{"id":"W2041589121","doi":"10.5555/2820518.2820597","title":"The Firefox temporal defect dataset","year":2015,"lang":"en","type":"article","venue":"Mining Software Repositories","topic":"Software Engineering Research","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Software bug; Process (computing); Software; Plan (archaeology); Data mining; Data science; Geography","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":[0.001224556,0.001504017,0.0006770855,0.007073648,0.0007636699,0.0009623092,0.002000227,0.001536521,0.002736955],"category_scores_gemma":[0.004839058,0.0004189938,0.00107491,0.004828651,0.0003826235,0.001204655,0.00099546,0.001033552,0.003986442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001423045,"about_ca_system_score_gemma":0.001479346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02717487,"about_ca_topic_score_gemma":0.05142295,"domain_scores_codex":[0.9986951,0.000120478,0.000167582,0.0003624988,0.0004977438,0.0001565209],"domain_scores_gemma":[0.9970922,0.0007083876,0.0005041599,0.0005778714,0.0008228387,0.0002943563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007958224,0.0009345521,0.08896516,0.001955222,0.0003254124,0.001293202,0.0004995013,0.005266598,0.005905402,0.00215731,0.7999632,0.09193867],"study_design_scores_gemma":[0.0007446969,0.0006951001,0.2622463,0.0004957426,0.0002899564,0.003235917,0.0007686901,0.02598711,0.008130996,0.003323412,0.6938828,0.0001992873],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.07422754,0.001330012,0.002124295,0.0005177837,0.0001270225,0.0002400476,0.9140127,0.003633623,0.00378708],"genre_scores_gemma":[0.02045078,0.0002843566,0.005572837,0.0001188521,0.00003730601,0.0002026177,0.9718784,0.0001154459,0.001339316],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02717487,"threshold_uncertainty_score":0.0540334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03020728126332903,"score_gpt":0.2824005254734659,"score_spread":0.2521932442101369,"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."}}