{"id":"W4293576324","doi":"10.1038/s41467-022-32012-w","title":"Synthesizing theories of human language with Bayesian program induction","year":2022,"lang":"en","type":"article","venue":"Nature Communications","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université du Québec; Mila - Quebec Artificial Intelligence Institute","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; National Science Foundation","keywords":"Computer science; Bayesian probability; Computational biology; Data science; Artificial intelligence; Biology","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":[0.00576153,0.001016679,0.001152435,0.002641981,0.001017609,0.002797006,0.0032595,0.001177729,0.002773598],"category_scores_gemma":[0.02975996,0.000996142,0.002276098,0.001394801,0.002346277,0.005322844,0.003000228,0.003072894,0.0008586636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001994555,"about_ca_system_score_gemma":0.002428015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003543936,"about_ca_topic_score_gemma":0.007109305,"domain_scores_codex":[0.9968653,0.001399495,0.0001695602,0.0006706488,0.0007819968,0.0001129913],"domain_scores_gemma":[0.9820225,0.01462134,0.0007369573,0.001559027,0.0008707976,0.000189314],"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":[0.000269678,0.0003561553,0.007817294,0.0007288482,0.0004331717,0.0002665687,0.001580887,0.3570413,0.007753395,0.1984751,0.007041076,0.4182366],"study_design_scores_gemma":[0.00002538783,0.00002292287,0.0002326879,0.00004378439,0.00003423494,0.00003401108,0.00007974327,0.7697648,0.002621924,0.2248245,0.00229786,0.00001812129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01355796,0.0001507089,0.981898,0.0005864553,0.00001607825,0.00007445412,0.0002545688,0.002280485,0.001181243],"genre_scores_gemma":[0.2209399,0.0002912104,0.7750373,0.0003497045,0.00006001304,0.0003138985,0.001500987,0.0005665636,0.0009403828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00576153,"threshold_uncertainty_score":0.03047025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01291467586665698,"score_gpt":0.3204863804193879,"score_spread":0.3075717045527309,"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."}}