{"id":"W4293765160","doi":"10.1061/9780784484364.005","title":"Geospatial Visual Analytics for Supporting Decision Making for Underground Utility Integrated Interventions","year":2022,"lang":"en","type":"article","venue":"International Conference on Transportation and Development 2022","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Geospatial analysis; Analytics; Computer science; Asset management; Visual analytics; Visualization; Data science; Data visualization; Sanitary sewer; Decision support system; Asset (computer security); Data analysis; Data mining; Engineering; Computer security; Business; Remote sensing","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0002635808,0.0001335571,0.0001293912,0.0001667931,0.0002532651,0.00006182334,0.000122042,0.00003549554,0.0005783765],"category_scores_gemma":[0.00003380594,0.0001439582,0.00007624269,0.0000794122,0.00001528845,0.0001037012,0.000008389898,0.0001518053,8.265403e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001494877,"about_ca_system_score_gemma":0.00008344805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004359702,"about_ca_topic_score_gemma":0.0001588622,"domain_scores_codex":[0.998937,0.000009902313,0.0004333201,0.0002084033,0.0002386313,0.000172705],"domain_scores_gemma":[0.9995694,0.00009209238,0.00008837844,0.00004980099,0.0001649408,0.00003535624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001465706,0.000262803,0.009486848,0.0004596022,0.0006270084,0.00001594942,0.006224157,0.05048,0.001922484,0.05662203,0.002988927,0.8694445],"study_design_scores_gemma":[0.004786502,0.000607041,0.09097508,0.0005862454,0.0001145182,0.00001124871,0.01713443,0.7801225,0.003774754,0.0240674,0.07651201,0.00130834],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1927808,0.00001485278,0.8047603,0.0000715557,0.001360821,0.0003486502,0.0002572162,0.00008127328,0.0003244829],"genre_scores_gemma":[0.9843228,0.00001020055,0.01410718,0.00006078143,0.00004829652,0.0002951403,0.001036088,0.00001778238,0.000101716],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8681362,"threshold_uncertainty_score":0.6332817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04585883499072958,"score_gpt":0.33833052245914,"score_spread":0.2924716874684104,"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."}}