{"id":"W4409897988","doi":"10.1017/s0890060425000083","title":"Enhancing TRIZ through environment-based design methodology supported by a large language model","year":2025,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Design Education and Practice","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"TRIZ; Computer science; Architectural engineering; Engineering; Systems engineering; Manufacturing engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01399356,0.00109276,0.000609197,0.002158198,0.0007589426,0.003545593,0.001764136,0.001340327,0.003267352],"category_scores_gemma":[0.01392352,0.0007130516,0.00131087,0.0007459842,0.00248215,0.004333576,0.003481381,0.001655873,0.0005788667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001580827,"about_ca_system_score_gemma":0.002588182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005385343,"about_ca_topic_score_gemma":0.000847653,"domain_scores_codex":[0.9878103,0.009053139,0.000645389,0.000721509,0.001526431,0.0002431808],"domain_scores_gemma":[0.9848986,0.01118379,0.0007203111,0.00148927,0.001502463,0.000205598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003858374,0.001530408,0.003937866,0.001702984,0.0001175964,0.0008739973,0.01978149,0.1437902,0.06040926,0.476078,0.002337072,0.2890553],"study_design_scores_gemma":[0.0002800993,0.001101849,0.0008634697,0.0006075862,0.0001492451,0.0005658616,0.003889815,0.7749441,0.04432731,0.1169991,0.05612932,0.0001422448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01450516,0.0000296048,0.9815014,0.0001782768,0.00001221267,0.0003995608,0.000025117,0.000411629,0.002936997],"genre_scores_gemma":[0.08716693,0.000048098,0.9109963,0.00005521599,0.000004067391,0.0007624953,0.00006086278,0.00006761083,0.0008384442],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01399356,"threshold_uncertainty_score":0.0740059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05799305597294572,"score_gpt":0.3134753793616325,"score_spread":0.2554823233886868,"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."}}