{"id":"W385412081","doi":"","title":"Identification of Road Irregularities via Vehicle Accelerations","year":2010,"lang":"en","type":"article","venue":"Research Portal (Queen's University Belfast)","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Queen's University; Queen's University Belfast","keywords":"Accelerometer; Range (aeronautics); Computer science; Acceleration; Entropy (arrow of time); Iterative method; Principle of maximum entropy; Algorithm; Simulation; Engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0002992739,0.00009820959,0.0001225198,0.0003076544,0.0002546557,0.00004005856,0.0002881812,0.0001179392,0.0001563282],"category_scores_gemma":[0.00004178653,0.0001138029,0.00006029381,0.0003852387,0.0001875507,0.0004284375,0.00008668229,0.0005644031,0.00002419314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005619983,"about_ca_system_score_gemma":0.00006087245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003104857,"about_ca_topic_score_gemma":0.00005841472,"domain_scores_codex":[0.9989132,0.00003518776,0.0001684463,0.0001618755,0.0003766593,0.0003445833],"domain_scores_gemma":[0.9991826,0.0000346562,0.00003457014,0.0003085998,0.0003369991,0.0001025462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003935756,0.0000873349,0.006817248,0.0002083347,0.0001362905,0.0001321925,0.001313403,0.002471899,0.9297465,0.04328974,0.004889496,0.01086821],"study_design_scores_gemma":[0.001167285,0.000136591,0.2897719,0.0000929496,0.0000522801,0.00001790958,0.007028122,0.01017055,0.6558049,0.001752002,0.03327651,0.0007289238],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874095,0.000006004635,0.001767961,0.0001706438,0.0004100221,0.0001882965,0.0000289362,0.0001313159,0.009887336],"genre_scores_gemma":[0.9970154,0.00003083471,0.0002487926,0.000001767073,0.0001426906,0.000001553792,0.00001981816,0.00001646688,0.002522671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2829547,"threshold_uncertainty_score":0.4640746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01153906250036676,"score_gpt":0.2520167665195168,"score_spread":0.2404777040191501,"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."}}