{"id":"W2060869072","doi":"10.1115/detc2009-86680","title":"Effective Analogical Transfer Using Biological Descriptions Retrieved With Functional and Biologically Meaningful Keywords","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":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Action (physics); Key (lock); Biological organism; Artificial intelligence; Quality (philosophy); Analogy; Human–computer interaction; Biological materials; Biochemical engineering; Linguistics; Epistemology; Engineering","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":[0.004821316,0.0006132779,0.0006306263,0.0003256188,0.0006436414,0.0005711808,0.0003142825,0.0003127014,0.0001392808],"category_scores_gemma":[0.0008875498,0.000422387,0.00008321543,0.0001186836,0.000845504,0.0004222031,0.0000433095,0.0005368728,0.000004127991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007353185,"about_ca_system_score_gemma":0.0002234703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000147325,"about_ca_topic_score_gemma":8.862876e-7,"domain_scores_codex":[0.9947802,0.003000326,0.00058212,0.0008889632,0.0003093511,0.0004390515],"domain_scores_gemma":[0.9915709,0.007269664,0.0001934526,0.0002271756,0.0003664421,0.0003723557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0264091,0.0005397628,0.0001886541,0.00006677466,0.001092553,0.000009877376,0.002540049,0.01873303,0.01191595,0.8899282,0.000842922,0.04773318],"study_design_scores_gemma":[0.01259984,0.007349258,0.01577992,0.0007950174,0.001041573,0.001263047,0.004395897,0.353058,0.01438723,0.5816104,0.004915265,0.002804579],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02671966,0.0004802329,0.9650753,0.003471029,0.0009099328,0.001842894,0.00003139114,0.0001472259,0.001322386],"genre_scores_gemma":[0.9491466,0.002447765,0.04542589,0.00168079,0.0003046131,0.0004440055,0.00004131974,0.00003358494,0.0004754097],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9224269,"threshold_uncertainty_score":0.9998228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1572137812953151,"score_gpt":0.3286757510187322,"score_spread":0.1714619697234171,"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."}}