{"id":"W7070732646","doi":"","title":"Quantitative genetics of human brain structure and function","year":2018,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Mental Health; Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Variation (astronomy); Perspective (graphical); Function (biology); Inheritance (genetic algorithm); Quantitative genetics; Human brain; Quantitative analysis (chemistry); Quantitative trait locus","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001086879,0.0002705351,0.0002686621,0.0005786772,0.0001932614,0.0009873883,0.0002482426,0.0004223969,0.003048829],"category_scores_gemma":[0.003197134,0.0001537326,0.0002600233,0.0007708854,0.000972953,0.0003994148,0.0003940341,0.0005345017,0.0003606576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005758637,"about_ca_system_score_gemma":0.0003828729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002451868,"about_ca_topic_score_gemma":0.002237348,"domain_scores_codex":[0.9991513,0.0003176011,0.00003013806,0.000207794,0.0002329945,0.00006008498],"domain_scores_gemma":[0.9991715,0.0005286055,0.0001501081,0.00004663603,0.00007378438,0.00002949337],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0002719077,0.00008467675,0.1387592,0.0007974363,0.0009155998,0.001255014,0.002866112,0.01612207,0.07369818,0.4923233,0.01386711,0.2590393],"study_design_scores_gemma":[0.00002815834,0.000156827,0.7358272,0.0002745161,0.0001681138,0.002351818,0.0007094255,0.009567902,0.005143112,0.212056,0.03362149,0.00009544439],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7267388,0.03443672,0.1616964,0.006519969,0.0003400524,0.000181511,0.005867798,0.0006294502,0.06358941],"genre_scores_gemma":[0.9650387,0.008033716,0.02035945,0.0003636627,0.0001427658,0.0001182763,0.0006912659,0.0001002846,0.005151883],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003048829,"threshold_uncertainty_score":0.01019937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03701864172414999,"score_gpt":0.2877534522870236,"score_spread":0.2507348105628737,"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."}}