{"id":"W82508868","doi":"","title":"A numerical approximation to the solution of a nonlocal obstacle thermistor problem","year":2001,"lang":"en","type":"book","venue":"Nova Science Publishers, Inc. eBooks","topic":"Numerical methods in inverse problems","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Thermistor; Piecewise; Uniqueness; Mathematics; Obstacle problem; Obstacle; Norm (philosophy); Piecewise linear function; Triangulation; Space (punctuation); Mathematical analysis; Applied mathematics; Mathematical optimization; Computer science; Geometry; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004273295,0.0002947114,0.0005204049,0.0001939165,0.0001924841,0.0004443219,0.0005792276,0.0007259632,0.002597434],"category_scores_gemma":[0.0009618157,0.0001936449,0.0002833134,0.000222039,0.0006174939,0.0006655859,0.0007473176,0.0008487703,0.0005625027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001788052,"about_ca_system_score_gemma":0.0004985685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007310256,"about_ca_topic_score_gemma":0.0006461507,"domain_scores_codex":[0.9998779,0.00004199387,0.000003794505,0.00001549685,0.00005220966,0.000008587137],"domain_scores_gemma":[0.9998204,0.00009924947,0.00001781477,0.00002196435,0.00002554164,0.0000151233],"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":[0.0001634997,0.0001065902,0.0006965632,0.0005671071,0.00002668975,0.0002725461,0.0002615711,0.4262353,0.04489193,0.4576683,0.002970111,0.06613968],"study_design_scores_gemma":[0.00001936756,0.00006032524,0.0001370695,0.00002599247,0.000004357926,0.00004666662,0.00001964119,0.9747964,0.002848098,0.01563107,0.006403816,0.00000728943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03013894,0.0004911118,0.9610851,0.0002036993,0.0001626931,0.00003624308,0.0000371257,0.0001073938,0.007737787],"genre_scores_gemma":[0.2930392,0.001277273,0.6705278,0.0001408092,0.0001043636,0.0002937797,0.0001808745,0.0001338311,0.03430215],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002597434,"threshold_uncertainty_score":0.008689225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.106512835935542,"score_gpt":0.3446534117757165,"score_spread":0.2381405758401745,"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."}}