara

Bibliographic record

VenueAdsorption Science & Technology · 2016
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsUniversité du Québec à Trois-RivièresInstitut de recherche Robert-Sauvé en santé et en sécurité du travailUniversité de Montréal
Fundersnot available
KeywordsCartridgeChemistryAdsorptionRespiratorChromatographyAnalytical Chemistry (journal)Chemical engineeringOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

Breakthrough experiments are essential for the characterization of the adsorption capacity and micropore volume of activated carbon respiratory cartridges and for the validation and determination of cartridge service life models. In an effort to gain better control over environmental conditions in breakthrough tests and to obtain reliable data, a novel experimental approach using a miniaturized (Mini) cartridge was designed to replicate a small section of a respiratory cartridge. The Mini device and the organic vapor respiratory cartridge were tested in single and parallel experiments where in the former, one filter was tested one at a time and in the latter both devices were exposed simultaneously to the same conditions. The Mini device gave comparable results to the 10% breakthrough times and adsorption capacities of the organic vapor cartridges. The reproducibility of the packed carbon bed of the Mini provided strong support for using the Mini in breakthrough experiments for the characterization of the activated carbon adsorption capacity and estimation of cartridge service life.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.033
GPT teacher head0.302
Teacher spread0.269 · 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 designBench or experimental
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

Citations3
Published2016
Admission routes1
Has abstractyes

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