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Record W2111634535 · doi:10.5589/m10-033

Spectral, spatial, and temporal sensitivity of correlating MODIS aerosol optical depth with ground-based fine particulate matter (PM<sub>2.5</sub>) across southern Ontario

2010· article· en· W2111634535 on OpenAlexvenueaboutno aff
Jie Tian, Dongmei Chen

Bibliographic record

VenueCanadian Journal of Remote Sensing · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsnot available
Fundersnot available
KeywordsModerate-resolution imaging spectroradiometerEnvironmental scienceParticulatesSpectroradiometerAerosolAtmospheric sciencesMorningGround levelRemote sensingAngstromMeteorologyReflectivityGeographySatelliteGeologyPhysicsChemistry

Abstract

fetched live from OpenAlex

This paper evaluates the sensitivity of the aerosol optical depth (AOD) measurements derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) at wavelengths of 0.47, 0.55, and 0.66 µm from both the Terra and Aqua satellites for the estimation of ground-level concentrations of fine particulate matter (PM2.5) in southern Ontario, Canada. The correlation between MODIS AOD and ground-based PM2.5 measurements is compared at different seasons and spatial aggregation levels. The results showed that the MODIS AOD data acquired in early afternoon (from Aqua) seem to have slightly higher agreement with the ground-based measurement of PM2.5 than the late morning Terra data. Moreover, MODIS AOD at 0.47 µm from both satellites had the highest correlation with ground-level PM2.5. More detailed examination suggested that the correlation was stronger in the spring and summer and weaker in the fall and winter. Aqua MODIS AOD appeared to have a better correlation with the average ground-based PM2.5 concentration over 1–3 h than with daily PM2.5. MODIS AOD values aggregated over 3 × 3 pixel groups correlate slightly better with PM2.5 than with the original single centre pixel values.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.716

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.195
Teacher spread0.187 · 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 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

Citations28
Published2010
Admission routes2
Has abstractyes

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