{"id":"W3128110793","doi":"10.5194/essd-13-237-2021","title":"Country-level and gridded estimates of wastewater production, collection, treatment and reuse","year":2021,"lang":"en","type":"article","venue":"Earth system science data","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":727,"is_retracted":false,"has_abstract":true,"ca_institutions":"United Nations University Institute for Water, Environment, and Health","funders":"Global Affairs Canada; Government of Canada","keywords":"Wastewater; Reuse; Environmental science; Production (economics); Population; Per capita; Sewage treatment; Sustainability; Water resource management; Environmental engineering; Engineering; Economics; Waste management; Environmental health","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004696132,0.0001359286,0.0002022612,0.00004757975,0.0004958615,0.00007707526,0.0003875493,0.00002443623,0.00003190145],"category_scores_gemma":[0.0002330405,0.0001004259,0.000008189304,0.0006210286,0.0008326468,0.0006846676,0.001321651,0.00002616433,0.00001431916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007843163,"about_ca_system_score_gemma":0.00006040941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008886408,"about_ca_topic_score_gemma":0.001093287,"domain_scores_codex":[0.9983793,0.00004333288,0.0002328127,0.0007424933,0.0003491197,0.0002529743],"domain_scores_gemma":[0.9986234,0.00003245612,0.00008731638,0.001117092,0.00004476915,0.00009491107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007933097,0.0004339579,0.1567551,0.0003779658,0.0001579696,0.00007623895,0.006683915,0.0003722898,0.8084624,0.002741856,0.02054735,0.003311698],"study_design_scores_gemma":[0.001032429,0.0004135477,0.1802911,0.0002755881,0.0001243925,0.001046684,0.004993379,0.005882368,0.7942939,0.0008617469,0.01019044,0.0005944726],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947791,0.0004072437,0.00005992308,0.0007267574,0.0004744605,0.0002674804,0.000557285,0.00005221723,0.002675537],"genre_scores_gemma":[0.9948184,0.00004021672,0.004135069,0.00001184464,0.00003252273,0.00001275734,0.00004171002,0.000006738109,0.0009006768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.023536,"threshold_uncertainty_score":0.4095249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04231712539561183,"score_gpt":0.2413172619593172,"score_spread":0.1990001365637054,"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."}}