{"id":"W2578999369","doi":"10.1609/icaps.v26i1.13752","title":"Efficient Representation of Pattern Databases Using Acyclic Random Hypergraphs","year":2016,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Heuristic; Computer science; Representation (politics); Abstraction; Benchmark (surveying); Database; Domain (mathematical analysis); Theoretical computer science; Table (database); Algorithm; Data mining; Mathematics; Artificial intelligence","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":[0.000415625,0.000136935,0.0001981794,0.0001898069,0.0001221521,0.00008868357,0.0006701426,0.0000443348,0.000006685181],"category_scores_gemma":[0.0003335743,0.00008599684,0.00006092417,0.0002186122,0.00007675692,0.0002031133,0.0002236753,0.0001106723,0.000001125667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002161423,"about_ca_system_score_gemma":0.00004665323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005477079,"about_ca_topic_score_gemma":2.719469e-7,"domain_scores_codex":[0.9987362,0.00001943834,0.0003543096,0.0003056737,0.0004216534,0.0001626948],"domain_scores_gemma":[0.9988047,0.0002548937,0.0004520065,0.000126742,0.0003163631,0.00004523677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001921203,0.0001049535,0.1694002,0.0001177873,0.0001716155,0.000003766351,0.001997368,0.02188736,0.764654,0.03142191,0.0001300333,0.009918833],"study_design_scores_gemma":[0.0006739466,0.00003388351,0.003392031,0.001984715,0.0000136469,0.00001936524,0.0001675659,0.909173,0.08389173,0.0005223148,0.00000636682,0.0001214385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9629533,0.00005545451,0.03491813,0.0008775318,0.0002457825,0.00009660408,0.00001682204,0.000155228,0.0006811705],"genre_scores_gemma":[0.9884077,0.000009048002,0.01147409,0.00005032058,0.00002457988,0.00000380685,0.000002020114,0.00000721149,0.00002122515],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8872856,"threshold_uncertainty_score":0.3506849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06536379554403488,"score_gpt":0.3140520566603513,"score_spread":0.2486882611163164,"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."}}