{"id":"W4411374939","doi":"10.1145/3724389.3731277","title":"Extracting Notional Machines for Databases","year":2025,"lang":"en","type":"article","venue":"","topic":"Computability, Logic, AI Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Notional amount; Computer science; Database; Information retrieval; Business","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":[0.002869268,0.00104418,0.0008928683,0.006649397,0.001533145,0.006171044,0.001955152,0.001884328,0.006259237],"category_scores_gemma":[0.02489981,0.001060644,0.00256716,0.004294624,0.002753452,0.01505239,0.004689198,0.003459818,0.003921521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001776787,"about_ca_system_score_gemma":0.001156911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005353448,"about_ca_topic_score_gemma":0.0006920986,"domain_scores_codex":[0.9955292,0.001295617,0.0006075987,0.0009581477,0.001295327,0.0003140248],"domain_scores_gemma":[0.9910305,0.005138765,0.0004064848,0.002166678,0.0009939403,0.0002637062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001033049,0.00005361673,0.001961106,0.000613126,0.00004456537,0.00028579,0.00143387,0.006662586,0.004395119,0.7866793,0.01421888,0.1835489],"study_design_scores_gemma":[0.00001272635,0.0000459404,0.0009844768,0.0001830303,0.00002786506,0.0005487123,0.0005180674,0.04749601,0.005666111,0.8746638,0.0698034,0.00004961636],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02394081,0.002691544,0.9590359,0.001580429,0.0004264473,0.0001640389,0.001782361,0.002317962,0.008060647],"genre_scores_gemma":[0.2622462,0.002072863,0.723386,0.0003046839,0.0004134593,0.0002300177,0.00550966,0.0006540435,0.005182913],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006649397,"threshold_uncertainty_score":0.02093923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03966962032184706,"score_gpt":0.3354051139444419,"score_spread":0.2957354936225948,"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."}}