{"id":"W4404735027","doi":"10.2139/ssrn.5035177","title":"Continuous Learning of Event-Based Systems by Using Pre-Conceptual Schemas","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Event (particle physics); Computer science; Conceptual model; Physics; Database","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003865648,0.0004540544,0.0006857304,0.0003061205,0.0002693593,0.0004264831,0.001304893,0.0004081434,0.000006302821],"category_scores_gemma":[0.000109395,0.0004354851,0.0003423197,0.0003079437,0.00009428107,0.000155245,0.0006433995,0.009147663,0.00001265188],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001094061,"about_ca_system_score_gemma":0.008868711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000470901,"about_ca_topic_score_gemma":0.00001120736,"domain_scores_codex":[0.9949605,0.0005082723,0.0008830175,0.0006784463,0.0007056894,0.00226401],"domain_scores_gemma":[0.9978943,0.0002035598,0.001022752,0.0004931335,0.0002389966,0.0001472356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006825372,0.0001391293,0.002135186,0.0008170848,0.001179217,0.00005059926,0.002470878,0.8570244,0.01024835,0.1137786,0.0008913671,0.01119702],"study_design_scores_gemma":[0.0007242672,0.0006863243,0.000007178869,0.003229009,0.0002004807,0.0005665009,0.001006342,0.969524,0.001470512,0.01985464,0.001868578,0.0008622063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1286182,0.04777746,0.8216024,0.0001749048,0.001266979,0.0002404702,0.00001141091,0.0001820558,0.0001261068],"genre_scores_gemma":[0.994553,0.0002706018,0.003340414,0.00002390313,0.0003429797,0.00001075267,0.00002191369,0.00005806475,0.001378406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8659347,"threshold_uncertainty_score":0.9998097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01179745601882343,"score_gpt":0.2548721384054755,"score_spread":0.243074682386652,"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."}}