{"id":"W4323308057","doi":"10.1139/cjce-2022-0427","title":"Dynamic segmentation of smartphone sensor-based pavement functional condition","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Pavement management; Computer science; Segmentation; Pavement engineering; Image processing; Transport engineering; Reliability engineering; Civil engineering; Artificial intelligence; Engineering; Image (mathematics)","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":[0.0001391332,0.0001030659,0.0001417462,0.0005175396,0.00003113215,0.00001519139,0.00006009062,0.00004581384,0.0001060529],"category_scores_gemma":[0.00002444113,0.000111034,0.00006724539,0.0002677388,0.00001359642,0.0001063645,0.000002264381,0.0001432617,0.000006098719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002666555,"about_ca_system_score_gemma":0.0001346525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005821057,"about_ca_topic_score_gemma":0.002025463,"domain_scores_codex":[0.9992856,0.000005136948,0.000282231,0.00005119103,0.0001459523,0.0002298693],"domain_scores_gemma":[0.9995817,0.0000279439,0.00006015754,0.00006827636,0.00009704858,0.0001648564],"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":[0.000001906822,9.419723e-7,0.000780042,0.00008356784,0.0000410738,0.00003804087,0.0001024998,0.9461533,0.05123278,0.00003477405,0.001135839,0.0003952572],"study_design_scores_gemma":[0.00251331,0.0002174584,0.1697527,0.00109143,0.000124602,0.0001605575,0.001076942,0.7183681,0.09608727,0.0002988091,0.009538785,0.0007700189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8571434,0.0002438226,0.1377356,0.000072891,0.004265914,0.0001014636,0.00004219668,0.00008875052,0.0003059669],"genre_scores_gemma":[0.9991392,0.00001213084,0.0006263928,0.00001248287,0.0001360299,0.000002905757,0.00002451937,0.00002593294,0.00002039549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2277851,"threshold_uncertainty_score":0.4527836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00568085370245791,"score_gpt":0.1851353017579081,"score_spread":0.1794544480554502,"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."}}