{"id":"W2559969327","doi":"10.1115/detc2016-59551","title":"Automated Extraction of System Structure Knowledge From Text","year":2016,"lang":"en","type":"article","venue":"","topic":"Design Education and Practice","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Autodesk (Canada)","funders":"","keywords":"Computer science; Parsing; Redundancy (engineering); Natural language processing; Artificial intelligence; Knowledge extraction; Knowledge-based systems; Information extraction; Set (abstract data type); Knowledge acquisition; Frame (networking); Knowledge representation and reasoning; Function (biology); Information retrieval; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00160224,0.002123061,0.001195455,0.01329689,0.001355226,0.002550627,0.001774402,0.001510872,0.006043628],"category_scores_gemma":[0.01354005,0.0009895256,0.00137951,0.004467004,0.00106302,0.005605638,0.002124769,0.00180667,0.004211644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001492211,"about_ca_system_score_gemma":0.003871926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005259912,"about_ca_topic_score_gemma":0.009069219,"domain_scores_codex":[0.9972522,0.0004917216,0.0003261965,0.0007503134,0.001041808,0.0001377916],"domain_scores_gemma":[0.9869499,0.008027158,0.001222854,0.001097805,0.002538538,0.0001638454],"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":[0.0001475913,0.0002881139,0.005018396,0.003645366,0.0001693857,0.001778432,0.002941477,0.008349226,0.03579546,0.009776469,0.02911813,0.902972],"study_design_scores_gemma":[0.000226863,0.0004872013,0.02351343,0.003054583,0.0009571761,0.003434106,0.005912221,0.3436818,0.1800116,0.08838753,0.3499246,0.0004088686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05693495,0.002642524,0.8700946,0.001673163,0.0002545881,0.001768508,0.02858412,0.02222913,0.01581844],"genre_scores_gemma":[0.1365777,0.001506638,0.8099174,0.0002805832,0.0001590982,0.0008524674,0.04480565,0.001037109,0.004863421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01329689,"threshold_uncertainty_score":0.02021801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01115126045612661,"score_gpt":0.2636369057100729,"score_spread":0.2524856452539462,"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."}}