{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002446787,0.00006858125,0.00008062054,0.00008299865,0.00008393336,0.0001119458,0.0004536027,0.00001868291,0.00003195967],"category_scores_gemma":[0.000005916145,0.00004557635,0.00002230723,0.0005185753,0.00002069854,0.0002188863,0.000153619,0.0001235783,0.0005334127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007148203,"about_ca_system_score_gemma":0.00003789999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002283243,"about_ca_topic_score_gemma":0.00004491967,"domain_scores_codex":[0.9991897,0.00004166957,0.00006721575,0.0002349885,0.0002393005,0.0002270751],"domain_scores_gemma":[0.999514,0.00006266671,0.00001125104,0.0003092216,0.00003462806,0.00006818759],"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":[0.000004379691,0.00002381425,0.0005790567,0.000009210266,0.00001512245,0.00006090269,0.001331583,0.02398925,0.0000534857,0.2169649,0.007841998,0.7491263],"study_design_scores_gemma":[0.0001945643,0.00007809782,0.0007608613,0.000007129617,6.6106e-7,0.000007771899,0.00007387692,0.9948013,0.0001660041,0.001614625,0.002209992,0.00008504979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01710118,0.00001822753,0.9235348,0.004312054,0.00006831863,0.00006474569,4.289783e-7,0.0008132506,0.05408695],"genre_scores_gemma":[0.9421256,0.00000488812,0.02233523,0.000343857,0.00004248008,0.000005988484,0.000001606196,0.000007515946,0.03513283],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9708121,"threshold_uncertainty_score":0.6856119,"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."}}