{"meta":{"query_hash":"a98dc5c3374c","filters":{"venue":"Hydrology"},"cohort_total":92,"direct_labels_cover":0,"predictions_cover":92,"exported":92,"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/a98dc5c3374c","api":"https://metacan.xera.ac/api/v1/cohort?venue=Hydrology"},"results":[{"id":"W1603801414","doi":"10.3390/hydrology2020093","title":"Evapotranspiration Trends Over the Eastern United States During the 20th Century","year":2015,"lang":"en","type":"article","venue":"Hydrology","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":41,"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":"Goddard Space Flight Center; U.S. Geological Survey; National Aeronautics and Space Administration","keywords":"Evapotranspiration; Environmental science; Hydrology (agriculture); Archaeology; Geography; Geology; Geotechnical engineering","score_opus":0.010007262449910894,"score_gpt":0.20479720097479795,"score_spread":0.19478993852488705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1603801414","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9862592,0.00099933,0.0003529828,0.00033089347,0.000024974303,0.0000040146356,0.0072813197,0.00004483369,0.004702456],"genre_scores_gemma":[0.9934275,0.0008788898,0.00037906368,0.00006570413,0.000024390509,0.000007127883,0.0037654075,0.0000069564335,0.0014449878],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99993515,0.000008505147,0.000009169572,0.000020894035,0.000017800141,0.000008465647],"domain_scores_gemma":[0.9997279,0.0000338904,0.00013756801,0.000014463212,0.00006639342,0.000019794354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018672147,0.00016515519,0.00007952772,0.0007032453,0.00019638252,0.00038071803,0.00011139031,0.00012927137,0.0009908694],"category_scores_gemma":[0.0005032305,0.00006828338,0.00012195588,0.001318526,0.00011848773,0.000476559,0.00026980796,0.00021177305,0.00013725527],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050497467,0.000037704496,0.96464634,0.000080071186,0.00011478421,0.00013506711,0.00069295673,0.003446173,0.0010141865,0.0011129522,0.0037115326,0.024957675],"study_design_scores_gemma":[0.000003057791,0.000013187925,0.9879652,0.000013858508,0.000024509603,0.00009635798,0.00020014393,0.0017615866,0.0004026588,0.00014712047,0.00936609,0.0000062588265],"about_ca_topic_score_codex":0.036695525,"about_ca_topic_score_gemma":0.085970834,"teacher_disagreement_score":0.036695525,"about_ca_system_score_codex":0.0006131633,"about_ca_system_score_gemma":0.00022084481,"threshold_uncertainty_score":0.07296389},"labels":[],"label_agreement":null},{"id":"W1991032810","doi":"10.3390/hydrology1010020","title":"Evaluating Three Hydrological Distributed Watershed Models: MIKE-SHE, APEX, SWAT","year":2014,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":239,"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 Guelph; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Watershed; Soil and Water Assessment Tool; Hydrology (agriculture); Streamflow; SWAT model; Environmental science; Calibration; Hydrological modelling; Drainage basin; Geography; Geology; Computer science; Cartography; Climatology; Statistics; Mathematics","score_opus":0.045734214607141443,"score_gpt":0.269925892202175,"score_spread":0.22419167759503356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991032810","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98412806,0.00009208841,0.009691282,0.0002666454,0.000030546897,0.00020464328,0.0017099264,0.0006943067,0.0031826212],"genre_scores_gemma":[0.9713083,0.00010581183,0.025131436,0.00006374278,0.0000086154905,0.00018813915,0.0023044953,0.00011763728,0.0007718797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999134,0.00031672744,0.00007658988,0.00016324487,0.00021285439,0.00009657919],"domain_scores_gemma":[0.9954331,0.0025031397,0.0003195822,0.00036278047,0.0010183196,0.00036313146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035043643,0.0010130797,0.00091385195,0.0010253154,0.0006517283,0.0011895705,0.0023106106,0.0011696612,0.0013681822],"category_scores_gemma":[0.0071803215,0.0005646098,0.0008843977,0.0013201665,0.000720277,0.0017624762,0.0008706826,0.00094774505,0.00019114694],"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":[0.00023870813,0.0002286952,0.018397693,0.000043689764,0.00010474234,0.000047484074,0.00004974827,0.97197515,0.0006341513,0.00080061855,0.00052646914,0.0069528944],"study_design_scores_gemma":[0.00012040229,0.00013805047,0.003449776,0.00000622756,0.000032951048,0.000009947589,0.00006422012,0.9944893,0.0009465615,0.00028690865,0.00043519182,0.000020421292],"about_ca_topic_score_codex":0.12605584,"about_ca_topic_score_gemma":0.13696212,"teacher_disagreement_score":0.12605584,"about_ca_system_score_codex":0.0041604554,"about_ca_system_score_gemma":0.0036559869,"threshold_uncertainty_score":0.2506442},"labels":[],"label_agreement":null},{"id":"W2041917558","doi":"10.3390/hydrology2010023","title":"Impacts of Forest Fires and Climate Variability on the Hydrology of an Alpine Medium Sized Catchment in the Canadian Rocky Mountains","year":2015,"lang":"en","type":"article","venue":"Hydrology","topic":"Fire effects on ecosystems","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":true,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Hydrology (agriculture); Environmental science; Drainage basin; Climate change; Alpine climate; Physical geography; Geography; Geology; Ecology; Geotechnical engineering; Oceanography","score_opus":0.010471217212951897,"score_gpt":0.2331148712410009,"score_spread":0.22264365402804898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041917558","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.999191,0.000037559672,0.00007273116,0.000032502547,9.471504e-7,0.0000037837667,0.00016484228,0.000007512327,0.00048910675],"genre_scores_gemma":[0.9996093,0.00003133885,0.00010538933,0.0000053885356,7.6986174e-7,0.000002002354,0.00013620779,0.000001403326,0.00010837271],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99985695,0.000020458725,0.0000046416158,0.000024116363,0.00003799283,0.000055778568],"domain_scores_gemma":[0.999866,0.000036564626,0.000017561542,0.000009256241,0.00003287637,0.000037765556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024008461,0.00016164487,0.00014177995,0.00035794114,0.00056936656,0.00067770336,0.00043884723,0.00022208811,0.0004795849],"category_scores_gemma":[0.0006412471,0.00008722525,0.00024407056,0.0005216641,0.0003589821,0.00016634684,0.00027935472,0.0001616626,0.000033602366],"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":[0.00018267232,0.00010935484,0.90076476,0.000055591005,0.00016901178,0.000591337,0.0005428484,0.07058836,0.006653357,0.0008989024,0.0007419192,0.018701978],"study_design_scores_gemma":[0.000009896122,0.000018191275,0.96787673,0.0000046631276,0.000025534157,0.0000388032,0.0004925723,0.030484872,0.00044931806,0.00006536064,0.0005204988,0.000013676759],"about_ca_topic_score_codex":0.936939,"about_ca_topic_score_gemma":0.95559156,"teacher_disagreement_score":0.063061,"about_ca_system_score_codex":0.005364866,"about_ca_system_score_gemma":0.0047665844,"threshold_uncertainty_score":0.12686473},"labels":[],"label_agreement":null},{"id":"W2094896410","doi":"10.3390/hydrology1010001","title":"Net Snowpack Accumulation and Ablation Characteristics in the Inland Temperate Rainforest of the Upper Fraser River Basin, Canada","year":2014,"lang":"en","type":"article","venue":"Hydrology","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":14,"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 Northern British Columbia","funders":"Ministry of Forests, Lands and Natural Resource Operations; Oregon State University; University of Alberta; University of Northern British Columbia","keywords":"Snowpack; Snow; Environmental science; Structural basin; Drainage basin; Snowmelt; Physical geography; Elevation (ballistics); Mesoscale meteorology; Hydrology (agriculture); Climatology; Temperate climate; Atmospheric sciences; Geology; Geography; Geomorphology; Ecology","score_opus":0.01399281388372982,"score_gpt":0.2028288453203433,"score_spread":0.18883603143661346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094896410","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9949456,0.0005838235,0.000058347698,0.00006638228,0.0000037074208,0.000010113766,0.001975901,0.000010927685,0.0023452805],"genre_scores_gemma":[0.9955909,0.00050199544,0.00017054891,0.00003866431,0.0000023774671,0.0000068451563,0.0015543412,0.0000051257634,0.002129157],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99979216,0.000008737735,0.00000976134,0.0000410766,0.00006173071,0.00008655268],"domain_scores_gemma":[0.99943775,0.00002672776,0.00006921344,0.000015299825,0.00032284798,0.00012816227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016035503,0.0002747018,0.00021854606,0.0013599136,0.0014084176,0.0008866772,0.00049761817,0.00016194572,0.0016225837],"category_scores_gemma":[0.0005151218,0.00016075568,0.00014608835,0.002458462,0.00036063767,0.00022557349,0.00027870853,0.00020889424,0.00024219474],"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":[0.00005900048,0.00002734561,0.98140645,0.000040799274,0.000048018035,0.00015748772,0.0009841922,0.0002359164,0.001276799,0.00007486337,0.0015011407,0.014188111],"study_design_scores_gemma":[9.658951e-7,0.0000031274683,0.9986596,0.000008637036,0.0000045906404,0.000030186899,0.0004898935,0.00008912587,0.000049168844,0.0000046420164,0.00065751374,0.000002488591],"about_ca_topic_score_codex":0.9936221,"about_ca_topic_score_gemma":0.9987134,"teacher_disagreement_score":0.011332384,"about_ca_system_score_codex":0.011332384,"about_ca_system_score_gemma":0.00929612,"threshold_uncertainty_score":0.08222252},"labels":[],"label_agreement":null},{"id":"W2177880339","doi":"10.3390/hydrology2040289","title":"Performance and Uncertainty Evaluation of Snow Models on Snowmelt Flow Simulations over a Nordic Catchment (Mistassibi, Canada)","year":2015,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Snowmelt; Streamflow; Snow; Environmental science; Calibration; Drainage basin; Climatology; Energy balance; Degree day; Water year; Meteorology; Hydrology (agriculture); Statistics; Geology; Mathematics; Geography","score_opus":0.03212635702927989,"score_gpt":0.2531515587834659,"score_spread":0.221025201754186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2177880339","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99802846,0.00008198259,0.001112629,0.000040816438,0.000007986544,0.000012140131,0.00014284211,0.00008729307,0.00048594092],"genre_scores_gemma":[0.99837697,0.000041013685,0.0011508734,0.000013069054,0.0000038799344,0.0000078018875,0.00024773699,0.000012397402,0.00014622534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995765,0.00016184962,0.00002442012,0.0000653805,0.0000740293,0.00009780045],"domain_scores_gemma":[0.9990983,0.0004161825,0.00008952903,0.000061086685,0.00021848151,0.00011642582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016745612,0.0010393566,0.00079261535,0.00055939227,0.0007860497,0.00090404047,0.0009365589,0.00067441066,0.0003728299],"category_scores_gemma":[0.0020143671,0.0005110857,0.0007664239,0.00045611715,0.0005210909,0.00050681055,0.00062900584,0.0004740251,0.00006057851],"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":[0.00020156485,0.0000633787,0.017972028,0.000021358705,0.00013879625,0.00004628883,0.000052234922,0.97581,0.001346807,0.0001009954,0.00008660874,0.004160008],"study_design_scores_gemma":[0.0000317167,0.00010366166,0.008718447,0.0000075975245,0.00003973653,0.00000769052,0.000039891867,0.98933345,0.0015396291,0.00005174265,0.00011378081,0.000012557969],"about_ca_topic_score_codex":0.3598385,"about_ca_topic_score_gemma":0.27276808,"teacher_disagreement_score":0.6401615,"about_ca_system_score_codex":0.0035053387,"about_ca_system_score_gemma":0.0025788539,"threshold_uncertainty_score":0.71548796},"labels":[],"label_agreement":null},{"id":"W2571921176","doi":"10.3390/hydrology4010005","title":"Spatial and Temporal Variability of Potential Evaporation across North American Forests","year":2017,"lang":"en","type":"article","venue":"Hydrology","topic":"Plant Water Relations and Carbon Dynamics","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":"Ministry of Forests; Natural Resources Canada; University of British Columbia; Canadian Forest Service","funders":"","keywords":"Environmental science; Spatial variability; Physical geography; Geography; Hydrology (agriculture); Geology; Geotechnical engineering","score_opus":0.0061379895423834254,"score_gpt":0.22996222801574023,"score_spread":0.2238242384733568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2571921176","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99878186,0.00015648991,0.00027728832,0.000038854876,0.0000027031786,0.0000022630186,0.00024722298,0.000014145216,0.00047919716],"genre_scores_gemma":[0.9990331,0.00011764895,0.00024936185,0.000008358922,0.0000035664425,0.0000067844235,0.0004170077,0.00000415978,0.00016008668],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998435,0.000033258588,0.000009575952,0.000068068446,0.000022956097,0.000022623464],"domain_scores_gemma":[0.99961483,0.00013803274,0.00009739948,0.000039132257,0.000082700426,0.000027881717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000657138,0.00017249143,0.00021821875,0.0005527986,0.00032930964,0.00062777085,0.0002771362,0.00024726588,0.0006137959],"category_scores_gemma":[0.0010806167,0.00017979863,0.00035612716,0.0008145925,0.00030395534,0.0006163515,0.00046410685,0.00025941388,0.00006843853],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010251874,0.000051878364,0.957723,0.000055226497,0.00021215795,0.00013834363,0.0009969051,0.023917355,0.0024786773,0.0004941858,0.0006500015,0.013179633],"study_design_scores_gemma":[0.0000022375393,0.0000068597387,0.9882516,0.000009381392,0.000018839608,0.00003436064,0.00025358828,0.010497173,0.00015117283,0.00014275813,0.0006209862,0.00001097855],"about_ca_topic_score_codex":0.065648496,"about_ca_topic_score_gemma":0.11947931,"teacher_disagreement_score":0.9343515,"about_ca_system_score_codex":0.00068241404,"about_ca_system_score_gemma":0.00036253047,"threshold_uncertainty_score":0.13053274},"labels":[],"label_agreement":null},{"id":"W2581530189","doi":"10.3390/hydrology4010007","title":"A Multi-Faceted Debris-Flood Hazard Assessment for Cougar Creek, Alberta, Canada","year":2017,"lang":"en","type":"article","venue":"Hydrology","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"BGC Engineering (Canada)","funders":"","keywords":"Debris; Flood myth; Hydrology (agriculture); Hazard; Geology; Hazard analysis; Archaeology; Geography; Physical geography; Geotechnical engineering; Ecology; Oceanography; Engineering","score_opus":0.011366576672712065,"score_gpt":0.2550573082627247,"score_spread":0.24369073159001264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2581530189","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9700836,0.0007692687,0.0029481465,0.00026276565,0.000016298734,0.00026657394,0.01087964,0.00016335792,0.014610343],"genre_scores_gemma":[0.9780442,0.0005750844,0.0040028505,0.000064840366,0.0000051999355,0.00004442321,0.006056255,0.00002303925,0.011184084],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99971896,0.000009543318,0.000008185712,0.000029763047,0.00016107115,0.00007231645],"domain_scores_gemma":[0.99929285,0.000036074543,0.00005040184,0.00001624454,0.00048627678,0.00011813844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003585882,0.0005028323,0.0002228917,0.0023153157,0.0018346136,0.0013009699,0.0008288342,0.00030981464,0.0023816333],"category_scores_gemma":[0.00059286057,0.00022641342,0.00024400097,0.0036137393,0.00040612085,0.0002720454,0.00055461493,0.00025169598,0.00021590557],"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":[0.00037072928,0.00023659747,0.79615974,0.00030469647,0.00014555134,0.0016498219,0.0019667533,0.04055735,0.005409725,0.0020441446,0.014452641,0.1367023],"study_design_scores_gemma":[0.00002472314,0.000097849734,0.9440501,0.0000889986,0.000068194,0.00018968341,0.0042789294,0.035219565,0.0008664605,0.00036892868,0.014694556,0.00005198589],"about_ca_topic_score_codex":0.9923396,"about_ca_topic_score_gemma":0.9980761,"teacher_disagreement_score":0.020961676,"about_ca_system_score_codex":0.020961676,"about_ca_system_score_gemma":0.029232277,"threshold_uncertainty_score":0.15208828},"labels":[],"label_agreement":null},{"id":"W2586586651","doi":"10.3390/hydrology4010009","title":"Application of HEC-HMS in a Cold Region Watershed and Use of RADARSAT-2 Soil Moisture in Initializing the Model","year":2017,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Canadian Space Agency","keywords":"Environmental science; Snowmelt; Hydrology (agriculture); Water content; Infiltration (HVAC); Watershed; Soil water; Hydrological modelling; Soil science; Water storage; Moisture; Snow; Meteorology; Inlet; Geology; Geotechnical engineering; Climatology; Geography","score_opus":0.027440059278942885,"score_gpt":0.2419673536947036,"score_spread":0.2145272944157607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586586651","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9922914,0.000021871501,0.00442353,0.00010774239,0.000009323237,0.000082932514,0.00039695,0.00023118286,0.0024351813],"genre_scores_gemma":[0.9934928,0.000021653452,0.005557565,0.000025967744,0.000003501108,0.000035713245,0.0003133552,0.000015836958,0.0005337119],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997657,0.00005912911,0.000012896505,0.000067181536,0.000038709204,0.000056420366],"domain_scores_gemma":[0.9995553,0.00017028872,0.000037573147,0.00004435059,0.00014830878,0.000044131448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007835497,0.000594945,0.00042292636,0.00036159868,0.0006413094,0.00078579696,0.0011604169,0.00069930684,0.0008524855],"category_scores_gemma":[0.001216797,0.0003625816,0.00044865225,0.00038762536,0.00043077528,0.0004348771,0.0004106332,0.000587529,0.00011892918],"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":[0.00008424444,0.00010842049,0.015277672,0.000015947871,0.000026946436,0.00011029562,0.000037733334,0.97703665,0.002368468,0.0002576474,0.00015382173,0.0045221224],"study_design_scores_gemma":[0.000034929162,0.000043176493,0.006610037,0.0000031422035,0.000013111871,0.000007195643,0.000039108658,0.991491,0.0014096982,0.00007842014,0.00026084587,0.000009317441],"about_ca_topic_score_codex":0.3775447,"about_ca_topic_score_gemma":0.3405863,"teacher_disagreement_score":0.3775447,"about_ca_system_score_codex":0.0036019443,"about_ca_system_score_gemma":0.0025969888,"threshold_uncertainty_score":0.7506943},"labels":[],"label_agreement":null},{"id":"W2612422058","doi":"10.3390/hydrology4020028","title":"Understanding the Effects of Parameter Uncertainty on Temporal Dynamics of Groundwater-Surface Water Interaction","year":2017,"lang":"en","type":"article","venue":"Hydrology","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":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Mount Royal University; University of Northern British Columbia","funders":"","keywords":"Environmental science; Uncertainty analysis; Groundwater; Groundwater flow; Watershed; Hydrology (agriculture); Climate change; Monte Carlo method; Greenhouse gas; Flow (mathematics); Surface water; Statistics; Environmental engineering; Geology; Mathematics; Aquifer; Computer science","score_opus":0.030502877498715025,"score_gpt":0.2550260190705862,"score_spread":0.22452314157187117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612422058","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8992105,0.0007659706,0.09526508,0.00050942687,0.000029889141,0.000046564677,0.00038579165,0.00013235876,0.003654409],"genre_scores_gemma":[0.9964317,0.00020189855,0.0030792416,0.000023844123,0.000009197042,0.000016558355,0.0000943981,0.000016744021,0.000126399],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992817,0.0002649228,0.00004802458,0.00015385872,0.00016397462,0.00008752071],"domain_scores_gemma":[0.99199706,0.0064367345,0.0008590304,0.0003062021,0.0003263168,0.00007470282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019589318,0.0006032513,0.00052098185,0.00076350727,0.0004214933,0.0011814676,0.00057249283,0.000916646,0.0005676099],"category_scores_gemma":[0.0117193395,0.000527876,0.0008235984,0.00069445866,0.00071605394,0.0024968144,0.00080022006,0.0011531024,0.000045055465],"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":[0.000050174982,0.00003734211,0.025562983,0.00006235992,0.00011882246,0.0002478774,0.00012520613,0.96030664,0.0037383426,0.0037939658,0.000110325025,0.0058459532],"study_design_scores_gemma":[0.000003080201,0.000024345574,0.008863957,0.000007911218,0.000035406625,0.00004851471,0.00007702001,0.9861201,0.0012709654,0.0032605038,0.00026712217,0.000021128999],"about_ca_topic_score_codex":0.011982045,"about_ca_topic_score_gemma":0.006341905,"teacher_disagreement_score":0.011982045,"about_ca_system_score_codex":0.0010085712,"about_ca_system_score_gemma":0.00074615236,"threshold_uncertainty_score":0.023824632},"labels":[],"label_agreement":null},{"id":"W2693951866","doi":"10.3390/hydrology4030033","title":"Numerical Tests of the Lookup Table Method in Solving Richards’ Equation for Infiltration and Drainage in Heterogeneous Soils","year":2017,"lang":"en","type":"article","venue":"Hydrology","topic":"Soil and Unsaturated Flow","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"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":"Lookup table; Richards equation; Drainage; Soil water; Water table; Nonlinear system; Robustness (evolution); Mathematics; Infiltration (HVAC); Applied mathematics; Geotechnical engineering; Mathematical optimization; Algorithm; Computer science; Soil science; Geology; Materials science","score_opus":0.019233444626391975,"score_gpt":0.26511143705394663,"score_spread":0.24587799242755465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2693951866","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.887397,0.0008882125,0.10511175,0.00024759205,0.00012704125,0.00011225891,0.00019554976,0.00043083995,0.0054897736],"genre_scores_gemma":[0.9701872,0.00024846144,0.028715102,0.000023253951,0.000011103593,0.000047033893,0.00012000393,0.00004248623,0.000605447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992724,0.0003095824,0.00007414327,0.00004932172,0.00023146792,0.00006314875],"domain_scores_gemma":[0.99249285,0.0058552492,0.00037379508,0.00054942403,0.0006008362,0.00012789089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017746008,0.00045015293,0.00053107494,0.00066961173,0.00031743583,0.0005596653,0.0006203593,0.0007497904,0.00082979165],"category_scores_gemma":[0.008309018,0.00019265227,0.00042052148,0.0009944433,0.00076226995,0.0007339186,0.000679018,0.0004207755,0.000116290015],"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":[0.0011907816,0.00035771908,0.013577187,0.0004281261,0.00010058192,0.00070455595,0.0004752642,0.87133956,0.03704912,0.010809163,0.00083601486,0.0631319],"study_design_scores_gemma":[0.00006628249,0.0005857081,0.0022942284,0.000027976195,0.000025141371,0.0001157133,0.00012952652,0.9741382,0.020830125,0.0012030668,0.0005530891,0.000030909],"about_ca_topic_score_codex":0.0033373625,"about_ca_topic_score_gemma":0.0021827882,"teacher_disagreement_score":0.0033373625,"about_ca_system_score_codex":0.00032315045,"about_ca_system_score_gemma":0.00047683463,"threshold_uncertainty_score":0.009385049},"labels":[],"label_agreement":null},{"id":"W2746355058","doi":"10.3390/hydrology4030040","title":"Water Balance Analysis over the Niger Inland Delta-Mali: Spatio-Temporal Dynamics of the Flooded Area and Water Losses","year":2017,"lang":"en","type":"article","venue":"Hydrology","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"United Nations University Institute for Water, Environment, and Health; University of Ottawa","funders":"West African Science Service Centre on Climate Change and Adapted Land Use; Bundesministerium für Bildung und Forschung","keywords":"Water balance; Environmental science; Hydrology (agriculture); Evapotranspiration; Inflow; Precipitation; Wetland; Streamflow; Drainage basin; Water resources; Flood myth; Geography; Geology; Meteorology; Ecology","score_opus":0.007507215325648837,"score_gpt":0.22290807479876465,"score_spread":0.21540085947311582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2746355058","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99903345,0.000041970172,0.00018036838,0.00002299155,0.0000014325195,0.0000049982154,0.00035050072,0.0000059548734,0.000358317],"genre_scores_gemma":[0.9990876,0.000067626715,0.00029713163,0.0000040951563,0.0000012720826,0.0000057693487,0.00033274374,0.0000011175628,0.00020261052],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999627,0.000006388134,0.00000482106,0.000011142155,0.00000705554,0.000007994524],"domain_scores_gemma":[0.99988675,0.000023969418,0.000040701598,0.0000075967055,0.000024739631,0.000016190887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015190044,0.00011465763,0.00013327379,0.00083802413,0.00017601474,0.0004024482,0.00010838236,0.00014160026,0.0004574332],"category_scores_gemma":[0.0002549685,0.00007900066,0.00009490358,0.00091407524,0.00011205669,0.00023956235,0.0002215471,0.00010214584,0.000056832367],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009207961,0.000045305085,0.9783353,0.000027971902,0.00004020769,0.00044761406,0.00039550653,0.0047550397,0.004336352,0.00026372107,0.0003057054,0.010955162],"study_design_scores_gemma":[0.000003866311,0.0000145454005,0.9856571,0.000010851457,0.000013192861,0.00007957331,0.0005176364,0.0125492085,0.0004980746,0.000069750975,0.0005810077,0.000005240811],"about_ca_topic_score_codex":0.040694974,"about_ca_topic_score_gemma":0.0560183,"teacher_disagreement_score":0.040694974,"about_ca_system_score_codex":0.0005725458,"about_ca_system_score_gemma":0.00026428772,"threshold_uncertainty_score":0.080916226},"labels":[],"label_agreement":null},{"id":"W2755656667","doi":"10.3390/hydrology4030044","title":"Bayesian Hierarchical Regression to Assess Variation of Stream Temperature with Atmospheric Temperature in a Small Watershed","year":2017,"lang":"en","type":"article","venue":"Hydrology","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Rowan University","keywords":"Riparian zone; Environmental science; Watershed; Air temperature; Atmospheric sciences; Seasonality; Mean radiant temperature; Hydrology (agriculture); Regression; Regression analysis; Physical geography; Climate change; Ecology; Geography; Statistics; Mathematics; Habitat; Geology; Biology","score_opus":0.009466611034962229,"score_gpt":0.22761119669074123,"score_spread":0.218144585655779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2755656667","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95655113,0.00006861255,0.042651024,0.000033091204,0.000002382389,0.000023750563,0.00014521801,0.00011665112,0.00040815503],"genre_scores_gemma":[0.9868015,0.000025607873,0.012628064,0.0000067877277,0.0000035404862,0.000022954397,0.0003199664,0.000021009932,0.00017059017],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986873,0.00077744276,0.00005655523,0.00021178288,0.00018703073,0.00007992583],"domain_scores_gemma":[0.9963871,0.0024728817,0.0005893711,0.00016691051,0.00027114892,0.000112520494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026494018,0.00031893956,0.0003646149,0.00087276334,0.0003311359,0.000430848,0.00047074567,0.00023063664,0.0004149424],"category_scores_gemma":[0.01078529,0.0003296963,0.00032543243,0.0009555211,0.00025636327,0.00056616374,0.00048014728,0.00036817364,0.000103687475],"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":[0.0003750983,0.00019909904,0.81769735,0.000045051795,0.0006629474,0.00008982634,0.0005309662,0.09919486,0.007742256,0.0009251661,0.0006244889,0.071912885],"study_design_scores_gemma":[0.000023821523,0.000114454044,0.4138434,0.000009951164,0.00006177731,0.00004985438,0.00012201674,0.58371574,0.0007410291,0.00093886553,0.000353611,0.000025452951],"about_ca_topic_score_codex":0.051129658,"about_ca_topic_score_gemma":0.06797008,"teacher_disagreement_score":0.051129658,"about_ca_system_score_codex":0.00060442876,"about_ca_system_score_gemma":0.0007254245,"threshold_uncertainty_score":0.101664126},"labels":[],"label_agreement":null},{"id":"W2768784310","doi":"10.3390/hydrology4040055","title":"RCP8.5-Based Future Flood Hazard Analysis for the Lower Mekong River Basin","year":2017,"lang":"en","type":"article","venue":"Hydrology","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"United Nations University Institute for Water, Environment, and Health","funders":"Ministry of Education, Culture, Sports, Science and Technology","keywords":"Mekong river; Flood myth; Hydrology (agriculture); Structural basin; Drainage basin; Water resource management; Environmental science; Hazard; Geology; Geography; Geomorphology; Archaeology; Geotechnical engineering; Cartography","score_opus":0.010657657920207972,"score_gpt":0.2505546914887489,"score_spread":0.23989703356854095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768784310","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98897374,0.00008839834,0.0019452884,0.00017878032,0.000013750213,0.000066936,0.0059742276,0.00016938531,0.0025894248],"genre_scores_gemma":[0.9939873,0.000051011622,0.0018046324,0.000012033216,0.000005470533,0.000041907348,0.00368605,0.000015056734,0.00039650942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998211,0.00004828588,0.000014336717,0.00003623578,0.00003615835,0.00004391832],"domain_scores_gemma":[0.99964,0.00005065744,0.000053825253,0.000042201653,0.00015796677,0.000055309378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005487598,0.00049541006,0.0002934576,0.0009795056,0.00031084602,0.0005843037,0.0008701373,0.00039473647,0.0017208674],"category_scores_gemma":[0.00077986607,0.00027370208,0.0006638729,0.0008762108,0.00016139924,0.00040494694,0.0005107959,0.000259179,0.00017623205],"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":[0.00027908888,0.00022440699,0.13360484,0.0001527746,0.00027516534,0.0010707603,0.00015932243,0.8378036,0.0032012642,0.0019616021,0.0037758513,0.017491374],"study_design_scores_gemma":[0.00007066858,0.000056317083,0.09876905,0.000023725997,0.00008872841,0.00007077717,0.00020612628,0.8964908,0.0011529147,0.00046031544,0.0025791184,0.00003155796],"about_ca_topic_score_codex":0.18457465,"about_ca_topic_score_gemma":0.11428332,"teacher_disagreement_score":0.18457465,"about_ca_system_score_codex":0.001904914,"about_ca_system_score_gemma":0.0019267356,"threshold_uncertainty_score":0.36700064},"labels":[],"label_agreement":null},{"id":"W2788895239","doi":"10.3390/hydrology5010010","title":"Comparing Machine Learning and Decision Making Approaches to Forecast Long Lead Monthly Rainfall: The City of Vancouver, Canada","year":2018,"lang":"en","type":"article","venue":"Hydrology","topic":"Climate variability and models","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"","keywords":"TOPSIS; Lead time; Geopotential height; Environmental science; Flood myth; Meteorology; Forecast skill; Computer science; Mathematics; Precipitation; Operations research; Geography; Engineering","score_opus":0.06294840363444888,"score_gpt":0.24139932322512936,"score_spread":0.1784509195906805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2788895239","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97194695,0.00089040207,0.010166103,0.0013604482,0.00006728499,0.00030387522,0.00122422,0.00017116225,0.013869486],"genre_scores_gemma":[0.9864768,0.00053593435,0.007942548,0.000056530018,0.0000118036905,0.00006482712,0.00094980694,0.000012581654,0.003949295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948114,0.00012019672,0.00003458544,0.000079193436,0.00016511044,0.00011976855],"domain_scores_gemma":[0.9987036,0.0004230761,0.00004994305,0.000033483462,0.0006173531,0.00017259734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010907078,0.0006636338,0.0004826061,0.0015049229,0.0012989696,0.002075492,0.001214465,0.0005611933,0.0014785187],"category_scores_gemma":[0.002925445,0.00025304174,0.00056445115,0.0018215637,0.00033875476,0.00047124756,0.00053180207,0.0005823847,0.00016522543],"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":[0.00067294925,0.00052022125,0.12379081,0.00027413914,0.0002411056,0.0004425084,0.0004259488,0.74000823,0.0015088659,0.0034224954,0.005143221,0.12354948],"study_design_scores_gemma":[0.000054923836,0.00009344704,0.036382876,0.00004408324,0.000041734507,0.000018773704,0.0013666983,0.9588769,0.0007995858,0.0006678327,0.0016131701,0.000040035065],"about_ca_topic_score_codex":0.9628453,"about_ca_topic_score_gemma":0.9634769,"teacher_disagreement_score":0.037154675,"about_ca_system_score_codex":0.018310878,"about_ca_system_score_gemma":0.01840103,"threshold_uncertainty_score":0.1328553},"labels":[],"label_agreement":null},{"id":"W2793081797","doi":"10.3390/hydrology5010011","title":"Floods and Countermeasures Impact Assessment for the Metro Colombo Canal System, Sri Lanka","year":2018,"lang":"en","type":"article","venue":"Hydrology","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"United Nations University Institute for Water, Environment, and Health","funders":"National Graduate Institute for Policy Studies; Japan International Cooperation Agency","keywords":"Flood myth; Environmental science; Sri lanka; Hydrology (agriculture); Flood mitigation; Water resource management; Geography; Geotechnical engineering; Engineering; Environmental planning","score_opus":0.009053872171041128,"score_gpt":0.29431963887771284,"score_spread":0.2852657667066717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793081797","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9981061,0.00006339738,0.00043812333,0.000058729922,0.000003193966,0.000033946504,0.00012589981,0.000026092132,0.0011445365],"genre_scores_gemma":[0.99920696,0.00006574272,0.000348995,0.000004856552,0.000001340857,0.000015584721,0.00008412375,0.0000022396187,0.00027026792],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9997063,0.000086991924,0.000016883561,0.000027129243,0.00006542217,0.00009718246],"domain_scores_gemma":[0.99971694,0.00008342628,0.00006433687,0.00001866505,0.0000703249,0.000046252382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036480423,0.0004229727,0.00037118563,0.00067814137,0.00052688346,0.0009152994,0.00043937273,0.0005712634,0.001030486],"category_scores_gemma":[0.00053804973,0.0001754191,0.0006511193,0.0006694056,0.0005318188,0.00040929974,0.0006526958,0.00026004633,0.00007203632],"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":[0.0008541497,0.00034537798,0.34459695,0.00041345065,0.00046929982,0.0059280694,0.0013425401,0.577439,0.030563118,0.0033217848,0.0015261755,0.033200108],"study_design_scores_gemma":[0.000159953,0.0022627278,0.54981965,0.000072238094,0.00042199108,0.0008180963,0.008304989,0.41985595,0.010286578,0.0012447977,0.0065667555,0.00018635488],"about_ca_topic_score_codex":0.048212398,"about_ca_topic_score_gemma":0.057681713,"teacher_disagreement_score":0.048212398,"about_ca_system_score_codex":0.0020748314,"about_ca_system_score_gemma":0.0013267354,"threshold_uncertainty_score":0.09586358},"labels":[],"label_agreement":null},{"id":"W2891282131","doi":"10.3390/hydrology5030050","title":"Characterization of Temporal and Spatial Variability of Phosphorus Loading to Lake Erie from the Western Basin Using Wavelet Transform Methods","year":2018,"lang":"en","type":"article","venue":"Hydrology","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":"McGill University","funders":"","keywords":"Environmental science; Streamflow; Spatial variability; Watershed; Hydrology (agriculture); Continuous wavelet transform; Wavelet; Surface runoff; Drainage basin; Discrete wavelet transform; Wavelet transform; Geology; Geography; Cartography; Ecology; Mathematics","score_opus":0.017334235701940443,"score_gpt":0.2688853916797054,"score_spread":0.25155115597776495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891282131","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99849343,0.000027256054,0.0011262187,0.0000107352525,9.994144e-7,0.0000027933681,0.000093117036,0.000008827698,0.00023668921],"genre_scores_gemma":[0.99731284,0.000070592505,0.0018619739,0.0000057385682,0.0000036371969,0.000010074517,0.00047748562,0.000006367136,0.0002511829],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999007,0.0000106819325,0.000009481382,0.0000230923,0.000031353815,0.000024649587],"domain_scores_gemma":[0.99979097,0.000051314753,0.000054414737,0.000012582299,0.00007846759,0.000012226833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023710185,0.00016346703,0.00015999474,0.00096024084,0.00014248605,0.0003985547,0.00013491015,0.00017168152,0.00021700641],"category_scores_gemma":[0.000638208,0.00009341492,0.00016115946,0.001404538,0.00009886685,0.00028351982,0.00026878403,0.00015626462,0.000065421766],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021166466,0.000098225224,0.84380174,0.000059256687,0.00009271069,0.00068872987,0.0012686299,0.006408169,0.06364691,0.00021793818,0.00040625996,0.08309973],"study_design_scores_gemma":[0.000004352801,0.00002785252,0.9816533,0.0000043479013,0.000020397287,0.00009064938,0.00051537785,0.014654974,0.0023614448,0.00003668037,0.000621133,0.000009564831],"about_ca_topic_score_codex":0.013959505,"about_ca_topic_score_gemma":0.018927133,"teacher_disagreement_score":0.9860405,"about_ca_system_score_codex":0.00016564314,"about_ca_system_score_gemma":0.00020700293,"threshold_uncertainty_score":0.027756512},"labels":[],"label_agreement":null},{"id":"W2952147374","doi":"10.3390/hydrology6020055","title":"Critical Analysis of the Snow Survey Network According to the Spatial Variability of Snow Water Equivalent (SWE) on Eastern Mainland Canada","year":2019,"lang":"en","type":"article","venue":"Hydrology","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Snow; Spatial distribution; Spatial variability; Environmental science; Scale (ratio); Spatial ecology; Variogram; Spatial analysis; Physical geography; Geography; Meteorology; Remote sensing; Cartography; Statistics; Kriging; Mathematics","score_opus":0.019880946045115106,"score_gpt":0.22856122702842827,"score_spread":0.20868028098331315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952147374","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99532026,0.00006329427,0.0026585618,0.00011842058,0.0000050414756,0.000039375638,0.000582068,0.000024521138,0.001188475],"genre_scores_gemma":[0.9979907,0.00004527091,0.0011211702,0.000008230022,0.0000026559353,0.000008993125,0.00058805966,0.000006259644,0.00022863978],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987538,0.00042322345,0.000061198894,0.00024100592,0.0003096189,0.00021113006],"domain_scores_gemma":[0.99116147,0.0038812878,0.00079271017,0.00035040927,0.0034502312,0.00036388496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026399612,0.000251448,0.00023598186,0.0018095796,0.0006804901,0.0011940127,0.00064501143,0.00022919204,0.00061872666],"category_scores_gemma":[0.01440671,0.00021391813,0.0004318908,0.0022344699,0.00085650355,0.0008321115,0.00073922693,0.000426356,0.00004194029],"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":[0.00028183198,0.000025234756,0.7911222,0.000047932797,0.00017662095,0.00037503953,0.00077468436,0.18158814,0.0019225971,0.003762152,0.0011016724,0.0188219],"study_design_scores_gemma":[0.000008631693,0.000044682074,0.58192617,0.000023779312,0.0000646271,0.00005866065,0.0015773175,0.413022,0.0010041945,0.0007577159,0.001486327,0.00002582503],"about_ca_topic_score_codex":0.56533265,"about_ca_topic_score_gemma":0.506129,"teacher_disagreement_score":0.43466735,"about_ca_system_score_codex":0.007250803,"about_ca_system_score_gemma":0.0033223901,"threshold_uncertainty_score":0.8744544},"labels":[],"label_agreement":null},{"id":"W2973752537","doi":"10.3390/hydrology6040083","title":"Evolution of Acid Mine Drainage from a Coal Waste Rock Pile Reclaimed with a Simple Soil Cover","year":2019,"lang":"en","type":"article","venue":"Hydrology","topic":"Mine drainage and remediation techniques","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University; Cape Breton University","funders":"Public Works and Government Services Canada","keywords":"Environmental science; Groundwater; Acid mine drainage; Drainage; Infiltration (HVAC); Surface runoff; Leaching (pedology); Water quality; Hydrology (agriculture); Surface water; Soil water; Geology; Environmental engineering; Soil science; Geotechnical engineering; Environmental chemistry","score_opus":0.004267556632609805,"score_gpt":0.19467196137961898,"score_spread":0.19040440474700918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973752537","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9989009,0.000012704345,0.000176492,0.000012547405,0.0000015936834,0.000008779539,0.00016004413,0.00001532764,0.00071165425],"genre_scores_gemma":[0.9990031,0.000023354865,0.0003951067,0.0000051063967,3.3512129e-7,0.0000049138703,0.00013347396,0.000002754164,0.00043198475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99995625,0.0000036414851,0.0000025360644,0.000010397099,0.000014033695,0.000013137451],"domain_scores_gemma":[0.99991107,0.000021712951,0.000015084077,0.000006040105,0.000025686202,0.000020384556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000067863184,0.00020692596,0.00017851115,0.00024775934,0.00034401455,0.0004941725,0.00033097685,0.00036890074,0.00047685506],"category_scores_gemma":[0.00020876402,0.00015028569,0.00022277207,0.00020880802,0.0002390075,0.00010553509,0.00017986329,0.00019088978,0.000103491795],"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":[0.00044320134,0.00030035214,0.2839596,0.00014659353,0.00005654531,0.002722794,0.0004143148,0.5222311,0.17716709,0.0005725895,0.0005865265,0.011399262],"study_design_scores_gemma":[0.000053600354,0.0003880374,0.2696974,0.000029068757,0.000038904454,0.00043981854,0.00070253847,0.6953126,0.032071028,0.00015360076,0.0010672058,0.000046201418],"about_ca_topic_score_codex":0.22672245,"about_ca_topic_score_gemma":0.22734691,"teacher_disagreement_score":0.22672245,"about_ca_system_score_codex":0.001913547,"about_ca_system_score_gemma":0.0012128985,"threshold_uncertainty_score":0.45080554},"labels":[],"label_agreement":null},{"id":"W3025710381","doi":"10.3390/hydrology7020027","title":"Analytical and Numerical Groundwater Flow Solutions for the FEMME-Modeling Environment","year":2020,"lang":"en","type":"article","venue":"Hydrology","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Golder Associates (Canada)","funders":"","keywords":"MODFLOW; Aquifer; Groundwater; Groundwater model; Groundwater flow; Surface water; Environmental science; Computer simulation; Hydrology (agriculture); Geology; Geotechnical engineering; Environmental engineering; Computer science; Simulation","score_opus":0.04062173518325114,"score_gpt":0.23767548704290786,"score_spread":0.19705375185965673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025710381","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008547138,0.00008562304,0.96922094,0.00011322243,0.000079207326,0.00010107717,0.001831564,0.0059220837,0.014099201],"genre_scores_gemma":[0.07404915,0.00030464825,0.8961646,0.00010973253,0.0000383793,0.0009794065,0.0037926508,0.0032635909,0.021297859],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964917,0.00007949325,0.000022055541,0.000041740535,0.0001628059,0.000044799723],"domain_scores_gemma":[0.99942136,0.00022276942,0.000041530333,0.000098623284,0.00018830619,0.000027280943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009301304,0.0007618244,0.0006123264,0.00067659817,0.00043178294,0.00073440844,0.0016573225,0.0009855945,0.025569871],"category_scores_gemma":[0.0021719362,0.0006754974,0.0009777178,0.0005695747,0.00042216506,0.0009864038,0.0012052457,0.0012915,0.0055283946],"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":[0.00016623433,0.00030174712,0.0023971794,0.00072477415,0.00009919737,0.0003599426,0.00056335516,0.5600117,0.04406074,0.15720296,0.04755714,0.18655504],"study_design_scores_gemma":[0.000058149988,0.000043379143,0.00049010763,0.000042203672,0.000010216445,0.00018002238,0.00003058888,0.904948,0.008326052,0.011817838,0.07401741,0.000036057427],"about_ca_topic_score_codex":0.002049608,"about_ca_topic_score_gemma":0.0030081489,"teacher_disagreement_score":0.025569871,"about_ca_system_score_codex":0.0004631112,"about_ca_system_score_gemma":0.001234023,"threshold_uncertainty_score":0.08553982},"labels":[],"label_agreement":null},{"id":"W3089029293","doi":"10.3390/hydrology7040070","title":"Integrated Surface Water and Groundwater Analysis under the Effects of Climate Change, Hydraulic Fracturing and its Associated Activities: A Case Study from Northwestern Alberta, Canada","year":2020,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Mount Royal University; Wilfrid Laurier University","funders":"","keywords":"Groundwater; Environmental science; Hydrology (agriculture); Surface water; Precipitation; Groundwater discharge; Climate change; Groundwater flow; Representative Concentration Pathways; Hydraulic fracturing; Water resources; Climate model; Geology; Aquifer; Petroleum engineering; Environmental engineering; Geography","score_opus":0.010442626165072253,"score_gpt":0.20582445121518123,"score_spread":0.19538182505010898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089029293","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9951206,0.00015778755,0.00061967556,0.00014451683,0.0000063391835,0.000041472507,0.0010336731,0.000035458128,0.0028405446],"genre_scores_gemma":[0.99547994,0.00024445992,0.0011687038,0.00003407935,0.0000034643613,0.000012345088,0.001089497,0.0000076148203,0.0019599411],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997397,0.000018829658,0.000008360129,0.00003246267,0.00010277511,0.0000979274],"domain_scores_gemma":[0.9997265,0.00003344757,0.000018362045,0.000010942093,0.00016052477,0.000050268816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003013872,0.0005078476,0.00032647283,0.0009986402,0.0013760935,0.001146633,0.00087926135,0.0005625978,0.0010182745],"category_scores_gemma":[0.00043725106,0.00026901375,0.0006721651,0.00259156,0.00072720204,0.00029490117,0.0004245609,0.00042919067,0.000080850055],"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":[0.00042009808,0.00060478545,0.53050715,0.00021301182,0.00029634463,0.005859211,0.00095194083,0.41078907,0.007860229,0.003004041,0.0040966426,0.035397556],"study_design_scores_gemma":[0.00015649595,0.00020073754,0.4974051,0.000058789894,0.00033210986,0.00040888254,0.00786095,0.4811745,0.0038108772,0.0008890607,0.007552776,0.0001495789],"about_ca_topic_score_codex":0.9909775,"about_ca_topic_score_gemma":0.9947054,"teacher_disagreement_score":0.02712033,"about_ca_system_score_codex":0.02712033,"about_ca_system_score_gemma":0.019826677,"threshold_uncertainty_score":0.1967727},"labels":[],"label_agreement":null},{"id":"W3106687614","doi":"10.3390/hydrology7040094","title":"Groundwater and Solute Budget (A Case Study from Sabkha Matti, Saudi Arabia)","year":2020,"lang":"en","type":"article","venue":"Hydrology","topic":"Groundwater and Isotope Geochemistry","field":"Earth and Planetary Sciences","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 Guelph; University of Waterloo","funders":"Saudi Aramco","keywords":"Sabkha; Geology; Groundwater recharge; Aquifer; Groundwater; Hydrology (agriculture); Groundwater flow; Geochemistry; Evaporite; Artesian aquifer; Hydrogeology; Vadose zone; Weathering; Geotechnical engineering; Sedimentary rock","score_opus":0.020106253030756714,"score_gpt":0.21118124052069517,"score_spread":0.19107498748993845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106687614","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99615943,0.00021851902,0.0003436371,0.00018195892,0.000003700418,0.000027025308,0.00088465784,0.000015724308,0.002165483],"genre_scores_gemma":[0.9965473,0.00035793183,0.00076645287,0.0000261053,0.0000045668107,0.0000105136505,0.0007293409,0.0000054096313,0.0015524891],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999181,0.000014229403,0.000006228602,0.000013329937,0.000018090268,0.000029990415],"domain_scores_gemma":[0.9998343,0.000040449377,0.000021462025,0.000009603026,0.00007056213,0.000023648405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020332108,0.00028549967,0.00018354852,0.0008520518,0.00065812236,0.0007530581,0.0003472897,0.0005852668,0.0014260522],"category_scores_gemma":[0.0003895372,0.00013537871,0.00028755644,0.0017555978,0.00027831554,0.00044970002,0.00040466452,0.00017516888,0.0001597312],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091924693,0.0007887016,0.66876745,0.00073381915,0.00028980002,0.02765267,0.0052307257,0.12142151,0.019793736,0.01125916,0.008873207,0.13426995],"study_design_scores_gemma":[0.00021239565,0.0005320407,0.70921797,0.00017524975,0.00025687553,0.0041369316,0.024900958,0.20670927,0.010502263,0.0033271103,0.039903827,0.00012509637],"about_ca_topic_score_codex":0.23779735,"about_ca_topic_score_gemma":0.25032136,"teacher_disagreement_score":0.23779735,"about_ca_system_score_codex":0.003158166,"about_ca_system_score_gemma":0.0011512038,"threshold_uncertainty_score":0.47282642},"labels":[],"label_agreement":null},{"id":"W3113856913","doi":"10.3390/hydrology8010001","title":"Assessment of Impacts of Climate Change on Tile Discharge and Nitrogen Yield Using the DRAINMOD Model","year":2020,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"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 Waterloo; University of Guelph","funders":"Ministry of Agriculture, Food and Rural Affairs; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Environmental science; Hydrology (agriculture); Evapotranspiration; Tile drainage; Water quality; Water table; Climate change; Precipitation; Soil and Water Assessment Tool; Point source pollution; Streamflow; Nonpoint source pollution; Groundwater; Soil water; Meteorology; Soil science; Drainage basin; Geography; Ecology","score_opus":0.045600567887553664,"score_gpt":0.27872087912256427,"score_spread":0.2331203112350106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113856913","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9906596,0.000060057027,0.0015620771,0.00008639128,0.000012040926,0.00003695263,0.0037679714,0.00025794475,0.0035570464],"genre_scores_gemma":[0.99518627,0.000057259032,0.0014007189,0.000014814807,0.000003196797,0.000028563287,0.002423081,0.000025158597,0.000860898],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99993014,0.0000126448385,0.0000040471373,0.000022886952,0.00001341383,0.000016758382],"domain_scores_gemma":[0.9997384,0.000107165666,0.000024143717,0.0000209957,0.00007545534,0.000033748198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028489306,0.00059377495,0.00034842832,0.00028825033,0.00040645045,0.00054653664,0.00080250605,0.00047501497,0.0015782192],"category_scores_gemma":[0.0007453809,0.00032144177,0.00050517236,0.00033299517,0.00032153432,0.00033203937,0.00030815968,0.00030872162,0.00013870688],"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":[0.00022444128,0.00009818588,0.071723446,0.00007301274,0.000076876495,0.000114045426,0.00006824638,0.9191082,0.0022578791,0.0004894965,0.0011369033,0.0046291617],"study_design_scores_gemma":[0.00008462491,0.00006395973,0.020820929,0.000006401047,0.000031532276,0.000013583104,0.000060001516,0.9765168,0.000952097,0.00014133996,0.0012927143,0.00001611333],"about_ca_topic_score_codex":0.50991464,"about_ca_topic_score_gemma":0.45452726,"teacher_disagreement_score":0.50991464,"about_ca_system_score_codex":0.0030994243,"about_ca_system_score_gemma":0.0025872716,"threshold_uncertainty_score":0.98594314},"labels":[],"label_agreement":null},{"id":"W3125231513","doi":"10.3390/hydrology8010011","title":"The Influence of Snow and Ice Albedo towards Improved Lake Ice Simulations","year":2021,"lang":"en","type":"article","venue":"Hydrology","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada; Canada Foundation for Innovation","keywords":"Sea ice; Arctic ice pack; Cryosphere; Arctic; Snow; Temperate climate; Shelf ice; Antarctic sea ice; Geology; Climatology; Sea ice thickness; Physical geography; Oceanography; Geomorphology; Ecology; Geography","score_opus":0.006921509395549537,"score_gpt":0.2131959357876722,"score_spread":0.20627442639212268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125231513","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93712145,0.0007705892,0.04735953,0.0006249194,0.00014433943,0.00014349699,0.0023833753,0.003626235,0.007826104],"genre_scores_gemma":[0.96951073,0.00015866957,0.028297303,0.000088797504,0.000018021512,0.000044080236,0.001035501,0.00033457924,0.0005121733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994491,0.00020595595,0.000043844364,0.0000980286,0.00011535468,0.000087801905],"domain_scores_gemma":[0.9980867,0.0009111593,0.00011317602,0.0002264897,0.00055656896,0.00010590363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017030443,0.0009122756,0.0006640577,0.00043874956,0.00042545467,0.0011211273,0.0013260589,0.0005717575,0.001508754],"category_scores_gemma":[0.0062137377,0.0004885186,0.0005533904,0.0004967727,0.0002661049,0.0012090218,0.0007114446,0.00067876047,0.0002687005],"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":[0.00023732579,0.00009460861,0.011658392,0.00006864889,0.00007288218,0.000059458387,0.00007488719,0.964668,0.0044995765,0.0003073301,0.0006983007,0.017560696],"study_design_scores_gemma":[0.00004745371,0.000027337102,0.002221731,0.000014403635,0.000020284755,0.000004945968,0.000018602344,0.995082,0.0019057675,0.00011640565,0.0005307758,0.000010350386],"about_ca_topic_score_codex":0.09054565,"about_ca_topic_score_gemma":0.08177675,"teacher_disagreement_score":0.09054565,"about_ca_system_score_codex":0.0011479252,"about_ca_system_score_gemma":0.0015703158,"threshold_uncertainty_score":0.18003726},"labels":[],"label_agreement":null},{"id":"W3127114526","doi":"10.3390/hydrology8010027","title":"Numerical Modeling of Venturi Flume","year":2021,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydraulic flow and structures","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"","keywords":"Flume; Venturi effect; Large eddy simulation; Turbulence; Discharge coefficient; Computational fluid dynamics; Mechanics; Curvilinear coordinates; Flow (mathematics); Environmental science; Meteorology; Geotechnical engineering; Simulation; Geology; Mathematics; Engineering; Inlet; Geometry; Physics; Mechanical engineering","score_opus":0.007573001080359946,"score_gpt":0.2029634033675743,"score_spread":0.19539040228721435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127114526","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5426172,0.00087826204,0.41870877,0.0004187156,0.00019380037,0.00025303575,0.0011212727,0.0011076416,0.034701295],"genre_scores_gemma":[0.95993644,0.0003241389,0.030727312,0.000045306184,0.000023439688,0.00021080216,0.00041475575,0.00005766193,0.008260029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998448,0.00003419429,0.000013473516,0.00003565287,0.00003977361,0.000032094755],"domain_scores_gemma":[0.9997718,0.00009308884,0.00003757018,0.000019910201,0.00005397222,0.000023748875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029658503,0.00060296356,0.00060947833,0.00057186786,0.0006263031,0.0009725008,0.00093354064,0.0013056272,0.0020930076],"category_scores_gemma":[0.00075198506,0.00029291198,0.0006120793,0.00047894372,0.0007149071,0.0005367233,0.00068547926,0.00046878698,0.000312744],"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":[0.000025132977,0.000023195022,0.0012663932,0.000026487249,0.0000067295678,0.00010302614,0.00004258064,0.98897845,0.004900315,0.002430821,0.00016932507,0.002027559],"study_design_scores_gemma":[0.0000051211005,0.000011617058,0.00028267276,0.0000032631017,0.000001815968,0.000016663107,0.00000778842,0.9985279,0.000421902,0.0002717504,0.00044438406,0.000005041744],"about_ca_topic_score_codex":0.008769786,"about_ca_topic_score_gemma":0.0038822023,"teacher_disagreement_score":0.008769786,"about_ca_system_score_codex":0.00079253234,"about_ca_system_score_gemma":0.00081460184,"threshold_uncertainty_score":0.017437458},"labels":[],"label_agreement":null},{"id":"W3129686462","doi":"10.3390/hydrology8010036","title":"On the Choice of Metric to Calibrate Time-Invariant Ensemble Kalman Filter Hyper-Parameters for Discharge Data Assimilation and Its Impact on Discharge Forecast Modelling","year":2021,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Rio Tinto","keywords":"Data assimilation; Ensemble Kalman filter; Kalman filter; Metric (unit); Calibration; Extended Kalman filter; Performance metric; Statistics; Mathematics; Computer science; Meteorology; Environmental science; Engineering; Geography","score_opus":0.05810568077155317,"score_gpt":0.2831337021600814,"score_spread":0.2250280213885282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129686462","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2650592,0.0042158975,0.7220639,0.001023128,0.00017137184,0.00019068074,0.0003908636,0.0012281043,0.0056568384],"genre_scores_gemma":[0.8162923,0.0008056637,0.18142976,0.00012407692,0.000039593047,0.00010314325,0.00051212515,0.00029770154,0.00039563363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9935003,0.0034531835,0.0005252438,0.00077220536,0.0014770625,0.00027186604],"domain_scores_gemma":[0.96774507,0.018849572,0.0029196525,0.0027740547,0.0072896187,0.0004219895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012196361,0.0014621323,0.0010452286,0.0020615375,0.00063074124,0.0023657614,0.0009001438,0.0012747438,0.00079162885],"category_scores_gemma":[0.061160017,0.00044342323,0.000701281,0.0019426701,0.0009748325,0.0032079648,0.0017830932,0.0014851268,0.0003019883],"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":[0.00038531117,0.00018560371,0.04801977,0.0006550227,0.00036855764,0.00024493044,0.00055702386,0.6306665,0.029273825,0.012930547,0.001828934,0.27488402],"study_design_scores_gemma":[0.000034403278,0.00035889115,0.042899672,0.00037521883,0.00011914023,0.00027707778,0.00032297894,0.9129314,0.0321341,0.005565609,0.004737928,0.00024364016],"about_ca_topic_score_codex":0.008416734,"about_ca_topic_score_gemma":0.0057254913,"teacher_disagreement_score":0.012196361,"about_ca_system_score_codex":0.0010124539,"about_ca_system_score_gemma":0.0012404906,"threshold_uncertainty_score":0.064501286},"labels":[],"label_agreement":null},{"id":"W3132678266","doi":"10.3390/hydrology8010035","title":"Surface and Groundwater Interactions: A Review of Coupling Strategies in Detailed Domain Models","year":2021,"lang":"en","type":"review","venue":"Hydrology","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Water Security Agency; University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Groundwater; Groundwater model; Coupling (piping); Groundwater flow; Surface water; Interface (matter); Computer science; Domain (mathematical analysis); Surface (topology); Environmental science; Geology; Aquifer; Geotechnical engineering; Mathematics; Engineering; Environmental engineering; Geometry","score_opus":0.043006237480268654,"score_gpt":0.3116393906548893,"score_spread":0.26863315317462066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132678266","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014098447,0.97808844,0.015088048,0.00036125735,0.00021139685,0.000039568644,0.00023382969,0.000087677014,0.00447996],"genre_scores_gemma":[0.006862227,0.98423463,0.0074596503,0.00011516538,0.0001122937,0.00005656771,0.00021402616,0.000029131339,0.0009162939],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996264,0.00007889089,0.00006387213,0.000083129176,0.00012194693,0.000025784706],"domain_scores_gemma":[0.99929273,0.00044952083,0.0000787202,0.000034996072,0.00012330204,0.000020737036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001208145,0.0014106883,0.0018575346,0.0019324356,0.00029549134,0.0013806033,0.0017453092,0.0011221509,0.0025428326],"category_scores_gemma":[0.0017505452,0.00074514194,0.0014741697,0.0032705318,0.00052393746,0.0021782243,0.0009913496,0.0011233878,0.001280193],"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":[0.00008498052,0.00012750896,0.0008390356,0.041818865,0.00040707504,0.00025443258,0.0003256675,0.039107196,0.0041430183,0.06654203,0.015416311,0.83093387],"study_design_scores_gemma":[0.000025273306,0.00021791639,0.0010465578,0.00863023,0.0005754685,0.00056998315,0.00017795214,0.015870659,0.0030304405,0.023578148,0.94612414,0.00015327566],"about_ca_topic_score_codex":0.004123702,"about_ca_topic_score_gemma":0.0027140626,"teacher_disagreement_score":0.004123702,"about_ca_system_score_codex":0.0008436788,"about_ca_system_score_gemma":0.0014020322,"threshold_uncertainty_score":0.008506596},"labels":[],"label_agreement":null},{"id":"W3136160691","doi":"10.3390/hydrology8010045","title":"Effects of River Discharge and Sediment Load on Sediment Plume Behaviors in a Coastal Region: The Yukon River, Alaska and the Bering Sea","year":2021,"lang":"en","type":"article","venue":"Hydrology","topic":"Methane Hydrates and Related Phenomena","field":"Environmental Science","cited_by":9,"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":"International Arctic Research Center, University of Alaska, Fairbanks; Japan Aerospace Exploration Agency; U.S. Geological Survey","keywords":"Plume; Sediment; Hydrology (agriculture); Geology; Oceanography; River delta; Discharge; Settling; Glacier; Sedimentary budget; Brackish water; River mouth; Sediment transport; Delta; Geomorphology; Salinity; Environmental science; Drainage basin; Geotechnical engineering","score_opus":0.005127463476864054,"score_gpt":0.1989756881279343,"score_spread":0.19384822465107027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136160691","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99989545,0.000009670926,0.000009674008,0.000003060975,3.3661397e-7,5.2747737e-7,0.000026592805,0.0000014960614,0.00005319593],"genre_scores_gemma":[0.9997608,0.000019619261,0.00002583721,0.0000035462274,2.8525173e-7,0.00000148895,0.00010681207,8.866354e-7,0.000080763035],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999354,0.0000109867215,0.000009499723,0.000017594763,0.000012323945,0.00001423478],"domain_scores_gemma":[0.99975425,0.000060527214,0.000047313708,0.000013015837,0.00006660826,0.000058276328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011809338,0.00013476846,0.00016837879,0.0003377411,0.00027418815,0.0003602679,0.00011996556,0.00017396711,0.0003734517],"category_scores_gemma":[0.000370804,0.00013113779,0.00017371848,0.00043361724,0.0002514571,0.00021505643,0.00033662195,0.00013752004,0.00005715679],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022030935,0.00008262878,0.9725528,0.000026084193,0.00008497102,0.00039430812,0.0005865785,0.0016893147,0.01990754,0.00004073732,0.00008462989,0.004330143],"study_design_scores_gemma":[0.0000030767073,0.000038617818,0.9979297,0.0000015219263,0.000014724669,0.000032664553,0.0005574086,0.0008276804,0.0005161934,0.000009143391,0.000064243475,0.0000049801533],"about_ca_topic_score_codex":0.06923624,"about_ca_topic_score_gemma":0.12072146,"teacher_disagreement_score":0.06923624,"about_ca_system_score_codex":0.00058085244,"about_ca_system_score_gemma":0.0004355031,"threshold_uncertainty_score":0.13766652},"labels":[],"label_agreement":null},{"id":"W3161455144","doi":"10.3390/hydrology8020079","title":"Reservoir Sizing at Draft Level of 75% of Mean Annual Flow Using Drought Magnitude Based Method on Canadian Rivers","year":2021,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Magnitude (astronomy); Hydrology (agriculture); Streamflow; Flow (mathematics); Mathematics; Environmental science; Standard deviation; Scale (ratio); Statistics; Geography; Geology; Drainage basin; Cartography; Geotechnical engineering","score_opus":0.05168161376199524,"score_gpt":0.28044342112908727,"score_spread":0.22876180736709203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161455144","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92115504,0.00013593704,0.07182957,0.00007195366,0.0000119495335,0.00009514102,0.002294499,0.00054213876,0.00386366],"genre_scores_gemma":[0.912365,0.000089678884,0.083663404,0.000010318761,0.0000034879952,0.000033430748,0.002170337,0.000043074382,0.0016211693],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997627,0.000024663303,0.000017081977,0.0000602398,0.00009117711,0.000044180193],"domain_scores_gemma":[0.9995757,0.000059308793,0.00003928568,0.000029217865,0.0002683982,0.000028109564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043034577,0.00022667523,0.00024769295,0.0013279269,0.00055978267,0.0006286716,0.00038121323,0.00013039152,0.0010341525],"category_scores_gemma":[0.0016984006,0.00012224975,0.00023323347,0.0012929991,0.00020289369,0.0002461047,0.00028448945,0.000165073,0.00008132343],"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":[0.00044415623,0.0000818853,0.22686854,0.00016651666,0.000103527134,0.0002791024,0.0007940941,0.28633386,0.033701804,0.007978821,0.0044285334,0.43881914],"study_design_scores_gemma":[0.00003450543,0.000087876724,0.3816786,0.000031664804,0.000050389917,0.00014236035,0.00041840997,0.5954152,0.013946886,0.0012311763,0.0068692956,0.00009358461],"about_ca_topic_score_codex":0.63863367,"about_ca_topic_score_gemma":0.7647364,"teacher_disagreement_score":0.36136633,"about_ca_system_score_codex":0.0024339745,"about_ca_system_score_gemma":0.0049517243,"threshold_uncertainty_score":0.726989},"labels":[],"label_agreement":null},{"id":"W3163431258","doi":"10.3390/hydrology8020078","title":"Implications of a Priori Parameters on Calibration in Conditions of Varying Terrain Characteristics: Case Study of the SAC-SMA Model in Eastern United States","year":2021,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","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 British Columbia; Fisheries and Oceans Canada","funders":"Islamic Development Bank","keywords":"A priori and a posteriori; Calibration; Terrain; Soil water; Environmental science; Hydrology (agriculture); Soil science; SMA*; Drainage basin; Surface runoff; Soil texture; Geology; Mathematics; Geotechnical engineering; Geography; Ecology; Statistics; Algorithm; Cartography","score_opus":0.026658540676477865,"score_gpt":0.26771893732124,"score_spread":0.24106039664476217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163431258","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9945798,0.00006219352,0.0039737565,0.00011328511,0.000006017867,0.000018079263,0.00020088186,0.0001240988,0.0009219892],"genre_scores_gemma":[0.9976058,0.000019623347,0.0021087595,0.00002174034,0.0000018233645,0.000007453711,0.00014835065,0.000016007436,0.000070518414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986953,0.0006706767,0.00011047823,0.00021829239,0.00018949734,0.00011567446],"domain_scores_gemma":[0.99132115,0.005796201,0.000802244,0.0009221403,0.001007019,0.000151313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038378974,0.0005889775,0.00049388997,0.0003848844,0.0008103703,0.0012072881,0.0009876762,0.00095801614,0.00044563366],"category_scores_gemma":[0.01778343,0.00038483628,0.00042924428,0.00076436414,0.0006893113,0.0014119962,0.00071640476,0.00077715307,0.000066958084],"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":[0.00027381312,0.00015307085,0.113553494,0.00005119155,0.000088305256,0.00030286144,0.00023792812,0.86888236,0.0034132292,0.0008623384,0.0006265021,0.011554918],"study_design_scores_gemma":[0.00006461318,0.000107454995,0.054064497,0.000033628974,0.000059732924,0.00009941062,0.00026815306,0.937343,0.006326835,0.0006262662,0.0009523262,0.000054090022],"about_ca_topic_score_codex":0.060828075,"about_ca_topic_score_gemma":0.05027956,"teacher_disagreement_score":0.060828075,"about_ca_system_score_codex":0.0014432033,"about_ca_system_score_gemma":0.0013219846,"threshold_uncertainty_score":0.120948076},"labels":[],"label_agreement":null},{"id":"W3166207126","doi":"10.3390/hydrology8020090","title":"Linking DPSIR Model and Water Quality Indices to Achieve Sustainable Development Goals in Groundwater Resources","year":2021,"lang":"en","type":"article","venue":"Hydrology","topic":"Groundwater and Isotope Geochemistry","field":"Earth and Planetary Sciences","cited_by":52,"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":"Hellenic Ministry of Rural Development and Food","keywords":"DPSIR; Groundwater; Environmental science; Sustainable development; Water resource management; Aquifer; Water Framework Directive; Water quality; Water resources; Environmental planning; Hydrology (agriculture); Geology","score_opus":0.015430387031630072,"score_gpt":0.2288127834312254,"score_spread":0.21338239639959533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166207126","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59208703,0.0007460187,0.3792927,0.001520983,0.0001579433,0.0004151194,0.0058064796,0.002609386,0.017364306],"genre_scores_gemma":[0.9537925,0.00019269192,0.041953832,0.00010968057,0.000020610767,0.00018310217,0.00208843,0.00009900681,0.0015600732],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939597,0.0002671055,0.00003253508,0.00013717117,0.000083142826,0.000084065156],"domain_scores_gemma":[0.9987684,0.0008236444,0.00008058014,0.000077281344,0.00020188895,0.000048103473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001960913,0.0007108648,0.0006824918,0.0010865662,0.00024974658,0.0012223527,0.0013339295,0.00090028497,0.0019641267],"category_scores_gemma":[0.0040845787,0.00030495474,0.00094846543,0.000899761,0.00029393952,0.0009550747,0.0010903983,0.000862501,0.00035149607],"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":[0.00003886488,0.00006387552,0.007223757,0.00005290641,0.0000661187,0.000031017593,0.000019042292,0.9812409,0.00031834343,0.0015121166,0.00063102547,0.008802013],"study_design_scores_gemma":[0.000007465397,0.000023359724,0.0007943355,0.0000036973915,0.00001112464,0.000008084947,0.000020394737,0.9970824,0.00019655417,0.0014272155,0.0004197,0.0000057147004],"about_ca_topic_score_codex":0.016077412,"about_ca_topic_score_gemma":0.012907824,"teacher_disagreement_score":0.016077412,"about_ca_system_score_codex":0.0010232687,"about_ca_system_score_gemma":0.0015210747,"threshold_uncertainty_score":0.03196764},"labels":[],"label_agreement":null},{"id":"W3173435539","doi":"10.3390/hydrology8030094","title":"Validation of Soil Survey Estimates of Saturated Hydraulic Conductivity in Major Soils of Puerto Rico","year":2021,"lang":"en","type":"article","venue":"Hydrology","topic":"Soil and Unsaturated Flow","field":"Engineering","cited_by":8,"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":"Natural Resources Conservation Service; U.S. Department of Agriculture","keywords":"Hydraulic conductivity; Pedotransfer function; Soil water; Soil science; Environmental science; Hydrology (agriculture); Mathematics; Statistics; Geology; Geotechnical engineering","score_opus":0.01904846043546655,"score_gpt":0.23519945569201883,"score_spread":0.2161509952565523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173435539","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9927745,0.00007029586,0.003509548,0.000030504118,0.000002931048,0.000045364046,0.0012656387,0.000076108925,0.0022250125],"genre_scores_gemma":[0.99538475,0.000048588583,0.0030266081,0.000021773203,0.0000019689996,0.000050105904,0.001254515,0.000012085545,0.00019958476],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99954766,0.0001588389,0.00003636994,0.00012929406,0.00008328053,0.00004453555],"domain_scores_gemma":[0.9983558,0.0005188168,0.00029831185,0.0003209726,0.00045940958,0.000046569126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012240688,0.00040026847,0.00018766962,0.001056997,0.00026664612,0.00032415453,0.00043988277,0.00020204428,0.00048411617],"category_scores_gemma":[0.0038726567,0.00012159116,0.00021423145,0.00083348475,0.00021472941,0.00032572512,0.0005657956,0.0001353649,0.00019843607],"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":[0.00035311605,0.000099804914,0.90232736,0.000109215754,0.0001682029,0.00020038888,0.0011619866,0.014553263,0.026780942,0.00037476863,0.00073600124,0.05313494],"study_design_scores_gemma":[0.000025329444,0.00008342579,0.9598314,0.000035619687,0.00004314514,0.00007986322,0.0007517212,0.02590482,0.01022062,0.00018885414,0.0028183593,0.000016730903],"about_ca_topic_score_codex":0.05882164,"about_ca_topic_score_gemma":0.08321797,"teacher_disagreement_score":0.05882164,"about_ca_system_score_codex":0.00063166535,"about_ca_system_score_gemma":0.00045204797,"threshold_uncertainty_score":0.11695856},"labels":[],"label_agreement":null},{"id":"W3190115807","doi":"10.3390/hydrology8030112","title":"Interdisciplinary Water Development in the Peruvian Highlands: The Case for Including the Coproduction of Knowledge in Socio-Hydrology","year":2021,"lang":"en","type":"article","venue":"Hydrology","topic":"Environmental and Cultural Studies in Latin America and Beyond","field":"Environmental Science","cited_by":10,"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":"Coproduction; Water resources; Agrarian society; Traditional knowledge; Sustainable development; Sociology of scientific knowledge; Environmental planning; Local community; Environmental resource management; Sociology; Political science; Geography; Environmental science; Public relations; Social science; Agriculture; Ecology","score_opus":0.020323023066704533,"score_gpt":0.27579306402041687,"score_spread":0.25547004095371234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190115807","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7943762,0.0037203315,0.008581114,0.055489402,0.00006699896,0.00021912623,0.00001844662,0.000054978656,0.13747333],"genre_scores_gemma":[0.99486536,0.0005634328,0.0019948292,0.0007313285,0.000017707023,0.00009888152,0.000006870359,0.000007925945,0.0017137725],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9914169,0.007139885,0.000090532805,0.00035183755,0.00029176255,0.00070904935],"domain_scores_gemma":[0.9919693,0.005204225,0.00049526495,0.0007462845,0.0005248324,0.0010601336],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.007517654,0.0003165881,0.00028580826,0.0012060157,0.008718099,0.0066291476,0.0017681649,0.0020874557,0.0023968015],"category_scores_gemma":[0.009947486,0.00026933942,0.00023061575,0.0014814184,0.01779301,0.0051112915,0.015335605,0.0024083448,0.00015070775],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057416568,0.0003792867,0.026558371,0.00034211297,0.00004415138,0.0030054064,0.64334494,0.000659645,0.0015631998,0.21283495,0.0023259446,0.10888459],"study_design_scores_gemma":[0.00006583319,0.0002518577,0.032062892,0.00096134405,0.00004889644,0.0009536185,0.65528536,0.0016445481,0.00092688797,0.117108166,0.19064775,0.00004283153],"about_ca_topic_score_codex":0.010871426,"about_ca_topic_score_gemma":0.022848802,"teacher_disagreement_score":0.9912819,"about_ca_system_score_codex":0.0053174715,"about_ca_system_score_gemma":0.011106009,"threshold_uncertainty_score":0.03975761},"labels":[],"label_agreement":null},{"id":"W3198848115","doi":"10.3390/hydrology8030134","title":"Modeling Impact of Climate Change on Surface Water Availability Using SWAT Model in a Semi-Arid Basin: Case of El Kalb River, Lebanon","year":2021,"lang":"en","type":"article","venue":"Hydrology","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 Guelph","funders":"American University of Beirut","keywords":"Streamflow; Environmental science; Climate change; Soil and Water Assessment Tool; Representative Concentration Pathways; SWAT model; Hydrology (agriculture); Arid; Surface runoff; Drainage basin; Water resources; Climate model; Climatology; Downscaling; Geography; Geology","score_opus":0.037911135574218754,"score_gpt":0.28266500074174633,"score_spread":0.24475386516752756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198848115","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9977552,0.000048612368,0.00077180605,0.0001041365,0.000008008639,0.00000968106,0.00026394127,0.0000410368,0.0009975648],"genre_scores_gemma":[0.9987301,0.000052562304,0.0005780211,0.000012527853,0.000004303239,0.00001403069,0.00021264401,0.000007066346,0.00038874915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983597,0.000061991224,0.000009470801,0.00003196411,0.000015135308,0.0000455096],"domain_scores_gemma":[0.99963164,0.0001696355,0.000047460693,0.000019044452,0.00008576139,0.000046473837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049068243,0.0005614183,0.00045288578,0.00058401335,0.0005925566,0.00094587763,0.0007962923,0.0010754367,0.0010021052],"category_scores_gemma":[0.0008097547,0.00030591188,0.00066735264,0.00057411194,0.00047219524,0.0006645062,0.00043631336,0.00043072127,0.00008833771],"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":[0.000058310008,0.000086237276,0.024104148,0.000018582494,0.000042081192,0.0004345235,0.00004713935,0.97178966,0.00084467133,0.00051786576,0.00028998277,0.0017666987],"study_design_scores_gemma":[0.000018356346,0.000021788017,0.004866748,0.0000033765014,0.000014818627,0.00001366603,0.0000716337,0.99445796,0.0002793146,0.00012177927,0.0001224864,0.000008068874],"about_ca_topic_score_codex":0.1625002,"about_ca_topic_score_gemma":0.10570846,"teacher_disagreement_score":0.1625002,"about_ca_system_score_codex":0.0019888803,"about_ca_system_score_gemma":0.0013052827,"threshold_uncertainty_score":0.32310867},"labels":[],"label_agreement":null},{"id":"W3201070295","doi":"10.3390/hydrology8030139","title":"Laboratory Experiments to Evaluate the Effectiveness of Persulfate to Oxidize BTEX in Saline Environment and at Elevated Temperature Using Stable Isotopes","year":2021,"lang":"en","type":"article","venue":"Hydrology","topic":"Arsenic contamination and mitigation","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","keywords":"BTEX; Ethylbenzene; Persulfate; Environmental chemistry; Benzene; Chemistry; Toluene; Xylene; Biodegradation; Sulfate; Isotope analysis; Organic chemistry; Catalysis; Geology","score_opus":0.010892310982653879,"score_gpt":0.2557213355374888,"score_spread":0.24482902455483493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201070295","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98728716,0.00044163957,0.010381362,0.000067703535,0.00006590361,0.000329096,0.00067436206,0.00009629534,0.00065654126],"genre_scores_gemma":[0.9634034,0.0013049241,0.030101458,0.00014002684,0.000049482052,0.0009793864,0.0012225326,0.0000477412,0.002751021],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993315,0.00010981999,0.00007813119,0.00019542608,0.00019675193,0.000088336295],"domain_scores_gemma":[0.9993253,0.00013011003,0.00017630601,0.00006730401,0.00024115048,0.00005986794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009578061,0.0009532688,0.0008184727,0.00028048173,0.00057782896,0.0005296097,0.00062692777,0.00072894216,0.00075875537],"category_scores_gemma":[0.00074129744,0.0002832999,0.0009607153,0.0003123209,0.0003602971,0.0004651889,0.00047834698,0.0010051505,0.00036039812],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013446984,0.00018776473,0.00060147373,0.00011318398,0.000025855978,0.000031169115,0.00004984288,0.00034947475,0.99749076,0.000033145352,0.000032083968,0.00095086184],"study_design_scores_gemma":[0.000040947445,0.004483054,0.0022744634,0.000012804428,0.000090529604,0.00009205614,0.000068552494,0.0011298091,0.99074566,0.0000659605,0.00096853817,0.000027444661],"about_ca_topic_score_codex":0.0024866846,"about_ca_topic_score_gemma":0.0027871986,"teacher_disagreement_score":0.0024866846,"about_ca_system_score_codex":0.00052036747,"about_ca_system_score_gemma":0.00063525635,"threshold_uncertainty_score":0.0050653815},"labels":[],"label_agreement":null},{"id":"W3207590597","doi":"10.3390/hydrology8040151","title":"Numerical Simulation of Flow in Parshall Flume Using Selected Nonlinear Turbulence Models","year":2021,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydraulic flow and structures","field":"Engineering","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 Ottawa","funders":"","keywords":"Flume; Turbulence; Computational fluid dynamics; Turbulence modeling; Nonlinear system; Marine engineering; Flow (mathematics); Engineering; Mechanics; Physics; Aerospace engineering","score_opus":0.01601133712513853,"score_gpt":0.2370460034947551,"score_spread":0.22103466636961658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207590597","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95512503,0.00013814517,0.033059057,0.0001981584,0.000052180727,0.000091957234,0.0004923363,0.000430173,0.010413096],"genre_scores_gemma":[0.9873117,0.00007875769,0.010573611,0.000021614516,0.0000063719394,0.00006433117,0.00021898428,0.00001683443,0.001707865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998281,0.000027797361,0.000012256803,0.000039124858,0.000048196962,0.00004457703],"domain_scores_gemma":[0.9996871,0.00016742355,0.000037374382,0.000026180958,0.000050571543,0.00003141487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026632295,0.00051105424,0.00044541285,0.00047985197,0.000856435,0.0007140614,0.00054284453,0.000876486,0.0011952945],"category_scores_gemma":[0.00051600684,0.00018485099,0.00039790932,0.0004703341,0.0006617476,0.00038828296,0.0004082474,0.0004542014,0.00014047316],"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":[0.00011653221,0.00009812939,0.0044432194,0.000049990747,0.000014411787,0.00016107014,0.0001813774,0.97319996,0.014518471,0.002178803,0.00035708197,0.0046809134],"study_design_scores_gemma":[0.000016820333,0.00006965972,0.0018552027,0.0000041264175,0.000005057648,0.000019612538,0.00006457135,0.99400216,0.0033394033,0.00023494336,0.0003722935,0.000016092285],"about_ca_topic_score_codex":0.029061155,"about_ca_topic_score_gemma":0.020732665,"teacher_disagreement_score":0.029061155,"about_ca_system_score_codex":0.00096961984,"about_ca_system_score_gemma":0.0013124445,"threshold_uncertainty_score":0.05778402},"labels":[],"label_agreement":null},{"id":"W3207642129","doi":"10.3390/hydrology8040152","title":"Flood Risk Communication Using ArcGIS StoryMaps","year":2021,"lang":"en","type":"article","venue":"Hydrology","topic":"Flood Risk Assessment and Management","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":"Institut National de la Recherche Scientifique","funders":"","keywords":"Flood myth; Flooding (psychology); ArcGIS Server; Watershed; Environmental resource management; Natural hazard; Hazard; Geographic information system; Climate change; Web mapping; Computer science; Geography; Cartography; World Wide Web; Environmental science; Web service; Web 2.0","score_opus":0.011304575212900096,"score_gpt":0.240448378064247,"score_spread":0.2291438028513469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207642129","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1498137,0.00086167385,0.2234024,0.0021344733,0.00028300105,0.0016774121,0.24240571,0.08749293,0.2919287],"genre_scores_gemma":[0.57515,0.001358614,0.28637224,0.00019585788,0.0000729716,0.0014672905,0.08673342,0.00535735,0.043292195],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995695,0.000114478185,0.000037753634,0.00006120036,0.00017770815,0.000039458075],"domain_scores_gemma":[0.99830675,0.0007176376,0.00011192707,0.00021910496,0.00053735246,0.00010724442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007252493,0.0005963825,0.00021990034,0.0042095473,0.0005906493,0.002230563,0.0006996348,0.00035009984,0.024769394],"category_scores_gemma":[0.0034100895,0.00026335078,0.00034665538,0.004493631,0.00031009802,0.0014741822,0.0010735308,0.00038641857,0.0031664607],"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":[0.00055272476,0.00022435354,0.026622584,0.0010578687,0.00012973767,0.0014474908,0.01652388,0.029552218,0.004858549,0.027221307,0.3088319,0.58297735],"study_design_scores_gemma":[0.00007497917,0.000055012995,0.02852381,0.0003109561,0.000053806976,0.00036766293,0.005925896,0.05420472,0.0063006342,0.010460505,0.89355916,0.00016298941],"about_ca_topic_score_codex":0.21123822,"about_ca_topic_score_gemma":0.26392046,"teacher_disagreement_score":0.21123822,"about_ca_system_score_codex":0.0016431286,"about_ca_system_score_gemma":0.0023427098,"threshold_uncertainty_score":0.4200173},"labels":[],"label_agreement":null},{"id":"W4200139874","doi":"10.3390/hydrology8040187","title":"Improving Hillslope Link Model Performance from Non-Linear Representation of Natural and Artificially Drained Subsurface Flows","year":2021,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":9,"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 Manitoba","funders":"Mid-America Transportation Center, University of Nebraska-Lincoln; Iowa Department of Transportation; U.S. Department of Transportation","keywords":"Hydrograph; Streamflow; Surface runoff; Hydrology (agriculture); Representation (politics); Benchmark (surveying); Environmental science; Scale (ratio); Tile drainage; Hydrological modelling; Computer science; Geology; Soil science; Soil water; Geotechnical engineering; Drainage basin; Cartography; Climatology; Geography","score_opus":0.011575856276902052,"score_gpt":0.22460677740495116,"score_spread":0.2130309211280491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200139874","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8874167,0.000116120806,0.10678186,0.00032133984,0.00004289208,0.000070770315,0.00036485188,0.0009886087,0.0038969566],"genre_scores_gemma":[0.98349607,0.000038478345,0.015668556,0.000036179135,0.0000060191924,0.000023191198,0.00021689519,0.000037894548,0.00047675392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997992,0.00008826729,0.000015437598,0.000033538097,0.000040080176,0.000023440847],"domain_scores_gemma":[0.9990522,0.00053683185,0.00010097571,0.00011513086,0.0001630302,0.00003176542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010914354,0.0005172648,0.00033530442,0.00028528963,0.00018917605,0.0007427166,0.0008907265,0.000545762,0.0007695272],"category_scores_gemma":[0.003288408,0.00028549615,0.00039656836,0.00022966912,0.0002942956,0.0010786221,0.0005891212,0.00069055724,0.00013880598],"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":[0.000035450175,0.00006415409,0.0052290633,0.000019762423,0.000018101222,0.000029052815,0.000022774062,0.98667693,0.0013162429,0.00066777173,0.00014994889,0.0057707056],"study_design_scores_gemma":[0.000007814404,0.0000148301515,0.00028300466,0.0000016147803,0.0000028612444,0.0000028129493,0.000003989985,0.9990103,0.0004986419,0.000094105446,0.00007691921,0.0000031161346],"about_ca_topic_score_codex":0.013373949,"about_ca_topic_score_gemma":0.011598904,"teacher_disagreement_score":0.013373949,"about_ca_system_score_codex":0.0007219091,"about_ca_system_score_gemma":0.0010406204,"threshold_uncertainty_score":0.026592195},"labels":[],"label_agreement":null},{"id":"W4210351272","doi":"10.3390/hydrology9020026","title":"Application of Numerical and Experimental Modeling to Improve the Efficiency of Parshall Flumes: A Review of the State-of-the-Art","year":2022,"lang":"en","type":"review","venue":"Hydrology","topic":"Hydraulic flow and structures","field":"Engineering","cited_by":11,"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 Ottawa","funders":"","keywords":"Flume; Computational fluid dynamics; Fluent; Flow (mathematics); Marine engineering; Software; Flow conditions; Simulation; Open-channel flow; Computer science; Fluid dynamics; Engineering; Mechanics","score_opus":0.013134239534840995,"score_gpt":0.2686425495662681,"score_spread":0.2555083100314271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210351272","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017771479,0.82157916,0.14111637,0.0012896409,0.00089691655,0.00023382364,0.000590113,0.00087065203,0.015652016],"genre_scores_gemma":[0.119870774,0.8079873,0.06559978,0.0003846491,0.00078699493,0.0002821581,0.0009345605,0.00027084074,0.0038830366],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99818027,0.00023341697,0.00020991595,0.00035146312,0.0009604115,0.00006456608],"domain_scores_gemma":[0.9974981,0.0013011259,0.0002873834,0.0001454388,0.00071969314,0.000048258116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030203164,0.0016702445,0.0016028611,0.003749036,0.0005447686,0.0028952153,0.0018224926,0.0014513002,0.003133288],"category_scores_gemma":[0.003831941,0.000872543,0.0018211008,0.0029037942,0.00096159626,0.0026537522,0.0008355452,0.0011432345,0.0010983616],"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":[0.00015316419,0.0003149725,0.005131224,0.020605719,0.00018363759,0.00032046853,0.00033454804,0.035041265,0.020099074,0.017828638,0.008227786,0.8917595],"study_design_scores_gemma":[0.00004493218,0.0011191525,0.013202431,0.011540403,0.000961886,0.0016101125,0.0010043812,0.14928529,0.075685,0.018711383,0.72616637,0.0006685961],"about_ca_topic_score_codex":0.0024394128,"about_ca_topic_score_gemma":0.0019228021,"teacher_disagreement_score":0.003749036,"about_ca_system_score_codex":0.0008880804,"about_ca_system_score_gemma":0.0012660265,"threshold_uncertainty_score":0.01597315},"labels":[],"label_agreement":null},{"id":"W4221107445","doi":"10.3390/hydrology9030048","title":"Evaluation of Future Streamflow in the Upper Part of the Nilwala River Basin (Sri Lanka) under Climate Change","year":2022,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":39,"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":"Faculty of Graduate Studies and Research, University of Alberta","keywords":"Streamflow; Climate change; Precipitation; Environmental science; Drainage basin; Watershed; Representative Concentration Pathways; Climate model; Climatology; Water resources; Structural basin; Hydrology (agriculture); Geology; Geography; Meteorology; Oceanography","score_opus":0.028333312863404646,"score_gpt":0.24875290627944682,"score_spread":0.22041959341604217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221107445","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9982255,0.000039832765,0.0004706235,0.00003373658,0.000003976356,0.000013469296,0.00046564048,0.000044769255,0.00070240896],"genre_scores_gemma":[0.9988385,0.000045276323,0.0005002805,0.00000523529,0.0000013060234,0.0000111147365,0.0004568488,0.000003150587,0.00013840623],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998491,0.00003736811,0.000013259931,0.00003314675,0.00003347216,0.000033704844],"domain_scores_gemma":[0.99978,0.00005737935,0.000026999243,0.000017607186,0.00009239091,0.00002561393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043436894,0.0002664362,0.00025475927,0.00043694107,0.00025777676,0.0007915081,0.00028025854,0.0003905212,0.00076712936],"category_scores_gemma":[0.00074109156,0.0001537945,0.00047203188,0.0006152713,0.00020018364,0.0005824718,0.00035118088,0.0002530649,0.00010461169],"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":[0.00017942028,0.00016720525,0.5932169,0.00010683128,0.0002644952,0.0006452604,0.00029269158,0.37178984,0.007918897,0.0007694768,0.00064875995,0.024000144],"study_design_scores_gemma":[0.00003271412,0.00031886293,0.48599756,0.000027311,0.00011887357,0.00012543163,0.00062346266,0.5076372,0.0036069008,0.0002760009,0.0011859987,0.00004964976],"about_ca_topic_score_codex":0.03639406,"about_ca_topic_score_gemma":0.040726174,"teacher_disagreement_score":0.03639406,"about_ca_system_score_codex":0.0008608506,"about_ca_system_score_gemma":0.00070521573,"threshold_uncertainty_score":0.07236445},"labels":[],"label_agreement":null},{"id":"W4229444723","doi":"10.3390/hydrology9050081","title":"Change in Winter Precipitation Regime across Ontario, Canada","year":2022,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"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 Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Precipitation; Environmental science; Snow; Climate change; Hydrology (agriculture); Water year; Trend analysis; Climatology; Drainage basin; Geography; Meteorology; Geology","score_opus":0.01454101439222226,"score_gpt":0.2290566293281551,"score_spread":0.21451561493593285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229444723","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9836511,0.0008136866,0.00025076067,0.00048636078,0.00001661631,0.000032668646,0.006881153,0.00002798487,0.007839728],"genre_scores_gemma":[0.9933642,0.00060108554,0.00028401858,0.00007011827,0.000005744554,0.000015421034,0.0023364814,0.000008433381,0.0033144217],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996606,0.000019251058,0.000016904216,0.00006444845,0.00014917998,0.000089582834],"domain_scores_gemma":[0.99903476,0.00004628112,0.00014668216,0.000021359505,0.00061214925,0.00013874612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026553543,0.0001747155,0.00020142682,0.0010612797,0.0019295581,0.0009896896,0.000442995,0.00012814863,0.001282725],"category_scores_gemma":[0.0008534778,0.00014306234,0.00023128118,0.0037371863,0.00049370545,0.0002431343,0.00037677577,0.00021762929,0.0001199101],"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":[0.00011833685,0.000025937605,0.970627,0.00008626859,0.00008355524,0.00021663489,0.0031427923,0.0006634815,0.0018938754,0.00041722835,0.0041475105,0.018577397],"study_design_scores_gemma":[0.0000028190086,0.0000047758444,0.99645966,0.0000071099703,0.000006700304,0.000016749314,0.00068160746,0.0001564559,0.000062571955,0.000013125953,0.0025843543,0.000004131328],"about_ca_topic_score_codex":0.9971957,"about_ca_topic_score_gemma":0.9990087,"teacher_disagreement_score":0.025248736,"about_ca_system_score_codex":0.025248736,"about_ca_system_score_gemma":0.026680294,"threshold_uncertainty_score":0.18319327},"labels":[],"label_agreement":null},{"id":"W4283070561","doi":"10.3390/hydrology9060109","title":"A Procedure for Estimating Drought Duration and Magnitude at the Uniform Cutoff Level of Streamflow: A Case of the Weekly Flows of Canadian Rivers","year":2022,"lang":"en","type":"article","venue":"Hydrology","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":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Magnitude (astronomy); Cutoff; Streamflow; Return period; Hydrology (agriculture); Mathematics; Environmental science; Truncation (statistics); Statistics; Flow (mathematics); Geography; Drainage basin; Geology; Flood myth","score_opus":0.019428015633489545,"score_gpt":0.23131252904730037,"score_spread":0.21188451341381082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283070561","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05185787,0.00007028395,0.9459131,0.000052522057,0.000009720923,0.00019416425,0.0004730767,0.0005619385,0.0008674702],"genre_scores_gemma":[0.26496783,0.00007980159,0.7325452,0.000022485152,0.000008486134,0.0002489829,0.00081300095,0.00006175988,0.0012523779],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920636,0.0001802914,0.00006352443,0.00024048935,0.00021916901,0.00009026137],"domain_scores_gemma":[0.99889416,0.00037892483,0.00014902467,0.00014453045,0.00038744253,0.000045974346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017852368,0.00050287065,0.00032661727,0.0014321575,0.0009769691,0.000515234,0.00070426037,0.0004169066,0.00094550283],"category_scores_gemma":[0.0052315257,0.00026348542,0.00042642813,0.0012827036,0.0006696918,0.00034381094,0.000484966,0.00060699484,0.00021259142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029501872,0.00011356729,0.06520272,0.00019891858,0.00015614118,0.00047371944,0.0010433131,0.11539228,0.05967646,0.027052268,0.0028748258,0.72752076],"study_design_scores_gemma":[0.000051167328,0.00025723866,0.15939778,0.00006397545,0.000103686456,0.0007762443,0.0005380442,0.7620422,0.05275814,0.010670368,0.013115799,0.00022528897],"about_ca_topic_score_codex":0.19993955,"about_ca_topic_score_gemma":0.24098979,"teacher_disagreement_score":0.80006045,"about_ca_system_score_codex":0.0013702674,"about_ca_system_score_gemma":0.0047293385,"threshold_uncertainty_score":0.39755154},"labels":[],"label_agreement":null},{"id":"W4283661702","doi":"10.3390/hydrology9070116","title":"Evaluating the Performance of Water Quality Indices: Application in Surface Water of Lake Union, Washington State-USA","year":2022,"lang":"en","type":"article","venue":"Hydrology","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":14,"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":"Water quality; Surface water; Categorization; Index (typography); Environmental science; Quality (philosophy); Hydrology (agriculture); Environmental resource management; Computer science; Environmental engineering; Engineering; Artificial intelligence; Ecology","score_opus":0.04159362353827674,"score_gpt":0.3275984095049375,"score_spread":0.2860047859666608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283661702","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9844158,0.00014756592,0.007209942,0.000059981827,0.0000095710975,0.00020854434,0.001388285,0.000122054895,0.006438122],"genre_scores_gemma":[0.9796072,0.00016209394,0.01747373,0.00001207252,0.0000036779659,0.000074902346,0.0013562412,0.000012170421,0.0012979388],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987431,0.00042020506,0.00011386743,0.0001454252,0.00050769997,0.00006980693],"domain_scores_gemma":[0.9968586,0.0008812968,0.00030858815,0.00014196009,0.00170966,0.000099859586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042941817,0.00035107377,0.000241453,0.0022966526,0.00042196648,0.0010923264,0.0002619682,0.00031230215,0.0007825229],"category_scores_gemma":[0.0061575808,0.000087984445,0.0002698926,0.0032886504,0.0002386022,0.0004997682,0.00065882393,0.00018167551,0.00016834846],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004947064,0.00043417237,0.738573,0.0004621177,0.00024632388,0.00017099448,0.0012833236,0.021107895,0.013886125,0.0010757692,0.0031972902,0.21906829],"study_design_scores_gemma":[0.000030386323,0.0012861827,0.8571965,0.00010180876,0.0001560156,0.00011850639,0.0043450026,0.103049755,0.0261721,0.0007781057,0.006683729,0.00008178652],"about_ca_topic_score_codex":0.04405669,"about_ca_topic_score_gemma":0.07244329,"teacher_disagreement_score":0.04405669,"about_ca_system_score_codex":0.0013426756,"about_ca_system_score_gemma":0.0010388037,"threshold_uncertainty_score":0.08760047},"labels":[],"label_agreement":null},{"id":"W4286587843","doi":"10.3390/hydrology9080129","title":"Growing Crops in Arid, Drought-Prone Environments: Adaptation and Mitigation","year":2022,"lang":"en","type":"article","venue":"Hydrology","topic":"Water resources management and optimization","field":"Engineering","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":"University of British Columbia","funders":"","keywords":"Water scarcity; Irrigation; Context (archaeology); Agriculture; Production (economics); Environmental science; Arid; Water conservation; Scarcity; Water use; Farm water; Water resource management; Water supply; Agroforestry; Deficit irrigation; Water resources; Climate change; Agricultural economics; Business; Irrigation management; Economics; Agronomy; Geography; Environmental engineering","score_opus":0.007355076914638605,"score_gpt":0.16560609181609717,"score_spread":0.15825101490145857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286587843","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9943895,0.00041652055,0.0023731857,0.00053922326,0.0000052153923,0.000018682807,0.000095508716,0.000013377378,0.0021487854],"genre_scores_gemma":[0.99841905,0.00047829977,0.0007482199,0.000025454156,0.000004859706,0.000009628912,0.000038241946,0.0000017804613,0.00027448442],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998524,0.00007269648,0.000004935592,0.00001793416,0.000009091814,0.00004299265],"domain_scores_gemma":[0.999793,0.000042440366,0.00009915611,0.000015512533,0.000018275827,0.000031673677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004804243,0.00014246716,0.00016939316,0.00024030652,0.00026075562,0.00057970895,0.00021208444,0.0002521908,0.000827906],"category_scores_gemma":[0.0006736522,0.000104611296,0.0001607793,0.00045658063,0.00037883234,0.000454992,0.0004179139,0.00017357028,0.0000771033],"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":[0.0004851579,0.00061481236,0.6150523,0.00045484488,0.0004239204,0.0006754733,0.0016531291,0.18069269,0.029679986,0.0134473,0.0032589284,0.15356152],"study_design_scores_gemma":[0.000034072396,0.00033557034,0.88121074,0.00006947217,0.000076597484,0.00018200745,0.006399228,0.087569945,0.0021968153,0.011657106,0.010226,0.000042343287],"about_ca_topic_score_codex":0.006817365,"about_ca_topic_score_gemma":0.014018389,"teacher_disagreement_score":0.006817365,"about_ca_system_score_codex":0.0006127987,"about_ca_system_score_gemma":0.00041805656,"threshold_uncertainty_score":0.0135554075},"labels":[],"label_agreement":null},{"id":"W4288041932","doi":"10.3390/hydrology9080133","title":"A Comparative Evaluation of Using Rain Gauge and NEXRAD Radar-Estimated Rainfall Data for Simulating Streamflow","year":2022,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Alberta Energy; Ministry of the Environment, Conservation and Parks; University of Guelph","funders":"","keywords":"Streamflow; Rain gauge; Environmental science; Radar; Precipitation; Surface runoff; Flood forecasting; Infiltration (HVAC); Hydrology (agriculture); Meteorology; Runoff model; Flood myth; Drainage basin; Geology; Geography; Geotechnical engineering","score_opus":0.16336104208553937,"score_gpt":0.3664756655690722,"score_spread":0.20311462348353282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288041932","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99498874,0.00010844094,0.0027330723,0.00005970008,0.0000112900125,0.000035313355,0.0006245755,0.00018936564,0.0012494524],"genre_scores_gemma":[0.9932173,0.00008590111,0.005008831,0.000025583056,0.000006667142,0.000021904103,0.001302308,0.00002810178,0.00030347376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980584,0.00080427807,0.00016014797,0.00037255287,0.00049294863,0.00011165959],"domain_scores_gemma":[0.9949884,0.0028432116,0.0003664648,0.00045931354,0.0011937565,0.00014884044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040209293,0.0008015537,0.0004702144,0.00090681884,0.0003370479,0.0010025764,0.0008802037,0.00056141213,0.00039902018],"category_scores_gemma":[0.010123921,0.0003234738,0.0004892451,0.0012907616,0.00035250984,0.0011544714,0.00046728642,0.0003263916,0.00013570247],"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":[0.0009140788,0.00039229827,0.38786122,0.00011387339,0.0005442636,0.00016741372,0.00038405237,0.54388565,0.0057940395,0.0006182884,0.000674963,0.058649898],"study_design_scores_gemma":[0.00014842593,0.00029810835,0.17835186,0.000027686105,0.00009526857,0.000044616194,0.0002850494,0.81199044,0.007329361,0.0001637446,0.0011995821,0.00006595242],"about_ca_topic_score_codex":0.20692183,"about_ca_topic_score_gemma":0.23815966,"teacher_disagreement_score":0.20692183,"about_ca_system_score_codex":0.0024642914,"about_ca_system_score_gemma":0.0015288637,"threshold_uncertainty_score":0.41143477},"labels":[],"label_agreement":null},{"id":"W4306925940","doi":"10.3390/hydrology9100186","title":"Development of a Machine Learning Framework to Aid Climate Model Assessment and Improvement: Case Study of Surface Soil Moisture","year":2022,"lang":"en","type":"article","venue":"Hydrology","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"Canadian Space Agency","keywords":"Climate model; Random forest; Climate change; Environmental science; Snowmelt; Emulation; Climatology; Meteorology; Relative humidity; Mean squared error; Snow; Water content; Downscaling; Range (aeronautics); Computer science; Machine learning; Statistics; Mathematics; Precipitation; Geography; Geology; Engineering","score_opus":0.012568029058669525,"score_gpt":0.26552232675113113,"score_spread":0.2529542976924616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306925940","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017034419,0.00009162063,0.979909,0.00035439094,0.000014393627,0.000080786565,0.00011576269,0.0008960291,0.0015036542],"genre_scores_gemma":[0.30866343,0.00011983933,0.6898406,0.000069687725,0.000034446974,0.00017344771,0.00020549823,0.000100970465,0.0007920401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946374,0.00025356785,0.000032424476,0.000075886965,0.00014131318,0.000033133463],"domain_scores_gemma":[0.9985827,0.00073402264,0.000102888465,0.0001260111,0.00040537777,0.000049135528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029896065,0.00072576304,0.0006679664,0.0007923688,0.00052489724,0.0010963436,0.0013865867,0.0009906782,0.0015176508],"category_scores_gemma":[0.0053925947,0.00035270583,0.00064988586,0.00055304,0.0006098549,0.001139921,0.0010445571,0.0012140984,0.00029882643],"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":[0.000024124836,0.000052805117,0.0014274884,0.000037766447,0.00002825554,0.00007499561,0.0000451069,0.9451051,0.0014190986,0.015126706,0.00063969917,0.03601889],"study_design_scores_gemma":[0.0000030855713,0.000007011836,0.00009535856,0.0000042393394,0.0000016708469,0.000005815938,0.0000044981666,0.9973527,0.00035904962,0.0018057285,0.00035767152,0.0000030815424],"about_ca_topic_score_codex":0.016742857,"about_ca_topic_score_gemma":0.014482107,"teacher_disagreement_score":0.016742857,"about_ca_system_score_codex":0.0011378706,"about_ca_system_score_gemma":0.0023955682,"threshold_uncertainty_score":0.033290803},"labels":[],"label_agreement":null},{"id":"W4308518061","doi":"10.3390/hydrology9110197","title":"Long Term Trend Analysis of River Flow and Climate in Northern Canada","year":2022,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Alberta Environment and Protected Areas; University of Calgary","funders":"","keywords":"Precipitation; Environmental science; Structural basin; Water resources; Glacier; Streamflow; Climate change; Drainage basin; Hydrology (agriculture); Trend analysis; Land cover; Climatology; Physical geography; Land use; Geography; Geology; Oceanography; Ecology; Meteorology","score_opus":0.005206242687282965,"score_gpt":0.19155527011785695,"score_spread":0.186349027430574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308518061","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9859548,0.00047839133,0.00042145117,0.00014420364,0.00000858501,0.000016195072,0.009955492,0.000054137985,0.0029667164],"genre_scores_gemma":[0.9904702,0.00040753532,0.0005827469,0.000022291684,0.0000038333956,0.0000149817515,0.0064999117,0.000010690844,0.00198791],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99967074,0.000014628607,0.000023506647,0.0000682703,0.00012192373,0.00010095982],"domain_scores_gemma":[0.9987783,0.000083169456,0.000118375945,0.000039162765,0.00085397146,0.00012707639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040679483,0.00019578385,0.00022613598,0.0021572763,0.0007920861,0.00080540916,0.00043251706,0.00013730266,0.0010867893],"category_scores_gemma":[0.0011475867,0.00013135806,0.00031581905,0.006244001,0.00025098093,0.0002573799,0.0003689857,0.00026536157,0.00013228596],"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":[0.000039874303,0.000014367773,0.98064464,0.000025319338,0.00007618673,0.000085137406,0.00045886004,0.0013721795,0.00062975555,0.0002646912,0.0014415358,0.014947504],"study_design_scores_gemma":[0.0000012118556,0.0000056497047,0.9966221,0.00000765687,0.000017408607,0.000016802685,0.0003902969,0.0013345983,0.00008818758,0.000020395237,0.0014894307,0.000006222285],"about_ca_topic_score_codex":0.9896186,"about_ca_topic_score_gemma":0.99284476,"teacher_disagreement_score":0.011350734,"about_ca_system_score_codex":0.011350734,"about_ca_system_score_gemma":0.014845464,"threshold_uncertainty_score":0.08235574},"labels":[],"label_agreement":null},{"id":"W4310478181","doi":"10.3390/hydrology9120216","title":"Forecasting High-Flow Discharges in a Flashy Catchment Using Multiple Precipitation Estimates as Predictors in Machine Learning Models","year":2022,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":3,"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; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada","keywords":"Quantitative precipitation estimation; Precipitation; Flow (mathematics); Machine learning; Computer science; Novelty; Meteorology; Radar; Artificial intelligence; Environmental science; Mathematics; Geography","score_opus":0.0365505414412861,"score_gpt":0.2420428748720379,"score_spread":0.2054923334307518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310478181","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91591996,0.0001225185,0.083163284,0.00011838117,0.000012985872,0.000012934726,0.000090104404,0.00017360611,0.00038619994],"genre_scores_gemma":[0.99337983,0.000033811,0.0063919025,0.0000053435683,0.0000055878904,0.000005064634,0.00006187949,0.000003268259,0.00011336305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976224,0.00009439874,0.000016974498,0.000057671394,0.000043049327,0.000025759917],"domain_scores_gemma":[0.9987722,0.0009190516,0.00011276209,0.00006000052,0.00009751694,0.000038439022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011812504,0.00037161438,0.00043431183,0.0007016744,0.00015343579,0.0005361974,0.00030107098,0.00033770854,0.00020785395],"category_scores_gemma":[0.0028746636,0.00018184795,0.00026667485,0.0006623802,0.0002726104,0.0008016778,0.000418409,0.0004965686,0.00005053019],"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":[0.0000600907,0.00005337676,0.01892414,0.000015223214,0.00004417718,0.00004917415,0.000028140239,0.9523954,0.0016327051,0.00037831385,0.000084072555,0.026335277],"study_design_scores_gemma":[0.0000015051828,0.000014698772,0.002166391,9.469434e-7,0.0000025632562,0.000003238622,0.0000050221734,0.9971308,0.00041802682,0.00023251692,0.000022346043,0.0000018491991],"about_ca_topic_score_codex":0.006411596,"about_ca_topic_score_gemma":0.0059768613,"teacher_disagreement_score":0.006411596,"about_ca_system_score_codex":0.00044587185,"about_ca_system_score_gemma":0.0003731761,"threshold_uncertainty_score":0.012748539},"labels":[],"label_agreement":null},{"id":"W4311858100","doi":"10.3390/hydrology9120221","title":"Trivariate Joint Distribution Modelling of Compound Events Using the Nonparametric D-Vine Copula Developed Based on a Bernstein and Beta Kernel Copula Density Framework","year":2022,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University","funders":"","keywords":"Vine copula; Copula (linguistics); Nonparametric statistics; Kernel density estimation; Mathematics; Joint probability distribution; Econometrics; Statistics; Estimator; Marginal distribution; Parametric statistics; Univariate; Random variable; Multivariate statistics","score_opus":0.03659844109157693,"score_gpt":0.2520943941845985,"score_spread":0.21549595309302158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311858100","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027963692,0.00013066082,0.9708816,0.000046361798,0.000013172724,0.000026934445,0.00007219617,0.00009541625,0.0007699724],"genre_scores_gemma":[0.878257,0.00046711776,0.11763215,0.000036775593,0.00003355274,0.00013318734,0.00032905923,0.00009837736,0.0030128357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988483,0.00048436664,0.000055656812,0.00030233103,0.00017482013,0.0001345382],"domain_scores_gemma":[0.99689084,0.0017352249,0.000539278,0.00029598028,0.00043756774,0.00010105885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023435426,0.0006714157,0.0009143414,0.0011327621,0.0003492884,0.0012184434,0.001428805,0.0006198534,0.0014557525],"category_scores_gemma":[0.007886301,0.0005532564,0.0013431144,0.001153092,0.0009989486,0.0015445439,0.0012633433,0.0011644268,0.00025876766],"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":[0.00002886102,0.00003046817,0.005468248,0.000049572394,0.000114886694,0.00019322665,0.0001650638,0.9315343,0.0010585812,0.03883218,0.00038886914,0.022135733],"study_design_scores_gemma":[0.0000010180264,0.000007730033,0.00076375477,0.0000032445566,0.0000052534533,0.00002673137,0.000013288088,0.9951395,0.00009262547,0.0037340082,0.00020528506,0.000007584477],"about_ca_topic_score_codex":0.017300969,"about_ca_topic_score_gemma":0.01065037,"teacher_disagreement_score":0.017300969,"about_ca_system_score_codex":0.00090143306,"about_ca_system_score_gemma":0.0009999265,"threshold_uncertainty_score":0.034400523},"labels":[],"label_agreement":null},{"id":"W4313319005","doi":"10.3390/hydrology10010008","title":"Trends and Variabilities in Rainfall and Streamflow: A Case Study of the Nilwala River Basin in Sri Lanka","year":2022,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":20,"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":"Norsk institutt for Bioøkonomi; Faculty of Graduate Studies and Research, University of Alberta; Wayamba University of Sri Lanka","keywords":"Streamflow; Environmental science; Flood forecasting; Hydrology (agriculture); Trend analysis; Drainage basin; Flood myth; Hydropower; Structural basin; Climatology; Water resources; Physical geography; Geography; Geology; Ecology; Cartography; Statistics","score_opus":0.007635409856372176,"score_gpt":0.20918751080218165,"score_spread":0.20155210094580947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313319005","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9992131,0.00004621131,0.00007224953,0.000032637236,7.675425e-7,0.000009091982,0.00021423148,0.0000060081493,0.00040568784],"genre_scores_gemma":[0.9993011,0.00009189623,0.00017234076,0.0000066522443,0.0000018451998,0.000011550517,0.0002830876,0.000001986286,0.00012961843],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99966,0.00009070914,0.00004444123,0.000064330205,0.000060333645,0.00008019358],"domain_scores_gemma":[0.9995308,0.00016186875,0.00011054598,0.000033782788,0.00010311617,0.000059983588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003812919,0.00018843364,0.00022733146,0.0014545053,0.0005430621,0.0008345577,0.00045582026,0.0003528972,0.0005988277],"category_scores_gemma":[0.0009030268,0.00020364783,0.00042238765,0.0029334857,0.00052627036,0.0004965849,0.00053870835,0.00028631583,0.00007389323],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008546714,0.000104476516,0.96987456,0.00010584888,0.0001677142,0.0081411805,0.0039916807,0.0034154705,0.0021413397,0.0004342557,0.00036880115,0.011169091],"study_design_scores_gemma":[0.000007015125,0.000078690944,0.9859897,0.00001912582,0.000057993853,0.0007860038,0.007777547,0.0040981155,0.0003261593,0.000099223216,0.0007373007,0.0000231113],"about_ca_topic_score_codex":0.06719824,"about_ca_topic_score_gemma":0.08685292,"teacher_disagreement_score":0.06719824,"about_ca_system_score_codex":0.0012976725,"about_ca_system_score_gemma":0.00056963036,"threshold_uncertainty_score":0.13361424},"labels":[],"label_agreement":null},{"id":"W4317743668","doi":"10.3390/hydrology10020031","title":"Assessing the Potential of Combined SMAP and In-Situ Soil Moisture for Improving Streamflow Forecast","year":2023,"lang":"en","type":"article","venue":"Hydrology","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Environmental science; Water content; Downscaling; Topsoil; Moisture; In situ; Data assimilation; Streamflow; Watershed; Soil science; Remote sensing; Precipitation; Soil water; Atmospheric sciences; Meteorology; Geology; Drainage basin; Geography","score_opus":0.01045047994612127,"score_gpt":0.23961975414583522,"score_spread":0.22916927419971395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317743668","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98320454,0.00018993339,0.014251249,0.0001239246,0.00003764788,0.000050214145,0.0005108304,0.00035346663,0.0012780926],"genre_scores_gemma":[0.98472726,0.000042995012,0.0146452775,0.00001728717,0.0000141261,0.000019632636,0.00037365776,0.00001732315,0.0001425585],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950826,0.00012453904,0.000030918847,0.000120591285,0.00014737072,0.00006839938],"domain_scores_gemma":[0.9991381,0.00030885078,0.00009730616,0.0001079195,0.00025852764,0.00008913831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017165468,0.00080684246,0.00048528367,0.0007912155,0.00033362655,0.0006759285,0.00067355245,0.00075644546,0.00052292837],"category_scores_gemma":[0.0029383486,0.00042287153,0.0006775063,0.0008868268,0.00020846903,0.001964697,0.00087774254,0.00054231286,0.000108608816],"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":[0.0014395982,0.0009531034,0.2522251,0.00017778334,0.0008841635,0.00025544295,0.00017580882,0.5404751,0.061376613,0.0008532074,0.0009889835,0.14019507],"study_design_scores_gemma":[0.000093733885,0.00038005982,0.08943395,0.000014208889,0.0002550533,0.000033713255,0.000077612356,0.8913658,0.01733725,0.00020904119,0.00076026865,0.000039344257],"about_ca_topic_score_codex":0.01909179,"about_ca_topic_score_gemma":0.023050075,"teacher_disagreement_score":0.01909179,"about_ca_system_score_codex":0.0005619451,"about_ca_system_score_gemma":0.0009662874,"threshold_uncertainty_score":0.037961304},"labels":[],"label_agreement":null},{"id":"W4319438872","doi":"10.3390/hydrology10020044","title":"Comparison of Spatio-Temporal Variability of Daily Maximum Flows in Cold-Season (Winter and Spring) in Southern Quebec (Canada)","year":2023,"lang":"en","type":"article","venue":"Hydrology","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":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Snowmelt; Environmental science; Spring (device); Snow; Spatial variability; Climatology; Snow cover; Winter season; Trend analysis; Atmospheric sciences; Hydrology (agriculture); Geography; Geology; Meteorology","score_opus":0.012898468419422488,"score_gpt":0.2356213231231966,"score_spread":0.22272285470377412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319438872","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99608576,0.00014488205,0.00022899057,0.00007973093,0.000004223163,0.000008543327,0.002060928,0.000024220673,0.0013625389],"genre_scores_gemma":[0.9970669,0.000066487155,0.00020149178,0.000015237526,0.0000024479873,0.000008371944,0.0019461104,0.000005036038,0.00068790617],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997532,0.0000265617,0.000012213412,0.00006313083,0.00006828544,0.00007666227],"domain_scores_gemma":[0.9991196,0.00013798068,0.00014517733,0.000033719138,0.00041020967,0.0001533697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041913477,0.00022054746,0.0002143371,0.0012412955,0.00080329325,0.00096303894,0.0004820778,0.00023802099,0.0015767142],"category_scores_gemma":[0.0010156826,0.00009976064,0.0002351096,0.0020165201,0.0004535816,0.00021941758,0.0003503646,0.00024944695,0.00013471693],"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":[0.00012054356,0.00004578194,0.97910815,0.000035636007,0.00012787354,0.00015026517,0.0008566867,0.002026422,0.001428627,0.00024316378,0.001900435,0.0139563875],"study_design_scores_gemma":[0.0000015885796,0.00000366254,0.9983215,0.0000048438756,0.0000052259475,0.0000072141534,0.0002222405,0.00090965134,0.000043076012,0.0000072621756,0.0004700553,0.0000037553102],"about_ca_topic_score_codex":0.9854578,"about_ca_topic_score_gemma":0.9912184,"teacher_disagreement_score":0.014542222,"about_ca_system_score_codex":0.009832669,"about_ca_system_score_gemma":0.0067578857,"threshold_uncertainty_score":0.071341336},"labels":[],"label_agreement":null},{"id":"W4321456748","doi":"10.3390/hydrology10020053","title":"Reservoir Capacity Estimation by the Gould Probability Matrix, Drought Magnitude, and Behavior Analysis Methods: A Comparative Study Using Canadian Rivers","year":2023,"lang":"en","type":"article","venue":"Hydrology","topic":"Water resources management and optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Magnitude (astronomy); Statistics; Environmental science; Scaling; Standardization; Streamflow; Hydrology (agriculture); Mathematics; Computer science; Geography; Geology; Drainage basin; Geotechnical engineering","score_opus":0.06018819471140105,"score_gpt":0.32138594553584,"score_spread":0.26119775082443897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321456748","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9693834,0.0010254435,0.023085061,0.00013133629,0.000010902722,0.00006818628,0.0008783795,0.00030622163,0.0051110806],"genre_scores_gemma":[0.97477144,0.00073227513,0.022441119,0.000013804512,0.000006797226,0.00003416141,0.0008797762,0.00005061244,0.0010700639],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983924,0.00034972923,0.000080245256,0.00021965602,0.00083172816,0.0001262646],"domain_scores_gemma":[0.99535,0.0022617208,0.00029807558,0.00024219882,0.0017631762,0.00008480689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033978878,0.0005759411,0.00065689755,0.004675028,0.0012009513,0.0013093294,0.0010554333,0.0003908564,0.000653429],"category_scores_gemma":[0.010085058,0.0003427926,0.0007552562,0.0051488453,0.0007359581,0.0010512257,0.0006101563,0.0003312638,0.00008864579],"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":[0.0005209456,0.00016239403,0.5112964,0.00060130854,0.00052929873,0.00042782194,0.0029044473,0.14188194,0.0071809767,0.006118432,0.0022357362,0.3261403],"study_design_scores_gemma":[0.00002359722,0.00014014752,0.67912227,0.00008002991,0.00025184653,0.00020189212,0.0018416626,0.30608904,0.0060292888,0.00071077066,0.0053091473,0.00020030855],"about_ca_topic_score_codex":0.86880046,"about_ca_topic_score_gemma":0.87574637,"teacher_disagreement_score":0.13119954,"about_ca_system_score_codex":0.005556595,"about_ca_system_score_gemma":0.0052122055,"threshold_uncertainty_score":0.26394433},"labels":[],"label_agreement":null},{"id":"W4366281264","doi":"10.3390/hydrology10040095","title":"A Machine-Learning Framework for Modeling and Predicting Monthly Streamflow Time Series","year":2023,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":16,"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 Calgary","funders":"","keywords":"Streamflow; Regression; Random forest; Computer science; Boosting (machine learning); Model selection; Gradient boosting; AdaBoost; Time series; Machine learning; Calibration; Ensemble learning; Regression analysis; Decision tree; Artificial intelligence; Ensemble forecasting; Predictive modelling; Data mining; Statistics; Mathematics; Geography; Support vector machine","score_opus":0.01032860140204604,"score_gpt":0.22219165880300812,"score_spread":0.2118630574009621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366281264","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005245541,0.000566589,0.9926675,0.00014645117,0.000031751526,0.000031720956,0.00020982845,0.0006546555,0.0004459569],"genre_scores_gemma":[0.39081714,0.0016021807,0.60319155,0.00015506322,0.00025115415,0.0004728897,0.0014175946,0.00011508143,0.0019773068],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936384,0.00023215676,0.000048964688,0.00015317387,0.00015509107,0.000046717796],"domain_scores_gemma":[0.9992931,0.00037221902,0.00011095141,0.000049424445,0.00014989679,0.000024494284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002034328,0.0013198816,0.0011694607,0.0013011508,0.00045671407,0.0009019,0.0015383088,0.0010416757,0.0008403535],"category_scores_gemma":[0.003028025,0.00043284372,0.0012859243,0.0014124169,0.00034542818,0.0011910769,0.0005593984,0.0017093502,0.00039731083],"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":[0.000016003602,0.00004801239,0.0009912709,0.000052083116,0.000078313395,0.00003668061,0.000025759,0.9475807,0.0005972649,0.008171764,0.00094952225,0.041452494],"study_design_scores_gemma":[0.0000012700607,0.000010022772,0.00013536938,0.0000044382436,0.0000054140983,0.0000060665448,0.0000018008732,0.99713624,0.00008858601,0.0021369131,0.00046996577,0.0000038320304],"about_ca_topic_score_codex":0.018290386,"about_ca_topic_score_gemma":0.017116632,"teacher_disagreement_score":0.018290386,"about_ca_system_score_codex":0.0008505041,"about_ca_system_score_gemma":0.0014899501,"threshold_uncertainty_score":0.036367893},"labels":[],"label_agreement":null},{"id":"W4367292832","doi":"10.3390/hydrology10050102","title":"Investigating the Use of Sentinel-1 for Improved Mapping of Small Peatland Water Bodies: Towards Wildfire Susceptibility Monitoring in Canada’s Boreal Forest","year":2023,"lang":"en","type":"article","venue":"Hydrology","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Hydrographic Service; Carleton University","funders":"Natural Resources Canada; Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; U.S. Forest Service","keywords":"Peat; Environmental science; Boreal; Surface water; Climate change; Hydrology (agriculture); Water cycle; Vegetation (pathology); Remote sensing; Global warming; Physical geography; Geology; Ecology; Oceanography; Geography","score_opus":0.041153897372402194,"score_gpt":0.2327015175477005,"score_spread":0.1915476201752983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367292832","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9925734,0.0002692426,0.004457799,0.000245431,0.000012003695,0.000058364167,0.00042186675,0.00008278159,0.001879075],"genre_scores_gemma":[0.9715649,0.00038652605,0.026211618,0.00010670978,0.000005341129,0.00002391175,0.00055760564,0.000018124967,0.0011251324],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99970144,0.000036411355,0.000008528107,0.00004157863,0.00011764001,0.00009434898],"domain_scores_gemma":[0.999169,0.00009633815,0.00005583723,0.000026778378,0.00056412,0.000088002096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010182152,0.00035099883,0.00017302022,0.00071723986,0.00078665966,0.000802667,0.00048425348,0.00023541487,0.0003183144],"category_scores_gemma":[0.0009248627,0.0001516012,0.00019503784,0.0008848048,0.00030381687,0.0005749725,0.00029686652,0.0002852679,0.000066989254],"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":[0.00029523717,0.00027386434,0.7270313,0.00022584114,0.00009095476,0.0003538261,0.0018814901,0.011249902,0.06752758,0.0006736419,0.0019940487,0.18840231],"study_design_scores_gemma":[0.000021278827,0.00020112345,0.8604238,0.000095581796,0.00012376106,0.00019150927,0.005584725,0.09811433,0.028738862,0.00029539317,0.0061493316,0.000060358387],"about_ca_topic_score_codex":0.8410693,"about_ca_topic_score_gemma":0.94956905,"teacher_disagreement_score":0.15893072,"about_ca_system_score_codex":0.0025992326,"about_ca_system_score_gemma":0.0057542147,"threshold_uncertainty_score":0.31973332},"labels":[],"label_agreement":null},{"id":"W4376136561","doi":"10.3390/hydrology10050108","title":"Reservoir Ice Conditions from Multi-Sensor Remote Sensing and ERA5-Land: The Manicouagan Hydroelectric Reservoir Case Study","year":2023,"lang":"en","type":"article","venue":"Hydrology","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Synthetic aperture radar; Satellite; Environmental science; Interferometric synthetic aperture radar; Geology; Remote sensing; Sea ice; Sea ice thickness; Hydropower; Water level; Cryosphere; Hydrology (agriculture); Climatology; Geography","score_opus":0.030166862309233704,"score_gpt":0.2660416466990664,"score_spread":0.23587478438983267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376136561","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9966671,0.00013681226,0.00028856596,0.00007707853,0.000004160445,0.000025785865,0.0012123415,0.00001754766,0.0015705557],"genre_scores_gemma":[0.9973455,0.00008682515,0.0006620385,0.000014528867,0.0000037545165,0.000013393342,0.0012103188,0.0000041986477,0.0006594682],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99982554,0.000023702702,0.000008215963,0.000038821618,0.000040293864,0.00006351708],"domain_scores_gemma":[0.9997409,0.00006273962,0.00005757081,0.000026146336,0.00007829652,0.00003435901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040140122,0.0004876065,0.0002349189,0.00084242714,0.00036390114,0.00069656776,0.0005861089,0.00048952317,0.00055712607],"category_scores_gemma":[0.00060084666,0.00016270444,0.00033785845,0.0014781336,0.00046287884,0.0003118047,0.00043727417,0.00026834005,0.000080352074],"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":[0.0004278423,0.0004167705,0.82086176,0.00017436473,0.00031329217,0.0059569697,0.00040043352,0.13438123,0.005727992,0.0007912638,0.0033126518,0.027235353],"study_design_scores_gemma":[0.000033446577,0.0000753409,0.7707934,0.000049488473,0.000111704736,0.0003918127,0.0014097746,0.22157584,0.0028319752,0.00018562206,0.0024707124,0.00007092359],"about_ca_topic_score_codex":0.68048763,"about_ca_topic_score_gemma":0.79457736,"teacher_disagreement_score":0.31951237,"about_ca_system_score_codex":0.00447754,"about_ca_system_score_gemma":0.002666068,"threshold_uncertainty_score":0.64278805},"labels":[],"label_agreement":null},{"id":"W4380083755","doi":"10.3390/hydrology10060130","title":"Use of Mixed Methods in the Science of Hydrological Extremes: What Are Their Contributions?","year":2023,"lang":"en","type":"article","venue":"Hydrology","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":4,"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 Laval","funders":"Ministère des relations internationales et de la Francophonie","keywords":"Flood myth; Citizen journalism; Management science; Computer science; Hydrology (agriculture); Geography; Engineering","score_opus":0.06398750430839828,"score_gpt":0.3483701773850219,"score_spread":0.2843826730766236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380083755","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004998193,0.24796563,0.6827748,0.04547798,0.0054504084,0.0014180872,0.00042453673,0.00020921734,0.011281142],"genre_scores_gemma":[0.11324949,0.12514117,0.7359405,0.009995629,0.006277028,0.0074702594,0.00021955538,0.00026416007,0.0014421048],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.669963,0.30093497,0.006734445,0.007408851,0.014049844,0.00090892636],"domain_scores_gemma":[0.3922428,0.5601382,0.011396991,0.020654859,0.013527856,0.00203931],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2323409,0.0032172455,0.007740971,0.009916038,0.0040447586,0.020034524,0.0057472405,0.008766591,0.004431947],"category_scores_gemma":[0.32430077,0.0019106079,0.005083376,0.013676126,0.014809947,0.01916883,0.013118104,0.010057799,0.0009305763],"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":[0.0004883655,0.00021783735,0.008877247,0.0140886195,0.0037616135,0.000359558,0.009649573,0.0054132915,0.00032295028,0.50443745,0.006057308,0.44632614],"study_design_scores_gemma":[0.00018399,0.0003882444,0.0021069013,0.016509384,0.0010398326,0.00041076727,0.0038634469,0.01731824,0.00061414955,0.8848471,0.07234027,0.00037767796],"about_ca_topic_score_codex":0.004742579,"about_ca_topic_score_gemma":0.004869718,"teacher_disagreement_score":0.76765907,"about_ca_system_score_codex":0.0072739115,"about_ca_system_score_gemma":0.009690956,"threshold_uncertainty_score":0.94666034},"labels":[],"label_agreement":null},{"id":"W4381679480","doi":"10.3390/hydrology10050105","title":"Examination of Measured to Predicted Hydraulic Properties for Low Impact Development Substrates","year":2023,"lang":"en","type":"article","venue":"Hydrology","topic":"Urban Stormwater Management Solutions","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":"Bioretention; Hydraulic conductivity; Pedotransfer function; Environmental science; Green roof; Infiltration (HVAC); Low-impact development; Geotechnical engineering; Richards equation; Soil water; Soil science; Water retention curve; Stormwater; Surface runoff; Roof; Geology; Engineering; Civil engineering; Materials science","score_opus":0.0358339179575286,"score_gpt":0.23754928937971653,"score_spread":0.20171537142218793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381679480","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.992733,0.000099943274,0.005809324,0.00001078237,0.000005801484,0.00001321032,0.00040408218,0.00010116902,0.0008227051],"genre_scores_gemma":[0.9974815,0.000052340474,0.0019663463,0.000005700394,0.0000010501726,0.00001567995,0.0002447728,0.000014694984,0.0002178741],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99935204,0.000095097996,0.000057017096,0.00017837269,0.00025638973,0.00006108734],"domain_scores_gemma":[0.99834645,0.0009129031,0.00019725469,0.00013370566,0.00037204695,0.00003772683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010703467,0.00040679815,0.0003373549,0.0006139442,0.00019725572,0.00066142634,0.00051730353,0.00047428746,0.0009855725],"category_scores_gemma":[0.0022805757,0.00020337073,0.00031263373,0.0006866978,0.0003726622,0.000718839,0.00025540133,0.00035596357,0.00029461423],"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":[0.0012160689,0.00051619037,0.20798826,0.00073641463,0.00012079865,0.0009579574,0.00059688283,0.12082856,0.5613285,0.0007564287,0.000566828,0.10438718],"study_design_scores_gemma":[0.000037352962,0.0014194681,0.4746292,0.000049396935,0.000091336165,0.0003382361,0.0007449484,0.14665528,0.3731062,0.00027260362,0.0025650256,0.00009091043],"about_ca_topic_score_codex":0.0022737535,"about_ca_topic_score_gemma":0.004216819,"teacher_disagreement_score":0.0022737535,"about_ca_system_score_codex":0.00071817276,"about_ca_system_score_gemma":0.00031238998,"threshold_uncertainty_score":0.0056605935},"labels":[],"label_agreement":null},{"id":"W4381839875","doi":"10.3390/hydrology10050110","title":"Predicting Optical Water Quality Indicators from Remote Sensing Using Machine Learning Algorithms in Tropical Highlands of Ethiopia","year":2023,"lang":"en","type":"article","venue":"Hydrology","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":67,"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":"International Development Research Centre; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Mean squared error; Random forest; Support vector machine; Water quality; Algorithm; AdaBoost; Artificial neural network; Regression; Machine learning; Statistics; Regression analysis; Mathematics; Remote sensing; Artificial intelligence; Environmental science; Computer science; Geography; Ecology","score_opus":0.039856665746569694,"score_gpt":0.2981025038010587,"score_spread":0.258245838054489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381839875","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99798155,0.00015326528,0.0011913155,0.00004004896,0.0000041140142,0.000014146517,0.000110310655,0.000012771066,0.0004924248],"genre_scores_gemma":[0.9968765,0.00016306563,0.0026073426,0.000012411455,0.0000038442004,0.000010663914,0.00013857849,0.0000023275131,0.00018524443],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981374,0.000060547154,0.000017466005,0.000037536945,0.000030572544,0.000040009574],"domain_scores_gemma":[0.99962914,0.00015948238,0.00007495118,0.000015689437,0.000083313615,0.00003737001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007501892,0.00054541125,0.00031769258,0.00091834756,0.00020073143,0.00074285,0.00037207257,0.00028170808,0.00023423096],"category_scores_gemma":[0.0010357507,0.00020880446,0.00040179671,0.0006722391,0.00020787497,0.00052224053,0.0002505993,0.00024000656,0.000086030755],"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":[0.0004325698,0.000609191,0.7366262,0.00019031756,0.00028960133,0.0012223629,0.00046065284,0.17899337,0.011893226,0.0008389051,0.00056043355,0.06788309],"study_design_scores_gemma":[0.00005377351,0.0003303039,0.42900592,0.00010087255,0.00012439728,0.00025353892,0.0021520052,0.5570362,0.008850206,0.00076039165,0.0012719585,0.000060401726],"about_ca_topic_score_codex":0.024449298,"about_ca_topic_score_gemma":0.020128934,"teacher_disagreement_score":0.024449298,"about_ca_system_score_codex":0.0008000748,"about_ca_system_score_gemma":0.000573243,"threshold_uncertainty_score":0.048613966},"labels":[],"label_agreement":null},{"id":"W4382560633","doi":"10.3390/hydrology10070139","title":"Predictive MPC-Based Operation of Urban Drainage Systems Using Input Data-Clustered Artificial Neural Networks Rainfall Forecasting Models","year":2023,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":7,"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":"Model predictive control; Computer science; Artificial neural network; Predictive modelling; Drainage; Control theory (sociology); Environmental science; Control (management); Machine learning; Artificial intelligence","score_opus":0.12872581540697395,"score_gpt":0.2800719510880925,"score_spread":0.15134613568111857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382560633","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50026226,0.00031375742,0.48419094,0.00024000678,0.00007174856,0.000096375006,0.00030287317,0.0013696534,0.013152405],"genre_scores_gemma":[0.99353915,0.000041046183,0.0056454283,0.00000728433,0.000003794459,0.000022620583,0.000058782152,0.0000076141932,0.0006742213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998889,0.000024528084,0.000006571124,0.00003162142,0.000029881796,0.000018415767],"domain_scores_gemma":[0.99981695,0.000069428344,0.000034015688,0.000013902125,0.00005764013,0.0000080822165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021130472,0.00043585425,0.00040766114,0.00021726087,0.00024480562,0.00054587336,0.0005952442,0.00031618177,0.00065230933],"category_scores_gemma":[0.00052890973,0.00020839629,0.000322589,0.00034195586,0.00022386029,0.00036313976,0.0003111342,0.0004572122,0.00007967369],"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":[0.000009924262,0.000007669825,0.00030606057,0.000006157004,0.000006276147,0.000010180744,0.000005689396,0.9958609,0.00032032534,0.00019427677,0.00005765265,0.003214969],"study_design_scores_gemma":[9.917395e-7,0.0000029672717,0.00010253454,4.6319803e-7,0.0000013458105,7.4882416e-7,0.0000013874284,0.99966013,0.00012387279,0.00007760912,0.000027281216,7.603981e-7],"about_ca_topic_score_codex":0.027532207,"about_ca_topic_score_gemma":0.017268507,"teacher_disagreement_score":0.027532207,"about_ca_system_score_codex":0.0006346004,"about_ca_system_score_gemma":0.0007556947,"threshold_uncertainty_score":0.054743946},"labels":[],"label_agreement":null},{"id":"W4385732815","doi":"10.3390/hydrology10080164","title":"Enhancing Flood Prediction Accuracy through Integration of Meteorological Parameters in River Flow Observations: A Case Study Ottawa River","year":2023,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":28,"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 Ottawa; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg; University of Ottawa; International Research and Exchanges Board","keywords":"Snowmelt; Precipitation; Flood myth; Environmental science; Watershed; Flood forecasting; Flooding (psychology); Hydrology (agriculture); Reliability (semiconductor); Hydrological modelling; Flow (mathematics); Stream flow; Variable (mathematics); Meteorology; Streamflow; Sensitivity (control systems); Computer science; Machine learning; Snow; Climatology; Mathematics; Drainage basin; Engineering; Geology; Geography","score_opus":0.046940799552213315,"score_gpt":0.2774889693912763,"score_spread":0.230548169839063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385732815","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9980818,0.00005210087,0.0010255328,0.00009151953,0.0000031069621,0.00001996342,0.00022750649,0.000040730738,0.0004577814],"genre_scores_gemma":[0.99737895,0.000053797005,0.0019348675,0.000008744536,0.000002188385,0.000009456072,0.00020296795,0.0000038426624,0.00040520838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9996031,0.00011833594,0.000030639887,0.00009830586,0.00007738238,0.000072278424],"domain_scores_gemma":[0.9986314,0.0007797582,0.000102589256,0.00011122659,0.00030880634,0.00006614696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008539241,0.00060507376,0.00034331653,0.0003786628,0.00077627,0.0009829842,0.0008687058,0.0006495645,0.00038998196],"category_scores_gemma":[0.0024604965,0.00026082102,0.00051757996,0.0009230667,0.00063367357,0.0005153703,0.0004520361,0.0005503246,0.000068944355],"study_design_candidate":"observational","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":[0.0004782674,0.00050095614,0.31375143,0.00016412325,0.00024201185,0.0018654701,0.000759773,0.62404174,0.0068166987,0.00063353917,0.0011300661,0.049615886],"study_design_scores_gemma":[0.000047841197,0.00021181627,0.18574482,0.00002398457,0.00017160503,0.00012581886,0.0010492803,0.80472803,0.00634365,0.00024570015,0.0012331808,0.00007422739],"about_ca_topic_score_codex":0.7991747,"about_ca_topic_score_gemma":0.8300726,"teacher_disagreement_score":0.20082527,"about_ca_system_score_codex":0.005292955,"about_ca_system_score_gemma":0.0033691158,"threshold_uncertainty_score":0.4040159},"labels":[],"label_agreement":null},{"id":"W4385758942","doi":"10.3390/hydrology10080169","title":"Using Ensembles of Machine Learning Techniques to Predict Reference Evapotranspiration (ET0) Using Limited Meteorological Data","year":2023,"lang":"en","type":"article","venue":"Hydrology","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Wind speed; Evapotranspiration; Environmental science; Decision tree; Sunshine duration; Meteorology; Mean squared error; Sensitivity (control systems); Relative humidity; Statistics; Computer science; Machine learning; Mathematics; Geography; Engineering","score_opus":0.09870527285679725,"score_gpt":0.29946995089410827,"score_spread":0.20076467803731102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385758942","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4683592,0.002269174,0.52318907,0.00037820157,0.00029078324,0.000103724975,0.0006405564,0.0013073091,0.0034620361],"genre_scores_gemma":[0.9441037,0.00042540996,0.05377731,0.00007045719,0.000063846215,0.000060232414,0.0006884891,0.000028988161,0.0007815105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994924,0.00015545504,0.00004708681,0.0001354153,0.000110791654,0.00005880353],"domain_scores_gemma":[0.9987779,0.00067591865,0.00011842446,0.000107384614,0.0002722782,0.000048052214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020697264,0.0010220517,0.0010616508,0.0010923226,0.00034376734,0.0007266966,0.00080670643,0.00062406686,0.0005094509],"category_scores_gemma":[0.003418153,0.00037068722,0.001142934,0.0009270453,0.0001730877,0.0010583703,0.00053889607,0.000910603,0.00019551281],"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":[0.00011397003,0.000100002995,0.0129182665,0.000057335285,0.0003215177,0.00006621101,0.00004542538,0.88047236,0.0013630441,0.0004331307,0.000741758,0.10336691],"study_design_scores_gemma":[0.0000037361763,0.000027265183,0.0013989334,0.00000828071,0.000026548229,0.000010135333,0.000008215595,0.9975884,0.00036652473,0.00037319795,0.00018107868,0.000007632273],"about_ca_topic_score_codex":0.013536785,"about_ca_topic_score_gemma":0.013724966,"teacher_disagreement_score":0.013536785,"about_ca_system_score_codex":0.00050672743,"about_ca_system_score_gemma":0.0008514361,"threshold_uncertainty_score":0.026915967},"labels":[],"label_agreement":null},{"id":"W4386134088","doi":"10.3390/hydrology10090177","title":"Modeling Hydrodynamic Behavior of the Ottawa River: Harnessing the Power of Numerical Simulation and Machine Learning for Enhanced Predictability","year":2023,"lang":"en","type":"article","venue":"Hydrology","topic":"Flood Risk Assessment and Management","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":true,"ca_institutions":"University of Ottawa; Université Laval","funders":"","keywords":"Predictability; Flood myth; Watershed; Hydrology (agriculture); Climate change; Streamflow; Environmental science; Flow (mathematics); Computer science; Process (computing); Drainage basin; Machine learning; Geology; Geography; Statistics; Geotechnical engineering; Mathematics","score_opus":0.012647606001061611,"score_gpt":0.2645232795877494,"score_spread":0.2518756735866878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386134088","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53260326,0.0011063407,0.45126668,0.0021704484,0.00015195477,0.00015102148,0.0008276904,0.0011206478,0.010602039],"genre_scores_gemma":[0.95043534,0.00028523538,0.047651507,0.00008378047,0.000039468538,0.00006165907,0.00030851507,0.000043385968,0.0010911663],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998324,0.000056672474,0.000010309069,0.00003377159,0.00004155466,0.000025334235],"domain_scores_gemma":[0.9990613,0.0005516689,0.000102177044,0.00008618147,0.00013896778,0.000059648468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005102297,0.00056936656,0.00045156278,0.00048677577,0.0004628309,0.001080835,0.0008682928,0.0007908692,0.00059953355],"category_scores_gemma":[0.0024237102,0.00040810808,0.00055556465,0.00038432083,0.00089307583,0.0008092938,0.0009855649,0.00084865466,0.000094131254],"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":[0.000009787781,0.000012686992,0.0024252466,0.000012454483,0.000011324429,0.000014113438,0.000021006912,0.99172556,0.0003097716,0.0011120571,0.00014902093,0.004196968],"study_design_scores_gemma":[9.3353253e-7,0.0000025501665,0.00017644235,0.00000153927,0.0000011772271,0.0000010410931,0.000003829838,0.99940395,0.000053673186,0.00024833737,0.000104561856,0.0000018891267],"about_ca_topic_score_codex":0.19668686,"about_ca_topic_score_gemma":0.15518975,"teacher_disagreement_score":0.80331314,"about_ca_system_score_codex":0.0015809836,"about_ca_system_score_gemma":0.0024734924,"threshold_uncertainty_score":0.39108402},"labels":[],"label_agreement":null},{"id":"W4386813952","doi":"10.3390/hydrology10090188","title":"Analysis of Groundwater Depletion in the Saskatchewan River Basin in Canada from Coupled SWAT-MODFLOW and Satellite Gravimetry","year":2023,"lang":"en","type":"article","venue":"Hydrology","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"MODFLOW; Groundwater; Hydrology (agriculture); Groundwater recharge; Environmental science; Soil and Water Assessment Tool; Structural basin; Water table; Water balance; Drainage basin; Climate change; SWAT model; Aquifer; Geology; Streamflow; Geography; Oceanography; Geomorphology","score_opus":0.012790935213047557,"score_gpt":0.19342270838359077,"score_spread":0.18063177317054321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386813952","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9940904,0.00013173549,0.00048254718,0.00006832243,0.000006065828,0.000024204264,0.0034182218,0.00008120014,0.001697282],"genre_scores_gemma":[0.9949378,0.00015098054,0.00086549984,0.00003822599,0.0000017937591,0.000019473922,0.0032580171,0.000013814835,0.00071436196],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998306,0.000012990616,0.000011019862,0.000041020867,0.00005682586,0.000047548656],"domain_scores_gemma":[0.99971527,0.000029934501,0.000027187034,0.000023432061,0.00016962447,0.00003458826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029908947,0.00047132012,0.00030666293,0.0013911547,0.0007465688,0.0010270106,0.00055751327,0.00045144442,0.0008288559],"category_scores_gemma":[0.00050817395,0.00029044872,0.00053296436,0.0031705645,0.0004025384,0.0003519697,0.00049485837,0.00026752974,0.00012305194],"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":[0.00031925002,0.00017590167,0.82997584,0.00018518315,0.0005575268,0.00071071385,0.00060676906,0.1067681,0.014689807,0.0011065108,0.0031133469,0.041791007],"study_design_scores_gemma":[0.000050290917,0.000024769322,0.8493116,0.000038725917,0.00012088279,0.00004981097,0.00078027294,0.14440016,0.0022050315,0.00017641025,0.0027678781,0.0000741969],"about_ca_topic_score_codex":0.9713666,"about_ca_topic_score_gemma":0.981975,"teacher_disagreement_score":0.028633416,"about_ca_system_score_codex":0.010185241,"about_ca_system_score_gemma":0.009695689,"threshold_uncertainty_score":0.07389945},"labels":[],"label_agreement":null},{"id":"W4387421254","doi":"10.3390/hydrology10100198","title":"Evaluation of Groundwater Quality Using the Water Quality Index (WQI) and Human Health Risk (HHR) Assessment in West Bank, Palestine","year":2023,"lang":"en","type":"article","venue":"Hydrology","topic":"Groundwater and Isotope Geochemistry","field":"Earth and Planetary Sciences","cited_by":14,"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":"","keywords":"Environmental science; Groundwater; Water quality; Aquifer; Nitrate; Water resource management; Water resources; Hazard; Hydrology (agriculture); Environmental engineering; Ecology; Engineering","score_opus":0.09429779496133688,"score_gpt":0.37404084656813963,"score_spread":0.27974305160680274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387421254","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9969754,0.00029692793,0.0006966125,0.000062847386,0.000003526678,0.00003916357,0.00043483757,0.0000055959463,0.0014850354],"genre_scores_gemma":[0.9975973,0.0002919166,0.0012464452,0.000019161444,0.0000026280848,0.000032290027,0.00027696157,0.0000011758725,0.0005322287],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99964845,0.00010428665,0.000042263557,0.000044536475,0.00010426974,0.000056090586],"domain_scores_gemma":[0.99973255,0.000043991684,0.00011643143,0.000010403004,0.00006940586,0.00002723806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009119527,0.0003056125,0.00029530827,0.0017776713,0.00037532425,0.0007594509,0.0002208006,0.00030893742,0.000562637],"category_scores_gemma":[0.00076200307,0.00018391605,0.0003570445,0.0025922132,0.00043894764,0.0003238018,0.00068158476,0.00025102447,0.00010563507],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020061395,0.000180459,0.95830476,0.00013560685,0.00010799217,0.0010141173,0.00144305,0.003561875,0.004663347,0.00040357138,0.00035217515,0.029632408],"study_design_scores_gemma":[0.000010099272,0.0003091431,0.9896483,0.00005017665,0.00004281367,0.00038470983,0.0024724514,0.004429603,0.0010847604,0.00028877257,0.0012628197,0.000016493412],"about_ca_topic_score_codex":0.035213057,"about_ca_topic_score_gemma":0.04842631,"teacher_disagreement_score":0.035213057,"about_ca_system_score_codex":0.0012058452,"about_ca_system_score_gemma":0.0010637292,"threshold_uncertainty_score":0.070016205},"labels":[],"label_agreement":null},{"id":"W4388627825","doi":"10.3390/hydrology10110211","title":"Investigating Multilayer Aquifer Dynamics by Combining Geochemistry, Isotopes and Hydrogeological Context Analysis","year":2023,"lang":"en","type":"article","venue":"Hydrology","topic":"Groundwater and Isotope Geochemistry","field":"Earth and Planetary 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":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal; Toronto and Region Conservation Authority; Geological Survey of Canada; Université Laval; Natural Resources Canada; Polytechnique Montréal; EnGlobe (Canada)","funders":"Natural Resources Canada","keywords":"Aquifer; Groundwater; Groundwater recharge; Geology; Hydrogeology; Groundwater flow; Bedrock; Watershed; Context (archaeology); Hydrology (agriculture); Earth science; Geochemistry; Geomorphology; Paleontology","score_opus":0.01295045431728017,"score_gpt":0.2153958832970649,"score_spread":0.20244542897978474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388627825","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.961856,0.00015006641,0.03505082,0.00006866741,0.0000023540815,0.000032021813,0.0009168753,0.00022586256,0.0016972066],"genre_scores_gemma":[0.98761976,0.000104536884,0.011723609,0.000009871406,0.0000015231974,0.00001263396,0.00026458793,0.000012821491,0.00025068226],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999287,0.0000068240033,0.000003911811,0.00002604989,0.000015895514,0.00001865274],"domain_scores_gemma":[0.99985385,0.000029450106,0.000041444106,0.000017774693,0.000039633534,0.000017753295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017728,0.00030886,0.00026707357,0.0015117631,0.00040021676,0.00090077927,0.0003909425,0.00024170431,0.0006932433],"category_scores_gemma":[0.0004968913,0.00020838875,0.0002571459,0.0016940823,0.0003895894,0.0007704404,0.0007611811,0.00017852017,0.00007013144],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007592749,0.00007253257,0.7604077,0.0001286979,0.0002147244,0.000310839,0.0011780928,0.0974553,0.05509694,0.0035483649,0.00042107207,0.0810898],"study_design_scores_gemma":[0.000007941064,0.000038004186,0.6228489,0.000022425742,0.00008710352,0.000085249354,0.0014453749,0.3619293,0.006380962,0.0047031916,0.0024002888,0.00005127085],"about_ca_topic_score_codex":0.24812086,"about_ca_topic_score_gemma":0.47941208,"teacher_disagreement_score":0.24812086,"about_ca_system_score_codex":0.0017675135,"about_ca_system_score_gemma":0.0011849282,"threshold_uncertainty_score":0.49335325},"labels":[],"label_agreement":null},{"id":"W4390176439","doi":"10.3390/hydrology11010001","title":"The Potential of Isotopic Tracers for Precise and Environmentally Clean Stream Discharge Measurements","year":2023,"lang":"en","type":"article","venue":"Hydrology","topic":"Soil and Water Nutrient Dynamics","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":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"TRACER; STREAMS; Laminar flow; Environmental science; Turbulence; Hyporheic zone; Dilution; Hydrology (agriculture); Chemistry; Surface water; Mechanics; Geology; Environmental engineering","score_opus":0.012705657583587628,"score_gpt":0.21739439944441585,"score_spread":0.20468874186082822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390176439","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29269645,0.0121994,0.6800819,0.0017563978,0.0005719694,0.00023307021,0.0014063556,0.0010285026,0.010025943],"genre_scores_gemma":[0.59337825,0.005417283,0.39566725,0.0005272999,0.00017734422,0.00029356356,0.00050044776,0.00029163118,0.0037469119],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99812716,0.00083767326,0.000081324404,0.00029738634,0.00060130615,0.000055125696],"domain_scores_gemma":[0.99674183,0.0016664177,0.00041907316,0.0005152963,0.00056502764,0.00009242208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044205603,0.00052934524,0.0004711809,0.0010445472,0.0003415981,0.0010861611,0.0007045648,0.0010866972,0.0007356626],"category_scores_gemma":[0.004821595,0.00033516923,0.00038616758,0.0012976083,0.00085662596,0.0014771572,0.0011035906,0.0011822432,0.00033524912],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054175727,0.0001045359,0.027361335,0.000670037,0.00009551337,0.00015322278,0.00025064714,0.0071598017,0.79480183,0.014027158,0.00072547974,0.1541087],"study_design_scores_gemma":[0.00006504987,0.00087914435,0.013233037,0.00019811759,0.0001569583,0.00052153395,0.000250619,0.03192622,0.8869381,0.017007625,0.048640043,0.00018361279],"about_ca_topic_score_codex":0.0015693218,"about_ca_topic_score_gemma":0.0027997033,"teacher_disagreement_score":0.0044205603,"about_ca_system_score_codex":0.0008081033,"about_ca_system_score_gemma":0.0010604766,"threshold_uncertainty_score":0.023378432},"labels":[],"label_agreement":null},{"id":"W4391335698","doi":"10.3390/hydrology11020014","title":"Ratingcurve: A Python Package for Fitting Streamflow Rating Curves","year":2024,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"National Science Foundation","keywords":"Streamflow; Python (programming language); Rating curve; Computer science; R package; Proxy (statistics); Measure (data warehouse); Probabilistic logic; Data mining; Hydrology (agriculture); Artificial intelligence; Machine learning; Geology; Programming language; Cartography","score_opus":0.01293954619628129,"score_gpt":0.25789361775155045,"score_spread":0.24495407155526916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391335698","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060379035,0.00014819311,0.44343406,0.00035934203,0.00016318834,0.00036514184,0.053175803,0.49099073,0.005325741],"genre_scores_gemma":[0.087728076,0.0004808233,0.62022835,0.0011002992,0.00014719543,0.0027865192,0.13023703,0.14157453,0.015717274],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992423,0.00014335639,0.00007922753,0.00019109566,0.00027256852,0.00007155182],"domain_scores_gemma":[0.99802184,0.00096285605,0.00015016925,0.0003148639,0.00043871976,0.000111560665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017526569,0.0015419663,0.000991291,0.0010833271,0.0004617764,0.0012890354,0.0030672434,0.00094734004,0.054561727],"category_scores_gemma":[0.009614251,0.0010359436,0.0014733839,0.0011762925,0.00044254737,0.0019087408,0.0018994305,0.0025925816,0.034164704],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004939343,0.0003309468,0.009323884,0.0014069933,0.0003070398,0.0003073619,0.00035259666,0.10010698,0.0069007375,0.010643092,0.64908344,0.22074294],"study_design_scores_gemma":[0.00018556404,0.0000939122,0.0056006294,0.00013662202,0.000045750516,0.00026626568,0.00005885692,0.7903949,0.010371793,0.027356682,0.165322,0.00016704197],"about_ca_topic_score_codex":0.006942411,"about_ca_topic_score_gemma":0.0086130025,"teacher_disagreement_score":0.054561727,"about_ca_system_score_codex":0.00066440296,"about_ca_system_score_gemma":0.0018479166,"threshold_uncertainty_score":0.18252718},"labels":[],"label_agreement":null},{"id":"W4391541084","doi":"10.3390/hydrology11020022","title":"Evaluation of BMPs in Flatland Watershed with Pumped Outlet","year":2024,"lang":"en","type":"article","venue":"Hydrology","topic":"Soil and Water Nutrient Dynamics","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":true,"ca_institutions":"McGill University; Ministry of the Environment, Conservation and Parks; Toronto and Region Conservation Authority; University of Guelph","funders":"Ministry of Agriculture, Food and Rural Affairs; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Buffer strip; Tillage; Watershed; Environmental science; Soil and Water Assessment Tool; SWAT model; Hydrology (agriculture); Cover crop; Soil conservation; Surface runoff; Drainage basin; Agronomy; Agroforestry; Engineering; Streamflow; Agriculture; Ecology; Geography; Geotechnical engineering; Biology","score_opus":0.013408894792520887,"score_gpt":0.2381117804582808,"score_spread":0.2247028856657599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391541084","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9987431,0.000014403784,0.00055934046,0.000012587777,0.0000024164312,0.000044434863,0.00009500869,0.000025163661,0.00050357304],"genre_scores_gemma":[0.99822825,0.000033026867,0.001377074,0.000006233121,8.7712795e-7,0.000024552472,0.00011753457,0.0000027699903,0.00020962956],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99971384,0.00007027307,0.000016297978,0.000051075083,0.0000748045,0.00007357955],"domain_scores_gemma":[0.99945027,0.00018147961,0.00010243434,0.000030202791,0.00015033515,0.00008523968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083921803,0.00037271835,0.00038895072,0.000431649,0.00035128603,0.0006835955,0.0006718811,0.0003546138,0.00047667444],"category_scores_gemma":[0.0013864883,0.0001931896,0.00031653527,0.0005511356,0.0005133149,0.00040064316,0.0002716965,0.0001996046,0.000045907316],"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":[0.0013297562,0.001853815,0.20736708,0.00022812276,0.00015308341,0.0007598533,0.00016147258,0.71870255,0.03376178,0.0008417914,0.00052539917,0.03431523],"study_design_scores_gemma":[0.00022313527,0.0023611933,0.23994777,0.000020727062,0.00016508067,0.000054406686,0.00059988926,0.73841333,0.016858341,0.00027929465,0.0010444814,0.00003237668],"about_ca_topic_score_codex":0.111751564,"about_ca_topic_score_gemma":0.19096969,"teacher_disagreement_score":0.111751564,"about_ca_system_score_codex":0.0027198535,"about_ca_system_score_gemma":0.0022003872,"threshold_uncertainty_score":0.22220218},"labels":[],"label_agreement":null},{"id":"W4392296289","doi":"10.3390/hydrology11030036","title":"Assessment of Water Quality as a Key Component in the Water–Energy–Food Nexus","year":2024,"lang":"en","type":"article","venue":"Hydrology","topic":"Water-Energy-Food Nexus Studies","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":"Component (thermodynamics); Water quality; Nexus (standard); Key (lock); Water energy; Environmental science; Water resource management; Natural resource economics; Environmental engineering; Environmental resource management; Engineering; Ecology; Economics; Biology","score_opus":0.021578297438537702,"score_gpt":0.2777039451697636,"score_spread":0.2561256477312259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392296289","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98822814,0.0018625316,0.0025703292,0.00016629409,0.000012874443,0.000058949092,0.00049960386,0.000008381076,0.006593045],"genre_scores_gemma":[0.9978483,0.0005761286,0.0009307036,0.000021071235,0.0000037272334,0.0000102663025,0.00015832827,0.0000016415138,0.00044987715],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996561,0.00010450558,0.000023089138,0.000050453687,0.00010851597,0.000057257235],"domain_scores_gemma":[0.9997346,0.00006067027,0.000081715705,0.000014506449,0.0000850377,0.00002337968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005542036,0.00026776426,0.0002994564,0.0019577595,0.0004103577,0.0011848424,0.0001795266,0.00026920054,0.0006685246],"category_scores_gemma":[0.0005204359,0.00008578204,0.00021887013,0.0024011116,0.00072210765,0.0005943519,0.0008817767,0.00020633098,0.00004960292],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026795123,0.00008199225,0.9041274,0.00044980957,0.00021918652,0.0003122733,0.0010839691,0.003724931,0.024501093,0.003210073,0.00038112525,0.061640177],"study_design_scores_gemma":[0.0000017768067,0.00013159154,0.99013406,0.000038415626,0.000049238144,0.00005522415,0.0017424785,0.0015997073,0.0034586089,0.00086588995,0.0019114328,0.000011550909],"about_ca_topic_score_codex":0.033297654,"about_ca_topic_score_gemma":0.056948315,"teacher_disagreement_score":0.033297654,"about_ca_system_score_codex":0.0012489608,"about_ca_system_score_gemma":0.0011979121,"threshold_uncertainty_score":0.06620771},"labels":[],"label_agreement":null},{"id":"W4401160176","doi":"10.3390/hydrology11080113","title":"Assessment of the Impact of Coal Mining on Water Resources in Middelburg, Mpumalanga Province, South Africa: Using Different Water Quality Indices","year":2024,"lang":"en","type":"article","venue":"Hydrology","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":9,"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":"Water quality; Environmental science; Groundwater; Surface water; Water resources; Pollution; Hydrology (agriculture); Water resource management; Water pollution; Environmental engineering; Geology; Ecology","score_opus":0.048400279629727566,"score_gpt":0.33014243621886347,"score_spread":0.28174215658913593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401160176","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9989102,0.00016872359,0.00014072051,0.00003229659,0.0000016326028,0.000014370557,0.00022589149,0.000002353602,0.00050371664],"genre_scores_gemma":[0.9986222,0.0003026436,0.0005635178,0.000008135608,0.000001938678,0.000016583983,0.00018880326,0.0000012300409,0.0002949795],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99983585,0.00003674978,0.000012908065,0.000027178803,0.000051184405,0.00003617716],"domain_scores_gemma":[0.9997603,0.000038963288,0.00009653537,0.0000066392568,0.00006548319,0.000032144977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026210584,0.0003274204,0.00021686224,0.0015921049,0.0003188009,0.0009135076,0.00016892554,0.00018941068,0.0003855237],"category_scores_gemma":[0.0005662342,0.00011205691,0.00019655356,0.0018343716,0.0002361563,0.00048884633,0.0003765993,0.00016973268,0.00005661324],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013054576,0.000081580794,0.9439743,0.00018863905,0.00015440589,0.0011113402,0.0021272877,0.002355826,0.016471704,0.00019040235,0.00021515251,0.03299875],"study_design_scores_gemma":[0.0000030950064,0.00006718273,0.9921536,0.000037739002,0.000035157434,0.00013290795,0.0034893954,0.0012807531,0.0015432147,0.00005385516,0.0011936122,0.000009445706],"about_ca_topic_score_codex":0.07126783,"about_ca_topic_score_gemma":0.18504088,"teacher_disagreement_score":0.07126783,"about_ca_system_score_codex":0.0015397092,"about_ca_system_score_gemma":0.0010000836,"threshold_uncertainty_score":0.14170605},"labels":[],"label_agreement":null},{"id":"W4401804303","doi":"10.3390/hydrology11090130","title":"Water Level Temporal Variability of Lake Mégantic during the Period 1920–2020 and Its Impacts on the Frequency of Heavy Flooding of the Chaudière River (Quebec, Canada)","year":2024,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","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":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Flooding (psychology); Period (music); Hydrology (agriculture); Environmental science; Water level; Physical geography; Geology; Geography","score_opus":0.010659739258561804,"score_gpt":0.1965374092873646,"score_spread":0.1858776700288028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401804303","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9949332,0.00033105467,0.000073892304,0.00018110551,0.0000072105836,0.000007898339,0.0028047455,0.000010530893,0.0016502335],"genre_scores_gemma":[0.99758303,0.00009219668,0.000048005986,0.000022334227,0.0000040714635,0.0000058685227,0.0016488885,0.000002125264,0.00059358485],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99983203,0.000011424281,0.000007165002,0.000036184134,0.000051670537,0.000061506275],"domain_scores_gemma":[0.99938047,0.00002747114,0.00010447956,0.00001924181,0.00032331998,0.0001449493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026221856,0.00022193803,0.00019006831,0.00084466086,0.0005651986,0.0006337355,0.00034144832,0.00031002265,0.0013364091],"category_scores_gemma":[0.0005721598,0.00010565491,0.00026683634,0.00164362,0.00034046537,0.00018653802,0.00035038212,0.00025944156,0.0001644559],"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":[0.00007352273,0.000017965804,0.992161,0.00001942976,0.000068982634,0.00013110043,0.0002871197,0.00048221086,0.0014390103,0.0000881151,0.001085565,0.0041459627],"study_design_scores_gemma":[8.6432857e-7,0.0000033510046,0.99915206,0.00000245445,0.0000033620227,0.0000106952,0.00008393506,0.00022304054,0.00002800295,0.000002414034,0.00048751204,0.000002251836],"about_ca_topic_score_codex":0.95370346,"about_ca_topic_score_gemma":0.9708127,"teacher_disagreement_score":0.046296537,"about_ca_system_score_codex":0.009098023,"about_ca_system_score_gemma":0.0045926753,"threshold_uncertainty_score":0.09313834},"labels":[],"label_agreement":null},{"id":"W4401857458","doi":"10.3390/hydrology11080126","title":"Review of River Ice Observation and Data Analysis Technologies","year":2024,"lang":"en","type":"article","venue":"Hydrology","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Centre For Cold Ocean Resources Engineering","funders":"Government of Canada","keywords":"Geology; Hydrology (agriculture); Remote sensing; Physical geography; Geography; Geotechnical engineering","score_opus":0.03153031686177611,"score_gpt":0.2643235714345819,"score_spread":0.2327932545728058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401857458","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020434766,0.972903,0.014015867,0.00085412466,0.00069522206,0.000061369166,0.0015421314,0.00018174796,0.007702977],"genre_scores_gemma":[0.007882032,0.97446865,0.012599079,0.00050276547,0.0008044972,0.000065540495,0.0022390087,0.000057883426,0.0013805891],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99918896,0.00013202582,0.00016023905,0.00017005982,0.000303962,0.000044795885],"domain_scores_gemma":[0.99754804,0.001387114,0.00021673304,0.00009889225,0.0007111908,0.00003795143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014628954,0.00080923445,0.0008097848,0.0045811995,0.00030722067,0.0013284006,0.001089591,0.0006407315,0.002760875],"category_scores_gemma":[0.0035569682,0.00040071155,0.00089387095,0.0059215715,0.0003576879,0.002265557,0.0006043642,0.0006240749,0.0013737882],"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":[0.000058212878,0.000033789263,0.0016973452,0.022555597,0.00015135616,0.0001998885,0.00014525178,0.0018867872,0.0022616545,0.0041169743,0.033927076,0.932966],"study_design_scores_gemma":[0.000010172599,0.00007832023,0.0057729813,0.01140281,0.00030949173,0.00068183604,0.00021689886,0.0020465003,0.0033916624,0.0045460914,0.971465,0.00007829222],"about_ca_topic_score_codex":0.0030246973,"about_ca_topic_score_gemma":0.0026205322,"teacher_disagreement_score":0.0045811995,"about_ca_system_score_codex":0.0005012266,"about_ca_system_score_gemma":0.0018249551,"threshold_uncertainty_score":0.009236097},"labels":[],"label_agreement":null},{"id":"W4402408621","doi":"10.3390/hydrology11090144","title":"Development of Statistical Downscaling Model Based on Volterra Series Realization, Principal Components, Climate Classification, and Ridge Regression","year":2024,"lang":"en","type":"article","venue":"Hydrology","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Environment and Climate Change Canada","keywords":"Downscaling; Ridge; Realization (probability); Principal component analysis; Series (stratigraphy); Climatology; Regression; Regression analysis; Principal component regression; Statistical analysis; Environmental science; Statistics; Geology; Climate change; Mathematics","score_opus":0.04492574042946953,"score_gpt":0.2667536947184368,"score_spread":0.22182795428896726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402408621","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040341284,0.000096496464,0.9567928,0.00010452729,0.000025243864,0.000032533175,0.00009413285,0.00044997482,0.002063087],"genre_scores_gemma":[0.80683464,0.00024946022,0.18937573,0.00004734241,0.000029827237,0.00011650282,0.00030709206,0.00011357732,0.002925818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998097,0.000033757042,0.0000130111475,0.000059463153,0.00006268252,0.000021396978],"domain_scores_gemma":[0.9998227,0.000053649197,0.000023289116,0.000015457174,0.00007728793,0.000007770094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005080538,0.00036528255,0.0004619296,0.00036186093,0.00033756145,0.00050672964,0.00082654424,0.00048229776,0.0006829876],"category_scores_gemma":[0.0007699424,0.00033488078,0.0007379741,0.00045186208,0.00023537334,0.0005833708,0.00034225773,0.00071937975,0.00018947716],"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":[0.000014800456,0.0000119129445,0.0011153778,0.000014709611,0.00001957756,0.000036933507,0.000025860378,0.97074753,0.0023313004,0.0042497157,0.00028122313,0.021150976],"study_design_scores_gemma":[8.5201896e-7,0.00000222139,0.0001139297,6.70628e-7,0.0000017968634,0.0000030272593,0.0000010830445,0.9991359,0.00025099784,0.00036554938,0.000121868834,0.0000021803428],"about_ca_topic_score_codex":0.020524284,"about_ca_topic_score_gemma":0.009701516,"teacher_disagreement_score":0.020524284,"about_ca_system_score_codex":0.000554015,"about_ca_system_score_gemma":0.00097200804,"threshold_uncertainty_score":0.04080963},"labels":[],"label_agreement":null},{"id":"W4402486348","doi":"10.3390/hydrology11090151","title":"Machine Learning Model for River Discharge Forecast: A Case Study of the Ottawa River in Canada","year":2024,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hydrology (agriculture); Discharge; Environmental science; Water discharge; Meteorology; Climatology; Geology; Geography; Drainage basin; Geotechnical engineering; Cartography","score_opus":0.02134855924654426,"score_gpt":0.23736043148606534,"score_spread":0.21601187223952106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402486348","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9698654,0.00024521718,0.021651115,0.0007124347,0.000023213051,0.00010410703,0.001129282,0.00032856426,0.005940796],"genre_scores_gemma":[0.98970234,0.000100893456,0.006290063,0.000023906909,0.0000045843603,0.000027501914,0.00040462954,0.000012239087,0.0034338403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985874,0.000026031628,0.000008241897,0.000032391323,0.000032639484,0.000041917127],"domain_scores_gemma":[0.9996451,0.00017036412,0.000021911277,0.0000136725785,0.0001238193,0.00002513029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004153756,0.0005263923,0.0003929941,0.00042382473,0.0009802656,0.0008726902,0.00084953377,0.0007456917,0.0013332391],"category_scores_gemma":[0.0011102384,0.0001589504,0.000346384,0.00085153437,0.00042188127,0.00032516604,0.0002778526,0.0006162995,0.00012039703],"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":[0.00004610839,0.000046036774,0.015262154,0.00003599181,0.000018653867,0.00031598323,0.000083725856,0.96903056,0.00040745112,0.001389175,0.0009075464,0.012456571],"study_design_scores_gemma":[0.000006826137,0.000014229794,0.00303681,0.000003525159,0.0000068282075,0.000014766689,0.00009242834,0.99579453,0.00022167437,0.0002443183,0.0005576686,0.0000064955807],"about_ca_topic_score_codex":0.94569033,"about_ca_topic_score_gemma":0.9306345,"teacher_disagreement_score":0.054309666,"about_ca_system_score_codex":0.009593156,"about_ca_system_score_gemma":0.007414196,"threshold_uncertainty_score":0.10925895},"labels":[],"label_agreement":null},{"id":"W4402814058","doi":"10.3390/hydrology11090154","title":"Estimating Non-Stationary Extreme-Value Probability Distribution Shifts and Their Parameters Under Climate Change Using L-Moments and L-Moment Ratio Diagrams: A Case Study of Hydrologic Drought in the Goat River Near Creston, British Columbia","year":2024,"lang":"en","type":"article","venue":"Hydrology","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":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Health, British Columbia","keywords":"Generalized extreme value distribution; Climate change; Moment (physics); Environmental science; Hydrology (agriculture); Distribution (mathematics); Extreme value theory; L-moment; Mathematics; Climatology; Statistics; Geology; Physics; Geotechnical engineering; Oceanography; Mathematical analysis","score_opus":0.03565131821448851,"score_gpt":0.26428584247991405,"score_spread":0.22863452426542552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402814058","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9834223,0.00006708877,0.0155136725,0.00007164086,0.0000019256192,0.0000113723045,0.0002462729,0.000120046825,0.0005457728],"genre_scores_gemma":[0.9907278,0.000023931701,0.008853114,0.000006000451,0.0000015810747,0.000005578907,0.0002238643,0.000012022832,0.000146177],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967587,0.00014033433,0.000018117651,0.00007281644,0.00005639443,0.00003653641],"domain_scores_gemma":[0.99723935,0.0020581207,0.00024982233,0.00013718082,0.00024149983,0.00007415999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012448196,0.00020481404,0.0002219044,0.00094785425,0.00034595633,0.0009245195,0.0005094235,0.00031406706,0.00053113315],"category_scores_gemma":[0.0068811965,0.00013071408,0.0003029757,0.0010538809,0.00033728045,0.00038461204,0.00035964712,0.00047681486,0.000058336973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019235768,0.00008617975,0.58153874,0.00007589462,0.00026589862,0.0005414375,0.001180477,0.3225555,0.006097383,0.0024162275,0.0008866725,0.08416325],"study_design_scores_gemma":[0.000008214902,0.000024431742,0.32458904,0.000011494001,0.000026335889,0.00008091434,0.0005850751,0.6721349,0.000731795,0.0010128979,0.00076345407,0.000031571133],"about_ca_topic_score_codex":0.37523574,"about_ca_topic_score_gemma":0.4735769,"teacher_disagreement_score":0.62476426,"about_ca_system_score_codex":0.0015469762,"about_ca_system_score_gemma":0.0011381551,"threshold_uncertainty_score":0.7461033},"labels":[],"label_agreement":null},{"id":"W4402951495","doi":"10.11648/j.hyd.20241203.12","title":"Assessing the Long-Term Changes in Selected Meteorological Parameters over the North-Rift, Kenya: A Regional Climatology Perspective","year":2024,"lang":"en","type":"article","venue":"Hydrology","topic":"Climate variability and models","field":"Environmental Science","cited_by":6,"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":"West African Science Service Centre on Climate Change and Adapted Land Use; International Development Research Centre","keywords":"Climatology; Term (time); Rift valley; Environmental science; Perspective (graphical); Geology; Meteorology; Geography","score_opus":0.03916007338755361,"score_gpt":0.30198803772887406,"score_spread":0.26282796434132044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402951495","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99603313,0.00049552973,0.0004353091,0.00012269912,0.000005098222,0.000011132292,0.0016379155,0.000011965333,0.0012471713],"genre_scores_gemma":[0.99786854,0.00037371312,0.0006113133,0.000010903764,0.0000064145047,0.000010041158,0.0007928528,0.0000023741436,0.00032380119],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99994123,0.000013191463,0.0000070402793,0.000016321845,0.000008847465,0.000013452026],"domain_scores_gemma":[0.99987006,0.00001843466,0.000057494024,0.000006526272,0.00003462788,0.000012779953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023577474,0.00016693922,0.000119140306,0.00058414054,0.00017802075,0.0003646177,0.00012339445,0.00017489852,0.0006701614],"category_scores_gemma":[0.00032723282,0.00007555113,0.00015960405,0.00092181476,0.0000909485,0.00030271616,0.00016929966,0.00013733133,0.00011260447],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045227996,0.00003516164,0.9684474,0.00012185768,0.00016854973,0.00026498924,0.0005948207,0.006765999,0.005376362,0.0002913045,0.0010929916,0.016795492],"study_design_scores_gemma":[0.000002048462,0.000025322366,0.99327624,0.000020327854,0.00003434704,0.00007321207,0.000816566,0.0041156113,0.0003123588,0.00003902983,0.0012781362,0.00000696387],"about_ca_topic_score_codex":0.044771377,"about_ca_topic_score_gemma":0.110632166,"teacher_disagreement_score":0.044771377,"about_ca_system_score_codex":0.00037419726,"about_ca_system_score_gemma":0.0004034158,"threshold_uncertainty_score":0.08902156},"labels":[],"label_agreement":null},{"id":"W4404277638","doi":"10.3390/hydrology11110191","title":"Evapotranspiration Estimation with the Budyko Framework for Canadian Watersheds","year":2024,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"","keywords":"Evapotranspiration; Environmental science; Estimation; Hydrology (agriculture); Water resource management; Geology; Ecology; Engineering; Geotechnical engineering","score_opus":0.01014602796830216,"score_gpt":0.23118915648734292,"score_spread":0.22104312851904076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404277638","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65165293,0.001111299,0.32399,0.00096154225,0.000057562807,0.000178024,0.002895569,0.001198065,0.017955014],"genre_scores_gemma":[0.94939333,0.0003609015,0.04699731,0.00003806719,0.000007693697,0.000053558364,0.0008054898,0.00010211606,0.0022414946],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99981683,0.000032192536,0.000009218004,0.00005112226,0.000050948696,0.000039754334],"domain_scores_gemma":[0.99979013,0.000047948764,0.000028366469,0.000012262697,0.00009687876,0.000024451676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045567195,0.0005956792,0.00043306805,0.0006400091,0.00083744095,0.0013159554,0.0012274085,0.00047975135,0.00063055434],"category_scores_gemma":[0.0010560342,0.0003221314,0.0005075681,0.00079322327,0.0004885321,0.00048788337,0.00066707254,0.00045746568,0.00010660733],"study_design_candidate":"observational","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":[0.000019671254,0.000014252861,0.0041797287,0.000019016043,0.000023577157,0.000040208077,0.000054452445,0.9820764,0.00052326365,0.0039537894,0.0003963,0.008699403],"study_design_scores_gemma":[0.0000032468151,0.0000020461184,0.0011306116,0.0000034396246,0.000006465955,0.0000049610912,0.000020238713,0.99768865,0.00010771496,0.00036652645,0.00065898296,0.0000071681984],"about_ca_topic_score_codex":0.89226437,"about_ca_topic_score_gemma":0.87359893,"teacher_disagreement_score":0.107735634,"about_ca_system_score_codex":0.0063692923,"about_ca_system_score_gemma":0.008272838,"threshold_uncertainty_score":0.21674019},"labels":[],"label_agreement":null},{"id":"W4404919742","doi":"10.3390/hydrology11120207","title":"Multi-Model Assessment of Climate Change Impacts on the Streamflow Conditions in the Kasai River Basin, Central Africa","year":2024,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management 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":"Polytechnique Montréal","funders":"","keywords":"Streamflow; Climate change; Stream flow; Hydrology (agriculture); Drainage basin; Environmental science; Climatology; Structural basin; Water resource management; Geology; Geography; Oceanography; Geomorphology; Geotechnical engineering","score_opus":0.03445983476454451,"score_gpt":0.2872041505415299,"score_spread":0.2527443157769854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404919742","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9974618,0.000074992946,0.0008292287,0.00016803302,0.000008918258,0.000017420716,0.0005603559,0.00003572444,0.0008435786],"genre_scores_gemma":[0.9988182,0.000048119775,0.00066490687,0.000014952529,0.0000032050878,0.000021607424,0.00027965725,0.0000053565936,0.00014397979],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997291,0.00012624868,0.000015346288,0.00005403558,0.00002152047,0.00005365604],"domain_scores_gemma":[0.99943334,0.00027838416,0.000081387814,0.000049029775,0.00008486318,0.000072971154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096582953,0.0006828601,0.0005106041,0.0006883286,0.0004981473,0.0012309953,0.00085267716,0.0009645545,0.0007113761],"category_scores_gemma":[0.0016075707,0.000277165,0.00082808745,0.0008065727,0.000583712,0.00086367695,0.00064266403,0.00057472044,0.00005649761],"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":[0.00008476778,0.00012983968,0.022586044,0.000023688764,0.00012709374,0.00011730709,0.00005364782,0.9730872,0.00082231476,0.00069525256,0.00022342028,0.0020495288],"study_design_scores_gemma":[0.000055674827,0.00006922557,0.015474009,0.000011823022,0.000054427514,0.000016351394,0.00013251459,0.98318475,0.0004134154,0.00025001343,0.00031620395,0.0000215399],"about_ca_topic_score_codex":0.119423114,"about_ca_topic_score_gemma":0.09266262,"teacher_disagreement_score":0.119423114,"about_ca_system_score_codex":0.0029356636,"about_ca_system_score_gemma":0.001742403,"threshold_uncertainty_score":0.23745602},"labels":[],"label_agreement":null},{"id":"W4404959085","doi":"10.3390/hydrology11120209","title":"Linking Land Use Change and Hydrological Responses: The Role of Agriculture in the Decline of Urmia Lake","year":2024,"lang":"en","type":"article","venue":"Hydrology","topic":"Transboundary Water Resource Management","field":"Social Sciences","cited_by":9,"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 Sherbrooke","funders":"","keywords":"Normalized Difference Vegetation Index; Evapotranspiration; Environmental science; Streamflow; Hydrology (agriculture); Soil and Water Assessment Tool; Agriculture; Irrigation; Digital elevation model; Climate change; Vegetation (pathology); Rangeland; Water resource management; Land use, land-use change and forestry; Water resources; Drainage basin; Geography; Remote sensing; Geology; Agroforestry","score_opus":0.039723517026704694,"score_gpt":0.28282089068527005,"score_spread":0.24309737365856537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404959085","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99853265,0.00013789067,0.00014491033,0.00027207303,0.000004687404,0.000005309357,0.00005953197,0.000005950861,0.0008369161],"genre_scores_gemma":[0.9997148,0.000054592037,0.00009073748,0.000020346068,0.000004425117,0.0000035850865,0.00003532982,8.9928113e-7,0.000075338736],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997433,0.000094191644,0.000016756705,0.000042911564,0.000031765965,0.000070993075],"domain_scores_gemma":[0.9996542,0.00007592516,0.000119185446,0.000018595954,0.00008549246,0.000046715686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042726877,0.00016841941,0.0001756794,0.0006696009,0.00042852003,0.0006533428,0.0002645199,0.00032142305,0.00070855406],"category_scores_gemma":[0.0012862851,0.00007759602,0.0002482696,0.0010050447,0.00045460468,0.0006594763,0.0006986221,0.00030589467,0.000058838577],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000591861,0.00006265378,0.972772,0.000041895968,0.00006434745,0.00036659057,0.0019405284,0.001255004,0.0016346724,0.00049185584,0.0004048603,0.020906435],"study_design_scores_gemma":[0.000001385349,0.000017271417,0.9942268,0.000010285968,0.000023936924,0.000044837543,0.0018682533,0.002749354,0.00015214198,0.00014422258,0.0007564482,0.000005162731],"about_ca_topic_score_codex":0.046490908,"about_ca_topic_score_gemma":0.07464696,"teacher_disagreement_score":0.046490908,"about_ca_system_score_codex":0.0010020459,"about_ca_system_score_gemma":0.0008231963,"threshold_uncertainty_score":0.092440605},"labels":[],"label_agreement":null},{"id":"W4405191680","doi":"10.3390/hydrology11120212","title":"Using Two Water Quality Indices for Evaluating the Health and Management of a Tropical Lake","year":2024,"lang":"en","type":"article","venue":"Hydrology","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":3,"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":"Bahir Dar University","keywords":"Water quality; Turbidity; Environmental science; Hydrology (agriculture); Trophic state index; Total suspended solids; Phytoplankton; Nutrient; Ecology; Environmental engineering; Chemical oxygen demand; Biology; Geology","score_opus":0.17293383358557338,"score_gpt":0.45980439691617386,"score_spread":0.28687056333060046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405191680","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9735247,0.00060758775,0.014584157,0.0002240827,0.000045724002,0.00031748592,0.0022486795,0.00011433712,0.008333352],"genre_scores_gemma":[0.9856405,0.000301991,0.012004655,0.0000646947,0.000015303613,0.00018327357,0.0009849973,0.0000082241995,0.00079632737],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991811,0.00014982626,0.0001275449,0.00010511748,0.00035853827,0.00007786974],"domain_scores_gemma":[0.9987901,0.00017638106,0.0004768106,0.000048001406,0.00041716223,0.0000914505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013896847,0.0005229075,0.00040762324,0.0028346854,0.00048178175,0.0011674227,0.00030591627,0.0003866638,0.0009100669],"category_scores_gemma":[0.0021106126,0.00013885852,0.0005369549,0.0033440178,0.0004117896,0.0007697054,0.0008720385,0.00035372056,0.0001215784],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024299926,0.00014803915,0.9081399,0.0003605665,0.00034516628,0.00013122185,0.0010861119,0.0033303713,0.010007234,0.00048681145,0.0012627664,0.074458875],"study_design_scores_gemma":[0.00002607306,0.0005979324,0.9730578,0.00008731729,0.00020120482,0.00015734343,0.0024978549,0.013367886,0.005018437,0.0007608098,0.0041466635,0.00008056106],"about_ca_topic_score_codex":0.014801518,"about_ca_topic_score_gemma":0.038435295,"teacher_disagreement_score":0.014801518,"about_ca_system_score_codex":0.0011407896,"about_ca_system_score_gemma":0.001015507,"threshold_uncertainty_score":0.029430747},"labels":[],"label_agreement":null},{"id":"W4406150270","doi":"10.3390/hydrology12010007","title":"Attribution of the Climate and Land Use Change Impact on the Hydrological Processes of Athabasca River Basin, Canada","year":2025,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Government of Alberta; University of Calgary","funders":"","keywords":"Evapotranspiration; Environmental science; Climate change; Streamflow; Groundwater recharge; Hydrology (agriculture); Precipitation; Watershed; Land use, land-use change and forestry; Land use; Shrubland; Structural basin; Drainage basin; Land cover; Surface runoff; Physical geography; Ecosystem; Geography; Geology; Ecology; Groundwater","score_opus":0.018552730311840772,"score_gpt":0.22752500406906317,"score_spread":0.2089722737572224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406150270","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99497426,0.00020610385,0.000103850805,0.00032353488,0.0000072663975,0.000015716989,0.0013378545,0.000013949135,0.0030175855],"genre_scores_gemma":[0.9987048,0.0001494508,0.00012272672,0.000032568445,0.0000019532624,0.000006432843,0.00038996743,0.0000030665399,0.0005890778],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99978703,0.000018503546,0.000009097664,0.000037934024,0.000060015278,0.00008740094],"domain_scores_gemma":[0.9994172,0.00004247283,0.00005664521,0.000018617468,0.00031274,0.00015235337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018184986,0.00014737822,0.00012881475,0.0010088278,0.0014159455,0.00076071627,0.0003457766,0.00016564583,0.0010686544],"category_scores_gemma":[0.00071716675,0.00008636991,0.00024108081,0.0017205446,0.00045855562,0.00017771278,0.00049932225,0.00028061896,0.000052893785],"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":[0.00004627852,0.000029069352,0.98144275,0.000031461543,0.000052955354,0.00018155355,0.00097489025,0.0013452735,0.0018340667,0.0007168171,0.0015688516,0.011776076],"study_design_scores_gemma":[0.000002093523,0.0000031459529,0.9963008,0.000010340693,0.000011064243,0.000019901683,0.0012475773,0.001133954,0.000092970964,0.00005498428,0.0011164312,0.0000066917387],"about_ca_topic_score_codex":0.99234104,"about_ca_topic_score_gemma":0.9960347,"teacher_disagreement_score":0.014348315,"about_ca_system_score_codex":0.014348315,"about_ca_system_score_gemma":0.019332794,"threshold_uncertainty_score":0.10410476},"labels":[],"label_agreement":null},{"id":"W4407039500","doi":"10.3390/hydrology12020023","title":"Modelling Hydrological Droughts in Canadian Rivers Based on Markov Chains Using the Standardized Hydrological Index as a Platform","year":2025,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","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":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Index (typography); Environmental science; Hydrology (agriculture); Markov chain; Physical geography; Water resource management; Geology; Geography; Statistics; Computer science; Mathematics; Geotechnical engineering","score_opus":0.016050197808553747,"score_gpt":0.249830557671992,"score_spread":0.23378035986343826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407039500","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8692007,0.00042667423,0.11738465,0.0010208185,0.000071477145,0.00021772839,0.0018457981,0.00052120356,0.009311014],"genre_scores_gemma":[0.9848439,0.0002677933,0.010944118,0.00006429154,0.000022401626,0.00010620595,0.00068008486,0.000026733054,0.0030444223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995197,0.000091690454,0.000022121605,0.00010623202,0.00007648746,0.00018376036],"domain_scores_gemma":[0.9983248,0.00089723006,0.00024851583,0.000050750692,0.00032107468,0.00015759084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013765437,0.0006423352,0.0007733772,0.0012306806,0.0011865135,0.0012891263,0.001565089,0.0009839367,0.001891518],"category_scores_gemma":[0.0041555273,0.000626378,0.0012601556,0.0010430769,0.0012417188,0.0007670051,0.0008945471,0.0010034866,0.000110217195],"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":[0.000020565043,0.000010512595,0.003134844,0.0000082032475,0.000019530584,0.000024104706,0.000046125075,0.9917487,0.00013876817,0.0035773974,0.00015369015,0.0011174865],"study_design_scores_gemma":[0.0000061796586,0.0000045419606,0.0009509474,0.0000034784755,0.000008345423,0.0000029423963,0.00001653977,0.9979898,0.000034808032,0.0008051943,0.00017046997,0.0000067521264],"about_ca_topic_score_codex":0.8726248,"about_ca_topic_score_gemma":0.82518977,"teacher_disagreement_score":0.12737519,"about_ca_system_score_codex":0.009471829,"about_ca_system_score_gemma":0.008822198,"threshold_uncertainty_score":0.25625062},"labels":[],"label_agreement":null},{"id":"W4407140708","doi":"10.3390/hydrology12020025","title":"Coupling HEC-RAS and AI for River Morphodynamics Assessment Under Changing Flow Regimes: Enhancing Disaster Preparedness for the Ottawa River","year":2025,"lang":"en","type":"article","venue":"Hydrology","topic":"Flood Risk Assessment and Management","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":true,"ca_institutions":"Toronto and Region Conservation Authority; Université Laval; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Beach morphodynamics; Preparedness; Hydrology (agriculture); Flow (mathematics); Geology; Environmental science; Geotechnical engineering; Sediment transport; Geomorphology; Sediment; Mechanics; Physics; Management","score_opus":0.007151167302525023,"score_gpt":0.27362438768576364,"score_spread":0.2664732203832386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407140708","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8880486,0.00030392664,0.094701394,0.001083934,0.00014352104,0.00015071791,0.0008750267,0.0020596855,0.012633159],"genre_scores_gemma":[0.9814057,0.00007061017,0.016643392,0.00007968565,0.000012378113,0.00004483485,0.00034652758,0.00003932503,0.001357634],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981755,0.000047862908,0.000008528634,0.000042450072,0.000035886245,0.000047664853],"domain_scores_gemma":[0.99948126,0.00021681123,0.000033866396,0.00006821813,0.00013477697,0.00006510946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056884316,0.0007125971,0.0004326977,0.00047507364,0.000558588,0.0009781212,0.0009769701,0.0008141416,0.001609479],"category_scores_gemma":[0.0018289874,0.0002650185,0.0005396154,0.00038470645,0.0006502365,0.0009175386,0.0009994773,0.00092492474,0.00022839603],"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":[0.000053458672,0.000044870212,0.0042942,0.000020694057,0.000023473109,0.000045119865,0.000043186832,0.98326313,0.00082295923,0.0005169588,0.0003601035,0.01051198],"study_design_scores_gemma":[0.000004637272,0.000019475849,0.0007255622,0.000002389204,0.0000050280873,0.000004024285,0.000029277115,0.9983393,0.00038485602,0.00022424446,0.00025617142,0.0000050293247],"about_ca_topic_score_codex":0.15698889,"about_ca_topic_score_gemma":0.17291851,"teacher_disagreement_score":0.84301114,"about_ca_system_score_codex":0.0013739824,"about_ca_system_score_gemma":0.0022746755,"threshold_uncertainty_score":0.31215024},"labels":[],"label_agreement":null},{"id":"W4407643438","doi":"10.3390/hydrology12020037","title":"Assessment of Heavy Metals in Surface Waters of the Santiago–Guadalajara River Basin, Mexico","year":2025,"lang":"en","type":"article","venue":"Hydrology","topic":"Heavy metals in environment","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":"University of Toronto","funders":"","keywords":"Heavy metals; Structural basin; Surface water; Hydrology (agriculture); Drainage basin; Environmental science; Geology; Geography; Environmental chemistry; Geomorphology; Environmental engineering; Geotechnical engineering; Chemistry","score_opus":0.008359679307026859,"score_gpt":0.2610652839746874,"score_spread":0.25270560466766057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407643438","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9931178,0.0016397705,0.0003550423,0.00022065282,0.000019077295,0.000042504147,0.0016595015,0.000027113736,0.0029185258],"genre_scores_gemma":[0.9925565,0.0015878356,0.0016592415,0.00010649225,0.000023338247,0.00009249031,0.0024776652,0.0000092608,0.0014872004],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998429,0.000019357121,0.000014308208,0.000052816595,0.000044687895,0.000025884294],"domain_scores_gemma":[0.9998597,0.000016787466,0.0000475208,0.000006135551,0.000058109876,0.000011701504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004188264,0.000410643,0.0003246509,0.0019499849,0.0006180097,0.0012815929,0.0003397517,0.00045839263,0.00044377527],"category_scores_gemma":[0.0003960978,0.00016755988,0.0003120215,0.0014517861,0.00032725063,0.00035895727,0.0006441413,0.0002140932,0.00006052695],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013414468,0.0000800754,0.95502245,0.00026470903,0.00028529405,0.00042151505,0.0008120291,0.0016882573,0.014243815,0.00021482121,0.000874636,0.025958193],"study_design_scores_gemma":[0.000008021851,0.000025355714,0.99493533,0.000022194521,0.000043109565,0.000041741165,0.00052921573,0.00057300285,0.00041197956,0.000031223444,0.0033733645,0.000005401175],"about_ca_topic_score_codex":0.13545223,"about_ca_topic_score_gemma":0.17120928,"teacher_disagreement_score":0.13545223,"about_ca_system_score_codex":0.00116151,"about_ca_system_score_gemma":0.00076604704,"threshold_uncertainty_score":0.26932758},"labels":[],"label_agreement":null},{"id":"W4411216136","doi":"10.3390/hydrology12060145","title":"Numerical Simulation of Turbulent Flow in River Bends and Confluences Using the k-ω SST Turbulence Model and Comparison with Standard and Realizable k-ε Models","year":2025,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Sediment Transport Processes","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Turbulence; Geology; K-epsilon turbulence model; Flow (mathematics); Turbulence modeling; Computer simulation; Geotechnical engineering; Mechanics; Hydrology (agriculture); Meteorology; Physics","score_opus":0.017472507346337506,"score_gpt":0.2656301897156093,"score_spread":0.2481576823692718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411216136","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95236444,0.00012522102,0.04124474,0.0001343264,0.00003445007,0.000055081517,0.00028642337,0.0002645141,0.005490933],"genre_scores_gemma":[0.98945135,0.00008803081,0.009400898,0.000013147453,0.000005594796,0.00004379338,0.00019260436,0.000018642708,0.00078606367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986017,0.000029383076,0.000014219002,0.00002125656,0.00003800872,0.000036988506],"domain_scores_gemma":[0.9996401,0.000145046,0.00005805009,0.000034059885,0.000073313844,0.00004945661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003051557,0.0005104615,0.0005267878,0.00042288424,0.00046458267,0.0006732919,0.00056037656,0.0007908563,0.0007769383],"category_scores_gemma":[0.0008326323,0.00021009211,0.0006787744,0.0005714646,0.0006778738,0.00040012426,0.0005113318,0.00045015034,0.000120369834],"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":[0.000044473552,0.0000589303,0.0037128776,0.000017557628,0.000009647504,0.000071569324,0.000043077835,0.9899512,0.0029571366,0.0010843889,0.00010671856,0.0019423316],"study_design_scores_gemma":[0.0000058376313,0.00002019752,0.00048080634,0.0000020087575,0.0000019091403,0.000005607617,0.00001361702,0.99891174,0.0004221912,0.00007423571,0.00005806076,0.0000038058674],"about_ca_topic_score_codex":0.018927071,"about_ca_topic_score_gemma":0.009438024,"teacher_disagreement_score":0.018927071,"about_ca_system_score_codex":0.00055940135,"about_ca_system_score_gemma":0.0009753815,"threshold_uncertainty_score":0.037633777},"labels":[],"label_agreement":null},{"id":"W4414628623","doi":"10.3390/hydrology12100253","title":"Forecasting the Athabasca River Flow Using HEC-HMS as Hydrologic Model for Cold Weather Applications","year":2025,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"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 Calgary","funders":"","keywords":"Precipitation; Stream flow; Water resources; Streamflow; Drainage basin; Climate change; Hydrology (agriculture); Hydrological modelling; Robustness (evolution)","score_opus":0.02932960273210993,"score_gpt":0.2565554117334958,"score_spread":0.22722580900138584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414628623","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98010147,0.00016352798,0.012520155,0.00032355974,0.000035781806,0.000061623745,0.0017061242,0.00071539485,0.0043724542],"genre_scores_gemma":[0.9938047,0.000037105743,0.004337985,0.000022574492,0.000009011219,0.000021387474,0.0011124247,0.000015610714,0.0006393243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998553,0.000035101824,0.000008757599,0.00003274516,0.000035081346,0.000033132448],"domain_scores_gemma":[0.9997942,0.00005836676,0.000025908641,0.000021650496,0.00006657746,0.000033273544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044104393,0.0006641836,0.00037736262,0.00042901235,0.0005301327,0.0006198833,0.0008076771,0.0005070439,0.0009880211],"category_scores_gemma":[0.00064001954,0.00019806519,0.00048799318,0.00044786456,0.00029112125,0.00034812817,0.00033597142,0.000496429,0.00011056078],"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":[0.000057112793,0.00006425064,0.008818159,0.000012558969,0.000026195365,0.00003379006,0.000015550375,0.9825555,0.0009822526,0.00030483797,0.0005679226,0.006561764],"study_design_scores_gemma":[0.000006988329,0.0000062558142,0.0020486463,0.0000010081275,0.000002892205,0.0000015305229,0.000007805187,0.99760073,0.00014776786,0.000035829737,0.00013763212,0.000002975755],"about_ca_topic_score_codex":0.46153566,"about_ca_topic_score_gemma":0.40674186,"teacher_disagreement_score":0.5384643,"about_ca_system_score_codex":0.0017573695,"about_ca_system_score_gemma":0.0023465424,"threshold_uncertainty_score":0.91769844},"labels":[],"label_agreement":null},{"id":"W4414768364","doi":"10.3390/hydrology12100261","title":"Using Entity-Aware LSTM to Enhance Streamflow Predictions in Transboundary and Large Lake Basins","year":2025,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":1,"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":"National Oceanic and Atmospheric Administration; Cooperative Institute for Great Lakes Research","keywords":"Streamflow; Hydrological modelling; Drainage basin; Scalability; Catchment hydrology; Water resources; Hydrology (agriculture)","score_opus":0.007936712895862253,"score_gpt":0.2681530302185337,"score_spread":0.2602163173226715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414768364","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8192957,0.00072007277,0.1702332,0.0010208181,0.00016520452,0.0000325079,0.0014190105,0.0038786468,0.0032349578],"genre_scores_gemma":[0.98128915,0.00010226606,0.016495008,0.00012374145,0.000021134972,0.000016198936,0.0010077525,0.00004403642,0.0009007618],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999188,0.000014751697,0.000005933456,0.00003247889,0.000009822823,0.000018171739],"domain_scores_gemma":[0.99983597,0.000069576214,0.000018779228,0.000022429584,0.000039447295,0.000013773063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037043958,0.0006125549,0.0003045256,0.0003297218,0.00021333927,0.0005814972,0.0006365284,0.0005755309,0.0010280581],"category_scores_gemma":[0.0011267166,0.00025952864,0.00037523106,0.0005053347,0.0002694157,0.0015634097,0.00068886863,0.0007638558,0.0002485908],"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":[0.000103764585,0.0000901794,0.010208511,0.000039096365,0.000105799256,0.00013067154,0.00008446209,0.8800225,0.0054142666,0.0010322938,0.0029845252,0.099783935],"study_design_scores_gemma":[0.0000036967167,0.0000068878667,0.0007166953,0.000002943827,0.0000061210503,0.0000040386662,0.000008695444,0.99769455,0.00076400116,0.0006206939,0.00016918124,0.000002466434],"about_ca_topic_score_codex":0.021940595,"about_ca_topic_score_gemma":0.036941003,"teacher_disagreement_score":0.021940595,"about_ca_system_score_codex":0.00060780445,"about_ca_system_score_gemma":0.0008931269,"threshold_uncertainty_score":0.043625772},"labels":[],"label_agreement":null},{"id":"W4414862207","doi":"10.3390/hydrology12100263","title":"Outdoor Ice Rinks in Ontario, Canada—An Oversimplified Model for Ice Water Equivalent and Operational Duration to Evaluate Changing Climate","year":2025,"lang":"en","type":"article","venue":"Hydrology","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Nipissing University","funders":"Colorado State University; U.S. Department of the Interior","keywords":"Precipitation; Climate change; Ice formation; Climate model; Wind speed; Ice water","score_opus":0.01761138539210702,"score_gpt":0.2507224579935938,"score_spread":0.23311107260148675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414862207","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9779863,0.00016862988,0.0029039513,0.00017355091,0.000024136312,0.00007892497,0.005600973,0.00016456704,0.012899024],"genre_scores_gemma":[0.99255496,0.00011529153,0.0014725277,0.000028042732,0.000004027314,0.000037945578,0.0022325285,0.000033887212,0.003520881],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998536,0.000012848017,0.0000056262165,0.000040811527,0.000028455357,0.00005865124],"domain_scores_gemma":[0.99966633,0.000058580335,0.000037008183,0.000019636182,0.00015838382,0.000059971015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023529168,0.000651979,0.00040905963,0.00033225722,0.0011073706,0.0010121135,0.0014341691,0.0006550483,0.0019890082],"category_scores_gemma":[0.00055577833,0.00033997514,0.0006157796,0.0006186497,0.0006195129,0.0004682783,0.00034252048,0.00045691093,0.00023606625],"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":[0.00013122082,0.000056921854,0.0325605,0.000038482285,0.000037808502,0.00014611863,0.00009783501,0.9608007,0.0011066173,0.0006093126,0.0014988991,0.002915565],"study_design_scores_gemma":[0.00007554997,0.000039433755,0.037278995,0.000017710698,0.000030397767,0.00002836506,0.00019366464,0.95839256,0.0004088809,0.0001868361,0.0033144031,0.00003317926],"about_ca_topic_score_codex":0.98416233,"about_ca_topic_score_gemma":0.98021185,"teacher_disagreement_score":0.018910268,"about_ca_system_score_codex":0.018910268,"about_ca_system_score_gemma":0.013155027,"threshold_uncertainty_score":0.13720423},"labels":[],"label_agreement":null},{"id":"W7114765137","doi":"10.3390/hydrology12120329","title":"Development of an Adapted Water Quality Index for the Danube River Using Objective Weighting Methods","year":2025,"lang":"en","type":"article","venue":"Hydrology","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":2,"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":"HORIZON EUROPE Framework Programme; European Commission","keywords":"Water quality; Weighting; Hydrology (agriculture); Index (typography); Robustness (evolution); Index method; Biochemical oxygen demand; Nitrate","score_opus":0.0735686113126627,"score_gpt":0.40465992339291135,"score_spread":0.33109131208024867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7114765137","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20868868,0.00023774288,0.78560215,0.00008274843,0.000031913594,0.0005011349,0.00043329285,0.00020295703,0.0042193937],"genre_scores_gemma":[0.5882497,0.00027178836,0.4075787,0.000037020243,0.000017193433,0.00074600626,0.0010533169,0.000053992862,0.0019923337],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851733,0.00043296738,0.00015599302,0.00021972292,0.0006130441,0.000060889117],"domain_scores_gemma":[0.9984909,0.00033928308,0.000192235,0.00011308815,0.00082310196,0.000041309104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029547138,0.00064562773,0.00058860204,0.0024772214,0.0002640467,0.0011060422,0.00059747684,0.0003842157,0.0005550594],"category_scores_gemma":[0.0042879963,0.00020301116,0.0005983023,0.0020350814,0.00026911072,0.0010483854,0.0013523819,0.00048465017,0.00013993972],"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":[0.00014029947,0.00023292091,0.086151354,0.00038057019,0.00037337944,0.00020031529,0.00038804812,0.23214617,0.040818255,0.0056451,0.0015872499,0.63193625],"study_design_scores_gemma":[0.000026747326,0.0002711543,0.057408825,0.000041767795,0.00008314547,0.000087833185,0.00024345299,0.91968733,0.014396513,0.00279249,0.004883097,0.00007761563],"about_ca_topic_score_codex":0.00578372,"about_ca_topic_score_gemma":0.009782614,"teacher_disagreement_score":0.00578372,"about_ca_system_score_codex":0.0007635483,"about_ca_system_score_gemma":0.0010520077,"threshold_uncertainty_score":0.015626192},"labels":[],"label_agreement":null},{"id":"W7116682038","doi":"10.3390/hydrology13010005","title":"Beyond the Flow: Multifractal Clustering of River Discharge Across Canada Using Near-Century Data","year":2025,"lang":"en","type":"article","venue":"Hydrology","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","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":"York University","funders":"York University","keywords":"Multifractal system; Scaling; Cluster analysis; Hurst exponent; Fractal; Exponent; Cluster (spacecraft)","score_opus":0.030601129839873113,"score_gpt":0.25006088561701695,"score_spread":0.21945975577714383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116682038","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9961343,0.00019239387,0.0018390673,0.00012378824,0.0000032284931,0.000008750565,0.0009761533,0.000025511208,0.0006969796],"genre_scores_gemma":[0.9978649,0.00007817785,0.0009958594,0.000007239739,0.0000024684555,0.0000024361887,0.0008595227,0.0000046396767,0.0001846985],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99970716,0.000046926518,0.0000172717,0.00007935326,0.00008679999,0.00006248326],"domain_scores_gemma":[0.99914694,0.00016401506,0.00015992866,0.000102930404,0.00032485515,0.00010132041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056152185,0.00014826976,0.00021522465,0.0023253686,0.00072575134,0.0008852299,0.00030436306,0.00017584229,0.00029010331],"category_scores_gemma":[0.003096114,0.00008142516,0.0002694139,0.003734368,0.0003935994,0.00037587585,0.0005579261,0.00026713085,0.000054800938],"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":[0.00006397215,0.000021571512,0.9336904,0.000033279943,0.00014308142,0.00011142543,0.0010968564,0.018077597,0.0015268037,0.0015974754,0.0013340458,0.04230338],"study_design_scores_gemma":[0.0000021983315,0.000005492431,0.9618889,0.000012282809,0.000016846509,0.000025620036,0.00053530483,0.03502899,0.00021087164,0.00055915857,0.001697773,0.00001664738],"about_ca_topic_score_codex":0.913379,"about_ca_topic_score_gemma":0.93392015,"teacher_disagreement_score":0.08662099,"about_ca_system_score_codex":0.0041910824,"about_ca_system_score_gemma":0.003979823,"threshold_uncertainty_score":0.17426217},"labels":[],"label_agreement":null},{"id":"W7117291955","doi":"10.3390/hydrology13010012","title":"Revealing Emerging Hydroclimatic Shifts: Advanced Trend Analysis of Rainfall and Streamflow in the Navasota River Watershed","year":2025,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management 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":"Institut National de la Recherche Scientifique","funders":"National Institute of Food and Agriculture","keywords":"Streamflow; Trend analysis; Precipitation; Watershed; Flood myth; Flood forecasting; Climate change; Hydrology (agriculture); Autocorrelation","score_opus":0.00677100950344394,"score_gpt":0.23977901560977286,"score_spread":0.2330080061063289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117291955","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9987984,0.000032052394,0.0004904463,0.000037256927,0.000001749263,0.0000040630566,0.00029401924,0.000015996735,0.0003260119],"genre_scores_gemma":[0.99810493,0.000045121218,0.0013201704,0.0000068452637,0.000004013078,0.000006147142,0.00040419868,0.0000037359546,0.00010470547],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99985826,0.000032119013,0.000011782594,0.00004212963,0.000027283866,0.00002832639],"domain_scores_gemma":[0.99958175,0.00009432537,0.00012257132,0.00004820375,0.00010022657,0.00005287557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042724638,0.00013682914,0.00015153112,0.001447517,0.00023204653,0.0006026323,0.00019184206,0.00016583753,0.00042284594],"category_scores_gemma":[0.0012204105,0.00008546109,0.00016395298,0.0018709903,0.00020651561,0.0005678981,0.00051850337,0.0002344099,0.000056105942],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005067483,0.00007244049,0.958445,0.000019300953,0.00005831541,0.00015079475,0.00087647437,0.0043467423,0.0032190427,0.00038600745,0.00028912624,0.032086022],"study_design_scores_gemma":[0.0000021075411,0.000024108056,0.9803176,0.000008917583,0.000015147402,0.00005405232,0.001224464,0.017001856,0.0004053889,0.00021509091,0.0007216171,0.0000095392725],"about_ca_topic_score_codex":0.04074394,"about_ca_topic_score_gemma":0.0770188,"teacher_disagreement_score":0.04074394,"about_ca_system_score_codex":0.00060706085,"about_ca_system_score_gemma":0.0005353081,"threshold_uncertainty_score":0.08101356},"labels":[],"label_agreement":null},{"id":"W7117475839","doi":"10.3390/hydrology13010013","title":"Climate Change Impacts on Agricultural Watershed Hydrology, Southern Ontario: An Integrated SDSM–SWAT Approach","year":2025,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management 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":true,"ca_institutions":"Ministry of the Environment, Conservation and Parks; Ministry of Environment; University of Guelph","funders":"","keywords":"Soil and Water Assessment Tool; Downscaling; Climate change; Watershed; Hydrology (agriculture); Evapotranspiration; Streamflow; Water resources; Aggradation; Land use, land-use change and forestry","score_opus":0.016857057237358888,"score_gpt":0.22640084522925696,"score_spread":0.20954378799189807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117475839","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9613935,0.00051124505,0.005052383,0.00090316345,0.000045094286,0.0001129327,0.016060889,0.00033598978,0.015584988],"genre_scores_gemma":[0.98397875,0.0004797658,0.005273598,0.00007057694,0.000015351978,0.000045929322,0.006577121,0.000054447522,0.0035042737],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983406,0.000016656079,0.0000091395905,0.000042405412,0.00006008144,0.000037557795],"domain_scores_gemma":[0.99971396,0.00002195462,0.000036276913,0.000023086293,0.00016105836,0.0000437023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023573585,0.0004252505,0.00027117864,0.0007503104,0.00088498864,0.0011842619,0.000795819,0.0004050576,0.0015724046],"category_scores_gemma":[0.00078030507,0.00029053455,0.00059156766,0.0018510028,0.00042942824,0.00039795655,0.00047284292,0.0003079758,0.00017848017],"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":[0.00015997396,0.00013536231,0.53912884,0.00024252257,0.0004904909,0.00049490697,0.00081635785,0.39917666,0.0047399006,0.0030945598,0.00954328,0.041977182],"study_design_scores_gemma":[0.00009143023,0.00003289806,0.4385494,0.00006885935,0.000196979,0.000047357444,0.0011034347,0.53393185,0.0010720778,0.0011529466,0.023653587,0.00009901751],"about_ca_topic_score_codex":0.9830027,"about_ca_topic_score_gemma":0.98961186,"teacher_disagreement_score":0.016997278,"about_ca_system_score_codex":0.013121061,"about_ca_system_score_gemma":0.014753968,"threshold_uncertainty_score":0.09520042},"labels":[],"label_agreement":null}]}