{"id":"W4241212385","doi":"10.2196/preprints.18540","title":"Geographic Differences in Cannabis Conversations on Twitter: Infodemiology Study (Preprint)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Smoking Behavior and Cessation","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; University of British Columbia","funders":"","keywords":"Cannabis; Recreation; Sentiment analysis; Legislature; Legislation; Social media; Advertising; Internet privacy; Government (linguistics); Political science; Psychology; Law; Business; Computer science; Psychiatry; Artificial intelligence","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":[0.0006120755,0.0001379759,0.0002435705,0.001064286,0.001300298,0.00146126,0.0002727378,0.0004481289,0.007069677],"category_scores_gemma":[0.005066114,0.0001875231,0.000308918,0.002084688,0.0005165529,0.001913053,0.001181211,0.0006541166,0.001993076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001000015,"about_ca_system_score_gemma":0.0009695704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05525079,"about_ca_topic_score_gemma":0.07738496,"domain_scores_codex":[0.9994542,0.0001339949,0.00006724469,0.00009495908,0.0001162627,0.0001333105],"domain_scores_gemma":[0.9968876,0.000944844,0.0009287623,0.0001662386,0.0006664713,0.0004061202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002739545,0.0001902838,0.9200681,0.0003484116,0.00006617926,0.0003867086,0.04651865,0.00003930332,0.0006356896,0.000464943,0.01433474,0.01667308],"study_design_scores_gemma":[0.000006493464,0.00005386581,0.9425524,0.00008554506,0.00002526056,0.0001267063,0.05083777,0.00009406789,0.0001646627,0.0000537886,0.005980055,0.00001938881],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886045,0.0001733478,0.00007775815,0.001021587,0.00005022719,0.00006809375,0.005679753,0.00001024712,0.00431456],"genre_scores_gemma":[0.9916742,0.0004844891,0.0001825954,0.0007207847,0.0001282563,0.0002039185,0.003756393,0.00002786261,0.002821508],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05525079,"threshold_uncertainty_score":0.1098584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08721593280741513,"score_gpt":0.3419305512311008,"score_spread":0.2547146184236856,"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."}}