{"id":"W6991634660","doi":"","title":"Health Hacks #217: Research behind magic mushrooms to treat depression","year":2023,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"MAGIC (telescope); Depression (economics); Health care; Magic bullet; Great Depression","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004792037,0.0003754853,0.0006030701,0.003562987,0.002242883,0.005958557,0.001007104,0.003383872,0.1125865],"category_scores_gemma":[0.02344237,0.0002420219,0.0008090637,0.006407746,0.002278969,0.003304281,0.00195524,0.003033053,0.01966413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003831652,"about_ca_system_score_gemma":0.008125335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03307615,"about_ca_topic_score_gemma":0.1018139,"domain_scores_codex":[0.997214,0.00114105,0.0001872604,0.0001384954,0.0009887647,0.0003304201],"domain_scores_gemma":[0.9747339,0.01476476,0.00145751,0.0008750921,0.004865823,0.003302832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001098312,0.00004710826,0.0005161982,0.001000548,0.0000249325,0.00004810685,0.0001226165,0.00001403562,0.00009647367,0.006826888,0.8587948,0.1323984],"study_design_scores_gemma":[0.0001529509,0.0001660637,0.004262862,0.004028196,0.00009956105,0.0000581806,0.0006043667,0.00004433904,0.0003613885,0.0077946,0.9824044,0.00002296064],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.002603994,0.1424013,0.0002945857,0.5717535,0.01144675,0.0002261829,0.01051431,0.0003226227,0.2604368],"genre_scores_gemma":[0.03624361,0.3618829,0.001611297,0.3045399,0.01743234,0.0005402896,0.005874142,0.000443827,0.2714317],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1125865,"threshold_uncertainty_score":0.3766395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2096528069240055,"score_gpt":0.4524556190828035,"score_spread":0.242802812158798,"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."}}