{"id":"W4327558474","doi":"10.1016/j.jhydrol.2023.129402","title":"A comprehensive investigation of wetting distribution pattern on sloping lands under drip irrigation: A new gradient boosting multi-filtering-based deep learning approach","year":2023,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Irrigation Practices and Water Management","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Drip irrigation; Resampling; Computer science; Gradient boosting; Surface runoff; Boosting (machine learning); Feature selection; Artificial neural network; Artificial intelligence; Mathematics; Random forest; Environmental science; Machine learning; Hydrology (agriculture); Irrigation; Engineering; Geotechnical engineering","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":[0.0002426052,0.0002207608,0.0003110974,0.0009194876,0.0001734522,0.0002926482,0.0003560768,0.000314942,0.0006387185],"category_scores_gemma":[0.0002857527,0.0001341983,0.0004409943,0.0006821231,0.0001449956,0.000452569,0.0002210633,0.0002424096,0.0001361843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000221344,"about_ca_system_score_gemma":0.0002803542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009040053,"about_ca_topic_score_gemma":0.01330467,"domain_scores_codex":[0.9999346,0.000006468335,0.000003044343,0.00002183827,0.00001551164,0.00001851456],"domain_scores_gemma":[0.9998689,0.00002616615,0.0000213868,0.00001501295,0.00005086915,0.00001776133],"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":[0.0004019561,0.0005028602,0.2963989,0.0002321678,0.0002847606,0.0005279367,0.0002653405,0.3671716,0.07759277,0.001486359,0.004841708,0.2502936],"study_design_scores_gemma":[0.000004962387,0.00002949757,0.09668983,0.000006746176,0.00003472542,0.00004256361,0.00005640101,0.8997356,0.002432073,0.0003563341,0.0005968653,0.00001443565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9692629,0.0002299797,0.02828293,0.00008595967,0.00001771694,0.00001367342,0.0006658231,0.0002789407,0.001162071],"genre_scores_gemma":[0.9921424,0.00008242863,0.006577371,0.00001378906,0.00000931416,0.00000535398,0.0006078971,0.00001648078,0.0005448696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009040053,"threshold_uncertainty_score":0.01797485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05642105166376792,"score_gpt":0.2564877021555291,"score_spread":0.2000666504917612,"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."}}