{"id":"W2397486511","doi":"10.7287/peerj.preprints.1260","title":"Comments on \"Researcher bias: The use of machine learning in software defect prediction\"","year":2016,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; McGill University","funders":"","keywords":"Metric (unit); Association (psychology); Construct (python library); Computer science; Reuse; Group (periodic table); Machine learning; Predictive modelling; Artificial intelligence; Software; Selection (genetic algorithm); Data mining; Data science; Econometrics; Psychology; Mathematics; Engineering; Operations management","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04840482,0.002000958,0.001436029,0.002409261,0.005958857,0.006100253,0.00711213,0.03303951,0.006271593],"category_scores_gemma":[0.2454176,0.001262102,0.00218852,0.003212775,0.007923996,0.007621877,0.004443184,0.03678506,0.005506236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005541444,"about_ca_system_score_gemma":0.008826547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01715823,"about_ca_topic_score_gemma":0.01418348,"domain_scores_codex":[0.9582209,0.0172501,0.004586947,0.004388088,0.01395527,0.001598721],"domain_scores_gemma":[0.6752614,0.2265363,0.014851,0.008160314,0.06844001,0.006751061],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000441507,0.00001871588,0.0008933928,0.0001183031,0.00002224398,0.0002763126,0.0008473462,0.0001597917,0.0001809013,0.001901261,0.9906417,0.004895996],"study_design_scores_gemma":[0.0001067852,0.0001419232,0.003949596,0.001459583,0.00009390068,0.001124063,0.00563316,0.001512394,0.001565065,0.008674436,0.9754169,0.0003222215],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0005882191,0.0008096992,0.0006689177,0.9795396,0.01743342,0.00002046045,0.0001523282,0.00009167969,0.0006956651],"genre_scores_gemma":[0.005880652,0.0009514168,0.0009192139,0.9703944,0.01957991,0.00007790981,0.00006698527,0.0001055761,0.002023994],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.9515952,"threshold_uncertainty_score":0.2559922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1040278633416342,"score_gpt":0.2983163283872558,"score_spread":0.1942884650456216,"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."}}