{"id":"W4400782410","doi":"10.1029/2023ea003473","title":"Multi‐Model Machine Learning Approach Accurately Predicts Lake Dissolved Oxygen With Multiple Environmental Inputs","year":2024,"lang":"en","type":"article","venue":"Earth and Space Science","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Svenska Forskningsrådet Formas","keywords":"Computer science; Artificial intelligence; Environmental science; Oxygen; Machine learning; Chemistry","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":[0.0005700285,0.001023898,0.0005539434,0.000459733,0.0003866352,0.0007636455,0.0007801816,0.001091199,0.0008961936],"category_scores_gemma":[0.0009163892,0.0005674731,0.0009616695,0.0002379114,0.0002420581,0.0008508745,0.0005191376,0.000911682,0.0002397922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001124267,"about_ca_system_score_gemma":0.00133222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02350169,"about_ca_topic_score_gemma":0.01880917,"domain_scores_codex":[0.9998565,0.0000322782,0.000009810499,0.00005502565,0.00002010557,0.00002627976],"domain_scores_gemma":[0.9996428,0.0001858558,0.00003380085,0.00002180768,0.00008983482,0.00002577608],"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":[0.00006065171,0.00006032555,0.002917187,0.0000158802,0.00005309443,0.00002318414,0.00001768054,0.9845216,0.002206946,0.0001338299,0.000123893,0.009865732],"study_design_scores_gemma":[0.000001727851,0.000008583703,0.000164231,5.269244e-7,0.000002391785,8.57604e-7,0.000001049244,0.9995677,0.000205042,0.00003284826,0.00001352756,0.000001482468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8512074,0.0002815406,0.1438924,0.0003006185,0.00006732974,0.0000548056,0.0003101705,0.001447454,0.002438145],"genre_scores_gemma":[0.9865203,0.00003073013,0.01241286,0.00003322605,0.000008501541,0.00004811851,0.0001605308,0.00001861084,0.0007672841],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02350169,"threshold_uncertainty_score":0.0467298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02260093135339985,"score_gpt":0.2250782394658485,"score_spread":0.2024773081124487,"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."}}