{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":692,"total_is_capped":false,"direct_labels_cover":1,"predictions_cover":692,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"0e3a95b3d883","filters":{"topic":"Hydrological Forecasting Using AI"}},"results":[{"id":"W2055002961","doi":"10.1175/jamc-d-11-0143.1","title":"Wet-Bulb Temperature from Relative Humidity and Air Temperature","year":2011,"lang":"en","type":"article","venue":"Journal of Applied Meteorology and Climatology","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":724,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Relative humidity; Wet-bulb temperature; Air temperature; Dry-bulb temperature; Environmental science; Humidity; Approximation error; Density of air; Apparent temperature; Atmospheric temperature range; Atmospheric sciences; Range (aeronautics); Meteorology; Thermodynamics; Materials science; Mathematics; Physics; Statistics; Composite material","retraction":null,"screen_n_in":null,"score":{"opus":0.0146778681332152,"gpt":0.2153732959083431,"spread":0.2006954277751279,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005203107,0.000206901,0.0005368462,0.00005573061,0.0001986712,0.00000879721,0.0001893675,0.0005927653,0.001133789],"category_scores_gemma":[0.00009967131,0.0001484618,0.0000581759,0.0001116896,0.0009240163,0.0001398483,0.0001918893,0.0009256206,0.00003705861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002435907,"about_ca_system_score_gemma":0.00001266202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002133632,"about_ca_topic_score_gemma":0.0000289553,"domain_scores_codex":[0.9986546,0.0001525695,0.0004206743,0.0003293468,0.0001282813,0.0003145256],"domain_scores_gemma":[0.999042,0.0002676681,0.0003491642,0.0001484626,0.00001835212,0.0001743554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.008193354,0.001063338,0.3809008,0.00006375879,0.001026305,0.001684304,0.01529909,0.0004080932,0.5354155,0.03672257,0.009758716,0.009464225],"study_design_scores_gemma":[0.005942549,0.003618354,0.5489659,0.00005841089,0.0008430791,0.005799716,0.0009235121,0.0001799547,0.04722343,0.3723052,0.01292728,0.001212614],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904398,0.0002840094,0.00001793994,0.0008487707,0.0002119475,0.00008867249,0.000006819736,0.00001881779,0.008083226],"genre_scores_gemma":[0.9920273,0.00013429,0.006188099,0.001545677,0.00005990365,0.000003436721,0.000002763318,0.0000119541,0.00002657049],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4881921,"threshold_uncertainty_score":0.9997793,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2062087947","doi":"10.1016/j.jhydrol.2014.03.057","title":"Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review","year":2014,"lang":"en","type":"review","venue":"Journal of Hydrology","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":724,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Wavelet; Hydrological modelling; Robustness (evolution); Artificial intelligence; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.066665002833659,"gpt":0.3391637691388423,"spread":0.2724987663051833,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002939919,0.0004260544,0.003307643,0.0003280498,0.00005347049,0.000008197471,0.001285631,0.0003478581,0.0007115992],"category_scores_gemma":[0.0004346478,0.0003205297,0.0007006499,0.0007007703,0.0006430952,0.0001186951,0.0003537883,0.001291014,0.0002764658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002258725,"about_ca_system_score_gemma":0.0001096699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002851733,"about_ca_topic_score_gemma":0.00001821151,"domain_scores_codex":[0.9947221,0.0008429707,0.003016774,0.0004806569,0.0004245384,0.0005129504],"domain_scores_gemma":[0.9958152,0.0005773807,0.002841941,0.0005539218,0.00004180891,0.0001697305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001541139,0.0002167846,0.000003788927,0.003992477,0.00005496437,0.00009128824,0.00003000355,0.01178391,0.000002474379,0.0003072769,0.0005199237,0.9829817],"study_design_scores_gemma":[0.0000730953,0.0006149382,4.911359e-7,0.007361617,0.0006452173,0.002668394,0.000001082424,0.01053734,0.000005567943,0.03511018,0.9426191,0.0003629643],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003738984,0.9863552,0.01080204,0.0004102716,0.0001722109,0.0007067577,0.00001043951,0.00001351105,0.001155673],"genre_scores_gemma":[0.004366949,0.9931152,0.001720764,0.0005190818,0.0001497979,0.00005544473,0.000009014501,0.00003910658,0.0000245733],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9826187,"threshold_uncertainty_score":0.9999247,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2041534329","doi":"10.1016/j.jhydrol.2004.12.001","title":"Groundwater level forecasting using artificial neural networks","year":2005,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":665,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Artificial neural network; Groundwater; Computer science; Feedforward neural network; Feed forward; Groundwater resources; Water resources; Machine learning; Environmental science; Artificial intelligence; Aquifer; Engineering; Ecology; Control engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.09678811329542164,"gpt":0.274602285667172,"spread":0.1778141723717503,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008237732,0.0001635304,0.0003055601,0.00007197897,0.0001780769,0.00003481537,0.0003034654,0.0001613369,0.001157581],"category_scores_gemma":[0.0001385921,0.0001255913,0.0001429988,0.0001710119,0.0002584069,0.0003175624,0.0001704709,0.0005043048,0.00005672059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001795677,"about_ca_system_score_gemma":0.000009453319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005011357,"about_ca_topic_score_gemma":0.00006615852,"domain_scores_codex":[0.9982255,0.0001470511,0.0006490653,0.0001946717,0.0002670189,0.0005166566],"domain_scores_gemma":[0.9991501,0.0001090938,0.000443354,0.0001292652,0.0000203761,0.000147802],"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":[0.00008145022,0.00008278174,0.005988738,8.84764e-7,0.00001370851,0.0001097186,0.00007859425,0.9675732,0.003894192,0.00002063424,0.0002222991,0.02193384],"study_design_scores_gemma":[0.000246497,0.000418768,0.001081259,0.000006364852,0.00003405704,0.002694572,0.000003627669,0.9919769,0.0003043254,0.001077654,0.00201858,0.0001373539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9793445,0.00003805032,0.0183538,0.001096762,0.0004926568,0.00005048918,4.885733e-7,0.00001669631,0.000606563],"genre_scores_gemma":[0.9873511,0.000001497441,0.01054504,0.001040798,0.0009979125,4.875185e-7,5.674079e-7,0.00001873803,0.00004382675],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02440378,"threshold_uncertainty_score":0.9997555,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2046785557","doi":"10.1016/j.jhydrol.2011.06.013","title":"A wavelet neural network conjunction model for groundwater level forecasting","year":2011,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":636,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoregressive integrated moving average; Groundwater; Artificial neural network; Environmental science; Aquifer; Watershed; Moving average; Autoregressive model; Wavelet; Hydrology (agriculture); Computer science; Meteorology; Statistics; Time series; Machine learning; Artificial intelligence; Engineering; Mathematics; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.157313920977,"gpt":0.2549453713901056,"spread":0.09763145041310561,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008847577,0.0001495805,0.0002926348,0.00004410248,0.0001554833,0.00001274099,0.0002531651,0.0001441387,0.0005167697],"category_scores_gemma":[0.0001511427,0.0001109252,0.0001578879,0.00009828243,0.0001933788,0.0002252578,0.0001084852,0.0002787015,0.00002843656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008359046,"about_ca_system_score_gemma":0.00001197301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000354006,"about_ca_topic_score_gemma":0.00004388903,"domain_scores_codex":[0.9985669,0.00007695232,0.0005108527,0.0001929121,0.000172221,0.00048022],"domain_scores_gemma":[0.9992011,0.0001026106,0.0004258222,0.0001199929,0.00003119499,0.0001192903],"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":[0.0007091399,0.0001494715,0.008362723,0.00000666192,0.0000450568,0.00006240129,0.0007331644,0.9731696,0.001457429,0.0001167781,0.00502057,0.01016703],"study_design_scores_gemma":[0.0006076749,0.001195382,0.001455837,0.000006947939,0.00004613425,0.0008352965,0.000003367866,0.9769845,0.0001107657,0.01773719,0.0008925772,0.000124313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9255456,0.00001400124,0.07191829,0.0003006464,0.0005201423,0.0001147302,0.000001641364,0.00001760396,0.00156738],"genre_scores_gemma":[0.9627937,0.000001717062,0.03560398,0.001041903,0.0002687182,0.000005258935,0.000001329114,0.00001916825,0.0002642027],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03724816,"threshold_uncertainty_score":0.5658265,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2017587036","doi":"10.1016/s0022-1694(00)00214-6","title":"Daily reservoir inflow forecasting using artificial neural networks with stopped training approach","year":2000,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":617,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Institut National de la Recherche Scientifique; Hydro-Québec; Natural Sciences and Engineering Research Council; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Overfitting; Inflow; Backpropagation; Artificial neural network; Computer science; Generalization; Training (meteorology); Artificial intelligence; Multivariate statistics; Early stopping; Machine learning; Time series; Meteorology; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.07284336732487058,"gpt":0.2531215800790667,"spread":0.1802782127541961,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001089949,0.0002300709,0.0004610103,0.00008282351,0.0002879472,0.00004919733,0.0004083365,0.0001939446,0.001084593],"category_scores_gemma":[0.0001210899,0.0001680636,0.0001191111,0.0003928794,0.0004402684,0.0003230765,0.00008683301,0.0008143366,0.00001331448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001202655,"about_ca_system_score_gemma":0.00002697931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006460836,"about_ca_topic_score_gemma":0.00003663661,"domain_scores_codex":[0.9976645,0.0002688222,0.0006744216,0.0003048657,0.0004038681,0.0006835757],"domain_scores_gemma":[0.9989755,0.0001460473,0.0004465732,0.0001977127,0.00002247277,0.0002116543],"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":[0.000483169,0.0000725645,0.00625881,0.000002281167,0.00002479121,0.0002268665,0.0004500777,0.9715375,0.0007621635,0.000005680143,0.00004556752,0.02013058],"study_design_scores_gemma":[0.0005101276,0.0009433444,0.001150095,0.00001691938,0.00005231939,0.00379647,0.00003449682,0.9923068,0.00002613469,0.0004481162,0.0005259478,0.0001892556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990133,0.00003378479,0.005700041,0.0003085783,0.0001420033,0.00009905145,7.627143e-7,0.00002903568,0.003553706],"genre_scores_gemma":[0.9803312,0.000002269927,0.01870173,0.0004705826,0.0004098017,0.000001530486,0.000001595495,0.00003000177,0.0000512251],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02076933,"threshold_uncertainty_score":0.9998286,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3005619874","doi":"10.1007/s00477-020-01776-2","title":"Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model","year":2020,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":557,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Mean squared error; Water quality; Coefficient of determination; Artificial intelligence; Correlation coefficient; Convolutional neural network; Computer science; Statistics; Machine learning; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.07775161905009657,"gpt":0.351285002543724,"spread":0.2735333834936274,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00171261,0.0002731829,0.0002826052,0.00004877205,0.001102912,0.0001221053,0.0002630223,0.0001076858,0.0009381071],"category_scores_gemma":[0.0001409945,0.0002084084,0.00006008764,0.0001302758,0.0007866158,0.0002888369,0.00112249,0.001136303,0.0001505863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006499402,"about_ca_system_score_gemma":0.00001731302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00021399,"about_ca_topic_score_gemma":0.000005541749,"domain_scores_codex":[0.9959815,0.0004677053,0.0004173614,0.0008910757,0.001289125,0.0009532352],"domain_scores_gemma":[0.9989014,0.0001710325,0.00006741924,0.0002478014,0.000007412042,0.0006049218],"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":[0.0001111439,0.0002580301,0.07081006,0.00001678997,0.00003929145,0.00001760177,0.0005264518,0.8273707,0.09452532,0.00003137788,0.00002768328,0.006265526],"study_design_scores_gemma":[0.0004101651,0.0006554002,0.02833773,0.00001661246,0.00003410236,0.00001387243,0.0001158213,0.9676805,0.0005058667,0.0019114,0.00006816276,0.0002503525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7720239,0.00003020911,0.2266825,0.0001026139,0.00003308069,0.0003907745,0.00006053852,0.00005874393,0.0006176602],"genre_scores_gemma":[0.9906858,0.00009502197,0.008787464,0.00005424949,0.00007967289,0.00004001264,0.00008201005,0.00003723843,0.0001385335],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2186619,"threshold_uncertainty_score":0.9999751,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2081670007","doi":"10.1029/2010wr009945","title":"Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada","year":2011,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":490,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"McGill University","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Autoregressive integrated moving average; Artificial neural network; Mean squared error; Wavelet; Autoregressive model; Linear regression; Statistics; Econometrics; Demand forecasting; Moving average; Computer science; Environmental science; Meteorology; Mathematics; Time series; Artificial intelligence; Geography; Operations research","retraction":null,"screen_n_in":null,"score":{"opus":0.1257209807763414,"gpt":0.3559706911852541,"spread":0.2302497104089127,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030722,0.0003359478,0.0006210255,0.0001160978,0.0006678123,0.00009414524,0.0003716139,0.0002224847,0.0001419671],"category_scores_gemma":[0.0005718917,0.0002004128,0.00006026119,0.000292548,0.0007446467,0.0001663826,0.0008530014,0.0008117874,0.000003201098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001317913,"about_ca_system_score_gemma":0.00002033356,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.175667,"about_ca_topic_score_gemma":0.2426257,"domain_scores_codex":[0.9952186,0.00115473,0.0008275112,0.0007620599,0.0005224287,0.001514641],"domain_scores_gemma":[0.9984019,0.0007838947,0.0001449015,0.0002954837,0.00008453887,0.00028926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002584414,0.0002510425,0.4592338,0.0001246893,0.00005576419,0.0001555971,0.02427887,0.4033152,0.01937119,0.000004177864,0.001629889,0.08899539],"study_design_scores_gemma":[0.000375196,0.0003483288,0.00879173,0.0001023197,0.00001332813,0.00001316745,0.0002021613,0.9671481,0.02037602,0.0004334391,0.001936352,0.0002598648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982603,0.0001349335,0.0004480002,0.0001680254,0.0001359766,0.0006703013,0.00001471821,0.0000299745,0.0001377306],"genre_scores_gemma":[0.9830372,0.000003602644,0.01634561,0.00004190345,0.0002843059,0.00004649918,0.00004613414,0.0000441476,0.0001505389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5638329,"threshold_uncertainty_score":0.8298223,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2001676017","doi":"10.1029/2000wr900368","title":"Artificial neural network modeling of water table depth fluctuations","year":2001,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":443,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval; University of Waterloo; Hydro-Québec; Institut National de la Recherche Scientifique","funders":"Northwestern University; Rockefeller Foundation","keywords":"Recurrent neural network; Artificial neural network; Water table; Computer science; Aquifer; Hydrometeorology; Groundwater; Water level; Table (database); Calibration; Probabilistic neural network; Probabilistic logic; Artificial intelligence; Hydrology (agriculture); Data mining; Machine learning; Time delay neural network; Statistics; Geology; Geotechnical engineering; Mathematics; Meteorology; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.1098094248536929,"gpt":0.3242874505308015,"spread":0.2144780256771086,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00175219,0.00011958,0.000163344,0.00007747178,0.0005296941,0.00008245436,0.0004440832,0.00009532257,0.003569339],"category_scores_gemma":[0.00007277722,0.00006891634,0.00005264328,0.0003828963,0.0003984381,0.0001288635,0.0006734113,0.0003807378,0.001166124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007453206,"about_ca_system_score_gemma":0.000003240112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001424704,"about_ca_topic_score_gemma":0.0002321078,"domain_scores_codex":[0.9970725,0.0002970963,0.0003222364,0.0003743979,0.0008367214,0.001097003],"domain_scores_gemma":[0.9994118,0.0000611744,0.00001753655,0.0003321311,0.00003659475,0.0001408294],"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":[0.00006120068,0.00006364855,0.0046641,0.000003675152,0.000005139974,0.00001549289,0.001609471,0.9466223,0.04428907,0.000006035363,0.0002842484,0.00237558],"study_design_scores_gemma":[0.00008848622,0.0001617067,0.0003592871,0.000011228,0.000004999595,0.00001307666,0.00003823661,0.9626286,0.01402176,0.004236508,0.01829959,0.0001365159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9852761,0.0000142286,0.0004169824,0.0006642275,0.00004929592,0.0001832836,0.000001230457,0.00004886145,0.01334577],"genre_scores_gemma":[0.9973157,0.000003412737,0.0004556024,0.00005258021,0.0001877169,0.00001779119,0.00001440442,0.00002114886,0.001931651],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03026731,"threshold_uncertainty_score":0.9996116,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3009964072","doi":"10.1016/j.scitotenv.2020.137612","title":"Improving prediction of water quality indices using novel hybrid machine-learning algorithms","year":2020,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":426,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"","keywords":"Random forest; Pruning; Water quality; Algorithm; Statistics; Mean squared error; Computer science; Pearson product-moment correlation coefficient; Decision tree; Tree (set theory); Data mining; Mathematics; Machine learning; Ecology","retraction":null,"screen_n_in":null,"score":{"opus":0.03510598550374761,"gpt":0.2329694438179496,"spread":0.197863458314202,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001602546,0.0001357994,0.0001645374,0.00001699615,0.0004725461,0.00002177455,0.0008104315,0.00002937552,0.0004019099],"category_scores_gemma":[0.0001790035,0.00006699941,0.00008852407,0.0002130936,0.002302472,0.000216969,0.001500488,0.0002433261,0.00003263846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000152305,"about_ca_system_score_gemma":0.00001169392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006729644,"about_ca_topic_score_gemma":3.23198e-7,"domain_scores_codex":[0.9978758,0.0001135828,0.0003619564,0.0003631842,0.0009530243,0.000332489],"domain_scores_gemma":[0.999238,0.00004187567,0.0002940189,0.000328929,0.000004021527,0.00009322643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007611498,0.000028131,0.0004591701,0.000004497088,0.000002976794,1.187051e-7,0.0005413449,0.4146618,0.5835908,0.000004181886,7.504845e-7,0.0006985574],"study_design_scores_gemma":[0.0001497047,0.00012573,0.009547941,0.000009265718,0.0000234338,0.00001018202,0.00005553366,0.6319755,0.3579108,0.00007379623,0.00002998207,0.00008816118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970371,0.00001004558,0.001699542,0.0006310502,0.00009903923,0.0001968654,0.00002174572,0.00001951739,0.0002851084],"genre_scores_gemma":[0.997354,0.000001836823,0.002505713,0.00004609431,0.0000271204,0.000002275829,0.000001107624,0.000008709714,0.0000530957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2256801,"threshold_uncertainty_score":0.8483555,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2004803051","doi":"10.1016/j.cageo.2010.07.005","title":"Quantile regression neural networks: Implementation in R and application to precipitation downscaling","year":2010,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":402,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Environment and Climate Change Canada","funders":"Division of Ocean Sciences","keywords":"Quantile regression; Quantile; Overfitting; Cumulative distribution function; Artificial neural network; Downscaling; Statistics; Computer science; Mathematics; Probability density function; Precipitation; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.01082930111063916,"gpt":0.2858230807523144,"spread":0.2749937796416753,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004725959,0.00008366223,0.00007703635,0.00005309358,0.0001754198,0.00007680762,0.000196066,0.00003936453,0.00004760161],"category_scores_gemma":[0.00003511981,0.00006673031,0.00001252794,0.0004209531,0.0001536357,0.000239284,0.0001632466,0.0001094168,0.00001827782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002541023,"about_ca_system_score_gemma":0.000003107169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006252537,"about_ca_topic_score_gemma":0.0009204732,"domain_scores_codex":[0.999011,0.00004074778,0.0001697494,0.0003663785,0.0001881259,0.0002239772],"domain_scores_gemma":[0.9996533,0.00007479822,0.0000672478,0.0001102545,0.000005401449,0.00008900921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000901041,0.00002930839,0.1925806,0.000002186031,5.00257e-7,0.000001133452,0.001189725,0.2857034,0.02399424,0.00008544469,0.0001991349,0.4962053],"study_design_scores_gemma":[0.00007414327,0.00005643259,0.2673339,0.000006545447,7.15113e-7,0.00000285855,0.00003901094,0.7316698,0.0001457265,0.0002985954,0.0002980661,0.00007417225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9451988,0.000006636699,0.05363202,0.0005004241,0.0003396833,0.0002203424,5.745588e-7,0.00003794007,0.00006355743],"genre_scores_gemma":[0.9878266,0.000001756692,0.01182082,0.0002818966,0.00003874671,0.00001742481,0.000004991887,0.00000328969,0.000004425827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4961312,"threshold_uncertainty_score":0.2721183,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2084678032","doi":"10.1016/j.jhydrol.2010.05.040","title":"Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques","year":2010,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":357,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial neural network; Principal component analysis; Data pre-processing; Preprocessor; Computer science; Singular spectrum analysis; Time series; Context (archaeology); Series (stratigraphy); Data mining; Benchmark (surveying); Mode (computer interface); Pattern recognition (psychology); Artificial intelligence; Machine learning; Singular value decomposition","retraction":null,"screen_n_in":null,"score":{"opus":0.02989246379809274,"gpt":0.2506433346021303,"spread":0.2207508708040376,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001063255,0.0001371038,0.0003273151,0.00006583464,0.00011946,0.00002385935,0.0004396074,0.0001817562,0.0003037636],"category_scores_gemma":[0.0001925092,0.00009982177,0.00004334007,0.0001862771,0.0006111825,0.0005383933,0.0002224752,0.0005635582,0.000003073757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003069247,"about_ca_system_score_gemma":0.00002399869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004749946,"about_ca_topic_score_gemma":0.00003096152,"domain_scores_codex":[0.9986055,0.0000881072,0.0005325167,0.0002368891,0.0002776016,0.0002593457],"domain_scores_gemma":[0.998821,0.00006287447,0.0006561881,0.0003312783,0.00004384236,0.00008485347],"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":[0.0003259961,0.00008068761,0.00946298,0.000004835604,0.00002524854,0.00005170673,0.00008056176,0.5739682,0.4136046,0.000003899567,0.00005158692,0.002339781],"study_design_scores_gemma":[0.0001591248,0.0006939835,0.001398382,0.00001577923,0.00006400862,0.001393827,0.000002672931,0.9920274,0.003370057,0.0005176825,0.0002700947,0.00008697535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874616,0.00001438227,0.01185381,0.0002550939,0.0001803476,0.00008119748,0.000004851351,0.00003416306,0.0001145099],"genre_scores_gemma":[0.9784017,0.00000209607,0.02120996,0.00009576753,0.0002567372,6.494077e-7,0.000006170782,0.0000160197,0.00001091947],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4180593,"threshold_uncertainty_score":0.4070613,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2898661956","doi":"10.5194/hess-22-5639-2018","title":"HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community","year":2018,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":348,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Energy; State Key Laboratory of Hydraulics and Mountain River Engineering; Sichuan University; National Science Foundation","keywords":"Grassroots; Computer science; Field (mathematics); Data science; Process (computing); Baseline (sea); Citizen science; Artificial intelligence; Nature versus nurture; Data sharing; Sociology; Political science; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02148488182457135,"gpt":0.2763075689827972,"spread":0.2548226871582259,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.005427252,0.0002522048,0.0003364556,0.0001465113,0.006109369,0.0001545028,0.001032433,0.0001646666,0.0004239134],"category_scores_gemma":[0.0008922081,0.0001905767,0.00004406585,0.001273154,0.01405221,0.0007260326,0.0008424158,0.0005271017,0.0008310581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000536777,"about_ca_system_score_gemma":0.00004398563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006825066,"about_ca_topic_score_gemma":0.0002199481,"domain_scores_codex":[0.9964966,0.0008565007,0.0003952692,0.0007704764,0.0005720055,0.0009091159],"domain_scores_gemma":[0.9986449,0.0004573278,0.0002707107,0.0003379289,0.00003504846,0.0002541165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003470684,0.0005489152,0.7529472,0.0002285102,0.00005567029,0.000112834,0.01716647,0.06384521,0.02532442,0.01828222,0.00009767598,0.1210438],"study_design_scores_gemma":[0.001520424,0.01463244,0.06194995,0.0003438625,0.00006303605,0.002351339,0.004732832,0.8640342,0.004424503,0.01298219,0.03115099,0.00181418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9140055,0.0002373775,0.0001110947,0.0002534708,0.0004023289,0.0001899661,0.000001285439,0.0002368939,0.08456203],"genre_scores_gemma":[0.9979796,0.0000188299,0.001341127,0.0004534749,0.00008170353,0.00001763752,0.00000189077,0.000007925409,0.00009774345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.800189,"threshold_uncertainty_score":0.9999469,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1964298122","doi":"10.1007/s00521-004-0413-4","title":"An ensemble of neural networks for weather forecasting","year":2004,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":318,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"","keywords":"Artificial neural network; Computer science; Perceptron; Ensemble forecasting; Multilayer perceptron; Ensemble learning; Weather forecasting; Artificial intelligence; Wind speed; Machine learning; Meteorology","retraction":null,"screen_n_in":null,"score":{"opus":0.02977035902122557,"gpt":0.2707457742965049,"spread":0.2409754152752794,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000145194,0.00009304846,0.0001117958,0.00001310324,0.0002805571,0.00002237831,0.0001366574,0.00004673419,0.000008644524],"category_scores_gemma":[0.00001843045,0.00008171366,0.00003601937,0.0001469344,0.0001304007,0.00005400347,0.00006939462,0.00009100421,0.000002182167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001890104,"about_ca_system_score_gemma":0.000002174168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006921167,"about_ca_topic_score_gemma":0.00001148089,"domain_scores_codex":[0.9992555,0.00001716149,0.0001768536,0.0002608455,0.00007446534,0.0002151782],"domain_scores_gemma":[0.9995735,0.00009673258,0.00009034872,0.0001532518,0.00001014593,0.00007597778],"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":[0.000005061166,0.00004426735,0.00262156,0.000004839012,0.00000164455,2.37589e-7,0.00007687527,0.9032612,0.003122292,0.0004531503,0.00001241768,0.09039645],"study_design_scores_gemma":[0.0002194609,0.0001470856,0.00203633,0.000006870233,0.000009356111,0.00001742217,0.000009806809,0.9943376,0.0003539916,0.002482382,0.0002831479,0.00009653658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8074493,0.00001534157,0.1916861,0.0001383054,0.00002178687,0.0002452234,0.000002152588,0.00006456597,0.0003771958],"genre_scores_gemma":[0.9873663,5.752335e-7,0.01230845,0.0001732882,0.00009938872,0.00002149791,0.00000712714,0.00001191305,0.00001143607],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.179917,"threshold_uncertainty_score":0.3332186,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2052914342","doi":"10.1177/0309133312444943","title":"Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting","year":2012,"lang":"en","type":"article","venue":"Progress in Physical Geography Earth and Environment","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":310,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University; Université Laval","funders":"","keywords":"Benchmarking; Computer science; Benchmark (surveying); Artificial neural network; Streamflow; Transparency (behavior); Field (mathematics); Set (abstract data type); Flood forecasting; Modular design; Operations research; Data science; Management science; Flood myth; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.03431854684248457,"gpt":0.261922041435436,"spread":0.2276034945929514,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003364247,0.0001889734,0.0002509856,0.00004276864,0.0001405903,0.00001382042,0.00008976486,0.00004329067,0.00001733745],"category_scores_gemma":[0.00001420793,0.0001514503,0.00007935915,0.0001103042,0.0008317383,0.000172085,0.0002405561,0.0001272629,0.000001290453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000104378,"about_ca_system_score_gemma":6.74191e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001364088,"about_ca_topic_score_gemma":0.000003318735,"domain_scores_codex":[0.9986003,0.00005805917,0.0001979016,0.0003266152,0.0002112605,0.0006058866],"domain_scores_gemma":[0.9994623,0.0001555731,0.0001004082,0.0001217033,0.000001664852,0.000158334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003325005,0.0001233337,0.7346699,0.0000275011,0.00001414442,9.566484e-7,0.00104809,0.006551264,0.00004766922,0.0001880579,0.000001298051,0.2572945],"study_design_scores_gemma":[0.0007768678,0.0003950354,0.9141327,0.00007533322,0.00004815618,0.000007282878,0.0001335932,0.0761485,0.0002897482,0.006696459,0.0009467012,0.0003496233],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936372,0.005613933,0.0000727013,0.0001308581,0.00003925251,0.0002830279,0.000004204941,0.00001860225,0.000200192],"genre_scores_gemma":[0.9914395,0.0003562026,0.007998115,0.0000217918,0.0001141209,0.00005160126,0.000002402728,0.00001413339,0.000002151064],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2569449,"threshold_uncertainty_score":0.6175964,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2046498361","doi":"10.4296/cwrj3203179","title":"A Review of Statistical Water Temperature Models","year":2007,"lang":"en","type":"review","venue":"Canadian Water Resources Journal / Revue canadienne des ressources hydriques","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":299,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Statistical model; Parametric statistics; Parametric model; Computer science; Econometrics; Environmental science; Statistics; Mathematics; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.0448873083798638,"gpt":0.2703972339283417,"spread":0.2255099255484779,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00300736,0.001160213,0.003095605,0.0008021601,0.0008417249,0.0002735587,0.001925213,0.0009382264,0.003362035],"category_scores_gemma":[0.0003545738,0.0006891919,0.000885159,0.0005776226,0.00150145,0.0003415515,0.0003011338,0.002265633,0.0002696087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002736012,"about_ca_system_score_gemma":0.00002624019,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.08407719,"about_ca_topic_score_gemma":0.1821648,"domain_scores_codex":[0.992317,0.000949276,0.002251626,0.001083895,0.0004573746,0.002940883],"domain_scores_gemma":[0.9948129,0.0002053538,0.0006020299,0.0009340462,0.0001576697,0.003287967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004200145,0.00009298261,0.00002922855,0.1086229,0.0006433019,0.007773348,0.09527439,0.002138757,0.00005697735,0.00000667192,0.002191204,0.7831283],"study_design_scores_gemma":[0.00009948666,0.0001997085,9.368843e-7,0.06998754,0.0005270028,0.005128564,0.00001524667,0.00008154477,0.00003182012,0.0007453422,0.9223061,0.0008767772],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.07684164,0.9181356,0.00002317176,0.0001597278,0.0002551441,0.0007533219,0.0003143747,0.00005727569,0.003459727],"genre_scores_gemma":[0.001591255,0.9945755,0.0006295347,0.001056059,0.0004686651,0.00003750269,0.0003014794,0.0002222048,0.001117808],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9201148,"threshold_uncertainty_score":0.9995559,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3196897376","doi":"10.1038/s41598-021-96751-4","title":"Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":279,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Prince Edward Island","funders":"University of Southern Queensland","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Residual; Artificial neural network; Convolution (computer science); Machine learning; Deep learning; Range (aeronautics); Flood forecasting; Streamflow; Pattern recognition (psychology); Data mining; Flood myth; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.06876589877112961,"gpt":0.2884703094740232,"spread":0.2197044107028936,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002560187,0.0001922883,0.0002265888,0.00004914006,0.0006190215,0.0001953302,0.0001030061,0.0001639562,0.0008556682],"category_scores_gemma":[0.0003043365,0.0001691557,0.00006541968,0.0006119031,0.0007901091,0.000217682,0.0001523263,0.000284546,0.000004691792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001679783,"about_ca_system_score_gemma":0.00006291062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005644982,"about_ca_topic_score_gemma":0.0001395396,"domain_scores_codex":[0.9969314,0.0005649781,0.0004356507,0.001121951,0.000464307,0.0004817569],"domain_scores_gemma":[0.9988604,0.0001474038,0.0001525738,0.0005647003,0.00004718093,0.0002277894],"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":[0.00001603317,0.00007493096,0.1399107,0.000002443391,0.000005321637,0.0005842309,0.00002919585,0.8485615,0.004724403,0.000002260289,0.0004732715,0.005615759],"study_design_scores_gemma":[0.00008278833,0.00007762055,0.05376567,0.0000312488,0.00002839332,0.0007048913,0.000008616347,0.9437166,0.0006692288,0.0005422452,0.0002149652,0.0001577221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9623967,0.00004233527,0.03227287,0.00004632643,0.004689116,0.0001564258,0.000003606953,0.0001080984,0.0002845564],"genre_scores_gemma":[0.986603,6.887076e-7,0.01262265,0.0001477181,0.0001578034,0.000006015415,0.0002267144,0.00001608935,0.0002193414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09515513,"threshold_uncertainty_score":0.9368966,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2025651937","doi":"10.1061/(asce)1084-0699(2006)11:3(199)","title":"Application of Support Vector Machine in Lake Water Level Prediction","year":2006,"lang":"en","type":"article","venue":"Journal of Hydrologic Engineering","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":268,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Support vector machine; Structural risk minimization; Artificial neural network; Computer science; Autoregressive model; Multilayer perceptron; Mean squared error; Quadratic programming; Correlation coefficient; Machine learning; Minification; Perceptron; Artificial intelligence; Least squares support vector machine; Data mining; Mathematical optimization; Mathematics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.008079776733601744,"gpt":0.1883173802355078,"spread":0.1802376035019061,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005614261,0.00009439137,0.0001862436,0.0000829487,0.00001643202,0.000005048696,0.0001437639,0.00007897164,0.0002426759],"category_scores_gemma":[0.00004514132,0.00006407413,0.00005706297,0.0001305771,0.00003684472,0.0001138149,0.00004832459,0.0002077642,0.00001922039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007768532,"about_ca_system_score_gemma":0.000002888435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005078697,"about_ca_topic_score_gemma":0.00004418262,"domain_scores_codex":[0.999,0.00001879196,0.0004685154,0.0001059464,0.0002125584,0.0001941883],"domain_scores_gemma":[0.9996969,0.00002920938,0.0001407211,0.00008783609,0.000009091289,0.00003630874],"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":[0.00001368415,0.0000564044,0.06902535,0.000006440894,0.000003217937,0.000015638,0.00002682478,0.7929084,0.1375567,0.00001063756,0.00004773197,0.0003290589],"study_design_scores_gemma":[0.0006678239,0.0005485358,0.3056697,0.00002285887,0.00002185236,0.0002517325,0.000001412835,0.6606923,0.02573401,0.0005801489,0.005647254,0.0001624207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9860212,0.00001763469,0.01318719,0.0001434511,0.00009883188,0.0000621577,0.000007066514,0.00002008746,0.0004423873],"genre_scores_gemma":[0.9976386,0.000002789457,0.002222857,0.0000214217,0.00006519651,0.000002461571,0.00000642964,0.000008173955,0.00003208303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2366443,"threshold_uncertainty_score":0.2657131,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2072106824","doi":"10.1061/(asce)he.1943-5584.0000245","title":"Comparison of Multivariate Regression and Artificial Neural Networks for Peak Urban Water-Demand Forecasting: Evaluation of Different ANN Learning Algorithms","year":2010,"lang":"en","type":"article","venue":"Journal of Hydrologic Engineering","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":257,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Artificial neural network; Water resources; Multilayer perceptron; Multivariate statistics; Bayesian multivariate linear regression; Water supply; Linear regression; Backpropagation; Computer science; Conjugate gradient method; Levenberg–Marquardt algorithm; Regression; Regression analysis; Multivariate adaptive regression splines; Perceptron; Machine learning; Environmental science; Statistics; Algorithm; Mathematics; Environmental engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.05244877557447699,"gpt":0.3018759457534602,"spread":0.2494271701789832,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001907729,0.0001646962,0.0004282445,0.00007373431,0.00008743817,0.00001566575,0.0001490449,0.000145988,0.00004096091],"category_scores_gemma":[0.0007318701,0.0001013283,0.0001005607,0.00007485678,0.000096679,0.0001135879,0.0001050274,0.000518956,2.111943e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003425605,"about_ca_system_score_gemma":0.000003506578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006450953,"about_ca_topic_score_gemma":0.000002623361,"domain_scores_codex":[0.9984114,0.00009402265,0.0006683016,0.0001661943,0.000390165,0.0002698831],"domain_scores_gemma":[0.9989949,0.0002344964,0.0005232661,0.00009168295,0.00006480753,0.00009087502],"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":[0.00005662665,0.0000607055,0.01391263,0.00001417202,0.00001305705,0.000001481636,0.0002874512,0.7492378,0.2296222,0.000003622046,0.000005009746,0.006785225],"study_design_scores_gemma":[0.0004925829,0.0007620556,0.007320754,0.00004529864,0.00008281102,0.00003624036,0.00001079725,0.9657755,0.02517933,0.0001652705,0.00002707243,0.0001023289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9851461,0.0000723876,0.01414665,0.00005242302,0.0003784497,0.0001711278,6.657821e-7,0.00001443572,0.00001778203],"genre_scores_gemma":[0.9964168,0.000002281912,0.003405871,0.000003777395,0.0001471378,0.000003826687,0.000002893674,0.00001348949,0.000003903533],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2165377,"threshold_uncertainty_score":0.4132048,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3092762910","doi":"10.5194/hess-25-2045-2021","title":"Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network","year":2021,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":253,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Global Water Futures; Horizon 2020 Framework Programme; Janssen Pharmaceuticals; Canada First Research Excellence Fund; Österreichische Forschungsförderungsgesellschaft; European Commission; Bundesministerium für Bildung, Wissenschaft und Forschung; Google; Compute Canada; Nvidia","keywords":"Benchmark (surveying); Process (computing); Temporal resolution; Flood myth; Deep learning; Reservoir computing; Long short term memory; Memory model","retraction":null,"screen_n_in":null,"score":{"opus":0.0189434626163235,"gpt":0.202268328648595,"spread":0.1833248660322715,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007465091,0.0001728633,0.0002454675,0.00002659238,0.0009428106,0.00006251218,0.0001721526,0.0001426026,0.0003093722],"category_scores_gemma":[0.00004192883,0.0001243357,0.00003882589,0.0003750316,0.001382367,0.0001961072,0.0002576542,0.0001245653,0.0001343551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000455493,"about_ca_system_score_gemma":0.00001931138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004988206,"about_ca_topic_score_gemma":0.001311398,"domain_scores_codex":[0.9980245,0.0002268668,0.0002426681,0.0006607619,0.0003162925,0.000528917],"domain_scores_gemma":[0.9993908,0.0001714167,0.00008011379,0.0001951888,0.0000111453,0.0001513511],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004668952,0.00004421957,0.8445187,0.00002182503,0.00001585326,0.000156925,0.0002302588,0.1484379,0.00435668,0.00002700704,0.0001992014,0.001944772],"study_design_scores_gemma":[0.0008974529,0.001668416,0.5663097,0.0002686165,0.00008922396,0.004054275,0.0001847616,0.4203535,0.003438668,0.00009701389,0.001990368,0.0006479545],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.984445,0.0002497855,0.0003153053,0.0001880757,0.0002508305,0.0001526628,0.000004220013,0.0001343359,0.01425983],"genre_scores_gemma":[0.9979444,0.00000616904,0.001089538,0.0001692091,0.0001033788,0.00001226906,0.00000781418,0.000007721297,0.0006594561],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2782089,"threshold_uncertainty_score":0.7251432,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3017246359","doi":"10.2166/hydro.2020.095","title":"Deep learning convolutional neural network in rainfall–runoff modelling","year":2020,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":247,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Computer science; Convolutional neural network; Surface runoff; Artificial intelligence; Evapotranspiration; Deep learning; Time series; Series (stratigraphy); Machine learning; Water resources; Artificial neural network; Data mining; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.02431721850055275,"gpt":0.2142705786362157,"spread":0.189953360135663,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006409973,0.0001253301,0.000253508,0.00003398776,0.0001049115,0.00003934197,0.000269801,0.00007008546,0.000330986],"category_scores_gemma":[0.0002303098,0.0001039569,0.0001001424,0.0003118778,0.00009446762,0.0005100672,0.0001348574,0.0006906214,0.0001280204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001193503,"about_ca_system_score_gemma":0.00001565067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007208929,"about_ca_topic_score_gemma":0.000003516683,"domain_scores_codex":[0.9982702,0.00005992124,0.0007965691,0.00007561048,0.0004643594,0.0003333642],"domain_scores_gemma":[0.9991491,0.0001142623,0.0004688471,0.00005930617,0.00001243973,0.0001959992],"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":[0.00003429313,0.00001373831,0.008213329,0.000007987111,0.000006287048,0.00002801564,0.001197876,0.9890962,0.00003667623,0.00002972508,0.0003866082,0.0009492782],"study_design_scores_gemma":[0.0003766147,0.0002181821,0.0007359058,0.00002578291,0.000009523736,0.0001239295,0.00005177785,0.9947847,0.000005181163,0.0005602345,0.002994118,0.0001140474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.977305,0.00005605581,0.01799355,0.0007622809,0.0001167172,0.00006163608,2.795422e-7,0.00002061975,0.003683896],"genre_scores_gemma":[0.9778121,0.00001679563,0.0209227,0.00105204,0.0001667072,3.924254e-7,0.000001449689,0.000009210095,0.00001857705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007477423,"threshold_uncertainty_score":0.423924,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2003002624","doi":"10.1029/01eo00031","title":"Global water data: A newly endangered species","year":2001,"lang":"en","type":"article","venue":"Eos","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":233,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Exploit; Endangered species; The Internet; Data science; Water sector; Computer science; Environmental resource management; Environmental science; Business; Water supply; World Wide Web; Ecology; Computer security; Environmental engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.05367552657852936,"gpt":0.2624263171201507,"spread":0.2087507905416214,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001440407,0.00008419891,0.00007917464,0.000005284186,0.00008714775,0.00003495942,0.0004155747,0.00004223243,0.008678379],"category_scores_gemma":[0.00004550838,0.00005463816,0.00002002133,0.0001060141,0.0001389268,0.0001540045,0.0005747837,0.00005298886,0.003967853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008865796,"about_ca_system_score_gemma":0.000001892194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003266563,"about_ca_topic_score_gemma":0.0002139393,"domain_scores_codex":[0.999099,0.00002609161,0.00009667935,0.0002886919,0.0001901977,0.0002993006],"domain_scores_gemma":[0.9994869,0.00001100743,0.00001079797,0.0004133112,0.000001710714,0.00007622263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001000373,0.0003648322,0.6152205,0.000007706579,0.00003417502,0.0006367328,0.0009089041,0.01402193,0.04474964,0.0001607197,0.2062961,0.1174986],"study_design_scores_gemma":[0.0001628627,0.00004993195,0.08261114,0.000003445572,0.000006854872,0.00007158847,0.000006727442,0.004137219,0.0007781186,0.000787223,0.9112234,0.0001615054],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9109083,0.000007752467,0.000726747,0.001012719,0.0001442199,0.00006070966,0.00002240083,0.00008984637,0.08702733],"genre_scores_gemma":[0.9927822,0.000002678407,0.001485883,0.000495509,0.0001126389,0.000001773418,0.00003015348,0.000005211336,0.005083894],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7049273,"threshold_uncertainty_score":0.9968077,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2398936495","doi":"10.1007/s00477-016-1265-z","title":"Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model","year":2016,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":226,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Extreme learning machine; Computational intelligence; Wavelet; Index (typography); Computer science; Artificial intelligence; Machine learning; Pattern recognition (psychology); Artificial neural network","retraction":null,"screen_n_in":null,"score":{"opus":0.06390041391793945,"gpt":0.3264049352160453,"spread":0.2625045212981059,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002103369,0.0003716173,0.0003327209,0.0001335551,0.001247226,0.00008632368,0.000305091,0.0001654503,0.0007095308],"category_scores_gemma":[0.0004253535,0.0002480421,0.00008257533,0.0002439239,0.001367266,0.0003367672,0.001311411,0.0009958094,0.0001051913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001373723,"about_ca_system_score_gemma":0.00002985447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000475554,"about_ca_topic_score_gemma":0.00006889229,"domain_scores_codex":[0.9955289,0.0005282172,0.000378343,0.0009959476,0.001354194,0.001214427],"domain_scores_gemma":[0.9980953,0.0009102285,0.0001754065,0.0003319843,0.000009907039,0.000477172],"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":[0.00032416,0.000693712,0.3012236,0.00002312461,0.0001161751,0.00009511125,0.0005084817,0.4079924,0.05277193,0.00009100295,0.00006532221,0.236095],"study_design_scores_gemma":[0.001101308,0.0008347444,0.02153604,0.00009787195,0.00002764071,0.00005299574,0.00006744868,0.9699187,0.0002140464,0.005689101,0.0001036595,0.0003564667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7719749,0.00006371283,0.2261953,0.00009766548,0.00003979723,0.0006304588,0.00004180063,0.00004624483,0.0009101409],"genre_scores_gemma":[0.989861,0.0001155039,0.008844857,0.00002181494,0.00006370051,0.00009726684,0.000008960717,0.00005738411,0.0009295717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5619263,"threshold_uncertainty_score":0.9999972,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1994234742","doi":"10.1016/j.jhydrol.2007.10.050","title":"Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system","year":2007,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":215,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec; Natural Sciences and Engineering Research Council; Institut National de la Recherche Scientifique; Hydro-Québec","funders":"","keywords":"Adaptive neuro fuzzy inference system; Jackknife resampling; Interpretability; Computer science; Artificial neural network; Neuro-fuzzy; Cluster analysis; Artificial intelligence; Data mining; Flood myth; Machine learning; Fuzzy logic; Fuzzy control system; Statistics; Mathematics; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.04319348890226257,"gpt":0.2746780606559225,"spread":0.23148457175366,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001808254,0.0001901522,0.0004299836,0.0001807884,0.0003374952,0.0000205233,0.0005415964,0.0001578214,0.0002407835],"category_scores_gemma":[0.0003245711,0.0001193948,0.0002798481,0.0008017329,0.0005066242,0.0001528907,0.000224801,0.0005139788,0.00006998816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004388865,"about_ca_system_score_gemma":0.00002494911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002370678,"about_ca_topic_score_gemma":0.0003799841,"domain_scores_codex":[0.9976779,0.0003638412,0.000700683,0.0002677942,0.0005204681,0.0004692818],"domain_scores_gemma":[0.9979218,0.0008099953,0.0007789893,0.0002707149,0.00004796614,0.0001705924],"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":[0.0003132658,0.0001259022,0.364864,0.000005415162,0.0005009452,0.0007704766,0.0005529562,0.564353,0.06722027,0.0006270441,0.0003471139,0.0003196762],"study_design_scores_gemma":[0.002218295,0.003618421,0.3815563,0.0000867464,0.003905335,0.01028304,0.0003142411,0.5815326,0.004116512,0.009209103,0.002037122,0.001122226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926598,0.00008885849,0.005024471,0.0006321454,0.0001779719,0.00007535694,0.000002175519,0.00003051507,0.001308684],"genre_scores_gemma":[0.9956063,0.000004936405,0.003507162,0.0006902753,0.00014772,6.115932e-7,0.000001095584,0.00001489363,0.00002697326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06310376,"threshold_uncertainty_score":0.4868778,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3139023302","doi":"10.1016/j.jhydrol.2021.126196","title":"Coupling a hybrid CNN-LSTM deep learning model with a Boundary Corrected Maximal Overlap Discrete Wavelet Transform for multiscale Lake water level forecasting","year":2021,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":215,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Tabriz","keywords":"Coupling (piping); Artificial intelligence; Boundary (topology); Computer science; Pattern recognition (psychology); Wavelet; Wavelet transform; Geology; Mathematics; Materials science; Mathematical analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.02701032289428389,"gpt":0.2345674026478447,"spread":0.2075570797535608,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008422714,0.0003084221,0.000606828,0.00007969357,0.0004710803,0.00007464228,0.0002852912,0.0001626207,0.0004879461],"category_scores_gemma":[0.0002720356,0.000209888,0.0002427817,0.0001464142,0.0003556982,0.0003378163,0.0001469906,0.0008438474,0.00001945889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000160273,"about_ca_system_score_gemma":0.00006336906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000193479,"about_ca_topic_score_gemma":0.0004223985,"domain_scores_codex":[0.9974456,0.00008211989,0.0007243438,0.0004371003,0.0004374468,0.0008734123],"domain_scores_gemma":[0.998925,0.0002345151,0.0003548783,0.0001655577,0.00009387956,0.0002261137],"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":[0.000889505,0.0001145343,0.003067388,0.00002131654,0.00008412564,0.0006217831,0.0007825178,0.9710192,0.01597451,0.0000032362,0.00008518466,0.007336708],"study_design_scores_gemma":[0.002056046,0.001245456,0.0003820214,0.00005428449,0.0001185625,0.004437442,0.00004220986,0.9809222,0.006762583,0.0009857671,0.002690211,0.0003032396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8200525,0.00004233249,0.1782008,0.000846842,0.0001750865,0.0001678958,0.00002033399,0.00003448935,0.0004597336],"genre_scores_gemma":[0.9633563,0.000009562829,0.03553808,0.0003240125,0.0001229889,0.00001114585,0.00004004759,0.00005504208,0.0005428188],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1433038,"threshold_uncertainty_score":0.8558986,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2102017823","doi":"10.1061/(asce)he.1943-5584.0000188","title":"Comparing Sigmoid Transfer Functions for Neural Network Multistep Ahead Streamflow Forecasting","year":2010,"lang":"en","type":"article","venue":"Journal of Hydrologic Engineering","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":211,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval; Environment and Climate Change Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sigmoid function; Artificial neural network; Perceptron; Computer science; Nonlinear system; Transfer function; Multilayer perceptron; Artificial intelligence; Streamflow; Algorithm; Mathematical optimization; Machine learning; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02780844563611802,"gpt":0.217777484010358,"spread":0.18996903837424,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008518681,0.0002306137,0.0003791414,0.00006332911,0.0002067528,0.00004690446,0.0003176975,0.0001569277,0.000195733],"category_scores_gemma":[0.0004263548,0.0001856693,0.0002324652,0.0002226481,0.00008666454,0.0002375904,0.0000714948,0.0008185655,0.00001298543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006828353,"about_ca_system_score_gemma":0.000007681651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001399155,"about_ca_topic_score_gemma":0.00004029265,"domain_scores_codex":[0.9983432,0.00002698551,0.0005742374,0.0002294953,0.0002499291,0.0005761387],"domain_scores_gemma":[0.9990985,0.0003681029,0.0001477644,0.0001613719,0.00002710502,0.0001971526],"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":[0.00004490269,0.00004135055,0.01871042,0.00001075654,0.00002210154,0.00002585437,0.00005044082,0.9522887,0.02589087,0.000021216,0.0001793906,0.002713947],"study_design_scores_gemma":[0.0006037679,0.0004267941,0.004108949,0.0000264625,0.00005033037,0.000461546,0.000004105972,0.9898188,0.0003775091,0.0001273944,0.003787131,0.0002072747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9528462,0.00003939407,0.04506807,0.0001518751,0.001266734,0.0001641209,0.000002558921,0.00007872868,0.0003822989],"genre_scores_gemma":[0.9750082,0.000001792539,0.02419732,0.00008211654,0.0006230998,0.000009144668,0.000002539233,0.00002885556,0.00004695403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03752998,"threshold_uncertainty_score":0.7571375,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1854912902","doi":"10.1002/wrcr.20517","title":"Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models","year":2013,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":205,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"McGill University; Ste. Anne's Hospital","funders":"","keywords":"Autoregressive integrated moving average; Autoregressive model; Wavelet; Statistics; Bootstrap aggregating; Econometrics; Artificial neural network; Computer science; Bootstrap model; Ensemble forecasting; Bootstrapping (finance); Moving average; Time series; Mathematics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.1317663813177193,"gpt":0.3268046037635469,"spread":0.1950382224458277,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00234636,0.0002756742,0.0002917154,0.00009263907,0.001096021,0.0004726807,0.0003738467,0.0001703903,0.001456741],"category_scores_gemma":[0.00003314519,0.0001569014,0.00006657315,0.0002179667,0.0007405108,0.0004054528,0.001461378,0.0007162988,0.0002375443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002486584,"about_ca_system_score_gemma":0.000005879034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003164397,"about_ca_topic_score_gemma":0.00005851382,"domain_scores_codex":[0.9953168,0.0006017262,0.0003865699,0.0007524127,0.001012084,0.001930404],"domain_scores_gemma":[0.9989966,0.0001648118,0.00003893636,0.0003858306,0.00004921469,0.000364602],"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":[0.00004069594,0.00005800304,0.01301898,0.00003248741,0.00002463808,0.0000696222,0.003689315,0.9090878,0.06712388,0.00001354659,0.001736239,0.005104727],"study_design_scores_gemma":[0.0002913174,0.0001875596,0.001215109,0.0000382148,0.000008604135,0.00007089373,0.00006966567,0.9862015,0.002650169,0.005328197,0.003659325,0.0002794243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910813,0.00003553728,0.0003221309,0.0004872596,0.0000513823,0.0005658142,0.00000154707,0.00007011319,0.007384907],"genre_scores_gemma":[0.9952342,0.000003804633,0.003054772,0.0001332705,0.0002001106,0.00005054528,0.00001126606,0.00004415286,0.001267903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07711366,"threshold_uncertainty_score":0.999456,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2132417139","doi":"10.5194/hess-14-1931-2010","title":"Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology","year":2010,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":205,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Genetic programming; Computer science; Data mining; Support vector machine; Evapotranspiration; Sampling (signal processing); Process (computing); Regression; Hydrological modelling; Machine learning; Artificial intelligence; Statistics; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.1123097722893943,"gpt":0.3235866012903875,"spread":0.2112768290009933,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002200108,0.00009993016,0.0002652182,0.00004970492,0.0001591979,0.000006364119,0.0004076499,0.000164944,0.00003383543],"category_scores_gemma":[0.0001799049,0.00006655126,0.000015932,0.0001760445,0.005481345,0.0002219225,0.0005175489,0.0001820281,8.382605e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007141409,"about_ca_system_score_gemma":0.00002219592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000776145,"about_ca_topic_score_gemma":0.0004897348,"domain_scores_codex":[0.9983076,0.0005767982,0.0003365084,0.0004242774,0.000149656,0.000205101],"domain_scores_gemma":[0.9991827,0.0003283224,0.0001736277,0.0002638938,0.000007714459,0.00004374936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008116341,0.00005403367,0.6746439,0.00007401466,0.00001676182,0.000003117894,0.01094079,0.04584394,0.263862,0.003392303,0.00001644982,0.001071456],"study_design_scores_gemma":[0.0002073446,0.0005537628,0.01189446,0.00005184914,0.0000147811,0.0001068376,0.001149946,0.9518704,0.03178514,0.002216376,0.0000298257,0.0001192455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984639,0.00007344874,0.000148985,0.0001902508,0.0001357111,0.0002098524,0.00001018824,0.00002019154,0.0007474173],"genre_scores_gemma":[0.9951862,0.000003623011,0.004714723,0.00005748684,0.00001651527,0.00001218429,0.000001556225,0.00000280544,0.000004901542],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9060265,"threshold_uncertainty_score":0.9972252,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1971665758","doi":"10.1061/(asce)1084-0699(2001)6:5(367)","title":"Multivariate Reservoir Inflow Forecasting Using Temporal Neural Networks","year":2001,"lang":"en","type":"article","venue":"Journal of Hydrologic Engineering","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":192,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval; Hydro-Québec; Centre de Géomatique du Québec; Institut National de la Recherche Scientifique","funders":"","keywords":"Inflow; Multivariate statistics; Recurrent neural network; Computer science; Artificial neural network; Multilayer perceptron; Reservoir computing; Time series; Hydropower; Multivariate analysis; Artificial intelligence; Hydrology (agriculture); Machine learning; Geology; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.04033891197865237,"gpt":0.2443177684813604,"spread":0.203978856502708,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001072879,0.0002494217,0.0003720398,0.0001085107,0.0001416086,0.00005143309,0.0004130067,0.0001696066,0.0002576413],"category_scores_gemma":[0.0006368628,0.0001947773,0.0001687639,0.0004628388,0.00008107617,0.0004099291,0.0002463326,0.0007223183,0.000009104103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002066937,"about_ca_system_score_gemma":0.000007660929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008349056,"about_ca_topic_score_gemma":0.000005154496,"domain_scores_codex":[0.9980469,0.00007747373,0.000679554,0.0002265405,0.0003671424,0.0006023414],"domain_scores_gemma":[0.9989854,0.0001750232,0.0004186378,0.0001831709,0.0000256981,0.0002120599],"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":[0.00004824284,0.00003104406,0.0501634,0.00000375214,0.00001725722,0.0005302952,0.000048646,0.9436733,0.004484931,0.000002199238,0.00003725014,0.0009597291],"study_design_scores_gemma":[0.0003868689,0.0002650481,0.003351989,0.00004187729,0.00002493842,0.001816579,0.000003682651,0.9928857,0.00006389811,0.00009059741,0.0008643628,0.0002044718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9677549,0.0001118043,0.03105776,0.0001493456,0.0004816069,0.00008212585,4.340886e-7,0.00006948371,0.0002925517],"genre_scores_gemma":[0.9790841,0.000007715875,0.02035797,0.0001344456,0.0003604145,0.000001142964,7.561142e-7,0.00002890509,0.00002454693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04921243,"threshold_uncertainty_score":0.7942789,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3020943814","doi":"10.1016/j.advwatres.2020.103595","title":"Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms","year":2020,"lang":"en","type":"article","venue":"Advances in Water Resources","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":190,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Groundwater; Artificial neural network; Computer science; Environmental science; Irrigation scheduling; Gradient boosting; Water resource management; Artificial intelligence; Hydrology (agriculture); Random forest; Soil science; Geology; Geotechnical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.07660228442083965,"gpt":0.223181709638159,"spread":0.1465794252173194,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002465316,0.0002854884,0.0002937703,0.00003465754,0.0002048651,0.0000713191,0.0003470098,0.00008427235,0.000468967],"category_scores_gemma":[0.00004332474,0.0001625874,0.00005271981,0.0002248677,0.0002759266,0.0005237302,0.0001755573,0.0004510866,0.0001183473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006108759,"about_ca_system_score_gemma":0.000002555737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005518827,"about_ca_topic_score_gemma":0.0005931154,"domain_scores_codex":[0.9980276,0.00003994811,0.0003205329,0.0006210108,0.0003602946,0.0006306368],"domain_scores_gemma":[0.9995251,0.0000593948,0.00006758912,0.0001353922,0.00000350087,0.0002089805],"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":[0.0001920416,0.00004648128,0.1204221,0.00003109432,0.000007899637,0.00005001,0.01003773,0.8397548,0.0009506406,0.00000276793,0.000006960891,0.0284975],"study_design_scores_gemma":[0.001480616,0.0008257445,0.002299156,0.00008643907,0.00002096612,0.00005408645,0.0002116631,0.8568395,0.01079705,0.0004113756,0.1262926,0.0006808658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985276,0.0001965843,0.01074343,0.001015317,0.00002756494,0.0002255085,0.000001709506,0.0001394563,0.002374417],"genre_scores_gemma":[0.9875913,0.00002087451,0.01145564,0.0002246173,0.0000814931,0.00001429659,0.00002006645,0.00004239267,0.0005493364],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1262856,"threshold_uncertainty_score":0.663012,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3129144742","doi":"10.1016/j.envsoft.2021.105159","title":"Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling","year":2021,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":184,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"","keywords":"Leverage (statistics); Process (computing); Artificial intelligence; Data science; Computer science; Earth system science; Field (mathematics); Management science; Engineering; Ecology; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01897362725078758,"gpt":0.2316734991690357,"spread":0.2126998719182481,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005503286,0.000493357,0.0004249692,0.00004904844,0.0006944441,0.0001174963,0.0003159613,0.0002016561,0.001857803],"category_scores_gemma":[0.0001517344,0.000518256,0.0001422948,0.0002814925,0.0004505683,0.0003078259,0.0005685179,0.0005426535,0.0003926352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006251683,"about_ca_system_score_gemma":0.00002060242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001064854,"about_ca_topic_score_gemma":0.00002046556,"domain_scores_codex":[0.9961133,0.0002211262,0.0005351829,0.00154028,0.0007683288,0.0008217641],"domain_scores_gemma":[0.9985352,0.0002201759,0.0001308627,0.0005700896,0.00001003483,0.0005336989],"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":[0.00005019704,0.0003150457,0.09108452,0.00003712329,0.000009313413,0.00003911974,0.0009109025,0.9022765,0.0005473324,0.000003183946,0.00001376293,0.004713049],"study_design_scores_gemma":[0.0004790613,0.0002456309,0.002192663,0.00005013265,0.000034164,0.00003299248,0.0002569273,0.9899974,0.002617911,0.001668664,0.001753338,0.0006711491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6142349,0.0001331309,0.3848201,0.0001695352,0.00005448289,0.0002809535,0.00001180124,0.0001842084,0.0001108684],"genre_scores_gemma":[0.9340585,0.00002938052,0.06470782,0.0006389847,0.00004382235,0.00008332487,0.0000864609,0.00007462367,0.000277093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3201123,"threshold_uncertainty_score":0.9997269,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2289152296","doi":"10.1007/s00477-016-1213-y","title":"Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay River, Iran","year":2016,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":179,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Adaptive neuro fuzzy inference system; Wavelet; Haar; Artificial intelligence; Statistics; Haar wavelet; Mathematics; Discrete wavelet transform; Salinity; Coefficient of determination; Computational intelligence; Pattern recognition (psychology); Artificial neural network; Wavelet transform; Computer science; Geology; Fuzzy logic; Fuzzy control system","retraction":null,"screen_n_in":null,"score":{"opus":0.1080984573204141,"gpt":0.389054215941687,"spread":0.2809557586212729,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002804981,0.0001759948,0.0002379526,0.00009008923,0.000336023,0.00002071727,0.0001949013,0.00006399229,0.0001265085],"category_scores_gemma":[0.0000827446,0.0001083655,0.00004569754,0.0001028583,0.00104991,0.0001994242,0.0004429485,0.0002492332,0.00002782266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004864689,"about_ca_system_score_gemma":0.00001108157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001890442,"about_ca_topic_score_gemma":0.0003464485,"domain_scores_codex":[0.9971358,0.0003401331,0.0005420127,0.0006754923,0.0007601415,0.0005464039],"domain_scores_gemma":[0.9988474,0.0005142366,0.00009694184,0.0003464377,0.000009553966,0.0001853876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001178521,0.008822677,0.197391,0.00006509561,0.0001223379,0.0002691261,0.006139862,0.1385376,0.06712765,0.0009100371,0.00002660084,0.5794095],"study_design_scores_gemma":[0.002219555,0.007550152,0.09181996,0.00006862033,0.00006303415,0.0001839489,0.004332505,0.7129755,0.005961867,0.1741446,0.00003989213,0.0006403399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6584646,0.000007838681,0.3400942,0.00007737346,0.00002412301,0.001179793,0.0001148882,0.00001081229,0.00002629168],"genre_scores_gemma":[0.9974545,0.00003730956,0.001997694,0.000004975203,0.00002587197,0.0004165056,0.00001169973,0.0000162825,0.00003512192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5787691,"threshold_uncertainty_score":0.4419018,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2986218094","doi":"10.2166/wcc.2019.321","title":"Flood prediction based on weather parameters using deep learning","year":2019,"lang":"en","type":"article","venue":"Journal of Water and Climate Change","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":160,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"","keywords":"Flood myth; Support vector machine; Artificial intelligence; Machine learning; Artificial neural network; Flood forecasting; Computer science; Deep learning; Meteorology; Internet of Things; Environmental science; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.0356374776589438,"gpt":0.2342438468991091,"spread":0.1986063692401653,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003870575,0.00008223252,0.0001243819,0.00003936695,0.00007501792,0.00002912472,0.00005778831,0.00005031131,0.0006166151],"category_scores_gemma":[0.000009821432,0.00004874256,0.0000475934,0.0000400409,0.0000357778,0.0001502243,0.000041887,0.0001675705,0.0000772365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005554636,"about_ca_system_score_gemma":7.123958e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001219111,"about_ca_topic_score_gemma":0.000001143564,"domain_scores_codex":[0.9992799,0.00005622046,0.000160077,0.0001083357,0.0001875425,0.0002079682],"domain_scores_gemma":[0.9997657,0.00002062525,0.00008619449,0.00005592771,0.00000583236,0.0000657354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000279739,0.0001514563,0.7295238,0.00004193265,0.00001936866,0.00004679184,0.002618144,0.1965078,0.05958935,0.000002824236,0.0000126363,0.01120617],"study_design_scores_gemma":[0.002392981,0.003972107,0.09324662,0.0003901224,0.0001306009,0.0002645428,0.0002469292,0.8863796,0.00970937,0.0002203509,0.002625511,0.0004212874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99857,0.00001211652,0.00007412544,0.0001169719,0.000156846,0.0000632291,0.000001140014,0.00001074572,0.0009948134],"genre_scores_gemma":[0.9990749,0.00001985287,0.000539757,0.0002844806,0.00005016279,7.531411e-7,0.000001354311,0.000009986959,0.00001869637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6898717,"threshold_uncertainty_score":0.6751503,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2097453622","doi":"10.2166/hydro.2011.044","title":"Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data","year":2011,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":159,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"McGill University","keywords":"Multivariate adaptive regression splines; Surface runoff; Watershed; Artificial neural network; Multivariate statistics; Mars Exploration Program; Environmental science; Hydrology (agriculture); Regression analysis; Bayesian multivariate linear regression; Computer science; Machine learning; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.1305215527434784,"gpt":0.2971876465423763,"spread":0.1666660937988979,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092884,0.0002616271,0.0006217171,0.0001220381,0.0001150083,0.00002943062,0.0005654053,0.0001185047,0.00003127383],"category_scores_gemma":[0.0001046623,0.0001537314,0.00007042022,0.000330341,0.0002534306,0.0008543847,0.000131858,0.0004266833,0.000001194404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009001583,"about_ca_system_score_gemma":0.0000279912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007765257,"about_ca_topic_score_gemma":0.0002448773,"domain_scores_codex":[0.9976059,0.00004238324,0.001327608,0.0001670207,0.0004217601,0.000435302],"domain_scores_gemma":[0.9980049,0.0001859892,0.00133935,0.000281159,0.00005811689,0.0001305004],"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":[0.005742378,0.0005142154,0.02833726,0.00008151014,0.00008907138,0.00004632142,0.008143621,0.9469816,0.001611393,0.00001264793,0.00007837067,0.008361587],"study_design_scores_gemma":[0.001501209,0.002500755,0.002331661,0.0003608458,0.00009793763,0.0001790628,0.0004222628,0.9905001,0.001808419,0.00006402359,0.00002284761,0.0002108949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9724242,0.00001144617,0.02683285,0.00006614977,0.00008225055,0.000392773,0.00001376472,0.00001323902,0.000163315],"genre_scores_gemma":[0.8906874,0.000002486081,0.109182,0.00003344493,0.00004149585,0.000002562304,0.00002426368,0.00002128463,0.000005104917],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08234912,"threshold_uncertainty_score":0.6268983,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1627096583","doi":"10.1029/2005wr003971","title":"Bayesian neural network for rainfall‐runoff modeling","year":2006,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":154,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Posterior probability; Artificial neural network; Bayesian probability; Gaussian; Bayesian linear regression; Conjugate prior; Prior probability; Computer science; Bayes' theorem; Bayesian hierarchical modeling; Bayesian network; Bayesian experimental design; Statistics; Mathematics; Bayesian inference; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.059830166891381,"gpt":0.3092752663541491,"spread":0.2494450994627682,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001993546,0.0001520624,0.0001644888,0.00006162449,0.0007472136,0.0001693875,0.0005242942,0.0001202041,0.0008005825],"category_scores_gemma":[0.00007337459,0.0001014713,0.00008911747,0.0002773025,0.0003283311,0.0001013864,0.0005143776,0.0003638638,0.0003216205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001218147,"about_ca_system_score_gemma":0.000003138529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002033152,"about_ca_topic_score_gemma":0.0002146432,"domain_scores_codex":[0.9968947,0.0002403262,0.0002668059,0.0005118444,0.0007088741,0.001377419],"domain_scores_gemma":[0.9993517,0.0001399366,0.00001965046,0.0003216317,0.00002484145,0.0001422791],"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":[0.00007126805,0.00003387808,0.004287077,0.000007679145,0.000003500913,0.00001139343,0.0003090659,0.9855541,0.003483825,0.00002045491,0.004399923,0.001817838],"study_design_scores_gemma":[0.0002344809,0.0001315441,0.00024334,0.000009618552,0.000002939552,0.000006035541,0.000007573031,0.9070616,0.0004849435,0.0114424,0.08022204,0.0001534878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845018,0.00003305274,0.002505347,0.001188442,0.00005939304,0.000409307,0.000004232786,0.0001033884,0.01119508],"genre_scores_gemma":[0.9923739,8.574174e-7,0.003217111,0.0001352045,0.0005219322,0.00006694081,0.00002907871,0.00003572901,0.003619231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07849249,"threshold_uncertainty_score":0.8765817,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2996139122","doi":"10.3390/w12010005","title":"Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning","year":2019,"lang":"en","type":"article","venue":"Water","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":149,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Dalhousie University; University of Prince Edward Island","funders":"","keywords":"Hydrology (agriculture); Environmental science; Evapotranspiration; Baseflow; Groundwater; Multilayer perceptron; Streamflow; Watershed; Artificial neural network; Geography; Machine learning; Drainage basin; Geology; Computer science; Cartography; Ecology","retraction":null,"screen_n_in":null,"score":{"opus":0.01732862895662417,"gpt":0.2256427347592902,"spread":0.208314105802666,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001095286,0.0001278724,0.0001576443,0.00001516755,0.0001410098,0.00005900532,0.00008620273,0.00008529083,0.0008432042],"category_scores_gemma":[0.000007318457,0.00008710732,0.00003136631,0.00003628454,0.0001370165,0.0001748744,0.0002239334,0.0002128104,0.000740196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004318219,"about_ca_system_score_gemma":3.771957e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000601711,"about_ca_topic_score_gemma":0.00001465186,"domain_scores_codex":[0.9989582,0.0001054284,0.0001372056,0.0003239701,0.000134915,0.0003403127],"domain_scores_gemma":[0.9997549,0.00003842769,0.00003530808,0.0001080171,0.000002475429,0.0000608664],"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":[0.00003524893,0.00003775004,0.04383795,0.000001301072,0.000005991387,0.000006646464,0.0003010137,0.860754,0.09156659,0.000004802327,0.000001853085,0.003446935],"study_design_scores_gemma":[0.0001572973,0.00008606363,0.0149915,0.00000318425,0.00001544557,0.000009448008,0.000002560099,0.9816273,0.001490173,0.001408812,0.00008178772,0.0001264101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963015,0.000003301121,0.003100619,0.0001250186,0.0001848423,0.0001032598,3.569539e-7,0.00005305497,0.000128007],"genre_scores_gemma":[0.9988284,1.491957e-7,0.0007954884,0.0001731525,0.0001001919,0.00000206449,0.00003589985,0.00001537585,0.00004925886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1208734,"threshold_uncertainty_score":0.9513969,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2990607379","doi":"10.1016/j.scitotenv.2019.135539","title":"Ensemble modelling framework for groundwater level prediction in urban areas of India","year":2019,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":142,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Science and Engineering Research Board","keywords":"Groundwater; Urbanization; Support vector machine; Population; Environmental science; Artificial neural network; Water resource management; Hydrology (agriculture); Computer science; Machine learning; Engineering; Ecology; Geotechnical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02794806011702378,"gpt":0.2185787917644305,"spread":0.1906307316474068,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011252,0.0001068094,0.00014015,0.00002895832,0.000137094,0.00001197101,0.0006652587,0.00005809139,0.0003183649],"category_scores_gemma":[0.00005449153,0.00005949472,0.00007318428,0.0002490795,0.001106343,0.0001489966,0.0004606161,0.0001480169,0.00007693675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002083023,"about_ca_system_score_gemma":0.00001134863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001170235,"about_ca_topic_score_gemma":6.212961e-7,"domain_scores_codex":[0.9985039,0.00004469517,0.0002619226,0.0002969569,0.0005823679,0.0003101753],"domain_scores_gemma":[0.9991978,0.0001003849,0.0001617209,0.0004963386,0.000002900458,0.00004078746],"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":[0.00002192195,0.00008005827,0.003340942,0.000005060689,0.000002422401,6.219474e-8,0.0006961433,0.9568063,0.0382745,0.0004631305,0.00001159348,0.0002978529],"study_design_scores_gemma":[0.0003866569,0.0004184764,0.1358508,0.0001011888,0.00002403138,0.000007278286,0.00009766782,0.7578199,0.0722928,0.03272894,0.00006741356,0.0002048712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932843,0.000007733981,0.004769699,0.0002193004,0.0001673256,0.0005100552,0.000007433082,0.000006336832,0.001027807],"genre_scores_gemma":[0.995011,0.000001996204,0.004229374,0.00002109699,0.00001365488,0.0000153902,5.165312e-7,0.00000778087,0.0006991972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1989864,"threshold_uncertainty_score":0.4076365,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2050292603","doi":"10.1007/s11269-006-0326-4","title":"An Intelligent Decision Support System for Management of Floods","year":2006,"lang":"en","type":"article","venue":"Digital Scholarship - UNLV (University of Nevada Reno)","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":141,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Western University","funders":"","keywords":"Decision support system; Flood myth; Flood forecasting; Flood control; Flooding (psychology); Computer science; Intelligent decision support system; Operations research; Engineering; Artificial intelligence; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.01942775047152231,"gpt":0.2255976167166251,"spread":0.2061698662451028,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003774667,0.0001452371,0.0002288429,0.00006371435,0.0001536933,0.00004729318,0.0005746908,0.0001036032,0.0002569075],"category_scores_gemma":[0.00001670183,0.0001547719,0.000127078,0.000286414,0.0002103465,0.0008248293,0.0002667963,0.00009689591,0.00009975635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002179219,"about_ca_system_score_gemma":0.000008231591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000225797,"about_ca_topic_score_gemma":0.00004887454,"domain_scores_codex":[0.9986453,0.00003083209,0.0002309227,0.0003747949,0.0004419135,0.0002762287],"domain_scores_gemma":[0.9992636,0.00006104912,0.0001786052,0.000343126,0.00002879566,0.0001247648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00224433,0.003499575,0.1293079,0.0009283323,0.0002923499,0.0005333126,0.001056199,0.0800394,0.02284609,0.01435326,0.003198804,0.7417004],"study_design_scores_gemma":[0.01307294,0.01114745,0.693652,0.002486892,0.001167857,0.0002131747,0.008651345,0.04474176,0.03343803,0.05552704,0.1311801,0.004721426],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9680943,0.000009554603,0.01281814,0.00003061448,0.00007228486,0.0002779156,0.00007818886,0.00006713323,0.0185518],"genre_scores_gemma":[0.9883381,0.000002328717,0.01101862,0.000009272195,0.00001081281,4.087236e-7,0.00007384863,0.000012171,0.0005344097],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.736979,"threshold_uncertainty_score":0.6311414,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2065572909","doi":"10.1016/s0043-1354(01)00287-1","title":"A neural network model to predict the wastewater inflow incorporating rainfall events","year":2002,"lang":"en","type":"article","venue":"Water Research","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":138,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Inflow; Artificial neural network; Sewage treatment; Wastewater; Process (computing); Environmental science; Engineering; Computer science; Environmental engineering; Artificial intelligence; Meteorology","retraction":null,"screen_n_in":null,"score":{"opus":0.115220329685276,"gpt":0.3114035109861496,"spread":0.1961831813008736,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002273391,0.0001431709,0.0001235062,0.00004052297,0.0006465259,0.000102708,0.0007483638,0.00007868526,0.001543832],"category_scores_gemma":[0.000135935,0.00007182259,0.00004822102,0.0003797837,0.0002676107,0.0001503368,0.001529,0.0005695807,0.004072386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001253445,"about_ca_system_score_gemma":0.000003121636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001771438,"about_ca_topic_score_gemma":0.00005425252,"domain_scores_codex":[0.9969029,0.0003924548,0.0002379924,0.0004171511,0.0009627209,0.001086778],"domain_scores_gemma":[0.9992289,0.00004797424,0.00002022663,0.0004587721,0.00001914409,0.0002249734],"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":[0.0000218994,0.00003692573,0.01179544,0.00000312377,0.000004773221,0.00001545043,0.001471162,0.96101,0.009774274,0.000007983992,0.01349836,0.002360544],"study_design_scores_gemma":[0.0001567798,0.0001719094,0.001014337,0.00001573826,0.000002409589,0.000009678931,0.000007234419,0.9899284,0.001054784,0.003610348,0.003896012,0.0001324121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.987393,0.000005564456,0.00008569337,0.005839813,0.00006523271,0.0004439608,0.000002009943,0.00006367898,0.006101052],"genre_scores_gemma":[0.9923604,8.232905e-7,0.001360231,0.00071415,0.0001558457,0.00007498942,0.000002804859,0.00002386042,0.005306862],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0289183,"threshold_uncertainty_score":0.9993689,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2154250668","doi":"10.1139/s03-071","title":"An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition","year":2004,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":135,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"Université Laval","funders":"","keywords":"Morlet wavelet; Wavelet; Artificial neural network; Wavelet transform; Surface runoff; Computer science; Series (stratigraphy); Artificial intelligence; Environmental science; Discrete wavelet transform; Geology; Ecology","retraction":null,"screen_n_in":null,"score":{"opus":0.01852569919110066,"gpt":0.2191362204370767,"spread":0.2006105212459761,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006004957,0.0001161858,0.0001578549,0.00005844207,0.0001632986,0.0000402457,0.0001793125,0.00003289895,0.00002765831],"category_scores_gemma":[0.00003097423,0.0000902699,0.00002880822,0.0002389424,0.0004142952,0.001184503,0.00005780296,0.0001476043,0.000001586986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001391346,"about_ca_system_score_gemma":0.00001097357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008398603,"about_ca_topic_score_gemma":0.000002695334,"domain_scores_codex":[0.9988305,0.00001564655,0.000306414,0.0001710885,0.0004318273,0.0002445361],"domain_scores_gemma":[0.9995089,0.00002718698,0.0002053533,0.00009604567,0.000005694227,0.000156791],"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":[0.00005229826,0.00006076145,0.001093417,0.000002251399,0.000002562947,0.00001237816,0.0002448765,0.8375804,0.1581397,0.00003584172,7.952538e-7,0.002774617],"study_design_scores_gemma":[0.0009594898,0.004300394,0.05123023,0.0001195325,0.00003304093,0.0004697649,0.0001126444,0.9235915,0.01795102,0.0008833568,0.00002875165,0.0003202794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846057,0.00001156804,0.0151166,0.00006219313,0.00009385491,0.00006395708,0.000001214566,0.00001057973,0.00003438261],"genre_scores_gemma":[0.9738538,0.000004453828,0.02604686,0.00002790635,0.00005523064,8.918906e-7,9.925623e-7,0.000008397696,0.000001481876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1401887,"threshold_uncertainty_score":0.36811,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2944550430","doi":"10.1016/j.jhydrol.2019.05.016","title":"Towards a time and cost effective approach to water quality index class prediction","year":2019,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":131,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"Institut Pengurusan dan Pemantauan Penyelidikan, Universiti Malaya; Universiti Kebangsaan Malaysia; Universiti Malaya","keywords":"Water quality; Biochemical oxygen demand; Decision tree; Index (typography); Quality (philosophy); Chemical oxygen demand; Environmental science; Computer science; Suspended solids; Hydrology (agriculture); Data mining; Environmental engineering; Engineering; Ecology","retraction":null,"screen_n_in":null,"score":{"opus":0.01154889388782263,"gpt":0.2465894769892087,"spread":0.2350405831013861,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001316429,0.0001127678,0.0003029357,0.00006087673,0.00004896344,0.00001543356,0.0001597529,0.0001464082,0.0004782221],"category_scores_gemma":[0.0001038491,0.00007065579,0.00005787224,0.00008780698,0.0001300794,0.0001307762,0.0001854269,0.0003121551,0.0004797519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001354321,"about_ca_system_score_gemma":0.000006168386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005147375,"about_ca_topic_score_gemma":0.000003032709,"domain_scores_codex":[0.9986641,0.0002741383,0.0003221334,0.0002179094,0.0002507507,0.000270933],"domain_scores_gemma":[0.9994962,0.00007389975,0.0001342439,0.0001248001,0.00001628548,0.0001545649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001600547,0.0005873894,0.284494,0.0000289625,0.0001446432,0.00003848992,0.002408209,0.4770592,0.2079786,0.00005950475,0.00248255,0.02311789],"study_design_scores_gemma":[0.003875551,0.006686315,0.698904,0.00003332689,0.0000978266,0.002007774,0.00002549175,0.2287918,0.005970051,0.003174806,0.04989104,0.0005420001],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9863014,0.000006070487,0.0007883786,0.0007873067,0.0001688879,0.0003666118,0.000003079501,0.00001448266,0.01156376],"genre_scores_gemma":[0.9983937,0.000001135543,0.0005262767,0.0007640891,0.00006790633,0.000007204639,0.000001666585,0.000008584373,0.000229447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.41441,"threshold_uncertainty_score":0.6166401,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2588810797","doi":"10.1007/s00477-017-1394-z","title":"Multi-step water quality forecasting using a boosting ensemble multi-wavelet extreme learning machine model","year":2017,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":126,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Extreme learning machine; Partial autocorrelation function; Adaptive neuro fuzzy inference system; Autocorrelation; Computer science; Mean squared error; Boosting (machine learning); Artificial intelligence; Wavelet; Discrete wavelet transform; Machine learning; Time series; Wavelet transform; Data mining; Statistics; Mathematics; Algorithm; Artificial neural network; Fuzzy logic; Autoregressive integrated moving average; Fuzzy control system","retraction":null,"screen_n_in":null,"score":{"opus":0.2678065094809721,"gpt":0.4108759280729035,"spread":0.1430694185919313,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.004317472,0.0004387473,0.0004382125,0.00009586078,0.005428371,0.0004097155,0.0006107488,0.0001928958,0.0003482217],"category_scores_gemma":[0.000894617,0.0003311638,0.0001087435,0.00006342408,0.001617089,0.0004894593,0.002910306,0.001524755,0.0001150818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008184176,"about_ca_system_score_gemma":0.00002747607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004287621,"about_ca_topic_score_gemma":0.0006029658,"domain_scores_codex":[0.9948001,0.0005498815,0.0005881199,0.001163728,0.001328891,0.001569312],"domain_scores_gemma":[0.9980231,0.0003507782,0.0003363693,0.0007316194,0.0000154087,0.0005427605],"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":[0.00014676,0.0009186135,0.2678121,0.00004153571,0.00007968272,0.00009320663,0.001023063,0.4358867,0.2396317,0.00001661458,0.00001189963,0.05433815],"study_design_scores_gemma":[0.001309216,0.0003025129,0.05452727,0.00006262595,0.00002977424,0.00003358965,0.0001723709,0.9422653,0.0004763218,0.0003733126,0.0000338376,0.0004138723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7036054,0.00004147779,0.2953421,0.0000675875,0.00004756359,0.0004726618,0.0000343301,0.00004349769,0.0003453871],"genre_scores_gemma":[0.8649037,0.00005483094,0.1337958,0.00001953083,0.00004949598,0.0000430606,0.00002718634,0.00005917878,0.001047196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5063786,"threshold_uncertainty_score":0.9999141,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4383215559","doi":"10.1016/j.envsoft.2023.105776","title":"Exploding the myths: An introduction to artificial neural networks for prediction and forecasting","year":2023,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":124,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"Australian Research Council","keywords":"Artificial neural network; Artificial intelligence; Computer science; Mythology; Machine learning; History","retraction":null,"screen_n_in":null,"score":{"opus":0.055063829364244,"gpt":0.2339230248312519,"spread":0.1788591954670079,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006589807,0.0002026013,0.0001517253,0.00003943967,0.0008418202,0.00008290662,0.0001765137,0.00009529848,0.00007820864],"category_scores_gemma":[0.00009317786,0.000166144,0.00005766031,0.0002118771,0.0001797546,0.0003043067,0.0002211785,0.0001991645,0.00004787514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001492232,"about_ca_system_score_gemma":0.00000148897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001694143,"about_ca_topic_score_gemma":0.000006861789,"domain_scores_codex":[0.9982119,0.00006958185,0.0003088973,0.0006482337,0.0002746791,0.0004867562],"domain_scores_gemma":[0.9993378,0.0001606351,0.00008341521,0.0002620785,0.000002230207,0.0001538562],"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":[0.00004699446,0.00002428094,0.003273071,0.000003097339,0.000004480405,0.000001381434,0.0005560881,0.9519518,0.001116391,0.00001122814,0.0003312455,0.04267994],"study_design_scores_gemma":[0.0001094163,0.0002217128,0.001688635,0.00000634023,0.00001842914,0.00001230956,0.0001474096,0.9953953,0.0001511242,0.0008323314,0.001234023,0.0001829934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7042104,0.000009856151,0.2941826,0.000628869,0.0002952593,0.0004026335,0.00002516116,0.0002386944,0.000006579841],"genre_scores_gemma":[0.988893,0.000006681194,0.009625751,0.0002351693,0.0008202436,0.0001222752,0.0001575091,0.00004668159,0.00009267344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2846826,"threshold_uncertainty_score":0.6775156,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2909151008","doi":"10.1080/19942060.2018.1564702","title":"Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates","year":2019,"lang":"en","type":"article","venue":"Engineering Applications of Computational Fluid Mechanics","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":120,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Wavelet; Arid; Artificial neural network; Support vector machine; Wavelet transform; Pan evaporation; Evaporation; Environmental science; Regression; Computer science; Pattern recognition (psychology); Meteorology; Artificial intelligence; Mathematics; Geography; Statistics; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.01344591644998661,"gpt":0.2295193529499361,"spread":0.2160734364999495,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002918559,0.0001404497,0.0001769029,0.00007780697,0.00007243951,0.00002710207,0.00008964622,0.00008887186,0.00002038335],"category_scores_gemma":[0.00003222076,0.0001432028,0.00002292274,0.0002434257,0.00002286362,0.0001364564,0.0001117214,0.0001425735,0.000004985556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007017024,"about_ca_system_score_gemma":0.000009526252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004280033,"about_ca_topic_score_gemma":0.000003750803,"domain_scores_codex":[0.9989477,0.00001985757,0.0003438797,0.0002960356,0.0002050635,0.0001874383],"domain_scores_gemma":[0.9996277,0.00008672786,0.0000740363,0.0001291829,0.00002345741,0.00005887189],"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":[0.000009437172,0.00002572661,0.0001323019,0.00001645682,0.000003197519,3.827602e-7,0.000057935,0.9794211,0.01646387,0.001885915,0.000002925108,0.001980762],"study_design_scores_gemma":[0.0001317036,0.00004693816,0.0003973225,0.00002414089,0.000008187498,0.000007845021,0.000008945308,0.995685,0.0005499654,0.002992292,0.00001124578,0.000136373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6908453,0.00002793296,0.3087564,0.00004094233,0.00003739218,0.0002399665,0.000007267834,0.00003380936,0.00001089809],"genre_scores_gemma":[0.9709517,0.000005431384,0.02887353,0.00002281449,0.00002885199,0.00002124106,0.00007642405,0.00001800899,0.000001957228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2801064,"threshold_uncertainty_score":0.583964,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2148300238","doi":"10.1007/s10666-015-9468-0","title":"Predicting Nitrate Concentration and Its Spatial Distribution in Groundwater Resources Using Support Vector Machines (SVMs) Model","year":2015,"lang":"en","type":"article","venue":"Environmental Modeling & Assessment","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":118,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Support vector machine; Groundwater; Nitrate; Aquifer; Water quality; Environmental science; Soil science; Hydrology (agriculture); Machine learning; Computer science; Engineering; Chemistry; Geotechnical engineering; Ecology","retraction":null,"screen_n_in":null,"score":{"opus":0.03776025100581817,"gpt":0.2710957640194148,"spread":0.2333355130135966,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006474329,0.0002657899,0.0002224062,0.00001904331,0.0001965062,0.00008458798,0.0001426977,0.0001221502,0.0001342468],"category_scores_gemma":[0.00002454573,0.0002474801,0.00003949243,0.0000763781,0.0001164707,0.0004630134,0.0003029635,0.0002764306,0.000026545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001122131,"about_ca_system_score_gemma":0.00002141416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008598881,"about_ca_topic_score_gemma":0.00008161912,"domain_scores_codex":[0.9978209,0.0001334954,0.0004460045,0.00056854,0.0005746121,0.0004564769],"domain_scores_gemma":[0.9994818,0.00002053638,0.0001245115,0.0001557343,0.000003664785,0.0002137821],"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":[0.00003312841,0.0001370435,0.1354156,0.000004262295,0.000005336502,0.00000927051,0.0005154926,0.8458291,0.01748329,0.000009562239,0.000004346818,0.0005535045],"study_design_scores_gemma":[0.0006447421,0.0001638471,0.01205712,0.00002209273,0.00002450364,0.00001777772,0.00006746671,0.9859937,0.0003154074,0.0004176094,0.00001455413,0.0002611958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9559855,0.00002831851,0.04309587,0.00009680918,0.0001001329,0.000311349,0.00003756408,0.00005494293,0.0002895335],"genre_scores_gemma":[0.9969537,0.00001059491,0.002662656,0.00008329165,0.00006172936,0.00002008106,0.0001425339,0.00002690457,0.00003856845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1401646,"threshold_uncertainty_score":0.9999977,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2166210698","doi":"10.5194/hess-13-1607-2009","title":"River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: case study of the Ganges river basin","year":2009,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":118,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Precipitation; Flow (mathematics); Streamflow; Artificial neural network; Hydrological modelling; Environmental science; Computer science; Drainage basin; Meteorology; Geology; Machine learning; Climatology; Mathematics; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.03461581209108301,"gpt":0.2154461920016238,"spread":0.1808303799105408,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007283388,0.0001707109,0.0002389053,0.00004486547,0.0007700629,0.00008503751,0.0001436934,0.00008325781,0.00000827747],"category_scores_gemma":[0.0000295868,0.0000958588,0.00001730472,0.0004472925,0.001081752,0.000723895,0.00005679177,0.0001294673,0.000001751779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001387432,"about_ca_system_score_gemma":0.00001493287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003321831,"about_ca_topic_score_gemma":0.000371591,"domain_scores_codex":[0.9985552,0.0002345093,0.0003023262,0.0003178347,0.000292935,0.0002971889],"domain_scores_gemma":[0.9994028,0.0001210043,0.0002551549,0.0001298232,0.00002549287,0.00006576737],"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":[0.000146097,0.00004764312,0.06212806,0.00002060806,0.00001100558,0.00004774697,0.009245025,0.9101593,0.0000854319,0.000004852243,7.357868e-7,0.01810346],"study_design_scores_gemma":[0.0003498234,0.00138181,0.08281054,0.00004930771,0.00004377811,0.001239358,0.00101793,0.9129274,0.00003627307,0.00002172424,0.000001383496,0.0001206418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984668,0.00003163433,0.0007401045,0.00006767824,0.00004927119,0.0004735462,0.000002250201,0.00002769862,0.0001410203],"genre_scores_gemma":[0.9971851,9.649443e-7,0.00269555,0.00007871247,0.00002389043,0.000004657868,0.000001308803,0.00000371351,0.000006083792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02068248,"threshold_uncertainty_score":0.5922779,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1999378232","doi":"10.1061/(asce)1084-0699(2000)5:4(424)","title":"Performance Evaluation of Artificial Neural Networks for Runoff Prediction","year":2000,"lang":"en","type":"article","venue":"Journal of Hydrologic Engineering","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":113,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"Lakehead University; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Mean squared error; Statistic; Computer science; Surface runoff; Regression; Linear regression; Nonlinear system; Statistics; Machine learning; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02266431642366356,"gpt":0.2318624977409896,"spread":0.209198181317326,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001536616,0.00009421449,0.0001720993,0.00004120025,0.00004988646,0.000008317461,0.0001384553,0.00008334058,0.0005827297],"category_scores_gemma":[0.0001636786,0.00007453769,0.00008801341,0.0001436929,0.00004327606,0.000173972,0.00001690612,0.0001714624,0.000004429478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000927015,"about_ca_system_score_gemma":0.000005851259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002124646,"about_ca_topic_score_gemma":3.894468e-7,"domain_scores_codex":[0.9988683,0.00003598911,0.0004339367,0.0001029318,0.0003614227,0.0001973594],"domain_scores_gemma":[0.9995877,0.0000651824,0.0001791116,0.00008289249,0.00003311142,0.00005204869],"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":[0.00005937943,0.00002821322,0.002922862,0.000003944925,0.000008622825,9.495079e-7,0.00002878957,0.9415579,0.003962793,0.000001240259,0.00004751336,0.05137783],"study_design_scores_gemma":[0.0002630246,0.0005765059,0.01130719,0.00001580025,0.00005347175,0.00005074104,9.573913e-7,0.9869057,0.0004918322,0.00005338106,0.0002201443,0.00006125247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959298,0.00005407269,0.003267827,0.00005006333,0.000275363,0.0001306131,0.000001282986,0.00001989102,0.000271048],"genre_scores_gemma":[0.9980274,0.0000129059,0.001709998,0.00002428277,0.0001998092,0.000005099692,0.000001476744,0.000008222733,0.00001080098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05131657,"threshold_uncertainty_score":0.6380481,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2038098184","doi":"10.1002/hyp.6928","title":"Stream temperature modelling using artificial neural networks: application on Catamaran Brook, New Brunswick, Canada","year":2008,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":111,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"Fisheries and Oceans Canada; Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tributary; Mean squared error; Environmental science; Air temperature; Hydrology (agriculture); Drainage basin; Mean radiant temperature; Coefficient of determination; Ecology; Meteorology; Statistics; Mathematics; Climate change; Geology; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.04036164724602984,"gpt":0.2286349219260893,"spread":0.1882732746800595,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001357666,0.0003402056,0.0003108646,0.00002624483,0.0006190174,0.00004551974,0.0004189042,0.0002619067,0.0002443098],"category_scores_gemma":[0.0001776483,0.0002622445,0.00005498695,0.0005809038,0.000307623,0.0001639343,0.0001316576,0.0005269907,0.00004709702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002452352,"about_ca_system_score_gemma":0.0003614716,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2520302,"about_ca_topic_score_gemma":0.1880808,"domain_scores_codex":[0.9975798,0.0000746525,0.0003805852,0.0007862343,0.0005259379,0.0006528081],"domain_scores_gemma":[0.9990427,0.0001658818,0.0001523055,0.0003063278,0.00002404476,0.00030867],"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":[0.00008117795,0.00009975348,0.004963519,0.000008037075,0.000004912851,0.00005222737,0.00003341697,0.9927241,0.0006595879,0.00005658982,0.0004340357,0.0008826432],"study_design_scores_gemma":[0.0001520184,0.0001933939,0.0008985409,0.00001214172,0.00001635748,0.0000892684,0.000003531209,0.9954849,0.0009176812,0.0009184402,0.0009564227,0.0003572952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862339,0.0001133022,0.01177841,0.0005370031,0.0001052358,0.0002818374,0.000004074981,0.0001767571,0.0007694643],"genre_scores_gemma":[0.9966662,0.00001773623,0.001000669,0.001769796,0.000346583,0.000007271897,0.00002938509,0.00002635292,0.0001359985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06394941,"threshold_uncertainty_score":0.999983,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2960137824","doi":"10.1016/j.agrformet.2019.107647","title":"On the applicability of maximum overlap discrete wavelet transform integrated with MARS and M5 model tree for monthly pan evaporation prediction","year":2019,"lang":"en","type":"article","venue":"Agricultural and Forest Meteorology","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":110,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Mars Exploration Program; Mean squared error; Pan evaporation; Wind speed; Discrete wavelet transform; Relative humidity; Multivariate adaptive regression splines; Coefficient of determination; Correlation coefficient; Wavelet; Environmental science; Statistics; Meteorology; Mathematics; Wavelet transform; Evaporation; Linear regression; Computer science; Geography; Bayesian multivariate linear regression; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.007900965922045517,"gpt":0.1859478138807081,"spread":0.1780468479586626,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002197254,0.0001406544,0.0001852039,0.00001038077,0.00009473523,0.00001185185,0.00008093452,0.0001069267,0.00002473852],"category_scores_gemma":[0.00002749715,0.00005710427,0.00003629407,0.00009548238,0.0002834241,0.000109888,0.0000301828,0.0001133811,0.00000334184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002926049,"about_ca_system_score_gemma":0.00000302792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001435796,"about_ca_topic_score_gemma":0.0004893183,"domain_scores_codex":[0.9991861,0.00004191009,0.000173223,0.0002965971,0.000118146,0.0001840477],"domain_scores_gemma":[0.9996119,0.0001395305,0.00008036673,0.0001084899,0.00001488943,0.0000448319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.007990188,0.0006657551,0.2575153,0.0002213718,0.0003110226,0.00000272969,0.003384599,0.4090683,0.1806989,0.03103552,0.001827736,0.1072786],"study_design_scores_gemma":[0.001096046,0.003797244,0.5578777,0.0000166002,0.0000817391,0.00001698131,0.0001296104,0.3973793,0.001245365,0.03790983,0.0002308209,0.0002188419],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959825,0.000006401937,0.0007696658,0.001086264,0.00002005852,0.0007952125,0.00006706259,0.00002232361,0.001250566],"genre_scores_gemma":[0.999048,0.000003975686,0.0005930865,0.00009585611,0.000008177992,0.00007115927,0.00007361954,0.000004792425,0.0001013277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3003623,"threshold_uncertainty_score":0.2328644,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2803907192","doi":"10.1016/j.jenvman.2018.05.072","title":"Novel hybrid linear stochastic with non-linear extreme learning machine methods for forecasting monthly rainfall a tropical climate","year":2018,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":106,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"","keywords":"Tropical climate; Extreme learning machine; Environmental science; Climatology; Meteorology; Econometrics; Mathematics; Statistics; Computer science; Machine learning; Geography; Ecology; Artificial neural network; Geology; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.03572237126299856,"gpt":0.280844081035535,"spread":0.2451217097725364,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001166851,0.0003574706,0.0004497054,0.0001114603,0.0003972969,0.00003930349,0.0004402831,0.00006772981,0.0005226437],"category_scores_gemma":[0.0001084585,0.0002701218,0.0002030848,0.0001487898,0.0004644878,0.0002520153,0.0005320831,0.0004155143,0.00006768804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003746983,"about_ca_system_score_gemma":0.0000051663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001363288,"about_ca_topic_score_gemma":0.000006593951,"domain_scores_codex":[0.9974403,0.0001047628,0.000739922,0.0004682314,0.000559927,0.000686823],"domain_scores_gemma":[0.9986534,0.0001814866,0.0006492977,0.0002481534,0.00001121158,0.0002565111],"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":[0.00238115,0.00140911,0.01157382,0.0000963378,0.0004479861,0.0002254758,0.0005671104,0.8207635,0.03135623,0.00003996276,0.0002596787,0.1308796],"study_design_scores_gemma":[0.00357872,0.004420773,0.01313964,0.0001558803,0.0003243596,0.0003497942,0.0001151943,0.9648536,0.0009392776,0.0001774812,0.01143867,0.000506587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4280971,0.00002841907,0.5699851,0.0001288524,0.0001599802,0.0004414519,0.00001373565,0.00002458795,0.001120784],"genre_scores_gemma":[0.6123088,0.000008141947,0.3869748,0.0001748484,0.0002008891,0.0000142238,0.000005765093,0.00004396066,0.0002685916],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1842117,"threshold_uncertainty_score":0.9999751,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2966421386","doi":"10.1007/s12665-019-8474-y","title":"Evaluation of data-driven models (SVR and ANN) for groundwater-level prediction in confined and unconfined systems","year":2019,"lang":"en","type":"article","venue":"Environmental Earth Sciences","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":105,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Mean squared error; Groundwater; Support vector machine; Hydrogeology; Artificial neural network; Mean absolute percentage error; Aquifer; Coefficient of determination; Correlation coefficient; Predictive modelling; Computer science; Machine learning; Data mining; Statistics; Engineering; Mathematics; Geotechnical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.1220507468108173,"gpt":0.2800154020876894,"spread":0.1579646552768721,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002253814,0.0001106448,0.0001617839,0.00004450454,0.0001006266,0.00003987399,0.0002056972,0.00005765181,0.0003263511],"category_scores_gemma":[0.00003090577,0.00009010884,0.00001231812,0.00009851411,0.0006489859,0.0006685839,0.0002405083,0.00005243788,0.00001960088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003711182,"about_ca_system_score_gemma":0.00001194511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004344083,"about_ca_topic_score_gemma":0.0001271764,"domain_scores_codex":[0.9982707,0.0001106913,0.000247741,0.0005228307,0.0006431843,0.000204866],"domain_scores_gemma":[0.9995746,0.0000669605,0.0001065019,0.0001931486,0.000003108185,0.00005569297],"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":[0.0000388592,0.000142165,0.1961463,0.0000265002,0.00001188862,5.354881e-7,0.0006564473,0.74791,0.03712065,0.000118146,0.00002824345,0.01780026],"study_design_scores_gemma":[0.0006324446,0.0003450285,0.1163428,0.00002030002,0.00001881237,0.000005935493,0.000116274,0.8815522,0.0002018871,0.000553425,0.0001203547,0.00009052304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975123,0.0000881403,0.000401485,0.00005132668,0.00009351387,0.0007338002,0.0001050562,0.00001045798,0.001003919],"genre_scores_gemma":[0.9981596,0.00001370684,0.001573492,0.00002048926,0.00001071327,0.00001837139,0.00003442314,0.00000504565,0.0001641432],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1336422,"threshold_uncertainty_score":0.3674532,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}