{"id":"W2900760155","doi":"10.3390/s18113845","title":"Road Surface Monitoring Using Smartphone Sensors: A Review","year":2018,"lang":"en","type":"review","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":197,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Road surface; Anomaly detection; Key (lock); Anomaly (physics); Smartphone application; Computer science; Real-time computing; Plan (archaeology); Remote sensing; Transport engineering; Computer security; Engineering; Artificial intelligence; Geography; Civil engineering; Multimedia","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003549319,0.000960193,0.00234407,0.0001673293,0.000138765,0.00007300657,0.0003941891,0.000484351,0.00006181829],"category_scores_gemma":[0.00009262878,0.0008397444,0.0006807024,0.0006357895,0.00007477876,0.0001139028,0.0001004246,0.000844905,0.0004946184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005067426,"about_ca_system_score_gemma":0.00009217159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003413111,"about_ca_topic_score_gemma":9.68092e-7,"domain_scores_codex":[0.9970334,0.0001306379,0.0009836499,0.0005734011,0.0003697984,0.00090914],"domain_scores_gemma":[0.9984646,0.00007573324,0.0002530843,0.0008837039,0.0001330753,0.0001898122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002578695,0.00001099802,0.00001104445,0.1973596,0.0006915863,0.0002061984,0.0001775097,0.00488599,0.00003060451,0.00000416317,0.005309959,0.7913098],"study_design_scores_gemma":[0.00006728898,0.00001132146,0.000002219965,0.1434776,0.0007099604,0.0002062108,0.00002010401,0.0003789626,0.00007501039,0.000003152704,0.8542483,0.0007998196],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001716853,0.9896806,0.00001751519,0.000002644031,0.006017046,0.0007091064,0.00003203657,0.0005533618,0.001270821],"genre_scores_gemma":[0.00002961116,0.9932801,0.002234133,0.000008700718,0.003627716,0.00001573268,0.000021178,0.0003141255,0.0004687032],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8489383,"threshold_uncertainty_score":0.9994053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04244868212734403,"score_gpt":0.3120904995979918,"score_spread":0.2696418174706477,"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."}}