{"id":"W4391878682","doi":"10.1101/2024.02.13.580143","title":"Predicting the macrovascular contribution to resting-state fMRI functional connectivity at 3 Tesla: A model-informed approach","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; University of Toronto","funders":"","keywords":"Resting state fMRI; Pairwise comparison; Limiting; Functional connectivity; Computer science; Artificial intelligence; Neuroscience; Pattern recognition (psychology); Physics; Psychology; Engineering","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":[0.0005058639,0.000591773,0.0004502459,0.0004848035,0.000262904,0.0006596388,0.0007337819,0.001140498,0.0006122531],"category_scores_gemma":[0.001735665,0.0005506256,0.0005841205,0.0002693322,0.0003629944,0.0006650194,0.0004187366,0.0006385606,0.0001822903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006884817,"about_ca_system_score_gemma":0.0009186981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01023876,"about_ca_topic_score_gemma":0.0114434,"domain_scores_codex":[0.9998935,0.00004928724,0.000004565966,0.00002695394,0.00001432604,0.00001134672],"domain_scores_gemma":[0.999468,0.0003777182,0.00004825042,0.00003706597,0.0000418749,0.00002716111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004629237,0.00002144802,0.0009861962,0.0000142247,0.0000243772,0.00004621937,0.00001279862,0.9908596,0.003224544,0.0005591438,0.0001222164,0.004082946],"study_design_scores_gemma":[9.789145e-7,0.000005225422,0.0001852707,7.014124e-7,0.000001754052,0.000006335757,0.000001249784,0.9991097,0.0002046036,0.0004589631,0.00002267719,0.000002549518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3547311,0.0003300454,0.6423988,0.0003937459,0.00001675509,0.00004224262,0.000327796,0.0006819161,0.001077657],"genre_scores_gemma":[0.9529187,0.0001346828,0.04599919,0.00007407605,0.00001433288,0.00007577142,0.0001907614,0.00004465711,0.0005479234],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01023876,"threshold_uncertainty_score":0.02035832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03030155831500092,"score_gpt":0.2353586680270864,"score_spread":0.2050571097120855,"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."}}