{"id":"W4232560806","doi":"10.1109/msr.2015.73","title":"The Firefox Temporal Defect Dataset","year":2015,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Process (computing); Software bug; Software; Plan (archaeology); Data science; Data mining; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001141628,0.001540202,0.0006642361,0.00624209,0.0007424774,0.0009105314,0.001905967,0.001536447,0.002973319],"category_scores_gemma":[0.004334252,0.0003995251,0.001064997,0.004143597,0.0003697687,0.001106204,0.0009217242,0.001053594,0.004137038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00141797,"about_ca_system_score_gemma":0.001386389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02740211,"about_ca_topic_score_gemma":0.05320118,"domain_scores_codex":[0.9988263,0.0001058914,0.0001448134,0.0003336292,0.0004417916,0.0001475511],"domain_scores_gemma":[0.9974011,0.0006411727,0.0004289594,0.0005143857,0.0007355566,0.0002788182],"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":[0.0008118515,0.0008798612,0.07959343,0.001692055,0.0002918326,0.001189083,0.0004022411,0.004940859,0.005365564,0.001928934,0.823799,0.07910528],"study_design_scores_gemma":[0.0007905677,0.000702373,0.2540245,0.0004610644,0.0002767382,0.003097463,0.0006941147,0.02491308,0.007630149,0.003171339,0.7040408,0.0001978527],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.06378667,0.001125812,0.001818652,0.0004923082,0.0001308938,0.0002128609,0.9256126,0.003182145,0.00363815],"genre_scores_gemma":[0.01872324,0.0002495966,0.004790925,0.0001200639,0.00003888692,0.0001879025,0.9744962,0.00010546,0.001287704],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02740211,"threshold_uncertainty_score":0.0544852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04834508987867862,"score_gpt":0.3027732577530527,"score_spread":0.2544281678743741,"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."}}