{"id":"W4405890268","doi":"10.1016/j.jag.2024.104347","title":"RTCNet: A novel real-time triple branch network for pavement crack semantic segmentation","year":2024,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Segmentation; Cartography; Geography; Computer science; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0003002506,0.0001085034,0.0001211694,0.000146987,0.00004818752,0.0001879411,0.00008325822,0.00005282077,0.0000266484],"category_scores_gemma":[0.000009861662,0.0000987073,0.00005631733,0.0001044347,0.000009951383,0.0007065762,0.00001075004,0.00009696406,0.0000110574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006348206,"about_ca_system_score_gemma":0.00002889939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002971779,"about_ca_topic_score_gemma":0.000003123618,"domain_scores_codex":[0.9990231,0.000002712144,0.0005072523,0.00006349691,0.0002717496,0.0001316376],"domain_scores_gemma":[0.9995126,0.00004783912,0.0001361954,0.00004467493,0.0002196855,0.0000390009],"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":[0.0002855765,0.00002544049,0.0003716915,0.0004630407,0.0005615548,0.000003433683,0.003954039,0.5651727,0.1133952,0.02139702,0.01138917,0.2829811],"study_design_scores_gemma":[0.00253036,0.0001327614,0.01892554,0.0004605131,0.00008157759,0.0000618683,0.0003507843,0.8735834,0.0182255,0.005777408,0.07951166,0.0003586048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5422173,0.0001283484,0.450848,0.0003878899,0.004090751,0.0004972728,0.00003552061,0.0001387157,0.001656176],"genre_scores_gemma":[0.9712735,0.0003625014,0.02624197,0.0002269126,0.001501682,0.00002397506,0.0001981079,0.00002296981,0.0001483923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4290562,"threshold_uncertainty_score":0.4025167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009352673317255188,"score_gpt":0.2272449419982582,"score_spread":0.217892268681003,"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."}}