{"id":"W2239461856","doi":"10.5539/mas.v10n1p154","title":"Mean Field Theory in Doing Logic Programming Using Hopfield Network","year":2015,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universiti Sains Malaysia","keywords":"Maxima and minima; Artificial neural network; Computer science; NetLogo; Field (mathematics); Hopfield network; Boltzmann machine; Artificial intelligence; Logic programming; Representation (politics); Theoretical computer science; Algorithm; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001461407,0.0001320983,0.0001404003,0.00009522961,0.0003009527,0.0003260311,0.001406308,0.00005271383,0.000002335829],"category_scores_gemma":[0.00002007514,0.0001163869,0.00002713992,0.001507077,0.0001564676,0.0003982427,0.0005362416,0.0002196637,0.00001141408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006909867,"about_ca_system_score_gemma":0.0001536912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003131814,"about_ca_topic_score_gemma":0.00003472263,"domain_scores_codex":[0.9981495,0.00002834149,0.0002148255,0.0005915394,0.0004060696,0.0006097196],"domain_scores_gemma":[0.9989901,0.0001199862,0.00008145371,0.0005754239,0.00004970499,0.0001833066],"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":[0.000007791227,0.00004143385,0.0002281822,0.000003317349,0.000001438982,0.000007531071,0.001153338,0.05890901,0.008665673,0.5712522,0.00009040607,0.3596396],"study_design_scores_gemma":[0.0001280425,0.00002066298,0.00003619588,0.0000140816,0.000001575929,0.00000748161,0.00005022415,0.8104204,0.0007915071,0.1878141,0.0005522253,0.0001634529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01187831,0.00009874947,0.9782913,0.0003850485,0.0001636876,0.0002750653,9.082133e-8,0.0001286504,0.00877912],"genre_scores_gemma":[0.8079746,0.000001939398,0.190954,0.0009170579,0.00009224197,0.00003105594,1.860057e-7,0.000005265455,0.00002365451],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7960963,"threshold_uncertainty_score":0.4746121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04810519078137467,"score_gpt":0.2859747937949412,"score_spread":0.2378696030135666,"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."}}