{"id":"W2402365003","doi":"10.1145/2901739.2903505","title":"The emotional side of software developers in JIRA","year":2016,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":120,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Politeness; Software; Affect (linguistics); Tracking (education); Data science; Software engineering; Human–computer interaction; Programming language; Psychology","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":[0.0003280828,0.00004230776,0.00004733356,0.0000618456,0.00002775143,0.00002031517,0.0006476223,0.00001968894,0.00001805028],"category_scores_gemma":[0.001390075,0.00002065182,0.00001744415,0.0002643849,0.00004164101,0.0001366581,0.0001843361,0.00004307875,0.00005171621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004416451,"about_ca_system_score_gemma":0.0001075249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001609008,"about_ca_topic_score_gemma":0.0000216454,"domain_scores_codex":[0.999326,0.00002048882,0.0001104115,0.0001213698,0.0002476972,0.0001740658],"domain_scores_gemma":[0.9979607,0.001663673,0.00001228989,0.0002671767,0.0000650648,0.00003106067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000006859426,0.00004116668,0.2888726,0.00001688486,0.00001947536,0.00001587575,0.0003436942,0.000314623,0.002264717,0.08781192,0.003459882,0.6168323],"study_design_scores_gemma":[0.0003380514,0.00002985632,0.9840308,0.00006036678,2.639302e-7,0.000007819548,0.00001163836,0.0009266047,0.00710073,0.003487189,0.003886599,0.0001200235],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07750544,0.00003873158,0.9198527,0.001953461,0.0001242802,0.00005728428,5.481963e-7,0.0001049216,0.0003626562],"genre_scores_gemma":[0.92745,0.00001132041,0.0710948,0.00003208705,0.00001238995,0.000007555181,8.554807e-8,0.000004385836,0.001387387],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8499445,"threshold_uncertainty_score":0.166415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01660011322032212,"score_gpt":0.2487457158679789,"score_spread":0.2321456026476568,"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."}}