{"id":"W1951676085","doi":"10.24908/pceea.v0i0.3825","title":"USING BIOLOGICAL ANALOGIES FOR ENGINEERING PROBLEM SOLVING AND DESIGN","year":2011,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Design Education and Practice","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Task (project management); Biological engineering; Artificial intelligence; Management science; Software engineering; Engineering; Systems engineering; Bioinformatics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.004834051,0.001221587,0.000750937,0.005008088,0.001241812,0.004859963,0.002298688,0.001778436,0.01176702],"category_scores_gemma":[0.0224722,0.0005609372,0.001305117,0.002709724,0.006748645,0.008681077,0.004131944,0.0019458,0.001724899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002220427,"about_ca_system_score_gemma":0.001461905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001065283,"about_ca_topic_score_gemma":0.001469767,"domain_scores_codex":[0.9937106,0.003482242,0.0007048858,0.0006963063,0.001294801,0.0001112268],"domain_scores_gemma":[0.9873428,0.00967261,0.0007836678,0.001503565,0.0005324539,0.0001648534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00006193747,0.0001601363,0.001047056,0.001343318,0.00006864809,0.0003441514,0.001971006,0.01701989,0.002179157,0.7382483,0.001983753,0.2355727],"study_design_scores_gemma":[0.00004052008,0.0001223184,0.0005311312,0.0006650421,0.00003394528,0.0003884089,0.0007283781,0.0161477,0.002181123,0.8984331,0.08065993,0.00006834291],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01264335,0.005334849,0.9020086,0.003004756,0.0002540087,0.0002687243,0.0002055549,0.0004570474,0.07582304],"genre_scores_gemma":[0.1359527,0.004433154,0.8525846,0.0008435636,0.0001488767,0.000705074,0.0003636781,0.0001063924,0.004862009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01176702,"threshold_uncertainty_score":0.03936464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05980751717878739,"score_gpt":0.2367313624595713,"score_spread":0.1769238452807839,"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."}}