{"id":"W2079194580","doi":"10.1109/saner.2015.7081815","title":"Measuring the quality of design pattern detection results","year":2015,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Design pattern; Software design pattern; Pattern detection; Engineering design process; Structural pattern; Quality (philosophy); Task (project management); Process (computing); Specification pattern; Artificial intelligence; Data mining; Software; Software design; Software engineering; Software development; Engineering; Systems engineering; Programming language","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08055682,0.002467117,0.00212268,0.01306365,0.001167165,0.007618563,0.002679222,0.002907111,0.001919167],"category_scores_gemma":[0.3914293,0.0007422136,0.001791892,0.004599261,0.001739541,0.005430216,0.003621477,0.001736269,0.001001577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00149295,"about_ca_system_score_gemma":0.001758673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001639545,"about_ca_topic_score_gemma":0.00165557,"domain_scores_codex":[0.8729921,0.04562463,0.02060947,0.009653957,0.04875457,0.002365307],"domain_scores_gemma":[0.4101267,0.4125316,0.04434707,0.05129207,0.07872447,0.002978097],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003595851,0.001215652,0.1183249,0.003955209,0.001700494,0.0008584181,0.005911116,0.03838752,0.0452223,0.006353015,0.0072898,0.7671858],"study_design_scores_gemma":[0.001036849,0.006090525,0.1731136,0.002054983,0.002700358,0.003315379,0.005848306,0.4821845,0.2665552,0.02940602,0.02645014,0.001244105],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4468865,0.003335322,0.5253568,0.001215354,0.000421368,0.0008881177,0.002234449,0.0120671,0.007594999],"genre_scores_gemma":[0.6935195,0.0005540517,0.2990493,0.0002552869,0.0001077195,0.0003828482,0.002908266,0.001880504,0.001342569],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9194432,"threshold_uncertainty_score":0.4260303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2239854316130997,"score_gpt":0.3308078191398476,"score_spread":0.1068223875267479,"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."}}