{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000527944,0.0003023311,0.0004449289,0.0004985045,0.0005139599,0.001098005,0.0007543223,0.0007250541,0.002877098],"category_scores_gemma":[0.001027155,0.0002228734,0.0006456966,0.0005511191,0.001072644,0.001546687,0.0006380575,0.0009746677,0.0003988671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008498552,"about_ca_system_score_gemma":0.0008426576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002873915,"about_ca_topic_score_gemma":0.002325497,"domain_scores_codex":[0.9998085,0.00005726744,0.0000116705,0.00004084114,0.00006243539,0.00001930719],"domain_scores_gemma":[0.9997918,0.0001214541,0.00002061998,0.00001596041,0.00003408732,0.00001612282],"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":[0.00004540955,0.00003787974,0.0006896461,0.0001537834,0.00003937435,0.0001476373,0.000176108,0.3939677,0.003264725,0.5408877,0.001583652,0.05900643],"study_design_scores_gemma":[0.000009023742,0.00001768943,0.00009088322,0.00001484744,0.000009380872,0.00003132506,0.00001134549,0.7548394,0.0006105804,0.2418336,0.002520751,0.00001118072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01078849,0.0007576938,0.9779229,0.0005419039,0.00005782419,0.00002821267,0.00003482162,0.0001435624,0.009724631],"genre_scores_gemma":[0.5934747,0.002067474,0.3868779,0.0003539346,0.0001743092,0.000245674,0.0001220534,0.0001068091,0.01657719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002877098,"threshold_uncertainty_score":0.009624839,"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."}}