{"id":"W1564675157","doi":"10.1002/hbm.22839","title":"Automated iterative reclustering framework for determining hierarchical functional networks in resting state f<scp>MRI</scp>","year":2015,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Baycrest Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Resting state fMRI; Computer science; Neuroscience; Functional connectivity; State (computer science); Psychology; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001535428,0.0003307253,0.0003908364,0.0003885647,0.0008884852,0.000243154,0.0002571355,0.0001484489,0.000005265615],"category_scores_gemma":[0.04892372,0.0003621771,0.00009746462,0.0006789427,0.0002064528,0.0004351644,0.0003551097,0.0007068138,0.00001270532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002900025,"about_ca_system_score_gemma":0.00009482828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000100387,"about_ca_topic_score_gemma":0.0000450711,"domain_scores_codex":[0.9968047,0.0004698477,0.000564612,0.0009460209,0.0004315667,0.0007832858],"domain_scores_gemma":[0.9723305,0.02684481,0.0002448692,0.0002776466,0.0001281007,0.0001741084],"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":[0.0003711671,0.0004264081,0.03614074,0.0004344449,0.0001672417,0.0005088698,0.05329598,0.5830097,0.1995349,0.04785812,0.07216655,0.006085861],"study_design_scores_gemma":[0.002278055,0.000403185,0.06639948,0.001020227,0.00001002161,0.00008606864,0.001899622,0.8459111,0.0008038397,0.07218814,0.008537811,0.0004624292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6441007,0.00004991667,0.3493787,0.002179753,0.001033993,0.0007898623,0.00001421429,0.0009185775,0.001534263],"genre_scores_gemma":[0.9812835,0.000001809846,0.01224062,0.004822908,0.0006652283,0.0002740221,0.00001406853,0.00007081782,0.0006270309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3371827,"threshold_uncertainty_score":0.999883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1248865108850263,"score_gpt":0.3247187033287145,"score_spread":0.1998321924436882,"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."}}