{"id":"W2101739091","doi":"10.1115/detc2010-28233","title":"An Engineering-to-Biology Thesaurus for Engineering Design","year":2010,"lang":"en","type":"article","venue":"","topic":"Design Education and Practice","field":"Engineering","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Civil, Mechanical and Manufacturing Innovation; Oregon State University; National Science Foundation","keywords":"Terminology; Computer science; Thesaurus; Process (computing); Domain (mathematical analysis); Engineering design process; Ingenuity; Function (biology); Field (mathematics); Software engineering; Artificial intelligence; Engineering; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002971558,0.0001401595,0.0001111099,0.0001192337,0.0000251483,0.00004717367,0.0001890428,0.0001028771,0.0002657062],"category_scores_gemma":[0.0002150923,0.0001399546,0.00003058769,0.000123544,0.000004181004,0.0001718209,0.000006059752,0.0001834459,0.0001171989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001534401,"about_ca_system_score_gemma":0.00001798803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000454541,"about_ca_topic_score_gemma":0.000003239409,"domain_scores_codex":[0.9994022,0.000008072669,0.000133294,0.0001483654,0.00003989676,0.0002681774],"domain_scores_gemma":[0.9991513,0.0003515549,0.000009540506,0.0002651283,0.00003904224,0.0001833654],"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":[0.00001202559,0.00003635362,0.00002854291,0.00003148984,0.00003430144,5.592481e-7,0.0003065812,0.1827978,0.7724674,0.02841463,0.004090607,0.01177966],"study_design_scores_gemma":[0.0001733158,0.00009942603,0.0003810229,0.000003088168,0.00001253261,0.00001171159,0.00002154053,0.5374555,0.04420158,0.0001333061,0.4171892,0.0003177407],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0161749,0.00001734617,0.9793149,0.0001729224,0.001860345,0.0003238284,0.000004538136,0.0007879586,0.001343238],"genre_scores_gemma":[0.7382503,0.000002743673,0.260964,0.000141746,0.0002983373,0.000132845,0.000007251339,0.00005087897,0.0001519508],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7282658,"threshold_uncertainty_score":0.5707182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01811482021564708,"score_gpt":0.2756772332481173,"score_spread":0.2575624130324702,"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."}}