{"id":"W4393753361","doi":"10.5281/zenodo.7849304","title":"An Exploratory Factor Analysis of Code Quality Metrics","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Technology and Data Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Factor (programming language); Quality (philosophy); Code (set theory); Computer science; Exploratory factor analysis; Programming language; Machine learning; Set (abstract data type); Physics","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":[0.008377312,0.00193575,0.001023404,0.003771934,0.001465705,0.001879788,0.002731271,0.001169596,0.04904983],"category_scores_gemma":[0.03518566,0.0008172391,0.002299313,0.006036783,0.0007434935,0.001201882,0.002449591,0.00249894,0.04532269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001614337,"about_ca_system_score_gemma":0.005091669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02190289,"about_ca_topic_score_gemma":0.03762434,"domain_scores_codex":[0.9941452,0.001419897,0.0007426353,0.001303502,0.001701065,0.0006877699],"domain_scores_gemma":[0.9731163,0.006692595,0.001426267,0.007133831,0.01106835,0.0005626566],"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":[0.0003764817,0.0003114404,0.005674664,0.0009202022,0.0001056048,0.0000484112,0.0002920577,0.000536512,0.0006926418,0.0009152376,0.9704006,0.01972599],"study_design_scores_gemma":[0.001704497,0.0002793185,0.08377624,0.0007552021,0.0002233817,0.0002061477,0.0009269026,0.002261243,0.003040749,0.004116815,0.902476,0.0002334283],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002994386,0.00003019743,0.001570089,0.0002391985,0.000097048,0.0006992449,0.9920961,0.0007851145,0.0014886],"genre_scores_gemma":[0.004980394,0.00003230652,0.006043707,0.0001006569,0.00002293625,0.006498082,0.9794684,0.0002791976,0.002574367],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04904983,"threshold_uncertainty_score":0.164088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09209335345079624,"score_gpt":0.3250921149550003,"score_spread":0.2329987615042041,"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."}}