{"id":"W4376505229","doi":"10.1145/3597208","title":"An Empirical Study on GitHub Pull Requests’ Reactions","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Software Engineering Research","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; École de Technologie Supérieure","funders":"","keywords":"Computer science; Leverage (statistics); Source code; Set (abstract data type); Code review; Open source; Software; Empirical research; Code (set theory); Process (computing); Static program analysis; Software engineering; World Wide Web; Software development; Programming language; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001682139,0.0002791346,0.000331084,0.0009412392,0.0002290365,0.00009465562,0.0008231766,0.0001864853,0.00001212399],"category_scores_gemma":[0.003736985,0.000279658,0.00008044556,0.001390867,0.00004034877,0.0002853027,0.00003735327,0.0007809347,0.00009463774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007916639,"about_ca_system_score_gemma":0.00005229976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003666237,"about_ca_topic_score_gemma":0.000004300312,"domain_scores_codex":[0.99761,0.0004273669,0.000292773,0.0007679894,0.0003409476,0.000560885],"domain_scores_gemma":[0.9900975,0.008033966,0.00003136807,0.001474987,0.00007511094,0.0002870562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001719916,0.002006342,0.02805945,0.0002142736,0.0006069946,0.0006532467,0.01030778,0.6426526,0.005558767,0.00145824,0.0008611592,0.3074492],"study_design_scores_gemma":[0.003336343,0.009950085,0.8594776,0.0002129364,0.0001378035,0.0006341665,0.001069271,0.09266257,0.008557026,0.002641142,0.01878126,0.00253978],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2131037,0.00001878702,0.7826081,0.0003610656,0.0008060211,0.0002219553,0.000006228491,0.002870681,0.000003481104],"genre_scores_gemma":[0.5497642,0.00004282277,0.449479,0.0001029839,0.00009470415,0.000214762,0.000005309545,0.00006267753,0.0002335662],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8314182,"threshold_uncertainty_score":0.9999655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1680616754714322,"score_gpt":0.4087685822272296,"score_spread":0.2407069067557973,"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."}}