{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02327443,0.001197044,0.002229482,0.006376599,0.0005528937,0.00711088,0.003063487,0.002241872,0.003769433],"category_scores_gemma":[0.05790279,0.0006069653,0.002040063,0.01301927,0.001488805,0.01249093,0.00565769,0.00414376,0.002066714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002385931,"about_ca_system_score_gemma":0.006688874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009354949,"about_ca_topic_score_gemma":0.008032679,"domain_scores_codex":[0.9902439,0.003957326,0.001500372,0.001143422,0.002804338,0.0003507541],"domain_scores_gemma":[0.9452456,0.0249701,0.005131286,0.007140893,0.01544955,0.002062616],"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":[0.0002345567,0.0001235607,0.03537247,0.0121482,0.001251916,0.000230192,0.0006239185,0.01287396,0.001490038,0.06772513,0.339996,0.5279301],"study_design_scores_gemma":[0.00006572228,0.0001064392,0.01668306,0.01129543,0.0005031354,0.0002145548,0.001531826,0.01648825,0.002021245,0.1379279,0.8129842,0.000178163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.02297289,0.2684925,0.1773525,0.3341627,0.005625719,0.0009839506,0.1498922,0.006936491,0.03358117],"genre_scores_gemma":[0.178838,0.2504767,0.3112314,0.02512453,0.004376651,0.001175219,0.2247747,0.0009379009,0.003064856],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02327443,"threshold_uncertainty_score":0.1230885,"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."}}