{"id":"W4283330994","doi":"10.3390/app12136358","title":"Construction and Application of Enterprise Knowledge Base for Product Innovation Design","year":2022,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Design Education and Practice","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"National Natural Science Foundation of China","keywords":"TRIZ; Computer science; Knowledge base; Construct (python library); Product (mathematics); Product design; Systems engineering; Manufacturing engineering; Industrial engineering; Engineering; Artificial intelligence; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008419655,0.00004042909,0.00004832697,0.0001244689,0.0001721705,0.00001534478,0.00007065704,0.000007833469,0.00001813067],"category_scores_gemma":[0.00003201585,0.00004246113,0.000005111481,0.0006314794,0.00007635222,0.000094275,0.00001216785,0.0000379934,0.000001725035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002027807,"about_ca_system_score_gemma":0.00004714336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001589597,"about_ca_topic_score_gemma":3.690027e-7,"domain_scores_codex":[0.9995938,0.00001771348,0.0001202343,0.000121291,0.000077071,0.00006991833],"domain_scores_gemma":[0.9997139,0.0001223352,0.00005245103,0.00006483636,0.00003422095,0.00001224404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004549505,0.00008672754,0.0003505659,0.0001036512,0.00001242648,2.572502e-8,0.002208989,0.02133275,0.4115427,0.181682,0.003539943,0.3790947],"study_design_scores_gemma":[0.001162478,0.0003210325,0.001379203,0.00001038821,0.00006381622,0.00003696686,0.008855784,0.2964142,0.4799735,0.03113536,0.1800296,0.0006176684],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0664399,0.0001871899,0.9248077,0.0001831098,0.000366315,0.0007823671,0.000006192561,0.00008253862,0.007144678],"genre_scores_gemma":[0.9685821,0.000004900655,0.03100354,0.00002307102,0.00002615601,0.0003297136,0.000005037432,0.000004017485,0.00002145159],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9021422,"threshold_uncertainty_score":0.1731514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03385987354822656,"score_gpt":0.2843288619913106,"score_spread":0.250468988443084,"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."}}