{"id":"W2044908638","doi":"10.1109/ccece.2008.4564709","title":"Harnessing overgeneralization in the synthesis of state machines from scenarios","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Spurious relationship; Generalization; Computer science; Set (abstract data type); Syntax; Finite-state machine; State (computer science); Programming language; Artificial intelligence; Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002422391,0.0002087361,0.0002537187,0.0003563088,0.0001240733,0.0002030346,0.0007405993,0.00008275117,0.000006626074],"category_scores_gemma":[0.0001161662,0.0001741143,0.00003209463,0.0006958658,0.0000516102,0.0004439317,0.00005240219,0.000269381,0.000002388359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007888359,"about_ca_system_score_gemma":0.0002501246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00543961,"about_ca_topic_score_gemma":0.0005043611,"domain_scores_codex":[0.9986284,0.00003302777,0.0003110101,0.0003917056,0.000254262,0.0003816327],"domain_scores_gemma":[0.9992746,0.0001065138,0.0001009343,0.0001786128,0.0001726037,0.000166713],"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":[0.00002738642,0.0001104996,0.02013024,0.0001048011,0.00004418692,0.00004516213,0.009613253,0.003145976,0.004839745,0.6169449,0.0002269795,0.3447669],"study_design_scores_gemma":[0.00008907213,0.00006496278,0.03811978,0.0001007664,0.00000376422,0.00002128302,0.00001033044,0.9580759,0.001856785,0.001343018,0.0001074349,0.0002068647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4097776,0.00007429403,0.5887838,0.0005063941,0.0001428684,0.000206626,0.000006030989,0.00007102211,0.0004313772],"genre_scores_gemma":[0.9585623,0.0001052824,0.041053,0.0001814064,0.00004758378,0.00003091005,0.000002351708,0.000009370057,0.000007845822],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9549299,"threshold_uncertainty_score":0.82231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02448213156951579,"score_gpt":0.2186512201645152,"score_spread":0.1941690885949994,"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."}}