{"id":"W4252037442","doi":"10.32920/ryerson.14649495","title":"Evaluation of the positional accuracy of subsurface utilities","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Underground infrastructure and sustainability","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Offset (computer science); Mains electricity; Sanitary sewer; Electricity; Computer science; Process (computing); Geospatial analysis; Transport engineering; Engineering; Remote sensing; Geography; Environmental engineering; Electrical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005812391,0.0001193558,0.0002000146,0.00002701399,0.00001969413,0.00001756127,0.0001884604,0.0001459066,0.001184901],"category_scores_gemma":[0.0002822784,0.00008923117,0.0001542496,0.00007881982,0.00007104415,0.00007132531,0.0001805971,0.0002307571,2.140144e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000192633,"about_ca_system_score_gemma":0.0004893957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001250722,"about_ca_topic_score_gemma":0.00005277599,"domain_scores_codex":[0.9987118,0.0001375223,0.0003066183,0.00012387,0.0006319458,0.00008822911],"domain_scores_gemma":[0.9983726,0.0001163746,0.00007508505,0.0004609216,0.0009608765,0.00001413635],"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":[0.000006997594,0.00005317809,0.003463141,0.001928065,0.0004317022,2.136264e-7,0.00235211,0.9766473,0.004239198,0.004340489,0.0009162251,0.005621406],"study_design_scores_gemma":[0.0004604484,0.00001969262,0.4046008,0.0002781398,0.0005959958,0.000005052967,0.006237588,0.2631596,0.09255121,0.2314476,0.0002164578,0.0004274314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9713846,0.0007905501,0.002622948,0.00007053393,0.0004768676,0.0002924597,0.00003042828,0.00002961368,0.024302],"genre_scores_gemma":[0.9992535,0.00001991852,0.0005766149,0.000008653397,0.00002421874,0.00001249858,0.00003276407,0.000008996411,0.00006290492],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7134876,"threshold_uncertainty_score":0.9997281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02128271476046937,"score_gpt":0.2589466984346255,"score_spread":0.2376639836741562,"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."}}