{"id":"W2096249340","doi":"10.1109/ipdps.2004.1303284","title":"A parallel QR factorization algorithm for solving toeplitz tridiagonal systems","year":2004,"lang":"en","type":"article","venue":"","topic":"Matrix Theory and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Toeplitz matrix; Tridiagonal matrix; QR decomposition; Computer science; Factorization; Scalability; Algorithm; Speedup; Parallel computing; Matrix decomposition; Parallel algorithm; Linear system; Mathematics; Eigenvalues and eigenvectors; Pure mathematics","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":[0.0003307202,0.0001345239,0.000160158,0.00007015574,0.0001772392,0.0002563489,0.0004830826,0.00006475369,0.00001175103],"category_scores_gemma":[0.00002927798,0.0001139552,0.00008482246,0.0002097268,0.00001670184,0.0005448859,0.00007900441,0.00006232507,0.00003763658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006258402,"about_ca_system_score_gemma":0.00009415279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003740347,"about_ca_topic_score_gemma":0.000001200502,"domain_scores_codex":[0.9989129,0.00002247242,0.0002372875,0.0003342501,0.0002008913,0.0002921798],"domain_scores_gemma":[0.99932,0.0001175103,0.00007576324,0.0002993782,0.00009091407,0.00009645849],"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":[0.000003387862,0.00005759129,0.000004790953,0.00002203735,0.00001577559,0.000003826414,0.0002324055,0.003364071,0.0001806155,0.942988,0.000181499,0.05294603],"study_design_scores_gemma":[0.002016369,0.0002090971,0.00005258459,0.00004224656,0.000009649861,0.00003702445,0.00009566679,0.8489993,0.0029271,0.1364698,0.008701256,0.0004398827],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000181062,0.0001244421,0.9970822,0.0002582877,0.0009931462,0.0004002065,0.000009748269,0.0002726811,0.0006782042],"genre_scores_gemma":[0.1164985,0.0000150493,0.8804888,0.0002084923,0.000596711,0.0001357867,0.00001972861,0.00001788568,0.002019104],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8456353,"threshold_uncertainty_score":0.4646957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01794944172908531,"score_gpt":0.2472664800805891,"score_spread":0.2293170383515038,"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."}}