{"id":"W4235727239","doi":"10.3897/rio.2.e10398","title":"Improved understanding of brain morphology through 3D printing: A brief guide","year":2016,"lang":"en","type":"article","venue":"Research Ideas and Outcomes","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research","keywords":"Brain morphometry; Morphology (biology); Computer science; Brain size; 3d model; Volume (thermodynamics); 3D printing; Cognitive science; Psychology; Artificial intelligence; Medicine; Magnetic resonance imaging; Engineering; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001288343,0.001543513,0.001031527,0.003986774,0.0005673754,0.001757906,0.002127368,0.002049199,0.03640825],"category_scores_gemma":[0.002673658,0.00140246,0.00117692,0.001638365,0.001101982,0.00226675,0.0009948299,0.002890749,0.0265507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007181009,"about_ca_system_score_gemma":0.00124479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002481108,"about_ca_topic_score_gemma":0.005711247,"domain_scores_codex":[0.9995766,0.00008381392,0.0000548994,0.00005446069,0.0002126189,0.00001762625],"domain_scores_gemma":[0.9985828,0.0006781606,0.00008539929,0.0001359883,0.0004129664,0.0001047025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001112064,0.0001900379,0.000497333,0.00335732,0.00004735009,0.001025004,0.0002553497,0.002975379,0.02853992,0.01947825,0.440244,0.5032788],"study_design_scores_gemma":[0.00001707756,0.0001297159,0.0009834495,0.0005419177,0.00001908297,0.002806443,0.0000601215,0.002229901,0.006531093,0.01481495,0.9717897,0.0000766054],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002936685,0.1208957,0.7583085,0.01609408,0.004674403,0.001865335,0.01213466,0.01009979,0.07299084],"genre_scores_gemma":[0.01012464,0.1685458,0.6952174,0.005967761,0.003187569,0.002718628,0.007810398,0.002216292,0.1042115],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03640825,"threshold_uncertainty_score":0.1217977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1978083425495885,"score_gpt":0.4086097278369628,"score_spread":0.2108013852873743,"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."}}