{"id":"W2140140973","doi":"10.1109/dmcc.1990.555433","title":"Conjugate Gradient Methods for Spline Collocation Equations","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Conjugate gradient method; Spline (mechanical); Collocation (remote sensing); Computer science; Collocation method; Applied mathematics; Mathematics; Mathematical analysis; Algorithm; Differential equation; Engineering; Ordinary differential equation; Mechanical engineering","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":[0.0002966324,0.00008680143,0.0001200225,0.00004842901,0.00004753876,0.00001182889,0.00007126547,0.0000302338,0.0000407409],"category_scores_gemma":[0.0004971008,0.0000834738,0.0000404353,0.0001534689,0.00001731225,0.00008569087,0.00001050494,0.00004751364,0.00002880482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000801425,"about_ca_system_score_gemma":0.000007325465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.471113e-7,"about_ca_topic_score_gemma":8.444866e-7,"domain_scores_codex":[0.9994471,0.00002514477,0.0002421373,0.00009623777,0.00006089594,0.0001285042],"domain_scores_gemma":[0.9979904,0.001734408,0.0000276293,0.0001168021,0.00008226639,0.00004850205],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001809081,0.00001860531,5.623243e-7,0.00002936466,0.00001431563,2.113791e-8,0.00006169738,0.5830448,0.001952118,0.1284844,0.0002215514,0.2861708],"study_design_scores_gemma":[0.0001409729,0.00001599376,0.000005821598,0.000004972039,0.00001009405,9.033089e-7,0.00001361494,0.8445723,0.008799947,0.125098,0.02124607,0.00009129341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001613057,0.00009954832,0.9963726,0.0003635296,0.0002082498,0.0003156978,0.000004453498,0.0003229149,0.002151673],"genre_scores_gemma":[0.01966888,0.000008478631,0.9796246,0.0001303878,0.000122166,0.0001388491,0.00001062733,0.00002548384,0.0002705386],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2860795,"threshold_uncertainty_score":0.3403963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05189543406151986,"score_gpt":0.4127076224574519,"score_spread":0.360812188395932,"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."}}