{"id":"W2947517210","doi":"10.48550/arxiv.1905.11346","title":"Error Analysis and Correction for Weighted A*'s Suboptimality (Extended Version)","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Heuristics; Variety (cybernetics); Upper and lower bounds; Mathematical optimization; Scale (ratio); Mathematics; Branch and bound; Computer science; Algorithm; Statistics; Mathematical analysis; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046569,0.0002848858,0.0004367472,0.0005061157,0.0002659849,0.0001479628,0.0007943272,0.0003533419,0.00002819023],"category_scores_gemma":[0.00003537192,0.0003245556,0.0002865324,0.0009104944,0.0000591912,0.0003266241,0.0007575931,0.0004601963,0.00003202957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001491405,"about_ca_system_score_gemma":0.0001566892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003793507,"about_ca_topic_score_gemma":0.00005223133,"domain_scores_codex":[0.9980489,0.0001558817,0.0001880152,0.001195174,0.00008823932,0.000323806],"domain_scores_gemma":[0.998078,0.0003580987,0.0002998457,0.0009226088,0.0001946935,0.0001467126],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003139492,0.0001540251,0.08508074,0.0002967965,0.001692974,0.00008678615,0.0005919242,0.8884706,0.00003173753,0.01607443,0.00474041,0.002465591],"study_design_scores_gemma":[0.0004444495,0.00008091718,0.009194246,0.00004939852,0.0006014791,0.000001420414,0.00003979922,0.9834609,0.00007277179,0.005084705,0.0005856349,0.0003842542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2042001,0.00005050617,0.793574,0.00009818739,0.0009091173,0.0003006204,0.0000490015,0.0002122446,0.0006062311],"genre_scores_gemma":[0.988817,0.00003911476,0.008031858,0.00007797574,0.00003856162,0.000001428095,0.0001643304,0.00001190579,0.002817781],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7855422,"threshold_uncertainty_score":0.9999207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04793379003341202,"score_gpt":0.2005591049034761,"score_spread":0.152625314870064,"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."}}