{"id":"W4395676174","doi":"10.3390/app14093699","title":"Product Improvement Using Knowledge Mining and Effect Analogy","year":2024,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Design Education and Practice","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Analogy; Computer science; Ranking (information retrieval); Selection (genetic algorithm); Dimension (graph theory); Data mining; Product design; Product (mathematics); Industrial engineering; Machine learning; Engineering; 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":[0.000556982,0.00006192014,0.00005754463,0.00008500642,0.00009013536,0.0001359552,0.00006125109,0.00001338848,0.00002824147],"category_scores_gemma":[0.00001381336,0.0000503552,0.000007810734,0.0003309249,0.00006628281,0.0001194117,0.00001401298,0.00004475633,0.00002453591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001773905,"about_ca_system_score_gemma":0.0000368257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003686643,"about_ca_topic_score_gemma":0.000003494425,"domain_scores_codex":[0.9995663,0.0000107048,0.00006793682,0.0001666292,0.00006325415,0.0001252331],"domain_scores_gemma":[0.9997537,0.0001492164,0.000006761405,0.000053524,0.000004743314,0.00003203636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002885474,0.00001307519,0.0002356107,0.0002982843,0.00003163293,0.000002169489,0.003375489,0.001473326,0.4806891,0.00664622,0.003171044,0.5040611],"study_design_scores_gemma":[0.0003922504,0.0003583992,0.001932837,0.0001701731,0.0001625156,0.00009398063,0.002421875,0.5506784,0.2262189,0.00141425,0.2151387,0.001017633],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9321723,0.002849264,0.001266809,0.00008652616,0.001020757,0.0001487891,4.247422e-7,0.000168167,0.06228696],"genre_scores_gemma":[0.9980489,0.00001713814,0.001688728,0.00002157713,0.0001125042,0.00001275016,3.353779e-7,0.000005928107,0.00009211611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5492051,"threshold_uncertainty_score":0.2053425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0279645403929136,"score_gpt":0.312019620852078,"score_spread":0.2840550804591644,"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."}}