{"id":"W1968178973","doi":"10.1016/j.scico.2013.11.021","title":"Understanding software artifact provenance","year":2013,"lang":"en","type":"article","venue":"Science of Computer Programming","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artifact (error); Sketch; Flexibility (engineering); Software; Software engineering; Component (thermodynamics); Java; Software development; Component-based software engineering; Software system; Human–computer interaction; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01305477,0.0005770697,0.000682933,0.005940391,0.002571904,0.00864943,0.001759867,0.002278874,0.002755101],"category_scores_gemma":[0.1161754,0.00119432,0.001303458,0.003328761,0.004728257,0.02457527,0.005952208,0.003559115,0.0005905851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001936309,"about_ca_system_score_gemma":0.005466409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01124038,"about_ca_topic_score_gemma":0.007821604,"domain_scores_codex":[0.9868869,0.004798226,0.001566798,0.001624477,0.004536794,0.0005868174],"domain_scores_gemma":[0.8984229,0.04988356,0.006411624,0.02732743,0.01686016,0.001094259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001273556,0.0001004586,0.01915849,0.0003692158,0.00006943198,0.0009020991,0.007617918,0.01114327,0.002338166,0.8316121,0.004278421,0.122283],"study_design_scores_gemma":[0.00002748528,0.00005256126,0.003014778,0.0003494976,0.0001285238,0.0005765251,0.00216413,0.07639355,0.005919709,0.8478037,0.06351636,0.00005325066],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.073333,0.0008799393,0.9009256,0.005900592,0.0002305689,0.0003152159,0.000705183,0.001626,0.0160838],"genre_scores_gemma":[0.7343896,0.001182051,0.2560317,0.0004070525,0.0001402202,0.0001790602,0.001664244,0.000793936,0.005212143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01305477,"threshold_uncertainty_score":0.06904107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2422656385648839,"score_gpt":0.3652303537855464,"score_spread":0.1229647152206625,"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."}}