{"id":"W3036740076","doi":"10.1680/jenes.20.00019","title":"Spatio-temporal variation of aerosols in ENSO events over Western India using satellite data","year":2020,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Aerosol; Moderate-resolution imaging spectroradiometer; Environmental science; Climatology; Satellite; Spectroradiometer; Seasonality; Atmospheric sciences; Spatial variability; El Niño Southern Oscillation; Spring (device); Atmosphere (unit); Variation (astronomy); Meteorology; Geography; Geology; Reflectivity","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001646921,0.0001510938,0.0001018046,0.0008124518,0.0001887114,0.0004883547,0.0001504134,0.0001430117,0.0004731375],"category_scores_gemma":[0.0003361997,0.00008490402,0.0002042059,0.001138608,0.0001362401,0.0002050788,0.0002576775,0.0001367144,0.0001212771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003642854,"about_ca_system_score_gemma":0.0002733071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05056506,"about_ca_topic_score_gemma":0.08041222,"domain_scores_codex":[0.9999036,0.000009902178,0.00001411159,0.00002669166,0.00002211402,0.000023543],"domain_scores_gemma":[0.9997122,0.00004803217,0.0001072778,0.00001904724,0.00007497385,0.00003850372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001023151,0.00003156468,0.988297,0.00004864421,0.00007722546,0.0002375105,0.0004446568,0.002568189,0.002356957,0.0001336575,0.0008099464,0.004892301],"study_design_scores_gemma":[0.00000191279,0.00000912309,0.997164,0.000005971177,0.00001659361,0.00005100193,0.0003202979,0.001556382,0.0002713169,0.00001229264,0.0005869614,0.00000424416],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974185,0.00004264866,0.00004956379,0.00001760642,0.000004021455,0.000003478839,0.001762457,0.00001760545,0.0006840994],"genre_scores_gemma":[0.9977316,0.00004925319,0.00009930553,0.000006156633,0.000004482597,0.000003971214,0.001919263,0.00000204059,0.0001840244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05056506,"threshold_uncertainty_score":0.1005415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01673643690229859,"score_gpt":0.223917900669842,"score_spread":0.2071814637675434,"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."}}