{"id":"W3102791339","doi":"10.5194/egusphere-egu2020-1870","title":"Prediction of Chlorophyll and Phosphorus in Lake Ontario by Ensemble of Neural Network Models","year":2020,"lang":"en","type":"article","venue":"","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Eutrophication; Water quality; Environmental science; Artificial neural network; Self-organizing map; Chlorophyll a; Secchi disk; Turbidity; Hydrology (agriculture); Computer science; Nutrient; Machine learning; Ecology; Chemistry; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003883465,0.0005549698,0.0003702946,0.0003894256,0.0003998432,0.0004261752,0.000487736,0.0004194241,0.0003112383],"category_scores_gemma":[0.001069755,0.000213313,0.0003792531,0.0004434369,0.000146321,0.00038055,0.0003081832,0.0002950227,0.00006162991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002454271,"about_ca_system_score_gemma":0.001547009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4728168,"about_ca_topic_score_gemma":0.4661775,"domain_scores_codex":[0.999861,0.00001618297,0.000009425219,0.00004064861,0.00003967969,0.0000330526],"domain_scores_gemma":[0.9996645,0.00008588061,0.0000502301,0.00001654621,0.0001581064,0.00002456164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001376378,0.00009406773,0.06764551,0.00003779326,0.0001236326,0.0001163108,0.0001159328,0.8888448,0.002568695,0.0001106769,0.0005635335,0.03964132],"study_design_scores_gemma":[0.000002577647,0.00001617389,0.01083649,0.000001415685,0.00001420399,0.000004173246,0.00001977568,0.9886917,0.0002864076,0.00003825376,0.00008403424,0.000004845258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921857,0.0001614311,0.00653033,0.0000717876,0.00001236121,0.00001082879,0.000200587,0.00007361275,0.0007533529],"genre_scores_gemma":[0.9972051,0.00006701861,0.001937062,0.000006419417,0.000003362047,0.000009211891,0.0002468923,0.000002674245,0.0005222476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4728168,"threshold_uncertainty_score":0.9401294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03635891927582627,"score_gpt":0.2132754057976398,"score_spread":0.1769164865218135,"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."}}