{"id":"W1994763309","doi":"10.1115/1.4027494","title":"Retrieving Causally Related Functions From Natural-Language Text for Biomimetic Design","year":2014,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Design Education and Practice","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Relation (database); Computer science; Sentence; Function (biology); Natural language; Identification (biology); Relationship extraction; Artificial intelligence; Natural language processing; Data mining; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002274485,0.0001767911,0.0003197024,0.0001822516,0.00008836223,0.00009047342,0.0002560089,0.0001728897,0.0003272467],"category_scores_gemma":[0.002846975,0.000153717,0.0001588373,0.0003070094,0.0000140563,0.0003338168,0.00001115861,0.0005042463,0.0001095085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001077349,"about_ca_system_score_gemma":0.00008400048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003874726,"about_ca_topic_score_gemma":6.620538e-7,"domain_scores_codex":[0.9982966,0.000411947,0.0006419323,0.0001465211,0.0002591455,0.0002438871],"domain_scores_gemma":[0.9953082,0.003829298,0.0002613526,0.0002150561,0.0001960798,0.0001900019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007209168,0.0001956812,0.000002854265,0.00005329079,0.000670686,0.00002922278,0.0008095052,0.02202535,0.7962103,0.001791123,0.08664146,0.09084959],"study_design_scores_gemma":[0.004114134,0.001665531,0.0002166735,0.0003368943,0.0009852852,0.0003652215,0.0007101077,0.8080536,0.1242059,0.01612322,0.04228814,0.0009353273],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002231943,0.00130261,0.9927044,0.0005206279,0.002660092,0.0002550203,0.000003672233,0.00009727028,0.0002243922],"genre_scores_gemma":[0.8585833,0.00007339994,0.1401449,0.0001996322,0.0004022862,0.000008461398,0.000004514451,0.00005479461,0.0005286899],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8563514,"threshold_uncertainty_score":0.6268397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0237644583712908,"score_gpt":0.2592052354026904,"score_spread":0.2354407770313996,"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."}}