{"id":"W2985253063","doi":"10.1145/3356773.3356806","title":"SST'19 - Software and Systems Traceability","year":2019,"lang":"en","type":"article","venue":"ACM SIGSOFT Software Engineering Notes","topic":"Software Engineering Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Traceability; Requirements traceability; Software engineering; Computer science; Context (archaeology); Software development; Software system; Change impact analysis; Event (particle physics); Systems engineering; Software; Engineering; Requirement; Programming language","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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001070381,0.0005337453,0.0005736349,0.0003811796,0.0001292023,0.0004789869,0.002090216,0.0002785655,0.0000351618],"category_scores_gemma":[0.3030201,0.0005481123,0.0001340162,0.0008398322,0.00006198182,0.0009158893,0.001175872,0.0006189276,0.0002323913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002049718,"about_ca_system_score_gemma":0.0001366206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009621511,"about_ca_topic_score_gemma":0.000001330588,"domain_scores_codex":[0.9964986,0.00008426444,0.0004903313,0.001134987,0.0007975511,0.000994285],"domain_scores_gemma":[0.8183672,0.1774911,0.0001131299,0.003165453,0.0002416512,0.0006215063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000009691129,0.00005200945,0.9805966,0.000882387,0.00005835984,0.00003802728,0.0004537028,0.009468371,0.0009174303,0.0007513216,0.00042892,0.006343151],"study_design_scores_gemma":[0.0009133117,0.0002442513,0.9800053,0.0002910639,0.00001976901,0.0002781112,0.00002281614,0.01018493,0.000706229,0.00009046469,0.005925467,0.001318309],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7281473,0.003019037,0.259673,0.0002424878,0.001766196,0.0008224208,0.00002596787,0.006301189,0.000002335783],"genre_scores_gemma":[0.8588812,0.00003186334,0.1405767,0.00005195859,0.0001317187,0.0000894977,0.00001184541,0.00008852326,0.0001366321],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3019498,"threshold_uncertainty_score":0.999697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01668425259771753,"score_gpt":0.242492195782453,"score_spread":0.2258079431847354,"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."}}