{"id":"W3172032788","doi":"10.1080/08839514.2021.1935590","title":"Near Real-time Map Building with Multi-class Image Set Labeling and Classification of Road Conditions Using Convolutional Neural Networks","year":2021,"lang":"en","type":"article","venue":"Applied Artificial Intelligence","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Convolutional neural network; Leverage (statistics); Artificial intelligence; Set (abstract data type); Pipeline (software); Deep learning; Data set; Class (philosophy); Computer vision; Pattern recognition (psychology); Data mining","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":[0.0001053322,0.0001670434,0.0001972514,0.00004882679,0.0001893535,0.00007897448,0.00008188203,0.00008843738,0.00003227351],"category_scores_gemma":[0.00001474174,0.0001721141,0.00002969772,0.0002188683,0.000206915,0.0001274367,0.00003667039,0.0002024877,0.000007594314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006125771,"about_ca_system_score_gemma":0.00003723632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000311549,"about_ca_topic_score_gemma":0.00001493557,"domain_scores_codex":[0.998983,0.00001659495,0.0003384177,0.0002451621,0.0001334583,0.0002833156],"domain_scores_gemma":[0.9994746,0.00006353558,0.00007847768,0.0001705335,0.0001491813,0.00006365447],"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":[0.00001168351,0.00000900478,0.00006388385,0.00002500729,0.00002179758,0.000005223434,0.0001520752,0.4813945,0.5102969,0.00545055,0.00001216554,0.002557276],"study_design_scores_gemma":[0.00004489255,0.000008897067,0.0004688271,0.00005034364,0.0000272824,0.00001419456,0.0004591871,0.8922715,0.105859,0.0006019307,0.0000234994,0.000170456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.674661,0.00008488508,0.3245642,0.00002144478,0.0002376382,0.0001400245,0.00002201994,0.00009987578,0.0001689158],"genre_scores_gemma":[0.9598843,0.00002170633,0.03982319,0.00001149245,0.000165284,0.00001347657,0.00004384632,0.00002942089,0.000007290133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.410877,"threshold_uncertainty_score":0.7018609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02865114961358872,"score_gpt":0.2744648216762371,"score_spread":0.2458136720626484,"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."}}