{"id":"W4401977862","doi":"10.3389/frwa.2024.1432280","title":"Advancing non-optical water quality monitoring in Lake Tana, Ethiopia: insights from machine learning and remote sensing techniques","year":2024,"lang":"en","type":"article","venue":"Frontiers in Water","topic":"Aquatic Ecosystems and Biodiversity","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre; Consortium of International Agricultural Research Centers; Styrelsen för Internationellt Utvecklingssamarbete; United States Agency for International Development","keywords":"Remote sensing; Water quality; Environmental science; Computer science; Artificial intelligence; Geography; Ecology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004723541,0.0005312286,0.0003451869,0.0008398684,0.0002028894,0.0007967277,0.0003502345,0.0004813516,0.000342191],"category_scores_gemma":[0.0005921033,0.0002169619,0.0002888393,0.0007346104,0.0002083435,0.0008784094,0.0002870751,0.0002062861,0.00009892447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003113523,"about_ca_system_score_gemma":0.0004179806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007313249,"about_ca_topic_score_gemma":0.01576805,"domain_scores_codex":[0.9998608,0.00004116495,0.0000127788,0.00003026113,0.00003591317,0.0000192198],"domain_scores_gemma":[0.9998024,0.00007472435,0.00005016497,0.000008088645,0.00005197611,0.00001270161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003057147,0.0004274785,0.6017635,0.000907708,0.0002726447,0.001029664,0.0007237287,0.04962078,0.1115041,0.0006992975,0.0004974387,0.2322479],"study_design_scores_gemma":[0.00003381126,0.0003026745,0.4483009,0.0001158605,0.0001980606,0.0003430978,0.001940017,0.5200744,0.02534237,0.001137249,0.002142608,0.00006910026],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886453,0.0009074382,0.008855349,0.0001669159,0.00002279229,0.00003521309,0.0001457182,0.00004273061,0.001178532],"genre_scores_gemma":[0.9855984,0.000606148,0.01318252,0.00005113323,0.000018082,0.0000211266,0.00009825321,0.000005628302,0.0004187776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007313249,"threshold_uncertainty_score":0.01454139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007351064612827416,"score_gpt":0.2286165347983898,"score_spread":0.2212654701855624,"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."}}