{"id":"W4311528069","doi":"10.3390/wevj13120231","title":"Toward Synthetic Data Generation to Enhance Skidding Detection in Winter Conditions","year":2022,"lang":"en","type":"article","venue":"World Electric Vehicle Journal","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Computer science; Set (abstract data type); Data set; Synthetic data; Artificial intelligence; Data mining; Network structure; Real-time computing; Machine learning","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":[0.0006119681,0.0001201622,0.000165155,0.0005465299,0.0002849393,0.0001279961,0.0003681727,0.00002310455,0.0001980648],"category_scores_gemma":[0.00002802558,0.0001384011,0.00004306597,0.001022553,0.000004071384,0.0002527309,0.00007797633,0.0006091781,0.00002223784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005846862,"about_ca_system_score_gemma":0.00003321533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002613146,"about_ca_topic_score_gemma":0.0004798575,"domain_scores_codex":[0.9986922,0.0001065925,0.0003744484,0.0002198104,0.0002633694,0.0003436291],"domain_scores_gemma":[0.9994976,0.0000343304,0.00005915283,0.0002758927,0.00003253292,0.000100494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002223091,0.00005201033,0.0003429532,0.00001079828,0.00005338724,0.00005445275,0.0002627939,0.3134068,0.5951035,0.00007339028,0.003439626,0.08717807],"study_design_scores_gemma":[0.0002241126,0.00006363988,0.001028734,0.00001499827,0.0000130823,0.0002293378,0.00005028322,0.9908426,0.003455814,0.00008033412,0.003824883,0.0001721399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9646015,0.0005632309,0.03179609,0.0006170937,0.001067715,0.0002570516,0.00002346642,0.0001065507,0.0009673198],"genre_scores_gemma":[0.99907,0.00002499089,0.00005807002,0.0001330734,0.0003508981,0.00005975469,0.00001366315,0.00003175173,0.000257785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6774359,"threshold_uncertainty_score":0.5643831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01475185721784091,"score_gpt":0.2448233025690681,"score_spread":0.2300714453512272,"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."}}