{"id":"W4367598895","doi":"10.3390/w15091736","title":"Adapted Water Quality Indices: Limitations and Potential for Water Quality Monitoring in Africa","year":2023,"lang":"en","type":"article","venue":"Water","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"World Bank Group","keywords":"Water quality; Quality (philosophy); Index (typography); Selection (genetic algorithm); Computer science; Process (computing); Statistics; Environmental science; Data mining; Mathematics; Machine learning; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001676628,0.0001598041,0.0001971635,0.0000847911,0.0002346393,0.00008919685,0.0001481615,0.0001040602,0.0004452396],"category_scores_gemma":[0.00001634873,0.00009155505,0.0000671267,0.00008835332,0.0001103127,0.0003373563,0.000264341,0.0001200763,0.001025452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009742602,"about_ca_system_score_gemma":0.000003328236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007560852,"about_ca_topic_score_gemma":0.0001539158,"domain_scores_codex":[0.9979444,0.0002649323,0.0004572479,0.000379441,0.0003043778,0.0006496706],"domain_scores_gemma":[0.9995754,0.00006116614,0.00003093678,0.0002166256,0.00001088023,0.0001050317],"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":[0.0002562139,0.0003103304,0.04929207,0.0001623809,0.00006875799,0.00001974279,0.09184642,0.00534645,0.8486808,0.0003174121,0.001196962,0.002502445],"study_design_scores_gemma":[0.001865804,0.0001062718,0.4012744,0.00002437363,0.00003094059,0.00000258219,0.002272278,0.0005675612,0.525965,0.008515269,0.05871766,0.0006579068],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942734,0.000003478167,0.0002801341,0.00402286,0.0002891618,0.0003337183,0.00003187086,0.00009122692,0.000674173],"genre_scores_gemma":[0.9961369,0.00000936849,0.0004532011,0.00008918954,0.00007518809,0.0001274658,0.0001301747,0.00001568119,0.002962814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3519823,"threshold_uncertainty_score":0.9997523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1432754935561779,"score_gpt":0.3344986731609612,"score_spread":0.1912231796047834,"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."}}