{"id":"W3046048985","doi":"10.3390/ijgi9080479","title":"Daily Water Level Prediction of Zrebar Lake (Iran): A Comparison between M5P, Random Forest, Random Tree and Reduced Error Pruning Trees Algorithms","year":2020,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Forests","funders":"","keywords":"Mean squared error; Random forest; Pruning; Statistics; Standard deviation; Tree (set theory); Decision tree; Algorithm; Computer science; Correlation coefficient; Mathematics; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007863742,0.0001651939,0.0003713763,0.0001428894,0.0001036246,0.0001034934,0.0003757288,0.000106131,0.0002085677],"category_scores_gemma":[0.0003882652,0.0001190654,0.0001317853,0.0001126689,0.0001460969,0.002042873,0.000147796,0.0002847089,0.0000421576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009392167,"about_ca_system_score_gemma":0.00001752403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006056323,"about_ca_topic_score_gemma":0.00002903411,"domain_scores_codex":[0.9975539,0.00008618777,0.001150133,0.0001143377,0.0008989143,0.0001964988],"domain_scores_gemma":[0.9987706,0.0001211613,0.0007437989,0.00007843561,0.0001361311,0.000149854],"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":[0.007520358,0.0002214329,0.3063571,0.00008753996,0.0007744136,0.00002490728,0.03344559,0.3665088,0.02375426,0.00004802055,0.006817637,0.2544399],"study_design_scores_gemma":[0.02290671,0.001333624,0.4211515,0.0002670659,0.0002420395,0.0001704152,0.0005834991,0.5196409,0.01261096,0.000638856,0.02003505,0.0004193295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.935332,0.00001228948,0.06106984,0.002121163,0.0003457952,0.0001965304,0.0001069053,0.00002652352,0.0007889647],"genre_scores_gemma":[0.9954773,0.00001110097,0.003893247,0.0002580921,0.000210051,0.000003322441,0.0001214016,0.000007582629,0.00001793075],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2540206,"threshold_uncertainty_score":0.4855348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04662398621044315,"score_gpt":0.2698469896894758,"score_spread":0.2232230034790327,"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."}}