{"id":"W4284890575","doi":"10.1155/2022/8697421","title":"Outdoor Clothing Design for Traffic Safety Based on Big Data and Artificial Intelligence","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Artificial Intelligence Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Zhanjiang Science and Technology Bureau","keywords":"Clothing; Context (archaeology); Artificial intelligence; Restricted Boltzmann machine; Big data; Computer science; Machine learning; Artificial neural network; Simulation; Engineering; Transport engineering; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001031053,0.0001093421,0.0001682751,0.0001820907,0.0003717121,0.00005119687,0.0008568101,0.00002573241,0.000007013969],"category_scores_gemma":[0.00008289568,0.0001109951,0.00005864969,0.0003815785,0.00003645333,0.0005634863,0.00001598458,0.0002183545,0.000001279003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006491506,"about_ca_system_score_gemma":0.0001477486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001751503,"about_ca_topic_score_gemma":0.00001799266,"domain_scores_codex":[0.9984177,0.00006685036,0.0006801745,0.0003001458,0.0003727067,0.0001624393],"domain_scores_gemma":[0.9984248,0.0005022144,0.0004423392,0.0003994636,0.0001555007,0.00007572847],"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":[0.0001928105,0.00007790581,0.000003759424,0.000005273161,0.00000538614,0.000003686422,0.0009267878,0.6321465,0.001045329,0.005036462,0.00001835948,0.3605377],"study_design_scores_gemma":[0.0002000901,0.000919863,0.0006048713,0.00002985269,0.00003963065,0.00000960312,0.001329923,0.9682933,0.007300313,0.01726146,0.003795184,0.0002159213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01581093,0.00006030936,0.9813382,0.00180172,0.0005219718,0.0003787359,0.00005401859,0.00002931581,0.000004774665],"genre_scores_gemma":[0.7758965,0.00001810033,0.2237257,0.0002039948,0.0000884582,0.00002630351,0.00002718495,0.00001032849,0.000003399385],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7600856,"threshold_uncertainty_score":0.4526247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1125160523578863,"score_gpt":0.3293151129712746,"score_spread":0.2167990606133884,"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."}}