{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004779571,0.002733042,0.001001188,0.0053489,0.0009936629,0.002176383,0.00181436,0.001632451,0.01798244],"category_scores_gemma":[0.01764982,0.00132147,0.001584879,0.001721446,0.0007870387,0.002767368,0.00352755,0.002134859,0.01028183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008902394,"about_ca_system_score_gemma":0.002692051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00175128,"about_ca_topic_score_gemma":0.003264853,"domain_scores_codex":[0.9966977,0.001128106,0.0003284057,0.0005565588,0.001162697,0.0001266046],"domain_scores_gemma":[0.9838173,0.01145239,0.001157166,0.001824245,0.001514124,0.0002347406],"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":[0.0006522785,0.0004377568,0.003692237,0.003476043,0.0002378951,0.001967736,0.004084557,0.01687158,0.09848344,0.02256289,0.1523517,0.6951818],"study_design_scores_gemma":[0.0003114261,0.0003623913,0.004077052,0.0006587699,0.0001642586,0.0024606,0.002052448,0.4727162,0.1097664,0.04553008,0.3615131,0.0003872634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004362185,0.0001040711,0.8624765,0.0002810904,0.00006226157,0.0005813889,0.005074296,0.1237917,0.0032664],"genre_scores_gemma":[0.02702725,0.0001689136,0.9535324,0.0002390383,0.00003529587,0.001117229,0.009559775,0.004703444,0.003616685],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01798244,"threshold_uncertainty_score":0.0601573,"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."}}