{"id":"W568381271","doi":"","title":"Must be Exact: When Done Right, Precision Milling Carries Heavy Benefits in Urban Areas","year":2006,"lang":"en","type":"article","venue":"Roads & bridges/Roads & bridges (Des Plaines, Ill. Online)","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Drum; Overlay; Engineering; Transport engineering; Civil engineering; Forensic engineering; Engineering drawing; Computer science; Mechanical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0006053411,0.001423161,0.001415768,0.0008922273,0.0005233475,0.000365239,0.0009818799,0.0006658181,0.00008282127],"category_scores_gemma":[0.0002391766,0.001402437,0.000435535,0.0008978098,0.000324395,0.001013626,0.000292708,0.001210335,0.00003912442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001006448,"about_ca_system_score_gemma":0.0001322119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005568053,"about_ca_topic_score_gemma":0.005580605,"domain_scores_codex":[0.9936155,0.0001206951,0.001746107,0.001335447,0.001023698,0.002158531],"domain_scores_gemma":[0.9972731,0.0002990485,0.0002922338,0.001206205,0.0005049678,0.0004244777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0007470097,0.0008666792,0.1701761,0.001184495,0.0004252355,0.001191416,0.004379686,0.6286818,0.01592905,0.001646176,0.06941777,0.1053545],"study_design_scores_gemma":[0.005255673,0.0004481885,0.6563033,0.003220162,0.0003371006,0.0006839481,0.001075371,0.06182326,0.04821391,0.001068166,0.2167229,0.004847996],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9782116,0.01125165,0.002638176,0.0004650506,0.002954862,0.0008730468,0.0007959979,0.001163263,0.001646364],"genre_scores_gemma":[0.9858438,0.001571401,0.004356306,0.0002529258,0.004883405,0.00009713628,0.001393504,0.0003686356,0.001232847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5668586,"threshold_uncertainty_score":0.9998518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0104515583716617,"score_gpt":0.2132387946611331,"score_spread":0.2027872362894714,"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."}}