{"id":"W4386090951","doi":"10.1371/journal.pwat.0000166","title":"Global microbial water quality data and predictive analytics: Key to health and meeting SDG 6","year":2023,"lang":"en","type":"article","venue":"PLOS Water","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"UNICEF; World Health Organization","keywords":"Geospatial analysis; Water quality; Data quality; Database; Water security; Quality (philosophy); Analytics; Water resources; Data science; Computer science; Business; Geography; Ecology; Remote sensing; Biology","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":[0.0005960691,0.0001049845,0.0001847694,0.00001983455,0.0002804444,0.00002243798,0.000287684,0.00006482587,0.0004994172],"category_scores_gemma":[0.0000330191,0.00006951158,0.0000117835,0.00006903894,0.0001783199,0.0001146785,0.002391405,0.0001074409,0.0006033555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005320488,"about_ca_system_score_gemma":0.000005781612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009908368,"about_ca_topic_score_gemma":0.002922218,"domain_scores_codex":[0.9987342,0.000291602,0.000180517,0.0003390377,0.00006444044,0.000390175],"domain_scores_gemma":[0.9994076,0.00004259262,0.00002101601,0.0003812731,0.000005022573,0.0001424255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005812378,0.0002804487,0.3240112,0.000172903,0.0002153266,0.00001649056,0.03479328,0.0007277001,0.4897943,0.00003239554,0.1481101,0.001264538],"study_design_scores_gemma":[0.00358429,0.002319824,0.6592929,0.0001741185,0.000199663,0.00007952926,0.004089683,0.009439058,0.06433672,0.01154663,0.2427538,0.002183728],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948897,0.000007332747,0.00001555578,0.003998315,0.000058639,0.0001732043,0.0002285487,0.0000380849,0.0005906071],"genre_scores_gemma":[0.9964699,0.00002074882,0.0001918257,0.002448373,0.0000363789,0.000003703607,0.0006056686,0.000006519911,0.000216935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4254576,"threshold_uncertainty_score":0.7755116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05248808400279641,"score_gpt":0.3033872669255241,"score_spread":0.2508991829227277,"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."}}