{"id":"W4400676397","doi":"10.1007/978-3-031-65112-0_1","title":"Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Inductive reasoning; Artificial intelligence; Inductive method; Inductive bias; Theoretical computer science; Mathematics education; Multi-task learning; Teaching method; 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":[0.0005577671,0.001024837,0.0007031213,0.0009591362,0.001159837,0.001989365,0.002902316,0.001049722,0.03438015],"category_scores_gemma":[0.003974208,0.0005117979,0.0008266325,0.002019327,0.001261776,0.003351695,0.002563092,0.002191274,0.005277863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001157576,"about_ca_system_score_gemma":0.00215787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004463324,"about_ca_topic_score_gemma":0.01027823,"domain_scores_codex":[0.9994138,0.0001310137,0.00003756977,0.0001677676,0.0001855695,0.00006419689],"domain_scores_gemma":[0.9987146,0.0007581497,0.00005366742,0.0002286956,0.0001832831,0.00006171989],"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":[0.0002598215,0.000157056,0.0004370601,0.0004020297,0.00003499695,0.0001477183,0.000243567,0.04774734,0.003993663,0.2382497,0.02864932,0.6796778],"study_design_scores_gemma":[0.0000941714,0.00004558446,0.0001653404,0.00005701759,0.00002561425,0.0001187287,0.0001445318,0.5629736,0.01040787,0.3923663,0.03357866,0.00002255876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009201641,0.0002972645,0.9547471,0.0003292412,0.0001398856,0.0001097573,0.0004057286,0.004444485,0.03032487],"genre_scores_gemma":[0.1294457,0.0002660924,0.8487033,0.0001731569,0.0001332108,0.0001897752,0.001219355,0.0009004173,0.01896903],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03438015,"threshold_uncertainty_score":0.115013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006533803507163764,"score_gpt":0.2260423456541138,"score_spread":0.21950854214695,"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."}}