{"id":"W2790424815","doi":"10.1007/s10915-018-0693-y","title":"A Second Order Energy Stable Linear Scheme for a Thin Film Model Without Slope Selection","year":2018,"lang":"en","type":"article","venue":"Journal of Scientific Computing","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Division of Mathematical Sciences; Higher Education Discipline Innovation Project; Shanghai University of Finance and Economics; National Natural Science Foundation of China","keywords":"Mathematics; Nabla symbol; Regularization (linguistics); Convergence (economics); Nonlinear system; Finite element method; Mathematical analysis; Energy (signal processing); Order (exchange); Stability (learning theory); Scheme (mathematics); Applied mathematics; Physics; Quantum mechanics","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":[0.001083802,0.0001302529,0.0002511545,0.0001972887,0.000259287,0.0001132369,0.0002118598,0.0000497501,0.00003417543],"category_scores_gemma":[0.0003819099,0.0001189709,0.0000787263,0.0006027725,0.00008363348,0.0002455743,0.0000491308,0.0001786601,0.000003570318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001120118,"about_ca_system_score_gemma":0.0001047291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.206386e-7,"about_ca_topic_score_gemma":0.000001851943,"domain_scores_codex":[0.9987016,0.00002684122,0.0005428605,0.0001611603,0.0003109418,0.0002566182],"domain_scores_gemma":[0.9982175,0.0003264701,0.0002666932,0.000108484,0.0009981398,0.00008270275],"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":[0.00001393685,0.00002313252,0.0000112272,0.00006717846,0.00003455321,4.836342e-7,0.0002812521,0.9797304,0.009732944,0.001310823,0.002407917,0.006386098],"study_design_scores_gemma":[0.0002640286,0.00006857805,0.00000472004,0.00008744858,0.00001124745,0.00003828289,0.00003565578,0.9596923,0.008541633,0.02399286,0.007133896,0.0001293667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0644343,0.00005041614,0.9335021,0.00002897435,0.001346697,0.00007102294,0.000003625007,0.0000653466,0.0004975034],"genre_scores_gemma":[0.2035913,8.432528e-7,0.795432,0.00004214561,0.0004619103,0.00000113202,9.798634e-7,0.00002663979,0.0004429727],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.139157,"threshold_uncertainty_score":0.4851491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0351854010327458,"score_gpt":0.3174500642585721,"score_spread":0.2822646632258263,"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."}}