{"id":"W4385764415","doi":"10.24963/ijcai.2023/624","title":"Levin Tree Search with Context Models","year":2023,"lang":"en","type":"article","venue":"","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Context (archaeology); Parameterized complexity; Artificial neural network; Tree (set theory); Convergence (economics); Pipeline (software); Artificial intelligence; Set (abstract data type); Mathematical optimization; Test set; Algorithm; Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001024904,0.0007458018,0.001124093,0.0006512776,0.0004417317,0.0009442492,0.001245085,0.001733334,0.004967541],"category_scores_gemma":[0.007739363,0.0005101215,0.0006260743,0.0008883283,0.0009485377,0.002683719,0.001808856,0.001494241,0.0007242375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001213062,"about_ca_system_score_gemma":0.001702617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004310024,"about_ca_topic_score_gemma":0.007148631,"domain_scores_codex":[0.9993266,0.0002925716,0.00003433524,0.0001351438,0.0001183511,0.00009315048],"domain_scores_gemma":[0.9977836,0.0016987,0.000131134,0.0001836463,0.000128115,0.00007486606],"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":[0.0001523964,0.00005023041,0.001008646,0.00008414033,0.00003123112,0.0001168046,0.00007891041,0.8881654,0.0006551365,0.05772636,0.002576418,0.04935429],"study_design_scores_gemma":[0.00001581897,0.00001881074,0.00003757374,0.000008304933,0.000003851596,0.00001328334,0.000008966475,0.9723949,0.000119477,0.02689571,0.0004798868,0.000003367807],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07885993,0.0008645513,0.9087884,0.0008683648,0.00007787962,0.00008610349,0.0002602184,0.001503563,0.00869096],"genre_scores_gemma":[0.7422461,0.0003727191,0.2489626,0.0005071355,0.00006422513,0.0003189957,0.0005687983,0.0003690172,0.006590412],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004967541,"threshold_uncertainty_score":0.01661807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03640010784762109,"score_gpt":0.2652189778720609,"score_spread":0.2288188700244398,"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."}}