{"id":"W4292458047","doi":"10.1155/2022/4684669","title":"Improvement of Multiclass Classification of Pavement Objects Using Intensity and Range Images","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Korea Institute of Construction Technology","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Feature (linguistics); Range (aeronautics); Pattern recognition (psychology); Object (grammar); Computer vision; Cognitive neuroscience of visual object recognition; Engineering","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":[0.0001312645,0.00006565134,0.0001692507,0.00009509376,0.00003084038,0.000002265059,0.0000418371,0.00001500544,0.000004209886],"category_scores_gemma":[0.00000530216,0.00006375017,0.00004590723,0.00008301447,0.00001867126,0.000157127,0.000002545193,0.0001144995,1.181295e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000795964,"about_ca_system_score_gemma":0.00001459757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007229695,"about_ca_topic_score_gemma":0.000004586256,"domain_scores_codex":[0.9992737,0.000006480055,0.0004075271,0.00005122621,0.0001852753,0.00007575059],"domain_scores_gemma":[0.9994592,0.00001321512,0.0002868318,0.00005278921,0.0001657743,0.00002217844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006014478,0.00001442286,0.00347589,0.0001033915,0.00002784265,0.000003126207,0.001502785,0.2245363,0.7654159,0.00001745586,0.000002164885,0.004840606],"study_design_scores_gemma":[0.001305004,0.0003044521,0.3874058,0.00009788842,0.00008526953,0.000008152363,0.006792944,0.003003936,0.6006399,0.000201685,0.00005401985,0.0001009572],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893904,0.0001914063,0.00982786,0.000009122504,0.0004604153,0.00009409389,0.00001406926,0.000006042618,0.000006610956],"genre_scores_gemma":[0.9926718,0.00007973753,0.007194751,0.000004462481,0.00003243494,0.000002346159,0.000003944634,0.000009206228,0.000001313547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3839299,"threshold_uncertainty_score":0.2599657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009304319417253596,"score_gpt":0.2299571426083365,"score_spread":0.2206528231910829,"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."}}