{"id":"W2062160031","doi":"10.1139/l09-102","title":"Utility impact rating with subsurface utility engineering in project development","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Underground infrastructure and sustainability","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Washington State Department of Transportation; Pennsylvania Department of Transportation","keywords":"Process (computing); Risk analysis (engineering); Service (business); Construction engineering; Transport engineering; Computer science; Engineering; Operations research; Civil engineering; Business","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005335116,0.0003301117,0.0004325519,0.0005506651,0.00005544075,0.00009384711,0.0002435723,0.0001186446,0.00009562993],"category_scores_gemma":[0.0001657676,0.0002974473,0.00009657368,0.000616618,0.00002007661,0.0004787339,0.000006052378,0.0006687159,6.640287e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008331061,"about_ca_system_score_gemma":0.001325365,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004606486,"about_ca_topic_score_gemma":0.03505981,"domain_scores_codex":[0.9982531,0.00001853169,0.0006193288,0.0001670998,0.0002173076,0.0007246324],"domain_scores_gemma":[0.9990525,0.00005678954,0.00006695569,0.0002203245,0.0001393798,0.0004641019],"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":[0.00002271698,0.00001424666,0.04864452,0.0001872829,0.0000893946,0.000260871,0.002054931,0.9438931,0.0004842936,0.00004989218,0.0003819995,0.003916693],"study_design_scores_gemma":[0.001103554,0.0002361964,0.7757378,0.0003874946,0.00002399765,0.0003428323,0.0003047679,0.2132898,0.001468825,0.0001145517,0.006208302,0.0007818547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9692569,0.0006502377,0.02812328,0.00002710725,0.0001833902,0.0002064494,0.000003270377,0.00007716693,0.001472155],"genre_scores_gemma":[0.9974332,0.000004760416,0.002435553,0.00001082787,0.00007155906,0.000002096623,0.0000023759,0.00003364154,0.000005920311],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7306033,"threshold_uncertainty_score":0.9999478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00636813205012601,"score_gpt":0.1962736065777992,"score_spread":0.1899054745276731,"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."}}