{"id":"W6931068843","doi":"10.5281/zenodo.3403463","title":"fMRIPrep: a robust preprocessing pipeline for functional MRI","year":2019,"lang":"en","type":"other","venue":"Figshare","topic":"Expert finding and Q&A systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Pipeline (software); Preprocessor; Workflow; Pattern recognition (psychology); Pipeline transport","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00006929308,0.0002585486,0.0002897834,0.0001722332,0.00008177254,0.0002402948,0.0007765841,0.0003212134,0.02679862],"category_scores_gemma":[0.0002037869,0.0002344743,0.0001432801,0.0001670101,0.000003425668,0.0001348887,0.0002149722,0.0001516973,0.004977411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004430888,"about_ca_system_score_gemma":0.0001916116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001318523,"about_ca_topic_score_gemma":0.00001028621,"domain_scores_codex":[0.9984604,0.00002257568,0.0002072585,0.0007309447,0.0002851366,0.0002936624],"domain_scores_gemma":[0.9986667,0.0001000026,0.0002853514,0.0007718764,0.0001041313,0.00007195656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001779775,0.0000147614,0.000001858811,0.000550881,0.00001637502,0.000001991267,0.00006224051,0.0001293829,0.000003100477,0.00006593821,0.9973108,0.001840909],"study_design_scores_gemma":[0.0002382214,0.00001709494,0.000003829655,0.00476144,0.00000358675,0.00001409398,0.00000480762,0.03440697,0.00002882252,0.00001358916,0.9602159,0.0002916613],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[3.660072e-8,0.0037553,0.1751663,0.0002990769,0.002042325,0.00132026,0.1032659,0.001495972,0.7126548],"genre_scores_gemma":[0.00003015653,0.000001824282,0.006530793,0.000210763,0.001891323,0.0005193246,0.03459496,0.0002321959,0.9559886],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2433338,"threshold_uncertainty_score":0.9957973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06619723203783366,"score_gpt":0.263244544971543,"score_spread":0.1970473129337094,"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."}}