{"id":"W2410175960","doi":"10.1007/bf03405121","title":"Indicators in Environmental Health: Identifying and Selecting Common Sets","year":2002,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Identification (biology); Selection (genetic algorithm); Process (computing); Health indicator; Computer science; Risk analysis (engineering); Process management; Environmental resource management; Management science; Data science; Business; Environmental health; Engineering; Environmental science; Medicine; Ecology; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02342106,0.001449783,0.00294405,0.01640115,0.001895602,0.005890876,0.002641909,0.001606153,0.001901135],"category_scores_gemma":[0.1048097,0.0007125204,0.002085776,0.0187042,0.002326851,0.009586938,0.00789993,0.003261902,0.0003294035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003666808,"about_ca_system_score_gemma":0.006205025,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01004587,"about_ca_topic_score_gemma":0.01221068,"domain_scores_codex":[0.9770551,0.01129798,0.002935666,0.002251698,0.005255166,0.001204403],"domain_scores_gemma":[0.9448562,0.03434492,0.007555886,0.004477436,0.007395897,0.001369696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005076393,0.0004495233,0.1208112,0.002584426,0.0008594838,0.0002409891,0.003499626,0.02544221,0.002245766,0.2832459,0.02018805,0.5399252],"study_design_scores_gemma":[0.0002805029,0.0005074492,0.07383905,0.001903303,0.001351743,0.0003734264,0.005924457,0.1402599,0.007811514,0.7143463,0.05309352,0.0003088591],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1759752,0.003483142,0.793023,0.006142593,0.0002374335,0.001153245,0.007788921,0.00104139,0.01115509],"genre_scores_gemma":[0.5019032,0.00170281,0.4853263,0.0002848654,0.0001231505,0.001454641,0.007983161,0.0001868378,0.001035023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9899541,"threshold_uncertainty_score":0.1238639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.132787021061996,"score_gpt":0.325364854346298,"score_spread":0.1925778332843019,"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."}}