{"id":"W3035799148","doi":"10.1609/aaai.v34i10.7227","title":"LGML: Logic Guided Machine Learning (Student Abstract)","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Counterexample; Consistency (knowledge bases); Function (biology); Algebraic expression; Phase (matter); Process (computing); Algorithm; Machine learning; Algebraic number; Mathematics; Programming language; Discrete mathematics","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":[0.002372466,0.0008720746,0.0005749048,0.001099104,0.0004837566,0.002474653,0.002958175,0.001668123,0.02013792],"category_scores_gemma":[0.009009409,0.0005237787,0.001098675,0.001079626,0.001846856,0.002975061,0.003391615,0.002893004,0.00728317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001132765,"about_ca_system_score_gemma":0.001318274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001615147,"about_ca_topic_score_gemma":0.002485389,"domain_scores_codex":[0.9981211,0.0007054398,0.0001063231,0.000359443,0.0006076152,0.0001000343],"domain_scores_gemma":[0.997008,0.001538012,0.0001852424,0.0006540717,0.0004977937,0.00011681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002592757,0.0002315179,0.001088181,0.0004586074,0.00009394353,0.0003123806,0.0002412011,0.08836792,0.005879763,0.2161557,0.06243379,0.6244777],"study_design_scores_gemma":[0.00007284513,0.00007702092,0.0001692692,0.0000979856,0.00001910879,0.0001238609,0.0000270567,0.6655113,0.007494788,0.2794955,0.04688374,0.00002750942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001676854,0.0002345538,0.9806721,0.0008983001,0.0001258408,0.00006175548,0.0002900001,0.0117031,0.004337475],"genre_scores_gemma":[0.09701785,0.000313191,0.8909994,0.001614978,0.0002324398,0.0002891924,0.001324757,0.001317131,0.006891044],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02013792,"threshold_uncertainty_score":0.06736809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1503808773953212,"score_gpt":0.3190635613215488,"score_spread":0.1686826839262275,"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."}}