{"id":"W2156672158","doi":"10.1109/acom.2007.4","title":"Identifying, Assigning, and Quantifying Crosscutting Concerns","year":2007,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Identification (biology); Modularity (biology); Software engineering; Business process reengineering; Ambiguity; Software quality; Code (set theory); Suite; Quality (philosophy); Software; Software development; Programming language; Engineering; Set (abstract data type)","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":[0.001345367,0.0001015917,0.0001016335,0.0001530739,0.0001657607,0.0004822469,0.000493119,0.00005148077,0.00002184819],"category_scores_gemma":[0.000545235,0.00009647524,0.00002710042,0.0003484211,0.00005183539,0.0004740712,0.0004042979,0.0001852433,0.00004264936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000384915,"about_ca_system_score_gemma":0.0000284271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007621882,"about_ca_topic_score_gemma":0.00001736286,"domain_scores_codex":[0.9986638,0.000015821,0.0001818505,0.0003443886,0.000337717,0.0004564249],"domain_scores_gemma":[0.9985982,0.0008292737,0.00003508379,0.0003273032,0.00006455048,0.000145565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000005429852,0.00004118371,0.7269545,0.0001286678,0.00005076597,0.0002915652,0.004147612,0.00007800267,0.02431059,0.1022663,0.002410888,0.1393145],"study_design_scores_gemma":[0.00118848,0.0001420844,0.8275663,0.0001853834,0.000007801937,0.0002132797,0.0004814757,0.04995488,0.1062207,0.001790571,0.01122291,0.001026212],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1710511,0.0002118163,0.8265797,0.00006910565,0.000247328,0.000062554,1.290657e-7,0.0005098192,0.001268484],"genre_scores_gemma":[0.8785484,0.000006423452,0.1206917,0.00006120668,0.00007118826,0.000002081614,2.478666e-7,0.00001050382,0.0006081762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7074974,"threshold_uncertainty_score":0.4650318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08655784220837867,"score_gpt":0.373265497336404,"score_spread":0.2867076551280253,"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."}}