{"id":"W2784031884","doi":"10.5194/amt-11-291-2018","title":"A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring","year":2018,"lang":"en","type":"article","venue":"Atmospheric measurement techniques","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":559,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Heinz Endowments; U.S. Environmental Protection Agency","keywords":"Calibration; Random forest; Environmental science; Approximation error; Linear regression; Statistics; Univariate; Air quality index; Computer science; Remote sensing; Machine learning; Mathematics; Meteorology; Multivariate statistics; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07173961059229887,"score_gpt":0.306617726598672,"score_spread":0.2348781160063731,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}