{"id":"W7039462082","doi":"","title":"A Machine learning and spatial analysis approach of religious affiliation and suicide rates in Toronto","year":2024,"lang":"en","type":"article","venue":"Universidade Nova de Lisboa's Repository (Universidade Nova de Lisboa)","topic":"Plant Diversity and Evolution","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nucleofection; Gestational period; TSG101; Dysgeusia; Liquation; Diafiltration; Fusible alloy; Proteogenomics; Triacetin","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003507061,0.0002690595,0.0004119119,0.0002890953,0.0002825795,0.0001588091,0.0002505265,0.0003434913,0.0001398477],"category_scores_gemma":[0.0000418187,0.0001921491,0.0001719273,0.00124684,0.0001666787,0.0007032922,0.0001926994,0.0003938813,0.000002966698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005944251,"about_ca_system_score_gemma":0.00005297438,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07344116,"about_ca_topic_score_gemma":0.01768515,"domain_scores_codex":[0.9981668,0.0002253818,0.0002570614,0.0006137067,0.0002932809,0.0004437724],"domain_scores_gemma":[0.9991977,0.0002731995,0.0001586592,0.00008913328,0.00008675923,0.0001945509],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008855851,0.0003046582,0.5915817,0.0002855187,0.0009080233,0.0009150296,0.006797024,0.001682706,0.3809931,0.002465841,0.0008742757,0.01230651],"study_design_scores_gemma":[0.001440828,0.0006999222,0.807444,0.0002987823,0.001510453,0.0003559071,0.02715622,0.1475991,0.005499765,0.0001929904,0.006659,0.001143022],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886408,0.002106141,0.0004184068,0.0004322165,0.0001114047,0.0001951211,0.00007678867,0.0001358609,0.007883274],"genre_scores_gemma":[0.9974959,0.0006463032,0.0005291033,0.00003342173,0.0001044177,8.095611e-7,0.0002101895,0.000003986754,0.0009758445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3754934,"threshold_uncertainty_score":0.986873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01317969276855347,"score_gpt":0.2203188901283997,"score_spread":0.2071391973598462,"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."}}