{"id":"W3108760172","doi":"10.1145/3428249","title":"Designing types for R, empirically","year":2020,"lang":"en","type":"article","venue":"Proceedings of the ACM on Programming Languages","topic":"Software Engineering Research","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Naval Research; Office of Naval Research Global; Natural Sciences and Engineering Research Council of Canada; European Commission; National Science Foundation","keywords":"Computer science; Compiler; Data type; Programming language; Code (set theory); Variety (cybernetics); Source code; Overhead (engineering); Process (computing); Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.000302887,0.0001081788,0.0001350145,0.00004826744,0.00007725821,0.0001414525,0.00302015,0.00004327288,0.000001821776],"category_scores_gemma":[0.01192488,0.00007566013,0.00008697521,0.0004697232,0.00003655201,0.0001520822,0.0008031594,0.0001476067,0.000005316935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001830959,"about_ca_system_score_gemma":0.00002473222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002830364,"about_ca_topic_score_gemma":8.825051e-8,"domain_scores_codex":[0.9989939,0.000004179346,0.0001410287,0.0002780658,0.0003107354,0.000272059],"domain_scores_gemma":[0.9988897,0.0004749194,0.00008224132,0.00029949,0.0001742378,0.00007935142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001820371,0.0002929289,0.05930092,0.00192964,0.0002614174,0.000007646906,0.01817801,0.0002294391,0.2022535,0.04644158,0.02987249,0.6410504],"study_design_scores_gemma":[0.001147878,0.001765782,0.008783817,0.0003921461,0.00004559929,0.00001561662,0.0008564451,0.004459711,0.9476553,0.004629121,0.02954101,0.000707568],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6872126,0.00162874,0.1895231,0.1123942,0.0005726761,0.004097979,0.00001166819,0.003521479,0.001037525],"genre_scores_gemma":[0.6179298,0.000001651479,0.381529,0.0003241414,0.00008140592,0.00006035773,2.800356e-7,0.00001637046,0.00005699551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7454019,"threshold_uncertainty_score":0.9963981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03649795393744953,"score_gpt":0.3018342192270644,"score_spread":0.2653362652896149,"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."}}