{"id":"W4223558620","doi":"10.3389/fninf.2022.843114","title":"NeoRS: A Neonatal Resting State fMRI Data Preprocessing Pipeline","year":2022,"lang":"en","type":"article","venue":"Frontiers in Neuroinformatics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Canadian Apheresis Group; Mila - Quebec Artificial Intelligence Institute; Polytechnique Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Craig H. Neilsen Foundation; Canada Research Chairs; Canada First Research Excellence Fund; Canada Foundation for Innovation","keywords":"Resting state fMRI; Preprocessor; Pipeline (software); Computer science; State (computer science); Pattern recognition (psychology); Artificial intelligence; Neuroscience; Psychology; Operating system; Programming language","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"],"consensus_categories":[],"category_scores_codex":[0.0007726091,0.0001978794,0.0002507616,0.0002966272,0.0006870861,0.0001130814,0.001083376,0.00002048389,0.00001614964],"category_scores_gemma":[0.0135164,0.0002181771,0.00003441111,0.0008955153,0.0001357393,0.001298727,0.002382905,0.000635254,0.00001066704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001399128,"about_ca_system_score_gemma":0.0001456156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001355972,"about_ca_topic_score_gemma":0.000008698601,"domain_scores_codex":[0.9975843,0.0001798604,0.00056852,0.0005154978,0.000709818,0.0004420156],"domain_scores_gemma":[0.9973204,0.001419565,0.0002717915,0.000895275,0.00002816005,0.00006477253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000607446,0.0003158387,0.01854904,0.0004896724,0.00002507394,0.0005999141,0.01572924,0.2018628,0.001237672,0.0002403725,0.5839914,0.1763515],"study_design_scores_gemma":[0.0007088605,0.00009483175,0.0003621423,0.00002861499,0.000008887346,0.0001443772,0.002456839,0.8824107,0.0008859276,0.001771686,0.1108131,0.0003140666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6925653,0.001341371,0.2442685,0.01514475,0.01758623,0.003119749,0.002156449,0.001616513,0.02220115],"genre_scores_gemma":[0.9180757,0.0001963552,0.05868329,0.01975558,0.0002925625,0.0001558045,0.0001698908,0.0001463869,0.002524442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6805479,"threshold_uncertainty_score":0.9947932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04985162829390084,"score_gpt":0.2726703172413241,"score_spread":0.2228186889474233,"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."}}