{"id":"W2954930023","doi":"10.22260/isarc2019/0160","title":"Pavement Crack Mosaicking Based on Crack Detection Quality","year":2019,"lang":"en","type":"article","venue":"Proceedings of the ... ISARC","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Computer science; Frame (networking); Video camera; Artificial intelligence; Computer vision; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006809741,0.000424424,0.0004261305,0.001751449,0.0001293047,0.0007242361,0.0002934553,0.0003375217,0.001040012],"category_scores_gemma":[0.002602467,0.0002077264,0.000349087,0.0005552499,0.0003000254,0.0007905086,0.0005352068,0.0003775513,0.0002872279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004152111,"about_ca_system_score_gemma":0.0002885567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00305176,"about_ca_topic_score_gemma":0.004519151,"domain_scores_codex":[0.999536,0.00004480674,0.00002934432,0.0001312447,0.0002075393,0.00005111262],"domain_scores_gemma":[0.9980245,0.0003714447,0.0002445485,0.000154951,0.001101202,0.0001034044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00122647,0.0001356041,0.06689388,0.0003778449,0.0002723409,0.0001789344,0.0002079374,0.05397789,0.3452134,0.0007609503,0.001325458,0.5294293],"study_design_scores_gemma":[0.00002919602,0.0002166687,0.09886926,0.00004503397,0.0002104861,0.0002821161,0.0001014703,0.7876155,0.1110351,0.0004742935,0.001064315,0.00005668048],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6070523,0.0007580902,0.387592,0.00009331691,0.0000577589,0.0001408811,0.0003162415,0.001019765,0.002969624],"genre_scores_gemma":[0.9280143,0.0002592325,0.07016852,0.00001677537,0.00001760066,0.00002047555,0.0003581379,0.00006366777,0.00108127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00305176,"threshold_uncertainty_score":0.006067991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006817415330286723,"score_gpt":0.2103999629399293,"score_spread":0.2035825476096425,"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."}}