{"id":"W4392763661","doi":"10.3390/app14062430","title":"Wiener Filter Using the Conjugate Gradient Method and a Third-Order Tensor Decomposition","year":2024,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Ministerul Cercetării, Inovării şi Digitalizării","keywords":"Wiener filter; Conjugate gradient method; Robustness (evolution); Algorithm; Mathematical optimization; Mathematics; Tensor decomposition; Applied mathematics; Third order; Computer science; Tensor (intrinsic definition)","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":[],"consensus_categories":[],"category_scores_codex":[0.0005983232,0.0001099541,0.0001126211,0.00007304434,0.0005421066,0.0002930855,0.0001673782,0.00003466781,0.00005714396],"category_scores_gemma":[0.00001236555,0.00006525018,0.00003576004,0.0004382363,0.0003020591,0.00009713517,0.00005606039,0.0001028753,0.00002759212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001687556,"about_ca_system_score_gemma":0.00002838995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001109869,"about_ca_topic_score_gemma":0.000006219992,"domain_scores_codex":[0.9990712,0.00004269102,0.0001797767,0.0003165056,0.0002037526,0.0001860591],"domain_scores_gemma":[0.9992703,0.0004416498,0.0000445975,0.0001621859,0.00003173872,0.00004957579],"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":[0.000003187479,0.00002842798,0.00001382409,0.00002189639,0.00001775927,0.000001411106,0.001044442,0.0001581467,0.05549112,0.9394897,0.001626907,0.002103232],"study_design_scores_gemma":[0.000230593,0.00004237972,0.0002796896,0.00007251176,0.000132534,0.0001693451,0.001245295,0.1315564,0.01571139,0.8374065,0.01280491,0.0003484328],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6390189,0.0002792046,0.33606,0.006091026,0.0002085188,0.000687765,0.00001861528,0.0003014684,0.01733452],"genre_scores_gemma":[0.8475498,0.00001000864,0.1514083,0.000712717,0.00006916784,0.00006859022,0.000001758892,0.00001090603,0.0001687966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2085309,"threshold_uncertainty_score":0.4169501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07959954090645134,"score_gpt":0.3947157876944449,"score_spread":0.3151162467879935,"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."}}