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Record W1976881220 · doi:10.1142/s0129156408005473

STANDOFF DETERMINATION OF BIOAEROSOL SIZE BASED ON DOUBLE SCATTERING MEASUREMENT WITH MFOV LIDAR: CONCEPT AND NUMERICAL SIMULATION

2008· article· en· W1976881220 on OpenAlexaff
G. Roy, Luc Bissonnette

Bibliographic record

VenueInternational Journal of High Speed Electronics and Systems · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsLidarBioaerosolScatteringRemote sensingVisibilityExtinction (optical mineralogy)OpticsRange (aeronautics)WavelengthLight scatteringParticle (ecology)RangingEnvironmental scienceAerosolPhysicsMaterials scienceMeteorologyGeographyGeologyGeodesy

Abstract

fetched live from OpenAlex

We investigate the use of multiple scattering via Multiple-Field-Of-View (MFOV) lidar signals to characterize bioaerosol particles size and concentration from ground based lidar over distances shorter than a few kilometers. The MFOV lidar signal is calculated for background aerosols at a wavelength 355 nm for a visibility of 30 km. The optical depths studied are small and the calculations are restricted to second order scattering. Also since background aerosols are constituted of relatively small particles which diffuse the light at large angles, the fields of view (FOV) range from 1 to 100 mrad full angle. We show that the MFOV lidar measurements contain exploitable information on particle size and extinction.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.221
Teacher spread0.210 · 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 designSimulation or modeling
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

Citations0
Published2008
Admission routes1
Has abstractyes

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