{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05549129,0.001731815,0.001783222,0.004435934,0.006109132,0.02990808,0.003637568,0.007505571,0.06752122],"category_scores_gemma":[0.2971062,0.002180226,0.002332592,0.004049656,0.01011214,0.05098649,0.01865667,0.01744493,0.08030297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002560654,"about_ca_system_score_gemma":0.01352208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00196875,"about_ca_topic_score_gemma":0.00208437,"domain_scores_codex":[0.9453455,0.02629179,0.004705172,0.006298042,0.015499,0.001860496],"domain_scores_gemma":[0.7176561,0.07848075,0.01086681,0.1078363,0.07092539,0.01423467],"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":[0.0001895245,0.00007607492,0.001879819,0.0007852517,0.0001399064,0.0005300413,0.006886896,0.0006081842,0.001413834,0.1875408,0.604508,0.1954416],"study_design_scores_gemma":[0.00002215149,0.0000184009,0.0001613718,0.0006279445,0.00003026551,0.0002968872,0.0007009501,0.0004176053,0.0008209185,0.07841568,0.918434,0.00005376611],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.002834344,0.008853843,0.5248841,0.2655658,0.05537612,0.001000651,0.00459019,0.02691099,0.1099839],"genre_scores_gemma":[0.08629825,0.02694681,0.5602995,0.07316877,0.03464911,0.002061589,0.0129216,0.04148552,0.1621689],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.06752122,"threshold_uncertainty_score":0.2934695,"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."}}