{"id":"W2123187470","doi":"10.1109/ase.2003.1240310","title":"Automatically inferring concern code from program investigation activities","year":2004,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Computer science; Source code; Session (web analytics); Task (project management); Program comprehension; Code (set theory); Context (archaeology); Set (abstract data type); Programming language; Program analysis; Program code; Object (grammar); Software engineering; Software; World Wide Web; Artificial intelligence; Software system; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001204626,0.00008799299,0.00009094479,0.00006595336,0.00005075599,0.0002649719,0.0004825465,0.00004613585,0.00001759598],"category_scores_gemma":[0.0003082851,0.00008115828,0.0000238004,0.0002384008,0.00006345837,0.0005577288,0.000190884,0.0001321869,0.00007295912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001073429,"about_ca_system_score_gemma":0.0001510143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002458983,"about_ca_topic_score_gemma":0.00003478981,"domain_scores_codex":[0.9990759,0.00001793858,0.0001246665,0.0002241536,0.0003281468,0.0002291167],"domain_scores_gemma":[0.9991294,0.000362305,0.00002049033,0.0003379654,0.00003668172,0.0001131806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009428276,0.0004168129,0.123993,0.0001605702,0.0001974283,0.00012537,0.01311039,0.03507875,0.02312608,0.385656,0.000958708,0.4171675],"study_design_scores_gemma":[0.001684172,0.0004187977,0.3594826,0.0002597464,0.000009124212,0.00001801385,0.0001162669,0.4086075,0.1403604,0.08661717,0.001557626,0.0008686906],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4904604,0.00001121624,0.5069003,0.0004538861,0.00008566996,0.0001321203,8.037704e-7,0.001651814,0.0003038864],"genre_scores_gemma":[0.698119,9.007735e-7,0.3016756,0.00007571489,0.00003209903,0.00003897982,0.000001973564,0.000006777656,0.00004890706],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4162988,"threshold_uncertainty_score":0.3309539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03761874027269257,"score_gpt":0.2994274565800896,"score_spread":0.261808716307397,"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."}}