{"id":"W4412000964","doi":"10.1016/j.ecoinf.2025.103313","title":"Evaluation of machine learning methods for forecasting turbidity in river networks using Sentinel-2 remote sensing data","year":2025,"lang":"en","type":"article","venue":"Ecological Informatics","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakes Environmental (Canada); University of Guelph","funders":"Science and Engineering Research Council; Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Turbidity; Remote sensing; Computer science; Artificial intelligence; Machine learning; Oceanography; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006611084,0.0001370163,0.0002566534,0.00005021517,0.000146327,0.0000311054,0.0002530184,0.0001769746,0.00004275737],"category_scores_gemma":[0.003834414,0.0001039669,0.00004799975,0.0004629607,0.0001204768,0.0002799529,0.0006694775,0.0003199294,0.000002404626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003227175,"about_ca_system_score_gemma":0.00002449729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001169354,"about_ca_topic_score_gemma":0.0001173971,"domain_scores_codex":[0.9982026,0.0004196658,0.0006411779,0.0001733595,0.0002865256,0.0002767294],"domain_scores_gemma":[0.998587,0.0006681797,0.000351145,0.0002833216,0.00007653648,0.00003386205],"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":[0.000007666256,0.0000162237,0.002256842,0.00002276384,0.00001314259,4.560782e-7,0.0002726927,0.6900137,0.0003568366,0.000003386743,0.0001599385,0.3068763],"study_design_scores_gemma":[0.0003876206,0.00001779721,0.01335634,0.00006050769,0.00008741126,0.000007408013,0.0001202608,0.9839889,0.0001426426,0.00108526,0.0006344343,0.0001114259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4592101,0.00002515931,0.5371017,0.00003659897,0.0001606059,0.0004375995,0.000002210005,0.00002341958,0.003002555],"genre_scores_gemma":[0.3780119,0.000007099443,0.6217661,0.0001054335,0.00002163946,1.544809e-7,0.00005388482,0.000004269474,0.00002945484],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3067649,"threshold_uncertainty_score":0.4590429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1408989555186764,"score_gpt":0.3795157952755416,"score_spread":0.2386168397568652,"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."}}