{"id":"W7163682816","doi":"10.13182/t130-32213","title":"Relaxation of Quasi-Static Approach via Polynomial Interpolation in the Predictor Corrector Quasi-Static Method","year":2020,"lang":"","type":"article","venue":"Transactions of the American Nuclear Society","topic":"Iterative Methods for Nonlinear Equations","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Predictor–corrector method; Relaxation (psychology); Polynomial; Interpolation (computer graphics); Polynomial interpolation; Linear interpolation","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002035219,0.0004645655,0.001089323,0.0001177153,0.0003927384,0.00006285214,0.001020011,0.0001575822,0.0002103519],"category_scores_gemma":[0.0008193916,0.0003498407,0.001194194,0.002451866,0.001232573,0.0003374055,0.00005102629,0.001164025,0.0000127462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002387434,"about_ca_system_score_gemma":0.000247283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007625374,"about_ca_topic_score_gemma":0.00004356997,"domain_scores_codex":[0.9928747,0.003648664,0.001675185,0.0005319763,0.0008540102,0.0004154919],"domain_scores_gemma":[0.9935031,0.002882842,0.002292245,0.0009321709,0.0002749814,0.0001146381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001361841,0.007520875,0.0008780396,0.002710577,0.002043623,9.943484e-7,0.8865105,0.01237496,0.02948962,0.002101539,0.003116009,0.05189145],"study_design_scores_gemma":[0.001046109,0.0013204,0.001404863,0.0001538314,0.000947671,0.000009847132,0.06501559,0.9279941,0.0005147518,0.001024601,0.0002410431,0.0003272255],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1116614,0.00003238444,0.8822069,0.003711109,0.0002653226,0.001572799,0.0003540928,0.00005185783,0.000144107],"genre_scores_gemma":[0.6063194,0.00003596826,0.3929082,0.0004841082,0.00008042183,0.00003261657,0.00001227802,0.00008130883,0.00004576549],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9156191,"threshold_uncertainty_score":0.9998953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0490701655524008,"score_gpt":0.3312509777917575,"score_spread":0.2821808122393567,"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."}}