{"id":"W4386716749","doi":"10.1007/978-3-031-42508-0_30","title":"Cannabis Use Estimators Within Canadian Population Using Social Media Based on Deep Learning Tools","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"HIV, Drug Use, Sexual Risk","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Cannabis; Computer science; Sample (material); Population; Classifier (UML); Estimator; Social media; Artificial intelligence; Statistics; World Wide Web; Demography; Psychology","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":[0.0006631186,0.0003565994,0.00028175,0.001191637,0.0006356015,0.0007490398,0.0009207298,0.0004840677,0.003151546],"category_scores_gemma":[0.003542724,0.0001865716,0.0005584293,0.001460745,0.0002629198,0.0004333464,0.0004414898,0.0006538081,0.0007494204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003705749,"about_ca_system_score_gemma":0.004724029,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9314598,"about_ca_topic_score_gemma":0.9377088,"domain_scores_codex":[0.9997563,0.00003998065,0.000009840399,0.00005333314,0.00006007699,0.00008046653],"domain_scores_gemma":[0.9984458,0.000466632,0.00008179097,0.00009157989,0.0007970021,0.0001172537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004218874,0.0001521972,0.8507078,0.00006799543,0.0002308726,0.0001725634,0.000424081,0.01980863,0.001022766,0.00211709,0.02070879,0.1041652],"study_design_scores_gemma":[0.00002748704,0.00006050914,0.6789939,0.00008777453,0.0002368805,0.0002344304,0.001386252,0.3051264,0.001717492,0.002039482,0.009992125,0.00009730293],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9559292,0.001307231,0.01202263,0.001069024,0.0001198007,0.0000509963,0.02347025,0.0003151407,0.0057158],"genre_scores_gemma":[0.9772724,0.0004366257,0.005894065,0.00008170198,0.00004814022,0.00002639034,0.01057345,0.00003259254,0.005634802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06854016,"threshold_uncertainty_score":0.1378875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07534237704909082,"score_gpt":0.3220288309887602,"score_spread":0.2466864539396694,"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."}}