{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005738215,0.0001655254,0.0001619728,0.000262336,0.0001274538,0.00008771108,0.0001933902,0.000161117,0.00002014174],"category_scores_gemma":[0.00105514,0.0001632851,0.00005573853,0.0003406859,0.00001313722,0.000328188,0.00001491637,0.000172644,0.000001765166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008429429,"about_ca_system_score_gemma":0.0002527256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000929117,"about_ca_topic_score_gemma":0.0001730316,"domain_scores_codex":[0.9991384,0.000006172224,0.0002578588,0.0001573596,0.0001208976,0.0003193348],"domain_scores_gemma":[0.9991729,0.0001396245,0.0001505639,0.00007143443,0.000303764,0.0001617585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008214913,0.0005906513,0.1548809,0.006637863,0.00242382,8.791506e-7,0.05123591,0.1176906,0.2993921,0.1852207,0.1578028,0.02404159],"study_design_scores_gemma":[0.001146932,0.0001786131,0.2374585,0.0009100995,0.0005296135,0.00006764742,0.003666187,0.5934738,0.05731644,0.00450984,0.09812092,0.002621386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8268941,0.003557366,0.1018465,0.003779978,0.01479406,0.008023979,0.0001530777,0.002554088,0.03839686],"genre_scores_gemma":[0.9142323,0.00003194576,0.08527355,0.00006324634,0.0001207556,0.0000806347,0.000002828901,0.00004286207,0.0001519126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4757832,"threshold_uncertainty_score":0.6658571,"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."}}