{"id":"W4246502189","doi":"10.32920/ryerson.14660571","title":"Cannabis Personalization: Curating Personalized Cannabis Experiences Through Machine Learning","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Cannabis and Cannabinoid Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cannabis; Legalization; Personalization; Internet privacy; Product (mathematics); Government (linguistics); Business; Computer science; World Wide Web; Marketing; Psychology; Psychiatry","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":[0.0006438871,0.000974774,0.0007220432,0.002180941,0.0006640105,0.001237223,0.0008547654,0.0008403042,0.003659586],"category_scores_gemma":[0.00363481,0.0002665213,0.0008767045,0.001573922,0.0005042648,0.001630908,0.00157655,0.001426963,0.003129205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007620933,"about_ca_system_score_gemma":0.0008032546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009684378,"about_ca_topic_score_gemma":0.0232508,"domain_scores_codex":[0.9990716,0.0001970983,0.00005128668,0.0003194499,0.0002308782,0.0001296141],"domain_scores_gemma":[0.9990324,0.0004219811,0.0001083782,0.0001652429,0.0001914166,0.00008059682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009530197,0.001273987,0.07518712,0.0008876077,0.0002765721,0.0007083855,0.00160401,0.01495113,0.01209452,0.003924239,0.0654324,0.8227069],"study_design_scores_gemma":[0.0001186415,0.0006367255,0.08639883,0.0005271453,0.0004156751,0.00124245,0.003527458,0.7185138,0.02023156,0.02539469,0.1426975,0.0002954459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3917928,0.01780211,0.4867095,0.005790698,0.001923476,0.001430516,0.02373949,0.02082441,0.04998709],"genre_scores_gemma":[0.7821354,0.003840439,0.1716995,0.001390919,0.0005146328,0.0004217306,0.01860558,0.0005967387,0.02079511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009684378,"threshold_uncertainty_score":0.01925606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03466790298912937,"score_gpt":0.3330138847161578,"score_spread":0.2983459817270285,"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."}}