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Record W2275801412 · doi:10.1080/02786826.2015.1131809

A light-weight, high-sensitivity particle spectrometer for PM2.5 aerosol measurements

2015· article· en· W2275801412 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAerosol Science and Technology · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsSpectrometerAerosolParticle sizeEnvironmental scienceElectronicsRange (aeronautics)Particle (ecology)OpticsRemote sensingMaterials sciencePhysicsEngineeringElectrical engineeringAerospace engineeringMeteorology

Abstract

fetched live from OpenAlex

A light-weight, low-cost optical particle spectrometer for measurements of aerosol number concentrations and size distributions has been designed, constructed, and demonstrated. The spectrometer is suitable for use on small, unmanned aerial vehicles (UAVs) and in balloon sondes. The spectrometer uses a 405 nm diode laser to count and size individual particles in the size range 140–3000 nm. A compact data system combines custom electronics with a single-board commercial computer. Power consumption is 7W at 9–15 V. 3D printing technology was used in the construction of the instrument to reduce cost, manufacturing complexity, and weight. The resulting Printed Optical Particle Spectrometer (POPS) instrument weighs about 800 g with an approximate materials cost of 2500 USD. Several POPS units have been constructed, tested in the laboratory, and deployed on UAVs. Here we present an overview of the instrument design and construction, laboratory validation data, and field engineering data for POPS.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.111
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.031
GPT teacher head0.232
Teacher spread0.201 · 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