{"id":"W4247822328","doi":"10.32920/ryerson.14665632.v1","title":"Under-Determined Blind Source Separation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Initialization; Blind signal separation; Cluster analysis; Matching pursuit; Matching (statistics); Algorithm; Computer science; Set (abstract data type); Matrix (chemical analysis); Mixing (physics); Segmentation; Pattern recognition (psychology); Source separation; Mathematics; Artificial intelligence; Statistics; Compressed sensing; Channel (broadcasting)","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004825884,0.000309966,0.0003380964,0.0002361281,0.00009348719,0.001491547,0.001328639,0.0004518043,0.0000898485],"category_scores_gemma":[0.0000390229,0.0003151428,0.000194514,0.0002931908,0.00003405192,0.0005113451,0.002148754,0.0006172845,0.00006345758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008732428,"about_ca_system_score_gemma":0.0004697758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007047503,"about_ca_topic_score_gemma":0.00008536372,"domain_scores_codex":[0.997703,0.0002452829,0.0004522294,0.0008940688,0.0004557141,0.0002497755],"domain_scores_gemma":[0.9976975,0.00009171999,0.0002547163,0.001593058,0.000255174,0.0001078622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000782906,0.00153315,0.0003734287,0.0005549821,0.0006378087,0.0001630238,0.02800129,0.08781236,0.01427025,0.6114627,0.05734613,0.1977666],"study_design_scores_gemma":[0.001159335,0.0001929921,0.0008896727,0.0002693716,0.00005424801,0.00008737531,0.0003440668,0.8045951,0.1020814,0.06328125,0.02478231,0.002262895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01858159,0.0000907854,0.9609914,0.002952434,0.0004180407,0.0003924968,0.000001135723,0.00154487,0.01502727],"genre_scores_gemma":[0.6781442,0.00004556411,0.3100475,0.003223432,0.0001371451,0.000109938,0.00009314719,0.00003028421,0.008168863],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7167827,"threshold_uncertainty_score":0.9999301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0444701577229451,"score_gpt":0.3316105414226108,"score_spread":0.2871403836996657,"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."}}