{"id":"W2109364550","doi":"10.24908/pceea.v0i0.3715","title":"THE USE OF BIONICS AND SEMANTIC PANEL FOR A PRODUCT DEVELOPMENT","year":2011,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Design Education and Practice","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bionics; Product design; Product (mathematics); Computer science; Creativity; New product development; Manufacturing engineering; Meaning (existential); Industrial engineering; Engineering; Artificial intelligence; Business; Mathematics; Marketing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004288324,0.00009574716,0.00009580532,0.0001188745,0.0001455367,0.00006549242,0.0001504111,0.00005839832,0.000005025956],"category_scores_gemma":[0.001336381,0.00008059035,0.00003191299,0.0002436292,0.00001472522,0.0002173908,0.000009554137,0.00009803461,0.000001639883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004585065,"about_ca_system_score_gemma":0.0004250983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009492121,"about_ca_topic_score_gemma":0.001623042,"domain_scores_codex":[0.9993427,0.000004019071,0.0002434743,0.00009517537,0.0001294675,0.0001851898],"domain_scores_gemma":[0.9990805,0.0001258214,0.000182467,0.00008157436,0.0004372443,0.00009233355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006486561,0.0005682527,0.2010641,0.006312012,0.002139397,1.217538e-7,0.08304515,0.004580605,0.03256094,0.1211278,0.4205743,0.1279624],"study_design_scores_gemma":[0.0002124295,0.00001877241,0.2446974,0.0001667893,0.0001121535,0.000005611887,0.0007468715,0.007844597,0.02566822,0.0002784502,0.7198867,0.0003619584],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.978185,0.001388804,0.0006447005,0.006439752,0.006684372,0.002601756,0.00004424175,0.0002562268,0.003755127],"genre_scores_gemma":[0.9918371,0.00005485637,0.006516395,0.00005962714,0.00005760433,0.00008956008,0.000003026407,0.00002746645,0.001354353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2993124,"threshold_uncertainty_score":0.3286379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05165993753449968,"score_gpt":0.20704458816472,"score_spread":0.1553846506302203,"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."}}