{"id":"W6893954683","doi":"10.5281/zenodo.6624789","title":"NiMARE: Neuroimaging Meta-Analysis Research Environment","year":2022,"lang":"en","type":"preprint","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Neuroimaging; Brain research; Set (abstract data type); Functional neuroimaging","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01518431,0.002777414,0.003679944,0.00675355,0.0007418158,0.006960334,0.005088617,0.002279208,0.439984],"category_scores_gemma":[0.08320624,0.003736023,0.005130854,0.006585419,0.0007720484,0.004053735,0.006435482,0.003578404,0.2524849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001683922,"about_ca_system_score_gemma":0.006224285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003173434,"about_ca_topic_score_gemma":0.00623827,"domain_scores_codex":[0.9933208,0.002923707,0.001046462,0.001177549,0.001262207,0.0002693798],"domain_scores_gemma":[0.9610251,0.02601912,0.002403369,0.006589475,0.002929982,0.001032962],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007177227,0.00003040326,0.0005695152,0.009202138,0.001457651,0.0001624037,0.000219565,0.00132397,0.0007653873,0.01125238,0.932334,0.04196492],"study_design_scores_gemma":[0.002511838,0.0001081375,0.002094704,0.002641865,0.001017653,0.0003644619,0.00004135106,0.004548896,0.002384032,0.04970565,0.9342937,0.0002876724],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.000992178,0.003002415,0.1259228,0.00277464,0.001258554,0.001045271,0.5282891,0.3151018,0.02161313],"genre_scores_gemma":[0.0158238,0.003964585,0.3509026,0.003335371,0.001021833,0.01057912,0.2381623,0.3422211,0.03398937],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.9848157,"threshold_uncertainty_score":0.7987949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8268824906981849,"score_gpt":0.4984501726369187,"score_spread":0.3284323180612662,"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."}}