{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007091725,0.0001571276,0.0002786068,0.001727149,0.001516001,0.001675306,0.0003223437,0.0004756625,0.00402065],"category_scores_gemma":[0.005876563,0.0001759676,0.0002829169,0.003042691,0.0006860982,0.002097897,0.001617613,0.000671376,0.001016148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001456733,"about_ca_system_score_gemma":0.001180459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06326991,"about_ca_topic_score_gemma":0.09548379,"domain_scores_codex":[0.9991748,0.0002271238,0.00008783073,0.0001326342,0.0001948524,0.0001827567],"domain_scores_gemma":[0.9959064,0.001185589,0.001344002,0.000236718,0.0008362966,0.0004909997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002705409,0.0001459645,0.9148491,0.0002705561,0.00005820993,0.0005504592,0.06176064,0.00005814434,0.0008768544,0.0006492254,0.004955083,0.01555524],"study_design_scores_gemma":[0.000006495163,0.00005754267,0.9095657,0.00007985785,0.00003114799,0.0002547713,0.08241778,0.0002428649,0.0002293251,0.00009483626,0.006992628,0.00002697686],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922475,0.000168131,0.0001059183,0.0006114469,0.0000253644,0.00006597718,0.002527406,0.000007663233,0.004240629],"genre_scores_gemma":[0.9964444,0.0003330521,0.0001741031,0.0003103733,0.00005510733,0.0001010649,0.001440695,0.00001657759,0.001124698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06326991,"threshold_uncertainty_score":0.1258033,"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."}}