{"id":"W7100394157","doi":"","title":"Improving Infrastructure Management: Municipal Investments in Water and Wastewater Infrastructure","year":2009,"lang":"en","type":"article","venue":"","topic":"Water Resources and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Water infrastructure; Investment (military); Revenue; Wastewater; Water industry; Variety (cybernetics); Water use","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00199899,0.0002349946,0.000156078,0.0009662621,0.002251314,0.003891422,0.0009120135,0.001124047,0.003292171],"category_scores_gemma":[0.006656832,0.0001523582,0.0001396662,0.00328479,0.001259819,0.001992756,0.002680492,0.0008840545,0.0001808268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02422642,"about_ca_system_score_gemma":0.05874802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3685279,"about_ca_topic_score_gemma":0.6815724,"domain_scores_codex":[0.9969807,0.0007996084,0.00005495096,0.0000817531,0.0006237145,0.001459218],"domain_scores_gemma":[0.9980645,0.0003255056,0.0003889666,0.00007391719,0.0004273352,0.0007198169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001178896,0.0004807166,0.1030184,0.0009355507,0.0001045069,0.0008644704,0.01107668,0.01645987,0.003324572,0.1908407,0.06273022,0.6100464],"study_design_scores_gemma":[0.00006223321,0.0004306316,0.4893824,0.001353953,0.0001514152,0.0002426233,0.04050471,0.008186223,0.003278462,0.03108771,0.4252593,0.0000603201],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6100152,0.008888585,0.005263526,0.1397544,0.000125166,0.0003372646,0.0003286356,0.0001756459,0.2351116],"genre_scores_gemma":[0.9888832,0.002120449,0.002395799,0.0008976806,0.00002621921,0.00002593291,0.00008755689,0.0000055656,0.005557642],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3685279,"threshold_uncertainty_score":0.7327657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006224778095023562,"score_gpt":0.2336276251426797,"score_spread":0.2274028470476561,"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."}}