{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00196909,0.001300858,0.001244726,0.006511478,0.0008863385,0.001665733,0.001836505,0.0007899468,0.003029384],"category_scores_gemma":[0.006455505,0.0004531851,0.002122068,0.004398116,0.0007518768,0.003589417,0.001565187,0.0007162181,0.0005987706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009543384,"about_ca_system_score_gemma":0.001659716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002690153,"about_ca_topic_score_gemma":0.002468809,"domain_scores_codex":[0.9970824,0.0006354856,0.0002674843,0.0007545636,0.001131621,0.0001284607],"domain_scores_gemma":[0.9972054,0.001444209,0.0002676211,0.0005103235,0.0005317878,0.00004068232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001416615,0.000487253,0.004318009,0.001103581,0.0002638701,0.0002686627,0.0004640282,0.04477402,0.008102824,0.01612641,0.00195253,0.921997],"study_design_scores_gemma":[0.0002212446,0.0006174271,0.01089712,0.0003353987,0.0009185824,0.0008930422,0.0007565379,0.8409611,0.03995094,0.07517279,0.02912239,0.000153556],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02572951,0.0006362618,0.9630694,0.0001759154,0.0000240779,0.000415146,0.0001844238,0.001361443,0.008403821],"genre_scores_gemma":[0.2532581,0.0009640331,0.7417036,0.0001086446,0.00003606121,0.0004181993,0.0008622977,0.00008615965,0.002562849],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006511478,"threshold_uncertainty_score":0.01041365,"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."}}