{"id":"W2904065829","doi":"10.1051/matecconf/201824700002","title":"Air quality health indices - review","year":2018,"lang":"en","type":"article","venue":"MATEC Web of Conferences","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Air quality index; Environmental health; Index (typography); Air pollution; Air pollutants; Epidemiology; Medicine; Population health; Public health; Air Pollution Index; Population; Geography; Meteorology; Computer science","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":[0.001718754,0.00010287,0.000336868,0.00002229563,0.0001404967,0.000009354766,0.0002828944,0.00004670792,0.01013767],"category_scores_gemma":[0.0001069468,0.0000803958,0.0000354969,0.0001862687,0.0005414565,0.0001489089,0.00009552533,0.00008264266,0.0007437094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004107888,"about_ca_system_score_gemma":0.0003952993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005192109,"about_ca_topic_score_gemma":0.002044766,"domain_scores_codex":[0.9984091,0.0002378185,0.0005072873,0.0001954803,0.0003610706,0.0002892242],"domain_scores_gemma":[0.999106,0.00006520536,0.000395114,0.0002392597,0.00001944551,0.000174901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009326836,0.0004041891,0.3435598,0.008136122,0.00003993283,0.000001755989,0.003438733,0.000003090929,0.0003199121,0.04403279,0.3004874,0.2994831],"study_design_scores_gemma":[0.0001862765,0.0005140259,0.4467449,0.001018839,0.000007328935,0.000001988843,0.0002454541,0.00003746829,0.00076932,0.003285773,0.5470021,0.0001864471],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5378326,0.005615505,0.0003326695,0.08192199,0.0004193209,0.0009362255,0.00007140269,0.000174127,0.3726962],"genre_scores_gemma":[0.9798506,0.003005503,0.0005328217,0.01630021,0.00003980969,0.000006633564,0.000004764327,0.000003974134,0.0002557122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.442018,"threshold_uncertainty_score":0.9907672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1027465676172858,"score_gpt":0.395777416607137,"score_spread":0.2930308489898512,"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."}}