{"id":"W3004198865","doi":"10.3808/jeil.201900017","title":"A Comparison of Two Data-Driven Models to Predict Hypolimnetic Dissolved Oxygen Concentration: A Case Study of the Seymareh Reservoir in Iran","year":2019,"lang":"en","type":"article","venue":"Journal of Environmental Informatics Letters","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Berger (Canada); York University","funders":"Natural Sciences and Engineering Research Council of Canada; York University","keywords":"Hypolimnion; Artificial neural network; Upstream (networking); DPSIR; Water quality; Linear regression; Mean squared error; Computer science; Selection (genetic algorithm); Environmental science; Data mining; Hydrology (agriculture); Statistics; Machine learning; Mathematics; Engineering; Ecology; Eutrophication","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008797953,0.001030315,0.0005818335,0.0006182056,0.0004339491,0.0007844622,0.001006046,0.000944323,0.0003852019],"category_scores_gemma":[0.001313381,0.0003904155,0.0008743973,0.0004186522,0.0002432317,0.0006434164,0.0004266985,0.0007526206,0.00008077484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00159833,"about_ca_system_score_gemma":0.00144834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06242861,"about_ca_topic_score_gemma":0.04788219,"domain_scores_codex":[0.9997873,0.00006236575,0.000018349,0.00005499487,0.00004484199,0.00003223001],"domain_scores_gemma":[0.9992445,0.0004320722,0.00005277909,0.00002836749,0.000207873,0.0000344475],"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":[0.0001358448,0.0002708398,0.02036051,0.0001063386,0.00009753078,0.0002489102,0.0001006165,0.9573912,0.001526716,0.0003675948,0.0003799594,0.01901392],"study_design_scores_gemma":[0.00001238397,0.0000561109,0.002261885,0.00000427218,0.00001587518,0.000008677263,0.00005095225,0.9965964,0.0007705851,0.0001015351,0.0001134737,0.000007869419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9774095,0.000231122,0.01937345,0.0003492124,0.00003938657,0.00008447945,0.0002702232,0.0001977127,0.002044826],"genre_scores_gemma":[0.9903468,0.00009969182,0.00869647,0.00001894206,0.00000678271,0.00004601584,0.0002353631,0.00001059073,0.000539377],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06242861,"threshold_uncertainty_score":0.1241304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06944895141034266,"score_gpt":0.2996956154021044,"score_spread":0.2302466639917618,"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."}}