{"id":"W2560513213","doi":"10.1101/091298","title":"Compositional Inductive Biases in Function Learning","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Child and Animal Learning Development","field":"Psychology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Companhia Brasileira de Metalurgia e Mineração; National Science Foundation","keywords":"Principle of compositionality; Computer science; Inductive bias; Artificial intelligence; Predictability; Property (philosophy); Prior probability; Function (biology); Process (computing); Machine learning; Natural language processing; Bayesian probability; Multi-task learning; Mathematics; Task (project management)","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":[0.004570021,0.0003523133,0.0002733563,0.0005526147,0.0001956224,0.001179026,0.0006537184,0.0006000092,0.003091882],"category_scores_gemma":[0.02696734,0.0003079888,0.0003088774,0.000294573,0.002128512,0.002661318,0.00155919,0.001346971,0.000360462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004488848,"about_ca_system_score_gemma":0.0003179477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003271652,"about_ca_topic_score_gemma":0.0003958477,"domain_scores_codex":[0.9977041,0.0009213715,0.0000928849,0.0004693996,0.0006893739,0.0001228748],"domain_scores_gemma":[0.9886851,0.007022161,0.001404266,0.001697658,0.0009421074,0.0002487839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007502973,0.0003143171,0.07068066,0.0006285913,0.0002675797,0.0004094236,0.006222025,0.03373509,0.1876947,0.4156517,0.001850244,0.2817955],"study_design_scores_gemma":[0.00007192085,0.00036654,0.07020877,0.0001375244,0.00008771974,0.0006285666,0.001145865,0.1081369,0.06461635,0.7465504,0.007930368,0.0001191144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7211449,0.0002997472,0.2593158,0.001093831,0.00004339582,0.00004545991,0.0001109751,0.0004514132,0.01749449],"genre_scores_gemma":[0.9800416,0.00007327665,0.01890591,0.0001262235,0.00001368814,0.00002088946,0.00005867529,0.00004969394,0.0007101201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004570021,"threshold_uncertainty_score":0.02416885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02411887202055137,"score_gpt":0.2536443436264952,"score_spread":0.2295254716059438,"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."}}