{"id":"W3082160916","doi":"10.2196/18540","title":"Geographic Differences in Cannabis Conversations on Twitter: Infodemiology Study","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; University of British Columbia","funders":"","keywords":"Cannabis; Sentiment analysis; Recreation; Legislation; Social media; Internet privacy; Legislature; Advertising; Government (linguistics); Content analysis; Public health; Psychology; Political science; Medicine; Computer science; Business; Law; Sociology; Psychiatry; Social science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008009139,0.0002163907,0.0007430763,0.0002323605,0.0001053916,0.0000350045,0.0001512397,0.00007883824,0.00008327868],"category_scores_gemma":[0.0009075557,0.0001858325,0.00005160104,0.0006824105,0.0001411265,0.00009299557,0.00007021164,0.0003558405,0.00002866083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005667162,"about_ca_system_score_gemma":0.0006595534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005833647,"about_ca_topic_score_gemma":0.001041262,"domain_scores_codex":[0.9973785,0.0005981503,0.0005621651,0.0005707507,0.0002866938,0.0006037765],"domain_scores_gemma":[0.998024,0.0001935471,0.0001622476,0.0003563852,0.00009944425,0.001164405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001588936,0.0002994019,0.9849694,0.0001402113,0.00002680804,0.00002561082,0.001988355,5.474219e-7,8.094797e-7,0.00008436816,0.008748623,0.003557016],"study_design_scores_gemma":[0.002018234,0.001313438,0.9622315,0.00001241291,6.776322e-7,0.000005074724,0.001355331,0.0003236286,2.88303e-8,0.00001069258,0.03258129,0.0001477538],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9068357,0.0004079474,0.00002193164,0.09054039,0.00009132861,0.001259422,0.0001260295,0.0001437816,0.0005734559],"genre_scores_gemma":[0.9725555,0.0002615377,0.00002438298,0.02662146,0.00009448834,0.0002093141,0.0001648664,0.00001578037,0.00005272132],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06571974,"threshold_uncertainty_score":0.7578029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06726751348316123,"score_gpt":0.3325211838282359,"score_spread":0.2652536703450747,"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."}}