{"id":"W4240395313","doi":"10.1145/2189751.2047884","title":"Monitoring aspects for the customization of automatically generated code for big-step models","year":2011,"lang":"en","type":"article","venue":"ACM SIGPLAN Notices","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Executable; Code generation; Programming language; Code (set theory); Personalization; Redundant code; Generator (circuit theory); Unreachable code; Dead code; Semantics (computer science); Reachability; Source code; Extension (predicate logic); Operating system; Theoretical computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0004879494,0.0001227703,0.0001815575,0.00006135012,0.0001010203,0.00003740401,0.001139066,0.00006225365,5.643053e-7],"category_scores_gemma":[0.002286156,0.00008932273,0.00005101784,0.0001750734,0.00003829955,0.0003323473,0.0001509508,0.00005668092,9.595624e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000190947,"about_ca_system_score_gemma":0.00003790483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007978028,"about_ca_topic_score_gemma":0.000005626959,"domain_scores_codex":[0.9991375,0.00004528314,0.0002390713,0.0002253755,0.0001313827,0.0002214067],"domain_scores_gemma":[0.9950068,0.004029587,0.000146297,0.0006055509,0.0001775787,0.00003417625],"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":[0.00006120974,0.00004818423,0.0001627412,0.0002016252,0.0001020691,0.000001650773,0.002768263,0.8993645,0.005281911,0.02902585,0.0001712665,0.0628107],"study_design_scores_gemma":[0.0003337646,0.0001241503,0.0005789945,0.0000408119,0.00003396279,0.000001387497,0.00006183648,0.9044585,0.05974754,0.03437303,0.00008858813,0.0001574844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006199228,0.0002309758,0.9916545,0.00007773564,0.001015214,0.0004251666,0.00001364013,0.0003355961,0.00004799477],"genre_scores_gemma":[0.3002512,0.000011164,0.6995114,0.00001395854,0.0001058967,0.00007571105,0.000001438465,0.00001228305,0.0000170091],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2940519,"threshold_uncertainty_score":0.3642475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1913483787223302,"score_gpt":0.3198090411456223,"score_spread":0.1284606624232921,"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."}}