{"id":"W4391097306","doi":"10.52293/wes.4.2.5270","title":"Evaluating semi-arid lake water quality. A synergy of water quality indices, multivariate statistics and geospatial technology","year":2024,"lang":"en","type":"article","venue":"Water and Environmental Sustainability","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Geospatial analysis; Multivariate statistics; Water quality; Arid; Quality (philosophy); Multivariate analysis; Environmental science; Statistics; Hydrology (agriculture); Water resource management; Geography; Mathematics; Remote sensing; Geology; Ecology; Biology","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":[0.002422768,0.0003572772,0.0004406876,0.00009320655,0.0002951453,0.00009206348,0.0001872747,0.0002329477,0.002634929],"category_scores_gemma":[0.00004023439,0.0002137667,0.00007457628,0.00006896731,0.001221849,0.0003439856,0.001118724,0.0003170103,0.00006382573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004382954,"about_ca_system_score_gemma":0.0000147486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003354563,"about_ca_topic_score_gemma":0.0004604008,"domain_scores_codex":[0.9964213,0.0006041679,0.0008644398,0.0008461887,0.0005070002,0.0007569015],"domain_scores_gemma":[0.9992221,0.00009331379,0.00006788847,0.0004259607,0.00001128203,0.0001794542],"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":[0.0004807179,0.001047297,0.3154086,0.002230539,0.0002813748,0.00007750285,0.04435996,0.001720242,0.5642391,0.001618805,0.0001109826,0.06842483],"study_design_scores_gemma":[0.002753699,0.001083483,0.3935488,0.00007349729,0.0002694946,0.00006353926,0.008193739,0.006005636,0.4666515,0.0886699,0.03084952,0.001837277],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952109,0.00007294529,0.002118715,0.001375393,0.0001267932,0.0005113833,0.0003732122,0.00007828194,0.0001323323],"genre_scores_gemma":[0.9977632,0.00003330137,0.0007461284,0.00008900758,0.0000316613,0.00006599674,0.0004254602,0.00002612819,0.0008191494],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09758768,"threshold_uncertainty_score":0.9982768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01765720797721471,"score_gpt":0.3200604992825933,"score_spread":0.3024032913053786,"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."}}