{"id":"W2002035275","doi":"10.1145/2594413.2594424","title":"Understanding the empirical hardness of <i>NP</i> -complete problems","year":2014,"lang":"en","type":"article","venue":"Communications of the ACM","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; 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":[0.01164113,0.00138547,0.00203628,0.002232919,0.002234041,0.006361587,0.004258552,0.004130556,0.006707335],"category_scores_gemma":[0.1392654,0.001498133,0.002121448,0.003090074,0.006184675,0.01456408,0.003426517,0.01234332,0.001259227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004069747,"about_ca_system_score_gemma":0.004109642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003635073,"about_ca_topic_score_gemma":0.003485611,"domain_scores_codex":[0.9903709,0.004107312,0.0005446702,0.002199139,0.00161331,0.001164747],"domain_scores_gemma":[0.7743731,0.1977028,0.007553382,0.01338296,0.003899671,0.003088108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001497625,0.001513964,0.04392542,0.001638278,0.0004206078,0.000415894,0.0008513699,0.3729193,0.003178301,0.3483447,0.1006611,0.1246335],"study_design_scores_gemma":[0.0001244768,0.0001443576,0.005141488,0.0001586911,0.00004357185,0.0002418958,0.000225504,0.4314807,0.001405973,0.5575637,0.003423407,0.00004624586],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4004439,0.01282033,0.4804265,0.04065452,0.001007144,0.0003394906,0.006041906,0.003758011,0.05450807],"genre_scores_gemma":[0.8872576,0.002629099,0.09277765,0.002666331,0.001526863,0.0004224492,0.006772414,0.001209897,0.004737628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01164113,"threshold_uncertainty_score":0.06156492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4601499494803349,"score_gpt":0.3555965632417745,"score_spread":0.1045533862385604,"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."}}