{"id":"W1969366857","doi":"10.1109/icpc.2010.46","title":"Studying the Impact of Social Structures on Software Quality","year":2010,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Software quality; Quality (philosophy); Software; Data science; Source code; Process (computing); Software metric; Software development; Product (mathematics); Code review; Software bug; Software engineering; Data mining","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":[0.0005361184,0.00008168244,0.0001041364,0.00005356713,0.0001160297,0.00007561056,0.0009833344,0.00004481327,0.00008269124],"category_scores_gemma":[0.001109095,0.00004445208,0.00009277938,0.0002433939,0.00005478948,0.00009522887,0.0002147358,0.0003326226,0.00001013906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002556036,"about_ca_system_score_gemma":0.00008257577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002717588,"about_ca_topic_score_gemma":0.00001389071,"domain_scores_codex":[0.9990765,0.00005296509,0.0001244792,0.0001630901,0.0003848835,0.0001981034],"domain_scores_gemma":[0.9984278,0.000926766,0.00003680868,0.0004868197,0.00008160708,0.00004020908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00004923796,0.0003124046,0.3740716,0.00006448699,0.0002803284,0.000009731979,0.01256728,0.00346494,0.02329195,0.4120303,0.01159644,0.1622613],"study_design_scores_gemma":[0.0001104707,0.00005937968,0.9945897,0.000001088816,6.625821e-7,0.000001482812,0.00001941452,0.0006859727,0.002035544,0.002387719,0.00003844985,0.00007013825],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9225364,0.000003155055,0.07665371,0.0001289795,0.000195192,0.0000935781,0.000002176935,0.0001762503,0.0002106015],"genre_scores_gemma":[0.9906874,1.240215e-7,0.009143485,0.00002133216,0.0000793666,0.000003849014,3.097452e-7,0.000005571727,0.00005862086],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6205181,"threshold_uncertainty_score":0.1827296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04254294862608747,"score_gpt":0.3741402883358084,"score_spread":0.3315973397097209,"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."}}