{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001254413,0.00120597,0.0009012222,0.001019649,0.0004034131,0.0006216419,0.001154336,0.0007948921,0.0006321426],"category_scores_gemma":[0.002286259,0.0002757887,0.00143797,0.0008600172,0.0001809679,0.0008688134,0.0003897508,0.0008164552,0.0002104602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007370376,"about_ca_system_score_gemma":0.00131165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04448447,"about_ca_topic_score_gemma":0.02739451,"domain_scores_codex":[0.9997031,0.00007272833,0.00002439568,0.0001000224,0.0000445749,0.00005516252],"domain_scores_gemma":[0.9993273,0.0003332946,0.00006833047,0.00003194126,0.0001931868,0.00004585544],"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":[0.0004434478,0.0002042423,0.03648705,0.0001863567,0.0002409871,0.000137386,0.00009958578,0.8390829,0.001969332,0.0004887845,0.003567838,0.1170921],"study_design_scores_gemma":[0.00002090447,0.00004684641,0.00453752,0.00001103899,0.00003440735,0.00001425387,0.00003116099,0.9943544,0.0004939617,0.0002171322,0.0002281759,0.00001030036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8955253,0.002131262,0.09385766,0.000880763,0.0001862734,0.00008887154,0.001940493,0.002639154,0.002750313],"genre_scores_gemma":[0.9527128,0.0004371629,0.04277293,0.000100606,0.00004296359,0.00006576229,0.003062633,0.0000825286,0.0007225223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04448447,"threshold_uncertainty_score":0.08845109,"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."}}