{"id":"W3083036831","doi":"10.1101/2020.09.03.282236","title":"SS-Detect: Development and Validation of a New Strategy for Source-Based Morphometry in Multi-Scanner Studies","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Michael Smith Health Research BC; H. Lundbeck A/S; Vancouver Coastal Health Research Institute; Fondation Brain Canada; Eli Lilly and Company","keywords":"Scanner; Pooling; Computer science; Voxel; Artificial intelligence; Pattern recognition (psychology); Reproducibility; Sample (material); Mathematics; Statistics; Chemistry","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":[0.01935346,0.001902172,0.001421502,0.00412801,0.0007272207,0.002093767,0.002777898,0.001786898,0.002376278],"category_scores_gemma":[0.05614904,0.001043701,0.002083446,0.001551274,0.001299209,0.001732253,0.003657794,0.001679438,0.0007586816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008420757,"about_ca_system_score_gemma":0.002106414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004149401,"about_ca_topic_score_gemma":0.005611157,"domain_scores_codex":[0.9942744,0.003249219,0.0004430417,0.0009166053,0.0009878628,0.0001288027],"domain_scores_gemma":[0.966953,0.02214305,0.002073901,0.003334285,0.004820683,0.000675037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002099632,0.0006296764,0.0391454,0.001084771,0.002784311,0.0008642409,0.001026951,0.1740193,0.06963215,0.01721975,0.005919303,0.6855745],"study_design_scores_gemma":[0.000143098,0.0002616992,0.00542189,0.00003308266,0.0001436174,0.0003596449,0.00008331005,0.9711238,0.01441733,0.005830594,0.00210501,0.00007702004],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01564981,0.0000920622,0.9813936,0.000105602,0.00003380864,0.0001877881,0.0001694249,0.002166763,0.0002012504],"genre_scores_gemma":[0.07723349,0.00006445507,0.9209399,0.0001070665,0.00003116799,0.0003910311,0.0003688647,0.0006257822,0.0002382629],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01935346,"threshold_uncertainty_score":0.1023521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1221059083617941,"score_gpt":0.2947087615601865,"score_spread":0.1726028531983924,"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."}}