{"id":"W2980946885","doi":"10.1101/806778","title":"Parallel Factor Analysis for multidimensional decomposition of fNIRS data","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; Université de Montréal; Cégep Marie-Victorin","funders":"Fonds de Recherche du Québec - Santé; Fonds de recherche du Québec – Nature et technologies; Hospital for Sick Children; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Artifact (error); Computer science; Functional near-infrared spectroscopy; Artificial intelligence; Independent component analysis; Pattern recognition (psychology); Principal component analysis; Multidimensional analysis; Mathematics; Statistics; Psychology","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":[0.006331936,0.002513171,0.001045223,0.002445824,0.0006679113,0.001426024,0.0008686561,0.000655842,0.009882412],"category_scores_gemma":[0.02291357,0.0004068899,0.002480667,0.002410924,0.0009168722,0.001349764,0.001172204,0.002097128,0.002767564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007766134,"about_ca_system_score_gemma":0.002358858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003578972,"about_ca_topic_score_gemma":0.002768928,"domain_scores_codex":[0.9962389,0.001734602,0.0003041263,0.0006830871,0.0008737437,0.000165428],"domain_scores_gemma":[0.9919092,0.004373802,0.0006145575,0.001218422,0.00168645,0.0001976081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009003154,0.0003935341,0.004826698,0.001007014,0.0006895342,0.0004148643,0.0006793193,0.0780564,0.04443273,0.01853067,0.008493773,0.8415751],"study_design_scores_gemma":[0.00007267837,0.0003992876,0.008673789,0.0001312608,0.0001059184,0.0002819869,0.0002340437,0.9327516,0.0193013,0.02631886,0.01159195,0.0001373988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009617923,0.0002030044,0.988122,0.0001119456,0.00005504984,0.0002156851,0.0003518742,0.001036819,0.0002856862],"genre_scores_gemma":[0.07494186,0.0002370585,0.9223058,0.00003219763,0.00003627133,0.0007926484,0.0007936003,0.000315275,0.0005452627],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009882412,"threshold_uncertainty_score":0.0334869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03403752094021335,"score_gpt":0.3207464084767206,"score_spread":0.2867088875365073,"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."}}