{"id":"W2791406502","doi":"10.1680/jenes.17.00021","title":"Using geographical information systems to address hydrogen sulfide in the sewer network of Leicestershire, UK","year":2017,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Odor and Emission Control Technologies","field":"Chemical Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hydrogen sulfide; Upstream (networking); Drainage basin; Environmental science; Geographic information system; Computer science; Sulfide; Environmental resource management; Risk analysis (engineering); Business; Geography; Computer network; Remote sensing; Materials science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005760149,0.0001515805,0.0001501904,0.001828331,0.0003316616,0.001157406,0.0003832291,0.0003013716,0.00373789],"category_scores_gemma":[0.005084353,0.0001332352,0.00008843345,0.003020665,0.0003012966,0.001007102,0.0009668017,0.0002006069,0.0004672258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002986551,"about_ca_system_score_gemma":0.002405296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3425435,"about_ca_topic_score_gemma":0.3862233,"domain_scores_codex":[0.9993161,0.0002328101,0.00007122228,0.00005820603,0.0002399132,0.00008167838],"domain_scores_gemma":[0.9978084,0.0007750219,0.0004783303,0.0000819445,0.0006974009,0.0001589935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002155848,0.00008907752,0.7203841,0.0007213161,0.00006254872,0.004083715,0.009960373,0.02191593,0.003637137,0.00323079,0.03581672,0.1998827],"study_design_scores_gemma":[0.00005450666,0.0002597829,0.847761,0.000378311,0.00005692259,0.0009988964,0.02727277,0.04441771,0.002447361,0.001465984,0.07478562,0.0001011235],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9492741,0.001534364,0.005649948,0.00532691,0.00005505137,0.0003611827,0.005889732,0.0003346101,0.03157409],"genre_scores_gemma":[0.9880387,0.0008610255,0.004674168,0.00008764612,0.00001236637,0.00007851208,0.001556054,0.00001418445,0.004677305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3425435,"threshold_uncertainty_score":0.6810994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01559633165864902,"score_gpt":0.2306856654420633,"score_spread":0.2150893337834143,"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."}}