{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008969274,0.0005359741,0.0007254204,0.001800661,0.0004720892,0.00170875,0.001737116,0.000691624,0.002395391],"category_scores_gemma":[0.006067924,0.0004716359,0.0007681795,0.003088803,0.000515153,0.002867952,0.001312305,0.00103301,0.0006040827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009105257,"about_ca_system_score_gemma":0.001413467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003180383,"about_ca_topic_score_gemma":0.003927747,"domain_scores_codex":[0.9987615,0.0004173851,0.0001163382,0.0002407154,0.0003793067,0.00008477485],"domain_scores_gemma":[0.9970154,0.001182538,0.0002300384,0.001137033,0.0003652311,0.00006989702],"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":[0.000263312,0.0001558911,0.001994475,0.0003307993,0.00008423055,0.0003874095,0.0004105917,0.4092803,0.01112861,0.1142259,0.009314467,0.4524241],"study_design_scores_gemma":[0.00003081028,0.00004808115,0.0002635204,0.00002580313,0.00002142227,0.0001617422,0.00009446729,0.9208171,0.00630741,0.06596977,0.006239705,0.00002007132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02133922,0.0001951062,0.9735059,0.0002025355,0.00001907587,0.0001565546,0.0006623243,0.002318335,0.001600981],"genre_scores_gemma":[0.2022601,0.0003574377,0.7915065,0.0001179327,0.00001788246,0.0004314142,0.00297111,0.0003142746,0.002023243],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003180383,"threshold_uncertainty_score":0.008013368,"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."}}