{"id":"W6986452401","doi":"","title":"Predicting air pollution spatial variation with street-level imagery","year":2020,"lang":"en","type":"article","venue":"Spiral (Imperial College London)","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Air pollution; Estimation; Air quality index; Geospatial analysis; Grid; Spatial analysis; Spatial variability; Satellite imagery; Satellite","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003250001,0.0002331522,0.0002698382,0.00002925828,0.0004102407,0.00003556053,0.0002033144,0.0001406541,0.001161383],"category_scores_gemma":[0.00021149,0.0002062677,0.00006128641,0.0004102776,0.0001513216,0.0006006476,0.0001532398,0.0002470207,0.0003270134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002600562,"about_ca_system_score_gemma":0.0001314739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00397173,"about_ca_topic_score_gemma":0.002326119,"domain_scores_codex":[0.9978957,0.0001523823,0.0004087868,0.0004554689,0.0005586483,0.0005290108],"domain_scores_gemma":[0.9991201,0.00004109736,0.0002021436,0.0001959285,0.00001844107,0.0004223149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.02440413,0.001644967,0.6293854,0.0009648875,0.0003478078,0.0006269708,0.02923753,0.03349833,0.09547349,0.01970787,0.09136333,0.07334533],"study_design_scores_gemma":[0.007850434,0.005897082,0.9407979,0.00008984115,0.0001110517,0.00002531361,0.0009173185,0.01831561,0.006801401,0.0009075106,0.01705473,0.001231762],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9766329,0.00001300689,0.005867349,0.01105016,0.0004554574,0.000776021,0.000337617,0.0002030856,0.00466442],"genre_scores_gemma":[0.985405,0.000004065804,0.003275023,0.01011209,0.001019627,0.00002247718,0.00003392941,0.00002810084,0.00009967556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3114126,"threshold_uncertainty_score":0.9997517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03147906556803934,"score_gpt":0.2528693537926123,"score_spread":0.221390288224573,"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."}}