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Record W2047091742 · doi:10.1175/jtech2006.1

Advancements in Techniques for Calibration and Characterization of In Situ Optical Particle Measuring Probes, and Applications to the FSSP-100 Probe

2007· article· en· W2047091742 on OpenAlexaff
Dagmar Nagel, Uwe Maixner, W. Strapp, M. A. Wasey

Bibliographic record

VenueJournal of Atmospheric and Oceanic Technology · 2007
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsAirspeedCalibrationCharacterization (materials science)SizingSpectrometerOpticsParticle (ecology)Materials scienceScatteringPhysicsChemistry

Abstract

fetched live from OpenAlex

Abstract Advancements in techniques for the operational calibration and characterization of instrument performance of the Particle Measuring Systems, Inc. (PMS), forward scattering spectrometer probe (FSSP) and optical array probes (OAPs) are presented, which also can be used for most in situ particle-measuring optical probes on the market. These techniques include the determination of a distortion matrix to correct for instrumental broadening of the measured particle size distribution. A new version of a monodisperse droplet generator is introduced for absolute calibration in the size range between 10 and 100 μm. In addition, a high-speed technique was employed for the determination of airspeed influence on the sample volume and the sizing of particles. The calibration of a PMS FSSP with real water droplets may be significantly different from the usual calibration with glass beads. High-speed measurements simulate particles at speeds of up to about 250 m s−1. Particle undersizing and the decrease of the sample volume with increasing airspeed are described. The use of the modular tools, built for this work, is discussed for probe alignment, functionality checks, and general characterization and diagnostics both in laboratory and field environments.

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)0.000
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 score0.244
Threshold uncertainty score0.202

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.010
GPT teacher head0.226
Teacher spread0.216 · 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 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

Citations21
Published2007
Admission routes1
Has abstractyes

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