{"id":"W2963678029","doi":"10.1109/aire.2018.00007","title":"ELICA: An Automated Tool for Dynamic Extraction of Requirements Relevant Information","year":2018,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Requirements elicitation; Leverage (statistics); Conversation; Process (computing); Domain (mathematical analysis); Set (abstract data type); Information extraction; Flexibility (engineering); Software engineering; Requirements engineering; Knowledge management; Data science; Software; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003489052,0.00005375312,0.00006165285,0.0001340609,0.00004455848,0.00006084,0.0003200757,0.00003804893,0.00001168922],"category_scores_gemma":[0.0003928093,0.00004881755,0.00001892088,0.000231634,0.00001972024,0.001964168,0.00005858053,0.00003479162,0.00004775258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006428688,"about_ca_system_score_gemma":0.00004232294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001665688,"about_ca_topic_score_gemma":0.000003333178,"domain_scores_codex":[0.9992843,0.00001179567,0.0002043412,0.0001081328,0.000234647,0.0001568256],"domain_scores_gemma":[0.999158,0.0001237223,0.00005518047,0.0003454298,0.0002832714,0.00003445673],"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":[0.0001751917,0.0004459797,0.005271424,0.0005280112,0.0001252886,0.000002493731,0.003576913,0.001914174,0.2366437,0.03813193,0.01538612,0.6977988],"study_design_scores_gemma":[0.000220895,0.0002810875,0.0328594,0.00001096953,0.000001286366,0.000003389426,0.000007268811,0.9534957,0.01175624,0.0002151635,0.001080468,0.00006808434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2259433,0.000001453175,0.7728245,0.00006218476,0.0001971468,0.0001965726,0.000001718534,0.0006879299,0.00008524584],"genre_scores_gemma":[0.8443609,0.000001311849,0.1555032,0.00002500864,0.00001584973,0.00002134221,0.000008782385,0.000003706621,0.00005981915],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9515816,"threshold_uncertainty_score":0.1990722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02206619614857035,"score_gpt":0.3488620810071728,"score_spread":0.3267958848586024,"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."}}