{"id":"W4392151222","doi":"10.1016/j.jenvman.2024.120394","title":"Predicting and analyzing the algal population dynamics of a grass-type lake with explainable machine learning","year":2024,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; Saint Mary's University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Algal bloom; Water quality; Eutrophication; Environmental science; Generalization; Computer science; Machine learning; Ecology; Mathematics; Biology; Phytoplankton","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":[0.0002600014,0.0002243753,0.0001759649,0.000279113,0.0003044089,0.0002725641,0.0003515776,0.0003741917,0.0003739644],"category_scores_gemma":[0.001026191,0.0001664381,0.0002818706,0.0002731083,0.0002397298,0.0004354433,0.0002196221,0.00033095,0.00003668712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007113883,"about_ca_system_score_gemma":0.0006394087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03716042,"about_ca_topic_score_gemma":0.04021687,"domain_scores_codex":[0.9999568,0.000008869366,0.0000026209,0.00001261106,0.000006987786,0.00001203916],"domain_scores_gemma":[0.9996941,0.0001769645,0.00004039514,0.00001634014,0.00004365066,0.00002864788],"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":[0.00006492539,0.00007377553,0.05459798,0.00001253769,0.0000458334,0.00007847488,0.0000553119,0.9308218,0.003275402,0.0005605324,0.0002002788,0.01021306],"study_design_scores_gemma":[0.000001738144,0.000005108292,0.003899122,2.269349e-7,0.00000222682,0.00000201386,0.000005811044,0.9958112,0.0001134665,0.0001464676,0.00001120355,0.000001455932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933997,0.00001575639,0.006226581,0.00007838661,0.000003403482,0.000003608065,0.00004300778,0.00002661071,0.0002029134],"genre_scores_gemma":[0.9982565,0.000006926067,0.001579295,0.000003830428,0.000002261229,0.000002438486,0.00004445784,0.000001547217,0.0001026913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03716042,"threshold_uncertainty_score":0.07388824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005304934060739995,"score_gpt":0.1954571582184385,"score_spread":0.1901522241576985,"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."}}