{"id":"W4288769727","doi":"10.3934/fods.2022014","title":"Statistical inference for persistent homology applied to simulated fMRI time series data","year":2022,"lang":"en","type":"article","venue":"Foundations of Data Science","topic":"Topological and Geometric Data Analysis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Persistent homology; Topological data analysis; Inference; Statistical inference; Computer science; Time series; Series (stratigraphy); Statistical hypothesis testing; Conditional independence; Data mining; Artificial intelligence; Machine learning; Algorithm; Mathematics; Statistics; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.01704321,0.0003699936,0.001037795,0.002081792,0.0007861766,0.0012855,0.001942486,0.001379292,0.001678752],"category_scores_gemma":[0.118531,0.0004310316,0.0009039178,0.001234392,0.003208864,0.002669144,0.002570922,0.001806761,0.0001493064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001148628,"about_ca_system_score_gemma":0.001619313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002152243,"about_ca_topic_score_gemma":0.001473485,"domain_scores_codex":[0.9912101,0.006330448,0.0003348227,0.001001489,0.0008448924,0.0002782667],"domain_scores_gemma":[0.7955059,0.1875094,0.005648932,0.007422945,0.00263325,0.001279591],"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":[0.0006598599,0.0002490759,0.02932281,0.0001832422,0.0003549769,0.0003332379,0.0004838484,0.6787398,0.004440066,0.2144174,0.0008086161,0.07000704],"study_design_scores_gemma":[0.00001480703,0.00007180744,0.001245892,0.000006326879,0.00000883632,0.00002961142,0.00002097488,0.9581091,0.0006663367,0.03969556,0.0001210603,0.000009694134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1172136,0.00007668226,0.8817478,0.0001576963,0.00001665063,0.00005726211,0.00007638549,0.0002646781,0.0003890119],"genre_scores_gemma":[0.8836926,0.00007670744,0.115262,0.00006670867,0.00004558173,0.0002247106,0.0002959715,0.00007024451,0.0002654354],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01704321,"threshold_uncertainty_score":0.09013414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08636553521497028,"score_gpt":0.3541447604522728,"score_spread":0.2677792252373026,"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."}}