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Record W2040521750 · doi:10.14356/kona.2014001

A PM<sub>1.0/2.5/10</sub> Trichotomous Virtual Impactor Based Sampler: Design and Applied to Arid Southwest Aerosols—Part II: Application to Arid Southwest Aerosols

2014· article· en· W2040521750 on OpenAlexaff
Virgil A. Marple, Dale A. Lundgren, Bernard A. Olson

Bibliographic record

VenueKONA Powder and Particle Journal · 2014
Typearticle
Languageen
FieldHealth Professions
TopicRadioactivity and Radon Measurements
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsAerosolVolume (thermodynamics)Range (aeronautics)AridParticle sizeMineralogyParticle (ecology)ChemistryAtmospheric sciencesEnvironmental scienceAnalytical Chemistry (journal)Materials scienceChromatographyGeologyPhysicsThermodynamics

Abstract

fetched live from OpenAlex

A PM1.0/2.5/10 Trichotomous sampler has been developed to determine if the particles in the saddle point between the coarse and fine particle modes (specifically the 1.0 μm to 2.5 μm size range) are primarily coarse or fine mode particles. The sampler consists of a standard high volume sampler with two high volume virtual impactors (one with a cut size of 2.5 μm and the other with a cut size of 1.0 μm) inserted between the PM10 inlet and the 8 × 10 inch after filter. Filters in this sampler were analyzed with ion chromatography (IC) to determine SO4−2 concentrations, representing a specie primarily found in the fine mode aerosol and proton-induced X-ray emission (PIXE) for determining concentrations of Si, S, Ca and Fe, representing species normally found in coarse mode aerosols. Application of this sampler to Phoenix, AZ, representing an arid region, showed that particles in the saddle point consisted of about 75% of particles from the coarse mode and about 25% from the fine mode.

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)0.001
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.054
GPT teacher head0.318
Teacher spread0.264 · 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
Published2014
Admission routes1
Has abstractyes

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