{"id":"W2155259264","doi":"10.1109/iembs.1990.691251","title":"A New Regularization Method Applied To Regression Problems In Electrocardiography","year":2005,"lang":"en","type":"article","venue":"","topic":"Statistical and numerical algorithms","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Regularization (linguistics); Inverse problem; Inverse; Mathematics; Applied mathematics; Regression; Linear regression; Matrix (chemical analysis); Algorithm; Regression analysis; Mathematical optimization; Computer science; Statistics; Artificial intelligence; Mathematical analysis","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":[0.0002202781,0.0001225728,0.0002345445,0.0001530305,0.00002758892,0.00001927515,0.00009763456,0.00006639606,0.000279299],"category_scores_gemma":[0.0001038356,0.00008635572,0.00005248428,0.0008364703,0.000006631597,0.00004116448,0.00003453738,0.000105044,0.00007938826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003095931,"about_ca_system_score_gemma":0.00001621824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002805104,"about_ca_topic_score_gemma":0.00002031134,"domain_scores_codex":[0.9989813,0.00004255688,0.000258739,0.000249312,0.000221732,0.0002463387],"domain_scores_gemma":[0.9994175,0.0002047683,0.00003640209,0.0001656655,0.00002302104,0.0001526355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004648799,0.0001259137,0.00009734403,0.00002963882,0.00002129076,0.000001291082,0.0003120867,0.0001463907,0.004425428,0.3523244,0.02130989,0.6211599],"study_design_scores_gemma":[0.0007241557,0.0001179139,0.000880439,0.00005216202,0.00002325001,0.000004797266,0.00002537274,0.00486677,0.01148745,0.9672968,0.01423084,0.000290068],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001825536,0.0000194295,0.9782949,0.001004278,0.00001849346,0.0003738078,9.077953e-7,0.0001071394,0.01999846],"genre_scores_gemma":[0.008481826,0.000004945124,0.9881805,0.0003751706,0.0001114716,0.00004609246,0.00000395584,0.00001768591,0.002778406],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6208698,"threshold_uncertainty_score":0.3521484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02399841455348571,"score_gpt":0.3262989955492875,"score_spread":0.3023005809958018,"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."}}