{"id":"W2024610583","doi":"10.1017/s0890060415000013","title":"Analogical thinking: An introduction in the context of design","year":2015,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Design Education and Practice","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Content (measure theory); Context (archaeology); Computer science; Action (physics); World Wide Web; Multimedia; Mathematics; History","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.002705906,0.001349757,0.0009580175,0.00271835,0.001839306,0.006464654,0.002278938,0.00321964,0.01026276],"category_scores_gemma":[0.005553801,0.0007202417,0.001400544,0.00373687,0.01264726,0.00826676,0.001882124,0.005484763,0.002416612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003532924,"about_ca_system_score_gemma":0.001429268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001214904,"about_ca_topic_score_gemma":0.001470533,"domain_scores_codex":[0.9976988,0.001382068,0.0001502372,0.000228372,0.0004328617,0.0001076786],"domain_scores_gemma":[0.993755,0.005460843,0.000133478,0.000232727,0.0002412988,0.0001765654],"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":[0.00002303027,0.00004113294,0.0001506358,0.0005084865,0.00001814813,0.0001673022,0.001502173,0.001565843,0.0001685663,0.944869,0.01000698,0.04097865],"study_design_scores_gemma":[0.000008183963,0.00002659572,0.0001412925,0.0004179868,0.000006084335,0.0002481552,0.0004357304,0.001455559,0.0001224621,0.8576514,0.1394737,0.00001291171],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005713867,0.2086929,0.3875041,0.01935944,0.005944587,0.0001282477,0.0001601971,0.0003712027,0.3721255],"genre_scores_gemma":[0.3626304,0.2302485,0.2989978,0.01232902,0.01592741,0.0009229694,0.0003758469,0.0005994603,0.07796863],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01026276,"threshold_uncertainty_score":0.03433233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09327727270580408,"score_gpt":0.2960551236818955,"score_spread":0.2027778509760914,"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."}}