{"id":"W2979603833","doi":"10.1109/usnc-ursi.2019.8861703","title":"Analytical Determinant of the Noise Parameter Extraction Matrix and Its Applications","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Extraction (chemistry); Noise (video); Computer science; Matrix (chemical analysis); Algorithm; Artificial intelligence; Materials science","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":[0.0009152994,0.0009045269,0.0005020308,0.00113917,0.0003428931,0.001154448,0.0007370482,0.0006942218,0.007695071],"category_scores_gemma":[0.005149422,0.000371004,0.0005151812,0.000777379,0.0007956814,0.001155791,0.001094464,0.0009495445,0.002593889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006947265,"about_ca_system_score_gemma":0.0006948284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001202207,"about_ca_topic_score_gemma":0.001248989,"domain_scores_codex":[0.9995253,0.0001079767,0.00002315977,0.00006740292,0.0002360043,0.00004020616],"domain_scores_gemma":[0.9989069,0.000545274,0.0001005441,0.0001076569,0.0003023848,0.00003739168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001043382,0.00008324219,0.001034173,0.0003354368,0.00003836984,0.0007576885,0.0003979773,0.2632152,0.04546387,0.5701801,0.007068744,0.1113209],"study_design_scores_gemma":[0.00001276503,0.00004021409,0.0005047295,0.00006625151,0.00001328171,0.0005460311,0.00005759507,0.8500612,0.009633013,0.1270323,0.01197333,0.00005917462],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008450504,0.0004087219,0.9800903,0.0002188605,0.00006641696,0.00002835361,0.00007705914,0.0002838942,0.01037587],"genre_scores_gemma":[0.4186441,0.001782138,0.5571755,0.0004018954,0.0002363097,0.000192079,0.0003832693,0.0007544272,0.02043038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007695071,"threshold_uncertainty_score":0.02574259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00704065946301074,"score_gpt":0.2642819446020855,"score_spread":0.2572412851390748,"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."}}