{"id":"W3200706878","doi":"10.51628/001c.28328","title":"Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics","year":2021,"lang":"en","type":"article","venue":"Neurons Behavior Data analysis and Theory","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; McGill University; Montreal Neurological Institute and Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Open Neuroscience Platform","keywords":"Resampling; Computer science; Human Connectome Project; Pipeline (software); Connectomics; Monte Carlo method; Preprocessor; Artificial intelligence; Connectome; Dimensionality reduction; Variance reduction; Algorithm; Machine learning; Noise (video); Curse of dimensionality; Range (aeronautics); Pattern recognition (psychology); Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005096858,0.0009606649,0.0008900253,0.000741596,0.0006415396,0.001545212,0.001243911,0.001328442,0.001997705],"category_scores_gemma":[0.02297489,0.0005963036,0.001145656,0.0007394144,0.001295906,0.002114853,0.001479963,0.003231758,0.0005831637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001024093,"about_ca_system_score_gemma":0.001276095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002959001,"about_ca_topic_score_gemma":0.004894982,"domain_scores_codex":[0.9987581,0.0005526883,0.00007847254,0.0003559505,0.000197742,0.00005706478],"domain_scores_gemma":[0.9930354,0.004067705,0.0005295927,0.001809751,0.0004457534,0.0001118665],"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":[0.000325543,0.0001508177,0.004713059,0.000142191,0.000153215,0.0001226551,0.0002076045,0.8708641,0.01235696,0.03354679,0.002043083,0.0753739],"study_design_scores_gemma":[0.00001160574,0.00003858612,0.0007630902,0.00001280755,0.000009883515,0.00003120493,0.00001022504,0.9765153,0.003201661,0.01856898,0.0008201079,0.00001658559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05908306,0.0001627279,0.9375352,0.0007368442,0.0000702827,0.0001196749,0.0002185773,0.001040026,0.0010337],"genre_scores_gemma":[0.4789774,0.0002211703,0.5172963,0.0004331959,0.00007795006,0.0004344609,0.0009982558,0.000268718,0.001292618],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005096858,"threshold_uncertainty_score":0.02695507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1783241388633127,"score_gpt":0.3721602847526841,"score_spread":0.1938361458893715,"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."}}