{"id":"W2955136383","doi":"10.1007/s11219-019-09456-3","title":"Pieces of contextual information suitable for predicting co-changes? An empirical study","year":2019,"lang":"en","type":"article","venue":"Software Quality Journal","topic":"Software Engineering Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Commit; Computer science; Artifact (error); Metadata; Baseline (sea); Software; Software engineering; Set (abstract data type); Software development; Data science; Data mining; Information retrieval; Artificial intelligence; World Wide Web; Database; 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":[],"consensus_categories":[],"category_scores_codex":[0.002956989,0.0003808511,0.0006928404,0.004253688,0.0008013612,0.001662137,0.0007876168,0.001006284,0.001884969],"category_scores_gemma":[0.04415865,0.0002713188,0.0004781865,0.007156562,0.0005700144,0.003307875,0.001344731,0.0008217033,0.0004357965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006116778,"about_ca_system_score_gemma":0.0006569677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005030767,"about_ca_topic_score_gemma":0.007489387,"domain_scores_codex":[0.9963742,0.001605434,0.0003561221,0.0005697481,0.0008587647,0.000235804],"domain_scores_gemma":[0.9070171,0.06651692,0.01194282,0.006101497,0.006206626,0.002215141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001069729,0.0009444422,0.9487245,0.0002039796,0.0001805713,0.000347991,0.002792575,0.001151502,0.001543834,0.0002894879,0.0004149483,0.04233639],"study_design_scores_gemma":[0.00003254128,0.0006482215,0.9772722,0.00007514185,0.0003097505,0.0003108196,0.005173414,0.01197816,0.001529232,0.0006279981,0.001998881,0.00004367818],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973459,0.0002058521,0.0009688049,0.00004461614,0.000008744604,0.00005191466,0.0004098354,0.00002404589,0.0009401875],"genre_scores_gemma":[0.9981856,0.00007318009,0.001077288,0.00001143536,0.000009976766,0.00002865648,0.0004848737,0.000009651314,0.0001193395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005030767,"threshold_uncertainty_score":0.01563823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06126340530962837,"score_gpt":0.3933497388779376,"score_spread":0.3320863335683092,"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."}}