{"id":"W2148276612","doi":"10.1115/detc2009-86681","title":"Supporting Biomimetic Design Through Categorization of Natural-Language Keyword-Search Results","year":2009,"lang":"en","type":"article","venue":"Volume 8: 14th Design for Manufacturing and the Life Cycle Conference; 6th Symposium on International Design and Design Education; 21st International Conference on Design Theory and Methodology, Parts A and B","topic":"Design Education and Practice","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Categorization; Computer science; Natural language; Process (computing); Natural language processing; Natural (archaeology); Information retrieval; Artificial intelligence; Programming language","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.008623172,0.0006360897,0.0007049799,0.0005299416,0.000482614,0.0005427555,0.0005778417,0.0002912075,0.0001038093],"category_scores_gemma":[0.001838214,0.0005141417,0.0001081026,0.0001633113,0.0005854572,0.0006444151,0.00006239966,0.0005248907,0.000009655999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007959027,"about_ca_system_score_gemma":0.0004075402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004572314,"about_ca_topic_score_gemma":7.335408e-7,"domain_scores_codex":[0.9933737,0.003669654,0.001046404,0.0008896895,0.0004705955,0.0005499477],"domain_scores_gemma":[0.9884672,0.009806647,0.0005352247,0.0003933996,0.0005036513,0.0002938824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.02880804,0.0007327272,0.00002444509,0.0002391875,0.001266481,0.00001055968,0.01910929,0.05196499,0.02432983,0.737634,0.004377509,0.131503],"study_design_scores_gemma":[0.008891938,0.002481475,0.0007611132,0.0006927011,0.0006251446,0.0002702825,0.007072905,0.5080505,0.1203215,0.346452,0.002601024,0.001779417],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002251799,0.0009360416,0.985791,0.004551316,0.001761521,0.00211848,0.00004039491,0.0001492221,0.002400253],"genre_scores_gemma":[0.9057643,0.003087094,0.08704276,0.001148571,0.0003225773,0.0003183694,0.00007804661,0.00005338525,0.002184918],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9035125,"threshold_uncertainty_score":0.999731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1027908757596722,"score_gpt":0.370387670596324,"score_spread":0.2675967948366518,"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."}}