{"id":"W4307381099","doi":"10.1115/1.4056076","title":"A Hybrid Semantic Networks Construction Framework for Engineering Design","year":2022,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bombardier (Canada); Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Word2vec; Information retrieval; Natural language processing; Artificial intelligence; Key (lock); Phrase; Thesaurus; Parsing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002151313,0.0001462134,0.0003576326,0.0002292459,0.0001800688,0.0000802081,0.0009531525,0.00005501822,0.00001965396],"category_scores_gemma":[0.000494945,0.0001397441,0.0002463796,0.0004043805,0.00001311134,0.0003547354,0.0001839327,0.0005942903,7.934627e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001664728,"about_ca_system_score_gemma":0.00008533111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.525982e-7,"about_ca_topic_score_gemma":1.805305e-8,"domain_scores_codex":[0.998233,0.0002570889,0.0005650136,0.0002209558,0.0004422669,0.0002817157],"domain_scores_gemma":[0.9977087,0.001208973,0.0004997643,0.0003008367,0.0001621988,0.0001195029],"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":[0.0001073236,0.00008036187,0.000001077722,0.000008188689,0.0001107324,0.00009298932,0.00002986898,0.7185278,0.003698449,0.2358597,0.001177843,0.04030572],"study_design_scores_gemma":[0.0001886318,0.0005985608,8.36421e-7,0.00002729696,0.00003848842,0.000517962,0.000007490544,0.7248951,0.009360417,0.2637861,0.0004569997,0.0001220903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00007798997,0.0002082261,0.9984194,0.0003393475,0.0005816758,0.0002611425,5.377545e-7,0.0001102439,0.000001409252],"genre_scores_gemma":[0.2161899,0.00002714362,0.7834485,0.0001404327,0.0001420106,0.00003192215,2.074094e-7,0.00001551601,0.0000043899],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2161119,"threshold_uncertainty_score":0.5698597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0248401259485364,"score_gpt":0.2625807066213537,"score_spread":0.2377405806728173,"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."}}