{"id":"W1994504150","doi":"10.5539/cis.v2n3p64","title":"Positive Affects Inducer on Software Quality","year":2009,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Software Engineering Research","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universiti Utara Malaysia","keywords":"Agile software development; Computer science; Extreme programming; Metric (unit); Quality (philosophy); Software; Contentment; Empirical research; Affect (linguistics); Extreme programming practices; Software development; Software engineering; Psychology; Statistics; Software development process; Operations management; Social psychology; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008197503,0.00009481812,0.00009033195,0.0003484015,0.0002298608,0.0006617206,0.0007354667,0.00003160423,0.000001762182],"category_scores_gemma":[0.0003594096,0.00008127911,0.00001946032,0.0009509028,0.00010175,0.007459538,0.0002169299,0.0001378639,0.00006941141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006033807,"about_ca_system_score_gemma":0.00009795493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002393593,"about_ca_topic_score_gemma":3.373751e-8,"domain_scores_codex":[0.9987198,0.00002306997,0.0001530254,0.0002077381,0.0006267072,0.0002696371],"domain_scores_gemma":[0.998999,0.0002537769,0.00004944665,0.0003548843,0.0001890384,0.0001538351],"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":[0.000004963398,0.00002549642,0.001155618,0.00001158551,0.000002068635,0.000001788326,0.002196217,0.0006888222,0.0001350944,0.1174743,0.0007434277,0.8775606],"study_design_scores_gemma":[0.0002421185,0.0003231763,0.9308624,0.00003332713,4.210478e-7,0.00001571328,0.000005546537,0.06455833,0.00229117,0.0005362902,0.0009436198,0.000187877],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1348977,0.000007910721,0.8630961,0.0004895463,0.0002746093,0.0001182791,9.7414e-7,0.0002309058,0.0008839115],"genre_scores_gemma":[0.9354114,0.000004259939,0.06201079,0.002526692,0.00003517629,0.000002483452,0.000001470208,0.000001188308,0.000006566594],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9297068,"threshold_uncertainty_score":0.6380987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0192490592782205,"score_gpt":0.3006171555897824,"score_spread":0.2813680963115618,"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."}}