{"id":"W4312969456","doi":"10.1109/icsme55016.2022.00035","title":"Stronger Together: On Combining Relationships in Architectural Recovery Approaches","year":2022,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"IBM Canada","keywords":"Computer science; Representation (politics); Process (computing); Architecture; Similarity (geometry); Software architecture; Class (philosophy); Data mining; Software engineering; Software; Artificial intelligence; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.03555468,0.002747932,0.001673723,0.01221302,0.004334084,0.009083548,0.005806417,0.003781663,0.00691402],"category_scores_gemma":[0.08611348,0.001933219,0.002969868,0.008684771,0.005953416,0.03835947,0.01953023,0.009071092,0.002046779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002658286,"about_ca_system_score_gemma":0.002903445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003529607,"about_ca_topic_score_gemma":0.005564163,"domain_scores_codex":[0.9517187,0.02456793,0.002606041,0.005250952,0.01414497,0.001711393],"domain_scores_gemma":[0.9317127,0.03268821,0.00721808,0.01876754,0.00819739,0.00141615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002580897,0.0003879284,0.01058337,0.0007722938,0.0004322791,0.000658964,0.01517248,0.02687439,0.004931047,0.4372224,0.005861327,0.4968454],"study_design_scores_gemma":[0.00006937006,0.0003243186,0.003967663,0.001053626,0.0006086332,0.0009430787,0.008424662,0.1747189,0.006395034,0.7340095,0.06928879,0.0001963949],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01446468,0.001240372,0.9691709,0.002462512,0.00007215217,0.0002740499,0.00008568805,0.0007265488,0.01150303],"genre_scores_gemma":[0.2115082,0.001644072,0.7793331,0.001166976,0.000179422,0.0004179486,0.0005885228,0.000837244,0.004324537],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03555468,"threshold_uncertainty_score":0.1880333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0853003857517976,"score_gpt":0.2524556322985324,"score_spread":0.1671552465467347,"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."}}