{"id":"W2012030515","doi":"10.1006/jcph.2000.6580","title":"Convolution–Thresholding Methods for Interface Motion","year":2001,"lang":"en","type":"article","venue":"Journal of Computational Physics","topic":"Cellular Automata and Applications","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Curvature; Thresholding; Convolution (computer science); Computer science; Cellular automaton; Motion (physics); Interface (matter); Computation; Artificial intelligence; Algorithm; Mathematics; Geometry; Artificial neural network","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":[0.0003931217,0.00007230768,0.0001328514,0.0000600405,0.0001086031,0.00008995183,0.0003612376,0.00002324204,0.000003068837],"category_scores_gemma":[0.00003114912,0.00006899819,0.0001257957,0.0002601312,0.00002348232,0.0005311792,0.00004816891,0.000091293,0.000006743282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005090851,"about_ca_system_score_gemma":0.00007485539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.380616e-7,"about_ca_topic_score_gemma":5.299162e-8,"domain_scores_codex":[0.9992592,0.00004416565,0.0003116588,0.0001056946,0.000175078,0.0001041757],"domain_scores_gemma":[0.9987442,0.0003146984,0.0003129452,0.0001147848,0.0004587979,0.0000545079],"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":[0.00001139868,0.0001531132,0.00005468,0.00001108006,0.0000476573,0.000001461736,0.0002487774,0.3398112,0.004468766,0.3634254,0.001428942,0.2903375],"study_design_scores_gemma":[0.0002762606,0.0000421649,0.0004748472,0.00001120653,0.000009258102,0.00006570916,0.000009261543,0.6207116,0.002109727,0.368899,0.007330778,0.00006018212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005735321,0.0001480338,0.9925386,0.001063573,0.0002540443,0.00009161622,0.00000144199,0.0000269786,0.0001404334],"genre_scores_gemma":[0.462513,0.000004788079,0.5371837,0.00009017333,0.0001724344,0.000002915222,0.000003032262,0.000004308788,0.00002564952],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4567777,"threshold_uncertainty_score":0.2813665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04060905330121274,"score_gpt":0.3770059370644711,"score_spread":0.3363968837632584,"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."}}