{"id":"W4322619775","doi":"10.32614/rj-2023-003","title":"Making Provenance Work for You","year":2023,"lang":"en","type":"article","venue":"The R Journal","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Ministry of Education, India; Harvard University; National Science Foundation","keywords":"Provenance; Trustworthiness; Scripting language; Computer science; Debugging; Programming language; Computer security","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":[],"consensus_categories":[],"category_scores_codex":[0.01664966,0.00004993809,0.00007851076,0.0001398657,0.0006625279,0.000681735,0.001238673,0.00001128175,0.00007753241],"category_scores_gemma":[0.002273945,0.00002482392,0.00006928333,0.001223636,0.00004124904,0.0001226835,0.0002729239,0.0001067992,0.0006794711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000142886,"about_ca_system_score_gemma":0.00002499313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.090665e-7,"about_ca_topic_score_gemma":0.00000102447,"domain_scores_codex":[0.9984174,0.0001056282,0.0002951216,0.0001725081,0.0007660893,0.0002432513],"domain_scores_gemma":[0.9981232,0.00102723,0.0001791295,0.0005221668,0.000111193,0.00003705798],"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":[0.00002168914,0.000005490705,0.0005938929,9.098374e-7,0.00000608127,0.000003499007,0.0005517189,0.001971815,0.00001630821,0.0009121727,0.7025086,0.2934078],"study_design_scores_gemma":[0.0002323308,0.00002871285,0.01686127,0.00004795748,0.00001137119,0.00003281327,0.001689027,0.01550969,0.00002370655,0.1242664,0.8412071,0.00008957144],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4207542,0.0005015712,0.500822,0.04124222,0.01723536,0.0008303897,0.00002877816,0.0002991748,0.01828632],"genre_scores_gemma":[0.9695391,0.00001096915,0.001977806,0.000368859,0.0006062014,0.00000427655,0.000001015044,0.000007390704,0.02748437],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5487849,"threshold_uncertainty_score":0.8733453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4460684727570378,"score_gpt":0.4782513959017604,"score_spread":0.0321829231447226,"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."}}