{"id":"W4232317444","doi":"10.1504/ejie.2018.10013866","title":"Latent Semantic Extraction and Analysis forTRIZ-Based Inventive Design","year":2018,"lang":"en","type":"article","venue":"European J of Industrial Engineering","topic":"Design Education and Practice","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Particle Physics","funders":"","keywords":"TRIZ; Computer science; Process (computing); Heuristic; Latent semantic analysis; Resolution (logic); Artificial intelligence; Natural language processing; Programming language","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":[0.0005753061,0.0001258125,0.0001644906,0.0003233429,0.0000313667,0.00003414861,0.00007561554,0.0000484701,0.00008468156],"category_scores_gemma":[0.000189731,0.0001351834,0.00005637855,0.0005877621,0.00002376176,0.0001542138,0.00001099544,0.0001768216,0.0000275336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003037845,"about_ca_system_score_gemma":0.00001752636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008680229,"about_ca_topic_score_gemma":8.709446e-7,"domain_scores_codex":[0.9992803,0.00008440539,0.0002531105,0.0001274815,0.0001086574,0.0001460154],"domain_scores_gemma":[0.9995068,0.0001513236,0.00006219471,0.0001467202,0.00005184219,0.00008115947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001140721,0.00008228346,0.002211108,0.00007532518,0.001257617,0.00002385489,0.0007582576,0.8995653,0.05213515,0.0001965554,0.002771206,0.04080928],"study_design_scores_gemma":[0.001158088,0.0002144866,0.01020466,0.0001114436,0.0007579584,0.000008476218,0.00008234039,0.9076194,0.07023682,0.000006868872,0.009154253,0.0004451913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2014358,0.0001187621,0.7937666,0.00005707674,0.001075022,0.0002435496,0.000005908765,0.0003133373,0.002983974],"genre_scores_gemma":[0.9958498,0.0000104115,0.003742294,0.00001201494,0.0002982603,0.000002690272,0.000007192844,0.00003367518,0.00004364263],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.794414,"threshold_uncertainty_score":0.5512617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06536545525848135,"score_gpt":0.2539248330574305,"score_spread":0.1885593777989491,"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."}}