{"id":"W4413822041","doi":"10.1109/tits.2025.3597944","title":"Conditional Pavement Crack Data Generation for Selective Data Augmentation Using GANs","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Data mining; Environmental science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007279075,0.0007460983,0.0003558977,0.0004319524,0.0001451678,0.0004106725,0.00110346,0.0005279447,0.001638776],"category_scores_gemma":[0.002237316,0.0002769943,0.0005158812,0.0003180651,0.0004458434,0.0007651607,0.0009449499,0.001245725,0.0004746793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004131445,"about_ca_system_score_gemma":0.0003793193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001945274,"about_ca_topic_score_gemma":0.003806007,"domain_scores_codex":[0.9997398,0.0000601218,0.000009553425,0.00009575019,0.00006336136,0.00003136187],"domain_scores_gemma":[0.9992907,0.0003274398,0.0000465715,0.0001717354,0.0001393407,0.00002410896],"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":[0.0001928444,0.0001186157,0.003700984,0.0001025043,0.00005052951,0.0001254893,0.0001014455,0.8270817,0.02310058,0.004812863,0.006899166,0.1337133],"study_design_scores_gemma":[0.000004415253,0.00001885377,0.0002751906,0.000004312008,0.000003325975,0.00001924863,0.00000647561,0.9931202,0.004470196,0.001406392,0.0006674729,0.000004003621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08641107,0.0002094306,0.904491,0.0003102819,0.000102177,0.0001289273,0.0008526936,0.004081489,0.003412945],"genre_scores_gemma":[0.8074966,0.00009920202,0.1873478,0.0002888342,0.00003158009,0.0001882779,0.002170976,0.0003067093,0.00207011],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001945274,"threshold_uncertainty_score":0.005482256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09718679236277118,"score_gpt":0.329085887198328,"score_spread":0.2318990948355568,"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."}}