{"id":"W4391427941","doi":"10.1016/j.stem.2023.12.014","title":"Bioprinting functional neural networks","year":2024,"lang":"en","type":"article","venue":"Cell stem cell","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of Victoria","funders":"","keywords":"Biology; Computational biology; Neuroscience","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001105261,0.000291061,0.0001739902,0.0001948044,0.0001287143,0.0003872166,0.0003635873,0.0004830161,0.002958294],"category_scores_gemma":[0.0003238253,0.0001805749,0.0001706444,0.0001415062,0.0002794194,0.0004592251,0.0003043776,0.0003816941,0.0007451252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003308532,"about_ca_system_score_gemma":0.000115893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003899033,"about_ca_topic_score_gemma":0.0008600714,"domain_scores_codex":[0.9999133,0.000007731996,0.000004695152,0.00002146085,0.00004193381,0.00001081048],"domain_scores_gemma":[0.9998908,0.00004338771,0.0000165068,0.00002180914,0.00002258035,0.000005036997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005926887,0.00004694652,0.0002024844,0.0001984266,0.00002904791,0.0002237234,0.00006486948,0.041891,0.8110473,0.0162282,0.002045324,0.1279635],"study_design_scores_gemma":[0.000009862736,0.0001290178,0.0006447872,0.00002538785,0.00002183403,0.0002804774,0.00002280001,0.2659368,0.7104423,0.006827469,0.01563874,0.00002050527],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3375855,0.003188157,0.5915074,0.0007759646,0.001013025,0.00006611143,0.0002380033,0.001440823,0.064185],"genre_scores_gemma":[0.8867844,0.001261794,0.07969711,0.0001836738,0.00008700694,0.00005434872,0.0001141933,0.0001328013,0.03168475],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002958294,"threshold_uncertainty_score":0.009896517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01658198825392126,"score_gpt":0.2215729463310903,"score_spread":0.204990958077169,"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."}}