{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001312066,0.001482614,0.0005335492,0.004489727,0.0006684299,0.001499871,0.001330819,0.0008089434,0.004223079],"category_scores_gemma":[0.003326881,0.000602301,0.00298904,0.002495228,0.0008082961,0.002963581,0.001818205,0.001298244,0.001542773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001528095,"about_ca_system_score_gemma":0.001650864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007014671,"about_ca_topic_score_gemma":0.01077549,"domain_scores_codex":[0.9988129,0.000373086,0.0001234436,0.0002966168,0.0003366508,0.00005728707],"domain_scores_gemma":[0.999102,0.0003821971,0.0001059469,0.000182133,0.0001885811,0.00003921594],"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":[0.00009219685,0.0001402664,0.002006022,0.0009470092,0.0002008237,0.0005698919,0.0008306663,0.2522102,0.007234517,0.314547,0.01120822,0.4100132],"study_design_scores_gemma":[0.0000163544,0.0000413304,0.0004281431,0.0001676394,0.00006352555,0.0001553786,0.0001493677,0.8008847,0.003487401,0.1502883,0.04428982,0.0000281082],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001764631,0.0001281197,0.9940951,0.0001453233,0.00001817781,0.00009569932,0.0006475145,0.00164931,0.001456043],"genre_scores_gemma":[0.06556241,0.0004176041,0.9271199,0.0001045188,0.0000295286,0.000474442,0.004048446,0.000293724,0.001949488],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007014671,"threshold_uncertainty_score":0.01412761,"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."}}