{"meta":{"query_hash":"3fc94882e1a7","filters":{"venue":"Environmental Modelling & Software"},"cohort_total":157,"direct_labels_cover":0,"predictions_cover":157,"exported":157,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/3fc94882e1a7","api":"https://metacan.xera.ac/api/v1/cohort?venue=Environmental+Modelling+%26+Software"},"results":[{"id":"W1527640955","doi":"10.1016/s1364-8152(01)00059-7","title":"Progress in integrated assessment and modelling1A Summary of a workshop on Integrated Assessment and Modelling, held at EcoSummit 2000: Integrating the Sciences, Halifax, June 18–22, 2000. See Costanza and Jorgensen (2001) for a further report on Ecosummit.1","year":2002,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Environmental and Social Impact Assessments","field":"Environmental Science","cited_by":232,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Saint Mary's University; Western University; Natural Resources Canada; University of Waterloo","funders":"","keywords":"Process (computing); Environmental impact assessment; Pessimism; Product (mathematics); Work (physics); Management science; Scale (ratio); Operations research; Environmental planning; Engineering; Computer science; Political science; Geography","score_opus":0.03821357398203234,"score_gpt":0.29093111555296297,"score_spread":0.2527175415709306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1527640955","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9480381,0.0015658111,0.046510715,0.00051299005,0.00019476778,0.0019486187,0.0003580173,0.000071823604,0.0007991579],"genre_scores_gemma":[0.95170605,0.0030772265,0.040619384,0.00034697884,0.000048741633,0.0002794692,0.00019083625,0.00011763084,0.0036136664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951686,0.0002849084,0.0010786479,0.0014551389,0.0010148709,0.0009978095],"domain_scores_gemma":[0.99800193,0.0005128783,0.0005635285,0.00055764866,0.000011241372,0.00035275987],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001301445,0.000824217,0.00076286134,0.0001684083,0.00082070724,0.00017676249,0.00040366262,0.00032670167,0.0007950405],"category_scores_gemma":[0.00002357375,0.00063948287,0.00015332963,0.00035213248,0.0014783842,0.00047819287,0.00044796316,0.00082449225,0.000019556914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035601907,0.0020330239,0.41963542,0.00009696036,0.00018260529,0.000105050145,0.0040438883,0.5428255,0.00044780062,0.00008835097,0.002476443,0.02770893],"study_design_scores_gemma":[0.0017045734,0.0007549717,0.005561068,0.0005368323,0.00010335153,0.000043324915,0.0023671447,0.9843167,0.00021974563,0.00087303837,0.0026209606,0.0008982772],"about_ca_topic_score_codex":0.00043585582,"about_ca_topic_score_gemma":0.00036280364,"teacher_disagreement_score":0.4414912,"about_ca_system_score_codex":0.0022206323,"about_ca_system_score_gemma":0.000061620056,"threshold_uncertainty_score":0.99960566},"labels":[],"label_agreement":null},{"id":"W1612806847","doi":"10.1016/j.envsoft.2015.04.013","title":"Automated calculation of surface energy fluxes with high-frequency lake buoy data","year":2015,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Oceanographic and Atmospheric Processes","field":"Earth and Planetary Sciences","cited_by":86,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Environment Research Council; Sight Research UK; Ministry of Business, Innovation and Employment; Global Lake Ecological Observatory Network; National Science Foundation","keywords":"Buoy; Sensible heat; Environmental science; Wind speed; Spectrum analyzer; Heat flux; Meteorology; Latent heat; Flux (metallurgy); Energy flux; Momentum (technical analysis); Wind wave; Surface water; Heat transfer; Remote sensing; Marine engineering; Geology; Mechanics; Engineering; Materials science; Physics; Telecommunications","score_opus":0.022675731797447574,"score_gpt":0.1931639957977069,"score_spread":0.17048826400025932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1612806847","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8786798,0.001561229,0.11888977,0.000019447563,0.00007637007,0.00006684998,0.00042458513,0.00019962082,0.000082331346],"genre_scores_gemma":[0.9271586,0.00014595516,0.070051,0.000033895285,0.000028192579,3.0196685e-7,0.0024885824,0.000009011155,0.0000844533],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988056,0.000034035213,0.0002109188,0.00034522545,0.00038776197,0.00021646728],"domain_scores_gemma":[0.99930763,0.00004825396,0.000120289915,0.00037876877,0.0000107184605,0.00013434046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011741635,0.00016676777,0.00017963003,0.000012569904,0.00008572316,0.000021454869,0.00033245582,0.00006769667,0.00020999588],"category_scores_gemma":[0.000007265295,0.00013150914,0.000021260807,0.00016039108,0.00015363505,0.00045188554,0.00003298001,0.00007032583,0.000019922596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021703567,0.00002710081,0.3778451,0.000010322414,0.00001900637,0.0000057459742,0.00008684148,0.62042874,0.000009258113,0.0000053534673,0.000113551105,0.0014272514],"study_design_scores_gemma":[0.00052764575,0.00021664273,0.044060312,0.000048684164,0.000056917226,0.000012738497,0.00017324304,0.95285827,0.00023757544,0.0007181012,0.00074272056,0.0003471477],"about_ca_topic_score_codex":0.0018866734,"about_ca_topic_score_gemma":0.0004931897,"teacher_disagreement_score":0.3337848,"about_ca_system_score_codex":0.0000056043345,"about_ca_system_score_gemma":0.000044089164,"threshold_uncertainty_score":0.53627867},"labels":[],"label_agreement":null},{"id":"W1776561125","doi":"10.1016/j.envsoft.2015.07.001","title":"Feature-preserving interpolation and filtering of environmental time series","year":2015,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Series (stratigraphy); Matching (statistics); SIGNAL (programming language); Feature (linguistics); Ranging; Interpolation (computer graphics); Computer science; Algorithm; Parametric statistics; Time series; Process (computing); Data mining; Mathematics; Artificial intelligence; Statistics; Machine learning; Geology; Telecommunications; Image (mathematics)","score_opus":0.013435024979130728,"score_gpt":0.18158151942950473,"score_spread":0.168146494450374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1776561125","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29612818,0.0007419929,0.70268273,0.00008800158,0.000058756916,0.00008565149,0.00002376802,0.00006914567,0.00012174958],"genre_scores_gemma":[0.79978985,0.000041746658,0.19944313,0.000016372456,0.000029661556,0.0000031585166,0.000020818747,0.000015420137,0.0006398518],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989367,0.000033282704,0.00020459431,0.00034489154,0.00027376608,0.00020675182],"domain_scores_gemma":[0.9993783,0.000031232838,0.00013621469,0.000329877,0.0000033201104,0.00012102226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015573291,0.00017152447,0.00020447801,0.00006169806,0.000107974,0.000070235736,0.0003184067,0.00006144429,0.000054068194],"category_scores_gemma":[0.000009143742,0.00017118848,0.00006536324,0.00006352288,0.00009088803,0.00089623523,0.0006098034,0.00011084505,0.000024398412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003005055,0.0005876323,0.06395399,0.00018772401,0.00045671308,0.00007698829,0.027269224,0.6508134,0.09979867,0.0009700809,0.0011346501,0.15445039],"study_design_scores_gemma":[0.0002482689,0.00013710062,0.0010064916,0.000044296485,0.000019890871,0.00003079105,0.00031007858,0.99385905,0.002197226,0.0008144121,0.0010752215,0.00025717038],"about_ca_topic_score_codex":0.000011493813,"about_ca_topic_score_gemma":6.9986896e-7,"teacher_disagreement_score":0.50366163,"about_ca_system_score_codex":0.0000604094,"about_ca_system_score_gemma":0.0000064632964,"threshold_uncertainty_score":0.6980864},"labels":[],"label_agreement":null},{"id":"W1970273717","doi":"10.1016/j.envsoft.2007.07.008","title":"Hybrid fuzzy-mechanistic models for addressing parameter variability","year":2007,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fuzzy logic; Computer science; Artificial intelligence","score_opus":0.030329214083534606,"score_gpt":0.23455283293857193,"score_spread":0.20422361885503731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970273717","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25755817,0.000040113024,0.740765,0.000046449964,0.00015698397,0.0004755934,0.000038113325,0.00011079088,0.00080881204],"genre_scores_gemma":[0.91728497,0.000027376775,0.08158511,0.00043419853,0.00005623095,0.00004418517,0.000063034095,0.000041933687,0.0004629363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977939,0.00005180591,0.00038141524,0.00074576016,0.00031287078,0.0007142578],"domain_scores_gemma":[0.9989402,0.00039837265,0.00010990851,0.00040836196,0.00000214804,0.00014098072],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009885039,0.00031235022,0.000268701,0.000040894938,0.0005658194,0.00002746565,0.00028128698,0.00010625951,0.00037646785],"category_scores_gemma":[0.00003917705,0.00030406128,0.0001450264,0.00005017935,0.00033388546,0.00033745036,0.0003592613,0.00019164258,0.00022314547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016309018,0.0002841857,0.010388379,0.00003628894,0.0000648209,0.0000215994,0.00051782856,0.97834176,0.00047103735,0.00035769684,0.00042990482,0.008923384],"study_design_scores_gemma":[0.0010576489,0.00022032425,0.0014713053,0.00003331139,0.00018911868,0.000013023769,0.00011626213,0.6375643,0.0030865076,0.35209662,0.0032178902,0.00093366747],"about_ca_topic_score_codex":0.000034354074,"about_ca_topic_score_gemma":0.000003619263,"teacher_disagreement_score":0.65972686,"about_ca_system_score_codex":0.00029293075,"about_ca_system_score_gemma":0.0000026384823,"threshold_uncertainty_score":0.9999412},"labels":[],"label_agreement":null},{"id":"W1976087357","doi":"10.1016/s1364-8152(02)00008-7","title":"Simulation of soil chemistry and nutrient availability in a forested ecosytem of southern Quebec. Part II. Application of the SAFE model","year":2002,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Soil Carbon and Nitrogen Dynamics","field":"Agricultural and Biological Sciences","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal; McGill University","funders":"","keywords":"Nutrient; Cycling; Environmental science; Nutrient cycle; Soil nutrients; Soil chemistry; Soil water; Soil science; Environmental chemistry; Hydrology (agriculture); Ecology; Chemistry; Geology; Forestry; Geography; Biology; Geotechnical engineering","score_opus":0.013333488415934082,"score_gpt":0.1748062986953336,"score_spread":0.16147281027939953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976087357","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9976024,0.0001394812,0.001916897,0.000018880208,0.0000050394297,0.00016428943,0.00013479574,0.0000073610863,0.000010824118],"genre_scores_gemma":[0.9996635,0.000028161478,0.00018975002,0.0000068047334,0.0000074504023,0.00000833774,0.000028399087,0.0000012980188,0.00006630349],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993162,0.0000204123,0.00026180386,0.0001633575,0.00015283599,0.00008539202],"domain_scores_gemma":[0.9996504,0.00007159931,0.00016146607,0.00008454997,0.0000074387103,0.000024529172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008325902,0.00008110756,0.00012822759,0.0000032349926,0.000041439824,0.0000020117088,0.00009316937,0.00007375478,0.000038997558],"category_scores_gemma":[0.000010787849,0.000038210976,0.000060226972,0.00006723618,0.000100540405,0.000029008375,0.00006193308,0.000057895322,8.3840496e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002040328,0.00019401852,0.08204696,0.000022576884,0.0000036488568,3.1082177e-8,0.00034413228,0.8997049,0.013581314,0.000004562528,5.2099426e-7,0.0040769484],"study_design_scores_gemma":[0.00012360123,0.000019496802,0.0062684235,0.000017682265,0.000008212037,1.5169876e-7,0.00012804408,0.9868469,0.0059421277,0.00057775097,0.0000075256944,0.00006003214],"about_ca_topic_score_codex":0.00052881864,"about_ca_topic_score_gemma":0.00053291395,"teacher_disagreement_score":0.087142065,"about_ca_system_score_codex":0.000038662416,"about_ca_system_score_gemma":0.0000020418179,"threshold_uncertainty_score":0.15581983},"labels":[],"label_agreement":null},{"id":"W1978177754","doi":"10.1016/s1364-8152(03)00028-8","title":"Watershed management modelling in Malawi: application and technology transfer","year":2003,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Environment and Climate Change Canada; University of Waterloo","funders":"University of Wisconsin-Milwaukee; World Bank Group","keywords":"Watershed; Deforestation (computer science); Water quality; Environmental science; Hydrology (agriculture); Watershed management; STREAMS; Agriculture; Structural basin; Water resource management; Drainage basin; Hydraulics; Environmental resource management; Engineering; Geography; Ecology; Computer science; Geology","score_opus":0.00813985811136109,"score_gpt":0.1811408680978521,"score_spread":0.173001009986491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978177754","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51989335,0.00014655683,0.47848505,0.00013653083,0.000023763661,0.00035950157,0.0000022415172,0.00006775965,0.00088523683],"genre_scores_gemma":[0.979426,0.0006033854,0.019105883,0.00013105324,0.0000049834607,0.00015174558,0.000015830863,0.000027910246,0.00053318666],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984222,0.000044217093,0.00027500093,0.00063382875,0.0001887734,0.0004359567],"domain_scores_gemma":[0.999622,0.00001608259,0.000028133963,0.0002723829,7.416082e-7,0.000060668346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022351777,0.00024458225,0.00019945222,0.00011899965,0.00020773824,0.000013294877,0.00016035751,0.00012992731,0.00019235951],"category_scores_gemma":[0.0000012687312,0.00024311258,0.000033761502,0.00017370036,0.00029746213,0.0001777206,0.00015459677,0.0001865293,0.00023546313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019634277,0.00014153434,0.1392457,0.000021322709,0.000029484041,0.000021581985,0.00052359485,0.8557466,0.00023896541,0.0010577124,0.000018099923,0.0029357264],"study_design_scores_gemma":[0.0052110106,0.000320245,0.010133408,0.00009982756,0.00029748253,0.00005075458,0.0023104835,0.82827157,0.005794825,0.10530829,0.039582904,0.0026192216],"about_ca_topic_score_codex":0.00003114277,"about_ca_topic_score_gemma":0.000008948766,"teacher_disagreement_score":0.45953268,"about_ca_system_score_codex":0.0001441031,"about_ca_system_score_gemma":8.2779286e-7,"threshold_uncertainty_score":0.9913843},"labels":[],"label_agreement":null},{"id":"W1979596695","doi":"10.1016/j.envsoft.2013.11.010","title":"A model library for dynamic transport and fate of micropollutants in integrated urban wastewater and stormwater systems","year":2013,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Wastewater Treatment and Nitrogen Removal","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Stormwater; Environmental science; Sewage treatment; Low-impact development; Wastewater; Scale (ratio); Computer science; Environmental engineering; Surface runoff; Stormwater management","score_opus":0.008703458509393285,"score_gpt":0.17598429924801004,"score_spread":0.16728084073861677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979596695","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9912093,0.00046217066,0.007196747,0.000024931976,0.000031153,0.00079775817,0.00022875196,0.00003871959,0.000010412839],"genre_scores_gemma":[0.96397877,0.00014025168,0.03452476,0.000018812096,0.0000056722756,0.0000836259,0.00011967529,0.00005398293,0.0010744333],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986787,0.000026488236,0.00034836636,0.00044435117,0.00015479872,0.0003473208],"domain_scores_gemma":[0.9996174,0.000021140913,0.00007309658,0.00016959455,0.0000010656549,0.00011772676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006289343,0.00027596985,0.0002959198,0.000062293424,0.00007148832,0.000028900535,0.000120335586,0.000108588516,0.0001287778],"category_scores_gemma":[5.9831285e-7,0.00022026396,0.00005629303,0.000049516162,0.00022634996,0.00053917,0.00008980644,0.00009668033,0.0000223537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033519886,0.00048056906,0.5369254,0.00020191038,0.000106764535,0.000023701306,0.005547855,0.24937326,0.20402516,0.000007758902,0.000118188815,0.0028542667],"study_design_scores_gemma":[0.0016348361,0.0001834231,0.0094092535,0.00010164147,0.000059910304,0.00002195663,0.0004748575,0.97069,0.016181258,0.00064620224,0.00011662107,0.00048004295],"about_ca_topic_score_codex":0.00026285514,"about_ca_topic_score_gemma":0.0000099454355,"teacher_disagreement_score":0.72131675,"about_ca_system_score_codex":0.00009153581,"about_ca_system_score_gemma":0.0000046022146,"threshold_uncertainty_score":0.89821035},"labels":[],"label_agreement":null},{"id":"W1979683159","doi":"10.1016/j.envsoft.2010.11.010","title":"Using AHP and Dempster–Shafer theory for evaluating sustainable transport solutions","year":2011,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":224,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"European Commission","keywords":"Analytic hierarchy process; Sustainability; Dempster–Shafer theory; Measure (data warehouse); Sustainable transport; Computer science; Intelligent transportation system; Process (computing); Operations research; Engineering; Transport engineering; Data mining","score_opus":0.5116639428476715,"score_gpt":0.41634163864533724,"score_spread":0.09532230420233423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979683159","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4183902,0.00038643865,0.5805605,0.0000078202065,0.00010499025,0.00039541323,0.00004301768,0.000042780426,0.00006883012],"genre_scores_gemma":[0.71116704,0.000011703301,0.28745326,0.00008520512,0.000040919764,0.000036711706,0.000009415791,0.00004380093,0.0011519563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99628186,0.0002327742,0.00084722164,0.00091583986,0.0010219216,0.00070036144],"domain_scores_gemma":[0.99759465,0.0012266472,0.00026273556,0.00065685005,0.00006319004,0.00019593778],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0046819495,0.00029850763,0.00037532367,0.00024018204,0.0009462798,0.00012998281,0.0005284717,0.0001466795,0.00073081424],"category_scores_gemma":[0.0005176604,0.00026352023,0.00019340063,0.00017307758,0.00023823178,0.00074576895,0.00026144285,0.00017883758,0.000047110127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001679805,0.00096304173,0.08594617,0.00014246274,0.00021427544,0.00013472512,0.040852647,0.68781465,0.007555908,0.011981201,0.00025904604,0.16245607],"study_design_scores_gemma":[0.0010198614,0.00013505481,0.003992945,0.00006016516,0.00011174506,0.000033088596,0.0056606457,0.7875281,0.0003770588,0.19960311,0.0009339083,0.000544329],"about_ca_topic_score_codex":0.00003819281,"about_ca_topic_score_gemma":0.0000021727594,"teacher_disagreement_score":0.2931072,"about_ca_system_score_codex":0.00015149523,"about_ca_system_score_gemma":0.000046638863,"threshold_uncertainty_score":0.9999817},"labels":[],"label_agreement":null},{"id":"W1979826640","doi":"10.1016/j.envsoft.2013.08.006","title":"A stepwise cluster analysis approach for downscaled climate projection – A Canadian case study","year":2013,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Climate variability and models","field":"Environmental Science","cited_by":106,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Downscaling; Climatology; Scale (ratio); Environmental science; Calibration; Projection (relational algebra); Climate change; Forcing (mathematics); Cluster (spacecraft); Precipitation; Climate model; Meteorology; Computer science; Statistics; Geography; Mathematics; Cartography; Algorithm; Ecology","score_opus":0.020167445112409494,"score_gpt":0.22331053416557273,"score_spread":0.20314308905316322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979826640","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75307274,0.0000109461,0.24367751,0.000030540334,0.000040835024,0.002766967,0.00014433166,0.00007694926,0.00017915046],"genre_scores_gemma":[0.95795786,0.000010494906,0.04012016,0.00018939418,0.00003159207,0.0012044786,0.00020455627,0.000046608846,0.0002348637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99740005,0.00011616269,0.00045187853,0.0009412506,0.00033232907,0.0007583295],"domain_scores_gemma":[0.99880034,0.000087284134,0.00011121303,0.00059259485,0.0000053611407,0.0004032032],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004935523,0.00034092992,0.00036015155,0.00016610902,0.0006570303,0.00011433178,0.0002256047,0.0001537724,0.0014007661],"category_scores_gemma":[0.000010587582,0.00032429287,0.00026497585,0.0003366495,0.00013475088,0.0004866883,0.0001898719,0.00019252441,0.00023699763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002841475,0.00074636546,0.16781926,0.000019380035,0.00014737087,0.00002792443,0.0030256957,0.826515,0.000056068657,0.0000012637624,0.00008908769,0.0015241656],"study_design_scores_gemma":[0.0008323541,0.00017563559,0.003016909,0.000003150673,0.000554815,0.000081157195,0.0030826489,0.9915264,0.0000111624995,0.00011069905,0.00015652143,0.00044852818],"about_ca_topic_score_codex":0.1702043,"about_ca_topic_score_gemma":0.029547878,"teacher_disagreement_score":0.20488508,"about_ca_system_score_codex":0.00081231166,"about_ca_system_score_gemma":0.000015269612,"threshold_uncertainty_score":0.9999209},"labels":[],"label_agreement":null},{"id":"W1980322125","doi":"10.1016/j.envsoft.2015.02.004","title":"Efficiency criteria for environmental model quality assessment: A review and its application to wastewater treatment","year":2015,"lang":"en","type":"review","venue":"Environmental Modelling & Software","topic":"Wastewater Treatment and Nitrogen Removal","field":"Environmental Science","cited_by":92,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Equivalence (formal languages); Computer science; Function (biology); Quality (philosophy); Operations research; Data mining; Mathematics","score_opus":0.08215300160986792,"score_gpt":0.34335874596473387,"score_spread":0.26120574435486593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980322125","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018091293,0.9648257,0.022935973,0.000047855145,0.000084789965,0.007864251,0.0022289446,0.00012633468,0.00007704738],"genre_scores_gemma":[0.0006459499,0.95132756,0.03936405,0.00014623765,0.00008152043,0.0031500848,0.002381414,0.00021638232,0.0026868077],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9951437,0.00023721482,0.0011811492,0.0018759691,0.00069780176,0.00086417806],"domain_scores_gemma":[0.997903,0.00009797221,0.00045165324,0.00090633705,0.0000035842702,0.000637485],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.00060235476,0.0012854324,0.0022139912,0.000101007005,0.0003663373,0.000070716946,0.0005404775,0.00035723657,0.00034468266],"category_scores_gemma":[0.000006899666,0.0010353689,0.0006240342,0.00013746298,0.00014823783,0.00030551254,0.00057282206,0.00023747474,0.00084746873],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008128702,0.0022481612,0.00010121798,0.011255218,0.00052032125,0.000017771734,0.000765233,0.02410633,0.0005381539,0.000019117066,0.0007327563,0.95961446],"study_design_scores_gemma":[0.0015721722,0.00095629145,0.000007923367,0.002963562,0.005323799,0.00008950521,0.00005309818,0.05413742,0.00015686687,0.00028547726,0.93166965,0.0027842538],"about_ca_topic_score_codex":0.000023185114,"about_ca_topic_score_gemma":0.0000023850873,"teacher_disagreement_score":0.9568302,"about_ca_system_score_codex":0.0025303538,"about_ca_system_score_gemma":0.000050649145,"threshold_uncertainty_score":0.99998975},"labels":[],"label_agreement":null},{"id":"W1984751909","doi":"10.1016/j.envsoft.2010.08.006","title":"Integration of numerical modeling and Bayesian analysis for setting water quality criteria in Hamilton Harbour, Ontario, Canada","year":2010,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":59,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Ministry of the Environment, Conservation and Parks; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Harbour; Credibility; Bayesian probability; Calibration; Process (computing); Computer science; Quality (philosophy); Consistency (knowledge bases); Bayesian network; Machine learning; Operations research; Bayesian inference; Remedial education; Management science; Risk analysis (engineering); Artificial intelligence; Engineering; Statistics; Mathematics; Business","score_opus":0.012308599463086786,"score_gpt":0.22460762506803317,"score_spread":0.21229902560494637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984751909","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.644898,0.00000385605,0.35474283,0.000075266,0.000052151823,0.00015598381,0.000014495815,0.000008461689,0.000048964444],"genre_scores_gemma":[0.96914417,0.000007779006,0.03040441,0.00016576861,0.000009630331,0.000040401883,0.000082097904,0.000011707016,0.00013403957],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998757,0.000036838293,0.00036318967,0.000385172,0.00017559301,0.0002822097],"domain_scores_gemma":[0.9996348,0.000063522384,0.00007181277,0.00017293489,0.0000021755309,0.000054747874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034788562,0.00016197664,0.0002686109,0.00004943592,0.0001740064,0.00001217653,0.0001130157,0.000076890385,0.0006536363],"category_scores_gemma":[0.000013954752,0.00014334399,0.000060935407,0.00006260633,0.00009594258,0.00016387191,0.00013414281,0.00019672766,0.0000019941576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028010201,0.00006504167,0.60157263,0.000013283456,0.00006051086,0.0000014196308,0.0019135684,0.39339563,0.0025607175,0.000009318285,0.000054354034,0.00032548272],"study_design_scores_gemma":[0.000299768,0.000030122614,0.20187223,0.000006529731,0.00010673377,4.8472737e-7,0.00028436052,0.7955927,0.0007291729,0.00065711245,0.00019211044,0.00022862483],"about_ca_topic_score_codex":0.50974035,"about_ca_topic_score_gemma":0.9355029,"teacher_disagreement_score":0.42576256,"about_ca_system_score_codex":0.00026456025,"about_ca_system_score_gemma":0.000008529359,"threshold_uncertainty_score":0.7156859},"labels":[],"label_agreement":null},{"id":"W1990751471","doi":"10.1016/j.envsoft.2013.10.005","title":"A layered approach to parallel computing for spatially distributed hydrological modeling","year":2013,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":56,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Natural Science Foundation of China","keywords":"Computer science; Parallel computing; Speedup; Grid; Computation; Upstream (networking); Routing (electronic design automation); Supercomputer; Parallel processing; Multi-core processor; Parallel algorithm; Focus (optics); Computational science; Flow routing; Distributed computing; Hydrological modelling; Flow (mathematics); Algorithm; Geology; Mathematics","score_opus":0.02297831566193703,"score_gpt":0.2076429916705053,"score_spread":0.18466467600856826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990751471","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39838412,0.000018868777,0.6001825,0.00018821695,0.00004065847,0.0008094771,0.000021332376,0.00010778889,0.0002470992],"genre_scores_gemma":[0.82016516,0.000011639718,0.1784847,0.0006763888,0.00004494975,0.0002553032,0.00017022056,0.00003083316,0.00016080141],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99784595,0.000050949682,0.000362778,0.0007726556,0.00027719748,0.0006904735],"domain_scores_gemma":[0.9993618,0.00007098941,0.000073889074,0.0002991454,0.0000029632815,0.00019119024],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021494886,0.00032665065,0.00031084803,0.00003490035,0.0005309997,0.000044639284,0.00037175903,0.00013724648,0.00023029654],"category_scores_gemma":[0.000019395118,0.0002941395,0.00012904224,0.00008341742,0.0001461852,0.00021139809,0.0006082159,0.0001786634,0.00076619146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039725706,0.0002031171,0.014812109,0.000009461235,0.00003631719,0.000001377179,0.0004632374,0.9828271,0.00013467556,0.000024342018,0.0005331199,0.00091537525],"study_design_scores_gemma":[0.00050721026,0.00011915692,0.0020522536,0.000006959312,0.000028798713,0.0000023147984,0.000088952715,0.9936755,0.00002469472,0.0025849107,0.00054190337,0.00036735518],"about_ca_topic_score_codex":0.00017447364,"about_ca_topic_score_gemma":0.0000029609532,"teacher_disagreement_score":0.42178106,"about_ca_system_score_codex":0.00016675137,"about_ca_system_score_gemma":0.0000020079647,"threshold_uncertainty_score":0.99995106},"labels":[],"label_agreement":null},{"id":"W1995363598","doi":"10.1016/j.envsoft.2004.02.009","title":"Global optimal real-time control of the Quebec urban drainage system","year":2004,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","cited_by":160,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Combined sewer; Reliability (semiconductor); Environmental science; Real-time Control System; Drainage; Control (management); Drainage network; Salient; Drainage system (geomorphology); Civil engineering; Hydrology (agriculture); Water resource management; Engineering; Computer science; Geography; Drainage basin; Cartography; Stormwater; Geotechnical engineering","score_opus":0.004811763447756084,"score_gpt":0.16685875288338026,"score_spread":0.16204698943562418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995363598","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6389759,0.00006444275,0.3571397,0.000092686896,0.00014798346,0.0006518361,0.00017179051,0.00019053911,0.0025650994],"genre_scores_gemma":[0.98994285,0.0000062121157,0.008309429,0.000067035944,0.00004050624,0.000035808753,0.0000205886,0.00003495226,0.0015426071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99789125,0.000078125115,0.00040126723,0.0005079108,0.0006413762,0.0004800774],"domain_scores_gemma":[0.99893594,0.000026305273,0.00020251678,0.0007058444,0.0000020072648,0.00012740711],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017726059,0.00029621896,0.0002643929,0.000021879774,0.00033987197,0.000029368672,0.00064432155,0.00010143034,0.00036174935],"category_scores_gemma":[0.00000543626,0.000245958,0.00021507683,0.00018069427,0.0005361441,0.000290031,0.00044653565,0.00012492575,0.00078689674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002324547,0.00016346303,0.037477955,0.000016348708,0.00004054579,0.000009552183,0.00042867247,0.95967495,0.0010174232,0.00066283456,0.00032289996,0.00016210195],"study_design_scores_gemma":[0.0152977435,0.00093888404,0.51088226,0.00085453124,0.0017218663,0.00016408862,0.0026582652,0.43937668,0.0042932057,0.0049097855,0.013991709,0.0049109734],"about_ca_topic_score_codex":0.0054426,"about_ca_topic_score_gemma":0.00013805475,"teacher_disagreement_score":0.5202983,"about_ca_system_score_codex":0.0023768349,"about_ca_system_score_gemma":0.000015257425,"threshold_uncertainty_score":0.9999993},"labels":[],"label_agreement":null},{"id":"W2007833012","doi":"10.1016/j.envsoft.2013.06.008","title":"Social influence and dynamic demand for new products","year":2013,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Innovation Diffusion and Forecasting","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Popularity; Imitation; Novelty; Product (mathematics); Microeconomics; Preference; Personality; Marketing; Economics; Business; Consumption (sociology); New product development; Psychology; Social psychology; Mathematics; Sociology","score_opus":0.05663825909304547,"score_gpt":0.30115089731799294,"score_spread":0.24451263822494745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007833012","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.694542,0.00008009289,0.30443633,0.00050953694,0.000059094928,0.00030351727,0.000008527151,0.00003353848,0.000027397276],"genre_scores_gemma":[0.9582097,0.000009118956,0.03790949,0.000342521,0.000076809796,0.000027279564,0.000014608479,0.00001806346,0.0033924123],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983426,0.000025632036,0.00040987332,0.0004739938,0.0005015492,0.00024636238],"domain_scores_gemma":[0.9992932,0.0002199455,0.00016744726,0.00019122218,0.000041095707,0.00008707018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000408775,0.00014576381,0.0001698434,0.00009374155,0.00040303494,0.00019265128,0.00022462153,0.00007531414,0.00019692896],"category_scores_gemma":[0.0003007386,0.000121429104,0.00004470628,0.00017148517,0.000114805145,0.00045275086,0.00012038745,0.000103383296,0.00020760256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089846886,0.00022577321,0.04873351,0.0000502748,0.000041532334,0.0000032971896,0.008280952,0.095813744,0.016069412,0.0013452808,0.012939434,0.81640697],"study_design_scores_gemma":[0.001685006,0.00014752052,0.098810576,0.000040222767,0.00002683259,0.00002136836,0.001017942,0.7217751,0.000980064,0.1588257,0.015828384,0.00084128795],"about_ca_topic_score_codex":0.000014834105,"about_ca_topic_score_gemma":0.0000010082313,"teacher_disagreement_score":0.81556565,"about_ca_system_score_codex":0.000040856416,"about_ca_system_score_gemma":0.000020681884,"threshold_uncertainty_score":0.49517354},"labels":[],"label_agreement":null},{"id":"W2010013147","doi":"10.1016/s1364-8152(00)00010-4","title":"Atmospheric environmental information — an overview with Canadian examples","year":2000,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Air quality index; Environmental science; Meteorology; Computer science; Geography","score_opus":0.02461132108152401,"score_gpt":0.2068732953926616,"score_spread":0.1822619743111376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010013147","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9891137,0.00023531653,0.009183604,0.000029989025,0.00004960147,0.0002869853,0.00010064141,0.00013956537,0.000860613],"genre_scores_gemma":[0.96098775,0.00038317934,0.037041295,0.0003415034,0.00006913457,0.000035353485,0.00032465407,0.000050363422,0.0007667962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979293,0.00006923773,0.00034596174,0.0004484938,0.00057348487,0.0006335641],"domain_scores_gemma":[0.9989132,0.000029452205,0.00009260096,0.00045435387,8.0843154e-7,0.0005095855],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001938345,0.00034591326,0.00020992594,0.000016528234,0.00057183165,0.000086236956,0.0003069,0.00012643672,0.012720744],"category_scores_gemma":[0.0000028131144,0.00032954867,0.00006520972,0.00010984842,0.0002490055,0.0015118589,0.00006247741,0.00024684364,0.0028013994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003854229,0.00013792889,0.13985111,0.000010882804,0.00001937699,0.000012293832,0.0020799846,0.63393956,0.00005118741,0.0000047883345,0.00006691268,0.22378741],"study_design_scores_gemma":[0.0023665521,0.0014895397,0.2364586,0.0002561422,0.00021672159,0.00020925982,0.0030000098,0.4357035,0.00065075053,0.0007553931,0.3150327,0.0038608734],"about_ca_topic_score_codex":0.02145973,"about_ca_topic_score_gemma":0.0011705107,"teacher_disagreement_score":0.31496575,"about_ca_system_score_codex":0.0008058035,"about_ca_system_score_gemma":0.00001608861,"threshold_uncertainty_score":0.99991566},"labels":[],"label_agreement":null},{"id":"W2010105821","doi":"10.1016/j.envsoft.2014.05.019","title":"Combining agent functional types, capitals and services to model land use dynamics","year":2014,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":111,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Division of Materials Research; European Commission","keywords":"Dynamics (music); Computer science; Land use; Engineering; Sociology; Civil engineering","score_opus":0.012334392326859745,"score_gpt":0.17905424407936213,"score_spread":0.1667198517525024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010105821","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8887175,0.00006981069,0.110668525,0.00006003807,0.00008942131,0.00016078366,0.00005775103,0.00006940763,0.000106778745],"genre_scores_gemma":[0.9890499,0.00006272712,0.009787825,0.00054934813,0.00003883107,0.000019506988,0.0001413287,0.000036437523,0.00031409116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862444,0.000031492702,0.00022248457,0.00048022153,0.0003206228,0.00032073996],"domain_scores_gemma":[0.99937546,0.00006028895,0.000068307476,0.0002648555,0.0000017783467,0.00022931368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001537257,0.00022729111,0.00019436584,0.000030884898,0.00026048723,0.000093100025,0.00016493893,0.000088621055,0.0002637307],"category_scores_gemma":[0.0000027197882,0.00020423801,0.000044812907,0.000051721352,0.00002548465,0.00041738595,0.00030707323,0.000106786654,0.0006042685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001625247,0.00003836327,0.2676279,0.000019015735,0.00001057194,0.000001285761,0.0003500115,0.73106855,0.00004672837,0.000022634109,0.000028228269,0.00077044516],"study_design_scores_gemma":[0.0002836097,0.000054032767,0.019396141,0.000038893748,0.00002746181,0.0000068785034,0.00005766691,0.97800165,0.000028197532,0.00070690917,0.0011070622,0.00029149704],"about_ca_topic_score_codex":0.000534758,"about_ca_topic_score_gemma":0.0005288482,"teacher_disagreement_score":0.24823175,"about_ca_system_score_codex":0.00016282256,"about_ca_system_score_gemma":0.0000024379856,"threshold_uncertainty_score":0.83285844},"labels":[],"label_agreement":null},{"id":"W2012385536","doi":"10.1016/j.envsoft.2014.11.026","title":"Modeling structural change in spatial system dynamics: A Daisyworld example","year":2014,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Earth Systems and Cosmic Evolution","field":"Earth and Planetary Sciences","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Austrian Science Fund","keywords":"Computer science; Python (programming language); Geographic information system; System dynamics; Software; Artificial intelligence; Geography; Cartography; Programming language","score_opus":0.019249230582975056,"score_gpt":0.18410249665948264,"score_spread":0.16485326607650758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012385536","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7227468,0.0004054571,0.27604717,0.0000096539825,0.00029230118,0.00021934851,0.00007154532,0.00008237956,0.00012534983],"genre_scores_gemma":[0.99564403,0.000017500302,0.0035513695,0.000052090014,0.00027323113,0.0000071642753,0.00040235367,0.000012415239,0.000039830345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984012,0.00010193412,0.00034983136,0.00042726743,0.00031715014,0.0004026081],"domain_scores_gemma":[0.9995145,0.00004642237,0.00007472131,0.00024589524,0.0000036140718,0.000114837545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026720713,0.00021269495,0.00023626181,0.00008714666,0.00018382088,0.00004055937,0.00017873697,0.00009574596,0.0001834512],"category_scores_gemma":[0.000003697653,0.00020100638,0.00006556287,0.000072320865,0.00003631735,0.00027711157,0.000021370697,0.00019674792,0.0001656518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011727707,0.00000410991,0.3129415,0.00003101403,0.0000026626083,0.00000298877,0.00027645213,0.6671255,0.0000011921717,0.000053238527,5.010063e-7,0.019549116],"study_design_scores_gemma":[0.00025724826,0.00004705303,0.062138006,0.00009047273,0.0000072797184,0.000012517821,0.0002143761,0.9366973,0.0000012285942,0.0002702965,0.000036602738,0.00022759811],"about_ca_topic_score_codex":0.06828154,"about_ca_topic_score_gemma":0.019529628,"teacher_disagreement_score":0.27289724,"about_ca_system_score_codex":0.000070223854,"about_ca_system_score_gemma":0.0000075735975,"threshold_uncertainty_score":0.9983614},"labels":[],"label_agreement":null},{"id":"W2017228450","doi":"10.1016/j.envsoft.2015.01.013","title":"Approximate dynamic programming for automated vacuum waste collection systems","year":2015,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Dynamic programming; Environmental science; Engineering; Waste management; Process engineering; Algorithm","score_opus":0.02577717846318871,"score_gpt":0.24064331290477844,"score_spread":0.21486613444158972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017228450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27792466,0.0011123841,0.7163579,0.0000048925926,0.00084904814,0.0013283172,0.000047743768,0.0023399915,0.000035079607],"genre_scores_gemma":[0.96996915,0.000031282623,0.028164351,0.0000025249137,0.000121519326,0.00074486557,0.00016913403,0.00016170304,0.00063544273],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981673,0.000051011557,0.00037947902,0.0003658475,0.00044902583,0.00058738067],"domain_scores_gemma":[0.99932563,0.000072995135,0.00005845488,0.00031731144,0.000017561217,0.00020804744],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040193446,0.00028197677,0.0002899403,0.00014560866,0.00017113853,0.0001236196,0.00019889887,0.00016303263,0.0000034930836],"category_scores_gemma":[0.000014195413,0.00030412097,0.000088112014,0.00016604231,0.000044318924,0.00019115677,0.00005172099,0.00018824489,0.00007362933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023674256,0.00003960143,0.0006794988,0.00026820655,0.00006305172,0.0000047648527,0.00050629367,0.9961274,0.00075928424,0.0000037137584,0.0006546463,0.00086983934],"study_design_scores_gemma":[0.0006329006,0.000085939326,0.000019412179,0.00010086956,0.000020953908,0.000026502554,0.0005045084,0.99431705,0.00047310692,0.000028155375,0.0034748854,0.00031574062],"about_ca_topic_score_codex":0.00003250976,"about_ca_topic_score_gemma":0.0000023547386,"teacher_disagreement_score":0.6920445,"about_ca_system_score_codex":0.0009463272,"about_ca_system_score_gemma":0.000022639084,"threshold_uncertainty_score":0.9999411},"labels":[],"label_agreement":null},{"id":"W2020703374","doi":"10.1016/j.envsoft.2004.03.013","title":"Mass balance modelling of priority toxic chemicals within the great lakes toxic chemical decision support system: RateCon model results for Lake Ontario and Lake Erie","year":2004,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Toxic Organic Pollutants Impact","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Environmental science; Balance (ability); Hydrology (agriculture); Water resource management; Oceanography; Environmental resource management; Environmental protection; Engineering; Geology; Biology","score_opus":0.017303229268085286,"score_gpt":0.21370384844555124,"score_spread":0.19640061917746596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020703374","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5971422,0.00006179395,0.4004756,0.00006176363,0.00008927099,0.0006329688,0.0013337766,0.000109154316,0.00009346112],"genre_scores_gemma":[0.90498716,0.000063638116,0.09429985,0.00009123937,0.00004404577,0.000034195153,0.00005994366,0.000088059955,0.00033186696],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99656767,0.00003729917,0.00096814916,0.0009902456,0.00075123954,0.00068538135],"domain_scores_gemma":[0.9982904,0.00020294226,0.00041690376,0.00077906786,0.0000038133915,0.00030687687],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007115686,0.0005268919,0.0005775599,0.00003822012,0.00029463365,0.000059317215,0.00057423435,0.0003150831,0.0002542386],"category_scores_gemma":[0.00006069013,0.00042963855,0.00016661924,0.00013189181,0.000524329,0.00043768156,0.00027335272,0.00043772836,0.000051366747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004797475,0.00012928424,0.0015877009,0.000033072974,0.000035161942,0.000009008549,0.0021090591,0.9668613,0.026919616,0.000015393873,0.00004731826,0.0017733182],"study_design_scores_gemma":[0.0043131714,0.0002804496,0.00039511075,0.00026590837,0.00017882307,0.00010594791,0.00027850395,0.91548383,0.0682471,0.008851089,0.0005562581,0.0010438152],"about_ca_topic_score_codex":0.00013326987,"about_ca_topic_score_gemma":0.005249907,"teacher_disagreement_score":0.30784494,"about_ca_system_score_codex":0.0010165075,"about_ca_system_score_gemma":0.00009084884,"threshold_uncertainty_score":0.9998155},"labels":[],"label_agreement":null},{"id":"W2020821169","doi":"10.1016/j.envsoft.2011.09.010","title":"Numerical assessment of metamodelling strategies in computationally intensive optimization","year":2011,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":139,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Metamodeling; Computer science; Mathematical optimization; Optimization problem; Emulation; Kriging; Function (biology); Machine learning; Mathematics; Algorithm","score_opus":0.029692641945791635,"score_gpt":0.2555625978735644,"score_spread":0.22586995592777276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020821169","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003104599,0.00008108815,0.9962049,0.00001243308,0.00010033415,0.0002424787,0.0000071370687,0.000094403666,0.00015258785],"genre_scores_gemma":[0.4671539,0.00003361629,0.5327172,0.000037474583,0.000005395902,0.000012769648,0.000019816714,0.000013420177,0.0000063845973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838567,0.00007966497,0.00045982414,0.00048404717,0.00035687294,0.00023394886],"domain_scores_gemma":[0.999246,0.00009854058,0.00023307792,0.00028322684,0.000068911475,0.000070245726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001390544,0.00020828054,0.00026633023,0.00016561887,0.00007327574,0.000032380864,0.00038638507,0.000070180424,0.000045397606],"category_scores_gemma":[0.000013136476,0.00022364393,0.000071868126,0.00022581697,0.00009051333,0.0010263784,0.00013271575,0.00019096097,0.0000056134363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006670843,0.0001684538,0.0017473346,0.0000069751313,0.000016694186,0.000008392676,0.0015399153,0.99331486,0.000021421212,0.002085813,3.5807471e-7,0.0010831075],"study_design_scores_gemma":[0.00037891127,0.00006745938,0.001608474,0.000026644751,0.0000069375856,0.0000057290135,0.0003528583,0.9931415,0.00027149334,0.003923598,0.0000017420754,0.00021465731],"about_ca_topic_score_codex":0.000035850175,"about_ca_topic_score_gemma":6.3726037e-7,"teacher_disagreement_score":0.4640493,"about_ca_system_score_codex":0.0001819471,"about_ca_system_score_gemma":0.00006394096,"threshold_uncertainty_score":0.9119935},"labels":[],"label_agreement":null},{"id":"W2037150067","doi":"10.1016/j.envsoft.2011.09.009","title":"Environmental decision support systems (EDSS) development – Challenges and best practices","year":2011,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":317,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Engineering and Physical Sciences Research Council","keywords":"Champion; Process management; Sustainability; Process (computing); Knowledge management; Revenue; Business; Operations management; Engineering; Computer science","score_opus":0.06770918547361696,"score_gpt":0.22680586688290377,"score_spread":0.15909668140928682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037150067","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45980692,0.0065554525,0.53265935,0.000013923862,0.00028526236,0.00021893244,0.000004664245,0.00015120943,0.00030425435],"genre_scores_gemma":[0.81546825,0.0023287535,0.18182983,0.000032509823,0.000045283272,0.000025923866,0.000008442039,0.00003424052,0.00022678013],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976667,0.00007103371,0.00041928515,0.0008650298,0.0005208802,0.00045705863],"domain_scores_gemma":[0.9986466,0.00014033064,0.0003403287,0.00063017855,0.0000038072612,0.00023876604],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040228994,0.00034157903,0.00026901843,0.00009524131,0.00038141522,0.00013134381,0.00048112994,0.00014236412,0.0000386792],"category_scores_gemma":[0.0000136717745,0.00033566117,0.00005773709,0.00003822884,0.0001221006,0.0007342869,0.0004787012,0.0002273879,0.00027446606],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054200536,0.0013235002,0.005370953,0.00009819872,0.00010911293,0.0002193074,0.028410373,0.00937734,0.00081482355,0.0005189015,0.000043187054,0.95366013],"study_design_scores_gemma":[0.0063225864,0.0043847775,0.042366005,0.0020312262,0.0004555774,0.0039893193,0.024313232,0.7114412,0.027119454,0.0052786632,0.16272227,0.009575649],"about_ca_topic_score_codex":0.000029855724,"about_ca_topic_score_gemma":0.0000027298654,"teacher_disagreement_score":0.94408447,"about_ca_system_score_codex":0.0001412705,"about_ca_system_score_gemma":0.000025118765,"threshold_uncertainty_score":0.9999095},"labels":[],"label_agreement":null},{"id":"W2039142341","doi":"10.1016/j.envsoft.2005.12.010","title":"Decision support for local environmental impact assessment","year":2006,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Variety (cybernetics); Sustainability; Context (archaeology); Computer science; Risk analysis (engineering); Informatics; Presentation (obstetrics); Decision support system; Key (lock); Business; Process management; Data science; Knowledge management; Computer security; Engineering","score_opus":0.006354004312295153,"score_gpt":0.2302320939355552,"score_spread":0.22387808962326003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039142341","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15681686,0.0002132521,0.84192973,0.000047653626,0.0002494294,0.00038722367,0.00010466413,0.00017598984,0.000075190066],"genre_scores_gemma":[0.71269906,0.000023623783,0.28640258,0.00028641455,0.000105529856,0.00004934856,0.00030194595,0.000037228212,0.000094278694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974073,0.000038306953,0.00044234734,0.00084672717,0.0006309329,0.0006343954],"domain_scores_gemma":[0.9987248,0.00020756814,0.00014896633,0.00072702765,0.000004467879,0.00018713584],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019504262,0.00040766835,0.0002893714,0.000115026814,0.00035689754,0.00012954266,0.0008901738,0.0001367474,0.00015214267],"category_scores_gemma":[6.998958e-7,0.0003645819,0.00030071737,0.000104697865,0.00008477586,0.0005301738,0.00037875056,0.00021785159,0.00012179529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058116322,0.00056835695,0.012829805,0.000021300935,0.000046670433,0.000025890608,0.0004289786,0.88396835,0.001079233,0.00058191747,0.000208125,0.10018323],"study_design_scores_gemma":[0.0013795586,0.00052976276,0.013306554,0.00003359758,0.000040798815,0.00005209511,0.00009079513,0.9614033,0.0020887265,0.012207634,0.008143227,0.000723945],"about_ca_topic_score_codex":0.00011035679,"about_ca_topic_score_gemma":0.000010061406,"teacher_disagreement_score":0.5558822,"about_ca_system_score_codex":0.00037898662,"about_ca_system_score_gemma":0.000039302056,"threshold_uncertainty_score":0.9998806},"labels":[],"label_agreement":null},{"id":"W2040126493","doi":"10.1016/s1364-8152(00)00086-4","title":"Space–time series modelling of beach and shoreline data","year":2001,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Coastal and Marine Dynamics","field":"Earth and Planetary Sciences","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Shore; Series (stratigraphy); Time series; Space (punctuation); Geology; Geography; Environmental science; Oceanography; Computer science; Machine learning","score_opus":0.022192135031117353,"score_gpt":0.18395569961209143,"score_spread":0.16176356458097407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040126493","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64806014,0.0012678499,0.34936354,0.00007565716,0.000059883463,0.00012096378,0.0007335295,0.000049312308,0.0002691255],"genre_scores_gemma":[0.9358885,0.0017529493,0.057537444,0.000028964836,0.00005994989,4.4405195e-7,0.0034404625,0.000010571891,0.0012807047],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998916,0.000019834446,0.00022498843,0.00037697286,0.0002222907,0.00023992138],"domain_scores_gemma":[0.99938464,0.000058250225,0.00006517159,0.000379965,0.0000037397394,0.0001082427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001412665,0.00017119737,0.00019661487,0.0000412629,0.0001162839,0.000023764302,0.0002571038,0.000060351456,0.00063101697],"category_scores_gemma":[0.000004988393,0.00015775762,0.00003255907,0.00006123812,0.00014354268,0.0003979041,0.0001493809,0.00012936702,0.000039831593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006478599,0.000028998902,0.08264566,0.000015000539,0.000014466895,0.000011386011,0.00012170814,0.890221,0.000018418907,0.000007066911,0.000032118005,0.026819397],"study_design_scores_gemma":[0.0001496064,0.000080098245,0.0017599717,0.000017498203,0.000025983469,0.000035660858,0.00007907135,0.9935536,0.00001460027,0.0010897104,0.0030049009,0.0001892916],"about_ca_topic_score_codex":0.0007930253,"about_ca_topic_score_gemma":0.00016285645,"teacher_disagreement_score":0.2918261,"about_ca_system_score_codex":0.0000045418114,"about_ca_system_score_gemma":0.000009219804,"threshold_uncertainty_score":0.6909193},"labels":[],"label_agreement":null},{"id":"W2049391094","doi":"10.1016/s1364-8152(02)00028-2","title":"GreenPro-I: a risk-based life cycle assessment and decision-making methodology for process plant design","year":2002,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Process Optimization and Integration","field":"Engineering","cited_by":96,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Life-cycle assessment; Process (computing); Engineering; Cleaner production; Product lifecycle; Product design; Product (mathematics); Sustainable design; Risk analysis (engineering); Process design; Design review (U.S. government); Decision-making; New product development; Manufacturing engineering; Work in process; Systems engineering; Computer science; Production (economics); Operations management; Sustainability; Process engineering; Process integration; Business; Waste management; Product testing","score_opus":0.05186571759120705,"score_gpt":0.27274388040253333,"score_spread":0.22087816281132627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049391094","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049716912,0.00071559014,0.9487627,0.000012663713,0.000079778416,0.00039120094,0.000081897815,0.00022342028,0.00001580757],"genre_scores_gemma":[0.52694386,0.00020568959,0.47261775,0.00006368757,0.00001955363,0.00009140854,0.000023859684,0.000031389685,0.0000028084955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901354,0.000043280314,0.00026378405,0.0002881094,0.00016423942,0.00022705838],"domain_scores_gemma":[0.99913144,0.0005562126,0.00007465261,0.00013818317,0.000008891494,0.000090597954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022948676,0.00020251493,0.00020023923,0.00007851533,0.00018129642,0.000041351184,0.00010572036,0.00011386452,0.00015834035],"category_scores_gemma":[0.00006607219,0.00019896218,0.000047500445,0.000057940604,0.000038325292,0.00018569286,0.000014382546,0.00016415924,0.000007961968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026457226,0.000040056988,0.0005928454,0.000041361498,0.000019328994,9.1094824e-7,0.000248812,0.95626456,0.0000237015,0.000010384865,0.000063369516,0.042668194],"study_design_scores_gemma":[0.00044883482,0.000076037846,0.00010650631,0.000060463983,0.000033845412,0.0000031302827,0.000066088745,0.99605465,0.0002770233,0.0025733684,0.00007231571,0.00022773125],"about_ca_topic_score_codex":0.0000015759283,"about_ca_topic_score_gemma":0.0000010529336,"teacher_disagreement_score":0.47722694,"about_ca_system_score_codex":0.000099877725,"about_ca_system_score_gemma":0.0000096475,"threshold_uncertainty_score":0.81134427},"labels":[],"label_agreement":null},{"id":"W2050158491","doi":"10.1016/j.envsoft.2010.01.006","title":"Information-driven receptor placement for contaminant source determination","year":2010,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; University of Waterloo","funders":"","keywords":"Detector; Bayesian probability; Monte Carlo method; Posterior probability; Sampling (signal processing); Markov chain Monte Carlo; Computer science; Algorithm; Point source; Statistics; Environmental science; Mathematical optimization; Mathematics; Physics; Optics","score_opus":0.005373733606615127,"score_gpt":0.186614359544443,"score_spread":0.18124062593782786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050158491","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46239904,0.000005393443,0.5367472,0.000031202242,0.0002069957,0.00043612815,0.000028244793,0.00006389894,0.00008189889],"genre_scores_gemma":[0.68634427,0.00003600785,0.31150925,0.0002807223,0.000060779177,0.00017715637,0.00020586405,0.000045373374,0.0013405947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983113,0.000020218476,0.00041548972,0.00034752506,0.0004389122,0.0004665721],"domain_scores_gemma":[0.9991534,0.00008307423,0.00020091966,0.00036543404,0.000002331174,0.00019488472],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00017523779,0.00031716426,0.00020459466,0.000015184903,0.0003842403,0.000051579293,0.00031833388,0.00018218176,0.0017925848],"category_scores_gemma":[0.000015863738,0.00031976716,0.00013324466,0.000060008988,0.00028896736,0.0007494589,0.00023278367,0.00027498355,0.0008519476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006899144,0.00017614753,0.043789875,0.000014256079,0.000014738503,7.428684e-7,0.0013001569,0.8864798,0.0045109033,0.000024771307,0.00035187945,0.06326772],"study_design_scores_gemma":[0.0012584361,0.00024310152,0.004646277,0.0000149687085,0.0000507658,0.0000139212225,0.0007501246,0.8755785,0.0012007635,0.0002826695,0.11521449,0.00074595795],"about_ca_topic_score_codex":0.000053222182,"about_ca_topic_score_gemma":0.000010062321,"teacher_disagreement_score":0.22523795,"about_ca_system_score_codex":0.00038887913,"about_ca_system_score_gemma":0.000006682776,"threshold_uncertainty_score":0.99992603},"labels":[],"label_agreement":null},{"id":"W2050699592","doi":"10.1016/j.envsoft.2008.02.008","title":"Modelling crop productivity and variability for policy and impacts of climate change in eastern Canada","year":2008,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"University of Guelph","keywords":"Climate change; Environmental science; Index (typography); Productivity; Geography; Physical geography; Statistic; Climatology; Ecology; Statistics; Mathematics; Economics","score_opus":0.04278828562494558,"score_gpt":0.22646853592852045,"score_spread":0.18368025030357488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050699592","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99756825,0.00033620332,0.00035143565,0.00049141655,0.000027670645,0.0005635103,0.0006357987,0.000019830211,0.000005864442],"genre_scores_gemma":[0.99767774,0.0012135515,0.0007665979,0.000069916496,0.0001483952,0.000032008593,0.00007853223,0.0000031371128,0.000010096894],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99875945,0.000043824704,0.00022824788,0.0004028186,0.00017825111,0.0003873865],"domain_scores_gemma":[0.9994986,0.00016295968,0.00010946726,0.000069384725,0.000009934649,0.00014963275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019039387,0.00019824921,0.0002603817,0.000014050487,0.00016330846,0.000014150702,0.0000848439,0.00008865059,0.00000981329],"category_scores_gemma":[0.00003480759,0.00009495366,0.000035369063,0.00010438345,0.00013291519,0.0002051866,0.00010135259,0.000105989646,3.276136e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025334756,0.0004902176,0.92244214,0.00026371167,0.00002283375,0.000014668126,0.0034013896,0.029924562,0.014759724,0.00005951504,0.000013555433,0.028354367],"study_design_scores_gemma":[0.001298505,0.0006372525,0.70001274,0.00030026387,0.000050061764,0.00011772561,0.0005970276,0.29060885,0.0028324393,0.001590497,0.0006850817,0.0012695625],"about_ca_topic_score_codex":0.15728556,"about_ca_topic_score_gemma":0.060444813,"teacher_disagreement_score":0.26068428,"about_ca_system_score_codex":0.00016530449,"about_ca_system_score_gemma":0.000014529436,"threshold_uncertainty_score":0.9566996},"labels":[],"label_agreement":null},{"id":"W2053494447","doi":"10.1016/s1364-8152(99)00025-0","title":"Relationships between topographically expressed zones of flow accumulation and sites of fault intersection: analysis by means of digital terrain modelling","year":2000,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Marine and environmental studies","field":"Earth and Planetary Sciences","cited_by":62,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Manitoba","funders":"","keywords":"Geology; Landform; Aquifer; Fault (geology); Groundwater; Water table; Terrain; Geomorphology; Groundwater flow; Hydrogeology; Surface runoff; Borehole; Hydrology (agriculture); Geotechnical engineering; Seismology","score_opus":0.03036114876709765,"score_gpt":0.20292968332803948,"score_spread":0.17256853456094184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053494447","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82797027,0.00036698088,0.17088385,0.000011758167,0.0000065003833,0.000082468585,0.00052774244,0.000011717058,0.00013872738],"genre_scores_gemma":[0.99004745,0.00043139543,0.008595133,0.0000033890801,0.000013615349,0.0000010583713,0.0008395215,0.0000048101474,0.000063596046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988656,0.000043961252,0.0004220275,0.00025126812,0.0002832654,0.00013384572],"domain_scores_gemma":[0.99947834,0.0001693804,0.00014274327,0.00014334093,0.0000042669385,0.000061906714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001255585,0.00014971168,0.00030733427,0.00009904174,0.00011405807,0.000012844994,0.0000916628,0.000087368506,0.00071404513],"category_scores_gemma":[0.0000055905957,0.00014040605,0.00012968353,0.00017555439,0.00021039454,0.0002837422,0.000021576057,0.00012143617,0.0000036351348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020020614,0.000021493308,0.4798004,0.0000110771825,0.00009560261,1.253421e-7,0.00049521204,0.5117082,0.000021854203,3.5458774e-7,0.0000020716348,0.007823598],"study_design_scores_gemma":[0.000198447,0.000110530076,0.17625959,0.000019447645,0.00023541742,4.7510937e-7,0.00045847704,0.82158476,0.00024088449,0.00067172706,0.000058950787,0.00016131895],"about_ca_topic_score_codex":0.0005398087,"about_ca_topic_score_gemma":0.000067820416,"teacher_disagreement_score":0.30987653,"about_ca_system_score_codex":0.000006177604,"about_ca_system_score_gemma":0.00000197758,"threshold_uncertainty_score":0.78182936},"labels":[],"label_agreement":null},{"id":"W2053897512","doi":"10.1016/j.envsoft.2014.06.024","title":"A parallel computational framework to solve flow and transport in integrated surface–subsurface hydrologic systems","year":2014,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":98,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Canadian Water Network; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Solver; Computer science; Jacobian matrix and determinant; Computational science; Parallel computing; Finite volume method; Convergence (economics); Nonlinear system; Range (aeronautics); Mathematical optimization; Applied mathematics; Mathematics","score_opus":0.010271154398707861,"score_gpt":0.19571522269060915,"score_spread":0.1854440682919013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053897512","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5240295,0.00007420497,0.47530338,0.00015037767,0.000058431557,0.00024689257,0.00001126316,0.00005029242,0.00007568979],"genre_scores_gemma":[0.8689945,0.000058080397,0.13026026,0.0004462638,0.000012414334,0.000032812102,0.000048543374,0.000022221457,0.0001249616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982849,0.00007271027,0.00031597778,0.00062180235,0.00027746975,0.00042719283],"domain_scores_gemma":[0.9994737,0.00013624791,0.000057954334,0.00019952463,0.000001299286,0.0001312682],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003610249,0.00028193885,0.00030769015,0.000041561427,0.00020838664,0.000018987766,0.00019218409,0.00015305159,0.00018318047],"category_scores_gemma":[0.00001142446,0.00026193768,0.000046052737,0.00012084369,0.00023963355,0.00014665368,0.0001612704,0.00027447022,0.00036285745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042488107,0.00007218451,0.21531548,0.000008553189,0.000015985519,0.000008850345,0.00096276856,0.7831998,0.000021310978,0.00004965603,0.000048249447,0.00025468817],"study_design_scores_gemma":[0.0004488895,0.00013786246,0.03499607,0.000041883563,0.000022819433,0.0000041752915,0.00014592281,0.9579349,0.000009077965,0.00462063,0.0012735178,0.00036423362],"about_ca_topic_score_codex":0.00022892503,"about_ca_topic_score_gemma":0.000025736113,"teacher_disagreement_score":0.34504312,"about_ca_system_score_codex":0.000138829,"about_ca_system_score_gemma":0.0000021460212,"threshold_uncertainty_score":0.9999833},"labels":[],"label_agreement":null},{"id":"W2064210876","doi":"10.1016/s1364-8152(01)00011-1","title":"Issues of EIS software design: some lessons learned in the past decade","year":2001,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Environment and Climate Change Canada","funders":"","keywords":"Interconnectivity; Software engineering; Computer science; Interface (matter); Software design; Software; Systems engineering; Engineering; Management science; Engineering management; Knowledge management; Risk analysis (engineering); Software development; Business; Artificial intelligence","score_opus":0.08137492536966552,"score_gpt":0.31169468018644947,"score_spread":0.23031975481678396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064210876","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66835237,0.004671705,0.31903547,0.0052429577,0.00031803863,0.0013730726,0.0000533794,0.00025285096,0.0007001411],"genre_scores_gemma":[0.98648185,0.0030561113,0.009454049,0.0001901671,0.00012781439,0.000065859844,0.000013134101,0.00001753589,0.0005934602],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9979098,0.00026777814,0.00043171897,0.00023549385,0.0007308478,0.00042439142],"domain_scores_gemma":[0.9991075,0.00035221654,0.00019268991,0.00027422138,0.000014590219,0.000058782138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001091905,0.00018010024,0.00025063346,0.00013103132,0.0006432069,0.00004906916,0.00045270525,0.00012853941,0.00007668753],"category_scores_gemma":[0.00006681352,0.0001504449,0.00011240025,0.00028780504,0.0004353311,0.00044055577,0.000078226316,0.00020738589,0.00008789277],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006974759,0.00037828914,0.45151448,0.0000644434,0.0000960019,0.000026339345,0.27341074,0.25021684,0.00003514683,0.0037485105,0.0007796542,0.019659774],"study_design_scores_gemma":[0.0041095563,0.0006065777,0.13196701,0.0008319899,0.0002707443,0.000043450345,0.4805999,0.009770612,0.0004172389,0.09691481,0.27126268,0.0032054263],"about_ca_topic_score_codex":0.0013706264,"about_ca_topic_score_gemma":0.00012482969,"teacher_disagreement_score":0.3195475,"about_ca_system_score_codex":0.00010517409,"about_ca_system_score_gemma":0.000023365705,"threshold_uncertainty_score":0.61349654},"labels":[],"label_agreement":null},{"id":"W2066769322","doi":"10.1016/j.envsoft.2004.10.003","title":"A wind driven three-dimensional pollutant transport model","year":2005,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Oceanographic and Atmospheric Processes","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Pollutant; Environmental science; Meteorology; Geography; Biology; Ecology","score_opus":0.010710356963520927,"score_gpt":0.17143763524791125,"score_spread":0.16072727828439032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066769322","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85794646,0.001487555,0.13978244,0.00010982424,0.0000570746,0.00012721732,0.00022722797,0.000092238006,0.00016999064],"genre_scores_gemma":[0.9106223,0.00011394444,0.08842579,0.00036307445,0.00010008864,7.418912e-7,0.00012327675,0.000012767557,0.0002380324],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984703,0.00001105253,0.00025952636,0.0004227051,0.0004300157,0.00040636517],"domain_scores_gemma":[0.9995088,0.000034529163,0.00006818995,0.0001902871,0.000003629394,0.00019453703],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007871686,0.0002463042,0.00019741333,0.000021071013,0.0002661739,0.000018682871,0.00022542579,0.0000985029,0.0017194457],"category_scores_gemma":[0.0000016803192,0.00021601508,0.0001194588,0.00009775624,0.00015500732,0.00026809465,0.000011990358,0.00019518971,0.00027085165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024897154,0.00004130678,0.1805736,0.000005215068,0.0000148749705,0.000008153561,0.00011584701,0.80630547,0.000014666421,0.0000032113287,0.000016943133,0.012875815],"study_design_scores_gemma":[0.00026710497,0.00005176763,0.026320405,0.000020819967,0.000028805574,0.0000140761285,0.000025066576,0.97125316,0.00004067235,0.0010126891,0.000662317,0.00030311284],"about_ca_topic_score_codex":0.00010115658,"about_ca_topic_score_gemma":0.00016518195,"teacher_disagreement_score":0.16494769,"about_ca_system_score_codex":0.000012521204,"about_ca_system_score_gemma":0.00003708316,"threshold_uncertainty_score":0.99919313},"labels":[],"label_agreement":null},{"id":"W2068421971","doi":"10.1016/j.envsoft.2014.11.009","title":"Reduction of waste biosolids by RAS-ozonation: Model validation and sensitivity analysis for biosolids reduction and nitrification","year":2014,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Wastewater Treatment and Nitrogen Removal","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biosolids; Reduction (mathematics); Environmental science; Nitrification; Sensitivity (control systems); Waste management; Environmental engineering; Environmental chemistry; Pulp and paper industry; Chemistry; Engineering; Mathematics; Nitrogen","score_opus":0.012819670544690466,"score_gpt":0.20459045812019844,"score_spread":0.19177078757550797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068421971","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6171274,0.00004848725,0.38244414,0.00005324399,0.000025073312,0.00021753523,0.00005368594,0.00002370252,0.0000067092883],"genre_scores_gemma":[0.9451878,0.00012353179,0.053904615,0.000008044691,0.000039840532,0.000026479735,0.00049852073,0.000021287504,0.00018987601],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986843,0.0000802258,0.0002837991,0.00051086664,0.00025214333,0.00018866968],"domain_scores_gemma":[0.99947953,0.000039356124,0.00017915167,0.00020997165,0.0000062400204,0.0000857595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032979457,0.00020151,0.00023005652,0.00009232978,0.00022062655,0.000028235952,0.000042101186,0.00010990652,0.000019140827],"category_scores_gemma":[0.0000074371364,0.00020352851,0.00008891162,0.0001599791,0.00021163147,0.0003652808,0.00005298034,0.00006326043,0.0000048918187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000077166915,0.0001370403,0.0064280448,0.000018095257,0.00010493408,1.28561e-7,0.00049910834,0.5560249,0.43189076,0.000018694036,0.00004465125,0.0047564665],"study_design_scores_gemma":[0.0004704348,0.000089802386,0.000689175,0.000007034747,0.0003772168,0.0000121323465,0.00012333102,0.7154223,0.28167248,0.00089608383,0.00003059498,0.00020942601],"about_ca_topic_score_codex":0.00007066757,"about_ca_topic_score_gemma":0.0000016950925,"teacher_disagreement_score":0.32853952,"about_ca_system_score_codex":0.00012361491,"about_ca_system_score_gemma":0.0000027795222,"threshold_uncertainty_score":0.8299652},"labels":[],"label_agreement":null},{"id":"W2069403900","doi":"10.1016/j.envsoft.2014.06.027","title":"An open framework for agent based modelling of agricultural land use change","year":2014,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":87,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Flexibility (engineering); Fidelity; Variety (cybernetics); Modular design; Land use; Agent-based model; Transparency (behavior); Land use, land-use change and forestry; Set (abstract data type); Ecosystem services; Scale (ratio); Conceptual framework; Environmental resource management; Data science; Management science; Ecosystem; Artificial intelligence; Ecology; Environmental science; Engineering; Geography; Civil engineering","score_opus":0.05882911796876044,"score_gpt":0.23906047405479827,"score_spread":0.18023135608603782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069403900","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54529554,0.000024612611,0.45402554,0.000021174734,0.00006438843,0.00045073312,0.00007498947,0.000031048963,0.000011950536],"genre_scores_gemma":[0.8571148,0.000027890199,0.14214906,0.00022795782,0.00009759541,0.00012776552,0.00019889508,0.00003695973,0.000019088435],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984093,0.00006522572,0.00032158228,0.00052725524,0.00030472368,0.0003719249],"domain_scores_gemma":[0.9990227,0.00015616887,0.00018101918,0.00045640013,0.000003406041,0.00018026681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026156293,0.00025318406,0.00030224808,0.000022858729,0.00023644618,0.00010539896,0.0006166437,0.00014466175,0.00034548357],"category_scores_gemma":[0.000005704034,0.0001978733,0.00009458542,0.00006903418,0.00003207403,0.0009091064,0.00020680457,0.0001110781,0.000085035295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004358089,0.00019100125,0.08611426,0.000036990532,0.0000115000375,4.6191516e-7,0.0004087802,0.91217875,0.00016039741,0.000024557386,0.0000126764135,0.0008170266],"study_design_scores_gemma":[0.0005583855,0.0002826497,0.014598857,0.00011282673,0.000049829403,0.0000013523829,0.00006729724,0.9806277,0.0009738144,0.0014229247,0.0009091193,0.00039523083],"about_ca_topic_score_codex":0.00093741645,"about_ca_topic_score_gemma":0.00008623322,"teacher_disagreement_score":0.31187648,"about_ca_system_score_codex":0.000102611484,"about_ca_system_score_gemma":0.0000027420413,"threshold_uncertainty_score":0.8069039},"labels":[],"label_agreement":null},{"id":"W2078102408","doi":"10.1016/j.envsoft.2009.06.003","title":"Multi-criteria analysis of wastewater treatment plant design and control scenarios under uncertainty","year":2009,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Wastewater Treatment and Nitrogen Removal","field":"Environmental Science","cited_by":76,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Controller (irrigation); Benchmark (surveying); Clarifier; Cascade; Control theory (sociology); Monte Carlo method; Process control; Process (computing); Effluent; Computer science; Control engineering; Engineering; Control (management); Environmental engineering; Mathematics","score_opus":0.023738298578566883,"score_gpt":0.22297886371778697,"score_spread":0.1992405651392201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078102408","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88567966,0.00019770305,0.11343887,0.000049658163,0.000026551781,0.00041958032,0.00013907286,0.00004483251,0.000004057835],"genre_scores_gemma":[0.919927,0.00011041698,0.07944402,0.00009320234,0.00001198914,0.000017007638,0.00016005975,0.000020495134,0.00021582602],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826145,0.000110786576,0.00035672454,0.0005676904,0.00029703215,0.00040633406],"domain_scores_gemma":[0.9992992,0.00007052736,0.00011665165,0.00033628094,0.000001481364,0.00017584818],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012986711,0.00036891017,0.0004920754,0.000104274084,0.00015841585,0.000031014744,0.00014654928,0.00010383216,0.0005991136],"category_scores_gemma":[0.0000015409346,0.00028470435,0.0002080694,0.00014232834,0.00018444625,0.00016448282,0.000052045434,0.000072152165,0.00005440085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020552559,0.0005522566,0.014539492,0.0000021410506,0.000482215,0.00002278653,0.00078489823,0.9313616,0.050909296,0.0000010309924,0.00000900059,0.0011297332],"study_design_scores_gemma":[0.002507971,0.00066934346,0.017095517,0.000014296623,0.0015593639,0.000013703524,0.00014182752,0.95906013,0.018290251,0.00013912638,0.00008316794,0.0004252826],"about_ca_topic_score_codex":0.00023643719,"about_ca_topic_score_gemma":0.000013907706,"teacher_disagreement_score":0.03424731,"about_ca_system_score_codex":0.00034586815,"about_ca_system_score_gemma":0.0000051928287,"threshold_uncertainty_score":0.9999605},"labels":[],"label_agreement":null},{"id":"W2078108775","doi":"10.1016/j.envsoft.2008.10.011","title":"A strategic classification support system for brownfield redevelopment","year":2008,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Brownfield; Redevelopment; Dominance (genetics); Plan (archaeology); Decision support system; Strategic planning; Preference; Set (abstract data type); Computer science; Business; Environmental planning; Management science; Operations research; Engineering; Artificial intelligence; Geography; Marketing; Economics; Civil engineering","score_opus":0.0782915154411669,"score_gpt":0.217533682465675,"score_spread":0.1392421670245081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078108775","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04217511,0.00008787343,0.95597476,0.00009653621,0.0002342728,0.00033337303,0.000014006565,0.00024478245,0.00083928223],"genre_scores_gemma":[0.74087554,0.000057229594,0.25852963,0.00008924101,0.000044639888,0.00007548865,0.000047025307,0.000012432889,0.00026875513],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860764,0.000019169143,0.0002944414,0.00049120554,0.00027849188,0.0003090796],"domain_scores_gemma":[0.99931836,0.000048994305,0.00010302313,0.00042041743,0.000007277639,0.00010192729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013232506,0.00017693092,0.00016295492,0.000044247783,0.0003605539,0.00005263942,0.00045641325,0.000094899944,0.000019988243],"category_scores_gemma":[0.0000021249139,0.00016419559,0.00008786493,0.00006807545,0.000047230165,0.0002286152,0.000089110414,0.00010077539,0.00007840914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036066442,0.0030525904,0.031396784,0.0009359661,0.0004944647,0.0007258841,0.02709144,0.6569577,0.005921577,0.1097535,0.012351324,0.15095814],"study_design_scores_gemma":[0.00095421064,0.0003590433,0.0018217254,0.000052474486,0.000018913546,0.0002119081,0.000595822,0.98496324,0.0011333456,0.004351708,0.0047539184,0.00078366965],"about_ca_topic_score_codex":0.000007051535,"about_ca_topic_score_gemma":3.0707096e-7,"teacher_disagreement_score":0.6987004,"about_ca_system_score_codex":0.00017848768,"about_ca_system_score_gemma":0.00004820306,"threshold_uncertainty_score":0.6695702},"labels":[],"label_agreement":null},{"id":"W2083398962","doi":"10.1016/s1364-8152(00)00011-6","title":"Integration of a nonpoint source pollution model with a decision support system","year":2000,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Environment and Climate Change Canada; University of Waterloo","funders":"Consejo Nacional de Ciencia y Tecnología; University of Waterloo; National Water Research Institute","keywords":"Nonpoint source pollution; Decision support system; Interface (matter); Computer science; Digital elevation model; Pollution; Systems engineering; Environmental science; Operations research; Data mining; Engineering; Remote sensing; Geography; Operating system","score_opus":0.008104755572998444,"score_gpt":0.18400506886495005,"score_spread":0.1759003132919516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083398962","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47838062,0.000012873967,0.52058095,0.000031435866,0.000013239386,0.00016399824,0.000010290694,0.000056456367,0.0007501237],"genre_scores_gemma":[0.9414507,0.000055760534,0.056656685,0.00010360686,0.000010691974,0.000029103592,0.000031599167,0.000023590945,0.0016382269],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861974,0.000031570962,0.0002950519,0.0004073291,0.00037297254,0.00027336125],"domain_scores_gemma":[0.99953794,0.000020434396,0.00009358653,0.00027923507,0.0000014888622,0.00006729505],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017474986,0.00021386589,0.00021397362,0.000040102015,0.0002188181,0.000009683001,0.00016712323,0.00008260414,0.0009225824],"category_scores_gemma":[0.000001940977,0.00017181745,0.00006945769,0.00008259101,0.00025433014,0.00024240573,0.00010456685,0.00012154918,0.00046962305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019511195,0.00010110689,0.00345742,0.000010482353,0.000020238575,0.000004930422,0.0009926759,0.9751739,0.00048262035,0.000019166191,0.00011609581,0.019426286],"study_design_scores_gemma":[0.0006750039,0.00027123056,0.0020518643,0.000077376804,0.00007991338,0.000016297095,0.00030865474,0.99398893,0.0010286336,0.00053588115,0.0006700146,0.0002962279],"about_ca_topic_score_codex":0.00010104434,"about_ca_topic_score_gemma":0.000016703483,"teacher_disagreement_score":0.46392426,"about_ca_system_score_codex":0.00023348334,"about_ca_system_score_gemma":0.000004112923,"threshold_uncertainty_score":0.9999907},"labels":[],"label_agreement":null},{"id":"W2084085078","doi":"10.1016/j.envsoft.2010.03.009","title":"An analytical framework to assist decision makers in the use of forest ecosystem model predictions","year":2010,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Lakehead University; Ministère des Ressources naturelles et des Forêts (Québec); Natural Resources Canada; Canadian Forest Service","funders":"Canadian Forest Service","keywords":"Computer science; Identification (biology); Natural resource management; Risk analysis (engineering); Forest management; Management science; Decision support system; Selection (genetic algorithm); Decision model; Operations research; Natural resource; Engineering; Business; Machine learning; Data mining; Ecology","score_opus":0.030985294971224354,"score_gpt":0.2582099677875824,"score_spread":0.22722467281635803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084085078","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63404745,0.000002215512,0.36535016,0.00007466526,0.00006510111,0.0002801538,0.00005473348,0.000025288098,0.000100253426],"genre_scores_gemma":[0.9025758,0.000009640798,0.09682931,0.00028487947,0.00004191353,0.000038321312,0.000029710685,0.000026689926,0.00016377168],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982906,0.000049671853,0.0003583582,0.000417736,0.00054245716,0.00034119326],"domain_scores_gemma":[0.9988099,0.00020897163,0.000073971445,0.0007406749,0.0000015628547,0.00016496996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002891961,0.00020180404,0.0001720058,0.00008853184,0.00016522652,0.000066474386,0.00052648433,0.0001408551,0.00056553684],"category_scores_gemma":[0.00004425007,0.00016156035,0.00009302209,0.00023665767,0.00015581441,0.0004034035,0.00018430821,0.00038015473,0.0003702533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027523904,0.00018121144,0.18864197,0.0000038063326,0.000004531323,0.0000029435432,0.00051824795,0.8084278,0.000035231602,0.0004982379,0.00053167617,0.0011268225],"study_design_scores_gemma":[0.00014465627,0.00007864279,0.038017765,0.000028511608,0.000023912648,0.0000026958733,0.000041067346,0.95618105,0.000007375574,0.0018368837,0.0034606862,0.00017673887],"about_ca_topic_score_codex":0.00023653975,"about_ca_topic_score_gemma":0.0006899099,"teacher_disagreement_score":0.26852834,"about_ca_system_score_codex":0.000114606126,"about_ca_system_score_gemma":0.000005325814,"threshold_uncertainty_score":0.658824},"labels":[],"label_agreement":null},{"id":"W2084193619","doi":"10.1016/j.envsoft.2006.11.003","title":"The assessment of emission-source contributions to air quality by using a coupled MM5-ARPS-CMAQ modeling system: A case study in the Beijing metropolitan region, China","year":2007,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":75,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"CMAQ; Beijing; MM5; Air quality index; Environmental science; Metropolitan area; Meteorology; Air pollution; Emission inventory; China; Geography; Mesoscale meteorology","score_opus":0.04748019161629316,"score_gpt":0.36149949322409486,"score_spread":0.31401930160780167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084193619","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5262923,0.000094239134,0.47234157,0.00026644353,0.000038879,0.00087856693,0.000031913274,0.00003188271,0.000024172294],"genre_scores_gemma":[0.9950002,0.00001983584,0.00439725,0.0004026259,0.000043539963,0.000047557663,0.000014148872,0.000036817204,0.00003801155],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995521,0.0007322125,0.0012385317,0.0005914481,0.0010561148,0.0008607011],"domain_scores_gemma":[0.9977947,0.0006843508,0.00037947088,0.00077103876,0.0000102661015,0.00036019867],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0058048298,0.00034016362,0.00043807703,0.00006683243,0.0017112808,0.000050176615,0.0005038939,0.00013160135,0.000015839616],"category_scores_gemma":[0.00013422169,0.00024225024,0.00012612472,0.0003860749,0.00019808844,0.00022980355,0.00028632864,0.0005458964,0.0000074703944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072267416,0.000723261,0.09433835,0.000042865926,0.000027458278,0.00012577127,0.008808426,0.8947652,0.00030624168,0.00006999556,0.00008822826,0.00063194643],"study_design_scores_gemma":[0.00080295186,0.00024431103,0.0058449586,0.00010651049,0.00006442926,0.0002075181,0.077563144,0.91446984,0.000041572224,0.000105139115,0.00019130144,0.0003583329],"about_ca_topic_score_codex":0.019927448,"about_ca_topic_score_gemma":0.0006280161,"teacher_disagreement_score":0.46870786,"about_ca_system_score_codex":0.0035289698,"about_ca_system_score_gemma":0.000046182708,"threshold_uncertainty_score":0.9995884},"labels":[],"label_agreement":null},{"id":"W2086874917","doi":"10.1016/j.envsoft.2005.04.003","title":"CoZMo-POP 2 – A fugacity-based dynamic multi-compartmental mass balance model of the fate of persistent organic pollutants","year":2005,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Toxic Organic Pollutants Impact","field":"Environmental Science","cited_by":110,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"Stockholms Universitet; Verband der Chemischen Industrie","keywords":"Fugacity; Environmental science; Pollutant; Water column; Hydrology (agriculture); Geology; Oceanography; Ecology; Chemistry","score_opus":0.01585316417899227,"score_gpt":0.2100619020379366,"score_spread":0.19420873785894432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086874917","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8603307,0.0002088508,0.13679828,0.0001403628,0.000097344615,0.00048013646,0.0018270407,0.000067717716,0.000049563143],"genre_scores_gemma":[0.9585295,0.00004434722,0.04055189,0.00023671982,0.000013967535,0.0000073101473,0.000007182318,0.0000794735,0.00052965106],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970582,0.000095739764,0.00067015627,0.0006182287,0.00091738807,0.0006402755],"domain_scores_gemma":[0.998456,0.00005121102,0.00042949631,0.0008511016,0.0000015705181,0.00021063525],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002145039,0.00044416595,0.00045732438,0.000050929706,0.00022160607,0.000014975107,0.00084234396,0.00015197844,0.0017745092],"category_scores_gemma":[0.000018956394,0.00037191427,0.00043509778,0.00022058765,0.00071386184,0.00028322346,0.00029630103,0.00030667818,0.00018825574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046739566,0.00041706229,0.014926278,0.000011133837,0.000048129685,0.0000012385827,0.0005114456,0.7738111,0.20699756,5.9733316e-7,0.000008161045,0.0032205805],"study_design_scores_gemma":[0.0012574202,0.00008540972,0.011214978,0.000048595735,0.00008687154,0.000009019481,0.000113928174,0.9373173,0.049391523,0.00006753443,0.000033752018,0.00037363247],"about_ca_topic_score_codex":0.00001665172,"about_ca_topic_score_gemma":0.000021474327,"teacher_disagreement_score":0.16350625,"about_ca_system_score_codex":0.0012279283,"about_ca_system_score_gemma":0.000045120894,"threshold_uncertainty_score":0.9998733},"labels":[],"label_agreement":null},{"id":"W2092179320","doi":"10.1016/j.envsoft.2009.02.001","title":"A decision support system for environmental effects monitoring","year":2009,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Environment and Climate Change Canada","funders":"","keywords":"Effluent; Decision support system; Environmental science; Environmental monitoring; Environmental impact assessment; Environmental resource management; Benthic zone; Engineering; Computer science; Environmental engineering; Ecology; Data mining; Biology","score_opus":0.009922170217744788,"score_gpt":0.20051570873581495,"score_spread":0.19059353851807015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092179320","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09167065,0.00024992938,0.90677875,0.00006726843,0.0004041415,0.00035689652,0.000011221565,0.00026782136,0.00019333523],"genre_scores_gemma":[0.7979632,0.000024403977,0.20122252,0.000053550917,0.00011925408,0.000039759747,0.000023405082,0.0000076274446,0.0005462548],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981761,0.000024456767,0.00029823836,0.0006746156,0.0003494006,0.000477206],"domain_scores_gemma":[0.9989718,0.00019241033,0.00010972843,0.00056149444,0.00000327276,0.00016125244],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019912125,0.0002795657,0.00023989448,0.000044656994,0.00032406277,0.0000761665,0.00062445,0.00013716903,0.000012143178],"category_scores_gemma":[0.000016255433,0.0002824057,0.0001587902,0.000054884935,0.00003591624,0.00031445562,0.0001659804,0.00015672743,0.00008722051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012433776,0.00069843204,0.010918599,0.00027853975,0.00007130426,0.00022086297,0.0015306665,0.38153508,0.04368747,0.0007102949,0.00036053642,0.55986387],"study_design_scores_gemma":[0.0035967825,0.0013697135,0.008522328,0.0006112622,0.00009480348,0.00029661172,0.00039503915,0.7107862,0.24315183,0.011148835,0.017974744,0.002051821],"about_ca_topic_score_codex":8.469313e-7,"about_ca_topic_score_gemma":1.8668949e-8,"teacher_disagreement_score":0.70629257,"about_ca_system_score_codex":0.00022854104,"about_ca_system_score_gemma":0.000009728124,"threshold_uncertainty_score":0.9999628},"labels":[],"label_agreement":null},{"id":"W2094261218","doi":"10.1016/j.envsoft.2006.08.001","title":"Modeling the effects of elevation data resolution on the performance of topography-based watershed runoff simulation","year":2006,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":74,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Regina","funders":"U.S. Geological Survey; U.S. Department of Agriculture","keywords":"Digital elevation model; Watershed; Surface runoff; Grid; Elevation (ballistics); Smoothing; Environmental science; Hydrology (agriculture); Remote sensing; Geology; Statistics; Mathematics; Computer science; Geodesy; Geometry","score_opus":0.015704622382172805,"score_gpt":0.20125095036381643,"score_spread":0.18554632798164364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094261218","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8290643,0.000052145842,0.1702911,0.00013988922,0.000046109108,0.00033707081,0.000009862801,0.000021055932,0.000038425816],"genre_scores_gemma":[0.99839073,0.00004192626,0.0012409203,0.00010624834,0.00002062297,0.000019383084,0.00012241211,0.000012942995,0.000044837034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988124,0.00008675239,0.00027346428,0.00029216972,0.00034156433,0.00019364234],"domain_scores_gemma":[0.99906975,0.00022250797,0.00012102623,0.0005713173,0.0000022887384,0.000013121753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036125447,0.00014838441,0.00012616813,0.000033771656,0.00032265333,0.000006582878,0.00037390456,0.000053758315,0.000041448413],"category_scores_gemma":[0.00001420512,0.00009242886,0.00005363521,0.00009907523,0.00027963004,0.00017766644,0.00016622091,0.00011448613,0.00002058871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057302885,0.000092836366,0.028193267,0.000028397893,0.00001525728,2.823087e-7,0.0001430769,0.96967024,0.0014340762,0.000020970472,0.00004266646,0.00030161513],"study_design_scores_gemma":[0.00022811933,0.000107757754,0.0064819735,0.000032061263,0.00004760124,8.349491e-8,0.00002383653,0.98812497,0.004426802,0.00035042636,0.000078823185,0.000097572185],"about_ca_topic_score_codex":0.00015329586,"about_ca_topic_score_gemma":0.0000063967464,"teacher_disagreement_score":0.16932636,"about_ca_system_score_codex":0.000051427633,"about_ca_system_score_gemma":0.0000017933182,"threshold_uncertainty_score":0.37691396},"labels":[],"label_agreement":null},{"id":"W2094809497","doi":"10.1016/j.envsoft.2011.12.001","title":"A parallelization framework for calibration of hydrological models","year":2012,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":186,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Coquitlam College","funders":"","keywords":"Computer science; Speedup; Hydrological modelling; Calibration; Cloud computing; Grid; Server; Supercomputer; Soil and Water Assessment Tool; Watershed; Distributed computing; Parallel processing; Parallel computing; Database; Real-time computing; Operating system; Machine learning; Streamflow","score_opus":0.0303153658838308,"score_gpt":0.2303103436457476,"score_spread":0.1999949777619168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094809497","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25730646,0.00012563675,0.74199504,0.0000743868,0.00006847169,0.0002660341,0.000011430226,0.000040243027,0.00011232445],"genre_scores_gemma":[0.89106,0.000097078046,0.108322434,0.00022315216,0.000045415763,0.00007651046,0.000052561798,0.000016314689,0.00010651979],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898356,0.00003762376,0.00022772714,0.00024120578,0.00018196469,0.00032789452],"domain_scores_gemma":[0.99956924,0.0000925617,0.00009702758,0.00017171381,9.2284483e-7,0.000068523455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018952304,0.00014925464,0.00016487396,0.00002109161,0.00017098518,0.000005092881,0.00012616774,0.00013122351,0.00035098428],"category_scores_gemma":[0.00001357906,0.0001343858,0.00007736908,0.000048267426,0.00018299301,0.00041498672,0.00015231656,0.00008558382,0.000040558567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004828643,0.00017987288,0.07015666,0.000010533403,0.000021783502,2.451046e-7,0.0008879611,0.9257137,0.000107729866,0.0021608628,0.00013697283,0.00057537877],"study_design_scores_gemma":[0.000383742,0.00015432455,0.0030316266,0.000013934408,0.00007663851,0.0000015054111,0.00009658145,0.867744,0.00093586044,0.12621866,0.0010292258,0.0003138591],"about_ca_topic_score_codex":0.000012038319,"about_ca_topic_score_gemma":6.306483e-7,"teacher_disagreement_score":0.63375354,"about_ca_system_score_codex":0.0000610611,"about_ca_system_score_gemma":9.083912e-7,"threshold_uncertainty_score":0.5480094},"labels":[],"label_agreement":null},{"id":"W2099133586","doi":"10.1016/j.envsoft.2009.03.011","title":"Dynamics of soil salinity in the Canadian prairies: Application of singular spectrum analysis","year":2009,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Government of British Columbia; Agriculture and Agri-Food Canada; Ministry of Agriculture; University of Manitoba","funders":"","keywords":"Groundwater; Singular spectrum analysis; Precipitation; Salinity; Water table; Environmental science; Soil salinity; Soil water; Water cycle; Hydrology (agriculture); Soil science; Atmospheric sciences; Climatology; Ecology; Geography; Geology; Meteorology; Mathematics; Oceanography","score_opus":0.009254997172821312,"score_gpt":0.20491202654361776,"score_spread":0.19565702937079646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099133586","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7832605,0.00011572104,0.2158947,0.00040383238,0.000010221101,0.000113414244,0.00007953722,0.0000065293266,0.00011551413],"genre_scores_gemma":[0.9924192,0.000013641318,0.0072227977,0.00009932862,0.000014043275,6.305131e-7,0.00021864238,0.0000015293193,0.00001021112],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990749,0.00008934029,0.00022576223,0.00017702456,0.00023442191,0.0001985709],"domain_scores_gemma":[0.9994991,0.00014108309,0.00008924826,0.00019758042,0.0000032985404,0.000069687114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026055262,0.000092431714,0.0002029338,0.00010327911,0.0000820093,0.000009066752,0.00019796865,0.000060499216,0.00004772708],"category_scores_gemma":[0.00001260762,0.000070033624,0.00009770052,0.0005035569,0.000086758075,0.00005028662,0.0000033487597,0.00014258022,0.000009101186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000105094405,0.000053656433,0.25289565,0.0000043181244,0.0000171978,0.000001279288,0.00022043654,0.68536466,0.0000038569565,0.000096314805,6.3711303e-7,0.06133148],"study_design_scores_gemma":[0.00004192878,0.00007667844,0.4692873,0.0000019991412,0.00004360208,3.8948622e-7,0.00003617225,0.511977,0.000044254906,0.018417068,0.000014417585,0.000059172435],"about_ca_topic_score_codex":0.18613867,"about_ca_topic_score_gemma":0.3021948,"teacher_disagreement_score":0.21639164,"about_ca_system_score_codex":0.000021940885,"about_ca_system_score_gemma":0.000021681157,"threshold_uncertainty_score":0.8192809},"labels":[],"label_agreement":null},{"id":"W2108191178","doi":"10.1016/j.envsoft.2013.08.001","title":"Foliar moisture content variations in lodgepole pine over the diurnal cycle during the red stage of mountain pine beetle attack","year":2013,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Joint Fire Science Program; Utah Agricultural Experiment Station","keywords":"Pinus contorta; Mountain pine beetle; Water content; Environmental science; Dendroctonus; Humidity; Crown (dentistry); Moisture; Relative humidity; Bark beetle; Atmospheric sciences; Forestry; Geography; Meteorology; Bark (sound); Geology","score_opus":0.011589394119672991,"score_gpt":0.20133733340977292,"score_spread":0.18974793929009992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108191178","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9836773,0.00017612342,0.014385646,0.00033336066,0.00012222114,0.0010389173,0.000083068364,0.000040424933,0.00014290657],"genre_scores_gemma":[0.9963339,0.0000502629,0.0010283883,0.00014322772,0.00005813035,0.00014998543,0.000016870128,0.000050179642,0.0021690521],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99782205,0.00017581253,0.0005309951,0.0004257217,0.00059133896,0.00045409397],"domain_scores_gemma":[0.99882406,0.00017077694,0.00026058624,0.0006433647,0.000003604457,0.00009763517],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003609405,0.00028373083,0.00027105413,0.000037384998,0.0003203166,0.00005148593,0.00052866316,0.00010446758,0.0020515],"category_scores_gemma":[0.000029783652,0.00019076126,0.0001249087,0.00017211697,0.00025907642,0.00037358957,0.0003437377,0.00037957888,0.00031666065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003384896,0.0003438366,0.24854827,0.000042941378,0.000045919613,0.0000090446565,0.001372492,0.72734827,0.021406984,0.000035985307,0.00022771362,0.00058467634],"study_design_scores_gemma":[0.0009099607,0.00006937935,0.72289246,0.000055542976,0.00001998527,0.000009726516,0.00030588626,0.27289796,0.0016396797,0.00024005608,0.00067402696,0.00028531931],"about_ca_topic_score_codex":0.0053629265,"about_ca_topic_score_gemma":0.00025662527,"teacher_disagreement_score":0.4743442,"about_ca_system_score_codex":0.00059214275,"about_ca_system_score_gemma":0.0000069534112,"threshold_uncertainty_score":0.9988608},"labels":[],"label_agreement":null},{"id":"W2114752817","doi":"10.1016/j.envsoft.2012.10.007","title":"Autocalibration experiments using machine learning and high performance computing","year":2012,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Calibration; GLUE; Set (abstract data type); Watershed; Simple (philosophy); Computer science; Algorithm; Flow (mathematics); Mathematical optimization; Machine learning; Mathematics; Statistics; Engineering","score_opus":0.01772892848450285,"score_gpt":0.21861848385153132,"score_spread":0.20088955536702846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114752817","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8798212,0.00030370388,0.119441785,0.000016254658,0.00009654961,0.0001327876,0.000001784315,0.00007983516,0.00010613409],"genre_scores_gemma":[0.9752223,0.00012642752,0.02424423,0.00010985439,0.000048837654,0.0000059003464,0.000024391044,0.000024302224,0.00019374081],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890226,0.000052287498,0.00017863832,0.00027545498,0.00019996741,0.00039139632],"domain_scores_gemma":[0.99970734,0.000025488576,0.00007818521,0.000101012396,4.4198904e-7,0.000087509565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018914662,0.00018786486,0.00014029435,0.000025884312,0.000679621,0.000017925311,0.00007666112,0.00006146999,0.00034407622],"category_scores_gemma":[0.0000033110414,0.00018281727,0.000024019244,0.00004400883,0.00019668888,0.00052063155,0.00041075962,0.00016336796,0.00012515385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009577704,0.000050470484,0.6632891,0.000006389443,0.000014048947,9.19125e-7,0.0009735515,0.33279127,0.0009989929,0.0000052481096,0.0000039466513,0.0018564812],"study_design_scores_gemma":[0.0003413924,0.000065632696,0.04137844,0.000016269814,0.000036778954,0.000009301276,0.00013966192,0.9535261,0.003201822,0.000050570852,0.00091747916,0.00031657558],"about_ca_topic_score_codex":0.00008592189,"about_ca_topic_score_gemma":5.447462e-7,"teacher_disagreement_score":0.6219107,"about_ca_system_score_codex":0.00011766389,"about_ca_system_score_gemma":7.136109e-7,"threshold_uncertainty_score":0.7455072},"labels":[],"label_agreement":null},{"id":"W2118849057","doi":"10.1016/j.envsoft.2012.11.001","title":"Are the applications of wildland fire behaviour models getting ahead of their evaluation again?","year":2012,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":119,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Credibility; Resource (disambiguation); Environmental science; Benchmark (surveying); Environmental resource management; Computer science; Operations research; Engineering; Geography; Cartography; Political science","score_opus":0.02207958766016667,"score_gpt":0.22811563809477767,"score_spread":0.20603605043461098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118849057","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9189928,0.0009873941,0.0786234,0.000027929884,0.00007152249,0.0010629696,0.000097201875,0.00003536855,0.00010137305],"genre_scores_gemma":[0.99678355,0.00003892904,0.0026859287,0.0000446277,0.00006161893,0.00024648517,0.000047882986,0.00004203536,0.00004892271],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800014,0.00017588427,0.00045208077,0.00031530982,0.0007015843,0.00035501432],"domain_scores_gemma":[0.99853,0.00019335317,0.0005680943,0.0006024743,0.000006417482,0.0000996712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009905766,0.00022883824,0.00025777853,0.000026698965,0.0002123004,0.000010249933,0.0003598832,0.000108027554,0.00020406004],"category_scores_gemma":[0.000015888914,0.00017670993,0.00012699283,0.00013197587,0.00022923756,0.00039677316,0.00019073223,0.00017964316,0.0000719425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008058214,0.00028353542,0.3616631,0.000027403812,0.00001896447,1.7093129e-7,0.001615968,0.61767155,0.00097447925,0.0000042892907,0.000049487204,0.017683005],"study_design_scores_gemma":[0.00037473324,0.000049213868,0.052605033,0.00008858446,0.00011893114,0.000008071312,0.0010161466,0.9420059,0.002580351,0.0006016766,0.0002303364,0.0003210614],"about_ca_topic_score_codex":0.00025137872,"about_ca_topic_score_gemma":0.000011941619,"teacher_disagreement_score":0.32433432,"about_ca_system_score_codex":0.0002626706,"about_ca_system_score_gemma":0.000006075038,"threshold_uncertainty_score":0.72060215},"labels":[],"label_agreement":null},{"id":"W2121783226","doi":"10.1016/j.envsoft.2006.02.004","title":"iCity: A GIS–CA modelling tool for urban planning and decision making","year":2006,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":274,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Geographic information system; Computer science; Cellular automaton; Focus (optics); Software; Interface (matter); Urban planning; Data mining; Engineering; Artificial intelligence; Geography; Civil engineering; Cartography","score_opus":0.013017033998814967,"score_gpt":0.21214380051193593,"score_spread":0.19912676651312097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121783226","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5235099,0.00040345287,0.4756378,0.0000076132856,0.000056621262,0.00023056827,0.000033497865,0.000055137236,0.00006541683],"genre_scores_gemma":[0.87717557,0.00003573118,0.12232518,0.00010428963,0.0001384286,0.00004891689,0.00005872075,0.000051582345,0.00006155651],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980561,0.000019102852,0.000399565,0.00065769325,0.0003620756,0.00050546293],"domain_scores_gemma":[0.9993165,0.00018505401,0.00013497261,0.0002785042,0.0000018859147,0.00008307099],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023111116,0.00030584857,0.00026913537,0.000044747794,0.0005147511,0.000103467886,0.00021439235,0.00014201296,0.00026391773],"category_scores_gemma":[0.0000032138726,0.0002820059,0.00010301952,0.0000689922,0.000041367537,0.0004462757,0.00021953422,0.00013585214,0.0001329645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006124946,0.000053215088,0.12539719,0.000028330309,0.000008929729,0.00000691667,0.00024464817,0.87092197,0.00008861231,0.000010370638,0.00020327423,0.002975307],"study_design_scores_gemma":[0.0005206745,0.000062427076,0.0024368356,0.00016624237,0.00004304052,0.000013837847,0.000062295396,0.98538697,0.00012698225,0.007325658,0.0034114604,0.00044355952],"about_ca_topic_score_codex":0.00014595981,"about_ca_topic_score_gemma":0.000018413832,"teacher_disagreement_score":0.35366568,"about_ca_system_score_codex":0.00017899953,"about_ca_system_score_gemma":0.0000034836416,"threshold_uncertainty_score":0.9999632},"labels":[],"label_agreement":null},{"id":"W2146840333","doi":"10.1016/j.envsoft.2004.03.017","title":"Human activities and global warming: a cointegration analysis","year":2004,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Cointegration; Radiative forcing; Greenhouse gas; Climatology; Environmental science; Forcing (mathematics); Carbon dioxide; Solar irradiance; Global temperature; Radiative transfer; Global warming; Series (stratigraphy); Atmospheric sciences; Steady state (chemistry); Econometrics; Climate change; Meteorology; Mathematics; Chemistry; Physics; Geology","score_opus":0.05018457824055156,"score_gpt":0.2308670099432637,"score_spread":0.18068243170271214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146840333","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8389666,0.0006042893,0.15926848,0.00008676346,0.00005015222,0.00008062857,0.0005060427,0.000042879925,0.00039412372],"genre_scores_gemma":[0.99501556,0.0002832119,0.004190169,0.0001569129,0.00005663235,0.000013910504,0.00013765683,0.000017111422,0.00012882498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999035,0.000004358998,0.000323413,0.0003767383,0.000025905974,0.0002345686],"domain_scores_gemma":[0.9995553,0.00001147404,0.00015890056,0.00019225103,9.899193e-7,0.00008111683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000108108376,0.00016347782,0.0003037786,0.00011501928,0.00020753441,0.000069519345,0.000087567765,0.000086309374,0.00015618603],"category_scores_gemma":[0.0000034779757,0.00020674418,0.00013049453,0.00009909874,0.0000890459,0.00025045837,0.000058943948,0.00008931305,0.00010375184],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028216728,0.00036258157,0.45033622,0.000048909566,0.0006469228,0.000009492994,0.0049224114,0.4675935,0.000066964036,0.07486545,0.000034435907,0.0010848911],"study_design_scores_gemma":[0.004052443,0.00041320134,0.12174752,0.000072627845,0.00054774265,0.00004577056,0.0035688172,0.13694613,0.000626818,0.7255091,0.0037006678,0.0027691536],"about_ca_topic_score_codex":0.00071659556,"about_ca_topic_score_gemma":0.00015082203,"teacher_disagreement_score":0.65064365,"about_ca_system_score_codex":0.00038999802,"about_ca_system_score_gemma":0.0000032291412,"threshold_uncertainty_score":0.8430783},"labels":[],"label_agreement":null},{"id":"W2161933013","doi":"10.1016/j.envsoft.2015.10.001","title":"A modified Sobol′ sensitivity analysis method for decision-making in environmental problems","year":2015,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Sobol sequence; Multiple-criteria decision analysis; Ranking (information retrieval); Robustness (evolution); Sensitivity (control systems); Stakeholder; Computer science; Decision analysis; Variance (accounting); Operations research; Mathematics; Machine learning; Engineering; Statistics; Economics","score_opus":0.08992107902443713,"score_gpt":0.3244997859705622,"score_spread":0.2345787069461251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161933013","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09686558,0.0003152266,0.90178126,0.000026111216,0.00016728113,0.0005788153,0.00016526072,0.000075899756,0.00002456922],"genre_scores_gemma":[0.6294819,0.000010170028,0.37020224,0.00004522758,0.000039510727,0.00005563804,0.000026932272,0.00003036683,0.00010797783],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99558663,0.00029220872,0.00093815307,0.0011522899,0.0014018889,0.0006288155],"domain_scores_gemma":[0.99438345,0.0042694295,0.0002429451,0.0007975308,0.000015652797,0.00029099337],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005394711,0.00037970705,0.0007171939,0.00054230046,0.00017333047,0.00012987746,0.00047804037,0.00020998159,0.00007005406],"category_scores_gemma":[0.0011673105,0.00032794275,0.00038707475,0.00060409197,0.00011875201,0.00035461446,0.00024674664,0.00027034976,0.00008843592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009236329,0.00018527763,0.0058172103,0.0000043181917,0.000076145596,0.000018273968,0.0010981943,0.97499377,0.000109103494,0.000051405503,0.00007129084,0.017482664],"study_design_scores_gemma":[0.0005481253,0.000066854336,0.001683825,0.00003623531,0.00013690323,0.000010956633,0.0003989674,0.9668556,0.000034799334,0.029553773,0.0002801383,0.0003938239],"about_ca_topic_score_codex":0.000036350077,"about_ca_topic_score_gemma":0.000023497503,"teacher_disagreement_score":0.5326164,"about_ca_system_score_codex":0.0005417457,"about_ca_system_score_gemma":0.000045572026,"threshold_uncertainty_score":0.99991727},"labels":[],"label_agreement":null},{"id":"W2278731849","doi":"10.1016/j.envsoft.2016.02.022","title":"Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops","year":2016,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact","funders":"Bundesanstalt für Landwirtschaft und Ernährung; National Oceanic and Atmospheric Administration; Bundesamt für Landwirtschaft; Institut National de la Recherche Agronomique; Bundesministerium für Ernährung und Landwirtschaft; Kungliga Tekniska Högskolan; Bundesministerium für Bildung und Forschung; Biotechnology and Biological Sciences Research Council; Svenska Forskningsrådet Formas","keywords":"Stratification (seeds); Sampling (signal processing); Stratified sampling; Statistics; Simple random sample; Mathematics; Yield (engineering); Sampling design; Environmental science; Crop; Spatial variability; Soil science; Agricultural engineering; Econometrics; Agronomy; Computer science","score_opus":0.15108252808534772,"score_gpt":0.32366333858658514,"score_spread":0.1725808105012374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2278731849","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94914055,0.00016932524,0.04974814,0.00025152904,0.000051641604,0.00042492626,0.00019267572,0.00001949832,0.0000017351795],"genre_scores_gemma":[0.97443944,0.00003094819,0.025271103,0.000030578794,0.00011661461,0.000013767305,0.00007163547,0.0000031568163,0.000022737959],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998656,0.000049992937,0.00041812626,0.0002829966,0.00034497763,0.00024793862],"domain_scores_gemma":[0.9977137,0.0018600409,0.00027300487,0.000084230946,0.000023472749,0.0000455412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040190268,0.0001619718,0.0002187786,0.000012996512,0.0001341935,0.000012873313,0.00022730387,0.000088386296,0.00017988426],"category_scores_gemma":[0.00024176903,0.000050145303,0.00011432238,0.000096462885,0.00009139809,0.000117045296,0.00008828432,0.00009027932,0.0000023211392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013773415,0.000110377936,0.0069816853,0.000019609508,0.000011913274,2.8745885e-7,0.00039758455,0.26788494,0.6196927,0.0000048238853,0.0000072774733,0.104751065],"study_design_scores_gemma":[0.0017413659,0.0012306878,0.024876764,0.0018529784,0.000073683055,0.0000081299295,0.00082567625,0.88264054,0.082751326,0.003086702,0.00027442834,0.0006377057],"about_ca_topic_score_codex":0.00020932977,"about_ca_topic_score_gemma":0.00008751051,"teacher_disagreement_score":0.6147556,"about_ca_system_score_codex":0.0000657147,"about_ca_system_score_gemma":0.0000044081544,"threshold_uncertainty_score":0.20448661},"labels":[],"label_agreement":null},{"id":"W2310987872","doi":"10.1016/j.envsoft.2016.03.006","title":"The diversity of socio-economic pathways and CO2 emissions scenarios: Insights from the investigation of a scenarios database","year":2016,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","cited_by":67,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Science Foundation of Sri Lanka; University Corporation for Atmospheric Research; National Center for Atmospheric Research","keywords":"Radiative forcing; Per capita; Diversity (politics); Forcing (mathematics); Productivity; Climate change; Economic geography; Natural resource economics; Environmental resource management; Environmental science; Economics; Ecology; Climatology; Economic growth; Political science; Biology","score_opus":0.0664707662165603,"score_gpt":0.19850763653756223,"score_spread":0.13203687032100192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2310987872","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98839694,0.001996181,0.0057375096,0.00052496255,0.0001158523,0.00016123972,0.0030223702,0.000012356382,0.000032574717],"genre_scores_gemma":[0.99457234,0.0044837496,0.000643696,0.000106831874,0.000057045403,0.000007728272,0.0000700486,0.000016900953,0.00004167961],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99901336,0.000021672986,0.0004563939,0.00030102514,0.000034256176,0.00017326992],"domain_scores_gemma":[0.99863416,0.00046994947,0.00041939324,0.00039474192,0.0000028887223,0.00007886409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021230208,0.0001402334,0.00024381721,0.000034627526,0.0005025974,0.000019514388,0.00027744597,0.00008152082,0.000094571275],"category_scores_gemma":[0.000032696862,0.00009627398,0.000083885396,0.000022981774,0.00043263217,0.0002320005,0.00045477308,0.00009260453,0.00006338512],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001039614,0.00009716953,0.9549959,0.000042195246,0.00018857142,0.0000012781485,0.017907772,0.007026753,0.0017127409,0.015520153,0.00043820005,0.0019653114],"study_design_scores_gemma":[0.0059758155,0.00034504704,0.19698888,0.0007478525,0.00020281553,0.000007981807,0.008712476,0.14425464,0.008655484,0.6221267,0.00994098,0.002041344],"about_ca_topic_score_codex":0.0009452709,"about_ca_topic_score_gemma":0.00006947281,"teacher_disagreement_score":0.758007,"about_ca_system_score_codex":0.00015026996,"about_ca_system_score_gemma":0.000012513322,"threshold_uncertainty_score":0.39259392},"labels":[],"label_agreement":null},{"id":"W2330742964","doi":"10.1016/j.envsoft.2016.03.008","title":"Multi-wheat-model ensemble responses to interannual climate variability","year":2016,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Plant Biotechnology Institute","funders":"National Institute of Food and Agriculture; Department for International Development; Biotechnology and Biological Sciences Research Council; U.S. Department of Agriculture; National Aeronautics and Space Administration","keywords":"Precipitation; Climate change; Environmental science; Climatology; Climate model; Yield (engineering); Coupled model intercomparison project; Growing season; Atmospheric sciences; Meteorology; Geography; Agronomy; Ecology","score_opus":0.042233152992614736,"score_gpt":0.24311783762283823,"score_spread":0.2008846846302235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2330742964","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9668903,0.00004896954,0.029486144,0.001173239,0.00010713501,0.0004119926,0.0016156093,0.00023630829,0.000030309246],"genre_scores_gemma":[0.98322815,0.00015647642,0.014899552,0.00059587805,0.00013236985,0.000050198145,0.00009609164,0.0000058373435,0.0008354458],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9978262,0.00012744011,0.0003362991,0.00070360675,0.00032808815,0.00067831745],"domain_scores_gemma":[0.99891186,0.00044932813,0.00008521515,0.00017554067,0.000016676804,0.0003613774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039745442,0.00033418136,0.00027213065,0.000017778482,0.00027700447,0.00006125555,0.00036026552,0.00017085239,0.0005734525],"category_scores_gemma":[0.00010305541,0.000113544214,0.00015230253,0.00011749257,0.000080139565,0.0002740823,0.00029690162,0.0001411307,0.00050253555],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034712412,0.00044413787,0.02229602,0.000010649946,0.000016432869,0.000009875132,0.00074666255,0.0039523873,0.92941993,0.000009688295,0.00041862318,0.042328432],"study_design_scores_gemma":[0.0075887167,0.0052875015,0.5958455,0.0021400864,0.00040776437,0.00028288134,0.005170571,0.12241719,0.20046806,0.0053709554,0.04383354,0.011187238],"about_ca_topic_score_codex":0.00003648112,"about_ca_topic_score_gemma":0.00007422783,"teacher_disagreement_score":0.72895193,"about_ca_system_score_codex":0.00025141728,"about_ca_system_score_gemma":0.000004602268,"threshold_uncertainty_score":0.64592457},"labels":[],"label_agreement":null},{"id":"W2488842558","doi":"10.1016/j.envsoft.2016.06.012","title":"A decision support system for updating and incorporating climate change impacts into rainfall intensity-duration-frequency curves: Review of the stakeholder involvement process","year":2016,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Climate variability and models","field":"Environmental Science","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Stakeholder; Stakeholder engagement; Process (computing); Process management; Duration (music); Decision support system; Legitimacy; Stakeholder analysis; Business; Environmental resource management; Computer science; Political science; Public relations; Economics; Data mining","score_opus":0.0468386794445431,"score_gpt":0.25543888774136214,"score_spread":0.20860020829681905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2488842558","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8960773,0.0026286559,0.09626306,0.00077533943,0.00020555583,0.0035277852,0.00034586864,0.00009595795,0.000080490005],"genre_scores_gemma":[0.975641,0.0062226886,0.016733043,0.0009990666,0.00003135915,0.00026674382,0.000060495644,0.000037776237,0.000007831974],"study_design_codex":"observational","study_design_gemma":"systematic_review","domain_scores_codex":[0.99766016,0.000075200616,0.0008141012,0.00056284864,0.00052566786,0.00036200875],"domain_scores_gemma":[0.9985999,0.00021477813,0.00055366923,0.00048413523,0.00001859211,0.00012893033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013190202,0.00027688153,0.00037305505,0.00002631615,0.00034155167,0.00002016913,0.0002970707,0.000084744424,0.00012298237],"category_scores_gemma":[0.00016483496,0.00017651297,0.00011368988,0.000116786374,0.00025140564,0.00061859394,0.00041521242,0.000108576794,0.000015687101],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040682015,0.0011965317,0.754264,0.08509789,0.00017459999,0.000015493755,0.01568119,0.010269261,0.032969844,0.000923241,0.00068039366,0.09832076],"study_design_scores_gemma":[0.0164997,0.0039984784,0.059751872,0.58540905,0.0024873659,0.00036836465,0.014294327,0.19070897,0.045096755,0.07010286,0.0017649238,0.00951732],"about_ca_topic_score_codex":0.00009786299,"about_ca_topic_score_gemma":0.000038365244,"teacher_disagreement_score":0.6945121,"about_ca_system_score_codex":0.00044183142,"about_ca_system_score_gemma":0.000016888158,"threshold_uncertainty_score":0.719799},"labels":[],"label_agreement":null},{"id":"W2494091213","doi":"10.1016/j.envsoft.2016.06.019","title":"An integrated knowledge-based and optimization tool for the sustainable selection of wastewater treatment process concepts","year":2016,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Robustness (evolution); Computer science; Process (computing); Selection (genetic algorithm); Effluent; Risk analysis (engineering); Engineering; Operations research; Artificial intelligence; Environmental engineering","score_opus":0.012374802481110076,"score_gpt":0.24740795021834813,"score_spread":0.23503314773723805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2494091213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44274628,0.000050185918,0.5565363,0.000051080162,0.000013362995,0.00051399844,0.000015541875,0.000068056295,0.000005172702],"genre_scores_gemma":[0.9723349,0.0000693722,0.026539423,0.000012633108,0.00000957323,0.00021628592,0.000027908121,0.000022061811,0.0007678048],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991007,0.00003718874,0.00019746687,0.00031977234,0.00012752379,0.00021738342],"domain_scores_gemma":[0.99952775,0.00012798353,0.00009580873,0.00020570222,0.000007682333,0.000035080335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012698556,0.00016435244,0.00012865513,0.000030983814,0.0002144635,0.000020657173,0.00013784946,0.000082135775,0.0004309788],"category_scores_gemma":[0.000019955949,0.000092891685,0.000036285237,0.00008237637,0.0002947901,0.00031505612,0.000029576257,0.000036115125,0.000010339315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007934093,0.00013852665,0.0059638224,0.000015133068,0.000010196428,2.200802e-7,0.00034129104,0.9749115,0.006296993,0.000012445973,0.000014682161,0.012215841],"study_design_scores_gemma":[0.0009314681,0.00057384546,0.0002639729,0.000020219619,0.000038137627,0.0000016830677,0.0006213318,0.7685966,0.22758807,0.00022011885,0.0009537873,0.00019076341],"about_ca_topic_score_codex":0.00003063489,"about_ca_topic_score_gemma":0.0000054049437,"teacher_disagreement_score":0.5299969,"about_ca_system_score_codex":0.00030243045,"about_ca_system_score_gemma":0.000014860038,"threshold_uncertainty_score":0.47189158},"labels":[],"label_agreement":null},{"id":"W2503483663","doi":"10.1016/j.envsoft.2016.05.022","title":"The INtegrated CAtchment model of phosphorus dynamics (INCA-P): Description and demonstration of new model structure and equations","year":2016,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trent University","funders":"NordForsk; Scottish Government; Rural and Environment Science and Analytical Services Division","keywords":"Phosphorus; Scale (ratio); Environmental science; Hydrology (agriculture); Drainage basin; Sorption; Land use; Computer science; Relevance (law); System dynamics; Operations research; Environmental resource management; Civil engineering; Mathematics; Chemistry; Geology; Geography; Engineering; Geotechnical engineering","score_opus":0.013450496042142577,"score_gpt":0.1868782817980542,"score_spread":0.17342778575591164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2503483663","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5153026,0.000099799094,0.48428825,0.00005697901,0.00001715209,0.00011464964,0.00010359062,0.000010575674,0.0000063934135],"genre_scores_gemma":[0.9747898,0.0009038377,0.023934234,0.000014074253,0.0000052034106,0.000006402507,0.000044089145,0.000018025576,0.00028435868],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889326,0.000021408641,0.00032353064,0.00028857155,0.00028025435,0.0001929623],"domain_scores_gemma":[0.99945945,0.000063706495,0.00015884872,0.00021679727,0.000004269855,0.00009693262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000099899116,0.0001828574,0.00015324401,0.000022558464,0.00017615243,0.000018398589,0.00013122016,0.000094952135,0.000006090499],"category_scores_gemma":[0.000009018191,0.000114790666,0.000039237355,0.000053848467,0.00040751838,0.00027043253,0.00013366608,0.00008766556,0.0000019539752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005111091,0.000062026025,0.117736936,0.000005689188,0.000014982248,1.6713004e-7,0.00071145274,0.849737,0.0036805961,0.0005038369,0.000012915502,0.027483318],"study_design_scores_gemma":[0.00040439324,0.00005438604,0.0013610475,0.000028034152,0.00003475824,0.0000018282259,0.00011070189,0.95948315,0.0016020059,0.036776338,0.00000671977,0.00013664702],"about_ca_topic_score_codex":0.00020010836,"about_ca_topic_score_gemma":0.000034955126,"teacher_disagreement_score":0.460354,"about_ca_system_score_codex":0.00023494862,"about_ca_system_score_gemma":0.00001570473,"threshold_uncertainty_score":0.46810275},"labels":[],"label_agreement":null},{"id":"W2509011425","doi":"10.1016/j.envsoft.2016.08.002","title":"Climate and human development impacts on municipal water demand: A spatially-explicit global modeling framework","year":2016,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Water resources management and optimization","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Deanship of Scientific Research, King Saud University; Natural Sciences and Engineering Research Council of Canada; King Abdulaziz University","keywords":"Urbanization; Scenario analysis; Climate change; Environmental resource management; Environmental planning; Environmental science; Environmental economics; Business; Natural resource economics; Economics; Ecology; Economic growth","score_opus":0.012286799471281204,"score_gpt":0.196917900700567,"score_spread":0.1846311012292858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2509011425","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57719356,0.00005338999,0.4223486,0.000011098957,0.000033284323,0.0001186727,0.000006867155,0.00017540029,0.00005917419],"genre_scores_gemma":[0.98402,0.00021004656,0.015485131,0.000050864008,0.000059199294,0.000023098328,0.00005711774,0.000057599478,0.000036964582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868256,0.000017234775,0.0002761659,0.0003110275,0.00022841747,0.00048462342],"domain_scores_gemma":[0.99961025,0.000015806145,0.000025231875,0.00022467424,0.0000022263362,0.000121788114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000103122824,0.0002822112,0.00017575422,0.000053477495,0.00022030892,0.000060408667,0.00014022089,0.000112528505,0.00007077299],"category_scores_gemma":[0.0000019846793,0.0001980229,0.000042585314,0.000027577891,0.000025168156,0.00018007477,0.0001513876,0.000105774576,0.00010939819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002110116,0.000029381205,0.0046259006,0.000034391727,0.00003433951,0.0000051092175,0.0009967107,0.9912143,0.00024112228,0.00006892889,0.0000035051896,0.002725254],"study_design_scores_gemma":[0.00076899154,0.00008547905,0.0004931931,0.00036155677,0.000035392026,0.0000023085993,0.000040637202,0.99289095,0.0031844361,0.0011940858,0.00037921467,0.0005637517],"about_ca_topic_score_codex":0.000009724482,"about_ca_topic_score_gemma":0.0000049680057,"teacher_disagreement_score":0.40686345,"about_ca_system_score_codex":0.00021438854,"about_ca_system_score_gemma":0.0000012183849,"threshold_uncertainty_score":0.80751395},"labels":[],"label_agreement":null},{"id":"W2519567241","doi":"10.1016/j.envsoft.2015.01.011","title":"Comparing interpolation techniques for monthly rainfall mapping using multiple evaluation criteria and auxiliary data sources: A case study of Sri Lanka","year":2015,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":101,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"Japan Aerospace Exploration Agency","keywords":"Inverse distance weighting; Kriging; Interpolation (computer graphics); Multivariate interpolation; Terrain; Geostatistics; Bayesian probability; Environmental science; Variable (mathematics); Spatial dependence; Statistics; Geography; Meteorology; Mathematics; Computer science; Cartography; Spatial variability; Bilinear interpolation; Artificial intelligence","score_opus":0.26063010120988156,"score_gpt":0.310371990809608,"score_spread":0.04974188959972642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2519567241","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82273513,0.00033710783,0.1762879,0.0000030164044,0.00003444082,0.0004573486,0.00010969391,0.000027552123,0.000007805251],"genre_scores_gemma":[0.94146734,0.000005068342,0.05760888,0.000009825247,0.000037858343,0.000004821239,0.00086005183,0.000004634951,0.0000015341035],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886,0.00011825868,0.00029124276,0.00029606785,0.00031182508,0.0001226129],"domain_scores_gemma":[0.9994357,0.00010263034,0.00015003902,0.00022077712,0.000022370881,0.00006843588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010442799,0.000116586816,0.00017646504,0.00009889223,0.00014972873,0.00004599717,0.00012972584,0.000036939484,0.000024916188],"category_scores_gemma":[0.000055478642,0.000114075585,0.000025069488,0.000058337428,0.00003422367,0.00045845204,0.000045473116,0.000055694654,0.0000012963826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035463694,0.000056603498,0.71077955,0.000018172539,0.000038258037,0.0000037944899,0.012218388,0.27090323,0.00011746394,3.026608e-8,0.0000045583593,0.0058244616],"study_design_scores_gemma":[0.00058192736,0.00009135392,0.0037634128,0.000037378388,0.0001164157,0.000008771166,0.022697391,0.9724643,0.000027746059,0.00006920033,0.000018204997,0.00012385166],"about_ca_topic_score_codex":0.0022931637,"about_ca_topic_score_gemma":0.000932461,"teacher_disagreement_score":0.70701617,"about_ca_system_score_codex":0.000018746432,"about_ca_system_score_gemma":0.000014284915,"threshold_uncertainty_score":0.46518672},"labels":[],"label_agreement":null},{"id":"W2521488166","doi":"10.1016/j.envsoft.2016.09.012","title":"Parameter uncertainty and temporal dynamics of sensitivity for hydrologic models: A hybrid sequential data assimilation and probabilistic collocation method","year":2016,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; University of Regina","funders":"Program for Innovative Research Team of Ningbo Novel Photoelectric Materials and Devices; National Natural Science Foundation of China","keywords":"Sensitivity (control systems); Data assimilation; Hydrological modelling; Propagation of uncertainty; Polynomial chaos; Probabilistic logic; Collocation (remote sensing); Uncertainty quantification; Streamflow; Evapotranspiration; Mathematics; Environmental science; Computer science; Statistics; Meteorology; Drainage basin; Monte Carlo method; Climatology; Geology","score_opus":0.043691899294049805,"score_gpt":0.2542823192196845,"score_spread":0.2105904199256347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2521488166","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3957331,0.000022975193,0.6033684,0.0002281562,0.000021796071,0.00035796006,0.00023857423,0.00002041695,0.000008628043],"genre_scores_gemma":[0.93182665,0.000090535636,0.06764161,0.00006287442,0.000010591544,0.000036931116,0.0002536138,0.000014038529,0.00006317961],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868625,0.0001228972,0.0002357353,0.00058447086,0.00015384472,0.00021678527],"domain_scores_gemma":[0.9991385,0.00036425737,0.00013197461,0.00031139134,0.000002654591,0.000051264396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063635176,0.00017548738,0.00021623397,0.000026581205,0.0001803337,0.000010411989,0.000107994325,0.0000700891,0.000022822413],"category_scores_gemma":[0.00004848957,0.00013311737,0.00002925277,0.000028560497,0.00045765273,0.0003864887,0.00048970454,0.000056785997,0.0000028777895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018935163,0.000113471426,0.058840558,0.000045431687,0.00006848256,0.0000035589899,0.00024471106,0.92114437,0.0005358501,0.00012653759,0.000066272194,0.018621398],"study_design_scores_gemma":[0.00045635662,0.00010015656,0.0013797231,0.000014621235,0.00009076184,0.0000061158953,0.000022858185,0.9660734,0.00009848581,0.031530257,0.000062958316,0.00016432966],"about_ca_topic_score_codex":0.000113883085,"about_ca_topic_score_gemma":0.00007224972,"teacher_disagreement_score":0.53609353,"about_ca_system_score_codex":0.00013436828,"about_ca_system_score_gemma":0.000003403794,"threshold_uncertainty_score":0.5428369},"labels":[],"label_agreement":null},{"id":"W2588298323","doi":"10.1016/j.envsoft.2017.01.022","title":"A GIS-based urban and peri-urban landscape representation toolbox for hydrological distributed modeling","year":2017,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fondo de Financiamiento de Centros de Investigación en Áreas Prioritarias; Comisión Nacional de Investigación Científica y Tecnológica; Centro de Desarrollo Urbano Sustentable; Fondo Nacional de Desarrollo Científico y Tecnológico; Institut National de Recherche en Sciences et Technologies pour l'Environnement et l'Agriculture; International Development Research Centre","keywords":"Terrain; Toolbox; Drainage; Drainage network; Geographic information system; Hydrology (agriculture); Urban planning; Representation (politics); Polygon mesh; Environmental science; Computer science; Remote sensing; Cartography; Drainage basin; Geography; Civil engineering; Geology; Engineering; Computer graphics (images)","score_opus":0.027268628776761464,"score_gpt":0.2372409472886541,"score_spread":0.20997231851189263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588298323","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64088494,0.000087210174,0.3578291,0.00039865743,0.0000667732,0.00038396823,0.00008326415,0.000074549425,0.00019152607],"genre_scores_gemma":[0.9904384,0.0000460411,0.008547051,0.00023048282,0.000064288884,0.0001339782,0.00026008833,0.000026502043,0.00025318272],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837893,0.00003848679,0.00024860975,0.0006860424,0.00022288293,0.00042505693],"domain_scores_gemma":[0.9991898,0.00007103407,0.0001339008,0.00049200834,0.000002051524,0.000111238056],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00021145577,0.00026035815,0.0002517818,0.000026229845,0.0014010316,0.00009446797,0.0003116932,0.00013408085,0.00018898638],"category_scores_gemma":[0.000044827517,0.00023830975,0.00010077904,0.000023323826,0.00040875337,0.00034582795,0.00031411037,0.00014889135,0.00005503577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102774226,0.0000803106,0.315634,0.0000102202775,0.000026672873,0.000007238711,0.00030264468,0.6826275,0.000086019805,0.000013995855,0.0005730508,0.0005355889],"study_design_scores_gemma":[0.0009895685,0.00013784796,0.014704611,0.000011482671,0.000074331074,0.0000015070251,0.000092683986,0.9810659,0.00012844472,0.0012400105,0.0012397381,0.00031384232],"about_ca_topic_score_codex":0.00006625171,"about_ca_topic_score_gemma":0.000011819654,"teacher_disagreement_score":0.34955344,"about_ca_system_score_codex":0.00007917002,"about_ca_system_score_gemma":0.0000023418684,"threshold_uncertainty_score":0.999899},"labels":[],"label_agreement":null},{"id":"W2592331921","doi":"10.1016/j.envsoft.2017.02.024","title":"Improvement of rank histograms for verifying the reliability of extreme event ensemble forecasts","year":2017,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Beijing Science and Technology Planning Project; National Natural Science Foundation of China","keywords":"Reliability (semiconductor); Histogram; Streamflow; Rank (graph theory); Computer science; Event (particle physics); Data mining; Set (abstract data type); Diagram; Statistics; Mathematics; Artificial intelligence; Image (mathematics); Geography","score_opus":0.03060629709753249,"score_gpt":0.23140574234607297,"score_spread":0.2007994452485405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592331921","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7703894,0.00010812022,0.22813416,0.00019230462,0.00015788966,0.0006976863,0.000025681138,0.00001658714,0.00027820712],"genre_scores_gemma":[0.9916928,0.000114035814,0.0075668064,0.000046988564,0.000019313988,0.00010356789,0.000011515933,0.000018997445,0.0004260103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860567,0.000024826017,0.00039691114,0.00038734553,0.00027957524,0.00030564907],"domain_scores_gemma":[0.9986895,0.00008776853,0.00038691296,0.00078893534,0.0000036702181,0.000043246073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005321642,0.00018806705,0.00026564504,0.000015021567,0.00066972204,0.00001198346,0.00051378616,0.00006795183,0.00016456588],"category_scores_gemma":[0.000040390532,0.00014122043,0.00019499515,0.000018701312,0.0007695836,0.00019581686,0.00054510986,0.00010013594,0.000016580285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005111893,0.00085901446,0.23384282,0.00026120894,0.00019023132,0.0000034058016,0.0034143324,0.647525,0.019338029,0.00007338493,0.0006388076,0.09334256],"study_design_scores_gemma":[0.009339182,0.0034410236,0.17636047,0.00036522647,0.0012111876,0.0000072087073,0.001661485,0.54898465,0.16149423,0.059753735,0.03488319,0.0024984288],"about_ca_topic_score_codex":0.00028195354,"about_ca_topic_score_gemma":0.00001987209,"teacher_disagreement_score":0.22130339,"about_ca_system_score_codex":0.00013913994,"about_ca_system_score_gemma":0.000002838216,"threshold_uncertainty_score":0.57588017},"labels":[],"label_agreement":null},{"id":"W2594815359","doi":"10.1016/j.envsoft.2017.02.027","title":"Using stage frequency distributions as objective functions for model calibration and global sensitivity analyses","year":2017,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"","keywords":"Sensitivity (control systems); Calibration; Stage (stratigraphy); Function (biology); Mathematics; Environmental science; Computer science; Statistics; Geology; Engineering","score_opus":0.061929140974326925,"score_gpt":0.30713666376289644,"score_spread":0.24520752278856953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594815359","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4349889,0.000023437413,0.56402814,0.000075152915,0.00004045908,0.00020358441,0.00039977452,0.000031368734,0.0002091839],"genre_scores_gemma":[0.97301406,0.00003595061,0.026215967,0.0000749556,0.00002437375,0.000034215867,0.000105256964,0.000012644269,0.00048256005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888813,0.000039321756,0.0001581541,0.00046970838,0.00015559039,0.00028909827],"domain_scores_gemma":[0.9994271,0.000039719063,0.00012754269,0.00031946457,0.0000024076976,0.000083773106],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00014516123,0.0001978215,0.00017082057,0.00001531597,0.0018600385,0.000070639435,0.00010417458,0.00008549301,0.000057054673],"category_scores_gemma":[0.00003488808,0.00019683882,0.00007886773,0.000027732885,0.00052121177,0.00064321945,0.00031920354,0.000089768015,0.000022291784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025111874,0.000070103204,0.1286411,0.000006011476,0.000057533496,0.000005076865,0.00014759459,0.8693274,0.0012017871,0.00020211173,0.00004453781,0.00027159962],"study_design_scores_gemma":[0.0003067024,0.00004749423,0.011474451,0.0000074397703,0.00014358979,0.0000043402147,0.00012562067,0.9751793,0.00032735904,0.012096965,0.000045023702,0.00024168352],"about_ca_topic_score_codex":0.0009048544,"about_ca_topic_score_gemma":0.0001393661,"teacher_disagreement_score":0.5380252,"about_ca_system_score_codex":0.00029385046,"about_ca_system_score_gemma":0.0000063268344,"threshold_uncertainty_score":0.9994394},"labels":[],"label_agreement":null},{"id":"W2735062580","doi":"10.1016/j.envsoft.2017.07.005","title":"Integrated modelling of urban spatial development under uncertain climate futures: A case study in Hungary","year":2017,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"International Institute for Sustainable Development","funders":"Natural Environment Research Council; Sight Research UK; Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"Urbanization; Spatial planning; Futures contract; Climate change; Urban planning; Stakeholder; Environmental planning; Urban climate; Population; Geography; Environmental resource management; Regional science; Business; Economic growth; Civil engineering; Environmental science; Economics; Engineering","score_opus":0.14742235680039514,"score_gpt":0.3315922622321554,"score_spread":0.18416990543176023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2735062580","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57049924,0.000116189345,0.42887685,0.00001168182,0.00013363092,0.00028407294,0.00002364105,0.000029480865,0.000025177249],"genre_scores_gemma":[0.96859586,0.00008230406,0.031050086,0.000030227744,0.000038439142,0.000039870898,0.000036032397,0.000040684605,0.00008646368],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957009,0.00023473536,0.0013450041,0.00091224496,0.0013190331,0.0004880766],"domain_scores_gemma":[0.99771214,0.00032672053,0.0006487615,0.0011211212,0.00004581747,0.0001454223],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0023551956,0.00036081037,0.0005307837,0.0003622018,0.000982609,0.00024824846,0.00081852765,0.00015906687,0.000078901205],"category_scores_gemma":[0.00008266371,0.00030775953,0.0001490987,0.00016808022,0.00018576487,0.00045534072,0.00030617884,0.00036847044,0.00003881199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009083074,0.00047355186,0.10643527,0.0000085177535,0.000028074499,0.00028128774,0.009828243,0.8589608,0.000027229738,0.000010742144,0.0000051993184,0.023850251],"study_design_scores_gemma":[0.0010410665,0.00012373221,0.0050814524,0.000088182846,0.000032599157,0.00004736682,0.032754146,0.9585826,0.000072097144,0.0017539231,0.000046566973,0.00037629364],"about_ca_topic_score_codex":0.003110225,"about_ca_topic_score_gemma":0.0015176871,"teacher_disagreement_score":0.39809662,"about_ca_system_score_codex":0.00015291489,"about_ca_system_score_gemma":0.000079333324,"threshold_uncertainty_score":0.9999375},"labels":[],"label_agreement":null},{"id":"W2765315715","doi":"10.1016/j.envsoft.2017.09.020","title":"Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services","year":2017,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":342,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Engineering and Physical Sciences Research Council; Horizon 2020 Framework Programme; University of Reading; Vetenskapsrådet; Natural Environment Research Council; Met Office; Svenska Forskningsrådet Formas; Sight Research UK","keywords":"Scale (ratio); Environmental science; Climate change; Environmental resource management; Thermal comfort; Identification (biology); Urban heat island; Energy consumption; Meteorology; Engineering; Geography","score_opus":0.01776253301243682,"score_gpt":0.22631090492018763,"score_spread":0.2085483719077508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765315715","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8114548,0.000065869484,0.18548173,0.00003356049,0.00021529538,0.0009987958,0.0015239938,0.00019377668,0.000032212316],"genre_scores_gemma":[0.90208644,0.00005484885,0.09499748,0.00022448851,0.00013926184,0.00019993642,0.0016554291,0.00011431904,0.00052780274],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969375,0.00007532026,0.00052075577,0.0010952321,0.00056472,0.00080645323],"domain_scores_gemma":[0.9980279,0.00006897579,0.00034490798,0.001240042,0.0000026777702,0.0003154669],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000337617,0.00053591054,0.00035838058,0.000049454276,0.0014819056,0.00023219475,0.00090922747,0.00026628195,0.001238007],"category_scores_gemma":[0.000010875994,0.0005345791,0.0002162744,0.00003407151,0.00052601146,0.0012675662,0.00034023446,0.00028361953,0.00043123355],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000174559,0.0005931455,0.927462,0.000045499757,0.000025641657,0.0000077391915,0.0008529158,0.056152288,0.008908599,0.0000020722314,0.00015733883,0.0056182393],"study_design_scores_gemma":[0.0032676777,0.00047258183,0.2211519,0.00010006131,0.00014946482,0.0000077209315,0.0002713534,0.7538106,0.009488426,0.00015698682,0.009946624,0.0011766452],"about_ca_topic_score_codex":0.0001910041,"about_ca_topic_score_gemma":0.00010837378,"teacher_disagreement_score":0.7063101,"about_ca_system_score_codex":0.00059550034,"about_ca_system_score_gemma":0.000012711923,"threshold_uncertainty_score":0.999818},"labels":[],"label_agreement":null},{"id":"W2768827643","doi":"10.1016/j.envsoft.2017.11.001","title":"Improving the catchment scale wetland modeling using remotely sensed data","year":2017,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Resources Conservation Service; U.S. Fish and Wildlife Service; National Aeronautics and Space Administration","keywords":"Scale (ratio); Wetland; Environmental science; Remote sensing; Hydrology (agriculture); Drainage basin; Environmental resource management; Geography; Water resource management; Physical geography; Cartography; Geology; Ecology","score_opus":0.05169146679663554,"score_gpt":0.24649823607954055,"score_spread":0.19480676928290502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768827643","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67117184,0.000092972856,0.3276416,0.00033449958,0.00017896717,0.00028009404,0.00002951692,0.000055296037,0.00021522936],"genre_scores_gemma":[0.97739285,0.00015016907,0.02162973,0.0002637904,0.00009866434,0.00000801297,0.000048883536,0.00003832163,0.0003696048],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978964,0.00006072488,0.00028895572,0.00081680383,0.0004049744,0.00053217646],"domain_scores_gemma":[0.99759203,0.000037767804,0.000200248,0.0020750195,0.000001463142,0.00009349159],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00045928668,0.0002942429,0.00022032499,0.000020052352,0.002944955,0.00014013101,0.0014050634,0.00009458339,0.00015985561],"category_scores_gemma":[0.000020862628,0.00022644724,0.0000687957,0.000025907762,0.00054440787,0.0006616814,0.003367824,0.00027481388,0.00024375693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028161527,0.000060119033,0.04679666,0.000009064171,0.00003819289,0.000017252896,0.00061673217,0.94651705,0.0016354353,0.000001414187,0.0001209054,0.0041589965],"study_design_scores_gemma":[0.00033577337,0.00002014813,0.0015920976,0.000017657692,0.00009775508,0.000009031051,0.00018241855,0.9961075,0.00028910328,0.0004914578,0.0005570417,0.0002999868],"about_ca_topic_score_codex":0.0019059642,"about_ca_topic_score_gemma":0.00008928668,"teacher_disagreement_score":0.306221,"about_ca_system_score_codex":0.00018719658,"about_ca_system_score_gemma":0.000004852392,"threshold_uncertainty_score":0.99835306},"labels":[],"label_agreement":null},{"id":"W2771100845","doi":"10.1016/j.envsoft.2017.11.023","title":"Transforming data into knowledge for improved wastewater treatment operation: A critical review of techniques","year":2017,"lang":"en","type":"review","venue":"Environmental Modelling & Software","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":207,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Sistema Nacional de Investigadores; Canadian Institute for Advanced Research","keywords":"Computer science; Component (thermodynamics); Fuzzy logic; Cluster analysis; Principal component analysis; Artificial intelligence; Data mining; Data science; Management science; Machine learning; Engineering","score_opus":0.2270736453883636,"score_gpt":0.3999897267181094,"score_spread":0.17291608132974579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771100845","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008163246,0.9499637,0.045869187,0.00008848068,0.00018344045,0.002953434,0.00067677535,0.00022677766,0.000030034498],"genre_scores_gemma":[0.000059443155,0.8842432,0.113026015,0.000012171343,0.00012008258,0.000985286,0.0011852133,0.00011270259,0.0002558776],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967115,0.0001269624,0.001156967,0.0012517803,0.0002740054,0.00047876354],"domain_scores_gemma":[0.9964307,0.00027156208,0.00037970796,0.002796435,0.000005175451,0.00011643525],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005664347,0.0007549579,0.0018717687,0.00007366737,0.00040540998,0.000065031556,0.0021163425,0.00045909724,0.00011870907],"category_scores_gemma":[0.00012008199,0.0005874085,0.0005031785,0.000056896133,0.00066081405,0.00057229167,0.001203149,0.00029504023,0.00010306297],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005661755,0.00023766459,0.0000030478466,0.029481893,0.000075250995,0.0000025556642,0.00015692988,0.000020054018,0.00003941329,0.0000066592543,0.0001781101,0.9697928],"study_design_scores_gemma":[0.00013060727,0.00026803193,8.9792906e-8,0.031368155,0.0008301127,0.00001236408,0.000015461863,0.00064810575,0.0013655135,0.0002758713,0.9644644,0.00062127993],"about_ca_topic_score_codex":0.00008379148,"about_ca_topic_score_gemma":0.0000059954673,"teacher_disagreement_score":0.96917146,"about_ca_system_score_codex":0.0009604175,"about_ca_system_score_gemma":0.00005376752,"threshold_uncertainty_score":0.99965775},"labels":[],"label_agreement":null},{"id":"W2791583331","doi":"10.1016/j.envsoft.2018.01.013","title":"Modelling marine trophic transfer of radiocarbon (14C) from a nuclear facility","year":2018,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Isotope Analysis in Ecology","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Environment Research Council; Sight Research UK","keywords":"Trophic level; Environmental science; Marine ecosystem; Microplastics; Fishing; Ecosystem; Oceanography; Ecology; Biology; Geology","score_opus":0.011586807350714913,"score_gpt":0.18277433148857625,"score_spread":0.17118752413786134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791583331","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72023565,0.00003991743,0.27880293,0.00002587736,0.00008682803,0.00018906768,0.000087582084,0.00006438987,0.00046776776],"genre_scores_gemma":[0.96782833,0.00012771023,0.031480007,0.00013926512,0.00008011159,0.00001347467,0.000088764165,0.00004938925,0.00019297251],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974303,0.00011807388,0.0005841282,0.0008292586,0.00053216686,0.0005060418],"domain_scores_gemma":[0.99891347,0.00008409618,0.00010292449,0.00073771545,0.0000042641036,0.00015754148],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00022870628,0.00034633104,0.0004694732,0.000057505196,0.00022615115,0.000017221984,0.00051162107,0.0002097129,0.0137505615],"category_scores_gemma":[0.000008965141,0.00035818102,0.00023150342,0.00015529501,0.0008867844,0.00024682577,0.00035191298,0.00029313803,0.0014216899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013481057,0.00029851717,0.04874828,0.0000071444265,0.00008434356,0.000010150801,0.0014195642,0.93957907,0.005214493,0.000014270796,0.000041410178,0.004447928],"study_design_scores_gemma":[0.00072018476,0.0002827377,0.0041025598,0.000014192098,0.00015722803,0.000005381189,0.00011935032,0.9852473,0.0036319501,0.0027522387,0.0024342823,0.00053257536],"about_ca_topic_score_codex":0.0016112508,"about_ca_topic_score_gemma":0.00005335543,"teacher_disagreement_score":0.24759266,"about_ca_system_score_codex":0.00034722107,"about_ca_system_score_gemma":0.0000067829556,"threshold_uncertainty_score":0.999887},"labels":[],"label_agreement":null},{"id":"W2793724872","doi":"10.1016/j.envsoft.2017.11.016","title":"A multi-lake comparative analysis of the General Lake Model (GLM): Stress-testing across a global observatory network","year":2018,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":172,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Environment; Ministry of the Environment, Conservation and Parks; University of Toronto","funders":"Natural Environment Research Council; Sight Research UK; Global Lake Ecological Observatory Network","keywords":"Environmental science; Thermocline; Observatory; Residence time (fluid dynamics); Climatology; Meteorology; Econometrics; Hydrology (agriculture); Geology; Mathematics; Geography","score_opus":0.05315431131901118,"score_gpt":0.2761532281447373,"score_spread":0.22299891682572612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793724872","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84625953,0.000059961523,0.15262666,0.00002715675,0.000105707266,0.00024554224,0.00039317328,0.000055185505,0.0002270845],"genre_scores_gemma":[0.95302045,0.000014778628,0.046055906,0.00035649142,0.000060078644,0.000028194545,0.00005852785,0.000014099242,0.00039147335],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99785984,0.0001044166,0.00040144759,0.0006178562,0.00040378247,0.0006126453],"domain_scores_gemma":[0.9990954,0.000057981928,0.00024050592,0.0005121178,0.0000059460876,0.00008808379],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025572587,0.0003140037,0.0004241414,0.000016780554,0.0008204959,0.000020383552,0.0005227028,0.00011108593,0.00026520737],"category_scores_gemma":[0.000011373338,0.00025041253,0.00022510576,0.00054179714,0.0012831825,0.00015992176,0.0010618645,0.00017886201,0.000079993784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016031074,0.000071313414,0.4608025,0.0000026987232,0.00022450916,0.0000012671071,0.0010005827,0.5375686,0.000042290263,0.000005118551,0.00009648859,0.00016854088],"study_design_scores_gemma":[0.00023815627,0.000035045152,0.29083288,0.000012707741,0.00031554833,4.794149e-7,0.00012149814,0.70780116,0.00008540096,0.00020042202,0.00016050121,0.00019621235],"about_ca_topic_score_codex":0.00013427481,"about_ca_topic_score_gemma":0.004863412,"teacher_disagreement_score":0.17023249,"about_ca_system_score_codex":0.00014430408,"about_ca_system_score_gemma":0.000005577102,"threshold_uncertainty_score":0.9999948},"labels":[],"label_agreement":null},{"id":"W2802990981","doi":"10.1016/j.envsoft.2018.03.021","title":"Downscaling of climate model output for Alaskan stakeholders","year":2018,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Climate variability and models","field":"Environmental Science","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Climate Program Office; Alaska Climate Adaptation Science Center, University of Alaska Fairbanks; National Oceanic and Atmospheric Administration; U.S. Geological Survey; U.S. Department of Energy","keywords":"Downscaling; Forcing (mathematics); Computer science; Visualization; Climate model; Northern Hemisphere; Climatology; Environmental science; Range (aeronautics); Software; Climate change; Precipitation; Selection (genetic algorithm); Meteorology; Environmental resource management; Data mining; Geography; Machine learning; Geology; Engineering","score_opus":0.05762448607979446,"score_gpt":0.242589485257294,"score_spread":0.18496499917749956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802990981","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5312022,0.000023978731,0.46744713,0.000029892453,0.00007913865,0.00037009554,0.00025800255,0.00006266725,0.0005269179],"genre_scores_gemma":[0.88593113,0.000116557116,0.113285325,0.00015958802,0.000054083073,0.00004839649,0.00007554558,0.000054511904,0.00027488673],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99776137,0.000031437845,0.0005112478,0.0006726492,0.0004010736,0.00062222895],"domain_scores_gemma":[0.9990104,0.0001146731,0.00017849717,0.0005239555,0.000005717475,0.00016679474],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043386154,0.00029239754,0.00031121084,0.000041635183,0.0003227176,0.000022175094,0.0003335336,0.00015328437,0.000614299],"category_scores_gemma":[0.000020248533,0.00029666105,0.00020452854,0.00008079665,0.0006003324,0.00030131865,0.00029398495,0.00013408205,0.00017315014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010293408,0.00022636744,0.006674358,0.000044109824,0.00001705566,5.275631e-7,0.0012555828,0.98423946,0.0029722974,0.000076083255,0.00011827985,0.0042729746],"study_design_scores_gemma":[0.00059174205,0.00017536755,0.0001965098,0.000035385565,0.000053722222,0.0000021465548,0.00015822702,0.98919356,0.0034332492,0.0047206013,0.0010806527,0.00035882407],"about_ca_topic_score_codex":0.0000618487,"about_ca_topic_score_gemma":0.000014919544,"teacher_disagreement_score":0.35472894,"about_ca_system_score_codex":0.00022615216,"about_ca_system_score_gemma":0.000009687236,"threshold_uncertainty_score":0.99994856},"labels":[],"label_agreement":null},{"id":"W2894470954","doi":"10.1016/j.envsoft.2018.09.017","title":"Short-term air temperature forecasting using Nonparametric Functional Data Analysis and SARMA models","year":2018,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Masdar Institute of Science and Technology","keywords":"Pooling; Term (time); Autoregressive model; Econometrics; Nonparametric statistics; Parametric statistics; Bayesian probability; Nonparametric regression; Statistics; Computer science; Mathematics; Artificial intelligence","score_opus":0.056450681270344576,"score_gpt":0.22281269264002074,"score_spread":0.16636201136967615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2894470954","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5354676,0.00060301565,0.4634234,0.0000013308979,0.00015014931,0.000052920386,0.00011587861,0.00013555077,0.000050147926],"genre_scores_gemma":[0.9360191,0.00011392026,0.06279741,0.000027459631,0.0003492256,0.000004326674,0.0005749751,0.00007297798,0.000040586452],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983564,0.00002122756,0.00032661142,0.0005830804,0.00029544404,0.00041726296],"domain_scores_gemma":[0.99916476,0.00008547005,0.000043890876,0.0005435381,0.000009313288,0.00015300734],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016259881,0.00032846892,0.00029801155,0.00029178077,0.00034336137,0.00007106414,0.0002542427,0.00016112918,0.00005444729],"category_scores_gemma":[0.000007913482,0.0003438503,0.00009138919,0.00049922086,0.00012149475,0.0006408486,0.00024684807,0.00029467585,0.0000072596886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000089325395,0.000019648523,0.013845682,0.000021283713,0.00026245526,0.000007803863,0.00018285056,0.9793414,0.0010373613,0.00000416053,0.00002235813,0.0052460944],"study_design_scores_gemma":[0.00014017113,0.00002040508,0.0013855757,0.00004292392,0.00032479616,0.00003500705,0.000037567937,0.99663234,0.00082719245,0.00011502054,0.00006230174,0.000376692],"about_ca_topic_score_codex":0.000020272475,"about_ca_topic_score_gemma":0.000007979472,"teacher_disagreement_score":0.40062597,"about_ca_system_score_codex":0.000111988826,"about_ca_system_score_gemma":0.0000090283465,"threshold_uncertainty_score":0.99990135},"labels":[],"label_agreement":null},{"id":"W2898336091","doi":"10.1016/j.envsoft.2018.10.005","title":"VARS-TOOL: A toolbox for comprehensive, efficient, and robust sensitivity and uncertainty analysis","year":2018,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":105,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; Global Institute for Water Security","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Sensitivity (control systems); Computer science; Latin hypercube sampling; Robustness (evolution); Toolbox; Emulation; Sampling (signal processing); Variogram; Uncertainty analysis; Data mining; Software; Stability (learning theory); Visualization; Mathematical optimization; Machine learning; Simulation; Mathematics; Engineering; Statistics; Monte Carlo method; Kriging","score_opus":0.08929391861524157,"score_gpt":0.28894128538363617,"score_spread":0.19964736676839462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898336091","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3969343,0.00018117134,0.60244745,0.000018640016,0.00006668523,0.00018500867,0.00010321554,0.00005586792,0.000007658016],"genre_scores_gemma":[0.8983834,0.000022500928,0.10125989,0.00008243266,0.000068951675,0.000012624534,0.0000225711,0.000018214592,0.0001294078],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979106,0.000097222575,0.00038489798,0.00078378915,0.00049491884,0.00032854365],"domain_scores_gemma":[0.9976541,0.001600444,0.00011455023,0.00044107158,0.000046529032,0.00014332158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008602098,0.00024092093,0.00041551975,0.00018153313,0.00038015825,0.00013369524,0.00015117101,0.000106215855,0.000033733384],"category_scores_gemma":[0.00018828051,0.00019720232,0.00013330484,0.00030563475,0.0004581728,0.00010423193,0.00013450756,0.00011099289,0.000021570842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047161477,0.000042947147,0.0036654735,0.000008274828,0.000085011576,0.0000037627872,0.00032940926,0.9924367,0.00018298527,0.000094276154,0.00010431969,0.0029996643],"study_design_scores_gemma":[0.0003420922,0.000085225205,0.0072176117,0.000010323871,0.000185697,0.000010028611,0.00014709917,0.9899757,0.000046633013,0.0010343551,0.0006877601,0.00025749003],"about_ca_topic_score_codex":0.000026367823,"about_ca_topic_score_gemma":0.0000083032955,"teacher_disagreement_score":0.5014491,"about_ca_system_score_codex":0.000056624584,"about_ca_system_score_gemma":0.000012328716,"threshold_uncertainty_score":0.8041677},"labels":[],"label_agreement":null},{"id":"W2898760395","doi":"10.1016/j.envsoft.2018.10.011","title":"Integrating organic chemical simulation module into SWAT model with application for PAHs simulation in Athabasca oil sands region, Western Canada","year":2018,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Athabasca University","funders":"Alberta Innovates","keywords":"Environmental science; Watershed; Oil sands; Surface runoff; Simulation modeling; Hydrology (agriculture); SWAT model; Environmental engineering; Asphalt; Geology; Geotechnical engineering; Ecology","score_opus":0.010595747689513803,"score_gpt":0.2131536601307385,"score_spread":0.2025579124412247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898760395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47817013,0.000011108091,0.5215672,0.00003482922,0.000016812144,0.00015659639,0.0000062163126,0.000026949652,0.000010133401],"genre_scores_gemma":[0.98684406,0.0000050487943,0.012064011,0.00015811199,0.00005976878,0.00012470648,0.00013664048,0.000042809537,0.0005648562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842066,0.000028564571,0.00033298624,0.00055038196,0.0003700036,0.00029739414],"domain_scores_gemma":[0.99940944,0.0001150728,0.00013163558,0.0002556011,0.000013096929,0.00007512781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001362938,0.00024826525,0.00019773086,0.000033277884,0.00026741292,0.000028289576,0.00017074752,0.000088011504,0.000030832016],"category_scores_gemma":[0.000015818974,0.0002282509,0.00003622724,0.00012376547,0.00013710048,0.0003473124,0.000113436945,0.00011673884,0.000020990532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057646077,0.000052098527,0.07153366,0.0000113921815,0.000007760073,7.687506e-7,0.0012804333,0.90593255,0.0015290861,0.0000028732952,0.000012041876,0.019579682],"study_design_scores_gemma":[0.0005528102,0.000055420216,0.002521583,0.000029253335,0.000018893548,0.0000012006398,0.00018055044,0.99372053,0.0018240731,0.0002238753,0.000581292,0.00029049296],"about_ca_topic_score_codex":0.012968358,"about_ca_topic_score_gemma":0.061155148,"teacher_disagreement_score":0.50950325,"about_ca_system_score_codex":0.0009989917,"about_ca_system_score_gemma":0.000025670795,"threshold_uncertainty_score":0.99360436},"labels":[],"label_agreement":null},{"id":"W2904251429","doi":"10.1016/j.envsoft.2018.12.002","title":"A multi-method Generalized Global Sensitivity Matrix approach to accounting for the dynamical nature of earth and environmental systems models","year":2018,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Sensitivity (control systems); Earth (classical element); Matrix (chemical analysis); Dynamical systems theory; Environmental accounting; Environmental science; Econometrics; Computer science; Mathematics; Accounting; Economics; Physics; Engineering; Materials science","score_opus":0.04618148887534965,"score_gpt":0.3014373271078217,"score_spread":0.255255838232472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904251429","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14144975,0.00091366074,0.8560084,0.00001978831,0.00024682822,0.00077573815,0.0005359318,0.00004188802,0.000008022174],"genre_scores_gemma":[0.5819398,0.00001326263,0.41776133,0.000037150905,0.000077758916,0.000030394473,0.000016924147,0.000018966522,0.00010439993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972777,0.00018888965,0.000553459,0.0007746609,0.0008158774,0.00038943795],"domain_scores_gemma":[0.99798894,0.0010894167,0.00018229385,0.0005663606,0.000020199848,0.00015281916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020517814,0.00029262144,0.00043234875,0.000061704566,0.000337942,0.00011875614,0.00039747028,0.00024977443,0.000006405201],"category_scores_gemma":[0.00020079793,0.0002006251,0.00015101307,0.00015092746,0.00028031424,0.0002090346,0.00029657123,0.00022895774,0.000011841837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006701034,0.00010591055,0.0008299821,0.000016209395,0.000035705434,0.000001060743,0.00033807158,0.99549574,0.0010135549,0.00057740265,0.000028956087,0.0014903885],"study_design_scores_gemma":[0.00047901276,0.000059563274,0.0009249434,0.000020541858,0.000046362435,0.000032055363,0.00044876762,0.99641466,0.000076123426,0.0010662345,0.00019517922,0.00023653852],"about_ca_topic_score_codex":0.0000408878,"about_ca_topic_score_gemma":0.0000017691901,"teacher_disagreement_score":0.44049007,"about_ca_system_score_codex":0.000101200094,"about_ca_system_score_gemma":0.000017460701,"threshold_uncertainty_score":0.8181254},"labels":[],"label_agreement":null},{"id":"W2905475537","doi":"10.1016/j.envsoft.2018.12.007","title":"Development and evaluation of a phosphorus (P) module in RZWQM2 for phosphorus management in agricultural fields","year":2018,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tile drainage; Phosphorus; Manure; Environmental science; Agriculture; Surface runoff; Drainage; Cropping; Hydrology (agriculture); Water quality; Tillage; Particulates; DNS root zone; Agricultural engineering; Environmental engineering; Agronomy; Soil water; Soil science; Engineering; Chemistry; Ecology","score_opus":0.01977788669194047,"score_gpt":0.2255703056565339,"score_spread":0.20579241896459344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905475537","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.986233,0.00021906926,0.0125116315,0.000017636783,0.00007821261,0.00064634666,0.000008243036,0.000014071232,0.0002717664],"genre_scores_gemma":[0.9586524,0.00011107702,0.040911727,0.00003307623,0.000011749737,0.00014595503,0.00003874522,0.000013497883,0.00008177276],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985708,0.000025150619,0.0003262696,0.00039313993,0.000402755,0.0002818802],"domain_scores_gemma":[0.9997011,0.000022334527,0.00007338485,0.00014734968,0.000003564646,0.000052298896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003860189,0.00016781554,0.00015776564,0.000046159646,0.00007468645,0.000011095739,0.00013307185,0.00008097701,0.000033726887],"category_scores_gemma":[0.000004247278,0.0001532892,0.000032988093,0.000111031986,0.00011985273,0.00014160293,0.00016787398,0.000075773285,0.00002199817],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001452453,0.00057156885,0.5624873,0.00002804799,0.000027344207,0.0000038461417,0.0057730386,0.24257436,0.00006989043,0.00005721631,0.000044719138,0.18821742],"study_design_scores_gemma":[0.003834539,0.00017857793,0.42384273,0.0001080176,0.00005583205,0.0000038742764,0.0011098483,0.5543039,0.0050295265,0.009444508,0.0015211826,0.0005674628],"about_ca_topic_score_codex":0.00012448942,"about_ca_topic_score_gemma":0.000045115357,"teacher_disagreement_score":0.31172952,"about_ca_system_score_codex":0.00042928077,"about_ca_system_score_gemma":0.0000043731297,"threshold_uncertainty_score":0.62509525},"labels":[],"label_agreement":null},{"id":"W2908365240","doi":"10.1016/j.envsoft.2018.12.006","title":"Global evaluation and sensitivity analysis of a physically based flow and reactive transport model on a laboratory experiment","year":2018,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Sensitivity (control systems); Hydraulic conductivity; Environmental science; Hydrogeology; Sorption; Ranking (information retrieval); Calibration; Hydrology (agriculture); Flow (mathematics); Soil science; Adsorption; Statistics; Computer science; Chemistry; Mathematics; Geotechnical engineering; Engineering; Machine learning; Soil water","score_opus":0.013844911098717523,"score_gpt":0.23767476863218004,"score_spread":0.22382985753346252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908365240","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6463802,0.000024028515,0.3532873,0.000014838124,0.000009676572,0.00013247607,0.00010738281,0.000011166841,0.0000329398],"genre_scores_gemma":[0.9913664,0.000008184405,0.00840413,0.00011890033,0.000010322494,0.000027459544,0.000035056375,0.000008805242,0.00002075715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869394,0.00007402746,0.0001689022,0.00043239424,0.00048112604,0.00014958504],"domain_scores_gemma":[0.99962807,0.00003983739,0.00007550403,0.00017690955,0.000009964993,0.00006971572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002616312,0.00016933431,0.0002285193,0.000038446095,0.00014312321,0.000008963783,0.000034278626,0.000048136306,0.0000622953],"category_scores_gemma":[0.0000049687305,0.00016126123,0.000058990547,0.00016335143,0.0003618887,0.00011825659,0.000048699174,0.000046202065,0.0000071440186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011919386,0.0002927654,0.09693033,0.000004811379,0.00018850162,0.0000028034772,0.0024186184,0.8774043,0.008770322,0.000009080828,0.0000060336224,0.013853266],"study_design_scores_gemma":[0.00034717625,0.00010990666,0.13920526,0.000008170623,0.00035350616,3.0646825e-7,0.00012666221,0.8569376,0.0026932494,0.000057134315,0.00001748472,0.00014352816],"about_ca_topic_score_codex":0.000089214634,"about_ca_topic_score_gemma":0.0000691248,"teacher_disagreement_score":0.3449862,"about_ca_system_score_codex":0.00024083171,"about_ca_system_score_gemma":0.00000813294,"threshold_uncertainty_score":0.65760416},"labels":[],"label_agreement":null},{"id":"W2913546406","doi":"10.1016/j.envsoft.2019.01.013","title":"Endocrine disruption: From a whole-lake experiment to a calibrated ecosystem model","year":2019,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Minnow; Ecosystem; Ecosystem model; Environmental science; Freshwater ecosystem; Biomass (ecology); Aquatic ecosystem; Ecology; Population; Endocrine disruptor; Biology; Fish <Actinopterygii>; Endocrine system; Fishery; Hormone","score_opus":0.010603538989513668,"score_gpt":0.20168737817808122,"score_spread":0.19108383918856756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913546406","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87103486,0.000045540357,0.12592384,0.00036976582,0.00021381408,0.000797169,0.0002251565,0.0001790927,0.0012107798],"genre_scores_gemma":[0.97706395,0.000035281897,0.015826063,0.001172295,0.00004137015,0.00020001551,0.00018134704,0.000050318777,0.0054293484],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978544,0.00004770213,0.000345889,0.0008437201,0.00039361106,0.0005146508],"domain_scores_gemma":[0.9991614,0.000046936137,0.000089577494,0.0005150543,0.0000016475706,0.00018538117],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00009173936,0.0003465437,0.00032054854,0.000041882755,0.00023756661,0.000038102637,0.00034087288,0.000088308014,0.006529604],"category_scores_gemma":[0.0000046695836,0.00034462675,0.000098535784,0.00010230856,0.00007240161,0.0003589464,0.000793585,0.00016430236,0.009765821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005713062,0.00020268006,0.08731571,0.000009062507,0.00005268002,0.000015812899,0.0009342285,0.90508515,0.0016383204,0.000016032685,0.0043809335,0.00029228957],"study_design_scores_gemma":[0.0018516382,0.00034644947,0.029392952,0.00008107957,0.000101454345,0.000006618448,0.00090685365,0.9102199,0.001979021,0.0011833189,0.052572597,0.0013581177],"about_ca_topic_score_codex":0.00007225472,"about_ca_topic_score_gemma":0.0003811324,"teacher_disagreement_score":0.11009777,"about_ca_system_score_codex":0.00032477087,"about_ca_system_score_gemma":0.000004103095,"threshold_uncertainty_score":0.9999006},"labels":[],"label_agreement":null},{"id":"W2931677578","doi":"10.1016/j.envsoft.2019.03.007","title":"A hybrid partial and general equilibrium modeling approach to assess the hydro-economic impacts of large dams – The case of the Grand Ethiopian Renaissance Dam in the Eastern Nile River basin","year":2019,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Water resources management and optimization","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Université Laval","funders":"","keywords":"Computable general equilibrium; Partial equilibrium; Structural basin; Water resources; General equilibrium theory; Drainage basin; Economic impact analysis; Economic equilibrium; Water resource management; Environmental science; Economics; Geography; Civil engineering; Geology; Mathematical economics; Engineering","score_opus":0.01658879336238253,"score_gpt":0.20539611323994925,"score_spread":0.18880731987756671,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2931677578","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88506424,0.00017426678,0.11397477,0.000048379785,0.000066095345,0.00053815066,0.000035253473,0.000015165702,0.0000836506],"genre_scores_gemma":[0.9989162,0.00002805068,0.0008352532,0.00006525411,0.00004194009,0.00002239782,0.000016812151,0.000030056372,0.000044046563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990873,0.000094561256,0.00024884436,0.00020107428,0.0001386076,0.00022966691],"domain_scores_gemma":[0.9994417,0.00004539153,0.000063768915,0.00041847656,0.000002003253,0.000028691082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035910704,0.00016162093,0.00015854042,0.000029054076,0.00008712496,0.000043616492,0.00032737813,0.000038155245,0.000006365008],"category_scores_gemma":[0.000002809324,0.00008829996,0.00006330339,0.000047984606,0.00007180465,0.00014498261,0.00016939135,0.00017834315,0.000004008653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001677485,0.000025113954,0.009112604,0.000041140196,0.000019207993,0.00000218706,0.0040068887,0.98652893,0.000095973475,0.00002792241,0.000018348928,0.00010489486],"study_design_scores_gemma":[0.0003421113,0.000016715609,0.00038087732,0.000030898576,0.000027254942,0.0000136386425,0.00029213302,0.9980817,0.00052017305,0.000078427925,0.00010507809,0.0001109803],"about_ca_topic_score_codex":0.00018625095,"about_ca_topic_score_gemma":0.000015164913,"teacher_disagreement_score":0.11385193,"about_ca_system_score_codex":0.00003850969,"about_ca_system_score_gemma":0.0000033162191,"threshold_uncertainty_score":0.36007679},"labels":[],"label_agreement":null},{"id":"W2962585917","doi":"10.1016/j.envsoft.2019.07.007","title":"Introductory overview of identifiability analysis: A guide to evaluating whether you have the right type of data for your modeling purpose","year":2019,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":200,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"National Nuclear Security Administration; Emil Aaltosen Säätiö; Academy of Finland; Sandia National Laboratories; U.S. Department of Energy","keywords":"Identifiability; Computer science; Forcing (mathematics); Estimation theory; Key (lock); Model parameter; Experimental data; Function (biology); Data mining; Type (biology); Econometrics; Data science; Mathematics; Algorithm; Statistics; Machine learning","score_opus":0.09422947615735792,"score_gpt":0.3392979842986203,"score_spread":0.24506850814126235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962585917","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59420705,0.00041334267,0.40455788,0.00004171556,0.00007322517,0.0005583573,0.00011623297,0.000011014001,0.000021181668],"genre_scores_gemma":[0.97270155,0.000045234574,0.025020393,0.000072480485,0.000030236768,0.0000380094,0.00013116174,0.00002360595,0.0019373422],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99776936,0.0001097213,0.0006004124,0.00067828374,0.000593662,0.00024855748],"domain_scores_gemma":[0.99828166,0.00012173493,0.00020479232,0.0013184588,0.000020240535,0.000053134543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013444172,0.0001998412,0.00039132577,0.000053620588,0.00013991214,0.000019820714,0.0007327015,0.000052168998,0.0007032302],"category_scores_gemma":[0.000056145283,0.00015164846,0.00015662146,0.00020913896,0.00012010236,0.00032958578,0.0008153781,0.00009441113,0.00008027999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053200813,0.000116009185,0.062012266,0.000049666647,0.00021912158,1.6238162e-7,0.0015078714,0.92423034,0.005166674,0.000008728257,0.000084854,0.0065511283],"study_design_scores_gemma":[0.00039026744,0.00013076363,0.0059908773,0.000038795548,0.00065107323,6.385725e-7,0.0010237341,0.986816,0.0010318618,0.0004027402,0.0032427248,0.00028051215],"about_ca_topic_score_codex":0.00034774147,"about_ca_topic_score_gemma":0.000073794996,"teacher_disagreement_score":0.3795375,"about_ca_system_score_codex":0.000153016,"about_ca_system_score_gemma":0.0000098113915,"threshold_uncertainty_score":0.76998776},"labels":[],"label_agreement":null},{"id":"W2979074416","doi":"10.1016/j.envsoft.2019.104540","title":"Assessing alfalfa production under historical and future climate in eastern Canada: DNDC model development and application","year":2019,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph; Carleton University; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada","keywords":"Environmental science; Cutting; Stock (firearms); Agronomy; Sowing; Biology; Horticulture; Geography","score_opus":0.008992553560440812,"score_gpt":0.17998087043749536,"score_spread":0.17098831687705454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979074416","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9374963,0.00016971912,0.061829936,0.00013263932,0.00007829484,0.00020963956,0.0000052264154,0.000018712595,0.00005955055],"genre_scores_gemma":[0.984863,0.00014904565,0.014506943,0.000058880687,0.00002078677,0.00001881955,0.000055069846,0.000018074166,0.00030940928],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988727,0.000016485428,0.0002207899,0.00042767223,0.00024183192,0.00022054356],"domain_scores_gemma":[0.99970716,0.000009697238,0.00006974063,0.00013936736,8.604706e-7,0.00007316663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011439328,0.0001519832,0.00012507403,0.00002684068,0.00012091133,0.000028677616,0.000061842235,0.0000748744,0.000013867532],"category_scores_gemma":[6.571188e-7,0.00015172658,0.000011269095,0.000048749273,0.00003104866,0.0002977772,0.00011389314,0.000147321,0.000014464232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006047535,0.000029867315,0.21911448,0.000010427501,0.0000027491433,0.0000012248321,0.00027607734,0.76743925,0.0004006597,0.000012997631,0.00000418071,0.012702068],"study_design_scores_gemma":[0.00016944025,0.0000065385125,0.049363185,0.000014324029,0.000007718302,0.0000097628,0.0001180317,0.94840086,0.00005339202,0.00017881811,0.0014515537,0.00022635562],"about_ca_topic_score_codex":0.006122491,"about_ca_topic_score_gemma":0.008047458,"teacher_disagreement_score":0.18096167,"about_ca_system_score_codex":0.0013507023,"about_ca_system_score_gemma":0.00001616459,"threshold_uncertainty_score":0.9255416},"labels":[],"label_agreement":null},{"id":"W2983385682","doi":"10.1016/j.envsoft.2020.104926","title":"The proper care and feeding of CAMELS: How limited training data affects streamflow prediction","year":2020,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":243,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Canada First Research Excellence Fund","keywords":"Streamflow; Training (meteorology); Computer science; Training set; Tree (set theory); Set (abstract data type); Machine learning; Data set; Sequence (biology); Predictive modelling; Data mining; Artificial intelligence; Mathematics; Meteorology; Geography; Drainage basin; Cartography","score_opus":0.04101356804679195,"score_gpt":0.19961134895728957,"score_spread":0.15859778091049762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2983385682","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9630593,0.00106937,0.03368945,0.0011721483,0.00009124916,0.00046579115,0.00012646367,0.000095529685,0.00023070781],"genre_scores_gemma":[0.9955777,0.000724654,0.0033114674,0.00014246514,0.00004713098,0.000015935519,0.000094646864,0.00002088078,0.00006513957],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987927,0.000052185263,0.00013709078,0.0004792727,0.00025034312,0.00028841378],"domain_scores_gemma":[0.99946123,0.000077770324,0.000081540464,0.00029441624,0.00000117784,0.00008384533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016074086,0.00017540426,0.00016128416,0.000012408361,0.00050848356,0.000030669402,0.00032362103,0.00006142705,0.000031640266],"category_scores_gemma":[0.000029949366,0.00012979722,0.000031072035,0.00006334629,0.00051710923,0.00032833876,0.0008985329,0.0001609659,0.0000151174045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019826823,0.000099797086,0.4250336,0.00019414518,0.00029053123,0.000024002005,0.051819358,0.40533286,0.004541892,0.000022818242,0.0021259594,0.11031675],"study_design_scores_gemma":[0.002178346,0.0013050147,0.028193686,0.0001705649,0.00051911623,0.000012286811,0.0344644,0.9005799,0.0029215573,0.00026454372,0.028329093,0.0010614558],"about_ca_topic_score_codex":0.000014923599,"about_ca_topic_score_gemma":0.0000058490023,"teacher_disagreement_score":0.49524707,"about_ca_system_score_codex":0.000050115083,"about_ca_system_score_gemma":0.0000023924567,"threshold_uncertainty_score":0.52929765},"labels":[],"label_agreement":null},{"id":"W2996541326","doi":"10.1016/j.envsoft.2019.104601","title":"Modeling riverine dissolved and particulate organic carbon fluxes from two small watersheds in the northeastern United States","year":2019,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"U.S. Department of Agriculture; National Aeronautics and Space Administration; National Science Foundation","keywords":"Biogeochemical cycle; Dissolved organic carbon; Environmental science; Particulates; Soil and Water Assessment Tool; Carbon cycle; Watershed; Hydrology (agriculture); Total organic carbon; Particulate organic carbon; Ecosystem; Flux (metallurgy); Carbon fibers; Aquatic ecosystem; Environmental chemistry; Drainage basin; Streamflow; Ecology; Geography; Chemistry; Phytoplankton; Geology; Nutrient","score_opus":0.010220475986548962,"score_gpt":0.18152063875396682,"score_spread":0.17130016276741786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996541326","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9882178,0.00012547582,0.010926344,0.0002090227,0.000060524973,0.00035440127,0.000013250329,0.00005012193,0.000043091775],"genre_scores_gemma":[0.99823993,0.0003049809,0.00074325374,0.0003539055,0.000019006815,0.000019957486,0.0001647795,0.000029326564,0.00012482882],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984738,0.00010434804,0.00026297173,0.0005164725,0.00022605674,0.00041634165],"domain_scores_gemma":[0.99952847,0.000071932765,0.00004687892,0.00029104526,0.0000010171645,0.00006066304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020103133,0.00026865295,0.00021836688,0.000045029945,0.00015498436,0.00003290886,0.00023816778,0.000059754915,0.00027685426],"category_scores_gemma":[0.000002742486,0.00019816121,0.000041953783,0.00010084169,0.00016946206,0.00013662931,0.0004189757,0.00021110616,0.00017945403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026725718,0.000039999195,0.3820868,0.000004170819,0.000021928123,0.000010569027,0.0031049345,0.6142338,0.0003094366,9.072245e-7,4.5967624e-7,0.00016027605],"study_design_scores_gemma":[0.00070412457,0.000059539776,0.015062018,0.000020187259,0.000049653965,0.0000018164687,0.00086309155,0.98166835,0.00014196784,0.0010870735,0.00007693256,0.00026524637],"about_ca_topic_score_codex":0.0035933196,"about_ca_topic_score_gemma":0.00048451676,"teacher_disagreement_score":0.3674346,"about_ca_system_score_codex":0.000081350285,"about_ca_system_score_gemma":0.0000010382832,"threshold_uncertainty_score":0.808078},"labels":[],"label_agreement":null},{"id":"W3016516095","doi":"10.1016/j.envsoft.2020.104728","title":"Flexible watershed simulation with the Raven hydrological modelling framework","year":2020,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":185,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Power Generation; National Research Council Canada; BC Hydro (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discretization; Watershed; Computer science; Interpolation (computer graphics); Routing (electronic design automation); Hydrological modelling; Set (abstract data type); Process (computing); Sensitivity (control systems); Mathematical optimization; Artificial intelligence; Mathematics; Machine learning; Geology","score_opus":0.02702613385837818,"score_gpt":0.2115041586081673,"score_spread":0.1844780247497891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016516095","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29464424,0.00005488981,0.7022826,0.0023110919,0.000024310068,0.0002880768,0.000004684808,0.00015203546,0.00023804339],"genre_scores_gemma":[0.97198886,0.000053889158,0.02437494,0.0031554,0.000080327845,0.000039293598,0.000029493012,0.000037150498,0.00024063142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816567,0.000085023836,0.00022500535,0.0006369093,0.00041213733,0.00047525414],"domain_scores_gemma":[0.9993284,0.00014876277,0.00008931476,0.0002980208,0.0000014287227,0.00013407055],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00016935129,0.00030540832,0.00023256715,0.000015557685,0.0006158424,0.00003726403,0.0003724565,0.00013730212,0.0009899305],"category_scores_gemma":[0.000007954938,0.00019492967,0.000085682674,0.00012326054,0.00044577755,0.00023586489,0.00039744691,0.0004012052,0.0009603968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015117913,0.00004964788,0.013782022,0.0000058385826,0.0000441869,0.000014254825,0.0024387455,0.9829285,0.000033573167,0.000027585676,0.00015134705,0.00037312554],"study_design_scores_gemma":[0.00030764448,0.00019880779,0.00046938017,0.000008375519,0.00006843787,0.0000015689202,0.00018138219,0.9895184,0.0001847927,0.0021641331,0.006586483,0.00031059026],"about_ca_topic_score_codex":0.000023450411,"about_ca_topic_score_gemma":9.569432e-7,"teacher_disagreement_score":0.6779077,"about_ca_system_score_codex":0.00008646442,"about_ca_system_score_gemma":0.0000018573552,"threshold_uncertainty_score":0.9999233},"labels":[],"label_agreement":null},{"id":"W3019703102","doi":"10.1016/j.envsoft.2020.104718","title":"A stochastic wavelet-based data-driven framework for forecasting uncertain multiscale hydrological and water resources processes","year":2020,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":54,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wavelet; Variable (mathematics); Probabilistic logic; Selection (genetic algorithm); Interval (graph theory); Decomposition; Streamflow; Econometrics; Computer science; Water resources; Mathematics; Statistics; Mathematical optimization; Artificial intelligence; Geography","score_opus":0.08482296300816525,"score_gpt":0.25163900807311707,"score_spread":0.16681604506495182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3019703102","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5065544,0.00005470762,0.4922552,0.00047923787,0.000022772268,0.00036531442,0.00012188371,0.00014040503,0.00000604669],"genre_scores_gemma":[0.7494347,0.000004442726,0.24910928,0.0010026444,0.00009965762,0.000060155326,0.00021999334,0.000053104806,0.000015999183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970715,0.00006455076,0.0004023266,0.0012942592,0.00043218973,0.00073515926],"domain_scores_gemma":[0.99843186,0.00066454225,0.00012410086,0.000425253,0.0000037563163,0.00035047036],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002673374,0.00040273517,0.0003741013,0.00002322861,0.0005184396,0.000082769475,0.0006401166,0.00024428006,0.00041376488],"category_scores_gemma":[0.00051800313,0.00030414242,0.0000701435,0.00011245912,0.00057155517,0.00023739084,0.0008702267,0.0003966051,0.00011603157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015694986,0.00010603494,0.0025377648,0.00006779974,0.0000143424295,0.000017978638,0.0012442609,0.9896986,0.0005360998,0.0000015792181,0.0000423043,0.0055762962],"study_design_scores_gemma":[0.00050860894,0.0004245538,0.00008138256,0.00006225867,0.000055268603,0.0000118111375,0.000041197218,0.9951905,0.00034802678,0.0017248753,0.0011137221,0.00043778238],"about_ca_topic_score_codex":0.000031738455,"about_ca_topic_score_gemma":0.0000042748648,"teacher_disagreement_score":0.24314593,"about_ca_system_score_codex":0.00010388553,"about_ca_system_score_gemma":0.000006954001,"threshold_uncertainty_score":0.99994105},"labels":[],"label_agreement":null},{"id":"W3092288189","doi":"10.1016/j.envsoft.2020.104891","title":"Scoping review of the potentials of fuzzy cognitive maps as a modeling approach for integrated environmental assessment and management","year":2020,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Federation of Canadian Municipalities","keywords":"Toolbox; Fuzzy cognitive map; Computer science; Complement (music); Fuzzy logic; Stakeholder; Cognitive map; Key (lock); Management science; Systems engineering; Cognition; Data mining; Data science; Artificial intelligence; Fuzzy set; Engineering; Membership function; Psychology","score_opus":0.03668887903617242,"score_gpt":0.26485434027500704,"score_spread":0.22816546123883463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092288189","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013826612,0.0045151752,0.9793263,0.000166117,0.00004138555,0.0018938323,0.00008221422,0.000028661922,0.00011969029],"genre_scores_gemma":[0.8293902,0.008330864,0.16124927,0.0008177512,0.0000141934825,0.00011823257,0.000050688133,0.000015699192,0.0000130475755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980918,0.00008580132,0.0004849188,0.00060188916,0.00048188274,0.00025370956],"domain_scores_gemma":[0.99931157,0.00007644747,0.00025021427,0.00025077068,0.0000134263655,0.000097561555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036389602,0.00021690154,0.00034887216,0.00004377332,0.0001779409,0.00003127282,0.0005064715,0.00004321621,0.0000100311145],"category_scores_gemma":[0.000020251011,0.00017738855,0.0001634187,0.00016217279,0.0001693318,0.00030834074,0.000661053,0.00012626794,0.0000021145115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002924172,0.0025139311,0.0065323445,0.07953887,0.0012103604,0.000032941756,0.012919536,0.53380996,0.019178424,0.007562871,0.00012219035,0.33628616],"study_design_scores_gemma":[0.0008153133,0.00018837572,0.00011944188,0.018084113,0.0001612473,0.000008194133,0.001198104,0.97560126,0.002316349,0.0011283862,0.000015639573,0.0003636099],"about_ca_topic_score_codex":0.0000059603476,"about_ca_topic_score_gemma":1.11351774e-7,"teacher_disagreement_score":0.818077,"about_ca_system_score_codex":0.00005833369,"about_ca_system_score_gemma":0.000039503215,"threshold_uncertainty_score":0.7233695},"labels":[],"label_agreement":null},{"id":"W3092390191","doi":"10.1016/j.envsoft.2020.104885","title":"Socio-technical scales in socio-environmental modeling: Managing a system-of-systems modeling approach","year":2020,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Complex Systems and Decision Making","field":"Decision Sciences","cited_by":83,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"Agricultural Research Service; U.S. Geological Survey; Australian Government; National Natural Science Foundation of China; National Socio-Environmental Synthesis Center; U.S. Department of Agriculture; National Science Foundation","keywords":"Variety (cybernetics); Conceptualization; Multidisciplinary approach; Process (computing); Management science; Documentation; Scale (ratio); Discipline; Computer science; System of systems; Data science; Environmental systems; Systems engineering; Sociotechnical system; Risk analysis (engineering); Engineering; Process management; Knowledge management; Systems design; Ecology; Sustainability; Geography; Artificial intelligence; Business; Political science","score_opus":0.12188710133620774,"score_gpt":0.2991505305568475,"score_spread":0.17726342922063976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092390191","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33236197,0.0027327903,0.66366905,0.000052022006,0.00016671483,0.0005473633,0.00007776572,0.00014736257,0.00024497733],"genre_scores_gemma":[0.97242534,0.00007033522,0.026954958,0.00007557073,0.00020821721,0.00007044357,0.000031675387,0.00010597399,0.000057464582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99103445,0.00043965434,0.002756144,0.0018014646,0.0031718202,0.0007964821],"domain_scores_gemma":[0.99739015,0.00051687734,0.0005965749,0.0010529174,0.000020384363,0.0004230683],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0021824145,0.00062375603,0.0013190688,0.00038922433,0.0004158324,0.00029230255,0.0015503296,0.00036081893,0.000054367625],"category_scores_gemma":[0.00011791229,0.00056402467,0.0005606292,0.00048017813,0.0002124342,0.00060215354,0.000947795,0.00068333006,0.0002099076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008277394,0.00016072551,0.011816993,0.00007375845,0.000022628366,0.000027009224,0.0014164528,0.9840584,0.00038331735,0.0008525797,0.00005633901,0.0010490279],"study_design_scores_gemma":[0.00074918644,0.000066071676,0.00007036787,0.0001542963,0.00002514931,0.000039175826,0.021030422,0.97365505,0.0000048713887,0.0035773397,0.000074990814,0.00055307936],"about_ca_topic_score_codex":0.00011220418,"about_ca_topic_score_gemma":0.0000018112765,"teacher_disagreement_score":0.6400634,"about_ca_system_score_codex":0.0006084994,"about_ca_system_score_gemma":0.000039258313,"threshold_uncertainty_score":0.9996811},"labels":[],"label_agreement":null},{"id":"W3092716745","doi":"10.1016/j.envsoft.2020.104902","title":"Cluster-based multi-objective optimization for identifying diverse design options: Application to water resources problems","year":2020,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Computer science; Flexibility (engineering); Multi-objective optimization; Benchmark (surveying); Data mining; Mathematical optimization; Cluster (spacecraft); Set (abstract data type); Pareto principle; Process (computing); Machine learning; Mathematics; Statistics; Geography","score_opus":0.050291367491627874,"score_gpt":0.2579614541183884,"score_spread":0.2076700866267605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092716745","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00044030533,0.00006413867,0.99599636,0.00033124493,0.00009489708,0.0025847603,0.000040707237,0.00044632694,0.0000012507263],"genre_scores_gemma":[0.0939713,0.000016688806,0.9042598,0.00071935914,0.000058746456,0.00075879117,0.00012400099,0.000058921538,0.000032369247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977689,0.00012814472,0.00037022255,0.0009918576,0.00034065128,0.00040024886],"domain_scores_gemma":[0.99901414,0.00016472694,0.00014674875,0.000388384,0.000053928885,0.00023210107],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025530736,0.00030535113,0.0002222622,0.0001295366,0.0004991009,0.0001605183,0.00048426952,0.00010269006,0.00001632245],"category_scores_gemma":[0.000053617536,0.0002990515,0.00010490274,0.00023704793,0.00005724118,0.00082944526,0.0002809138,0.00014410053,0.00009427411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004600352,0.000120748206,0.000057738216,0.00002482695,0.00001603312,0.0000012115065,0.0044811815,0.99071306,0.0012128626,0.000010697909,0.000008296672,0.0033073248],"study_design_scores_gemma":[0.0011284923,0.00014575555,0.000016147955,0.000027651828,0.000019832882,0.0000014809891,0.00016157405,0.99086505,0.0068894164,0.00014041342,0.0002164509,0.00038771826],"about_ca_topic_score_codex":0.000008915856,"about_ca_topic_score_gemma":8.357723e-7,"teacher_disagreement_score":0.09353099,"about_ca_system_score_codex":0.00029431935,"about_ca_system_score_gemma":0.000015669893,"threshold_uncertainty_score":0.9999462},"labels":[],"label_agreement":null},{"id":"W3093290892","doi":"10.1016/j.envsoft.2020.104900","title":"Speaking their language – Development of a multilingual decision-support tool for communicating invasive species risks to decision makers and stakeholders","year":2020,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Coral and Marine Ecosystems Studies","field":"Environmental Science","cited_by":75,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"Fundação para a Ciência e a Tecnologia; Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção","keywords":"Decision support system; First language; Computer science; Knowledge management; Business; Artificial intelligence; Linguistics","score_opus":0.09620331037860465,"score_gpt":0.26828530239614096,"score_spread":0.17208199201753632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093290892","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78506136,0.00011946335,0.21402715,0.000041849802,0.000040083047,0.00055749074,0.00006787102,0.000036585676,0.000048133512],"genre_scores_gemma":[0.7514459,0.000056122837,0.24818958,0.00016302979,0.000021125054,0.000038589445,0.000023619707,0.00002617295,0.000035858513],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9983384,0.000024028552,0.00051527284,0.000488309,0.0003229662,0.00031102766],"domain_scores_gemma":[0.9988434,0.0005589265,0.00016724407,0.0002688618,0.00000488966,0.00015672305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024888394,0.0002638594,0.0003351648,0.000031543696,0.0003419845,0.00002777339,0.0003075896,0.000054833512,0.00022186253],"category_scores_gemma":[0.0001618182,0.00023187972,0.000087710476,0.000088806526,0.00011564856,0.00013722946,0.0012074929,0.00012573587,0.000057221234],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037887602,0.00015270812,0.0984202,0.00010488259,0.00008107957,0.000011350276,0.080415264,0.052645713,0.013392796,0.000002192559,0.00033567738,0.75405926],"study_design_scores_gemma":[0.014292666,0.0032850474,0.27609155,0.0025422766,0.00042956174,0.00007018879,0.23933592,0.1832884,0.18381293,0.0014293568,0.088017724,0.0074043944],"about_ca_topic_score_codex":0.00008492145,"about_ca_topic_score_gemma":0.00019368791,"teacher_disagreement_score":0.74665487,"about_ca_system_score_codex":0.00015751296,"about_ca_system_score_gemma":0.000013305503,"threshold_uncertainty_score":0.94557804},"labels":[],"label_agreement":null},{"id":"W3112134271","doi":"10.1016/j.envsoft.2020.104954","title":"The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support","year":2020,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":592,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; Global Institute for Water Security; University of Saskatchewan","funders":"Office of Science; Universitat Oberta de Catalunya; Universitetet i Bergen; Sandia National Laboratories; Joint Research Centre; National Nuclear Security Administration; Advanced Scientific Computing Research; U.S. Department of Energy; European Commission; Global Water Futures; Australian Government; National Socio-Environmental Synthesis Center; National Science Foundation","keywords":"Warrant; Multidisciplinary approach; Variety (cybernetics); Structuring; Management science; Context (archaeology); Risk analysis (engineering); Computer science; Perspective (graphical); Data science; Engineering ethics; Knowledge management; Engineering; Political science; Artificial intelligence; Sociology; Business; Social science","score_opus":0.044575074106422384,"score_gpt":0.2903875998439404,"score_spread":0.24581252573751802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112134271","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11514866,0.00051497837,0.88348484,0.0002943517,0.00010282128,0.00023882856,0.00017727891,0.000036246012,0.0000019797835],"genre_scores_gemma":[0.9843371,0.00008653877,0.015004861,0.00003378997,0.00040358066,0.0000135183345,0.000052288175,0.000019147787,0.000049185364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980373,0.000102861064,0.0005280617,0.0004972919,0.0005907646,0.00024374701],"domain_scores_gemma":[0.99874204,0.00050250586,0.00015376708,0.00038575206,0.000025165307,0.00019074378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011254651,0.00017521769,0.00036669883,0.0000838745,0.0003095948,0.000119533164,0.00027016026,0.00008293902,0.000004665758],"category_scores_gemma":[0.00013762807,0.00011468564,0.0001626835,0.00029533738,0.00012331885,0.00019197242,0.00012860005,0.00011467928,0.0000038379026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004939999,0.000025739757,0.0004739873,0.000012969623,0.00007335947,0.0000016745462,0.0006950613,0.996576,0.00015015704,0.00059371913,0.000018807907,0.0013291409],"study_design_scores_gemma":[0.00016514715,0.00007296057,0.00007580563,0.000004539063,0.00013913267,0.0000027643714,0.0010832961,0.9964221,0.000024862406,0.0016747835,0.00020112666,0.000133452],"about_ca_topic_score_codex":0.000018913728,"about_ca_topic_score_gemma":0.0000043677396,"teacher_disagreement_score":0.8691884,"about_ca_system_score_codex":0.00003279309,"about_ca_system_score_gemma":0.000033106626,"threshold_uncertainty_score":0.46767446},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.018973627250787578,"score_gpt":0.2316734991690357,"score_spread":0.21269987191824813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129144742","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6142349,0.00013313092,0.3848201,0.00016953523,0.000054482887,0.00028095354,0.00001180124,0.00018420842,0.00011086842],"genre_scores_gemma":[0.9340585,0.000029380524,0.064707816,0.00063898467,0.000043822354,0.00008332487,0.0000864609,0.00007462367,0.000277093],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961133,0.00022112625,0.00053518295,0.00154028,0.0007683288,0.00082176406],"domain_scores_gemma":[0.99853516,0.00022017595,0.00013086268,0.0005700896,0.000010034828,0.00053369894],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005503286,0.00049335696,0.00042496918,0.00004904844,0.0006944441,0.000117496296,0.0003159613,0.0002016561,0.0018578032],"category_scores_gemma":[0.00015173445,0.00051825604,0.00014229475,0.0002814925,0.00045056827,0.00030782592,0.00056851795,0.0005426535,0.00039263524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050197035,0.00031504573,0.091084525,0.00003712329,0.000009313413,0.000039119743,0.00091090245,0.90227646,0.0005473324,0.0000031839459,0.000013762928,0.004713049],"study_design_scores_gemma":[0.00047906133,0.00024563092,0.0021926626,0.000050132654,0.000034163997,0.00003299248,0.00025692728,0.9899974,0.0026179112,0.0016686644,0.0017533384,0.00067114906],"about_ca_topic_score_codex":0.00010648539,"about_ca_topic_score_gemma":0.000020465563,"teacher_disagreement_score":0.3201123,"about_ca_system_score_codex":0.00062516826,"about_ca_system_score_gemma":0.000020602416,"threshold_uncertainty_score":0.9997269},"labels":[],"label_agreement":null},{"id":"W3129198752","doi":"10.1016/j.envsoft.2021.105016","title":"Replication of an agent-based model using the Replication Standard","year":2021,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Replication (statistics); Computer science; Replicate; Biology; Virology; Mathematics","score_opus":0.029511142986198228,"score_gpt":0.24650542491295244,"score_spread":0.2169942819267542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129198752","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68597573,0.00008101154,0.31359482,0.000053165906,0.000024167928,0.00014243579,0.000055017576,0.00003224125,0.00004143428],"genre_scores_gemma":[0.96186066,0.00003714887,0.037627973,0.00018982223,0.000022819697,0.000019659694,0.00017546293,0.000026714028,0.00003974039],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836403,0.000071219154,0.00031311894,0.00064868934,0.0004027468,0.00020019163],"domain_scores_gemma":[0.9980908,0.00003124738,0.00018462376,0.001610141,0.000005546322,0.000077617784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003523189,0.000147023,0.00015352998,0.000014227405,0.00025122444,0.000028426803,0.00028196548,0.000073873154,0.00034598509],"category_scores_gemma":[0.000010239167,0.00011658203,0.00007821279,0.00010913303,0.00004623228,0.0002801336,0.00012146592,0.00009246615,0.000035813657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022701472,0.00008738772,0.011141292,0.000014471864,0.000007156806,0.0000016224168,0.00023579014,0.97638375,0.009793446,0.000004857802,0.000011186711,0.002296332],"study_design_scores_gemma":[0.00020192255,0.000031209584,0.0008552712,0.000021982694,0.00003617358,0.0000056383333,0.00010449842,0.9589569,0.03858285,0.0007153184,0.00034063766,0.00014758609],"about_ca_topic_score_codex":0.00011677121,"about_ca_topic_score_gemma":0.00006252356,"teacher_disagreement_score":0.27596685,"about_ca_system_score_codex":0.00021583072,"about_ca_system_score_gemma":0.00001930355,"threshold_uncertainty_score":0.47540772},"labels":[],"label_agreement":null},{"id":"W3136446618","doi":"10.1016/j.envsoft.2021.105018","title":"Performance improvements to modern hydrological models via lookup table optimizations","year":2021,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pointwise; Lookup table; Computer science; Function (biology); Algorithm; Table (database); Process (computing); Reduction (mathematics); Mathematical optimization; Computational science; Data mining; Mathematics","score_opus":0.013673245904823886,"score_gpt":0.19091882119503745,"score_spread":0.17724557529021356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136446618","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43273193,0.00006849137,0.5650055,0.0001764247,0.00008515049,0.00023882836,0.000017049731,0.00009689441,0.0015797841],"genre_scores_gemma":[0.9457006,0.00025129798,0.04838083,0.0015653851,0.000030258303,0.000111063724,0.00009300847,0.000038815524,0.0038287498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99778944,0.000049049533,0.00031587254,0.000821182,0.00039493182,0.0006295422],"domain_scores_gemma":[0.9992513,0.000028234996,0.00006459971,0.00045892302,0.000003737626,0.00019320035],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00014738573,0.0003107074,0.00025709087,0.000038052123,0.00059431145,0.000031424788,0.00031007515,0.0001246848,0.0016449115],"category_scores_gemma":[0.0000066641264,0.00030818683,0.000080056336,0.00017400493,0.00017782323,0.00048369542,0.000992564,0.0002067199,0.0010996993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002422695,0.00022920984,0.020214288,0.0000071013674,0.000038681355,0.000020801908,0.0004683623,0.97411907,0.0010268865,0.000008197473,0.00029534826,0.0035478112],"study_design_scores_gemma":[0.0003764995,0.00011246336,0.0010971068,0.0000103042175,0.000048805934,0.000007382569,0.000039647846,0.992468,0.0013254822,0.0027299328,0.0013854565,0.00039892131],"about_ca_topic_score_codex":0.00003563388,"about_ca_topic_score_gemma":0.0000067614487,"teacher_disagreement_score":0.5166246,"about_ca_system_score_codex":0.00022528427,"about_ca_system_score_gemma":0.0000055861497,"threshold_uncertainty_score":0.999937},"labels":[],"label_agreement":null},{"id":"W3161079635","doi":"10.5555/2772070.2772111","title":"A stepwise cluster analysis approach for downscaled climate projection - A Canadian case study","year":2013,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Climate variability and models","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cluster (spacecraft); Projection (relational algebra); Climatology; Environmental science; Meteorology; Geography; Econometrics; Environmental resource management; Computer science; Mathematics; Geology; Algorithm","score_opus":0.020483291219020445,"score_gpt":0.22380696928636676,"score_spread":0.2033236780673463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161079635","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7558573,0.000011032301,0.24089667,0.00002986401,0.000040437553,0.0027644376,0.0001478948,0.0000770745,0.00017528277],"genre_scores_gemma":[0.9581866,0.000010455783,0.039895274,0.00018939085,0.000031709726,0.0011996878,0.00020968197,0.00004654453,0.00023063649],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974029,0.00011605795,0.00045423605,0.00093816454,0.00033159647,0.00075701234],"domain_scores_gemma":[0.9988031,0.00008665797,0.00011103128,0.00059147016,0.0000052075034,0.000402578],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004906384,0.0003405002,0.00035962148,0.00016545816,0.00066242873,0.00011543006,0.00022524782,0.0001536896,0.0014521695],"category_scores_gemma":[0.000010509833,0.000323859,0.0002634938,0.0003348732,0.00013620355,0.0004958887,0.00018974405,0.00019420698,0.00023664752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028587609,0.000758094,0.16779761,0.000019493269,0.000149076,0.000028036886,0.003103949,0.82649255,0.000055401986,0.0000012718023,0.000091101494,0.0014748585],"study_design_scores_gemma":[0.00083006005,0.00017670095,0.0030233702,0.0000031604277,0.00055879285,0.00008041202,0.003066608,0.9915349,0.000010915864,0.00011328211,0.00015441334,0.0004474007],"about_ca_topic_score_codex":0.17815773,"about_ca_topic_score_gemma":0.02899742,"teacher_disagreement_score":0.20232931,"about_ca_system_score_codex":0.00081590476,"about_ca_system_score_gemma":0.000015248284,"threshold_uncertainty_score":0.9999213},"labels":[],"label_agreement":null},{"id":"W3179973283","doi":"10.1016/j.envsoft.2021.105117","title":"A new modelling framework to assess biogenic GHG emissions from reservoirs: The G-res tool","year":2021,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Groupe de recherche interuniversitaire en limnologie; National Science Foundation","keywords":"Greenhouse gas; Environmental science; Carbon footprint; Empirical modelling; Downstream (manufacturing); Ecology; Computer science; Engineering","score_opus":0.026835480750093257,"score_gpt":0.23053997025209214,"score_spread":0.20370448950199888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3179973283","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41175944,0.00042793143,0.586721,0.0004395492,0.0001679481,0.0002458587,0.000043433945,0.00009244199,0.00010240422],"genre_scores_gemma":[0.4273839,0.0004493009,0.5674254,0.0011703413,0.00017682696,0.00004477439,0.00008221511,0.000104543535,0.0031627407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962111,0.00014140455,0.0005715867,0.0012327337,0.0010080811,0.0008350615],"domain_scores_gemma":[0.9976097,0.0003605458,0.00014462603,0.0013294,0.0000028184186,0.00055293227],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00020532151,0.0005579119,0.00038651534,0.00001303589,0.00074917247,0.00014966888,0.00091102114,0.00029990225,0.007100636],"category_scores_gemma":[0.000046547648,0.00047674592,0.00029089194,0.0003536644,0.00023308871,0.0003628357,0.0013700242,0.00067980395,0.0015307738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003686721,0.00013834782,0.024943247,0.0000030729852,0.000050239472,0.000041334213,0.0011671568,0.9653349,0.0014362779,0.000035390218,0.0007877706,0.006025376],"study_design_scores_gemma":[0.0007297614,0.00013460827,0.008320172,0.00022325313,0.00023899377,0.000039006754,0.0022961062,0.9111712,0.0029143307,0.02505541,0.04703404,0.0018431322],"about_ca_topic_score_codex":0.0010341159,"about_ca_topic_score_gemma":0.000028792962,"teacher_disagreement_score":0.054163735,"about_ca_system_score_codex":0.00068361056,"about_ca_system_score_gemma":0.000037783746,"threshold_uncertainty_score":0.99976844},"labels":[],"label_agreement":null},{"id":"W3184121115","doi":"10.1016/j.envsoft.2021.105140","title":"Improving the simulation of soil temperature within the EPIC model","year":2021,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; International Development Research Centre","funders":"Agence Nationale de la Recherche","keywords":"Environmental science; Trigonometric functions; EPIC; Climate change; Range (aeronautics); Soil science; Air temperature; Ecosystem; Atmospheric sciences; Ecology; Engineering; Geology; Mathematics","score_opus":0.008426054506816067,"score_gpt":0.1825301643094443,"score_spread":0.17410410980262822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184121115","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80821174,0.0001415532,0.19119531,0.000076661236,0.000059373335,0.00012982552,0.0000453831,0.000024076828,0.000116082876],"genre_scores_gemma":[0.993896,0.000033455686,0.004850189,0.00016760814,0.00002003531,0.000010864571,0.00006309395,0.000021617125,0.0009371008],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988482,0.00006188898,0.00026101427,0.00028450397,0.00036248495,0.00018188269],"domain_scores_gemma":[0.99931306,0.00009631501,0.0001307148,0.00041574726,0.0000029442222,0.0000411929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019980561,0.0001539549,0.00011579734,0.000010220023,0.00032234497,0.000035427936,0.00023615237,0.00008998001,0.00013122824],"category_scores_gemma":[0.000012212914,0.00009637629,0.00008969498,0.000102892314,0.00018336567,0.00016119752,0.00021316206,0.00027135698,0.00003733855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057343855,0.000037152808,0.0038847052,0.000003784664,0.0000093010185,0.0000026228872,0.00071732217,0.98836964,0.00628202,0.00004649568,0.000006659678,0.000634528],"study_design_scores_gemma":[0.00011424153,0.000009760994,0.00041763028,0.0000100885945,0.000031467524,0.00000859891,0.000109968576,0.9954672,0.0022952945,0.0013698697,0.000040921328,0.00012498563],"about_ca_topic_score_codex":0.000056202378,"about_ca_topic_score_gemma":0.000026194906,"teacher_disagreement_score":0.18634512,"about_ca_system_score_codex":0.000107780455,"about_ca_system_score_gemma":0.000013659565,"threshold_uncertainty_score":0.3930111},"labels":[],"label_agreement":null},{"id":"W3186399057","doi":"10.1016/j.envsoft.2021.105139","title":"Quantile regression as a generic approach for estimating uncertainty of digital soil maps produced from machine-learning","year":2021,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":101,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Forests; Ministry of Agriculture, Food and Rural Affairs; Dalhousie University; University of Guelph; Simon Fraser University","funders":"Natural Resources Canada","keywords":"Quantile regression; Regression; Computer science; Quantile; Machine learning; Artificial intelligence; Statistics; Regression analysis; Econometrics; Mathematics","score_opus":0.020931206289641634,"score_gpt":0.22483004288185887,"score_spread":0.20389883659221725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186399057","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42421746,0.00021887096,0.57474846,0.000013573164,0.000085328895,0.00020470169,0.000299288,0.000051334704,0.00016097217],"genre_scores_gemma":[0.6677778,0.000026803484,0.33007059,0.000028699762,0.000045037996,0.00003891523,0.001628634,0.00004224663,0.0003412632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99801075,0.000048786234,0.00039689516,0.0007416119,0.00043194837,0.00036997825],"domain_scores_gemma":[0.99909896,0.00018339804,0.0002452014,0.00034195054,0.000006576347,0.00012392062],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014545767,0.0002772025,0.00030986976,0.000025005906,0.00029375695,0.00006369889,0.00020597214,0.00009609511,0.0002529552],"category_scores_gemma":[0.00015605136,0.00026804503,0.00013566352,0.000114536204,0.00013435727,0.00020184112,0.00034486328,0.00020940114,0.000048454353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003998332,0.00018664746,0.023665475,0.000040131064,0.000027332333,0.000009276469,0.0006908945,0.9447118,0.0076242387,0.000010007333,0.00007332953,0.02292087],"study_design_scores_gemma":[0.00047903814,0.00007312602,0.00024318011,0.000059225702,0.00004267579,0.000009915304,0.00042886383,0.98980534,0.005885326,0.0018644726,0.0007725993,0.0003362127],"about_ca_topic_score_codex":0.00038233845,"about_ca_topic_score_gemma":0.000004979942,"teacher_disagreement_score":0.24467786,"about_ca_system_score_codex":0.00015356162,"about_ca_system_score_gemma":0.000019397618,"threshold_uncertainty_score":0.9999772},"labels":[],"label_agreement":null},{"id":"W3194341172","doi":"10.1016/j.envsoft.2021.105163","title":"Sensitivity analysis on distance-adjusted propensity score matching for wildfire effect quantification using national forest inventory data","year":2021,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Covariate; Propensity score matching; Statistics; Matching (statistics); Observational study; Sample size determination; Sample (material); Spatial analysis; Econometrics; Computer science; Environmental science; Mathematics","score_opus":0.07333189026922655,"score_gpt":0.25974367436850093,"score_spread":0.1864117840992744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194341172","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6094552,0.000046866277,0.3894124,0.000021261196,0.00012507368,0.000541933,0.000320547,0.00006330909,0.000013348847],"genre_scores_gemma":[0.98788786,0.000010071325,0.008869944,0.00009529159,0.00008645933,0.000043040312,0.0028593908,0.00006184046,0.00008610553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965885,0.0003894274,0.0004088579,0.001292304,0.00090101355,0.00041984388],"domain_scores_gemma":[0.9980867,0.00045258465,0.0002689416,0.0010387963,0.000008346386,0.0001446622],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012802505,0.00035846562,0.00046028532,0.00007803313,0.00054515025,0.00009515554,0.0003279081,0.00014241562,0.00007495807],"category_scores_gemma":[0.00014040137,0.0003657285,0.00020600931,0.0003791385,0.00015228183,0.00062546553,0.00040615755,0.00024126317,0.0001080823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006651375,0.0001687502,0.3156237,0.000047671227,0.0001257446,0.000018539777,0.000093154835,0.677924,0.0047044978,0.0000069758817,0.000056395365,0.0011640585],"study_design_scores_gemma":[0.0003910424,0.00005699265,0.09164926,0.00008565147,0.00031120845,0.000013615484,0.000022241868,0.90528744,0.0014579016,0.00012600006,0.00021069664,0.00038793136],"about_ca_topic_score_codex":0.00063626387,"about_ca_topic_score_gemma":0.00082159956,"teacher_disagreement_score":0.3805425,"about_ca_system_score_codex":0.0010686036,"about_ca_system_score_gemma":0.000028638618,"threshold_uncertainty_score":0.9998795},"labels":[],"label_agreement":null},{"id":"W3196588212","doi":"10.1016/j.envsoft.2021.105185","title":"Stream power index for networks (SPIN) toolbox for decision support in urbanizing watersheds","year":2021,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Toolbox; Sinuosity; Computer science; Channel (broadcasting); Python (programming language); Stream power; Suite; Hydrology (agriculture); Environmental science; Watershed; Data mining; Erosion; Geology; Geography; Machine learning; Telecommunications; Geomorphology","score_opus":0.011026152497057273,"score_gpt":0.2310930812792957,"score_spread":0.22006692878223844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196588212","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22149724,0.0001319711,0.77700245,0.000062802515,0.0002910337,0.00075206166,0.000034880904,0.00005326374,0.00017428261],"genre_scores_gemma":[0.88508534,0.00012988069,0.112581715,0.00031506547,0.00005678386,0.00022355124,0.0002598193,0.00006648432,0.0012813732],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977084,0.00002712088,0.00044523398,0.00078850944,0.0003718948,0.000658857],"domain_scores_gemma":[0.9991874,0.00017884887,0.000098124336,0.00040195952,0.0000036938047,0.00012997356],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00031153788,0.00031077757,0.00028546312,0.000053265725,0.00021852936,0.00007626349,0.00027575847,0.0001522107,0.0011022628],"category_scores_gemma":[0.000019299585,0.00031452064,0.0001872378,0.00012599827,0.00007723173,0.0003850881,0.00029923796,0.00016548121,0.00006650437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011576627,0.00032733154,0.07912336,0.00001576825,0.000026188703,0.000020780872,0.0003259629,0.8948233,0.0002291368,0.000043143427,0.0020075738,0.02294169],"study_design_scores_gemma":[0.003256307,0.00048360263,0.024156155,0.00011580558,0.00009670918,0.000008047624,0.00090103276,0.9185935,0.0014905131,0.003167715,0.046624195,0.0011063772],"about_ca_topic_score_codex":0.000050378712,"about_ca_topic_score_gemma":0.0000791837,"teacher_disagreement_score":0.6644207,"about_ca_system_score_codex":0.00045178243,"about_ca_system_score_gemma":0.000012007032,"threshold_uncertainty_score":0.9999307},"labels":[],"label_agreement":null},{"id":"W3200654331","doi":"10.1016/j.envsoft.2021.105206","title":"The chaos in calibrating crop models: Lessons learned from a multi-model calibration exercise","year":2021,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":108,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"BonaRes; National Science Fund for Distinguished Young Scholars; Department of Agriculture and Fisheries, Queensland Government; National Institute of Food and Agriculture; China Scholarship Council; National Natural Science Foundation of China; Priority Academic Program Development of Jiangsu Higher Education Institutions; Bundesministerium für Bildung und Forschung; National Key Research and Development Program of China; Higher Education Discipline Innovation Project; Institut National de la Recherche Agronomique; Academy of Finland; Deutsche Forschungsgemeinschaft; Ministerstvo Školství, Mládeže a Tělovýchovy; U.S. Department of Agriculture; University of Southern Queensland; Agriculture and Agri-Food Canada; National Science Foundation","keywords":"Calibration; Computer science; Process (computing); Data mining; Cover (algebra); Component (thermodynamics); Estimation; Machine learning; Statistics; Mathematics; Systems engineering; Engineering","score_opus":0.128102544163915,"score_gpt":0.2591314116276055,"score_spread":0.1310288674636905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200654331","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9810358,0.002179791,0.013130196,0.0026284412,0.00007497051,0.0002902385,0.00051821955,0.0001247325,0.000017609376],"genre_scores_gemma":[0.98981357,0.0028767246,0.0054611736,0.00026225692,0.00008509814,0.000059441103,0.0009613,0.0000070417773,0.00047337264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981806,0.00009962119,0.00034270558,0.0005825552,0.0003321923,0.00046228277],"domain_scores_gemma":[0.99934256,0.00023243461,0.000119553035,0.00014944398,0.000010536961,0.00014545151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014124264,0.0002688993,0.00023580641,0.000008253806,0.0005172739,0.0002223633,0.00027961613,0.00019201949,0.00013609427],"category_scores_gemma":[0.000029831219,0.000109597786,0.00012030484,0.00018227696,0.00007158266,0.0004386893,0.00020498628,0.0003251184,0.000021068265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000433885,0.0003039693,0.0014552954,0.000006064729,0.000012766981,0.000029075769,0.0018478684,0.84598255,0.11140567,0.000037167512,0.00009132657,0.03878486],"study_design_scores_gemma":[0.00026203092,0.000018023398,0.0015742101,0.00008688988,0.000015489086,0.0000027835918,0.0019026287,0.98919404,0.0033216532,0.003157566,0.00016248302,0.0003022246],"about_ca_topic_score_codex":0.00041956975,"about_ca_topic_score_gemma":0.001454685,"teacher_disagreement_score":0.14321147,"about_ca_system_score_codex":0.00012981851,"about_ca_system_score_gemma":0.000012418158,"threshold_uncertainty_score":0.4469268},"labels":[],"label_agreement":null},{"id":"W3202052128","doi":"10.1016/j.envsoft.2021.105209","title":"Making spatial-temporal marine ecosystem modelling better – A perspective","year":2021,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Fisheries and Oceans Canada","funders":"Mitacs; Ministerio de Ciencia e Innovación; Bundesministerium für Bildung und Forschung; Agence Nationale de la Recherche; Natural Environment Research Council; European Commission; Biodiversa+; Consejo Superior de Investigaciones Científicas; Commonwealth Scientific and Industrial Research Organisation; Natural Sciences and Engineering Research Council of Canada; Sight Research UK; FP7 Coherent Development of Research Policies","keywords":"Credibility; Software deployment; Sustainability; Perspective (graphical); Computer science; Temporal scales; Marine ecosystem; Ecosystem; Environmental resource management; Scalability; Environmental science; Data science; Ecology; Political science; Artificial intelligence","score_opus":0.02784547466865563,"score_gpt":0.23868413356294155,"score_spread":0.2108386588942859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202052128","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27413383,0.00006904874,0.69831705,0.00028878642,0.00015555763,0.00033888107,0.000058384518,0.00014407687,0.026494395],"genre_scores_gemma":[0.94336116,0.00010697807,0.05255608,0.00030365252,0.00016940782,0.000049515176,0.00013526937,0.000088827604,0.0032290989],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968835,0.00012989207,0.00042232822,0.0010045554,0.0008377411,0.0007219658],"domain_scores_gemma":[0.9989595,0.0000729437,0.00011546688,0.00063476554,0.000010321254,0.00020703215],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00022621402,0.0003806027,0.0003494256,0.000050189126,0.00037845346,0.00012231075,0.00036724907,0.00016060987,0.024609525],"category_scores_gemma":[0.000015422493,0.00040383966,0.00020356158,0.00019093545,0.00015939193,0.0003797102,0.0013319142,0.0004964206,0.0008408603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000081586855,0.00033653033,0.38394037,0.000041629773,0.0000756516,0.00045325817,0.0012483668,0.58350176,0.00026970112,0.00007192462,0.00023446901,0.029744789],"study_design_scores_gemma":[0.00069018384,0.00010503534,0.001762644,0.000037440037,0.000045863788,0.000098743025,0.0011064623,0.9536107,0.00068515737,0.0039295834,0.03702587,0.00090232823],"about_ca_topic_score_codex":0.0015616873,"about_ca_topic_score_gemma":0.00036065874,"teacher_disagreement_score":0.66922736,"about_ca_system_score_codex":0.0008813487,"about_ca_system_score_gemma":0.000024412244,"threshold_uncertainty_score":0.9999371},"labels":[],"label_agreement":null},{"id":"W3205978700","doi":"10.1016/j.envsoft.2021.105226","title":"Sensitivity analysis: A discipline coming of age","year":2021,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"","keywords":"Sensitivity (control systems); Engineering","score_opus":0.023482165211959893,"score_gpt":0.2383706673188567,"score_spread":0.2148885021068968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205978700","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27879655,0.00019415643,0.720682,0.00007203391,0.00006250997,0.000058785725,0.000017735492,0.00006998891,0.000046176698],"genre_scores_gemma":[0.84114885,0.00004017305,0.15845048,0.000071944116,0.000024148776,0.0000048537695,0.00003960599,0.000012382387,0.00020752221],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981947,0.00013107233,0.0003758728,0.00057777244,0.00039489998,0.000325675],"domain_scores_gemma":[0.99872863,0.00021146491,0.0001345294,0.00079846906,0.000018291443,0.00010861697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003271019,0.0001814732,0.0003425043,0.00011368205,0.00017238967,0.000069611684,0.00032801874,0.00006832389,0.000048042893],"category_scores_gemma":[0.000030156643,0.00019601716,0.00023801198,0.0005998277,0.00011272237,0.00039511526,0.00051167683,0.0001655843,0.000049557057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005916917,0.00025101483,0.0076155686,0.000015840791,0.00016304743,0.00039959952,0.0019187732,0.9716314,0.011759756,0.0014568693,0.000007522594,0.0047746743],"study_design_scores_gemma":[0.0000806046,0.000027553537,0.0014796542,0.000026715303,0.0001096339,0.000013303886,0.00035013148,0.9178823,0.075697504,0.003948563,0.000117364994,0.00026663629],"about_ca_topic_score_codex":0.000080392434,"about_ca_topic_score_gemma":0.000062676925,"teacher_disagreement_score":0.5623523,"about_ca_system_score_codex":0.00009762402,"about_ca_system_score_gemma":0.00002726503,"threshold_uncertainty_score":0.7993348},"labels":[],"label_agreement":null},{"id":"W3208191372","doi":"","title":"The Nutrient App: Developing a smartphone application for on-site instantaneous community-based [formula omitted] and [formula omitted] monitoring","year":2020,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Eutrophication; Environmental science; Nutrient; Wetland; Pollution; Agriculture; Nutrient pollution; Remedial action; Environmental engineering; Environmental resource management; Contamination; Hydrology (agriculture); Ecology; Engineering; Environmental remediation","score_opus":0.03879153155867717,"score_gpt":0.23716786185894748,"score_spread":0.1983763303002703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208191372","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6896371,0.00008377673,0.30833337,0.0006934283,0.000087206354,0.00072872994,0.000046556946,0.0003802784,0.000009540534],"genre_scores_gemma":[0.9470051,0.00016049061,0.05188593,0.0003652519,0.00008679186,0.00031532024,0.00007967444,0.00007038188,0.000031094267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977941,0.00010785947,0.00046502313,0.0005726412,0.0004920828,0.00056831003],"domain_scores_gemma":[0.99839795,0.0005825324,0.00019875579,0.0006500601,0.000005391362,0.00016533714],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00044361828,0.0003970915,0.00027883847,0.00003973287,0.0015976162,0.0001121038,0.0006541551,0.00018473298,0.000004046112],"category_scores_gemma":[0.000060821036,0.0003393558,0.000097408636,0.0001806245,0.00038526522,0.0001938389,0.00056528504,0.00060508394,0.00010702261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015217287,0.00093725906,0.22072028,0.00048725918,0.00019506844,0.000033705408,0.015185563,0.5756279,0.04613256,0.0006558441,0.000395392,0.13810743],"study_design_scores_gemma":[0.006918165,0.0021261706,0.019066978,0.0005654962,0.0002358861,0.000048575726,0.0055944123,0.32063952,0.5262533,0.01861925,0.095908016,0.004024238],"about_ca_topic_score_codex":0.00011455357,"about_ca_topic_score_gemma":0.000005359967,"teacher_disagreement_score":0.48012075,"about_ca_system_score_codex":0.00063871074,"about_ca_system_score_gemma":0.000011880121,"threshold_uncertainty_score":0.9999058},"labels":[],"label_agreement":null},{"id":"W4200392092","doi":"10.1016/j.envsoft.2021.105282","title":"The pie sharing problem: Unbiased sampling of N+1 summative weights","year":2021,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Global Water Futures; Canada First Research Excellence Fund","keywords":"Sampling (signal processing); Independent and identically distributed random variables; Simple random sample; Mathematics; Slice sampling; Set (abstract data type); Algorithm; Probability sampling; Statistics; Cumulative distribution function; Probability distribution; Simple (philosophy); Probability density function; Sensitivity (control systems); Calibration; Random variable; Importance sampling; Computer science; Monte Carlo method","score_opus":0.10021791195030288,"score_gpt":0.29413041540825147,"score_spread":0.19391250345794858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200392092","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.091690436,0.0013327112,0.9063237,0.000064275795,0.00017443557,0.00016259313,0.000029970824,0.000052659627,0.00016922517],"genre_scores_gemma":[0.86576504,0.00014340409,0.1324934,0.000024392593,0.000052259595,0.000018235289,0.000016921807,0.000027694394,0.0014586494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973969,0.00008109463,0.00068337965,0.00056501303,0.00094349956,0.00033013063],"domain_scores_gemma":[0.9970773,0.0018364592,0.00021153422,0.00072551466,0.000044507553,0.00010471745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010595606,0.00019813984,0.00028550363,0.0000568714,0.00040775767,0.00012640293,0.00066632475,0.00008252144,0.00013740588],"category_scores_gemma":[0.00041987473,0.00013231805,0.00015988843,0.0002497742,0.00019524798,0.00017667472,0.00028059343,0.00022038005,0.00008950221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016216369,0.00006767237,0.0024044618,0.000012101293,0.000033029894,0.000008768431,0.00070739305,0.9890174,0.0005827771,0.0022103842,0.00006819657,0.0048715714],"study_design_scores_gemma":[0.0006383872,0.00007910449,0.0020085163,0.00023428102,0.00006493547,0.000021932907,0.001233065,0.71238005,0.008448295,0.2677789,0.0065116268,0.0006009263],"about_ca_topic_score_codex":0.000008403924,"about_ca_topic_score_gemma":0.0000025667912,"teacher_disagreement_score":0.7740746,"about_ca_system_score_codex":0.000099314944,"about_ca_system_score_gemma":0.000045590812,"threshold_uncertainty_score":0.53957736},"labels":[],"label_agreement":null},{"id":"W4205127571","doi":"10.1016/j.envsoft.2022.105318","title":"Increasing the uptake of ecological model results in policy decisions to improve biodiversity outcomes","year":2022,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Sustainability and Climate Change Governance","field":"Environmental Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Multidisciplinary approach; Stakeholder; Process (computing); Citizen journalism; Stakeholder engagement; Process management; Interpersonal communication; Management science; Sustainable development; Business; Knowledge management; Environmental resource management; Computer science; Ecology; Engineering; Psychology; Political science; Economics; Public relations","score_opus":0.029321116410142713,"score_gpt":0.23933711976035568,"score_spread":0.21001600335021298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205127571","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99421746,0.000022674101,0.0033628175,0.0010152049,0.000049828806,0.0004351621,0.00074770744,0.000025981184,0.00012314532],"genre_scores_gemma":[0.9932322,0.00003930477,0.0057034544,0.0007541568,0.000008396559,0.00005608984,0.000024583773,0.000010123214,0.00017167587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9980323,0.00011590214,0.00039565793,0.00049772905,0.0005631365,0.0003952742],"domain_scores_gemma":[0.9989816,0.00032247297,0.00013660872,0.0004532086,0.000001908254,0.00010419268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007613468,0.00017781425,0.00021551001,0.000051156774,0.00049361546,0.00001239192,0.0005743102,0.00005552323,0.00036582202],"category_scores_gemma":[0.00028623157,0.00014682444,0.00011468679,0.00027275961,0.00021409169,0.00012881002,0.0016725481,0.0002813616,0.000056089888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018858396,0.00031669313,0.0810507,0.000001913571,0.000004084208,0.0000072048874,0.004340882,0.9116583,0.00029259763,0.000011364444,0.00009138989,0.0020363198],"study_design_scores_gemma":[0.0019420933,0.00046715123,0.714899,0.000016331172,0.00003626955,0.000014123519,0.016747264,0.25538808,0.00026896156,0.0059249382,0.003576358,0.00071939547],"about_ca_topic_score_codex":0.0013205546,"about_ca_topic_score_gemma":0.00011284392,"teacher_disagreement_score":0.6562702,"about_ca_system_score_codex":0.00144379,"about_ca_system_score_gemma":0.000021386124,"threshold_uncertainty_score":0.5987327},"labels":[],"label_agreement":null},{"id":"W4205535841","doi":"10.1016/j.envsoft.2022.105326","title":"A stochastic conceptual-data-driven approach for improved hydrological simulations","year":2022,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Ensemble forecasting; Quantile; Streamflow; Probabilistic logic; Interval (graph theory); Computer science; Data set; Ensemble learning; Set (abstract data type); Data mining; Mathematics; Statistics; Artificial intelligence","score_opus":0.03979231752446499,"score_gpt":0.23278041241106476,"score_spread":0.19298809488659976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205535841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1686246,0.00006601893,0.8292688,0.0001710791,0.00008826853,0.0008962383,0.0006657757,0.00012270854,0.000096486925],"genre_scores_gemma":[0.9551101,0.0000068535405,0.042053554,0.00059533765,0.00003925381,0.00032655624,0.0012412382,0.000031336636,0.0005957939],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981528,0.00008338637,0.00025727757,0.000811663,0.00024699315,0.00044791435],"domain_scores_gemma":[0.99915147,0.00014474252,0.00009839201,0.0005216218,9.662363e-7,0.00008283696],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021448419,0.00023286128,0.00022916883,0.000031075102,0.001208122,0.000013892436,0.00062127295,0.00006283076,0.0012902991],"category_scores_gemma":[0.000017599052,0.00022942119,0.00008188377,0.00007932019,0.0004610452,0.00019633668,0.0019323494,0.0002513825,0.00006027091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068106725,0.00026984626,0.0020556832,0.0000028520253,0.00004677866,0.0000014985453,0.0005849828,0.99579906,0.00010100344,0.000019432971,0.00064901717,0.00040171598],"study_design_scores_gemma":[0.0006657906,0.00019254074,0.00018027688,7.420235e-7,0.00007500449,0.0000032747869,0.00028309118,0.9926934,0.000004680034,0.00083861494,0.00478957,0.00027300243],"about_ca_topic_score_codex":0.000024161058,"about_ca_topic_score_gemma":0.0000020659766,"teacher_disagreement_score":0.7872153,"about_ca_system_score_codex":0.00021661585,"about_ca_system_score_gemma":0.0000034445127,"threshold_uncertainty_score":0.99962264},"labels":[],"label_agreement":null},{"id":"W4205968361","doi":"10.1016/j.envsoft.2022.105328","title":"Sequential surface and subsurface flow modeling in a tropical aquifer under different rainfall scenarios","year":2022,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Groundwater recharge; Aquifer; Hydrology (agriculture); Groundwater; Depression-focused recharge; Environmental science; Groundwater flow; Groundwater model; Watershed; Precipitation; Geology; Geography; Geotechnical engineering; Meteorology","score_opus":0.024009965248592578,"score_gpt":0.20653389456525365,"score_spread":0.18252392931666106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205968361","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68749255,0.0002169349,0.3117737,0.00012719906,0.00008775045,0.00021793196,0.000023752395,0.000044911954,0.000015298818],"genre_scores_gemma":[0.9940583,0.00007323122,0.0044124876,0.00024366137,0.000018474044,0.00005724401,0.00004244674,0.000040199197,0.0010539606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774784,0.00015060931,0.0003470878,0.00066092244,0.0006232482,0.00047032215],"domain_scores_gemma":[0.99953246,0.000050593466,0.000055996148,0.00023865784,0.0000014204592,0.00012087274],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015633399,0.00030131073,0.00027964753,0.000040485156,0.00053114444,0.00004642866,0.00022199682,0.000063815896,0.0011914313],"category_scores_gemma":[0.0000030096119,0.00030164394,0.000081849175,0.00009595899,0.0001539964,0.00021390748,0.0009132934,0.0003886005,0.000057943405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042764037,0.00016329039,0.11740333,0.000004491628,0.000014773313,0.00001424354,0.0015815899,0.87826,0.00082226284,0.0000077068935,0.000019327326,0.0016661922],"study_design_scores_gemma":[0.00095203787,0.00008319332,0.016183386,0.000008504776,0.00002322662,0.000015300187,0.0009955287,0.9803015,0.00008766586,0.00039110982,0.00055167085,0.00040686107],"about_ca_topic_score_codex":0.00027715627,"about_ca_topic_score_gemma":0.00010304535,"teacher_disagreement_score":0.3073612,"about_ca_system_score_codex":0.000717015,"about_ca_system_score_gemma":0.000006299905,"threshold_uncertainty_score":0.99994355},"labels":[],"label_agreement":null},{"id":"W4220828128","doi":"10.1016/j.envsoft.2022.105370","title":"A review of parallel computing applications in calibrating watershed hydrologic models","year":2022,"lang":"en","type":"review","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Waterloo; University of Guelph","funders":"","keywords":"Speedup; Computer science; Suite; Parallel computing; Supercomputer; Watershed; Cost efficiency; Machine learning","score_opus":0.04831261435215807,"score_gpt":0.264457847169482,"score_spread":0.21614523281732395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220828128","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004679445,0.94060117,0.056958713,0.000018023968,0.00003918021,0.0017747132,0.00005280719,0.000075383265,0.0004332245],"genre_scores_gemma":[0.00043208615,0.99012476,0.0076494003,0.00030553603,0.000018737765,0.0006025154,0.0006916802,0.000068715046,0.0001065433],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966433,0.00034656783,0.0011539067,0.0009291089,0.0003832245,0.0005438829],"domain_scores_gemma":[0.99850464,0.0002482713,0.0005899442,0.0005789481,7.710118e-7,0.00007744324],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006140757,0.0005825694,0.001592914,0.00011096796,0.00032972693,0.000009264961,0.00073022384,0.00018894806,0.0013331823],"category_scores_gemma":[0.000010255594,0.00050711376,0.00042603465,0.00031145994,0.00029595871,0.00018993905,0.0015407164,0.0006365809,0.00010494002],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005361723,0.00032467398,0.0007446961,0.026310593,0.00012932338,0.000020110332,0.0005041239,0.8329408,1.7559736e-7,0.000031704843,0.00025091282,0.13873751],"study_design_scores_gemma":[0.0002698328,0.000085838976,0.0000048429292,0.010701137,0.00065259734,0.000012837963,0.00008400969,0.057809886,3.529617e-7,0.0016070794,0.9278009,0.0009707085],"about_ca_topic_score_codex":0.00008632049,"about_ca_topic_score_gemma":0.0000025144388,"teacher_disagreement_score":0.92754996,"about_ca_system_score_codex":0.0004268411,"about_ca_system_score_gemma":0.000010487238,"threshold_uncertainty_score":0.99973804},"labels":[],"label_agreement":null},{"id":"W4224315380","doi":"10.1016/j.envsoft.2022.105402","title":"Machine-learning approach for predicting the occurrence and timing of mid-winter ice breakups on canadian rivers","year":2022,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Environment and Climate Change Canada; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Resources Canada; Environment and Climate Change Canada","keywords":"Computer science; Robustness (evolution); Probabilistic logic; Machine learning; Preprocessor; Scale (ratio); Model selection; Data mining; Artificial intelligence; Geography; Cartography","score_opus":0.02385358181453426,"score_gpt":0.203775625469272,"score_spread":0.17992204365473774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224315380","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9462894,0.00014248834,0.05188,0.000098659046,0.00009818437,0.0004769617,0.00035917803,0.000061009283,0.0005941063],"genre_scores_gemma":[0.98835766,0.00001299324,0.011057791,0.00025224977,0.000017951537,0.00005833396,0.0000923242,0.000024334991,0.0001263404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983962,0.00011608435,0.00023195562,0.0004883596,0.0003587982,0.0004085591],"domain_scores_gemma":[0.9992881,0.00022311986,0.00013815334,0.0002040737,0.0000013712626,0.00014517338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048282105,0.00019562819,0.00016750643,0.000043664284,0.0011486104,0.000021472091,0.00033818555,0.000051471467,0.00047476997],"category_scores_gemma":[0.000049704522,0.00016636206,0.00008008704,0.00009669076,0.00029592757,0.000082415885,0.00035312242,0.00046418162,0.000010160527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003174664,0.00004680404,0.047005653,0.000009133986,0.000010011623,0.0000021898009,0.0013656238,0.9466549,0.00011669591,0.0000024866472,0.00005537093,0.0046993657],"study_design_scores_gemma":[0.00026588116,0.00025048625,0.00080695876,0.00001556171,0.000030584266,0.000020633823,0.00024701183,0.9950206,0.0001143603,0.00012368293,0.0028907624,0.00021343071],"about_ca_topic_score_codex":0.005582607,"about_ca_topic_score_gemma":0.000107959524,"teacher_disagreement_score":0.04836573,"about_ca_system_score_codex":0.00038080078,"about_ca_system_score_gemma":0.0000097191705,"threshold_uncertainty_score":0.8834299},"labels":[],"label_agreement":null},{"id":"W4229043503","doi":"10.1016/j.envsoft.2022.105408","title":"Arc Hydro Hillslope and Critical Duration: New tools for hillslope-scale runoff analysis","year":2022,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Surface runoff; Hydrograph; Watershed; Geology; Hydrology (agriculture); Curvature; Fluvial; Subsurface flow; Flow (mathematics); Environmental science; Geomorphology; Structural basin; Geotechnical engineering; Geometry; Mathematics; Groundwater; Ecology","score_opus":0.0173122593613999,"score_gpt":0.22106485288428177,"score_spread":0.20375259352288186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229043503","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5013251,0.00037160257,0.49455655,0.0021408498,0.00019700745,0.0006311305,0.00018041382,0.00012594358,0.00047143595],"genre_scores_gemma":[0.97439575,0.00010361909,0.021649193,0.0010937199,0.000058231646,0.00017560567,0.00022107312,0.00002972743,0.002273061],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807584,0.000070957336,0.00032694938,0.00071075506,0.00037292665,0.00044256233],"domain_scores_gemma":[0.99929714,0.00018927203,0.000072836825,0.00030312725,0.0000013472547,0.00013628707],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000270392,0.0002502023,0.00029886304,0.000073338175,0.0011745804,0.000057827805,0.00024826592,0.00006082101,0.0031044278],"category_scores_gemma":[0.0000173683,0.00026247348,0.00016196637,0.00019329225,0.00031655005,0.00040115166,0.00078603753,0.00019476318,0.00008646467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001107499,0.00022561628,0.07684254,0.000014493534,0.0002942276,0.000016056734,0.0022514965,0.90920895,0.00021297159,0.00006913479,0.003518455,0.0072352774],"study_design_scores_gemma":[0.0035406107,0.0012995263,0.046225503,0.000018203562,0.003445796,0.00004578495,0.0026417722,0.6205194,0.0006474609,0.01977661,0.2990773,0.0027620436],"about_ca_topic_score_codex":0.000092296694,"about_ca_topic_score_gemma":0.000033848253,"teacher_disagreement_score":0.47307068,"about_ca_system_score_codex":0.00021927521,"about_ca_system_score_gemma":0.000004453427,"threshold_uncertainty_score":0.9999828},"labels":[],"label_agreement":null},{"id":"W4283332913","doi":"10.1016/j.envsoft.2022.105448","title":"Regional sub-daily stochastic weather generator based on reanalyses for surface water stress estimation in central Tunisia","year":2022,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Agence Nationale de la Recherche","keywords":"Univariate; Wind speed; Variable (mathematics); Environmental science; Scale (ratio); Covariate; Calibration; Multivariate statistics; Meteorology; Series (stratigraphy); Weather station; Computer science; Climatology; Statistics; Mathematics; Geography; Geology","score_opus":0.014404629660950605,"score_gpt":0.21227817245917252,"score_spread":0.19787354279822192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283332913","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6893156,0.000031665622,0.31005442,0.0001799139,0.000045167104,0.00023014923,0.000098865494,0.00003142147,0.0000127815165],"genre_scores_gemma":[0.9877855,0.0000042372067,0.010707572,0.0003990551,0.000022552127,0.00010095676,0.00068719673,0.0000393769,0.00025355184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980087,0.000138442,0.00028967447,0.00058672985,0.0004805013,0.0004958995],"domain_scores_gemma":[0.9993136,0.00011087042,0.00007504651,0.00039299813,0.0000012285783,0.000106239895],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00025723505,0.00024180324,0.00022500493,0.000065988475,0.0004656731,0.000018780158,0.0003126761,0.00007862713,0.0023565318],"category_scores_gemma":[0.0000072295707,0.0002214926,0.00015192019,0.00011173357,0.00013262183,0.0001385917,0.00016307671,0.00023695505,0.000111658606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015420272,0.00030066783,0.024047118,0.0000037220295,0.000019521636,0.00000927736,0.0003912602,0.97308344,0.001632986,0.0000046939804,0.00016371232,0.00018937998],"study_design_scores_gemma":[0.0006089162,0.00013077038,0.0014821553,0.0000072124917,0.000060278002,0.0000020742518,0.00008061418,0.99479455,0.0017629405,0.0005259873,0.00026670238,0.0002778217],"about_ca_topic_score_codex":0.00017254365,"about_ca_topic_score_gemma":0.00004591124,"teacher_disagreement_score":0.29934686,"about_ca_system_score_codex":0.0005749324,"about_ca_system_score_gemma":0.000009633469,"threshold_uncertainty_score":0.9985554},"labels":[],"label_agreement":null},{"id":"W4285404854","doi":"10.1016/j.envsoft.2022.105461","title":"An innovative approach to correct data from in-situ turbidity sensors for surface water monitoring","year":2022,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; Ministry of the Environment, Conservation and Parks; Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Turbidity; In situ; Surface water; Environmental science; Remote sensing; Surface (topology); Environmental engineering; Geology; Meteorology; Mathematics; Geography; Oceanography","score_opus":0.08814018567760774,"score_gpt":0.2773752184361988,"score_spread":0.18923503275859105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285404854","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9291153,0.000024438144,0.068793446,0.00005911102,0.00041384157,0.0007021758,0.00058796036,0.0002716039,0.00003213836],"genre_scores_gemma":[0.8584794,0.00000582549,0.14014594,0.000035082703,0.00007462075,0.00013473789,0.00095133553,0.00006099294,0.00011208834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.996942,0.00015966408,0.0003898593,0.0012785593,0.00060333137,0.00062660355],"domain_scores_gemma":[0.99828655,0.00011581291,0.000079749705,0.001405365,0.0000027185527,0.00010980614],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00067724776,0.000326929,0.00029700412,0.000065229484,0.00043332845,0.000055214943,0.0015961163,0.0001023315,0.00011245452],"category_scores_gemma":[0.000021531745,0.0003180579,0.000041245854,0.00024214698,0.00014011688,0.000526231,0.0028537253,0.00048007202,0.00011869526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007161433,0.0003502745,0.11352061,0.000004831121,0.000013165495,0.0000051913767,0.0038315367,0.8281529,0.053017464,8.397306e-7,0.00013798973,0.000893597],"study_design_scores_gemma":[0.0019119912,0.0007161916,0.05073561,0.000045678204,0.00006348176,0.00001334213,0.012412794,0.22597687,0.6925802,0.002492618,0.010347448,0.002703752],"about_ca_topic_score_codex":0.00097428914,"about_ca_topic_score_gemma":0.000007496643,"teacher_disagreement_score":0.6395628,"about_ca_system_score_codex":0.0009938487,"about_ca_system_score_gemma":0.000006038718,"threshold_uncertainty_score":0.99992716},"labels":[],"label_agreement":null},{"id":"W4288926139","doi":"10.1016/j.envsoft.2022.105473","title":"The Dynamic Temperate and Boreal Fire and Forest-Ecosystem Simulator (DYNAFFOREST): Development and evaluation","year":2022,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Agricultural Research Institute, California State University; National Institute of Food and Agriculture; Environmental Defense Fund; U.S. Department of Energy; Gordon and Betty Moore Foundation; Royal Bank of Canada; University of California; Zegar Family Foundation; National Science Foundation","keywords":"Biome; Taiga; Boreal; Temperate climate; Environmental science; Disturbance (geology); Temperate rainforest; Temperate forest; Fire regime; Climate change; Forest dynamics; Forest ecology; Boreal ecosystem; Fire ecology; Ecosystem; Environmental resource management; Ecology; Geography; Physical geography; Forestry; Geology","score_opus":0.007383606729635929,"score_gpt":0.19943716436580963,"score_spread":0.1920535576361737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288926139","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99494797,0.002019052,0.0017491414,0.00005284488,0.00012490364,0.0009829653,0.00004494336,0.00005670891,0.00002149306],"genre_scores_gemma":[0.9979018,0.00018854924,0.0012940752,0.000044408374,0.000013885845,0.00029272243,0.000063846885,0.000045963156,0.00015473622],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977898,0.00021269178,0.00032784487,0.0005978182,0.0007069319,0.00036493992],"domain_scores_gemma":[0.9992327,0.00020816067,0.00013259842,0.00027035517,0.000002095213,0.00015406543],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0009858375,0.00026933782,0.0001930975,0.000024428778,0.0016194396,0.00009618261,0.00016978527,0.000058632322,0.00008045979],"category_scores_gemma":[0.000014946845,0.00022804794,0.000028340384,0.00006936846,0.00016524186,0.00021148274,0.0006266656,0.000210843,0.000024234805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000695904,0.00008139845,0.28102115,0.0000626256,0.000047960744,0.000012070744,0.0020396858,0.59252256,0.0001982358,0.000010865629,0.00006630171,0.12386753],"study_design_scores_gemma":[0.0005222482,0.000106434854,0.06966285,0.000019669627,0.000028554578,0.000050092942,0.00030047473,0.9255528,0.000031390613,0.00017799925,0.0032700826,0.00027743008],"about_ca_topic_score_codex":0.0001752049,"about_ca_topic_score_gemma":0.00029580801,"teacher_disagreement_score":0.3330302,"about_ca_system_score_codex":0.0006867449,"about_ca_system_score_gemma":0.000015231672,"threshold_uncertainty_score":0.99968034},"labels":[],"label_agreement":null},{"id":"W4309630228","doi":"10.1016/j.envsoft.2022.105577","title":"The prediction of mid-winter and spring breakups of ice cover on Canadian rivers using a hybrid ontology-based and machine learning model","year":2022,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada","keywords":"Ontology; Computer science; Breakup; Artificial intelligence; Machine learning","score_opus":0.029284710218175518,"score_gpt":0.18537527919332666,"score_spread":0.15609056897515114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309630228","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99168324,0.0008903064,0.0038507879,0.000030240917,0.000052596475,0.00008996315,0.003369971,0.0000068227987,0.000026041023],"genre_scores_gemma":[0.9989146,0.00018297732,0.00049260375,0.00007799254,0.000008995495,8.583949e-7,0.00029254484,0.0000053955405,0.000024039125],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935293,0.000043015407,0.00013123617,0.00016647701,0.0001458727,0.00016048037],"domain_scores_gemma":[0.9996546,0.000117815085,0.00007230504,0.00008032199,0.0000021349729,0.00007280675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012541038,0.00008993652,0.00010535801,0.00005433547,0.0004303306,0.000010326191,0.000065010885,0.000021262826,0.00026943928],"category_scores_gemma":[0.0000043320974,0.00007939036,0.000028864828,0.000025211946,0.00011088526,0.000058100915,0.000021763279,0.00015744258,0.0000012630825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004457469,0.0000049005766,0.3742848,0.00000786885,0.000005817296,0.000002451752,0.00026671865,0.62441814,0.00015241622,3.5011337e-7,0.0000018045997,0.00081013545],"study_design_scores_gemma":[0.00023603605,0.0001094117,0.016415933,0.000016564052,0.000019219711,0.000008212511,0.0001704074,0.9825326,0.00017003776,0.000037446644,0.00021520197,0.00006887993],"about_ca_topic_score_codex":0.06655564,"about_ca_topic_score_gemma":0.016996183,"teacher_disagreement_score":0.35811448,"about_ca_system_score_codex":0.00002938144,"about_ca_system_score_gemma":0.000018497627,"threshold_uncertainty_score":0.9484269},"labels":[],"label_agreement":null},{"id":"W4316037002","doi":"10.1016/j.envsoft.2023.105625","title":"Residual correlation and ensemble modelling to improve crop and grassland models","year":2023,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"CHIST-ERA; Natural Environment Research Council; Biotechnology and Biological Sciences Research Council; Analyses et Expérimentations pour les Ecosystèmes; Scotland’s Rural College; Bundesministerium für Bildung und Forschung; Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement; Agence Nationale de la Recherche; Agriculture and Agri-Food Canada; Sight Research UK","keywords":"Biogeochemical cycle; Calibration; Residual; Grassland; Environmental science; Vegetation (pathology); Sustainability; Computer science; Statistics; Ecology; Mathematics","score_opus":0.012719858269192665,"score_gpt":0.18969120131554235,"score_spread":0.17697134304634968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316037002","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6051761,0.00006483562,0.39421016,0.00004351675,0.000047422818,0.00018753103,0.000054422035,0.00008626625,0.00012974127],"genre_scores_gemma":[0.9801889,0.0004637064,0.017732179,0.000057449,0.000026219535,0.000026560278,0.000119277094,0.000040690946,0.0013450147],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984796,0.00002874193,0.00023984109,0.00057190284,0.00031624385,0.00036364922],"domain_scores_gemma":[0.99947095,0.000060163653,0.000055811015,0.00021383571,0.0000016006596,0.00019763224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001923367,0.00022394443,0.00016809194,0.00006149462,0.00029686812,0.00006699846,0.00010172995,0.000121125304,0.000034341283],"category_scores_gemma":[0.0000035183382,0.00022597624,0.00003179385,0.00013352692,0.00010293191,0.0003619249,0.00029687872,0.00017566829,0.0002357756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019561347,0.000017438872,0.010409234,0.0000055737974,0.000007686899,0.000008251991,0.0007239642,0.98522615,0.000968768,0.00004962348,0.00006390206,0.002499825],"study_design_scores_gemma":[0.00025360606,0.000054782005,0.0015121092,0.000016814294,0.000023276569,0.000012677948,0.000051844607,0.98946637,0.00009781546,0.007999166,0.00022675844,0.00028476765],"about_ca_topic_score_codex":0.00014877782,"about_ca_topic_score_gemma":0.000014924413,"teacher_disagreement_score":0.37647796,"about_ca_system_score_codex":0.00010513872,"about_ca_system_score_gemma":0.0000034317977,"threshold_uncertainty_score":0.9215044},"labels":[],"label_agreement":null},{"id":"W4324361274","doi":"10.1016/j.envsoft.2023.105682","title":"Regional frequency analysis of stream temperature at ungauged sites using non-linear canonical correlation analysis and generalized additive models","year":2023,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Pacific Institute for Climate Solutions; University of Victoria; University of New Brunswick; Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Bundesamt für Umwelt; Natural Sciences and Engineering Research Council of Canada","keywords":"Canonical correlation; Generalized additive model; Generalized linear model; Linearity; Mathematics; Linear correlation; Nonlinear system; Econometrics; Statistics; Environmental science; Engineering; Physics","score_opus":0.03392986769235722,"score_gpt":0.24586149877508112,"score_spread":0.2119316310827239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324361274","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9383174,0.00008784328,0.060796175,0.000022090204,0.000026424059,0.00018602576,0.00043156144,0.00011221529,0.000020257541],"genre_scores_gemma":[0.97043884,0.00013049477,0.02742111,0.00007450539,0.000024941626,0.000012556768,0.0016598345,0.000042087962,0.00019562214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974358,0.00013485992,0.00051152124,0.0008323948,0.0006515831,0.00043381055],"domain_scores_gemma":[0.9989491,0.00022285363,0.00024888947,0.00037553228,0.0000071966087,0.00019640196],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028742015,0.00033241327,0.000605762,0.00032905175,0.00039653113,0.00002162648,0.00019865585,0.00026246865,0.0006681453],"category_scores_gemma":[0.000026706934,0.0003125658,0.00036115403,0.001829078,0.00046362297,0.00024983188,0.00032092928,0.0002627379,0.00004885326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003314229,0.00006017262,0.1730944,0.0000030172032,0.0005347109,0.000011087316,0.00040482415,0.8146524,0.011056554,0.0000050334816,0.000025486834,0.000119207],"study_design_scores_gemma":[0.0002785483,0.000052227355,0.03987048,0.000012443354,0.0020898592,0.0000033002473,0.000026642147,0.9563304,0.00061536004,0.0004030682,0.000011509037,0.00030615603],"about_ca_topic_score_codex":0.0009262114,"about_ca_topic_score_gemma":0.0001913737,"teacher_disagreement_score":0.14167805,"about_ca_system_score_codex":0.0004821125,"about_ca_system_score_gemma":0.000012059037,"threshold_uncertainty_score":0.99993265},"labels":[],"label_agreement":null},{"id":"W4327980589","doi":"10.1016/j.envsoft.2023.105678","title":"Capturing sub-grid temperature and moisture variations for wildland fire modeling","year":2023,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Canadian Forest Service","funders":"Los Alamos National Laboratory; Strategic Environmental Research and Development Program","keywords":"Environmental science; Grid; Scale (ratio); Meteorology; Intensity (physics); Combustion; Moisture; Atmospheric sciences; Fire protection; Geography; Engineering; Geology; Physics; Civil engineering","score_opus":0.009530184532716842,"score_gpt":0.19057699804689404,"score_spread":0.1810468135141772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327980589","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9431139,0.00028037472,0.054804567,0.0001527888,0.00031597496,0.0007637132,0.00023078591,0.0003179598,0.000019884841],"genre_scores_gemma":[0.9921026,0.00017949197,0.006502111,0.000109620676,0.00019423895,0.0001784074,0.00030640027,0.000088541485,0.000338569],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980711,0.00004550898,0.00029797954,0.00070728577,0.00035946612,0.00051867904],"domain_scores_gemma":[0.9992347,0.00016056333,0.00008041314,0.0003446429,0.0000025626764,0.0001771358],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002925967,0.00032727048,0.0002620891,0.000048275895,0.0005699362,0.00008537855,0.00020829045,0.0002053167,0.000059137397],"category_scores_gemma":[0.000029470693,0.00032379138,0.00010258651,0.0001652067,0.00008052427,0.00034190746,0.00021959387,0.00025864446,0.0002346765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018551094,0.000039211267,0.013414758,0.00004753503,0.000022923145,0.000008578225,0.000799531,0.97938913,0.003640833,0.000004510648,0.00073838327,0.0018760732],"study_design_scores_gemma":[0.00047083406,0.00005257699,0.0024638958,0.000054414268,0.000037085163,0.000014788533,0.00010297505,0.9947401,0.00027294995,0.00045521726,0.00093640335,0.0003987282],"about_ca_topic_score_codex":0.00026663515,"about_ca_topic_score_gemma":0.00004197565,"teacher_disagreement_score":0.04898867,"about_ca_system_score_codex":0.00024315527,"about_ca_system_score_gemma":0.0000063932384,"threshold_uncertainty_score":0.99992144},"labels":[],"label_agreement":null},{"id":"W4361225447","doi":"10.1016/j.envsoft.2023.105688","title":"BasinMaker 3.0: A GIS toolbox for distributed watershed delineation of complex lake-river routing networks","year":2023,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Ontario Power Generation; University of Waterloo","funders":"","keywords":"Routing (electronic design automation); Watershed; Hydrology (agriculture); Toolbox; Environmental science; Channel (broadcasting); Hydrological modelling; Computer science; Geology; Computer network; Machine learning","score_opus":0.025341327164892544,"score_gpt":0.22218162736824368,"score_spread":0.19684030020335114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361225447","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35787058,0.000015803294,0.64119095,0.00012900592,0.000075405704,0.0003776233,0.00016651259,0.00012957116,0.000044554337],"genre_scores_gemma":[0.98715085,0.0000841799,0.0098532885,0.00017579549,0.000046658013,0.00007484373,0.0019824123,0.000034265195,0.0005976877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983917,0.000045931192,0.00037861982,0.00045533167,0.00024188547,0.00048654436],"domain_scores_gemma":[0.99944025,0.00011265832,0.00013306433,0.0002452167,0.0000033819636,0.000065414104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029994955,0.00023413493,0.00026361676,0.000043203712,0.00035819886,0.000013253683,0.00019936233,0.000108002525,0.00040482095],"category_scores_gemma":[0.00001739039,0.00022178603,0.00013110289,0.00016435437,0.00032058265,0.00018528408,0.00027271293,0.0001084408,0.00020143247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059857422,0.000060944512,0.095454544,0.000014067781,0.00005309455,0.0000038068983,0.0007483034,0.8987555,0.00020254545,0.00000913307,0.001762674,0.0028755572],"study_design_scores_gemma":[0.0007663982,0.00008280396,0.043677468,0.000019643625,0.000084336716,9.276497e-7,0.0001627562,0.94356334,0.000255256,0.0004059078,0.010670294,0.00031088237],"about_ca_topic_score_codex":0.000047161244,"about_ca_topic_score_gemma":0.000018543215,"teacher_disagreement_score":0.63133764,"about_ca_system_score_codex":0.000099838275,"about_ca_system_score_gemma":0.000001518896,"threshold_uncertainty_score":0.9044172},"labels":[],"label_agreement":null},{"id":"W4362586924","doi":"10.1016/j.envsoft.2023.105693","title":"Applicability of a flood forecasting system for Nebraska watersheds","year":2023,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Flood forecasting; Flood myth; Snowmelt; Environmental science; Snow; Flooding (psychology); Preparedness; Hydrology (agriculture); Hydrological modelling; Meteorology; Flood warning; Climatology; Engineering; Geography; Geology","score_opus":0.026109653119224356,"score_gpt":0.2053311603068117,"score_spread":0.17922150718758734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362586924","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8248893,0.000023866849,0.17364398,0.00007151334,0.00011877599,0.0006465396,0.00005615631,0.00023868792,0.0003112027],"genre_scores_gemma":[0.9898632,0.000016613407,0.009315727,0.000038426115,0.000035031615,0.00022811466,0.00007373514,0.00003378422,0.00039534623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983847,0.00003574127,0.0003483379,0.00051482534,0.000247872,0.00046850802],"domain_scores_gemma":[0.9993741,0.0001285202,0.00010780506,0.00031639458,0.0000016656841,0.00007150828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042785765,0.00021591468,0.0002773396,0.000047857913,0.00031445728,0.000008870146,0.0002497401,0.00009236239,0.000099707046],"category_scores_gemma":[0.000011992926,0.00019822935,0.00014105669,0.00014480585,0.00027403014,0.00013908195,0.00042518688,0.00009263507,0.0003266722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000094819305,0.0001323247,0.16229205,0.00027059665,0.00008980937,0.00000876664,0.0017458433,0.82799053,0.0014239044,0.00004524998,0.00046769256,0.0054384223],"study_design_scores_gemma":[0.0023392353,0.00048579797,0.016014695,0.00012579482,0.00029060422,0.000012124282,0.0030953551,0.95179737,0.013815629,0.0049266755,0.0059785508,0.0011181767],"about_ca_topic_score_codex":0.000041730582,"about_ca_topic_score_gemma":0.0000033968004,"teacher_disagreement_score":0.16497394,"about_ca_system_score_codex":0.00015422155,"about_ca_system_score_gemma":0.000001782649,"threshold_uncertainty_score":0.8083558},"labels":[],"label_agreement":null},{"id":"W4366694877","doi":"10.1016/j.envsoft.2023.105708","title":"Development of a knowledge-sharing parallel computing approach for calibrating distributed watershed hydrologic models","year":2023,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Environment and Climate Change Canada; Esri (Canada); University of Guelph","funders":"Canada First Research Excellence Fund","keywords":"Computer science; Speedup; Overhead (engineering); Distributed computing; Partitioned global address space; Parallel computing; Watershed; Fault tolerance; Calibration; Programming paradigm; Machine learning","score_opus":0.04298056328621872,"score_gpt":0.2325229029034208,"score_spread":0.18954233961720207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366694877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40599534,0.00003291363,0.59318805,0.000016357943,0.0000330525,0.00037312126,0.000024720226,0.00016926951,0.00016717393],"genre_scores_gemma":[0.78109306,0.0000144694895,0.21776801,0.000033722787,0.000019261497,0.00010204761,0.0007381277,0.000032985325,0.00019832762],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99781895,0.000038798447,0.00053814315,0.0007111515,0.00024090207,0.0006520401],"domain_scores_gemma":[0.9993884,0.00008627788,0.00016314452,0.0002700276,0.0000024037383,0.000089773624],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00051981217,0.00031039488,0.00036041456,0.0000682739,0.0006523378,0.000017494232,0.00040032135,0.00012392206,0.000045857643],"category_scores_gemma":[0.00000969782,0.0002870876,0.000121032484,0.00018774004,0.0002281077,0.00022778861,0.001040593,0.0001489491,0.00009592089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037185408,0.00014453586,0.016444752,0.000068639216,0.0000705144,0.0000022181252,0.004655224,0.9767709,0.0004083863,0.000035799123,0.00013463631,0.0012272173],"study_design_scores_gemma":[0.00054202595,0.0000398447,0.000819935,0.000017594402,0.000034996658,0.0000010723124,0.0004570694,0.99479204,0.000592725,0.0020003463,0.00038025086,0.00032208016],"about_ca_topic_score_codex":0.000015378057,"about_ca_topic_score_gemma":0.0000022557504,"teacher_disagreement_score":0.37542003,"about_ca_system_score_codex":0.00016409319,"about_ca_system_score_gemma":0.000004823456,"threshold_uncertainty_score":0.9999581},"labels":[],"label_agreement":null},{"id":"W4366751894","doi":"10.1016/j.envsoft.2023.105709","title":"Regional thermal index model for river temperature frequency analysis in ungauged basins","year":2023,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Index (typography); Environmental science; Drainage basin; Thermal; Hydrology (agriculture); Meteorology; Geology; Geography; Geotechnical engineering; Computer science; Cartography","score_opus":0.028455278669464814,"score_gpt":0.2304190309993476,"score_spread":0.20196375232988278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366751894","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90049225,0.000030203246,0.09846035,0.00015905178,0.000045912544,0.00033693973,0.00015769176,0.00026622208,0.000051372186],"genre_scores_gemma":[0.97451574,0.000026589974,0.023764582,0.00038841568,0.00003918902,0.00008550287,0.00028370708,0.00006528384,0.00083096646],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974916,0.000072972056,0.0003864942,0.00083822256,0.00053095707,0.00067978195],"domain_scores_gemma":[0.9991868,0.00014361239,0.00010545889,0.0003995805,0.000002666371,0.0001618916],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038451896,0.0003323741,0.0003447716,0.00018422534,0.00030350735,0.000033151955,0.00038925186,0.00027206022,0.00037098472],"category_scores_gemma":[0.000030568623,0.0003131956,0.0002790783,0.00083406764,0.00034925755,0.00024733337,0.00021009312,0.00038243204,0.0003936432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003249463,0.00009702753,0.15710637,0.000004334227,0.00004218087,0.0000137633215,0.000749851,0.8394134,0.0019041505,0.000009112407,0.00014522522,0.0004820914],"study_design_scores_gemma":[0.0004214362,0.000041820433,0.05029313,0.000012747956,0.00007777914,0.000001771942,0.000019075671,0.9449414,0.000070150774,0.0036630414,0.00010054053,0.00035713223],"about_ca_topic_score_codex":0.00014733752,"about_ca_topic_score_gemma":0.00008836618,"teacher_disagreement_score":0.10681324,"about_ca_system_score_codex":0.0004387262,"about_ca_system_score_gemma":0.000012106673,"threshold_uncertainty_score":0.999932},"labels":[],"label_agreement":null},{"id":"W4376878797","doi":"10.1016/j.envsoft.2023.105713","title":"Modeling agent decision and behavior in the light of data science and artificial intelligence","year":2023,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Evolutionary Game Theory and Cooperation","field":"Social Sciences","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"H2020 European Research Council; Economic and Social Research Council; National Science Foundation; European Research Council; Horizon 2020 Framework Programme; Alan Turing Institute","keywords":"Computer science; Artificial intelligence; Reinforcement learning; Key (lock); Marketing and artificial intelligence; Data science; Artificial neural network; Management science; Intelligent decision support system; Engineering; Computer security","score_opus":0.09284377815386316,"score_gpt":0.32394438195162856,"score_spread":0.2311006037977654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376878797","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88732976,0.00025599278,0.1119698,0.00015124076,0.000046509962,0.00017731605,0.000014465076,0.000018809835,0.000036109293],"genre_scores_gemma":[0.99609923,0.0008715025,0.0029352177,0.000023256336,0.000030043868,0.000010491488,0.000013928523,0.0000045061447,0.0000118434855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881345,0.00007755622,0.00018151994,0.00028597907,0.0004762533,0.00016525618],"domain_scores_gemma":[0.9995858,0.0001138423,0.000026767279,0.00022299185,0.000009048092,0.00004154267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020115355,0.000063829524,0.000068205205,0.00007813916,0.00055880414,0.000046755937,0.00036888817,0.000036924102,0.000013519264],"category_scores_gemma":[0.000085112515,0.000053644446,0.000009146551,0.0002910805,0.0004836939,0.000593747,0.00021081624,0.00008715984,0.000009102044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000076263525,0.00030239095,0.004919652,0.000015434101,0.00000435786,0.000015310135,0.05117196,0.6344457,0.002467511,0.03262525,0.000010560184,0.27394563],"study_design_scores_gemma":[0.00003776832,0.00003584489,0.00097320264,0.000032018244,0.000015760368,0.000001957287,0.010808674,0.96664214,0.0001305728,0.021084948,0.00012067291,0.00011644941],"about_ca_topic_score_codex":0.000109470675,"about_ca_topic_score_gemma":0.000083400984,"teacher_disagreement_score":0.33219644,"about_ca_system_score_codex":0.00004891181,"about_ca_system_score_gemma":0.000040048955,"threshold_uncertainty_score":0.42979258},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.055063829364244005,"score_gpt":0.23392302483125188,"score_spread":0.17885919546700788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383215559","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7042104,0.000009856151,0.29418257,0.000628869,0.0002952593,0.00040263348,0.000025161156,0.00023869435,0.000006579841],"genre_scores_gemma":[0.98889303,0.0000066811936,0.009625751,0.00023516934,0.00082024356,0.00012227517,0.00015750907,0.000046681587,0.00009267344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99821186,0.000069581845,0.00030889732,0.0006482337,0.00027467907,0.0004867562],"domain_scores_gemma":[0.9993378,0.00016063513,0.00008341521,0.00026207854,0.0000022302067,0.00015385618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006589807,0.00020260134,0.00015172525,0.000039439674,0.0008418202,0.00008290662,0.00017651367,0.00009529848,0.000078208635],"category_scores_gemma":[0.00009317786,0.000166144,0.000057660312,0.00021187711,0.00017975461,0.00030430665,0.00022117846,0.00019916451,0.000047875135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004699446,0.00002428094,0.0032730715,0.0000030973388,0.0000044804046,0.0000013814342,0.0005560881,0.9519518,0.0011163906,0.000011228135,0.00033124548,0.042679936],"study_design_scores_gemma":[0.00010941625,0.00022171285,0.0016886351,0.00000634023,0.00001842914,0.000012309561,0.00014740959,0.9953953,0.00015112423,0.00083233137,0.0012340227,0.0001829934],"about_ca_topic_score_codex":0.000016941425,"about_ca_topic_score_gemma":0.000006861789,"teacher_disagreement_score":0.28468263,"about_ca_system_score_codex":0.00014922317,"about_ca_system_score_gemma":0.0000014889699,"threshold_uncertainty_score":0.67751557},"labels":[],"label_agreement":null},{"id":"W4383226627","doi":"10.1016/j.envsoft.2023.105777","title":"Beyond engineering: A review of reservoir management through the lens of wickedness, competing objectives and uncertainty","year":2023,"lang":"en","type":"review","venue":"Environmental Modelling & Software","topic":"Water resources management and optimization","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Australian Research Council","keywords":"Wickedness; Risk analysis (engineering); Order (exchange); Control (management); Reservoir engineering; Environmental resource management; Management science; Computer science; Business; Engineering; Economics; Artificial intelligence; Geology","score_opus":0.02849753714268733,"score_gpt":0.23116890730730374,"score_spread":0.20267137016461642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383226627","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000043392833,0.9734048,0.024893248,0.0000071490604,0.00010005053,0.0010492872,0.00007577945,0.00015387966,0.00027239032],"genre_scores_gemma":[0.00015262683,0.99412173,0.0050579538,0.000009989425,0.00003785712,0.00009666143,0.0002791245,0.00012613469,0.000117928124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99835587,0.000056955185,0.0007103356,0.00031760955,0.00031084864,0.00024841347],"domain_scores_gemma":[0.9991567,0.00014595507,0.00023985052,0.00042391292,0.0000056436556,0.000027923497],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023770734,0.00041331493,0.0009724384,0.000099652694,0.000065636465,0.000016501994,0.00033738947,0.00010280734,0.00001670162],"category_scores_gemma":[0.000009052003,0.00032182582,0.0002450285,0.00024608668,0.000104777646,0.00011471461,0.000296019,0.00024973878,0.000008908325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015437565,0.000016620264,0.0000026597256,0.18157007,0.0003681093,0.0000032593896,0.00046073183,0.7990294,1.5809023e-7,0.00012680098,0.0000603672,0.01836026],"study_design_scores_gemma":[0.00030643857,0.00005577099,0.0000051160646,0.19003916,0.0023567108,0.0000059403214,0.00040310645,0.14083949,0.0000052410815,0.00034068953,0.6646552,0.000987099],"about_ca_topic_score_codex":0.000009638341,"about_ca_topic_score_gemma":5.38496e-7,"teacher_disagreement_score":0.6645949,"about_ca_system_score_codex":0.000096350675,"about_ca_system_score_gemma":0.0000042808333,"threshold_uncertainty_score":0.9999234},"labels":[],"label_agreement":null},{"id":"W4387490902","doi":"10.1016/j.envsoft.2023.105850","title":"Agent decision-making: The Elephant in the Room - Enabling the justification of decision model fit in social-ecological models","year":2023,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Complex Systems and Decision Making","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Economic and Social Research Council; Medical Research Council; Vetenskapsrådet; Svenska Forskningsrådet Formas; Scottish Government; Rural and Environment Science and Analytical Services Division","keywords":"Context (archaeology); Management science; R-CAST; Rational planning model; Computer science; Sociology; Ecology; Psychology; Business decision mapping; Data science; Knowledge management; Decision support system; Artificial intelligence; Engineering; Economics; Geography; Management; Biology","score_opus":0.21057070032493855,"score_gpt":0.3729950262017509,"score_spread":0.16242432587681235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387490902","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50225127,0.0005460973,0.49625513,0.00020854078,0.00013654218,0.0005038498,0.000024615707,0.000028522025,0.000045443514],"genre_scores_gemma":[0.993446,0.000121643425,0.0058087786,0.00025507645,0.00008651179,0.00011469757,0.0000056414838,0.000030506151,0.00013113751],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9929839,0.00058877014,0.0018394127,0.0009092904,0.0031063303,0.0005722952],"domain_scores_gemma":[0.98629946,0.011769187,0.0005498918,0.0012820329,0.000044576325,0.00005484847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0086064525,0.00032367304,0.00051245827,0.00043997105,0.0009028077,0.0003156496,0.0024973305,0.00019588547,0.00009605931],"category_scores_gemma":[0.00095130265,0.00015637839,0.0003297455,0.0016902237,0.00020775228,0.0003611403,0.00075190794,0.0005488356,0.00016705431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007737003,0.00013267116,0.0008499836,0.0000025789525,0.000004849033,0.000017304472,0.005011564,0.9516777,0.00006350912,0.0013895122,0.0014040776,0.039368913],"study_design_scores_gemma":[0.0002217798,0.000020146845,0.0064475858,0.0000662098,0.000008346542,0.000006605938,0.004113403,0.71056426,0.000005288504,0.2780067,0.0004008603,0.00013880488],"about_ca_topic_score_codex":0.000033497356,"about_ca_topic_score_gemma":0.000050723516,"teacher_disagreement_score":0.49119475,"about_ca_system_score_codex":0.00021491124,"about_ca_system_score_gemma":0.000052949632,"threshold_uncertainty_score":0.6943758},"labels":[],"label_agreement":null},{"id":"W4388340910","doi":"10.1016/j.envsoft.2023.105866","title":"Incorporating physically-based water temperature predictions into the National water model framework","year":2023,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"National Oceanic and Atmospheric Administration; National Science Foundation","keywords":"Environmental science; Scale (ratio); Water quality; Water model; Meteorology; Hydrology (agriculture); Engineering; Chemistry; Ecology; Cartography; Geography; Molecular dynamics","score_opus":0.013652207129549095,"score_gpt":0.21700577651430228,"score_spread":0.20335356938475319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388340910","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83632755,0.000011901877,0.15840384,0.003918533,0.00015251776,0.00037885015,0.000026933718,0.00038073302,0.0003991174],"genre_scores_gemma":[0.98715883,0.000017176384,0.009476275,0.0017526778,0.00010634077,0.00017262303,0.00024723366,0.000039055663,0.0010298002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811417,0.000064182284,0.00024244469,0.00051453436,0.0005837723,0.00048092645],"domain_scores_gemma":[0.9995558,0.00007743232,0.00003819711,0.00025443084,0.0000034740606,0.000070632115],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00035335554,0.00026313635,0.00015520255,0.000048338337,0.0013276816,0.000045721437,0.00033329832,0.00014356212,0.0003428836],"category_scores_gemma":[0.000010954634,0.00014933532,0.000109810484,0.000117982054,0.00046362873,0.00024596025,0.0005507471,0.00041438686,0.002638257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015515936,0.000071752234,0.005578174,0.0000065387676,0.000033461365,0.0000039870874,0.002634515,0.9863256,0.004050566,0.000062337545,0.0011344305,0.00008310342],"study_design_scores_gemma":[0.00020508892,0.000036852733,0.00052425155,0.000015033515,0.00003574856,7.541927e-7,0.00016618604,0.9521668,0.0050325487,0.040422812,0.001112426,0.0002814784],"about_ca_topic_score_codex":0.00002412876,"about_ca_topic_score_gemma":0.000005064838,"teacher_disagreement_score":0.15083125,"about_ca_system_score_codex":0.00024988852,"about_ca_system_score_gemma":0.0000039914657,"threshold_uncertainty_score":0.99997246},"labels":[],"label_agreement":null},{"id":"W4388702939","doi":"10.1016/j.envsoft.2023.105880","title":"EnviroFutures: Envisioning the next century of environmental sciences","year":2023,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Sustainability and Climate Change Governance","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"","keywords":"Environmental science; Engineering","score_opus":0.03069293278784508,"score_gpt":0.22931781362089762,"score_spread":0.19862488083305255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388702939","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9963417,0.0009825062,0.0011981191,0.0002319409,0.00021215556,0.0004059241,0.0001391559,0.00012515501,0.0003633267],"genre_scores_gemma":[0.9947146,0.003020008,0.0013264205,0.00023837636,0.00008226227,0.000046925477,0.000051354393,0.000045532324,0.00047451077],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99686664,0.0001235781,0.00045837028,0.00075505517,0.0010632164,0.00073313434],"domain_scores_gemma":[0.99866974,0.00034787972,0.00024611087,0.0006035843,9.543786e-7,0.00013172839],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00065886544,0.00034578008,0.0002706997,0.00005812003,0.0009326212,0.00005299363,0.0008413214,0.00013594257,0.0035965508],"category_scores_gemma":[0.000038056303,0.00026901718,0.00020938403,0.000381594,0.0018719011,0.0004985849,0.0008560712,0.00030616933,0.0012865929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008705186,0.00050892174,0.132781,0.000058778984,0.00004884911,0.00004398296,0.019026944,0.79899436,0.016144155,0.00013535202,0.0012438585,0.030926771],"study_design_scores_gemma":[0.0030670364,0.001318182,0.41910142,0.00034481485,0.00030539813,0.000117757765,0.16094871,0.20309839,0.014888671,0.015748842,0.1769645,0.0040962794],"about_ca_topic_score_codex":0.00007901122,"about_ca_topic_score_gemma":0.00000561298,"teacher_disagreement_score":0.59589595,"about_ca_system_score_codex":0.0003723682,"about_ca_system_score_gemma":0.00001097319,"threshold_uncertainty_score":0.9999762},"labels":[],"label_agreement":null},{"id":"W4388773445","doi":"10.1016/j.envsoft.2023.105879","title":"Is the climate getting WARMer? A framework and tool for climate data comparison","year":2023,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Environmental Monitoring and Data Management","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Climate model; Climate science; Informatics; Downscaling; Climate change; Data science; Computer science; Analytics; Data analysis; Visualization; Environmental science; Data mining; Climatology; Meteorology; Geography; Engineering; Precipitation","score_opus":0.058441274565362925,"score_gpt":0.2699864198149663,"score_spread":0.21154514524960338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388773445","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9221051,0.0015697022,0.06925058,0.00054221123,0.000521245,0.0007300216,0.004981276,0.00025462377,0.00004524622],"genre_scores_gemma":[0.9575159,0.0032389523,0.035758615,0.0003945371,0.00018591328,0.000013664557,0.0028100149,0.000020063511,0.0000623443],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980893,0.000042180425,0.00030125753,0.00065063284,0.00032357112,0.000593069],"domain_scores_gemma":[0.99855435,0.00048153952,0.00010498092,0.0007709395,0.0000011215175,0.00008709127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006362896,0.00022242196,0.00018881477,0.000039458304,0.0008337636,0.00014871609,0.0005462709,0.00007569981,0.00016003264],"category_scores_gemma":[0.000021776434,0.0001742257,0.000051644427,0.00008776472,0.00013721887,0.00035141985,0.00040590038,0.00021051505,0.00047055725],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000398541,0.000025665582,0.8376782,0.000106304,0.000035325607,0.0000047727444,0.00057077175,0.10282609,0.0000056976555,0.000014102921,0.00065308023,0.05804015],"study_design_scores_gemma":[0.0002759098,0.000092194015,0.2343269,0.00008747634,0.0001003805,0.000003378688,0.0010652234,0.74905103,0.00004106576,0.0008264266,0.013738125,0.00039188418],"about_ca_topic_score_codex":0.00008052379,"about_ca_topic_score_gemma":0.000005717927,"teacher_disagreement_score":0.6462249,"about_ca_system_score_codex":0.000011764671,"about_ca_system_score_gemma":0.0000026634027,"threshold_uncertainty_score":0.7104718},"labels":[],"label_agreement":null},{"id":"W4389504600","doi":"10.1016/j.envsoft.2023.105926","title":"Real-time peak flow prediction based on signal matching","year":2023,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Watershed; Flood myth; Flow (mathematics); SIGNAL (programming language); Precipitation; Environmental science; Streamflow; Flood forecasting; Matching (statistics); Event (particle physics); Computer science; Real-time computing; Hydrology (agriculture); Meteorology; Geography; Statistics; Engineering; Drainage basin; Geotechnical engineering; Mathematics; Machine learning","score_opus":0.009247829665881864,"score_gpt":0.20009623514280198,"score_spread":0.1908484054769201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389504600","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75733745,0.000008654679,0.23629022,0.00019750072,0.0002714578,0.0006463104,0.00011931828,0.0011005453,0.0040285536],"genre_scores_gemma":[0.96371084,0.0001440543,0.030917957,0.00018946416,0.00011134483,0.00008762783,0.00056749687,0.00008827581,0.004182962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976669,0.00006891657,0.0002835232,0.0006544634,0.0008195705,0.0005066179],"domain_scores_gemma":[0.9992729,0.000086986474,0.00008489411,0.00040241974,9.2277526e-7,0.00015187205],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00031934987,0.0003010999,0.00018544175,0.00009455085,0.00038161216,0.000053403164,0.00027654084,0.00009470148,0.00418048],"category_scores_gemma":[0.0000028765344,0.00029949204,0.00012832132,0.00020630614,0.0001015903,0.00026978317,0.00025173576,0.00020483775,0.0082219355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003164962,0.0001423838,0.0066783517,0.000008567448,0.000012678023,0.000022315297,0.00020862397,0.98155856,0.0022764448,0.000004880844,0.0033002696,0.0057552936],"study_design_scores_gemma":[0.00045528225,0.00016208012,0.014606759,0.000034809378,0.000034333454,7.9830426e-7,0.00007976369,0.98084474,0.00034743862,0.00066531735,0.0024522613,0.00031643597],"about_ca_topic_score_codex":0.00016579377,"about_ca_topic_score_gemma":0.000004222701,"teacher_disagreement_score":0.20637338,"about_ca_system_score_codex":0.00041957578,"about_ca_system_score_gemma":0.0000058223254,"threshold_uncertainty_score":0.9999457},"labels":[],"label_agreement":null},{"id":"W4390535373","doi":"10.1016/j.envsoft.2024.105940","title":"A practice-oriented framework for stationary and nonstationary flood frequency analysis","year":2024,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Flood myth; Workflow; Computer science; Software; Reproducibility; Process (computing); Exploratory analysis; Environmental science; Statistics; Data science; Mathematics; Geography; Programming language","score_opus":0.008945333658240415,"score_gpt":0.24937153944542254,"score_spread":0.24042620578718213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390535373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15718384,0.0011493852,0.8405645,0.0003462649,0.00007870898,0.00021667819,0.00017703867,0.00015390532,0.00012966525],"genre_scores_gemma":[0.65670294,0.00019545258,0.34216818,0.00027246453,0.000038402108,0.00007503476,0.0003132805,0.00002956265,0.00020467534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983706,0.000069006695,0.0002936672,0.0006633522,0.00031679394,0.00028659665],"domain_scores_gemma":[0.99880457,0.00075742527,0.00007111091,0.00023963486,0.000003400069,0.00012385598],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00026029808,0.00021129406,0.00021184985,0.00012579965,0.00031923957,0.000044935226,0.000113349495,0.0001503138,0.0011245387],"category_scores_gemma":[0.00005224032,0.00021016516,0.00018325937,0.0004656799,0.00023419544,0.0005968045,0.00009240529,0.0002244435,0.00028245975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010295057,0.00035240725,0.0945452,0.000039325667,0.0016305461,0.00009182307,0.0038215427,0.88410676,0.00017106772,0.003996496,0.00032300153,0.010818873],"study_design_scores_gemma":[0.00025807135,0.00014295703,0.004686871,0.000025476014,0.0023675386,0.000024297718,0.0005636832,0.9144116,0.000046182042,0.06893138,0.008045674,0.0004962815],"about_ca_topic_score_codex":0.000081135695,"about_ca_topic_score_gemma":0.00001292316,"teacher_disagreement_score":0.4995191,"about_ca_system_score_codex":0.00015776654,"about_ca_system_score_gemma":0.000011184045,"threshold_uncertainty_score":0.9997886},"labels":[],"label_agreement":null},{"id":"W4391147710","doi":"10.1016/j.envsoft.2023.105822","title":"Corrigendum to “Development of a knowledge-sharing parallel computing approach for calibrating distributed watershed hydrologic models” [Environ. Model. Software 164 (2023) 105708]","year":2024,"lang":"en","type":"erratum","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Environment and Climate Change Canada; Esri (Canada); University of Guelph","funders":"","keywords":"Watershed; Computer science; Software; Hydrological modelling; Hydrology (agriculture); Distributed computing; Geology; Programming language; Machine learning; Geotechnical engineering","score_opus":0.039069310765285435,"score_gpt":0.23151333663393264,"score_spread":0.1924440258686472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391147710","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041652983,0.0013589681,0.98683244,0.000064710104,0.0026603932,0.002289646,0.00091866835,0.00046442804,0.0012454508],"genre_scores_gemma":[0.19346127,0.00026531052,0.6981397,0.00039039174,0.00051484,0.0011803546,0.016273946,0.0005685263,0.08920562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9927859,0.00010412056,0.0016580615,0.0028409208,0.00086205074,0.0017490035],"domain_scores_gemma":[0.9979165,0.0000898922,0.0005523546,0.0010239932,0.000011542272,0.00040570655],"candidate_categories":["metaepi_narrow"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0008056913,0.0015001583,0.0015487954,0.0002734009,0.0011592238,0.00010549411,0.0015339718,0.0009912737,0.00018722434],"category_scores_gemma":[0.0000360748,0.0014402716,0.00054946117,0.00032922352,0.0005206215,0.0003908527,0.0042015086,0.001369447,0.00036179184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010321323,0.00039178177,0.00071153365,0.00070823106,0.00036905805,0.000012918243,0.0047424715,0.9077734,0.000036559988,0.000015735146,0.08405189,0.0010832564],"study_design_scores_gemma":[0.0005445114,0.00016270998,0.0000363609,0.00027793343,0.00036376095,0.0000047844646,0.00027248537,0.97553074,0.00006738725,0.0027269851,0.018539274,0.0014730415],"about_ca_topic_score_codex":0.00007032022,"about_ca_topic_score_gemma":0.000020190724,"teacher_disagreement_score":0.28869268,"about_ca_system_score_codex":0.0011669291,"about_ca_system_score_gemma":0.000060635128,"threshold_uncertainty_score":0.99977475},"labels":[],"label_agreement":null},{"id":"W4395478374","doi":"10.1016/j.envsoft.2024.106055","title":"PyLandslide: A Python tool for landslide susceptibility mapping and uncertainty analysis","year":2024,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Python (programming language); Computer science; Programming language","score_opus":0.011396398518896267,"score_gpt":0.21917893746868108,"score_spread":0.20778253894978482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395478374","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6200773,0.0007905247,0.37820286,0.0000736632,0.00011752531,0.00032727187,0.00015405707,0.00017648283,0.000080302954],"genre_scores_gemma":[0.97966206,0.00038965407,0.018240932,0.00009803405,0.00007792025,0.000057447025,0.00023825154,0.000046669353,0.0011890531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792594,0.000042876225,0.0003688217,0.00086899527,0.0003305589,0.000462831],"domain_scores_gemma":[0.99923325,0.00020276353,0.00005693229,0.0003513483,0.000002202127,0.00015348695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045545844,0.00030320344,0.00033170477,0.00009723745,0.00029039785,0.00013500893,0.00016622794,0.00018340134,0.00088810624],"category_scores_gemma":[0.0000127199355,0.00024378237,0.00030115942,0.0003239608,0.00018246782,0.00023650791,0.00020176749,0.0002497996,0.000117401585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000077402474,0.000090750524,0.2345173,0.00009159602,0.00044465685,0.000028736029,0.0014373703,0.72362673,0.00072973466,0.000025550868,0.0003813734,0.038548816],"study_design_scores_gemma":[0.0007223761,0.00015929596,0.02132855,0.00007745154,0.0008382494,0.000022483486,0.00025097208,0.9290998,0.00012423047,0.0027302464,0.04383813,0.00080824207],"about_ca_topic_score_codex":0.00016362326,"about_ca_topic_score_gemma":0.000053270393,"teacher_disagreement_score":0.35996193,"about_ca_system_score_codex":0.00030073922,"about_ca_system_score_gemma":0.000008790428,"threshold_uncertainty_score":0.99411565},"labels":[],"label_agreement":null},{"id":"W4397033162","doi":"10.1016/j.envsoft.2024.106076","title":"PyCoSMoS: An advanced toolbox for simulating real-world hydroclimatic data","year":2024,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Ministero dell'Istruzione e del Merito; Natural Sciences and Engineering Research Council of Canada; Ministero dell’Istruzione, dell’Università e della Ricerca; Grantová Agentura České Republiky","keywords":"Toolbox; Computer science; Climatology; Meteorology; Data science; Environmental science; Geology; Geography; Programming language","score_opus":0.04272100474556216,"score_gpt":0.2846574913178727,"score_spread":0.24193648657231054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4397033162","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57836086,0.00042329935,0.41704398,0.00027361952,0.00041896774,0.0010289669,0.00023782568,0.00063099567,0.0015815168],"genre_scores_gemma":[0.9419696,0.00017360058,0.05484302,0.00026568671,0.000086879416,0.00008955642,0.00062925695,0.00007070619,0.0018717295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977719,0.000044832985,0.00035657026,0.0010138869,0.00028589444,0.0005269061],"domain_scores_gemma":[0.99879694,0.00022531074,0.00006405486,0.00080253655,8.793657e-7,0.000110264875],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003733382,0.0003060358,0.0002481666,0.000059553084,0.0004658543,0.00006982087,0.00058539107,0.000071046525,0.0005786683],"category_scores_gemma":[0.000016132151,0.00028992206,0.00007774446,0.00012615015,0.00020822733,0.0011106238,0.00064079545,0.00018504703,0.0005074026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038179693,0.00011507985,0.010656562,0.00008313327,0.00006523249,0.000018998575,0.000876988,0.965886,0.00026734482,0.000066354885,0.0006104788,0.021315627],"study_design_scores_gemma":[0.00031062172,0.00011992214,0.00067249243,0.00006101667,0.00011262285,0.0000017684373,0.00016142806,0.9744737,0.0001090342,0.004598732,0.01896408,0.0004145707],"about_ca_topic_score_codex":0.000076839024,"about_ca_topic_score_gemma":0.000065121254,"teacher_disagreement_score":0.36360875,"about_ca_system_score_codex":0.00020908298,"about_ca_system_score_gemma":0.0000037576417,"threshold_uncertainty_score":0.9999553},"labels":[],"label_agreement":null},{"id":"W4402105903","doi":"10.1016/j.envsoft.2024.106201","title":"PyMTRD: A Python package for calculating the metrics of temporal rainfall distribution","year":2024,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Climate variability and models","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Gansu Province; National Natural Science Foundation of China; Lanzhou University","keywords":"Python (programming language); Computer science; Index (typography); Seasonality; Environmental science; Statistics; Mathematics; Programming language","score_opus":0.023761349471642157,"score_gpt":0.24190655358666383,"score_spread":0.21814520411502167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402105903","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.414025,0.00023027748,0.5845843,0.000097299286,0.00010804713,0.0003844771,0.00045347807,0.00007206297,0.000045046836],"genre_scores_gemma":[0.98621476,0.00006019846,0.012923907,0.000052293675,0.000049671857,0.00006288198,0.00041468034,0.00003841359,0.00018321909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839646,0.000052362433,0.00038468343,0.0004555361,0.00037930664,0.00033167496],"domain_scores_gemma":[0.99903697,0.00044241268,0.000087358014,0.00035374754,0.0000024527205,0.00007705253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000654776,0.00020310107,0.00019048242,0.000027065546,0.00023890393,0.000049060156,0.00023372445,0.00011069704,0.0002809826],"category_scores_gemma":[0.0000619925,0.00015626574,0.00021282567,0.00021634817,0.00025582992,0.00022842358,0.00020375909,0.00018777428,0.00006469761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006854202,0.00029620997,0.027617833,0.00022639698,0.00006690304,0.000007694897,0.0028759716,0.93866825,0.0061320574,0.0008353753,0.0008695786,0.02233519],"study_design_scores_gemma":[0.0002656261,0.000103804894,0.0009658175,0.00006568029,0.00009006678,0.0000066409693,0.00020189343,0.9795094,0.0026190188,0.0067201746,0.009149945,0.00030189977],"about_ca_topic_score_codex":0.00018203446,"about_ca_topic_score_gemma":0.000008752896,"teacher_disagreement_score":0.57218975,"about_ca_system_score_codex":0.0003429021,"about_ca_system_score_gemma":0.000009471615,"threshold_uncertainty_score":0.6372332},"labels":[],"label_agreement":null},{"id":"W4402932681","doi":"10.1016/j.envsoft.2024.106234","title":"Using national hydrologic models to obtain regional climate change impacts on streamflow basins with unrepresented processes","year":2024,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Science Foundation","keywords":"Streamflow; Environmental science; Climate change; Hydrological modelling; Hydrology (agriculture); Stream flow; Drainage basin; Climatology; Geography; Geology; Oceanography; Cartography","score_opus":0.0865341667995819,"score_gpt":0.2660546496550705,"score_spread":0.17952048285548858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402932681","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88106096,0.00033193108,0.115123354,0.0010838864,0.000101298676,0.00080028106,0.00018185162,0.00033590794,0.0009805297],"genre_scores_gemma":[0.99004054,0.0003268224,0.0076376945,0.0015121466,0.000091345515,0.0001246013,0.000075199445,0.000051920168,0.00013975741],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99763787,0.000054385462,0.00021918828,0.000824796,0.0006697477,0.00059399055],"domain_scores_gemma":[0.9994699,0.00010164215,0.00005326005,0.00021462608,0.000004305739,0.00015627606],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022409561,0.00035519866,0.00021198466,0.00012297186,0.00041101448,0.00005746243,0.00022571297,0.00009811772,0.0002357826],"category_scores_gemma":[0.000008575515,0.0002852049,0.00006351854,0.0002762978,0.00023428106,0.00058867934,0.000307273,0.0002150538,0.00046471495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017678754,0.0001642026,0.00806567,0.000049872102,0.000058318794,0.00006553929,0.0012802521,0.9891325,0.00007426086,0.00007128284,0.00025006643,0.00061119685],"study_design_scores_gemma":[0.00038036736,0.00040786405,0.0011011898,0.0002477751,0.00007597045,0.000029939625,0.00017103547,0.9905505,0.00022455795,0.0046019773,0.0016346866,0.00057413406],"about_ca_topic_score_codex":0.00009366614,"about_ca_topic_score_gemma":0.00003554173,"teacher_disagreement_score":0.10897955,"about_ca_system_score_codex":0.00043513972,"about_ca_system_score_gemma":0.000012103062,"threshold_uncertainty_score":0.99996},"labels":[],"label_agreement":null},{"id":"W4403538275","doi":"10.1016/j.envsoft.2024.106247","title":"A Machine Learning-based framework and open-source software for Non Intrusive Water Monitoring","year":2024,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Open source software; Software; Open source; Software engineering; Artificial intelligence; Operating system","score_opus":0.019080346002798603,"score_gpt":0.24136375962935525,"score_spread":0.22228341362655665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403538275","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44738463,0.00028160086,0.55124015,0.00012678963,0.00018485932,0.0004441434,0.000030257608,0.0002897192,0.000017884688],"genre_scores_gemma":[0.85818595,0.00004452471,0.14035913,0.00018652319,0.00012229335,0.00012465613,0.00008934934,0.00012057623,0.000766993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99760073,0.00006499828,0.00032130713,0.0009950144,0.00035761544,0.0006603058],"domain_scores_gemma":[0.9989943,0.00041994575,0.000058178888,0.00030160157,0.0000030090657,0.00022295944],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038897427,0.00040372557,0.0003191439,0.000052780793,0.0006056374,0.00031911614,0.00046101233,0.00024558484,0.00073912396],"category_scores_gemma":[0.000102877486,0.00032277362,0.00010772685,0.00012335106,0.00029991803,0.00033249316,0.000846957,0.00064688915,0.00040394845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007123029,0.00007897718,0.024965525,0.000055713634,0.000024087905,0.000023908398,0.0010482946,0.9491907,0.0010200477,0.000003043163,0.000042298183,0.023476193],"study_design_scores_gemma":[0.00044952254,0.0003985687,0.00034497102,0.0003073463,0.00007058854,0.000018283421,0.000032543947,0.9663139,0.0053837085,0.0031950807,0.022852905,0.00063258887],"about_ca_topic_score_codex":0.00013639707,"about_ca_topic_score_gemma":0.0000019364156,"teacher_disagreement_score":0.410881,"about_ca_system_score_codex":0.00033289628,"about_ca_system_score_gemma":0.000008154645,"threshold_uncertainty_score":0.99992245},"labels":[],"label_agreement":null},{"id":"W4405679893","doi":"10.1016/j.envsoft.2024.106309","title":"Environmental-Health Convergence: A deep learning-oriented decision support system for catalyzing sustainable healthy food systems","year":2024,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Agriculture Sustainability and Environmental Impact","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Convergence (economics); Sustainable development; Decision support system; Computer science; Artificial intelligence; Biology; Ecology; Economics; Economic growth","score_opus":0.008262421785830617,"score_gpt":0.22402280020169615,"score_spread":0.21576037841586554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405679893","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45894605,0.005684762,0.53041047,0.00013013599,0.0008643194,0.0028940665,0.000286295,0.00067305355,0.0001108147],"genre_scores_gemma":[0.989088,0.00073070114,0.0061381822,0.00014439732,0.00018362307,0.0005104868,0.0007874963,0.00018459305,0.002232538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9937898,0.00022050903,0.0011717016,0.0017742759,0.001145109,0.0018986122],"domain_scores_gemma":[0.9978405,0.0003323879,0.00031894012,0.0006800824,0.0000046142936,0.0008234598],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012119738,0.00084511936,0.000749311,0.000118977274,0.0015051218,0.00023651836,0.0005223655,0.00036068316,0.0008249081],"category_scores_gemma":[0.000038738715,0.0007739384,0.0004315084,0.00035002062,0.00045228377,0.001100695,0.0005780102,0.0007280607,0.0010668075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038296691,0.0006846783,0.017697506,0.0016995639,0.00018204655,0.00014016774,0.0058625517,0.942746,0.0003385573,0.00039995564,0.0021109318,0.027755119],"study_design_scores_gemma":[0.0030254924,0.0065623494,0.0060330573,0.0006197533,0.00034627656,0.0005669305,0.058583584,0.6130724,0.00054405036,0.0008232073,0.3065056,0.0033172937],"about_ca_topic_score_codex":0.0004230472,"about_ca_topic_score_gemma":0.000010573839,"teacher_disagreement_score":0.53014195,"about_ca_system_score_codex":0.007951615,"about_ca_system_score_gemma":0.00006186395,"threshold_uncertainty_score":0.9997948},"labels":[],"label_agreement":null},{"id":"W4406069343","doi":"10.1016/j.envsoft.2025.106317","title":"OFPO &amp; KGFPO: Ontology and knowledge graph for flood process observation","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Trudell Medical International; Military Health System; National Key Research and Development Program of China; China Scholarship Council; Ministry of Water Resources; Magee-Womens Research Institute; Natural Science Foundation of Hubei Province; National Natural Science Foundation of China; Rare Disease Foundation","keywords":"Ontology; Computer science; Graph; Flood myth; Knowledge graph; Process (computing); Knowledge management; Information retrieval; Theoretical computer science; Programming language; Geography; Epistemology; Philosophy; Archaeology","score_opus":0.03008632792475968,"score_gpt":0.2694087393100672,"score_spread":0.23932241138530752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406069343","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3388184,0.0013720277,0.6590618,0.00019211187,0.00018375252,0.0002037016,0.0000047588724,0.00012226259,0.000041183623],"genre_scores_gemma":[0.8285342,0.000117579606,0.17047025,0.00022228813,0.000024069726,0.000081061386,0.000022622782,0.000009433436,0.00051849097],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989247,0.000027494756,0.00021055414,0.0004792468,0.000096780044,0.00026127353],"domain_scores_gemma":[0.9993815,0.00018321158,0.00005948584,0.000319357,0.000011999495,0.00004448853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013117968,0.00016629511,0.00019634987,0.00008228825,0.00021756101,0.000055901193,0.00038497796,0.00009193175,0.0000035184835],"category_scores_gemma":[0.000028763825,0.00016144752,0.000058899954,0.0001148883,0.000093629285,0.00027960286,0.00014995862,0.00009172138,0.000010180135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022868626,0.0020862948,0.48023704,0.0014645811,0.00041948466,0.000017920014,0.016793048,0.11866962,0.0016521362,0.06744982,0.0021455425,0.3088358],"study_design_scores_gemma":[0.0025503992,0.00026203514,0.040355813,0.0002334793,0.00012475632,0.000027739705,0.00043220582,0.63903207,0.0036301583,0.30004737,0.012277305,0.001026684],"about_ca_topic_score_codex":0.000013320406,"about_ca_topic_score_gemma":0.00003241386,"teacher_disagreement_score":0.52036244,"about_ca_system_score_codex":0.000042170923,"about_ca_system_score_gemma":0.000031983465,"threshold_uncertainty_score":0.6583639},"labels":[],"label_agreement":null},{"id":"W4406536189","doi":"10.1016/j.envsoft.2025.106321","title":"Integrated models of nutrient dynamics in lake and reservoir watersheds: A systematic review and integrated modelling decision pathway","year":2025,"lang":"en","type":"review","venue":"Environmental Modelling & Software","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"Wageningen University and Research; Grantová Agentura České Republiky; European Commission; H2020 Marie Skłodowska-Curie Actions; Global Lake Ecological Observatory Network","keywords":"Environmental science; Dynamics (music); Hydrology (agriculture); Geology; Geotechnical engineering; Psychology","score_opus":0.01810771169769557,"score_gpt":0.2291411057575664,"score_spread":0.21103339405987084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406536189","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00053015543,0.72958237,0.26599118,0.0000041036888,0.00005720254,0.0027370802,0.0010132313,0.000036634825,0.00004805076],"genre_scores_gemma":[0.0015797247,0.9844184,0.012337024,0.000032466785,0.0000059705176,0.0003129091,0.0009023164,0.00008819605,0.00032301032],"study_design_codex":"systematic_review","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952202,0.00040763617,0.0022180523,0.0011053247,0.0005483171,0.0005004789],"domain_scores_gemma":[0.9975504,0.00062594144,0.00079694384,0.000823157,0.000009771112,0.00019378767],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009997314,0.00091024616,0.003600482,0.0002360297,0.00012930165,0.00005305246,0.00054111815,0.0005061061,0.0000698483],"category_scores_gemma":[0.000061440325,0.00069982273,0.00030419944,0.00049275527,0.0002252262,0.000291191,0.00061517576,0.0007317954,0.000020480622],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002259581,0.0002307291,0.0001832378,0.7240336,0.00015499932,0.000030954787,0.0003649066,0.25193986,1.3443024e-7,0.00024426606,0.00002066472,0.022774074],"study_design_scores_gemma":[0.00019501854,0.000046817848,2.1968492e-7,0.35556135,0.0007992992,0.000027612288,0.000109081906,0.639311,2.2113201e-7,0.0012644189,0.0021800448,0.0005048889],"about_ca_topic_score_codex":0.0002771362,"about_ca_topic_score_gemma":0.000640771,"teacher_disagreement_score":0.38737118,"about_ca_system_score_codex":0.0011043687,"about_ca_system_score_gemma":0.00006251636,"threshold_uncertainty_score":0.9995453},"labels":[],"label_agreement":null},{"id":"W4408211520","doi":"10.1016/j.envsoft.2025.106415","title":"Simulation decomposition analysis of the Iowa food-water-energy system","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Decomposition; Environmental science; Computer science; Ecology; Biology","score_opus":0.008927524689422033,"score_gpt":0.2042948591221628,"score_spread":0.19536733443274076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408211520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4546562,0.00015320074,0.5430091,0.00005566335,0.0001721119,0.00013476345,0.00008821068,0.00008392276,0.0016468224],"genre_scores_gemma":[0.99784404,0.0000068042864,0.0014277912,0.00011029161,0.000016692293,0.000031213916,0.000076375734,0.000022094277,0.00046468017],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981443,0.000117726464,0.0004451895,0.00051054906,0.00045481932,0.00032740043],"domain_scores_gemma":[0.99913853,0.00011568645,0.00013988171,0.0005494898,0.0000041208577,0.000052276097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013024917,0.00025642113,0.00033850176,0.00012439613,0.00041180855,0.0000193732,0.00035587,0.000105629624,0.00012840987],"category_scores_gemma":[0.000004990191,0.00018383736,0.0002890572,0.00044668117,0.0002179372,0.00017100446,0.00054240687,0.00009236643,0.000023672683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018673827,0.00008723962,0.012387522,0.000013719608,0.00035872785,8.395261e-7,0.00022933586,0.98296076,0.0023683042,0.0008221032,0.000015432443,0.00073732313],"study_design_scores_gemma":[0.00027187233,0.000064770495,0.011697138,0.0000605863,0.0009057829,7.143222e-7,0.00020304482,0.95420897,0.027302215,0.004510542,0.0004899324,0.0002844105],"about_ca_topic_score_codex":0.0003808103,"about_ca_topic_score_gemma":0.00012263292,"teacher_disagreement_score":0.54318786,"about_ca_system_score_codex":0.00061411463,"about_ca_system_score_gemma":0.0000036022561,"threshold_uncertainty_score":0.749667},"labels":[],"label_agreement":null},{"id":"W4408305758","doi":"10.1016/j.envsoft.2025.106429","title":"A hybrid framework for regional climate seasonality study and trend analysis","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Climate variability and models","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"York University","keywords":"Seasonality; Climatology; Regional science; Geography; Environmental science; Statistics; Geology; Mathematics","score_opus":0.02401354583576435,"score_gpt":0.2669317317068602,"score_spread":0.24291818587109584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408305758","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61033076,0.00008525504,0.388771,0.000073066316,0.00003523704,0.00041861844,0.00019199026,0.000050972714,0.000043089334],"genre_scores_gemma":[0.96124816,0.00011069989,0.037859663,0.00031205342,0.000019103198,0.00013792573,0.00013229933,0.000018925039,0.00016117287],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797857,0.00008271665,0.00034956718,0.0008523701,0.00031465437,0.0004221087],"domain_scores_gemma":[0.99891794,0.00038330018,0.00009088894,0.00047473956,0.0000017624573,0.00013137636],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048567465,0.0002648669,0.0003564384,0.00005636497,0.00043960137,0.000055366498,0.00021641499,0.00009058672,0.00042154652],"category_scores_gemma":[0.000019956458,0.0002667246,0.00021388396,0.00021979575,0.0002427657,0.00017120503,0.00035670746,0.00018368082,0.00002140774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002003052,0.00094023766,0.63634455,0.00003153288,0.00027682228,0.0000038253434,0.0007563916,0.35732275,0.000035067176,0.00039372587,0.00007679304,0.0036180168],"study_design_scores_gemma":[0.0011356886,0.0001931485,0.14413185,0.000038395174,0.0014529385,0.0000030815763,0.000595717,0.7913594,0.000052953477,0.058575742,0.001827825,0.0006332417],"about_ca_topic_score_codex":0.00010892616,"about_ca_topic_score_gemma":0.000040996056,"teacher_disagreement_score":0.49221268,"about_ca_system_score_codex":0.00023218006,"about_ca_system_score_gemma":0.000005604599,"threshold_uncertainty_score":0.9999785},"labels":[],"label_agreement":null},{"id":"W4409472480","doi":"10.1016/j.envsoft.2025.106481","title":"Approximate Bayesian inference for calibrating the IPCC tier-2 steady-state soil organic carbon model for Canadian croplands using long-term experimental data","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph; Environment and Climate Change Canada; Agriculture and Agri-Food Canada","funders":"","keywords":"Term (time); Bayesian inference; Soil carbon; Inference; Environmental science; Bayesian probability; Calibration; Econometrics; Steady state (chemistry); Soil science; Computer science; Mathematics; Statistics; Soil water; Artificial intelligence; Chemistry","score_opus":0.035744416063126096,"score_gpt":0.2636576378787344,"score_spread":0.22791322181560833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409472480","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35071957,0.0001504848,0.6474298,0.000038827282,0.0001311723,0.00074893684,0.0006941743,0.000039720366,0.00004731581],"genre_scores_gemma":[0.95632076,0.000028438273,0.042000216,0.0002983273,0.00004137766,0.00013349293,0.00057974306,0.000071628296,0.0005259947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976605,0.000036682137,0.00042710968,0.00084096467,0.00027066056,0.00076404883],"domain_scores_gemma":[0.9986303,0.00019071817,0.00014742788,0.00081127015,0.0000049814557,0.00021530276],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030215687,0.00037149523,0.00027273202,0.00006275414,0.0009768163,0.00016010935,0.0007993977,0.0001157815,0.00007192469],"category_scores_gemma":[0.000037423964,0.0003405261,0.000073713556,0.00012051779,0.00023262882,0.00029073088,0.0005903121,0.0001964923,0.000004298834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039154835,0.00007570144,0.020735465,0.000058110032,0.000046614165,0.0000046797936,0.0014573538,0.97214097,0.0026366417,0.00003678677,0.000099648365,0.002668892],"study_design_scores_gemma":[0.00052987813,0.000035549525,0.0002958706,0.000064904045,0.000068547764,0.0000035998287,0.00023888823,0.99595827,0.0012810822,0.0010731601,0.000058097994,0.00039216262],"about_ca_topic_score_codex":0.0075645135,"about_ca_topic_score_gemma":0.0074731447,"teacher_disagreement_score":0.6056012,"about_ca_system_score_codex":0.00064498786,"about_ca_system_score_gemma":0.0001272905,"threshold_uncertainty_score":0.9999047},"labels":[],"label_agreement":null},{"id":"W4409543205","doi":"10.1016/j.envsoft.2025.106474","title":"NSVineCopula: R package for modeling non-stationary multivariate dependence","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Chinese Academy of Sciences; National Natural Science Foundation of China; Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"Multivariate statistics; R package; Statistics; Mathematics; Econometrics; Computer science","score_opus":0.0317951248391797,"score_gpt":0.23340559140550793,"score_spread":0.20161046656632822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409543205","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22794107,0.0013968063,0.76926607,0.00006955993,0.00029795483,0.00040123475,0.00037841394,0.00006267517,0.00018625006],"genre_scores_gemma":[0.88739246,0.00024037294,0.11104464,0.00017186932,0.000064776315,0.000109803135,0.00017347108,0.000041490668,0.0007611145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980776,0.000009495876,0.00072241283,0.0007163017,0.000062771665,0.00041147473],"domain_scores_gemma":[0.9992498,0.000107398286,0.00016538893,0.0003932838,0.000011887817,0.000072249895],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035307326,0.00025651278,0.00038784748,0.0001688737,0.0003777981,0.000052486248,0.00027071583,0.00017124147,0.00005594904],"category_scores_gemma":[0.00007266317,0.00032762485,0.00020143151,0.000111698944,0.000041450767,0.00034106578,0.00010646873,0.00021325558,0.00013332107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007053772,0.00015096263,0.018713214,0.00007711578,0.00004240976,0.0000019261702,0.0005446882,0.96874994,0.00009132447,0.008714994,0.0000675656,0.002775326],"study_design_scores_gemma":[0.00059394806,0.000028110702,0.0008147341,0.00005125698,0.000012170848,4.492429e-7,0.00006737471,0.9126616,0.00011104107,0.08468373,0.0006810447,0.00029454453],"about_ca_topic_score_codex":0.00027711765,"about_ca_topic_score_gemma":0.000008085719,"teacher_disagreement_score":0.6594514,"about_ca_system_score_codex":0.00023156375,"about_ca_system_score_gemma":0.00002453218,"threshold_uncertainty_score":0.99991757},"labels":[],"label_agreement":null},{"id":"W4409878501","doi":"10.1016/j.envsoft.2025.106507","title":"Urban flood modelling: Challenges and opportunities - A stakeholder-informed analysis","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"Gouvernement de l'Île-du-Prince-Édouard; Natural Sciences and Engineering Research Council of Canada","keywords":"Flood myth; Stakeholder; Environmental planning; Stakeholder engagement; Stakeholder analysis; Environmental resource management; Environmental science; Geography; Political science; Public relations; Archaeology","score_opus":0.07045342286573593,"score_gpt":0.2346962455175058,"score_spread":0.16424282265176987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409878501","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35653982,0.006831439,0.6244111,0.0005311931,0.00015767096,0.00064642314,0.000047892547,0.0002786661,0.010555784],"genre_scores_gemma":[0.95853007,0.018358694,0.016536685,0.00030762507,0.000024740251,0.00007584252,0.00009925157,0.000031825475,0.006035254],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786645,0.000052717973,0.00040318284,0.0007040141,0.00047839605,0.0004952261],"domain_scores_gemma":[0.9991186,0.00008088432,0.00011564332,0.00051079,0.0000023774971,0.00017166151],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002324372,0.00038001896,0.00038538847,0.00022060092,0.000318478,0.00007600108,0.00031062204,0.00012099364,0.00044476055],"category_scores_gemma":[0.00000282474,0.00037629125,0.00016246738,0.00018313367,0.000271297,0.0004458294,0.00050592294,0.00019583569,0.000062000916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036479494,0.000297431,0.014723935,0.00006626305,0.00066859793,0.000020392521,0.002202406,0.9374338,0.000024006504,0.0011799164,0.0010804307,0.042266335],"study_design_scores_gemma":[0.0010393433,0.00013714896,0.007317083,0.000061286686,0.0014932384,0.0000020620728,0.0028805803,0.9255266,0.00013253644,0.0034379393,0.05704432,0.0009278294],"about_ca_topic_score_codex":0.00016055975,"about_ca_topic_score_gemma":0.00008344691,"teacher_disagreement_score":0.60787445,"about_ca_system_score_codex":0.00026289464,"about_ca_system_score_gemma":0.000014201901,"threshold_uncertainty_score":0.9998689},"labels":[],"label_agreement":null},{"id":"W4410218632","doi":"10.1016/j.envsoft.2025.106502","title":"Network analysis of ground-level ozone: Implications for environmental policy and air quality management","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ozone; Air quality index; Environmental science; Ground Level Ozone; Quality (philosophy); Environmental resource management; Meteorology; Geography; Physics","score_opus":0.02489879173268395,"score_gpt":0.2471917361214809,"score_spread":0.22229294438879696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410218632","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5893012,0.00073927076,0.40786752,0.00013298614,0.000032311797,0.0002696666,0.001199828,0.000025551468,0.00043162255],"genre_scores_gemma":[0.96334577,0.00031507996,0.03360949,0.0002609396,0.000042512806,0.000012425322,0.0010150746,0.0000046075397,0.001394127],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988915,0.000024708008,0.000321969,0.00037443897,0.00012679477,0.0002606189],"domain_scores_gemma":[0.9993313,0.00019725635,0.0001151824,0.00027635397,0.0000020262723,0.00007790494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015396209,0.00016440752,0.00025974476,0.000023368471,0.0002628247,0.000015682594,0.00016531693,0.00006952194,0.00018271034],"category_scores_gemma":[0.00000554559,0.00016647522,0.00012296363,0.00028851294,0.00015824036,0.000088997986,0.000045916902,0.00006767495,0.000004042947],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003437351,0.00005281993,0.41086018,0.00004043559,0.00035538146,3.005545e-7,0.00006258122,0.5593916,0.00006922795,0.00022319733,0.00003508909,0.028874783],"study_design_scores_gemma":[0.00031220564,0.000026061243,0.96642834,0.000015463458,0.00045430672,6.676001e-7,0.00024735395,0.027755147,0.00006398813,0.0036104606,0.00087513047,0.00021086357],"about_ca_topic_score_codex":0.00023250961,"about_ca_topic_score_gemma":0.000028437838,"teacher_disagreement_score":0.55556816,"about_ca_system_score_codex":0.000031989577,"about_ca_system_score_gemma":0.00001095919,"threshold_uncertainty_score":0.67886627},"labels":[],"label_agreement":null},{"id":"W4410278078","doi":"10.1016/j.envsoft.2025.106519","title":"Applying user-centred design to climate and environmental tools","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Innovative Approaches in Technology and Social Development","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Wageningen University and Research; Agricultural Research Service; Consortium of International Agricultural Research Centers; Vlaamse Overheid; Foreign, Commonwealth and Development Office; Department for International Development; Australian Government; International Development Research Centre; Vlaamse regering; University of Nebraska-Lincoln; Bill and Melinda Gates Foundation; U.S. Department of Agriculture","keywords":"Computer science; Environmental resource management; Human–computer interaction; Environmental science","score_opus":0.030452547056390517,"score_gpt":0.2054198524161084,"score_spread":0.17496730535971788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410278078","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2844232,0.00035784987,0.7120387,0.00031360346,0.0002835229,0.0015014593,0.000024415547,0.00032519817,0.0007320603],"genre_scores_gemma":[0.95696694,0.00009938572,0.03922802,0.0027245525,0.00009247352,0.0003678512,0.00011949549,0.000037583624,0.00036368854],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984654,0.000016083097,0.0003187188,0.0005267886,0.00020926105,0.00046374617],"domain_scores_gemma":[0.99958336,0.00006030485,0.00010043986,0.00023286058,0.0000043723244,0.00001867592],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025437784,0.00029048655,0.00024607198,0.0002132057,0.0005904985,0.00014621875,0.00026051744,0.00016982484,0.00008161328],"category_scores_gemma":[0.00002457219,0.00031461852,0.000051054776,0.00022518085,0.00016573654,0.00050099683,0.0006926857,0.00023799305,0.00019740997],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063197245,0.0012937044,0.31103173,0.00045047936,0.00053679466,0.000088763954,0.0024108782,0.066613354,0.0064154943,0.062654026,0.0038653058,0.54400754],"study_design_scores_gemma":[0.010029451,0.00022558385,0.09118213,0.0013553504,0.00093079754,0.000022397782,0.028792858,0.22369827,0.022547713,0.093772605,0.5188729,0.008569925],"about_ca_topic_score_codex":0.000009660386,"about_ca_topic_score_gemma":6.1608085e-7,"teacher_disagreement_score":0.6728107,"about_ca_system_score_codex":0.00017490472,"about_ca_system_score_gemma":0.000008190803,"threshold_uncertainty_score":0.99993056},"labels":[],"label_agreement":null},{"id":"W4410384667","doi":"10.1016/j.envsoft.2025.106490","title":"Stochastic generator for rainfall with a Hawkes process marked by an extended generalized Pareto and a vine copula","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; American Society of Neuroradiology","keywords":"Vine copula; Copula (linguistics); Pareto principle; Vine; Generalized Pareto distribution; Econometrics; Mathematics; Generator (circuit theory); Economics; Statistical physics; Statistics; Physics; Biology; Extreme value theory; Ecology","score_opus":0.00622623735565904,"score_gpt":0.22268546375987477,"score_spread":0.21645922640421572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410384667","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5634648,0.0002831471,0.4356625,0.00007463373,0.000018412024,0.00035107502,0.0000636574,0.000053237964,0.000028540522],"genre_scores_gemma":[0.9749621,0.000039510174,0.022420574,0.00064642925,0.000023373173,0.00027946878,0.00022109217,0.00003800702,0.001369425],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823,0.00006891198,0.00027527803,0.00076955045,0.00023999765,0.00041627476],"domain_scores_gemma":[0.999302,0.000068991016,0.0000948917,0.00031921035,0.000003541861,0.00021135659],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018809589,0.00032304964,0.00033988638,0.000048820042,0.0004368014,0.000043111224,0.00022238598,0.00014434803,0.00056962896],"category_scores_gemma":[0.000016390773,0.00028108878,0.00006878205,0.00013734806,0.00036162927,0.0002634545,0.00009256256,0.00013695848,0.000018353056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009274429,0.00052010297,0.03983896,0.00004804084,0.00020783368,0.0000104705605,0.0010346211,0.9480848,0.003990684,0.00002991121,0.0009548365,0.004352323],"study_design_scores_gemma":[0.0027776023,0.00039748865,0.0015267156,0.000037507838,0.0003845746,0.000012243195,0.00024118424,0.9883348,0.0015887934,0.002535382,0.0014546864,0.00070901733],"about_ca_topic_score_codex":0.00007767874,"about_ca_topic_score_gemma":0.00007693296,"teacher_disagreement_score":0.41324192,"about_ca_system_score_codex":0.00012085871,"about_ca_system_score_gemma":0.000015711856,"threshold_uncertainty_score":0.9999641},"labels":[],"label_agreement":null},{"id":"W4410635325","doi":"10.1016/j.envsoft.2025.106534","title":"Ice-jam flood predictions using an interpretable machine learning approach","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada; Global Institute for Water Security; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Global Institute for Water Security, University of Saskatchewan; University of Saskatchewan","keywords":"Flood myth; Machine learning; Computer science; Artificial intelligence; Geography; Archaeology","score_opus":0.01389199834786998,"score_gpt":0.22489492617308593,"score_spread":0.21100292782521596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410635325","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2812348,0.0002160476,0.71460307,0.000018128261,0.00016330813,0.0003447694,0.000017072554,0.00021793116,0.0031848722],"genre_scores_gemma":[0.89590967,0.00015680485,0.099882945,0.0001334007,0.000039937226,0.000050820312,0.0001503476,0.0000444781,0.0036316123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793684,0.00010566238,0.00033593352,0.0007361436,0.00040282405,0.00048259116],"domain_scores_gemma":[0.9992835,0.00002774882,0.00009819964,0.0004487088,0.0000017481523,0.00014010441],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025250416,0.00032281407,0.00023792427,0.0000969679,0.00065785134,0.000087552464,0.00039818673,0.00011108461,0.00089712953],"category_scores_gemma":[0.000006409918,0.0003297552,0.000110503046,0.00021865999,0.0001685443,0.0006534129,0.00059898256,0.00041035796,0.00012871914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001724519,0.0005000745,0.053435545,0.000016007712,0.000049033784,0.0000029564428,0.00036443124,0.94204557,0.0007755988,0.000037985064,0.00008750797,0.0026680266],"study_design_scores_gemma":[0.00042931226,0.00008587501,0.0020908099,0.000031051197,0.00013346567,0.000003799707,0.00034368236,0.9911537,0.00019511401,0.00038825604,0.004819816,0.0003251101],"about_ca_topic_score_codex":0.00077876076,"about_ca_topic_score_gemma":0.000021647833,"teacher_disagreement_score":0.6147201,"about_ca_system_score_codex":0.00049111346,"about_ca_system_score_gemma":0.000009905494,"threshold_uncertainty_score":0.9999154},"labels":[],"label_agreement":null},{"id":"W4410980007","doi":"10.1016/j.envsoft.2025.106555","title":"Towards simulating solute transport in complex, regional-scale fracture networks: a rapid upscaled approach","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scale (ratio); Fracture (geology); Geology; Geotechnical engineering; Geography; Cartography","score_opus":0.018614743064097543,"score_gpt":0.21892805664660928,"score_spread":0.20031331358251173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410980007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2584203,0.00032992617,0.739844,0.00015908838,0.000051533032,0.00032135527,0.000009329954,0.00008231521,0.0007821398],"genre_scores_gemma":[0.97565496,0.000111215806,0.020930128,0.0011099242,0.000039105515,0.00008193182,0.00020915158,0.000030980867,0.0018326112],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978329,0.00007281257,0.00047207705,0.00069362996,0.00042475646,0.0005038544],"domain_scores_gemma":[0.9994262,0.00006533463,0.000093387294,0.0003237429,0.0000027405276,0.00008859292],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024016073,0.0003435484,0.0003718439,0.00007144357,0.0003379362,0.00002856311,0.00031068234,0.00016018211,0.0004117678],"category_scores_gemma":[0.000003131462,0.0003352796,0.00015604506,0.0002381162,0.00024836572,0.00025851594,0.00018346563,0.0003403035,0.000045658366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003308102,0.00019486058,0.1657838,0.000013116408,0.000022907438,0.000005653427,0.001031509,0.8182892,0.00005418389,0.000005452839,0.00026159023,0.014304666],"study_design_scores_gemma":[0.001105192,0.00003255529,0.33458897,0.000049533945,0.00004820502,0.0000038013725,0.0005692069,0.64660835,0.000042232237,0.0003453212,0.01611586,0.0004907612],"about_ca_topic_score_codex":0.00031183442,"about_ca_topic_score_gemma":0.00006675191,"teacher_disagreement_score":0.7189139,"about_ca_system_score_codex":0.00038353886,"about_ca_system_score_gemma":0.000007665372,"threshold_uncertainty_score":0.99990994},"labels":[],"label_agreement":null},{"id":"W4411114766","doi":"10.1016/j.envsoft.2025.106567","title":"Development of an alpine hydrological model considering the recharge of stream water to alluvial plain aquifers","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Environment and Climate Change Canada","funders":"Science and Technology Plan Projects of Tibet Autonomous Region; Fundamental Research Funds for the Central Universities; Natural Science Foundation of Gansu Province; National Natural Science Foundation of China","keywords":"Groundwater recharge; Aquifer; Hydrology (agriculture); Alluvium; Alluvial plain; Geology; Groundwater; Depression-focused recharge; Alluvial soils; Environmental science; Geomorphology; Geotechnical engineering","score_opus":0.019184054656879675,"score_gpt":0.22337395110927985,"score_spread":0.20418989645240018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411114766","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83258,0.000020813672,0.16644946,0.00022707702,0.000048275673,0.00030966447,0.000011836964,0.0000344957,0.00031844142],"genre_scores_gemma":[0.9355114,0.00002100034,0.06363259,0.00039837777,0.0000073493266,0.000046159017,0.000027133989,0.000013489823,0.00034246503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984045,0.000059900307,0.000438715,0.00044186838,0.0002719607,0.0003830448],"domain_scores_gemma":[0.99948376,0.000042385385,0.000068667505,0.00033333377,0.0000017811287,0.00007004443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040813957,0.00022641958,0.0002757377,0.000052206102,0.0003100633,0.0000066475263,0.0003596978,0.00009436115,0.0003074342],"category_scores_gemma":[0.000009155265,0.0001514838,0.000067035675,0.00006502681,0.0003599874,0.000119810844,0.0007657059,0.0001492378,0.0000706899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011041796,0.00021155334,0.006183352,0.000014763247,0.00007051868,0.0000031385275,0.0040018745,0.9751047,0.0115966,0.00004778977,0.00006579924,0.002589526],"study_design_scores_gemma":[0.0014439682,0.00036269214,0.0037648233,0.00008663436,0.00017796262,0.000003435574,0.001194029,0.7547628,0.22643077,0.0059370035,0.005014858,0.00082101417],"about_ca_topic_score_codex":0.0000318116,"about_ca_topic_score_gemma":0.000025907908,"teacher_disagreement_score":0.22034186,"about_ca_system_score_codex":0.00011559851,"about_ca_system_score_gemma":0.000006184015,"threshold_uncertainty_score":0.61773294},"labels":[],"label_agreement":null},{"id":"W4412749294","doi":"10.1016/j.envsoft.2025.106632","title":"Framework for stochastic urban flood hazard mapping using coupled and industry-standard hydrologic and hydraulic models","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Flood myth; Hazard; Environmental science; Hydrological modelling; Hydrology (agriculture); Water resource management; Computer science; Geology; Geography; Geotechnical engineering; Climatology","score_opus":0.025148132622223488,"score_gpt":0.2481794266182749,"score_spread":0.22303129399605143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412749294","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4385381,0.00043911085,0.5601956,0.00009675134,0.00007732094,0.00053554395,0.00001993165,0.000059316135,0.00003831506],"genre_scores_gemma":[0.83476955,0.0001184703,0.16438328,0.00036362887,0.000035284018,0.00007749895,0.000023772975,0.000034334047,0.00019418908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980371,0.000037270325,0.0003341997,0.0007685797,0.00031057448,0.0005122892],"domain_scores_gemma":[0.9992798,0.00014559423,0.000116759744,0.0003062069,0.0000022918537,0.0001493238],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025916877,0.00035940178,0.000345194,0.000088328095,0.0005228552,0.00009605719,0.00020294306,0.00030878148,0.000083144165],"category_scores_gemma":[0.000017220498,0.00036393924,0.000074156495,0.0001354529,0.00030311977,0.00033118157,0.0005280936,0.0004092499,0.000008637054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060343344,0.000075542106,0.017055446,0.000041362917,0.00006336303,0.0000037537648,0.000424847,0.9796688,0.00027144357,0.0005887847,0.00009335353,0.0016529541],"study_design_scores_gemma":[0.0007669629,0.000101868165,0.0007652228,0.000107888125,0.00013206217,0.0000030952685,0.00027555265,0.94166124,0.000057014204,0.05537956,0.00038282006,0.0003666876],"about_ca_topic_score_codex":0.00006724386,"about_ca_topic_score_gemma":0.0000049455302,"teacher_disagreement_score":0.39623144,"about_ca_system_score_codex":0.0002959165,"about_ca_system_score_gemma":0.000011318813,"threshold_uncertainty_score":0.99988127},"labels":[],"label_agreement":null},{"id":"W4412980970","doi":"10.1016/j.envsoft.2025.106640","title":"An efficient knowledge-driven method for generating complex labels on geological maps","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Chinese Government Scholarship; China Geological Survey; China Scholarship Council; University of Saskatchewan","keywords":"Computer science; Artificial intelligence; Data mining","score_opus":0.046968914587331594,"score_gpt":0.3382873444515013,"score_spread":0.2913184298641697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412980970","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11375575,0.00016504529,0.8825881,0.00017768472,0.00029225132,0.00073960394,0.00007011545,0.0001829515,0.002028536],"genre_scores_gemma":[0.8214875,0.000024682906,0.17716199,0.00030777566,0.00011680951,0.00014150486,0.00007142515,0.000011200284,0.00067712873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984025,0.00020915229,0.00035483806,0.000345394,0.0002888452,0.0003993134],"domain_scores_gemma":[0.9991543,0.00040371643,0.00011165992,0.00021398538,0.000027657972,0.00008865545],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0007865717,0.00018115588,0.0002452136,0.00011131373,0.0016469306,0.000068046145,0.00026495848,0.0001311012,0.000052933796],"category_scores_gemma":[0.000049315473,0.00017117309,0.00011799073,0.00014746306,0.00021272364,0.00009067156,0.0000801156,0.00012955104,0.00005632854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017462813,0.00018921154,0.004257881,0.00002536725,0.000040808558,5.257565e-7,0.012694211,0.9630722,0.000113977956,0.013767084,0.000333059,0.005488253],"study_design_scores_gemma":[0.0007004456,0.00015714558,0.0011824445,0.000072999865,0.00004320967,5.7491735e-7,0.009927207,0.947187,0.00011275927,0.0023367554,0.037889577,0.00038986138],"about_ca_topic_score_codex":0.00012524836,"about_ca_topic_score_gemma":0.000049209488,"teacher_disagreement_score":0.7077317,"about_ca_system_score_codex":0.00019469706,"about_ca_system_score_gemma":0.000027626988,"threshold_uncertainty_score":0.9996528},"labels":[],"label_agreement":null},{"id":"W4415081672","doi":"10.1016/j.envsoft.2025.106733","title":"A feature-level ensemble framework for improving daily PM2.5 estimation across the contiguous United States (2000–2023)","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Extrapolation; Estimation; Ensemble forecasting; Ensemble average; Air quality index; Ensemble learning","score_opus":0.026844857147689576,"score_gpt":0.27148681373582567,"score_spread":0.2446419565881361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415081672","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3143824,0.00018746241,0.68404007,0.00040894703,0.00028061305,0.0004176996,0.00014681993,0.00011251451,0.000023456436],"genre_scores_gemma":[0.79393214,0.000029199173,0.20152196,0.00046134472,0.00007908451,0.00012372385,0.00024110677,0.00004378909,0.003567677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797237,0.00007522344,0.0003266871,0.0005948383,0.0003732062,0.0006577045],"domain_scores_gemma":[0.9985839,0.00063849107,0.00018731868,0.00048322277,0.0000061500464,0.00010087846],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005641451,0.00032606575,0.00023911393,0.00003943673,0.0011795291,0.00014735977,0.00041296604,0.00021577321,0.00004846989],"category_scores_gemma":[0.0001646299,0.00027439542,0.00013613827,0.00027425087,0.00030585093,0.00023253326,0.00031148983,0.00044884768,0.00008659227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007230291,0.00008380829,0.0047256346,0.000046586218,0.00003111957,0.0000023092362,0.0031990085,0.90396434,0.00031331656,0.000089785746,0.0005588917,0.08691291],"study_design_scores_gemma":[0.00064801023,0.00011188819,0.0037306584,0.00016595636,0.000080615944,0.0000054243756,0.002281281,0.9685709,0.0022054585,0.013706191,0.007958427,0.0005352131],"about_ca_topic_score_codex":0.00043789885,"about_ca_topic_score_gemma":0.000011976229,"teacher_disagreement_score":0.4825181,"about_ca_system_score_codex":0.00054871634,"about_ca_system_score_gemma":0.000015329117,"threshold_uncertainty_score":0.9999708},"labels":[],"label_agreement":null},{"id":"W4415943058","doi":"10.1016/j.envsoft.2025.106763","title":"Transitional fresh-saltwater assessment in the integrated surface-groundwater hydrology of the regulated La Paz tidal watershed in Mexico","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Coastal wetland ecosystem dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Universidad de Guanajuato; China Scholarship Council; Consejo Nacional de Ciencia y Tecnología","keywords":"Groundwater recharge; Hydrology (agriculture); Watershed; Groundwater; Arid; Saltwater intrusion; Water resources; Seawater; Resource (disambiguation)","score_opus":0.005610120936049428,"score_gpt":0.19997170813582615,"score_spread":0.19436158719977673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415943058","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98160833,0.000020188443,0.016580345,0.0007563269,0.00009918187,0.00049027195,0.00005537014,0.00002242715,0.00036756706],"genre_scores_gemma":[0.9980203,0.00001618447,0.0012372897,0.00026458534,0.000005689697,0.00003451941,0.00013892331,0.000021229758,0.00026126194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99777365,0.00046639272,0.00052771234,0.000433878,0.0004075158,0.00039088132],"domain_scores_gemma":[0.99930626,0.00015430954,0.00008646574,0.0004170833,0.0000019665233,0.00003391651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006734366,0.00025494405,0.0002736836,0.0000580713,0.000115417046,0.0000285844,0.0005211441,0.00018549462,0.0005036906],"category_scores_gemma":[0.000004272321,0.00015776881,0.0001152824,0.00026621934,0.0003190826,0.0001506062,0.0002517337,0.000514488,0.00003135553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058723537,0.00024613002,0.38958627,0.0000122786205,0.0000130652925,0.000010554488,0.0009617989,0.6064986,0.002372881,0.000028385099,0.000042616237,0.00016869135],"study_design_scores_gemma":[0.0014767145,0.00007950147,0.41100544,0.00013014708,0.00003333684,0.000023432418,0.0005484211,0.5808232,0.00156818,0.0028969608,0.0010655456,0.0003491312],"about_ca_topic_score_codex":0.0013174443,"about_ca_topic_score_gemma":0.0020009943,"teacher_disagreement_score":0.025675412,"about_ca_system_score_codex":0.00040608866,"about_ca_system_score_gemma":0.00001902922,"threshold_uncertainty_score":0.6433626},"labels":[],"label_agreement":null},{"id":"W4416406955","doi":"10.1016/j.envsoft.2025.106779","title":"MSFlood-Net: A physically informed deep learning model integrating multi-source data for flood inundation mapping","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Special Project of Central Government for Local Science and Technology Development of Hubei Province; Guangxi Key Research and Development Program; Natural Science Foundation of Guangxi Province; National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Flood myth; Deep learning; Digital elevation model; Robustness (evolution); Synthetic aperture radar; Feature learning; Consistency (knowledge bases); Representation (politics); Scalability","score_opus":0.027508719118568337,"score_gpt":0.2632804599263815,"score_spread":0.23577174080781316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416406955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12001366,0.00008592644,0.8782525,0.00007697886,0.00010356758,0.00084070885,0.000034399312,0.0002076038,0.00038462345],"genre_scores_gemma":[0.5895101,0.00012878739,0.40700507,0.00024049592,0.000041016378,0.00013404147,0.00088703213,0.000047860107,0.0020055955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975341,0.00005132222,0.00052043714,0.0009054353,0.0004210996,0.0005676107],"domain_scores_gemma":[0.9987826,0.00015842081,0.00021072588,0.00073806156,0.000004646099,0.00010554595],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003739901,0.00038839685,0.00030325723,0.0000960973,0.00064628996,0.00013156618,0.0008380644,0.00012369537,0.00012335584],"category_scores_gemma":[0.000079890946,0.0003944599,0.00012073751,0.00020414767,0.00014564885,0.00092096487,0.0013971083,0.00036967237,0.000109673296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023640572,0.00024011033,0.0074304235,0.0000495275,0.000059515878,6.9415813e-7,0.00095365907,0.90313286,0.0008119478,0.000042787648,0.00026941826,0.08698545],"study_design_scores_gemma":[0.001033248,0.0000539267,0.000905598,0.000087550034,0.00009113491,4.7915705e-7,0.0009928918,0.98710716,0.00015879226,0.00080460845,0.008354502,0.0004101146],"about_ca_topic_score_codex":0.0001407246,"about_ca_topic_score_gemma":0.00010638711,"teacher_disagreement_score":0.47124746,"about_ca_system_score_codex":0.00047718483,"about_ca_system_score_gemma":0.00002359805,"threshold_uncertainty_score":0.99985075},"labels":[],"label_agreement":null},{"id":"W4416701898","doi":"10.1016/j.envsoft.2025.106792","title":"Advancing geospatial data infrastructure in Dataverse via metadata automation, interactive tools and LLM case study","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Research Data Management Practices","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Artificial Intelligence in Medicine (Canada)","funders":"Office of Advanced Cyberinfrastructure; Alfred P. Sloan Foundation; National Institutes of Health; National Science Foundation","keywords":"Geospatial analysis; Metadata; Geospatial metadata; Geospatial PDF; Metadata management; Meta Data Services; Reuse; Metadata modeling; Metadata repository","score_opus":0.04018958650219973,"score_gpt":0.3170950997399523,"score_spread":0.2769055132377526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416701898","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20065509,0.00007219408,0.798076,0.000077864526,0.00011634043,0.00062513666,0.00028900956,0.000074534175,0.000013872742],"genre_scores_gemma":[0.83484113,0.00019143982,0.16405605,0.000111302,0.000018236615,0.000030270103,0.0006368608,0.000012960957,0.00010174796],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742055,0.00026274586,0.0003923239,0.0011421056,0.00043383517,0.00034843278],"domain_scores_gemma":[0.9968272,0.0004886069,0.00015458486,0.0024313715,0.000008697097,0.00008953656],"candidate_categories":["scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.0009112262,0.00023403649,0.00022856514,0.00026240657,0.00023997457,0.0015661595,0.0022918948,0.000048218026,0.000025126223],"category_scores_gemma":[0.00026972755,0.00023784228,0.000018161445,0.00029406903,0.00006606167,0.053413033,0.007424796,0.00041118916,0.000015060948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016664826,0.0024262685,0.11217556,0.00022903526,0.0006763206,0.008367228,0.007944576,0.4024822,0.00019008719,0.001089643,0.0012573102,0.4629951],"study_design_scores_gemma":[0.00081129035,0.00008517717,0.0062121437,0.000047363545,0.000050672388,0.00011685064,0.0030157608,0.9845209,0.00002997583,0.0005059712,0.0043289135,0.00027495806],"about_ca_topic_score_codex":0.0008568608,"about_ca_topic_score_gemma":0.0003570794,"teacher_disagreement_score":0.634186,"about_ca_system_score_codex":0.0001997215,"about_ca_system_score_gemma":0.0000451084,"threshold_uncertainty_score":0.9994703},"labels":[],"label_agreement":null},{"id":"W4416825208","doi":"10.1016/j.envsoft.2025.106806","title":"Bayesian-factorial analysis for unveiling multi-factor interactive effect on water demand in Central Asia","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Water resources management and optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"National Natural Science Foundation of China","keywords":"Bayesian probability; Key (lock); Irrigation; Central asia; Agriculture; Range (aeronautics); Virtual water; Bayesian inference","score_opus":0.006862130538194108,"score_gpt":0.2002591476222562,"score_spread":0.1933970170840621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416825208","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38991424,0.000028908198,0.60931695,0.00000562998,0.00026413432,0.00033936373,0.000019545509,0.000088818226,0.000022433233],"genre_scores_gemma":[0.99430877,0.000017262086,0.0050203996,0.000014507632,0.000065494496,0.000059270107,0.00029427782,0.000036858048,0.00018316288],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989053,0.000034371133,0.00025182954,0.00031426034,0.00012345404,0.00037081106],"domain_scores_gemma":[0.9996678,0.00009324249,0.000024076753,0.0001649355,0.0000022949864,0.000047619113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007755328,0.00024844776,0.00027093603,0.00030822848,0.00007491016,0.000056388813,0.00012271073,0.000098616896,0.000049364644],"category_scores_gemma":[0.0000061194596,0.00021248231,0.00016952847,0.00012405534,0.000016902572,0.00016366622,0.00004088494,0.00016403891,0.000009580884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001223021,0.00005239944,0.03360225,0.00005343485,0.00029132146,0.0000023151931,0.00072403834,0.9637781,0.0003790998,0.0000023036519,0.0000070176116,0.0009854112],"study_design_scores_gemma":[0.0010671197,0.000058308153,0.0027390867,0.000038809827,0.00016786919,5.3037002e-8,0.000029356153,0.9778038,0.017471394,0.000035542726,0.00036525037,0.00022336109],"about_ca_topic_score_codex":0.000009246028,"about_ca_topic_score_gemma":0.0000090488575,"teacher_disagreement_score":0.60439456,"about_ca_system_score_codex":0.00031414305,"about_ca_system_score_gemma":0.0000015070984,"threshold_uncertainty_score":0.8664777},"labels":[],"label_agreement":null},{"id":"W4417027410","doi":"10.1016/j.envsoft.2025.106816","title":"Modelling near-surface ice content and midwinter melt events in mineral soils","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo; University of Guelph; Wilfrid Laurier University; Queen's University","funders":"ArcticNet; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Flooding (psychology); Flood myth; Hydrology (agriculture); Soil water; Hydrological modelling; Climate model; Ice formation","score_opus":0.04194900295298828,"score_gpt":0.21868526036576758,"score_spread":0.1767362574127793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417027410","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9713546,0.0027248128,0.024807652,0.00013804557,0.00020461295,0.00020712538,0.00037756815,0.000029921854,0.00015565977],"genre_scores_gemma":[0.99354136,0.00081800856,0.0037819971,0.00038686086,0.000029703284,0.0000021850942,0.00062859687,0.000008496798,0.00080278376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987103,0.000047838243,0.00028027975,0.000412403,0.00017751331,0.00037168083],"domain_scores_gemma":[0.9995644,0.000112084716,0.000048298847,0.00017101632,0.0000036408296,0.0001005767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015421232,0.00021854826,0.00022691426,0.00003572374,0.00016977415,0.000043831904,0.00014578682,0.00010113413,0.00076796935],"category_scores_gemma":[0.000003396237,0.00020682365,0.000062440355,0.0000776319,0.000088431414,0.00023128171,0.00004169636,0.00020894347,0.000099304394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042731357,0.000023612234,0.42515016,0.000019305404,0.000008949753,0.0000068326954,0.0005366697,0.5728729,0.00006550781,0.0000013255772,0.000025086336,0.0012469437],"study_design_scores_gemma":[0.00051926216,0.00003791347,0.037932117,0.00012376328,0.000017303528,0.0000049313217,0.00030509275,0.95986557,0.00007868645,0.00035991558,0.000520624,0.00023483082],"about_ca_topic_score_codex":0.0040421076,"about_ca_topic_score_gemma":0.0008736163,"teacher_disagreement_score":0.38721803,"about_ca_system_score_codex":0.000024370156,"about_ca_system_score_gemma":0.000011390955,"threshold_uncertainty_score":0.8434024},"labels":[],"label_agreement":null},{"id":"W7116704148","doi":"10.1016/j.envsoft.2025.106843","title":"Taming the non-linearity: An iterative conceptual routing model for improving flood peak prediction","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Stantec (Canada)","funders":"Science and Engineering Research Board; Ministry of Education, India; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Routing (electronic design automation); Flow (mathematics); Flood myth; Streamflow; Magnitude (astronomy); Flow routing; Function (biology)","score_opus":0.014777430914944711,"score_gpt":0.23359245372894416,"score_spread":0.21881502281399945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116704148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26882172,0.000046928624,0.7296606,0.00006421333,0.00020672425,0.000810854,0.000059806956,0.00009736025,0.00023179922],"genre_scores_gemma":[0.93969786,0.000024793246,0.05712255,0.00039352226,0.00009146741,0.00022480913,0.00020598243,0.000037824593,0.0022012107],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980176,0.000055900036,0.00037778215,0.0006941873,0.00037799394,0.00047649807],"domain_scores_gemma":[0.99926335,0.00009296388,0.00012701284,0.0004265841,0.0000042772344,0.000085842024],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004413108,0.00030589546,0.00020508037,0.00004140579,0.0009684846,0.00012429584,0.00040063952,0.000105956045,0.00007436572],"category_scores_gemma":[0.000013646793,0.00025734853,0.00012363233,0.00010935028,0.00027603394,0.0006580069,0.00044430976,0.0002712899,0.000026873375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000329193,0.00015579273,0.0063686823,0.000014771681,0.000032154414,8.314715e-7,0.004256965,0.9748823,0.001654101,0.00018680032,0.00011827993,0.0122964075],"study_design_scores_gemma":[0.0006951234,0.00010722924,0.0010842455,0.000026843432,0.00011313652,5.5472754e-7,0.0022498835,0.99352515,0.0005527527,0.0008442617,0.000546915,0.00025391486],"about_ca_topic_score_codex":0.00016418674,"about_ca_topic_score_gemma":0.000033872184,"teacher_disagreement_score":0.67253804,"about_ca_system_score_codex":0.00040034967,"about_ca_system_score_gemma":0.000017688775,"threshold_uncertainty_score":0.9999879},"labels":[],"label_agreement":null},{"id":"W7117258432","doi":"10.1016/j.envsoft.2025.106837","title":"A reliable deep ensemble hybrid model for urban air quality health index forecasting in maritime Canada","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Nova Scotia Department of Agriculture; University of Guelph; University of Prince Edward Island","funders":"Natural Sciences and Engineering Research Council of Canada; University of Prince Edward Island; University of Guelph","keywords":"Feature selection; Resampling; Ensemble forecasting; Air quality index; Benchmarking; Feature (linguistics); Ensemble learning; Index (typography); Interpretability","score_opus":0.029956856469111438,"score_gpt":0.24929837475496985,"score_spread":0.2193415182858584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117258432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33018163,0.00021741618,0.66833824,0.00020877644,0.00015935212,0.00048908865,0.00007921889,0.00007307642,0.00025321887],"genre_scores_gemma":[0.9482275,0.000022439895,0.04823796,0.0007556433,0.00005695941,0.00011734733,0.00009076308,0.000049068716,0.0024422945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969091,0.0000831732,0.00079979724,0.0008079707,0.00048567244,0.000914276],"domain_scores_gemma":[0.99887455,0.000262788,0.00022983192,0.00042575478,0.000004160919,0.00020290668],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00086320465,0.00035149022,0.0004426502,0.00006373286,0.00054558343,0.000029228866,0.000339208,0.00010240456,0.000043253916],"category_scores_gemma":[0.00007176588,0.000406553,0.0001140948,0.00017484886,0.00011857976,0.00023468773,0.00030133058,0.00037671888,0.000009917409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051384322,0.0000962528,0.1373729,0.00007463752,0.000010100937,0.000004099998,0.00044327622,0.8480816,0.00001707644,0.000020607675,0.0010340622,0.012794048],"study_design_scores_gemma":[0.00062411465,0.000039022867,0.0042984206,0.00013352152,0.000009297255,0.0000031233656,0.00024348834,0.99014354,0.00015439303,0.002491376,0.0014711691,0.00038851847],"about_ca_topic_score_codex":0.18541679,"about_ca_topic_score_gemma":0.06057197,"teacher_disagreement_score":0.62010026,"about_ca_system_score_codex":0.0026795634,"about_ca_system_score_gemma":0.00015762281,"threshold_uncertainty_score":0.99983865},"labels":[],"label_agreement":null},{"id":"W819596111","doi":"10.1016/j.envsoft.2015.06.009","title":"From meta-studies to modeling: Using synthesis knowledge to build broadly applicable process-based land change models","year":2015,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Svenska Forskningsrådet Formas; European Research Council; National Socio-Environmental Synthesis Center; National Science Foundation","keywords":"Computer science; Conceptualization; Process (computing); Metamodeling; Aggregate (composite); Data science; Management science; Software engineering; Artificial intelligence; Engineering; Programming language","score_opus":0.2046372097797259,"score_gpt":0.3036795302175079,"score_spread":0.09904232043778197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W819596111","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7602597,0.0013512634,0.2370532,0.00012829395,0.00010877443,0.0006954777,0.0001677296,0.00013840012,0.00009713885],"genre_scores_gemma":[0.9615698,0.00003697342,0.036739863,0.00056940847,0.00020916839,0.0007098964,0.000032756478,0.00008968163,0.00004245982],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971762,0.0000725017,0.000443918,0.0010555226,0.0006081693,0.00064369227],"domain_scores_gemma":[0.99854475,0.000105015155,0.00010198737,0.00060940644,0.000012299457,0.00062655064],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00037138994,0.0005026554,0.0006991244,0.00008260097,0.00028560185,0.00007094384,0.0005419779,0.00014554175,0.00038508323],"category_scores_gemma":[0.000018191513,0.00041624834,0.00017405226,0.0002501305,0.000029898269,0.0005934038,0.00044629304,0.00014298085,0.0018307634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006427691,0.00016057446,0.004403729,0.000034472836,0.00017254564,0.0000053188264,0.003712703,0.990759,0.000081580016,5.4527095e-7,0.00008569058,0.0005195374],"study_design_scores_gemma":[0.00027975594,0.00005265883,0.000013890639,0.000095194235,0.0005151558,0.0000019649608,0.0005572187,0.99448466,0.0010401398,0.0017456665,0.0006200838,0.000593618],"about_ca_topic_score_codex":0.0036647173,"about_ca_topic_score_gemma":0.0004135461,"teacher_disagreement_score":0.20131008,"about_ca_system_score_codex":0.00054246234,"about_ca_system_score_gemma":0.000017841394,"threshold_uncertainty_score":0.99982893},"labels":[],"label_agreement":null}]}