{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002678222,0.001105494,0.0003878625,0.002490463,0.0009230372,0.004251936,0.0008995034,0.001779325,0.01143673],"category_scores_gemma":[0.00404061,0.0004725931,0.0009159562,0.001731617,0.004422937,0.00627096,0.003351613,0.001946914,0.003185224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009477056,"about_ca_system_score_gemma":0.001318612,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005050523,"about_ca_topic_score_gemma":0.0004611392,"domain_scores_codex":[0.9963134,0.001919322,0.0001863969,0.0006033449,0.0008571561,0.0001204274],"domain_scores_gemma":[0.9976185,0.001197808,0.0001542756,0.0006182688,0.0002932544,0.0001178959],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001289694,0.00007843131,0.0006315758,0.0007593524,0.0000330431,0.0005086716,0.005853588,0.003336658,0.02580317,0.7520057,0.005558063,0.2053028],"study_design_scores_gemma":[0.000046369,0.0002689138,0.001287117,0.0005557644,0.00006365617,0.001756471,0.001974849,0.01971077,0.02899972,0.2655852,0.6796196,0.0001316352],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006039734,0.0009455169,0.9301018,0.0005046948,0.0001950615,0.0001757749,0.0001064412,0.001185613,0.0607454],"genre_scores_gemma":[0.1093732,0.0009556371,0.8691055,0.0002803174,0.00008717642,0.0005317395,0.0002319927,0.0005265009,0.01890792],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.999495,"threshold_uncertainty_score":0.03825969,"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."}}