{"id":"W4238372560","doi":"10.20944/preprints201901.0128.v2","title":"Conjunctivitis and Exposure to Ambient Ozone","year":2019,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada","funders":"","keywords":"Akaike information criterion; Goodness of fit; Ozone; Lag; Demography; Medicine; Toxicology; Statistics; Mathematics; Meteorology; Geography; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0006931694,0.0002866509,0.000282504,0.0006169207,0.0002741163,0.0006270001,0.0002127691,0.0003211177,0.001327062],"category_scores_gemma":[0.002819605,0.0002030644,0.0004220695,0.001012668,0.0004004839,0.0002013675,0.0004824329,0.0003372157,0.0001408378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006561102,"about_ca_system_score_gemma":0.0007656457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04455245,"about_ca_topic_score_gemma":0.03392611,"domain_scores_codex":[0.9992797,0.0002086188,0.00004443373,0.0001239448,0.0002049683,0.0001383713],"domain_scores_gemma":[0.9985839,0.0003967781,0.000632392,0.00009682279,0.0001509068,0.0001391756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000182751,0.00002900584,0.9967457,0.00002635142,0.00008657628,0.0000797461,0.00005616943,0.0001284999,0.0005848348,0.00002240096,0.00003827226,0.00201965],"study_design_scores_gemma":[0.000001748556,0.00004903103,0.9995036,0.000003845134,0.00001963196,0.0001121811,0.0000390526,0.00008847706,0.00007001094,0.00001774979,0.0000930747,0.000001555647],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978927,0.001025109,0.000210586,0.00003406263,0.000005318873,0.000008106849,0.0002716361,0.000005968467,0.0005465242],"genre_scores_gemma":[0.9991048,0.0003217321,0.0001197947,0.000009387899,0.000007335188,0.000002763769,0.0002038094,0.000001695107,0.0002285218],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04455245,"threshold_uncertainty_score":0.08858627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1347187307781553,"score_gpt":0.357883563621144,"score_spread":0.2231648328429887,"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."}}