{"id":"W2332935455","doi":"10.1115/detc2012-70732","title":"Automatic Extraction of Causally Related Functions From Natural-Language Text for Biomimetic Design","year":2012,"lang":"en","type":"article","venue":"","topic":"Design Education and Practice","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Information extraction; Parsing; Natural language processing; Natural language; Categorization; Relationship extraction; Artificial intelligence; Representation (politics); Question answering; Domain (mathematical analysis); Action (physics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00256174,0.002118658,0.0007929315,0.007668831,0.001006565,0.00193647,0.00148708,0.001298836,0.006481507],"category_scores_gemma":[0.01475911,0.0008114688,0.001528391,0.003730586,0.0009664016,0.004134212,0.001087336,0.001402779,0.004192916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001283829,"about_ca_system_score_gemma":0.002683001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001296152,"about_ca_topic_score_gemma":0.00192066,"domain_scores_codex":[0.9976798,0.0008039503,0.0003443543,0.000512064,0.00059421,0.0000657749],"domain_scores_gemma":[0.9807487,0.01388766,0.001659095,0.001394074,0.00215061,0.000159799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003519345,0.0005085262,0.006726098,0.005310468,0.0001341062,0.001831829,0.002940617,0.009270506,0.06175054,0.03253597,0.02499723,0.8536422],"study_design_scores_gemma":[0.000288399,0.0005253637,0.01719951,0.001697046,0.0005097582,0.003720561,0.003627198,0.3617112,0.2002629,0.1505587,0.2595408,0.0003586149],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0366512,0.0008558226,0.9280208,0.001088807,0.0001279589,0.001381706,0.01159756,0.01381125,0.006464944],"genre_scores_gemma":[0.07840267,0.0006189527,0.9056219,0.0001484982,0.00005392347,0.0007412028,0.01212224,0.0006993765,0.001591176],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007668831,"threshold_uncertainty_score":0.0216828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02135103804033923,"score_gpt":0.2841725328529365,"score_spread":0.2628214948125973,"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."}}