{"id":"W1558294391","doi":"10.1007/3-540-44886-1_8","title":"Monadic Memoization towards Correctness-Preserving Reduction of Search","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Logic, programming, and type systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Memoization; Computer science; Correctness; Reduction (mathematics); Table (database); Pace; Theoretical computer science; Programming language; Data mining; Mathematics","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"],"consensus_categories":[],"category_scores_codex":[0.001402565,0.0003731191,0.000498903,0.0007933787,0.0002064624,0.0003285141,0.002502187,0.0003305805,0.00002385635],"category_scores_gemma":[0.0001033376,0.0003367084,0.0001208368,0.0009639096,0.000497935,0.0005888063,0.0007727779,0.0005345501,0.00001663128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002703709,"about_ca_system_score_gemma":0.0006975222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001997184,"about_ca_topic_score_gemma":0.00004968726,"domain_scores_codex":[0.9964572,0.00008645432,0.0005647218,0.001165445,0.001183627,0.0005425186],"domain_scores_gemma":[0.997647,0.0001203021,0.000335551,0.001236999,0.0005227874,0.0001373248],"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":[0.000005464744,0.0000415078,0.00009086107,0.0001683209,0.0000173008,0.0000291212,0.002005688,0.03096141,0.0004379049,0.1486161,0.00006087512,0.8175654],"study_design_scores_gemma":[0.0004535254,0.0004347724,0.0001577298,0.0002565729,0.00001833431,0.0003129496,0.000002121075,0.5202346,0.01557665,0.4576718,0.003807926,0.001072936],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001017387,0.0006186083,0.9862425,0.0002928606,0.003804597,0.0004418817,8.859432e-7,0.0001118172,0.008385165],"genre_scores_gemma":[0.8726743,0.00007718014,0.1251405,0.0002137795,0.0005155574,0.00001177365,0.000008917805,0.0000413772,0.001316617],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8725725,"threshold_uncertainty_score":0.9999085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03158398901796504,"score_gpt":0.2656718112774615,"score_spread":0.2340878222594964,"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."}}