{"id":"W4417336957","doi":"10.1109/tits.2025.3642011","title":"Boundary-Guided Real-Time Semantic Segmentation and Pixel-Level Quantification of Pavement Cracks","year":2025,"lang":"","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 Waterloo","funders":"Basic Research Program of Jiangsu Province; National Natural Science Foundation of China","keywords":"Pruning; Segmentation; Representation (politics); Inference; Feature (linguistics); Boundary (topology); Enhanced Data Rates for GSM Evolution; Pattern recognition (psychology)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000504949,0.0005861712,0.0007360028,0.000819201,0.000366136,0.0001690688,0.0002024915,0.0003519229,0.0001190358],"category_scores_gemma":[0.000004087319,0.0006513653,0.0002559224,0.0007411657,0.0001600297,0.0003714,5.995155e-7,0.0003830597,0.00004269838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004138735,"about_ca_system_score_gemma":0.000159432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008664182,"about_ca_topic_score_gemma":0.0001290305,"domain_scores_codex":[0.9959802,0.0001269761,0.002171108,0.0006622181,0.0005843397,0.0004751667],"domain_scores_gemma":[0.9982629,0.0001796963,0.0004133383,0.0005195852,0.000486938,0.0001375244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001721954,0.0002762768,0.0003755222,0.004032955,0.0009956539,0.00000569763,0.006048456,0.5563581,0.4119366,0.001838475,0.0001982432,0.0177618],"study_design_scores_gemma":[0.001462667,0.0003086548,0.004967596,0.003842031,0.001153322,0.000008883256,0.00577235,0.06976471,0.9103693,0.0001973694,0.001258171,0.0008949886],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1934239,0.0004361233,0.798343,0.00005347062,0.005478052,0.001456615,0.0003965288,0.0001485591,0.0002637838],"genre_scores_gemma":[0.9930077,0.003134727,0.001143307,0.00001690578,0.00008863043,0.0002379377,0.0001190484,0.00007363604,0.00217806],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7995839,"threshold_uncertainty_score":0.9995937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02536170484696415,"score_gpt":0.2734229811537139,"score_spread":0.2480612763067498,"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."}}