{"id":"W2127619534","doi":"10.1109/wcre.1998.723173","title":"Evaluating architectural extractors","year":2002,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Workbench; Computer science; Software engineering; Reverse engineering; Software; Visualization; Source code; Architectural pattern; Software system; Software construction; Programming language; Artificial intelligence","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":[0.0002120548,0.00005908035,0.00004946788,0.00008016577,0.00004256779,0.00009250249,0.0005487581,0.00001826839,0.0003851715],"category_scores_gemma":[0.0006081525,0.00004851935,0.0000270276,0.0002643367,0.00001196385,0.000165355,0.0001358459,0.0001227485,0.0003830709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002302268,"about_ca_system_score_gemma":0.000006241164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000986234,"about_ca_topic_score_gemma":6.763718e-7,"domain_scores_codex":[0.9991417,0.00002407963,0.00008030125,0.0001847441,0.0003352502,0.000233923],"domain_scores_gemma":[0.9990035,0.0005395151,0.000009884096,0.0003469557,0.00002898743,0.00007120891],"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":[5.443009e-7,0.00003374115,0.006811267,0.000009986479,0.00001016493,0.00002116467,0.000969332,0.003065795,0.003460948,0.004977318,0.001859758,0.97878],"study_design_scores_gemma":[0.0001496849,0.00007935306,0.0323652,0.000006384506,6.70399e-7,0.00004963114,0.0000033914,0.9638458,0.002169842,0.0004147475,0.0007642257,0.0001509956],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.472482,0.0001039208,0.5226033,0.0005637608,0.0002214187,0.00007648819,1.032287e-7,0.0007297342,0.003219219],"genre_scores_gemma":[0.8640548,7.729736e-7,0.1342165,0.00004637833,0.00003887355,0.000005553203,8.33943e-8,0.000004683721,0.001632395],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.978629,"threshold_uncertainty_score":0.4923729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09881032934449571,"score_gpt":0.344662248214615,"score_spread":0.2458519188701193,"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."}}