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Record W2266058414 · doi:10.1063/1.3117015

Preliminary Analysis of Night‐time Aerosol Optical Depth Retrievals at a Rural, Near‐urban Site in Southern Canada

2009· article· en· W2266058414 on OpenAlexaffabout
Konstantin Baibakov, Norman T. O’Neill, B. Firanski, K. B. Strawbridge

Bibliographic record

VenueAIP conference proceedings · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsEnvironmental scienceRemote sensingLidarAerosolZenithCalibrationBackscatter (email)HomogeneousSpectral lineOptical depthCorrelation coefficientExtinction (optical mineralogy)Atmospheric sciencesMeteorologyGeologyOpticsPhysicsMathematicsComputer science

Abstract

fetched live from OpenAlex

In the summer of 2007, a SPSTAR03 starphotometer was installed at Egbert, Canada (44°13′ N, 79°45′ W, alt 264 m) and a continuous series of initial measurements was performed between August 26 and September 19. Several sunphotometry parameters such as the aerosol optical depth (AOD) and the “fine” and “coarse” optical depths were extracted from the SPSTAR03 extinction spectra. The SPSTAR03 data was analyzed in conjunction with sunphotometry and zenith‐pointing lidar data acquired during the same time period. Preliminary results show coarse continuity between the day‐ and night time AOD values (with the mean difference between the measured and the interpolated values being 0.05) as well as a qualitative correlation between the “fine” and “coarse” optical depths and the normalized lidar backscatter coefficient profiles. It was also found that the spectra produced with the differential two‐star measurement method were sensitive to non‐horizontally homogeneous differences in the line‐of‐sight conditions of both stars. The one‐star method helps to reduce the uncertainties but requires the determination of a calibration constant.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.205
Teacher spread0.197 · 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.

Study designObservational
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

Citations15
Published2009
Admission routes2
Has abstractyes

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