{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00837024,0.001663695,0.002077088,0.01996631,0.001379385,0.004678453,0.002279367,0.001322523,0.006842388],"category_scores_gemma":[0.0452771,0.0005423568,0.001420639,0.01031963,0.001234564,0.008050294,0.003211902,0.000758249,0.003628053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00158277,"about_ca_system_score_gemma":0.003506213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003278228,"about_ca_topic_score_gemma":0.006068171,"domain_scores_codex":[0.9904944,0.003914196,0.001674938,0.001035936,0.002598076,0.0002825218],"domain_scores_gemma":[0.9527262,0.0361439,0.00257027,0.003465949,0.00449109,0.0006025895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001103001,0.0008235612,0.007702229,0.00890069,0.0003263262,0.0009985963,0.008125813,0.01094978,0.05404126,0.04091338,0.02470928,0.841406],"study_design_scores_gemma":[0.0008707809,0.001451553,0.01149048,0.003362113,0.0006060339,0.002868793,0.0138615,0.3010268,0.1039366,0.3105417,0.2493286,0.0006550507],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1094993,0.004394765,0.8258843,0.002061374,0.0001651348,0.002762964,0.01132418,0.02183256,0.02207541],"genre_scores_gemma":[0.1407518,0.0014775,0.8418491,0.0003191418,0.00006821968,0.001191232,0.01181185,0.0007049548,0.001826149],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01996631,"threshold_uncertainty_score":0.04426658,"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."}}