{"meta":{"query_hash":"dfc4fde3847c","filters":{"venue":"Water Resources Management"},"cohort_total":229,"direct_labels_cover":0,"predictions_cover":229,"exported":229,"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/dfc4fde3847c","api":"https://metacan.xera.ac/api/v1/cohort?venue=Water+Resources+Management"},"results":[{"id":"W1075870924","doi":"10.1007/s11269-015-1080-1","title":"Streamflow Forecast Errors and Their Impacts on Forecast-based Reservoir Flood Control","year":2015,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Inflow; 100-year flood; Flood control; Flood myth; Environmental science; Flood forecasting; Hydrology (agriculture); Streamflow; Quantitative precipitation forecast; Precipitation; Routing (electronic design automation); Water level; Meteorology; Climatology; Geology; Computer science; Drainage basin; Geography","score_opus":0.014469005393596928,"score_gpt":0.1861678356043905,"score_spread":0.17169883021079357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1075870924","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.955103,0.00018388039,0.010413597,0.00063736393,0.00024989457,0.0010833577,0.000023901146,0.0006536044,0.03165144],"genre_scores_gemma":[0.99743956,0.000019090616,0.0006665278,0.0002808965,0.0001265427,0.00009162521,0.000097791526,0.00009485772,0.001183102],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99821764,0.000067575515,0.0003251869,0.000393162,0.00034736338,0.0006490901],"domain_scores_gemma":[0.9991427,0.000020300457,0.000045904315,0.0004889952,0.000035837635,0.0002662708],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038185288,0.00042517687,0.00030216613,0.0004007376,0.000115939176,0.00026254702,0.00032824842,0.000084229236,0.000039333543],"category_scores_gemma":[0.0000066843795,0.00028665666,0.00008745075,0.00016822397,0.000056489902,0.00019240603,0.00014970425,0.00014720907,0.00009205128],"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":[0.00031170223,0.000094698196,0.0014591939,0.00034782517,0.0003508495,0.00005412587,0.00389983,0.98008466,0.00006556344,0.00007633768,0.0056009707,0.0076542385],"study_design_scores_gemma":[0.005481039,0.00051518367,0.00096647115,0.00017224444,0.00013134096,0.0000029185499,0.0011991777,0.73644155,0.0036004048,0.00045224777,0.25030425,0.0007331339],"about_ca_topic_score_codex":0.000027407697,"about_ca_topic_score_gemma":0.000041345877,"teacher_disagreement_score":0.2447033,"about_ca_system_score_codex":0.00010764251,"about_ca_system_score_gemma":0.0000018944714,"threshold_uncertainty_score":0.9999586},"labels":[],"label_agreement":null},{"id":"W1122458939","doi":"10.1007/s11269-015-1105-9","title":"Exploring Generational Differences Towards Water Resources and Policy Preferences of Water Re-Allocation in Alberta, Canada","year":2015,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Environmental Education and Sustainability","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Lethbridge","funders":"Division of Graduate Education; University of Lethbridge","keywords":"Context (archaeology); Water use; Government (linguistics); Business; Natural resource economics; Water resources; Intervention (counseling); Resource (disambiguation); Water conservation; Resource allocation; Environmental planning; Environmental resource management; Economics; Water resource management; Geography; Environmental science; Ecology","score_opus":0.03764622168374414,"score_gpt":0.22721599293238118,"score_spread":0.18956977124863705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1122458939","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98619765,0.00001217104,0.000008016414,0.0061228476,0.00007197283,0.00028787172,0.0000017459694,0.000009360286,0.007288375],"genre_scores_gemma":[0.9950408,0.000024664934,0.00013209153,0.00022670181,0.000040606086,0.00011819451,0.000030247142,0.000010317704,0.004376384],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982184,0.00012244868,0.00034879387,0.0003933673,0.00053420686,0.0003827745],"domain_scores_gemma":[0.9995563,0.00001009532,0.000035415054,0.00024804875,0.000009772152,0.00014033749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039360506,0.00017792113,0.00017126884,0.00009874011,0.00009578125,0.000054116306,0.0002463917,0.000032690943,0.00045906098],"category_scores_gemma":[0.000012147738,0.00010138514,0.000022555332,0.00007475151,0.00016178303,0.0002736071,0.00050130347,0.00007403964,0.000030043546],"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":[0.00020664862,0.0005438598,0.6864538,0.0003083731,0.00009458385,0.000018232147,0.27005935,0.013294488,0.0036318616,0.00024013847,0.0011329522,0.0240157],"study_design_scores_gemma":[0.0008139989,0.0001365191,0.7785731,0.000032108648,0.000027992779,0.000002265523,0.02581628,0.00050634413,0.051705588,0.0024391764,0.13944001,0.00050659553],"about_ca_topic_score_codex":0.6775777,"about_ca_topic_score_gemma":0.37759992,"teacher_disagreement_score":0.29997775,"about_ca_system_score_codex":0.00052808656,"about_ca_system_score_gemma":0.000012085574,"threshold_uncertainty_score":0.63375723},"labels":[],"label_agreement":null},{"id":"W1127605711","doi":"10.1007/s11269-015-1108-6","title":"Using Data Mining to Understand Drinking Water Advisories in Small Water Systems: a Case Study of Ontario First Nations Drinking Water Supplies","year":2015,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Water supply; Decision tree; Certification; Hydrogeology; Data mining; Computer science; Environmental science; Engineering; Environmental engineering","score_opus":0.07730290404225432,"score_gpt":0.24032933995306788,"score_spread":0.16302643591081356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1127605711","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9900566,0.000028110968,0.006156516,0.00004781592,0.00053851906,0.0013575349,0.0000044404915,0.00014022224,0.0016702452],"genre_scores_gemma":[0.99654806,0.0000015958742,0.00146001,0.000009335762,0.00009060285,0.00007394107,0.00013453848,0.00008269575,0.0015992393],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.99739623,0.000083829036,0.0008293361,0.00055294984,0.00038867973,0.00074896455],"domain_scores_gemma":[0.9989115,0.000014231205,0.000036626203,0.0008479356,0.00006991119,0.000119778335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008493657,0.00037367304,0.00044902158,0.00077982363,0.00027609678,0.0004269549,0.00050748064,0.00008231747,0.000026126128],"category_scores_gemma":[0.000002702099,0.00022158753,0.000036509708,0.00013429359,0.00002583049,0.00042257056,0.0012044627,0.0001315984,0.000022491286],"study_design_candidate":"qualitative","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":[0.000021799864,0.0000717551,0.0064374837,0.00035727196,0.00017891378,0.000571505,0.35973227,0.6324492,0.00009808266,0.00000842783,0.000056503264,0.000016783548],"study_design_scores_gemma":[0.008836056,0.00073197845,0.0003568247,0.0024589514,0.00089308363,0.00063004036,0.59434044,0.2261804,0.019906962,0.00010128844,0.14238435,0.0031796405],"about_ca_topic_score_codex":0.036320645,"about_ca_topic_score_gemma":0.4692624,"teacher_disagreement_score":0.43294176,"about_ca_system_score_codex":0.00066555425,"about_ca_system_score_gemma":0.000005846424,"threshold_uncertainty_score":0.9700966},"labels":[],"label_agreement":null},{"id":"W1560095848","doi":"10.1023/a:1013059108329","title":"An Assessment of the Impact of Mingoa Stream Input to the Bacteriological Quality of the Municipal Lake of Yaoundé (Cameroon)","year":2001,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Fecal coliform; Tributary; Environmental science; Water quality; Indicator bacteria; Water pollution; Pollution; Hydrology (agriculture); Environmental chemistry; Geography; Biology; Ecology; Geology; Chemistry","score_opus":0.03711608443508209,"score_gpt":0.35065900938912026,"score_spread":0.3135429249540382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1560095848","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.993079,0.00000432257,0.000113982765,0.0009779541,0.00006175362,0.0005911409,0.000057365047,0.000007753909,0.00510675],"genre_scores_gemma":[0.9988728,0.000009033387,0.00023962375,0.00016642532,0.000015457552,0.000020486264,0.0000065951344,0.0000074748564,0.00066206703],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99723566,0.0008796185,0.0007031526,0.00024809988,0.0006485395,0.00028492932],"domain_scores_gemma":[0.9983354,0.00004339436,0.0003937915,0.001151457,0.000015965397,0.00005998934],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001522303,0.00017157565,0.00031317174,0.000034144286,0.000116958756,0.000019215737,0.0012730071,0.00004895608,0.0010147458],"category_scores_gemma":[0.000010332232,0.00006725732,0.00026600264,0.00025985303,0.0004825551,0.000072097624,0.0011690443,0.00011999114,0.000007665769],"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":[0.00021119232,0.001484477,0.87579477,0.00012415244,0.00032115643,0.0000013158972,0.017880136,0.064688765,0.03282735,0.0006424126,0.0003478836,0.005676396],"study_design_scores_gemma":[0.0002788876,0.0002772982,0.97945476,0.000035131045,0.000036438756,7.575308e-7,0.00095767947,0.00028102382,0.0044199615,0.00015381293,0.014007746,0.00009650658],"about_ca_topic_score_codex":0.004746112,"about_ca_topic_score_gemma":0.0016659308,"teacher_disagreement_score":0.10366,"about_ca_system_score_codex":0.0000983114,"about_ca_system_score_gemma":0.0000056577555,"threshold_uncertainty_score":0.99989843},"labels":[],"label_agreement":null},{"id":"W1575730668","doi":"10.1023/a:1021993222371","title":"Optimal Operation of Reservoir Systems using Simulated Annealing","year":2002,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":111,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Manitoba Hydro","keywords":"Simulated annealing; Mathematical optimization; Computer science; Integer programming; Benchmark (surveying); Nonlinear system; Algorithm; Mathematics","score_opus":0.020890834915430267,"score_gpt":0.19710525750235938,"score_spread":0.17621442258692913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1575730668","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9711302,0.00042468144,0.015420159,0.000027527134,0.00022714309,0.00055344007,0.0000032921748,0.00033837784,0.011875171],"genre_scores_gemma":[0.9960378,0.000073946096,0.0015495622,0.000011668378,0.00008908372,0.000006554001,0.0000357254,0.000056996512,0.0021386924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985887,0.000048048485,0.0004477962,0.00023761936,0.00031282404,0.0003650393],"domain_scores_gemma":[0.99952054,0.000006437485,0.0000494232,0.00033462376,0.000033801985,0.00005515133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019842296,0.00021488704,0.00022305334,0.0003180893,0.000107346226,0.0001680183,0.00026395908,0.00006403181,0.00015749689],"category_scores_gemma":[0.0000014624325,0.00017710747,0.00006142864,0.0001940761,0.000028907161,0.00023398684,0.00015115934,0.000080316306,0.000063283725],"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":[0.0000099267745,0.000021987851,0.00018078754,0.00040066254,0.0001382058,0.000015014482,0.002101251,0.99596435,0.0005869287,0.000035238565,0.00033123285,0.00021440926],"study_design_scores_gemma":[0.0004369906,0.000028129385,0.00003428033,0.00008483114,0.00006738339,0.0000013087728,0.00029995042,0.9673144,0.0023606722,0.000003931758,0.029147698,0.0002203849],"about_ca_topic_score_codex":0.000041752126,"about_ca_topic_score_gemma":8.564204e-7,"teacher_disagreement_score":0.028816465,"about_ca_system_score_codex":0.00006608429,"about_ca_system_score_gemma":2.2291161e-7,"threshold_uncertainty_score":0.72222334},"labels":[],"label_agreement":null},{"id":"W1580543518","doi":"10.1023/a:1015561427616","title":"Assessment of the Significance of Sample Serial Correlation by the Bootstrap Test","year":2002,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Monte Carlo method; Statistics; Lag; Correlation coefficient; Parametric statistics; Series (stratigraphy); Hydrogeology; Test (biology); Mathematics; Computer science; Geology; Geotechnical engineering","score_opus":0.08245601452670483,"score_gpt":0.35407705964815644,"score_spread":0.27162104512145163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1580543518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05633474,0.000026556685,0.9295133,0.0004757495,0.00013285466,0.00090902264,0.000095430216,0.000021230353,0.012491122],"genre_scores_gemma":[0.9084878,0.000010517742,0.08815078,0.000050515846,0.000026353227,0.00005003311,0.0000031902198,0.00001426935,0.003206556],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989359,0.000120531506,0.0003271207,0.00015008809,0.0003066347,0.00015974474],"domain_scores_gemma":[0.9988401,0.00057077676,0.00015912394,0.00038066925,0.000028356526,0.000020973095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034839872,0.000099250705,0.00015876966,0.000017332995,0.000087626984,0.000013985147,0.0002576476,0.000025142845,0.00017699649],"category_scores_gemma":[0.000088989655,0.00004679777,0.000058812944,0.00006819474,0.00011644477,0.000030068793,0.00012753939,0.00008175438,0.0000017381468],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012199564,0.0030511015,0.009056875,0.0028565172,0.0007727674,0.000008740896,0.01749376,0.020643141,0.028019506,0.7641044,0.030941946,0.122929215],"study_design_scores_gemma":[0.0019716371,0.00046024082,0.007928919,0.00024704117,0.0005205735,0.0000022732097,0.0014331393,0.08208715,0.021921588,0.73072135,0.15216205,0.00054405315],"about_ca_topic_score_codex":0.000025644542,"about_ca_topic_score_gemma":0.000004060134,"teacher_disagreement_score":0.85215306,"about_ca_system_score_codex":0.000021094786,"about_ca_system_score_gemma":9.88919e-7,"threshold_uncertainty_score":0.19379874},"labels":[],"label_agreement":null},{"id":"W1809792443","doi":"10.1007/s11269-015-1162-0","title":"Protecting Water from Agricultural Diffuse Pollutions: Between Action Territories and Hydrogeological Demarcation","year":2015,"lang":"en","type":"article","venue":"Water Resources Management","topic":"French Urban and Social Studies","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"ASTER","funders":"Région Normandie; Institut National de la Recherche Agronomique","keywords":"Agriculture; Environmental planning; Action (physics); Environmental resource management; Business; Environmental protection; Geography; Environmental science","score_opus":0.06947036470812759,"score_gpt":0.2657983499954048,"score_spread":0.1963279852872772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1809792443","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9808419,0.00023582995,0.00007877226,0.0041562533,0.00024738614,0.0004113915,0.0000036233125,0.00013609254,0.013888773],"genre_scores_gemma":[0.9944419,0.00004820492,0.0001799442,0.000044819277,0.0009518288,0.00007576738,0.000032152406,0.000005129929,0.0042202105],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99874127,0.00022623478,0.0001595718,0.00022833455,0.00032509404,0.00031947246],"domain_scores_gemma":[0.9997212,0.00002379551,0.00003451813,0.000077237,0.00003771748,0.000105493345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004425902,0.00011218267,0.00013794379,0.000040745057,0.0009736398,0.00018284161,0.00012539557,0.00007515323,0.000020934905],"category_scores_gemma":[0.000035313293,0.00006231327,0.000035159766,0.0000633841,0.00022425046,0.00018687749,0.00020083574,0.0001028272,0.000037914273],"study_design_candidate":"qualitative","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":[0.000069017355,0.00026104847,0.086152494,0.00007589453,0.00064065534,0.000019637228,0.8621731,0.000032308555,0.00081539404,0.0071655903,0.003867451,0.03872743],"study_design_scores_gemma":[0.0006451292,0.00006821975,0.10908297,0.000027821452,0.00010044429,2.513568e-7,0.06467581,0.000008338208,0.0012062329,0.014586872,0.80929786,0.00030003427],"about_ca_topic_score_codex":0.014262187,"about_ca_topic_score_gemma":0.0018806404,"teacher_disagreement_score":0.8054304,"about_ca_system_score_codex":0.00019809058,"about_ca_system_score_gemma":0.000002005539,"threshold_uncertainty_score":0.99230194},"labels":[],"label_agreement":null},{"id":"W1832189138","doi":"10.1007/s11269-014-0902-x","title":"A Generalized Interval Fuzzy Chance-Constrained Programming Method for Domestic Wastewater Management Under Uncertainty – A Case Study of Kunming, China","year":2015,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Major Science and Technology Program for Water Pollution Control and Treatment","keywords":"Interval (graph theory); China; Fuzzy logic; Hydrogeology; Wastewater; Mathematical optimization; Mathematics; Water resource management; Statistics; Econometrics; Operations research; Environmental science; Computer science; Environmental engineering; Engineering; Geography; Artificial intelligence; Geotechnical engineering","score_opus":0.023805372766123486,"score_gpt":0.26893547251039734,"score_spread":0.24513009974427385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1832189138","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8920747,0.00007639224,0.09947094,0.00009405515,0.0003801863,0.0045239874,0.000008337836,0.0004914806,0.0028798988],"genre_scores_gemma":[0.94594085,0.000010184466,0.04958863,0.000042129766,0.000117267,0.0011211125,0.00009417921,0.00012090243,0.0029647388],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997159,0.00015238363,0.0007891835,0.00061918923,0.0004964908,0.0007837443],"domain_scores_gemma":[0.99897474,0.000017689528,0.00012736728,0.000629541,0.00006554166,0.0001851313],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00092612265,0.00052126707,0.0005609485,0.0006093845,0.00014251405,0.00022120569,0.00046850674,0.000076612596,0.000026438045],"category_scores_gemma":[0.0000035777343,0.0003990152,0.00017228944,0.0003203157,0.000056578006,0.00018373731,0.0004852199,0.00012450143,0.000012637594],"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":[0.0002586706,0.0004895615,0.000096925956,0.0013912811,0.0016725459,0.0010550158,0.031605437,0.93837947,0.00005293239,0.00030552185,0.00052846596,0.024164151],"study_design_scores_gemma":[0.026051806,0.0024192177,0.00013894078,0.00047034523,0.0028852038,0.00035079694,0.10143534,0.7498644,0.001270678,0.0021115167,0.1105678,0.0024339752],"about_ca_topic_score_codex":0.0002685746,"about_ca_topic_score_gemma":0.00009156325,"teacher_disagreement_score":0.18851511,"about_ca_system_score_codex":0.00016296843,"about_ca_system_score_gemma":0.000001964123,"threshold_uncertainty_score":0.99984616},"labels":[],"label_agreement":null},{"id":"W1966566909","doi":"10.1007/s11269-014-0554-x","title":"Adaptation Investigations to Respond to Climate Change Projections in Gansu Province, Northern China","year":2014,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Precipitation; Climate change; Desertification; Environmental science; China; Current (fluid); Cropping; Irrigation; Representative Concentration Pathways; Climatology; Population; Geography; Physical geography; Water resource management; Climate model; Agriculture; Ecology; Geology; Meteorology","score_opus":0.03434290920319691,"score_gpt":0.22783866205632677,"score_spread":0.19349575285312987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966566909","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97750324,0.0000073102437,0.0000114231525,0.016393432,0.000072892886,0.0016803149,0.000037785965,0.0001307767,0.004162804],"genre_scores_gemma":[0.9954884,0.000013672236,0.00037005724,0.0017183571,0.00028506079,0.0005653112,0.00013660673,0.0000036656738,0.0014188709],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99845326,0.00011201197,0.00024445084,0.00041615736,0.00027000485,0.00050408975],"domain_scores_gemma":[0.9995833,0.000020900568,0.000048144535,0.00011184701,0.00003595912,0.0001998604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003778658,0.00018783203,0.00015176724,0.00011206977,0.0002270624,0.00016619112,0.0002777588,0.000054630924,0.00008106795],"category_scores_gemma":[0.000025732348,0.00006844617,0.00004365235,0.00068047596,0.000018245224,0.00015458962,0.00025433025,0.00008951674,0.00035965734],"study_design_candidate":"observational","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":[0.0005789627,0.001143206,0.08038341,0.00037749833,0.0000828926,0.00006557807,0.12740447,0.004299098,0.10620526,0.001599868,0.0048772534,0.6729825],"study_design_scores_gemma":[0.00018752927,0.00042940662,0.6100076,0.00013033385,0.000015773847,0.0000021146661,0.0026584587,0.00024787665,0.0006697917,0.00018042908,0.38513973,0.0003309503],"about_ca_topic_score_codex":0.0010361264,"about_ca_topic_score_gemma":0.04756661,"teacher_disagreement_score":0.6726515,"about_ca_system_score_codex":0.000095913805,"about_ca_system_score_gemma":8.7311963e-7,"threshold_uncertainty_score":0.9698128},"labels":[],"label_agreement":null},{"id":"W1967151810","doi":"10.1007/s11269-009-9556-5","title":"Regional Dry Spells Frequency Analysis by L-Moment and Multivariate Analysis","year":2009,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":62,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Skewness; Arid; Homogeneity (statistics); Multivariate statistics; Principal component analysis; Spatial distribution; Spatial dependence; Frequency distribution; Statistics; Spatial variability; Moment (physics); L-moment; Homogeneous; Environmental science; Geography; Physical geography; Mathematics; Geology","score_opus":0.005608748671026941,"score_gpt":0.20959786653230014,"score_spread":0.2039891178612732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967151810","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9540089,0.00014002534,0.009276825,0.0021654584,0.00001627884,0.00020219084,0.0000073690585,0.00007161767,0.034111336],"genre_scores_gemma":[0.9817994,0.00007820836,0.0021080067,0.0013914958,0.000020474743,0.0000064336677,0.000083901185,0.000009934614,0.014502138],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978589,0.0001264597,0.00035717554,0.0007270335,0.00045359155,0.00047687002],"domain_scores_gemma":[0.9992243,0.00001189492,0.00009286293,0.0005029252,0.000005143148,0.00016287235],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00043440412,0.0002513749,0.00040996802,0.0004266441,0.00017936209,0.00007884995,0.00032482,0.00008167713,0.00409046],"category_scores_gemma":[0.0000013471234,0.00018215667,0.00031722663,0.0013816275,0.00015834112,0.0001496234,0.00023892714,0.00010762974,0.001029374],"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":[0.00015507391,0.0010157537,0.8628081,0.00002287942,0.028179068,0.0002676625,0.008695519,0.069399126,0.0042607915,0.000729828,0.010346056,0.014120124],"study_design_scores_gemma":[0.0010734766,0.00019272149,0.7089466,0.0000063766383,0.022459863,0.0000015064765,0.0002732905,0.03532346,0.00093294523,0.003788134,0.22600858,0.0009930691],"about_ca_topic_score_codex":0.00087891234,"about_ca_topic_score_gemma":0.00021330138,"teacher_disagreement_score":0.21566251,"about_ca_system_score_codex":0.00009255559,"about_ca_system_score_gemma":4.5120578e-7,"threshold_uncertainty_score":0.99974847},"labels":[],"label_agreement":null},{"id":"W1967455052","doi":"10.1007/s11269-005-3275-3","title":"Reservoir Operation Using a Dynamic Programming Fuzzy Rule–Based Approach","year":2005,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Mathematical optimization; Fuzzy logic; Set (abstract data type); Dynamic programming; Fuzzy rule; Function (biology); Variance (accounting); Fuzzy set; Algorithm; Mathematics; Artificial intelligence","score_opus":0.011032010071420371,"score_gpt":0.20623676194787627,"score_spread":0.1952047518764559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967455052","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7342861,0.0002293917,0.22045451,0.00028119763,0.00016592754,0.0015602767,0.0000032254402,0.0013931607,0.041626185],"genre_scores_gemma":[0.9045499,0.000016283078,0.092515394,0.00010926089,0.00015242951,0.00013607055,0.0001988749,0.00010299614,0.0022187848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815756,0.000052907224,0.00038647553,0.00040642015,0.0003935406,0.000603108],"domain_scores_gemma":[0.9994025,0.000004194502,0.000038729235,0.0004434739,0.000023014403,0.00008807004],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003012325,0.0003230306,0.0002077054,0.00040911682,0.00021842185,0.00042062777,0.00037855504,0.00007826766,0.000061101964],"category_scores_gemma":[0.0000012075234,0.00026582505,0.00009214921,0.00023100093,0.00003774057,0.0003542494,0.00018804274,0.00014027594,0.000121658115],"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":[0.000019192012,0.0000687704,0.00010903523,0.0003255418,0.000095935924,0.000008445983,0.0012212129,0.9834234,0.00039276062,0.000044506723,0.0001490575,0.014142135],"study_design_scores_gemma":[0.00050789985,0.000018486673,0.0001017127,0.000039044877,0.00007515468,0.0000013165112,0.00020023593,0.824406,0.0009699713,0.000024709463,0.17331462,0.0003408117],"about_ca_topic_score_codex":0.000013849394,"about_ca_topic_score_gemma":0.000010315267,"teacher_disagreement_score":0.17316556,"about_ca_system_score_codex":0.00022192049,"about_ca_system_score_gemma":0.0000012927229,"threshold_uncertainty_score":0.9999794},"labels":[],"label_agreement":null},{"id":"W1968997236","doi":"10.1007/s11269-013-0477-y","title":"Stormwater Capture Efficiency of Bioretention Systems","year":2013,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","cited_by":68,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Bioretention; Stormwater; Surface runoff; Environmental science; Stormwater management; Low-impact development; Probabilistic logic; Hydrogeology; Hydrology (agriculture); Environmental engineering; Mathematics; Geotechnical engineering; Statistics; Engineering","score_opus":0.005627691374690555,"score_gpt":0.16774871256704038,"score_spread":0.16212102119234983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968997236","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.917537,0.00004419186,0.0030804365,0.00028071756,0.00027621537,0.0011204081,0.0000031534914,0.00011799049,0.07753992],"genre_scores_gemma":[0.9647045,0.0000060683938,0.0003635834,0.00006258683,0.000024889307,0.0001609229,0.000017622975,0.000022615535,0.034637216],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99804074,0.00006854599,0.000380601,0.00040793823,0.0005472078,0.0005549597],"domain_scores_gemma":[0.9992608,0.000004872559,0.0000960477,0.0005380211,0.0000128530155,0.00008740564],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00027313438,0.00020663482,0.00018061818,0.0001445593,0.00015356179,0.00009397687,0.0004826234,0.000053762637,0.003097116],"category_scores_gemma":[0.0000014019098,0.00013737516,0.00008892271,0.00019492161,0.00019181763,0.00030635344,0.00081117847,0.000077139004,0.004638997],"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":[0.00015948062,0.003975513,0.26630595,0.0026699007,0.001692059,0.00019306548,0.049889717,0.09332379,0.14983062,0.0079228105,0.4099784,0.014058715],"study_design_scores_gemma":[0.0015055549,0.0003068466,0.09574542,0.00012530244,0.00030353563,0.0000087431235,0.00321632,0.0086486405,0.0069054263,0.00078935456,0.88127685,0.0011679862],"about_ca_topic_score_codex":0.0023764947,"about_ca_topic_score_gemma":0.000015487476,"teacher_disagreement_score":0.4712985,"about_ca_system_score_codex":0.0001510924,"about_ca_system_score_gemma":4.001266e-7,"threshold_uncertainty_score":0.9978142},"labels":[],"label_agreement":null},{"id":"W1970244596","doi":"10.1007/s11269-011-9875-1","title":"Reservoirs Effects on the Interannual Variability of Winter and Spring Streamflow in the St-Maurice River Watershed (Quebec, Canada)","year":2011,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Streamflow; Environmental science; Hydrology (agriculture); Spring (device); Watershed; Precipitation; Climate change; Climatology; Drainage basin; Geology; Geography; Meteorology; Oceanography","score_opus":0.007846709343010103,"score_gpt":0.17559712658259036,"score_spread":0.16775041723958026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970244596","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97221833,0.0000055682885,0.00003100658,0.002913692,0.00006320893,0.0005716541,0.0000015456039,0.000011599883,0.024183366],"genre_scores_gemma":[0.99781966,0.000008469322,0.00004970426,0.0011145595,0.000012338914,0.00006856848,0.0000015556096,0.000008483163,0.00091663963],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983469,0.00039734208,0.0002248656,0.00035540963,0.0003207483,0.0003547319],"domain_scores_gemma":[0.9993389,0.00012239382,0.000046633017,0.000458318,0.000003890822,0.00002985298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010109077,0.00018363078,0.00016464952,0.000046681842,0.00015816154,0.00002042737,0.0005611267,0.000030770272,0.00027862],"category_scores_gemma":[0.0000129817,0.00008476427,0.000036316418,0.00008846494,0.00031656685,0.00009076438,0.00092876574,0.00015204042,0.000023319537],"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":[0.00082172465,0.0005046623,0.81346905,0.0004987039,0.000635323,0.0003915154,0.16311358,0.0015705186,0.00017849928,0.0011486742,0.013244597,0.004423147],"study_design_scores_gemma":[0.00060166034,0.00013973699,0.95373005,0.000063682004,0.00009148734,6.827241e-7,0.0029990617,0.00017321043,0.0025604267,0.0015214371,0.03787406,0.00024448065],"about_ca_topic_score_codex":0.1969834,"about_ca_topic_score_gemma":0.28579214,"teacher_disagreement_score":0.16011451,"about_ca_system_score_codex":0.000091074544,"about_ca_system_score_gemma":9.599393e-7,"threshold_uncertainty_score":0.808364},"labels":[],"label_agreement":null},{"id":"W1971029240","doi":"10.1007/s11269-008-9247-7","title":"Characterization of a Regional Aquifer System in the Maritimes Basin, Eastern Canada","year":2008,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Institut National de la Recherche Scientifique; Geological Survey of Canada","funders":"","keywords":"Aquifer; Geology; Hydrogeology; Groundwater recharge; Bedrock; Structural basin; Groundwater flow; Groundwater; Hydrology (agriculture); Sedimentary rock; Hydraulic conductivity; Geomorphology; Geochemistry; Soil science; Geotechnical engineering","score_opus":0.009878190094348906,"score_gpt":0.16672869559138676,"score_spread":0.15685050549703786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971029240","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.991521,0.000007530529,0.00031889093,0.00066450343,0.000039892242,0.00020741382,0.0000022787924,0.000009150462,0.007229353],"genre_scores_gemma":[0.9904555,0.00000647415,0.000017925138,0.0004779139,0.000015640966,0.00004532274,0.000016003176,0.000004954648,0.008960228],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99903756,0.00006720629,0.0001861967,0.0001514283,0.00039907175,0.00015852618],"domain_scores_gemma":[0.9997759,0.0000068282484,0.000039400657,0.00015678236,0.0000052624305,0.00001585815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014584725,0.00008397472,0.0000972357,0.000031501437,0.00010273851,0.0000138141095,0.00020514638,0.000012718388,0.00007511775],"category_scores_gemma":[4.80071e-7,0.00004935333,0.000021942573,0.00008463361,0.000064831394,0.000068905174,0.00015166955,0.000031399755,0.000040628707],"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":[0.00010501402,0.00025985666,0.9173728,0.00030065206,0.00012151783,0.00052568986,0.044819437,0.00026716106,0.004352751,0.0005152021,0.006249679,0.02511022],"study_design_scores_gemma":[0.00019478293,0.000012384767,0.64931005,0.000020908623,0.000008656712,0.000009898982,0.0014868509,0.00012042377,0.00038515253,0.00000254601,0.34837362,0.00007474936],"about_ca_topic_score_codex":0.06518966,"about_ca_topic_score_gemma":0.025089756,"teacher_disagreement_score":0.34212393,"about_ca_system_score_codex":0.00010372149,"about_ca_system_score_gemma":0.0000016945712,"threshold_uncertainty_score":0.9926998},"labels":[],"label_agreement":null},{"id":"W1975115035","doi":"10.1007/s11269-009-9545-8","title":"Characteristics of Rainfall, Snowmelt and Runoff in the Headwater Region of the Main River Watershed in Germany","year":2009,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Snowmelt; Snow; Environmental science; Precipitation; Watershed; Surface runoff; Hydrology (agriculture); Hydrograph; Water year; Climatology; Meteorology; Drainage basin; Geology; Geography","score_opus":0.012307762895529163,"score_gpt":0.18857688992910243,"score_spread":0.17626912703357328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975115035","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9954977,0.000072536895,0.000007330029,0.003043135,0.00003801616,0.00026651836,0.000004920717,0.0000033057443,0.0010665109],"genre_scores_gemma":[0.99875104,0.0001322346,0.00005612047,0.0007260047,0.000017458677,0.0000017417477,0.000009123942,0.000001455347,0.000304823],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992005,0.000074363525,0.00025000353,0.00012655772,0.0001641618,0.0001844178],"domain_scores_gemma":[0.9997077,0.000021308777,0.00005470588,0.00019561443,0.000007758353,0.000012884773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029418457,0.00008472108,0.00013664298,0.00003812711,0.000060103936,0.00001914223,0.0002268618,0.000023123484,0.000045315308],"category_scores_gemma":[0.0000027046028,0.00003807689,0.000032071748,0.00013385985,0.00009438614,0.000047948844,0.000042131054,0.00006504551,0.0000030998476],"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":[0.00010205613,0.000060987528,0.9403416,0.000084905376,0.000025322279,0.000041679683,0.03840486,0.0004865471,0.00003976037,0.0001255412,0.00043094595,0.019855822],"study_design_scores_gemma":[0.0002452247,0.000046217785,0.9745043,0.000030018935,0.000010163755,0.0000016870312,0.00061500503,0.0003080388,0.000025042946,0.00041115694,0.023747673,0.0000554918],"about_ca_topic_score_codex":0.0010596497,"about_ca_topic_score_gemma":0.0007402201,"teacher_disagreement_score":0.03778985,"about_ca_system_score_codex":0.0000030947501,"about_ca_system_score_gemma":9.651957e-7,"threshold_uncertainty_score":0.16018805},"labels":[],"label_agreement":null},{"id":"W1975439857","doi":"10.1007/s11269-015-0945-7","title":"Composite Drought Indices of Monotonic Behaviour for Assessing Potential Impact of Climate Change to a Water Resources System","year":2015,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Science Council; University of Alberta","keywords":"Environmental science; Climate change; Vulnerability (computing); Water resources; Water scarcity; Index (typography); Economic shortage; Resilience (materials science); Monotonic function; Climatology; Hydrology (agriculture); Water resource management; Mathematics; Computer science; Geology; Geotechnical engineering; Ecology","score_opus":0.016923534524941147,"score_gpt":0.2635102343746043,"score_spread":0.24658669984966317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975439857","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99395144,0.000034736506,0.0005236642,0.00016649475,0.00006556642,0.00073571905,0.000021859063,0.00004378455,0.0044567455],"genre_scores_gemma":[0.99843675,0.000005539062,0.000981208,0.00005586958,0.00005599118,0.00013050661,0.00003480866,0.000025832433,0.00027350514],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978782,0.00013638232,0.0005041601,0.0004334859,0.00044247034,0.0006053014],"domain_scores_gemma":[0.99921584,0.0000105093195,0.00017723526,0.0004057077,0.000020236954,0.0001704559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086242845,0.00024015723,0.00043953065,0.0002282483,0.00015647097,0.000068622714,0.00047126441,0.0000925734,0.00013126788],"category_scores_gemma":[0.0000017490402,0.00014658552,0.00026198782,0.00017462298,0.00012607114,0.000262987,0.00078805164,0.00007276759,0.00013140649],"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":[0.002028231,0.0010588862,0.74169326,0.0012005278,0.0016332949,0.00021541522,0.105586335,0.10676113,0.031188838,0.000052460597,0.0006672948,0.007914329],"study_design_scores_gemma":[0.011606961,0.004777563,0.7059608,0.0011761554,0.0065527805,0.00008261115,0.017299566,0.06347503,0.14417516,0.00068988785,0.040690485,0.0035129774],"about_ca_topic_score_codex":0.0012392288,"about_ca_topic_score_gemma":0.000057108133,"teacher_disagreement_score":0.112986326,"about_ca_system_score_codex":0.00017434964,"about_ca_system_score_gemma":0.0000011300816,"threshold_uncertainty_score":0.5977584},"labels":[],"label_agreement":null},{"id":"W1981921504","doi":"10.1007/s11269-014-0658-3","title":"Assessment of Suspended Solid Removal in a Surface Flow Constructed Wetland Using a Three-Dimensional Numerical Model","year":2014,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Constructed Wetlands for Wastewater Treatment","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Suspended solids; Total suspended solids; Hydrogeology; Constructed wetland; Wetland; Flow (mathematics); Environmental science; Flow conditions; Hydrology (agriculture); Mechanics; Environmental engineering; Geotechnical engineering; Wastewater; Geology; Ecology; Physics","score_opus":0.011169884059889041,"score_gpt":0.23579011361278462,"score_spread":0.2246202295528956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981921504","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9856813,0.0000056232716,0.0058878814,0.000078757905,0.000067386,0.0005058569,0.000009971372,0.000045691166,0.007717531],"genre_scores_gemma":[0.89839643,0.0000013018333,0.10128689,0.000037814647,0.000011193692,0.000010894128,0.000022322061,0.000026617054,0.00020655093],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974514,0.00012630872,0.00055001234,0.00060310645,0.0006965574,0.000572604],"domain_scores_gemma":[0.9992115,0.000028152655,0.00012467716,0.00049710134,0.000011140552,0.00012745635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038809745,0.00030850075,0.0004193304,0.00012061052,0.000095140706,0.000037513048,0.00032336632,0.00007296327,0.00082931516],"category_scores_gemma":[0.0000038452035,0.00022692274,0.000100092846,0.00021202215,0.00021772104,0.00011423005,0.00081499567,0.00014425129,0.000039817678],"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":[0.00008605536,0.00021629926,0.111910224,0.00004035681,0.000104080325,0.00009516015,0.00038806637,0.86816317,0.015284581,0.000102800484,0.000030012163,0.0035791951],"study_design_scores_gemma":[0.0019590617,0.00008317986,0.02886815,0.00005925711,0.00007227789,0.000049950635,0.00005168686,0.9631561,0.0033262472,0.0017431453,0.00033044515,0.00030047298],"about_ca_topic_score_codex":0.0006626483,"about_ca_topic_score_gemma":0.00026909544,"teacher_disagreement_score":0.09539901,"about_ca_system_score_codex":0.00031820216,"about_ca_system_score_gemma":0.000006857682,"threshold_uncertainty_score":0.9253641},"labels":[],"label_agreement":null},{"id":"W1982103663","doi":"10.1007/s11269-012-0036-y","title":"Comprehensive Evaluation Method of Urban Water Resources Utilization Based on Dynamic Reduct","year":2012,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Reduct; Beijing; Water resources; Computer science; Resource (disambiguation); Data mining; Operations research; Water scarcity; Environmental economics; China; Rough set; Engineering; Geography; Economics","score_opus":0.028102977894714408,"score_gpt":0.2640560891369288,"score_spread":0.23595311124221438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982103663","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8574107,0.00053055736,0.09120407,0.00023360408,0.00080848136,0.0024359936,0.000012652024,0.000714213,0.046649676],"genre_scores_gemma":[0.9930587,0.00003096243,0.0047730105,0.000118854165,0.00012959952,0.00013149767,0.00040720552,0.00009820027,0.0012520046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99725586,0.00028894047,0.00054277136,0.0003823585,0.00084322353,0.000686829],"domain_scores_gemma":[0.9990646,0.000026853106,0.000080602025,0.0006268109,0.0000915672,0.00010956341],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00090845773,0.00038945358,0.0003294274,0.0006689195,0.00013594965,0.00009646913,0.00034635427,0.0001030408,0.0004003398],"category_scores_gemma":[0.000004420706,0.0002752494,0.00013134396,0.00024697048,0.00005480613,0.00026573005,0.0001651548,0.00013513942,0.0001434776],"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":[0.0001115631,0.00015719481,0.0006339585,0.00070477603,0.00024575062,0.0000029179964,0.010350242,0.9684298,0.0026108057,0.00005531882,0.0009733052,0.015724353],"study_design_scores_gemma":[0.0011355168,0.000098804165,0.0027560117,0.00011341737,0.00032350724,0.000001038559,0.0006164784,0.7195105,0.026871584,0.000083854175,0.24802116,0.00046814335],"about_ca_topic_score_codex":0.0000124792095,"about_ca_topic_score_gemma":0.0000014431488,"teacher_disagreement_score":0.24891932,"about_ca_system_score_codex":0.00016538726,"about_ca_system_score_gemma":9.2410494e-7,"threshold_uncertainty_score":0.99996996},"labels":[],"label_agreement":null},{"id":"W1982227551","doi":"10.1007/s11269-014-0622-2","title":"Floodplain Inundation Analysis Combined with Contingent Valuation: Implications for Sustainable Flood Risk Management","year":2014,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Environment and Protected Areas","funders":"","keywords":"Flood myth; Floodplain; 100-year flood; Natural hazard; Contingent valuation; Flood control; Flood mitigation; Environmental science; Water resource management; Risk management; Zoning; Environmental resource management; Socioeconomic status; Willingness to pay; Environmental planning; Geography; Business; Civil engineering; Economics; Population; Engineering; Cartography; Environmental health; Finance","score_opus":0.006008809721333298,"score_gpt":0.21779028335395043,"score_spread":0.21178147363261712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982227551","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36185232,0.000019744879,0.49530444,0.0020835437,0.000083284605,0.005293111,0.000010050793,0.00029874826,0.13505477],"genre_scores_gemma":[0.9517976,0.00005272327,0.021169944,0.000298085,0.00005246499,0.0017317417,0.0003375116,0.000042776595,0.024517177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973184,0.00016354772,0.00044839235,0.00081156736,0.00053113105,0.000726975],"domain_scores_gemma":[0.998732,0.000036843292,0.00021846006,0.00084422604,0.00004282745,0.00012564752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014725403,0.0003245984,0.00031529457,0.00035036542,0.0007406853,0.00030471178,0.00052704854,0.00004634091,0.00040146662],"category_scores_gemma":[0.000006826653,0.00023654051,0.00016705737,0.00076722226,0.000094084935,0.00029709603,0.00070012314,0.00008265626,0.00017132984],"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":[0.0010770323,0.0021071301,0.25972384,0.00091263227,0.012000179,0.000029208817,0.00495931,0.4238927,0.000090324436,0.14377053,0.02078267,0.13065444],"study_design_scores_gemma":[0.004200787,0.0007230706,0.2536737,0.000021373115,0.006320594,5.258716e-7,0.0019555655,0.026403217,0.00029500414,0.015433106,0.6901976,0.0007754391],"about_ca_topic_score_codex":0.00028915342,"about_ca_topic_score_gemma":0.00037113577,"teacher_disagreement_score":0.66941494,"about_ca_system_score_codex":0.0002641895,"about_ca_system_score_gemma":0.0000013867487,"threshold_uncertainty_score":0.9645842},"labels":[],"label_agreement":null},{"id":"W1985589884","doi":"10.1007/s11269-012-0253-4","title":"Generation of Daily and Hourly Weather Variables for use in Climate Change Vulnerability Assessment","year":2013,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Climate variability and models","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Precipitation; Climate change; Environmental science; Climatology; Flood myth; Vulnerability (computing); Meteorology; Extreme weather; Geography; Computer science; Geology","score_opus":0.06337183965579622,"score_gpt":0.26508519084227505,"score_spread":0.20171335118647882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985589884","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9947744,0.000006652736,0.0015504785,0.00041047818,0.000037750746,0.0013528302,0.00001726503,0.000016696078,0.0018334477],"genre_scores_gemma":[0.99055624,0.000044010576,0.0083290795,0.0002048666,0.000022606668,0.00059214595,0.000025249154,0.000011861372,0.00021391547],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988541,0.00008066478,0.00026983552,0.0003528218,0.00016145712,0.0002811177],"domain_scores_gemma":[0.99959064,0.000030670653,0.000049950286,0.0002775835,0.000007349369,0.0000438269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072073267,0.000119341224,0.00015156719,0.000047094993,0.00007353003,0.00008047123,0.00011211794,0.000042654876,0.0005247307],"category_scores_gemma":[0.000004111989,0.000086611806,0.00003120567,0.00005519196,0.00007592689,0.00038699928,0.00033118424,0.000044048942,0.000017806204],"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":[0.00010928405,0.0012401087,0.8897565,0.00087953696,0.000083529405,0.000004244145,0.013676945,0.013069071,0.031461667,0.003306989,0.0011361144,0.045276023],"study_design_scores_gemma":[0.0019270836,0.00027516374,0.7651349,0.00007103795,0.000079249454,9.570921e-7,0.0005331054,0.19960555,0.0017972755,0.006321222,0.023754304,0.0005001668],"about_ca_topic_score_codex":0.002266469,"about_ca_topic_score_gemma":0.00034465472,"teacher_disagreement_score":0.18653648,"about_ca_system_score_codex":0.000080393576,"about_ca_system_score_gemma":4.3815504e-7,"threshold_uncertainty_score":0.5745433},"labels":[],"label_agreement":null},{"id":"W1990673078","doi":"10.1007/s11269-006-9072-9","title":"Evaluation of three unit hydrograph models to predict the surface runoff from a Canadian watershed","year":2006,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Hydrograph; Watershed; Surface runoff; Hydrology (agriculture); Environmental science; Base flow; Predictability; Runoff model; Computer science; Geography; Statistics; Geology; Mathematics; Drainage basin; Cartography; Ecology","score_opus":0.021814560301920114,"score_gpt":0.2072426167847428,"score_spread":0.18542805648282268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990673078","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92604095,0.00004613639,0.00041499714,0.002440352,0.000076718745,0.0010455343,0.000013310662,0.000041502342,0.06988049],"genre_scores_gemma":[0.9977845,0.000006531925,0.0002776314,0.0003903695,0.00002970014,0.000094929244,0.000045043504,0.00001716541,0.0013541154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9977047,0.0001855011,0.00028875162,0.0004274081,0.0008630093,0.0005306249],"domain_scores_gemma":[0.99928606,0.000013720624,0.000051065377,0.0005380761,0.000020056075,0.000091009315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014695738,0.00020691626,0.00016780215,0.00011107568,0.0003309505,0.00004394549,0.0005648936,0.000049762286,0.00083149405],"category_scores_gemma":[0.0000020480636,0.00012427653,0.00006987879,0.00022069861,0.00019791712,0.00013226485,0.00057269377,0.00007638049,0.00039596512],"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":[0.000037184443,0.00006699124,0.069930136,0.000010070623,0.0003079865,0.0000111442905,0.0038945025,0.9173534,0.00024723692,0.00014970305,0.0056621134,0.0023295505],"study_design_scores_gemma":[0.0025501244,0.00024852742,0.49465087,0.00006421541,0.002073895,0.0000011262689,0.001724262,0.17866893,0.0036158601,0.092189044,0.22320627,0.0010068701],"about_ca_topic_score_codex":0.29013997,"about_ca_topic_score_gemma":0.22312048,"teacher_disagreement_score":0.7386845,"about_ca_system_score_codex":0.00014137468,"about_ca_system_score_gemma":0.000002388759,"threshold_uncertainty_score":0.9104277},"labels":[],"label_agreement":null},{"id":"W1992094946","doi":"10.1007/s11269-010-9658-0","title":"Renovation and Innovation: It’s Time for the Great Lakes Regime to Respond","year":2010,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Resources and Governance","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; ArcelorMittal (Canada)","funders":"Joyce Foundation","keywords":"Scholarship; Corporate governance; Objectivity (philosophy); Political science; Commission; Presentation (obstetrics); Public relations; Sociology; Public administration; Law; Management; Economics","score_opus":0.01960632989354298,"score_gpt":0.2677256073188337,"score_spread":0.24811927742529072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992094946","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8882545,0.000025472327,0.00037885812,0.07424573,0.00024023463,0.0012142945,0.000007868243,0.00007315639,0.03555985],"genre_scores_gemma":[0.4581811,0.000025693238,0.001316521,0.0046895193,0.0008947386,0.00027677687,0.000017545286,0.00003193379,0.53456616],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987995,0.000040227867,0.00020892364,0.00027954776,0.0003433384,0.00032844042],"domain_scores_gemma":[0.9994566,0.000061155726,0.00006858249,0.00029568363,0.00006641469,0.000051607036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011744028,0.00011455561,0.00010254119,0.00016039051,0.0007267461,0.00031773045,0.00036003126,0.000054602955,0.00015198071],"category_scores_gemma":[0.00004900346,0.00006933015,0.000028970493,0.00043440986,0.000140548,0.00012371888,0.0001632878,0.000090029054,0.00009568211],"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":[0.00092370034,0.0001253942,0.002579943,0.00017318063,0.00034135501,0.000026754791,0.235289,0.00014680941,0.011027531,0.23600504,0.35033536,0.16302595],"study_design_scores_gemma":[0.00022988435,0.000038371923,0.0017241762,0.000015702402,0.000019479246,5.5106915e-7,0.0007793261,0.000046456174,0.00046164455,0.001323915,0.9952366,0.00012391586],"about_ca_topic_score_codex":0.00023356265,"about_ca_topic_score_gemma":0.0006799761,"teacher_disagreement_score":0.6449012,"about_ca_system_score_codex":0.000023127508,"about_ca_system_score_gemma":0.0000040499767,"threshold_uncertainty_score":0.5589617},"labels":[],"label_agreement":null},{"id":"W1995889581","doi":"10.1007/s11269-011-9953-4","title":"A Hybrid Dynamic Dual Interval Programming for Irrigation Water Allocation under Uncertainty","year":2012,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Interval (graph theory); Dual (grammatical number); Mathematical optimization; Irrigation; Dynamic programming; Computer science; Reliability (semiconductor); Operations research; Process (computing); Irrigation district; Water scarcity; Water resources; Economic shortage; Mathematics","score_opus":0.010142302951172383,"score_gpt":0.21151614661851176,"score_spread":0.20137384366733938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995889581","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77943027,0.0001009907,0.21342087,0.000428166,0.00065248396,0.0018319864,0.000005795733,0.0008102102,0.0033192008],"genre_scores_gemma":[0.99200344,0.00001179957,0.0032589477,0.00010796918,0.00020706054,0.00046546946,0.0008182568,0.00008704813,0.003039994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982135,0.00003800937,0.0003767628,0.00028243693,0.0002721724,0.00081706664],"domain_scores_gemma":[0.99952364,0.000008740681,0.00003556863,0.00029662752,0.000033914675,0.000101509395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043974517,0.0002959326,0.00019011783,0.00025555003,0.00016518975,0.00022002694,0.00020863675,0.000054673812,0.000077658675],"category_scores_gemma":[0.000001556554,0.00020800577,0.000111960755,0.00007099027,0.00003616716,0.00041568224,0.00017480232,0.000091194386,0.00015977593],"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":[0.00010709931,0.00017801388,0.00017954351,0.0011226682,0.0006044613,0.00000567341,0.009759403,0.94015235,0.0017250268,0.0008358092,0.0012554049,0.044074558],"study_design_scores_gemma":[0.0014367108,0.000108915396,0.0004955965,0.00009117031,0.00030628778,0.0000056421104,0.0010620995,0.40443274,0.012264439,0.0011507388,0.5778327,0.000812929],"about_ca_topic_score_codex":0.000011906441,"about_ca_topic_score_gemma":0.000007846648,"teacher_disagreement_score":0.5765773,"about_ca_system_score_codex":0.00019359833,"about_ca_system_score_gemma":5.20579e-7,"threshold_uncertainty_score":0.848223},"labels":[],"label_agreement":null},{"id":"W1997146567","doi":"10.1007/s11269-014-0799-4","title":"Evaluation of the Performance of Eight Record-Extension Techniques Under Different Levels of Association, Presence of Outliers and Different Sizes of Concurrent Records: A Monte Carlo Study","year":2014,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Ste. Anne's Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Outlier; Statistics; Percentile; Variance (accounting); Extension (predicate logic); Computer science; Ordinary least squares; Monte Carlo method; Contrast (vision); Data mining; Mathematics; Artificial intelligence; Accounting","score_opus":0.0835099427494799,"score_gpt":0.3554605009844133,"score_spread":0.2719505582349334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997146567","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9735523,0.000048287362,0.024847727,0.00003468723,0.0000874775,0.0012361243,0.000027509293,0.000008095099,0.00015781696],"genre_scores_gemma":[0.99345607,0.000059343693,0.006220274,0.000003869129,0.000009363943,0.0000554299,7.2154734e-7,0.000013670255,0.00018125113],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99710435,0.00070418976,0.0008219496,0.00022927177,0.0009880441,0.00015217383],"domain_scores_gemma":[0.99783254,0.00042568892,0.00082206534,0.00042281332,0.0004695174,0.000027364806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020450104,0.00016274423,0.00056227256,0.00008848861,0.000037152524,0.000004701688,0.00020497468,0.000044182867,0.000013773653],"category_scores_gemma":[0.00031305273,0.00008908967,0.00007697098,0.000069117355,0.000113695234,0.000044102813,0.00028818112,0.00007705324,4.7809447e-8],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070245453,0.006065435,0.18457532,0.012367887,0.0026845902,5.658633e-7,0.04508534,0.0036441977,0.03615881,0.010008358,0.00024286078,0.69846416],"study_design_scores_gemma":[0.004056295,0.0033699202,0.42145628,0.0026304745,0.0031627377,4.254571e-7,0.004750569,0.08655102,0.3660419,0.107173584,0.00026156096,0.0005452259],"about_ca_topic_score_codex":0.000037157297,"about_ca_topic_score_gemma":0.000027928476,"teacher_disagreement_score":0.69791895,"about_ca_system_score_codex":0.00006550063,"about_ca_system_score_gemma":0.000004119823,"threshold_uncertainty_score":0.36329713},"labels":[],"label_agreement":null},{"id":"W1998377782","doi":"10.1007/s11269-015-0930-1","title":"Future Irrigation Demand of South Saskatchewan River Basin under the Combined Impacts of Climate Change and El Niño Southern Oscillation","year":2015,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Climate variability and models","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta; Alberta Environment and Protected Areas","funders":"Alberta Innovates","keywords":"Irrigation; Climate change; Environmental science; Water resource management; Drainage basin; Irrigation district; Agriculture; Hydrology (agriculture); Geography; Geology","score_opus":0.0218036692271391,"score_gpt":0.21815562272927716,"score_spread":0.19635195350213805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998377782","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9964201,0.000021004747,0.00017161835,0.0007823131,0.00004780889,0.00045842055,0.000020709225,0.000015975085,0.0020620935],"genre_scores_gemma":[0.99946827,0.000018683604,0.00017671562,0.00015657974,0.00003221422,0.000012374579,0.000014668253,0.000010105918,0.000110398825],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99897087,0.0000965623,0.00022219493,0.00020347611,0.00030558676,0.00020131904],"domain_scores_gemma":[0.9995347,0.000011542112,0.000117671145,0.00026103886,0.0000108662625,0.00006417684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007137099,0.00011565355,0.00014017688,0.00003594561,0.000077638244,0.000021971064,0.00013156801,0.000047227466,0.00008444602],"category_scores_gemma":[0.0000020236143,0.00006732152,0.000036502635,0.00008274168,0.00019835378,0.000115247065,0.00033847013,0.00004610469,0.000031347874],"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":[0.00069251866,0.00029936913,0.31210363,0.0005076866,0.0001464397,0.0000034959717,0.6650906,0.011625572,0.0020712803,0.0011386131,0.00014593183,0.006174842],"study_design_scores_gemma":[0.004872362,0.00068375765,0.7952707,0.00019129536,0.0004380175,0.0000035097755,0.14024706,0.014127465,0.0025840166,0.0333608,0.0075123087,0.000708741],"about_ca_topic_score_codex":0.0007312434,"about_ca_topic_score_gemma":0.00027774175,"teacher_disagreement_score":0.5248436,"about_ca_system_score_codex":0.000045618257,"about_ca_system_score_gemma":0.0000010849188,"threshold_uncertainty_score":0.2745292},"labels":[],"label_agreement":null},{"id":"W2000154656","doi":"10.1007/s11269-013-0284-5","title":"Assessing the Impacts of Four Land Use Types on the Water Quality of Wetlands in Japan","year":2013,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","cited_by":165,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Wetland; Water quality; Environmental science; Hydrology (agriculture); Turbidity; Land use; Drainage basin; Ecology; Geography; Geology; Biology","score_opus":0.041032845213453066,"score_gpt":0.25829263870275065,"score_spread":0.21725979348929758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000154656","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98517805,0.0000027300541,0.00003485219,0.0022719793,0.00003893755,0.0005404769,0.0000015357859,0.000015524454,0.011915916],"genre_scores_gemma":[0.99613446,0.0000049605396,0.00007753884,0.0002627971,0.00001257017,0.000047877336,0.00000499805,0.000012115442,0.0034426616],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99843055,0.00022390678,0.0003575655,0.00021829728,0.00042160923,0.00034807288],"domain_scores_gemma":[0.999229,0.00007669406,0.00008974037,0.00056486734,0.000007120245,0.000032535485],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010744574,0.00014015606,0.00015702617,0.00007393367,0.00011650818,0.00013049979,0.00046469312,0.00002774533,0.0010570836],"category_scores_gemma":[0.000012374703,0.00005060074,0.00006255584,0.00011972025,0.00019743283,0.0003176577,0.0006554339,0.00009726848,0.00035163417],"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":[0.000024497456,0.00019104069,0.9755072,0.00007888068,0.00010658684,0.0000032754322,0.0071143033,0.002621249,0.009858238,0.0002228922,0.0028346528,0.0014371681],"study_design_scores_gemma":[0.0002521409,0.000036953053,0.9784339,0.00003207571,0.000026067193,2.620898e-7,0.0006426214,0.00024344341,0.004713338,0.0004921923,0.015013516,0.00011350014],"about_ca_topic_score_codex":0.004876759,"about_ca_topic_score_gemma":0.0005710082,"teacher_disagreement_score":0.012178863,"about_ca_system_score_codex":0.00006985569,"about_ca_system_score_gemma":4.8938153e-7,"threshold_uncertainty_score":0.9998561},"labels":[],"label_agreement":null},{"id":"W2003776028","doi":"10.1007/s11269-013-0275-6","title":"Structural and Non-Structural Climate Change Adaptation Strategies for the Péribonka Water Resource System","year":2013,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec; Université de Sherbrooke; École de Technologie Supérieure","funders":"","keywords":"Hydropower; Environmental science; Electricity generation; Greenhouse gas; Computer science; Climate change; Resource (disambiguation); Adaptation (eye); Turbine; Water resources; Power station; Power (physics); Mathematical optimization; Engineering; Mathematics; Geology; Ecology","score_opus":0.013216578175882067,"score_gpt":0.18642512370641434,"score_spread":0.17320854553053228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003776028","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9802698,0.00028357978,0.0074200574,0.000575063,0.00041245972,0.0037196337,0.000013190143,0.0006673674,0.0066388687],"genre_scores_gemma":[0.99710727,0.00004106397,0.00077675184,0.00010671119,0.0003060643,0.00094002235,0.00013600299,0.000079679405,0.00050645793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99821275,0.00003684763,0.00037396862,0.00036373167,0.00027190198,0.00074076856],"domain_scores_gemma":[0.99943835,0.000020391239,0.000046055036,0.00037947306,0.000038035265,0.00007770127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002186829,0.00036967365,0.00023763962,0.00017985707,0.0004695298,0.0009072227,0.0003576276,0.00007518873,0.000055241024],"category_scores_gemma":[6.299613e-7,0.00019523592,0.00008338971,0.000081262784,0.00007248626,0.00059433724,0.0002898463,0.000110806344,0.00007246844],"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":[0.00020206846,0.000015351894,0.0009528721,0.007365258,0.0010450826,0.000027892085,0.088823386,0.8352296,0.0008167991,0.0045508533,0.002823975,0.05814688],"study_design_scores_gemma":[0.0009174487,0.00006519958,0.0039538713,0.000101474114,0.00019092877,0.0000058529554,0.01554648,0.9427925,0.0005740808,0.00033395152,0.035014894,0.0005033171],"about_ca_topic_score_codex":0.00010237877,"about_ca_topic_score_gemma":0.000019388026,"teacher_disagreement_score":0.10756292,"about_ca_system_score_codex":0.000061716135,"about_ca_system_score_gemma":4.1814064e-7,"threshold_uncertainty_score":0.8748369},"labels":[],"label_agreement":null},{"id":"W2008978077","doi":"10.1007/s11269-009-9416-3","title":"A Fuzzy Robust Nonlinear Programming Model for Stream Water Quality Management","year":2009,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Interval (graph theory); Mathematical optimization; Fuzzy logic; Linearization; Nonlinear system; Piecewise; Fuzzy number; Mathematics; Computer science; Fuzzy set; Artificial intelligence","score_opus":0.020667563792411473,"score_gpt":0.22906471307984982,"score_spread":0.20839714928743835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008978077","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29832676,0.00018217112,0.56752753,0.001175509,0.00048805517,0.0070851683,0.00003396258,0.0035247495,0.121656105],"genre_scores_gemma":[0.8065116,0.00013610693,0.16169609,0.0005376279,0.0003506465,0.0007111532,0.00085296243,0.00020045758,0.029003354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970801,0.000036023932,0.0006647562,0.0006295714,0.00047899885,0.001110534],"domain_scores_gemma":[0.9990935,0.0000055556634,0.000049262097,0.0006665028,0.000042409913,0.00014279986],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005235838,0.00050692307,0.00037182192,0.0003938374,0.00024953266,0.0003976761,0.0005730997,0.00010136603,0.00003137726],"category_scores_gemma":[0.0000011389282,0.0003607462,0.00021760372,0.00015383837,0.00003690522,0.00027806824,0.00025101998,0.00013605916,0.0001088581],"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":[0.000096214324,0.0001731983,0.000033862256,0.0008327774,0.0003275699,0.000023162162,0.002900016,0.9500701,0.00009926264,0.0009962216,0.0012820134,0.043165617],"study_design_scores_gemma":[0.0018295711,0.00011781069,0.00014727967,0.00008485642,0.000266139,0.0000010572171,0.00046869527,0.7693207,0.0020509223,0.0018005426,0.22305445,0.00085793546],"about_ca_topic_score_codex":0.0000057986604,"about_ca_topic_score_gemma":0.0000070453452,"teacher_disagreement_score":0.50818485,"about_ca_system_score_codex":0.00012662452,"about_ca_system_score_gemma":6.106366e-7,"threshold_uncertainty_score":0.9998844},"labels":[],"label_agreement":null},{"id":"W2011628948","doi":"10.1023/b:warm.0000024702.40031.b2","title":"Sensitivity of the Red River Basin Flood Protection System to Climate Variability and Change","year":2004,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":58,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Deutsches Klimarechenzentrum","keywords":"Climate change; Flood myth; Environmental science; Streamflow; Drainage basin; Precipitation; Flood control; Flood forecasting; Hydrology (agriculture); Flooding (psychology); 100-year flood; Structural basin; Vulnerability assessment; Climatology; Geography; Meteorology; Geology","score_opus":0.010307731240591164,"score_gpt":0.18528843336597464,"score_spread":0.17498070212538347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011628948","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9895649,0.0000018409863,0.00068665703,0.0034488211,0.00009493149,0.0012766926,0.000003150408,0.000048731108,0.004874291],"genre_scores_gemma":[0.99913526,0.000005266439,0.0002680705,0.00027749885,0.000020104259,0.00012147347,7.7678317e-7,0.000007154471,0.00016438954],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988378,0.00021161637,0.00015718005,0.000328979,0.00019670784,0.00026769686],"domain_scores_gemma":[0.9995968,0.0000063045677,0.000045017172,0.00031183835,0.0000039551146,0.000036128695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010668465,0.00012362622,0.00014047696,0.0000357969,0.00025930456,0.000013661029,0.0001175039,0.000033302927,0.000019764171],"category_scores_gemma":[0.0000053416625,0.00007282096,0.000038458875,0.00011328846,0.0001826851,0.0000845098,0.0012764991,0.00005703879,0.00007913928],"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":[0.0012175692,0.0014237941,0.71045077,0.0050154645,0.0011259683,0.00025420295,0.15550162,0.063289836,0.019027647,0.004204058,0.00066906755,0.037820004],"study_design_scores_gemma":[0.0007874818,0.0001229519,0.97151095,0.00012293669,0.00014320867,0.000005983983,0.0005077254,0.00044899512,0.0070991907,0.0006551683,0.018345486,0.00024993042],"about_ca_topic_score_codex":0.0010006854,"about_ca_topic_score_gemma":0.00011117365,"teacher_disagreement_score":0.26106018,"about_ca_system_score_codex":0.000109092085,"about_ca_system_score_gemma":1.9162333e-7,"threshold_uncertainty_score":0.29695526},"labels":[],"label_agreement":null},{"id":"W2012414214","doi":"10.1007/s11269-012-0061-x","title":"Water Cognition and Cognitive Affective Mapping: Identifying Priority Clusters Within a Canadian Water Efficiency Community","year":2012,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"International Development Research Centre","keywords":"Cognition; Psychology; Context (archaeology); Cognitive psychology; Social psychology; Geography","score_opus":0.027755932219738953,"score_gpt":0.24483157515700982,"score_spread":0.21707564293727086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012414214","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94064766,0.000050405157,0.044665374,0.00061145227,0.0004662754,0.00092857744,0.0000045903653,0.00014965233,0.012476003],"genre_scores_gemma":[0.9975746,0.0000062611866,0.00058889453,0.0010897951,0.000071460556,0.000085034524,0.000035709574,0.000016664397,0.0005315744],"study_design_codex":"qualitative","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.996821,0.0005099029,0.0003203005,0.0005256394,0.00045836554,0.0013647989],"domain_scores_gemma":[0.998929,0.000055726574,0.000053471093,0.00040500154,0.00012583766,0.00043096533],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0026246249,0.000317714,0.0002425085,0.00062109425,0.0014777813,0.0006433868,0.00067756337,0.00007704272,0.000037230886],"category_scores_gemma":[0.000018936506,0.00019755987,0.000077024306,0.00023737275,0.00024398084,0.0013683677,0.0016340205,0.0003928773,0.0003243885],"study_design_candidate":"qualitative","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":[0.000057346253,0.00047817055,0.006363854,0.0006230669,0.00044960887,0.00015565798,0.92827684,0.00003732864,0.014351708,0.0008331911,0.00011260605,0.0482606],"study_design_scores_gemma":[0.009478319,0.001069117,0.12750383,0.0024782326,0.001086471,0.00038904138,0.16788438,0.014958687,0.5886712,0.015462961,0.06468824,0.006329529],"about_ca_topic_score_codex":0.007232163,"about_ca_topic_score_gemma":0.003052002,"teacher_disagreement_score":0.7603925,"about_ca_system_score_codex":0.00014033627,"about_ca_system_score_gemma":0.0000084153335,"threshold_uncertainty_score":0.99982214},"labels":[],"label_agreement":null},{"id":"W2012491321","doi":"10.1007/s11269-014-0568-4","title":"A Simplified Model for Predicting Drought Magnitudes: a Case of Streamflow Droughts in Canadian Prairies","year":2014,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multiplicative function; Mathematics; Streamflow; Statistics; Markov chain; Probability distribution; Hydrology (agriculture); Environmental science; Climatology; Drainage basin; Geography; Geology","score_opus":0.008241540000745796,"score_gpt":0.22055609292180048,"score_spread":0.2123145529210547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012491321","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9713367,0.000008577677,0.004721956,0.0005052627,0.000022638245,0.00044532586,0.000013067127,0.00002621907,0.022920225],"genre_scores_gemma":[0.9937531,0.00000246817,0.002170005,0.00024235105,0.000021318065,0.0001071421,0.00002063603,0.000016210217,0.0036668198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847543,0.0000681832,0.0003326959,0.00039140857,0.00015225935,0.0005799976],"domain_scores_gemma":[0.9994177,0.000034909488,0.00006485143,0.0003299342,0.0000065247887,0.00014603419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006190848,0.00016784748,0.00024109356,0.00017436886,0.00018037594,0.000027250393,0.0002494278,0.00007925234,0.00016986075],"category_scores_gemma":[0.000017916645,0.00013079982,0.0000832454,0.0001686193,0.0001392556,0.0001299078,0.00018452968,0.00007828046,0.000053969154],"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":[0.00024912282,0.0003788435,0.21671882,0.00047119876,0.0003691765,0.0007407842,0.042667672,0.720442,0.00022191672,0.0015201329,0.002030645,0.014189664],"study_design_scores_gemma":[0.00086627965,0.00009544582,0.0017296914,0.000027517943,0.00014679263,0.000022458806,0.00046042734,0.95799077,0.0003263527,0.0043265843,0.033738464,0.00026921302],"about_ca_topic_score_codex":0.1369125,"about_ca_topic_score_gemma":0.73639566,"teacher_disagreement_score":0.5994832,"about_ca_system_score_codex":0.00012614435,"about_ca_system_score_gemma":0.000003862993,"threshold_uncertainty_score":0.8688349},"labels":[],"label_agreement":null},{"id":"W2012554008","doi":"10.1007/s11269-007-9199-3","title":"Modelling the Effectiveness of Agricultural Measures to Reduce the Amount of Pesticides Entering Surface Waters","year":2007,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Environmental science; Buffer strip; Soil and Water Assessment Tool; Hydrology (agriculture); Pesticide; Agriculture; Water quality; Surface water; Drainage basin; Water resource management; SWAT model; Surface runoff; Structural basin; Environmental engineering; Geography; Ecology; Engineering; Streamflow; Geology","score_opus":0.012531192248281826,"score_gpt":0.21314173006352494,"score_spread":0.2006105378152431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012554008","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98253644,0.00002702473,0.008936542,0.0004940551,0.00009369909,0.0006671913,8.2294446e-7,0.000021348647,0.0072228583],"genre_scores_gemma":[0.9990543,0.000015128404,0.00028924932,0.0000725103,0.000014471519,0.000021397269,0.0000015647721,0.000009346406,0.0005220173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99849457,0.00019782648,0.00028601722,0.00026575927,0.00037356143,0.00038228306],"domain_scores_gemma":[0.9994759,0.000106699685,0.0000667786,0.00030410485,0.000011276465,0.00003523025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024733418,0.00016732974,0.00018433767,0.000036706835,0.00024152985,0.00002036402,0.0005279264,0.0000250138,0.000024167628],"category_scores_gemma":[0.0000061419937,0.00007015549,0.00007514247,0.00014681318,0.00029081042,0.00006696695,0.0008732189,0.00007848272,0.00004392786],"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":[0.00021008743,0.000041985266,0.010864422,0.000094909235,0.00016891948,0.0000044754634,0.007290668,0.96513945,0.01568316,0.00010424123,0.000087493274,0.0003102034],"study_design_scores_gemma":[0.00088265224,0.0003230179,0.5519657,0.00028621237,0.00041934237,0.000005247832,0.008296798,0.0021042954,0.41278228,0.00094556966,0.021420501,0.00056836515],"about_ca_topic_score_codex":0.00067353767,"about_ca_topic_score_gemma":0.000021878395,"teacher_disagreement_score":0.96303517,"about_ca_system_score_codex":0.00005784563,"about_ca_system_score_gemma":2.487595e-7,"threshold_uncertainty_score":0.28608578},"labels":[],"label_agreement":null},{"id":"W2015158104","doi":"10.1007/s11269-008-9348-3","title":"Decline in the Index of Biotic Integrity of the Fish Assemblage as a Response to Reservoir Aging","year":2008,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Fish Biology and Ecology Studies","field":"Agricultural and Biological Sciences","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Biological Sciences","funders":"Science and Engineering Research Board","keywords":"Index of biological integrity; Environmental science; Omnivore; Biotic index; Sediment; Ecology; Indicator species; Abundance (ecology); Fish <Actinopterygii>; Biotic component; Biology; Abiotic component; Species richness; Fishery","score_opus":0.025702303892742116,"score_gpt":0.2336298177910868,"score_spread":0.20792751389834468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015158104","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9572475,0.000021253809,3.2627554e-7,0.041036263,0.000045062665,0.00029212362,0.0000042610855,0.000008679938,0.001344512],"genre_scores_gemma":[0.9965378,0.000024163604,0.000010233394,0.0025913701,0.00002320645,0.00001931544,0.0000024313576,5.436102e-7,0.0007909054],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998757,0.00043304518,0.00024068113,0.00018259582,0.00016420729,0.00022247253],"domain_scores_gemma":[0.9995538,0.00021828116,0.000060422764,0.00012582849,0.000024290228,0.000017407343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012002684,0.00009254632,0.00016104637,0.000028064589,0.00019683976,0.0000073024908,0.0006556244,0.000061040184,0.000043075364],"category_scores_gemma":[0.00009263029,0.000022899378,0.00006969106,0.0003003286,0.00021865394,0.000023791636,0.00070692465,0.00014610065,0.000010824982],"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":[0.0012944753,0.0003902292,0.95720756,0.000048908758,0.00011351401,0.00007147477,0.012699976,0.00012198658,0.019914566,0.000088636014,0.0058352984,0.0022133803],"study_design_scores_gemma":[0.00012249539,0.00020184567,0.9552132,0.000026806847,0.0000082923425,0.0000037430043,0.002294588,0.0000051057777,0.0030197767,0.00029036714,0.038756143,0.000057662142],"about_ca_topic_score_codex":0.0005626636,"about_ca_topic_score_gemma":0.003679571,"teacher_disagreement_score":0.039290316,"about_ca_system_score_codex":0.000013191,"about_ca_system_score_gemma":0.0000012368689,"threshold_uncertainty_score":0.20532869},"labels":[],"label_agreement":null},{"id":"W2018495176","doi":"10.1007/s11269-012-0164-4","title":"Using Information-Gap Decision Theory for Water Resources Planning Under Severe Uncertainty","year":2012,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":95,"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":"Engineering and Physical Sciences Research Council; University of Exeter; Natural Environment Research Council; Sight Research UK; Canadian Centre for Applied Research in Cancer Control","keywords":"Rainwater harvesting; Water resources; Futures contract; Computer science; Robustness (evolution); Scenario planning; Demand management; Environmental economics; Operations research; Environmental resource management; Environmental science; Business; Economics; Engineering","score_opus":0.02390306870263433,"score_gpt":0.23700295734272311,"score_spread":0.21309988864008877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018495176","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72540057,0.00022037637,0.25548607,0.00006749374,0.0005828295,0.0010664308,0.000010909842,0.00055202685,0.016613273],"genre_scores_gemma":[0.98933166,0.000019339532,0.0077924295,0.0003352589,0.00035344387,0.00012379121,0.0002545543,0.00009327274,0.0016962473],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99767655,0.00006375743,0.00055144617,0.0002495985,0.00043337594,0.0010252416],"domain_scores_gemma":[0.9992601,0.000050032355,0.000060430077,0.00043484115,0.000044299926,0.00015024646],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008582943,0.00040321596,0.00028358205,0.00049949886,0.00037076147,0.00038878998,0.0004192571,0.00012218948,0.00020544672],"category_scores_gemma":[0.0000054616053,0.00027014563,0.0001417051,0.00014210344,0.00004784308,0.0011116563,0.00036523014,0.00013931553,0.00020082934],"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":[0.00014676063,0.000027550448,0.0004400335,0.00033863986,0.00024773402,0.0000020672899,0.016105056,0.9763612,0.00010141569,0.00047159338,0.0011407358,0.004617194],"study_design_scores_gemma":[0.0015624637,0.00004796747,0.00043999427,0.00018501085,0.00026358213,0.000006609944,0.003919759,0.15695785,0.0019424624,0.0030354979,0.8307733,0.0008655062],"about_ca_topic_score_codex":0.000012343313,"about_ca_topic_score_gemma":0.0000013408967,"teacher_disagreement_score":0.8296326,"about_ca_system_score_codex":0.00016639834,"about_ca_system_score_gemma":7.593218e-7,"threshold_uncertainty_score":0.9999751},"labels":[],"label_agreement":null},{"id":"W2020229756","doi":"10.1007/s11269-014-0666-3","title":"Compromise Programming Based Model for Augmenting Food Production with Minimum Water Allocation in a Watershed: a Case Study in the Indian Himalayas","year":2014,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Watershed; Environmental science; Agriculture; Water resources; Water resource management; Farm water; Production (economics); Water scarcity; Compromise; Scarcity; Water use; Agricultural productivity; Livestock; Food processing; Agricultural engineering; Watershed area; Water conservation; Geography; Computer science; Ecology; Engineering; Economics; Forestry","score_opus":0.015344375539003512,"score_gpt":0.2016997874811442,"score_spread":0.1863554119421407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020229756","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96699727,0.0000051216603,0.027541466,0.00033650006,0.000055689557,0.0047227866,6.7883985e-7,0.00016371516,0.00017676587],"genre_scores_gemma":[0.9944538,7.145485e-7,0.003103815,0.00005832864,0.000056959787,0.0019386648,0.00008836971,0.0000646538,0.0002347021],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99806637,0.00012299644,0.00042832375,0.0004692137,0.0003016992,0.00061139505],"domain_scores_gemma":[0.9994665,0.000010676728,0.000042451433,0.0004189527,0.00002308274,0.000038341757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011707263,0.00029965615,0.00021707582,0.0005208796,0.00015829531,0.000283798,0.0002845053,0.00004567781,0.0000031330885],"category_scores_gemma":[0.0000027336478,0.00017132753,0.00004478932,0.00021379102,0.00002774974,0.00022510206,0.00008820221,0.00013258419,0.0000050889726],"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":[0.000102474005,0.00030600926,0.0029883846,0.0005692235,0.00007318857,0.000107448664,0.06840577,0.92444587,0.000070076116,0.000005399746,0.000024816012,0.0029013592],"study_design_scores_gemma":[0.0028342223,0.00036606521,0.00024668573,0.00008190472,0.00010203087,0.000013214926,0.0114731835,0.9812388,0.0008111851,0.000035601875,0.0024337345,0.00036336877],"about_ca_topic_score_codex":0.000070822796,"about_ca_topic_score_gemma":0.001362142,"teacher_disagreement_score":0.056932587,"about_ca_system_score_codex":0.00009742635,"about_ca_system_score_gemma":0.0000010040525,"threshold_uncertainty_score":0.6986534},"labels":[],"label_agreement":null},{"id":"W202107547","doi":"10.1007/s11269-012-0225-8","title":"Application of ANN, Fuzzy Logic and Decision Tree Algorithms for the Development of Reservoir Operating Rules","year":2012,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":80,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Inflow; Fuzzy logic; Hydrogeology; Artificial neural network; Water resources; Computer science; Elevation (ballistics); Algorithm; Decision tree; Data mining; Operations research; Hydrology (agriculture); Mathematical optimization; Mathematics; Engineering; Machine learning; Geology; Artificial intelligence; Geotechnical engineering","score_opus":0.020078832240849383,"score_gpt":0.23244263515024163,"score_spread":0.21236380290939225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W202107547","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48781008,0.00065067195,0.5079577,0.00004842692,0.000073475,0.0009328282,0.0000033295835,0.00006593242,0.002457551],"genre_scores_gemma":[0.88132125,0.000058789978,0.11811925,0.000014088265,0.0000630758,0.000180807,0.00003977883,0.000027612461,0.00017535772],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989793,0.0000136276985,0.00039936588,0.0001451003,0.00020648516,0.00025613216],"domain_scores_gemma":[0.9995896,0.000039093768,0.00006167159,0.00024331322,0.00003061391,0.00003569568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000541479,0.00014475301,0.00015644854,0.00013127743,0.00012623037,0.00003836231,0.0002328634,0.000036697715,0.000006008182],"category_scores_gemma":[0.0000037313068,0.0000883978,0.000038058533,0.00008921763,0.00003647549,0.00012368154,0.00021902943,0.00003871734,0.0000053662247],"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":[0.0000713081,0.00010802381,0.0035532967,0.0013354535,0.00038814594,3.5951803e-7,0.018300243,0.32111457,0.0014532598,0.0011077499,0.00035459243,0.652213],"study_design_scores_gemma":[0.0020621591,0.00011281196,0.03151575,0.0002163494,0.00030276168,0.0000012492511,0.0036587266,0.6994336,0.028672695,0.00198366,0.23136404,0.00067615585],"about_ca_topic_score_codex":0.0000054053357,"about_ca_topic_score_gemma":0.000005043507,"teacher_disagreement_score":0.6515368,"about_ca_system_score_codex":0.00002377994,"about_ca_system_score_gemma":5.4723483e-7,"threshold_uncertainty_score":0.36047575},"labels":[],"label_agreement":null},{"id":"W2022571528","doi":"10.1007/s11269-008-9375-0","title":"Dual-Interval Two-Stage Optimization for Flood Management and Risk Analyses","year":2008,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Federal Emergency Management Agency","keywords":"Stage (stratigraphy); Hydrogeology; Flood risk management; Flood myth; Interval (graph theory); Dual (grammatical number); Environmental science; Risk management; Water resource management; Hydrology (agriculture); Geology; Mathematics; Geography; Business; Geotechnical engineering","score_opus":0.021545899035635713,"score_gpt":0.23329070996021425,"score_spread":0.21174481092457853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022571528","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61772853,0.00040056207,0.35405698,0.00006167777,0.00028979479,0.0018419569,0.000029187939,0.000939345,0.024651982],"genre_scores_gemma":[0.9012051,0.0033337525,0.06792036,0.000119921104,0.00028442443,0.00050971983,0.00038542235,0.0002064354,0.026034852],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998142,0.00005168411,0.00044030114,0.0004950763,0.00031817198,0.00055275596],"domain_scores_gemma":[0.999357,0.00001442432,0.000070654736,0.0004185169,0.000030524046,0.000108876804],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023892993,0.0003800929,0.00030008744,0.00047941194,0.0003533326,0.00018433473,0.00024268818,0.000055891192,0.00013293778],"category_scores_gemma":[0.000002208629,0.0003125148,0.0001291485,0.00020557288,0.0000693459,0.00025991452,0.0003074511,0.00009872479,0.000039552997],"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":[0.00007293851,0.00005493689,0.0011017356,0.00050327636,0.0008133167,0.000064911335,0.001544219,0.991434,0.00001613042,0.000112792666,0.0013958061,0.0028859768],"study_design_scores_gemma":[0.0027527579,0.00009692928,0.0015820022,0.000060345985,0.00060736394,0.000006631778,0.00049787475,0.82529277,0.0013581131,0.000080011625,0.16701809,0.00064711337],"about_ca_topic_score_codex":0.000027457352,"about_ca_topic_score_gemma":0.000009415296,"teacher_disagreement_score":0.28613663,"about_ca_system_score_codex":0.00006213781,"about_ca_system_score_gemma":4.7646756e-7,"threshold_uncertainty_score":0.9999327},"labels":[],"label_agreement":null},{"id":"W2022612120","doi":"10.1007/s11269-012-9996-1","title":"Identifying Optimal Water Resources Allocation Strategies through an Interactive Multi-Stage Stochastic Fuzzy Programming Approach","year":2012,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Fuzzy logic; Computer science; Stochastic programming; Context (archaeology); Set (abstract data type); Operations research; Fuzzy set; Water resources; Function (biology); Constraint (computer-aided design); Resource allocation; Mathematics; Artificial intelligence","score_opus":0.03719081956893387,"score_gpt":0.2659706656902892,"score_spread":0.22877984612135532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022612120","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.529715,0.00020340286,0.4557517,0.00003419131,0.0003750583,0.0012852005,0.000004168167,0.0011024164,0.011528844],"genre_scores_gemma":[0.9560502,0.000027279662,0.03966748,0.000044628738,0.00035679975,0.00038507467,0.0004416532,0.00016691288,0.0028599575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966978,0.00014494854,0.00061123964,0.000623938,0.00056964875,0.0013524513],"domain_scores_gemma":[0.99902475,0.00001184098,0.000090134876,0.00063331664,0.00005055615,0.00018938926],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005657784,0.0006104409,0.00037263992,0.000423308,0.00037412692,0.0011326578,0.00063362764,0.00013537327,0.00010133812],"category_scores_gemma":[0.0000025876677,0.0004458078,0.00013681005,0.00021673486,0.000104930055,0.0033982866,0.0004970891,0.00031663218,0.0002109953],"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":[0.00008265508,0.0003600258,0.00014900208,0.00076441374,0.00047249102,0.000013129882,0.13946727,0.85383433,0.0010453118,0.00038682442,0.000061637424,0.0033628852],"study_design_scores_gemma":[0.004027129,0.00032479793,0.0017653431,0.00037642606,0.00093479257,0.000021700593,0.13267477,0.5161714,0.014576891,0.0002454404,0.3253433,0.003537987],"about_ca_topic_score_codex":0.000082973835,"about_ca_topic_score_gemma":0.000012071539,"teacher_disagreement_score":0.4263352,"about_ca_system_score_codex":0.00017378354,"about_ca_system_score_gemma":0.0000011037656,"threshold_uncertainty_score":0.9999043},"labels":[],"label_agreement":null},{"id":"W2022900140","doi":"10.1007/s11269-008-9315-z","title":"Incorporating Eco-environmental Water Requirements in Integrated Evaluation of Water Quality and Quantity—A Study for the Yellow River","year":2008,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"National Science Fund for Distinguished Young Scholars; National Natural Science Foundation of China","keywords":"Environmental science; Water quality; Water resources; Water resource management; Ecosystem; River ecosystem; Environmental flow; Agriculture; Hydrology (agriculture); Environmental resource management; Environmental engineering; Ecology; Engineering","score_opus":0.10126478674556193,"score_gpt":0.3221717149386637,"score_spread":0.22090692819310176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022900140","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99653244,0.000010862061,0.0003004527,0.0005192716,0.00008425533,0.0020178815,0.000010266979,0.000016671855,0.00050790823],"genre_scores_gemma":[0.998331,0.000010801127,0.00033576533,0.0001506237,0.000016335867,0.00022935122,0.000052926538,0.000015109683,0.0008580683],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9970733,0.0006110448,0.00064204715,0.0004391785,0.0008575754,0.0003768779],"domain_scores_gemma":[0.99944323,0.000025489533,0.0000974161,0.0003740763,0.000010214686,0.00004959245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042758617,0.00020760714,0.00022615606,0.0000822724,0.00032368777,0.000039196213,0.000279476,0.000044688448,0.0006364674],"category_scores_gemma":[0.0000051679954,0.00010133871,0.000060287835,0.00006092645,0.00035955914,0.00024048163,0.00060027564,0.00009850487,0.00010789864],"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":[0.0003915202,0.0016661605,0.854318,0.0001225294,0.0003223293,0.000017754734,0.10128878,0.012229068,0.022201167,0.000043382344,0.00028318135,0.007116074],"study_design_scores_gemma":[0.0049570496,0.0004124725,0.92639583,0.000034768753,0.00024077114,0.0000032039948,0.007870831,0.0067096776,0.039148107,0.0013741311,0.012359035,0.00049410533],"about_ca_topic_score_codex":0.0015753313,"about_ca_topic_score_gemma":0.0003688845,"teacher_disagreement_score":0.09341795,"about_ca_system_score_codex":0.00025496702,"about_ca_system_score_gemma":0.0000016639642,"threshold_uncertainty_score":0.6968872},"labels":[],"label_agreement":null},{"id":"W2023347862","doi":"10.1007/s11269-008-9382-1","title":"Enhancing Inflow Forecasting Model at Aswan High Dam Utilizing Radial Basis Neural Network and Upstream Monitoring Stations Measurements","year":2008,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":68,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Ottawa Mental Health Centre; Royal Military College of Canada","funders":"","keywords":"Inflow; Upstream (networking); Artificial neural network; Hydrogeology; Hydrology (agriculture); Water level; Environmental science; Computer science; Meteorology; Engineering; Machine learning; Cartography; Geotechnical engineering; Geography","score_opus":0.061487234655311154,"score_gpt":0.22976944227456722,"score_spread":0.16828220761925605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023347862","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9938646,0.000026292968,0.0007275518,0.000116552575,0.00016631762,0.0003149228,0.0000023106888,0.00014370891,0.0046377718],"genre_scores_gemma":[0.98457247,0.000012218395,0.014095076,0.00011852627,0.00014412554,0.000039035287,0.000009661379,0.000036424317,0.000972456],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975766,0.00009403222,0.000377122,0.00057853176,0.00058976334,0.00078397203],"domain_scores_gemma":[0.9993975,0.00003428243,0.00009904998,0.00028547132,0.000008688819,0.00017498947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048680347,0.00027439074,0.00021653368,0.00006270408,0.0011561728,0.00008471463,0.00025868617,0.00006238774,0.00013632505],"category_scores_gemma":[0.000021656846,0.00021902121,0.000059213748,0.00016839529,0.00018109471,0.00021765906,0.0009811225,0.000158903,0.00006712502],"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":[0.00003357404,0.000030413348,0.10470796,0.000019130248,0.000036703506,0.00004222855,0.0025259648,0.88443565,0.0014279595,0.0000025592988,0.000118121425,0.0066197473],"study_design_scores_gemma":[0.0015864079,0.0002446988,0.06602605,0.00022082364,0.00019790194,0.00006627253,0.0001788928,0.9106089,0.013826695,0.00080331205,0.0051466064,0.0010934336],"about_ca_topic_score_codex":0.00024792002,"about_ca_topic_score_gemma":0.00006753073,"teacher_disagreement_score":0.038681906,"about_ca_system_score_codex":0.0002911821,"about_ca_system_score_gemma":0.0000013824908,"threshold_uncertainty_score":0.8931426},"labels":[],"label_agreement":null},{"id":"W2023845885","doi":"10.1007/s11269-012-0196-9","title":"Multi-Source Multi-Sector Sustainable Water Supply Under Multiple Uncertainties: An Inexact Fuzzy-Stochastic Quadratic Programming Approach","year":2012,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"National Science Foundation","keywords":"Robustness (evolution); Water supply; Mathematical optimization; Quadratic programming; Fuzzy logic; Computer science; Quadratic equation; Stochastic programming; Scale (ratio); Environmental science; Mathematics; Environmental engineering; Artificial intelligence","score_opus":0.019301033919962043,"score_gpt":0.21289212257754236,"score_spread":0.19359108865758032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023845885","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39049235,0.0002737809,0.6030223,0.000057386722,0.00032604605,0.0026609288,0.000004337779,0.001568009,0.0015948799],"genre_scores_gemma":[0.9565546,0.000012164068,0.025918383,0.00010539135,0.00030499315,0.00060034153,0.00053521513,0.0002677465,0.015701197],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952659,0.0001642745,0.00070156006,0.00070865307,0.00059018144,0.0025694557],"domain_scores_gemma":[0.9985551,0.000022835084,0.000074947726,0.00087614835,0.000071713606,0.00039920432],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008057994,0.0007867387,0.00050778623,0.00067850616,0.00050910615,0.0006801181,0.00074443995,0.00018251638,0.00015538582],"category_scores_gemma":[0.000008786958,0.00055643724,0.00018220003,0.00031873147,0.00011991496,0.0011287696,0.0006516954,0.00032717828,0.0002730066],"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":[0.000060451035,0.0007171941,0.0023910762,0.0014356434,0.00045210842,0.000017634953,0.028756412,0.9634811,0.0005064402,0.00008950429,0.0002205201,0.0018718658],"study_design_scores_gemma":[0.0035111913,0.00014738136,0.0020187069,0.00009045896,0.00037390902,0.0000090994445,0.02374175,0.81686586,0.0017999497,0.000034517285,0.14969647,0.0017107044],"about_ca_topic_score_codex":0.00025078128,"about_ca_topic_score_gemma":0.000031917127,"teacher_disagreement_score":0.5771039,"about_ca_system_score_codex":0.00033402973,"about_ca_system_score_gemma":0.0000022553568,"threshold_uncertainty_score":0.9996887},"labels":[],"label_agreement":null},{"id":"W2024954848","doi":"10.1007/s11269-012-0184-0","title":"Re-Framing Environmental Social Science Research for Sustainable Water Management in a Changing Climate","year":2012,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":11,"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":"Economic and Social Research Council; Engineering and Physical Sciences Research Council; Canadian Centre for Applied Research in Cancer Control","keywords":"Framing (construction); Climate change; Blame; Openness to experience; Public relations; Sociology; Environmental resource management; Marketing; Business; Economics; Political science; Engineering; Social psychology; Psychology; Ecology","score_opus":0.018210341044001384,"score_gpt":0.2546178154988378,"score_spread":0.2364074744548364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024954848","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8261535,0.0002030639,0.0037423111,0.00034684132,0.00029591122,0.0033662496,0.000004206537,0.00041868116,0.16546921],"genre_scores_gemma":[0.99170846,0.00007326541,0.0009670635,0.000087010274,0.00023978615,0.0006459199,0.00006730148,0.000089626286,0.0061215805],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9944785,0.000065087195,0.00039179996,0.0004731168,0.0007592492,0.0038322508],"domain_scores_gemma":[0.99945843,0.000012719083,0.000024217501,0.00035864802,0.000022447919,0.00012350989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004220368,0.00030001212,0.00022190805,0.002528626,0.0010865498,0.00042163147,0.00062063447,0.00006777118,0.00007896262],"category_scores_gemma":[0.0000020405707,0.0002343238,0.000080064085,0.00069160823,0.00017846697,0.0008201381,0.00181415,0.00019195375,0.00013449411],"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":[0.0011843432,0.0023948709,0.015702704,0.023394005,0.0017603403,0.0009279541,0.5496194,0.21013601,0.010286057,0.057843838,0.009657128,0.117093384],"study_design_scores_gemma":[0.0027540822,0.0000939007,0.0030498663,0.00012702764,0.00013785684,0.0000017794804,0.0801401,0.032021694,0.024327526,0.00094012107,0.8551838,0.0012222605],"about_ca_topic_score_codex":0.0000046723667,"about_ca_topic_score_gemma":0.0000012665788,"teacher_disagreement_score":0.84552664,"about_ca_system_score_codex":0.00060765806,"about_ca_system_score_gemma":5.38495e-7,"threshold_uncertainty_score":0.9555447},"labels":[],"label_agreement":null},{"id":"W2025731018","doi":"10.1007/s11269-011-9938-3","title":"Future Water Supply and Demand in the Okanagan Basin, British Columbia: A Scenario-Based Analysis of Multiple, Interacting Stressors","year":2011,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Environment and Climate Change Canada; Impact; University of British Columbia","funders":"U.S. Army Corps of Engineers; University of British Columbia","keywords":"Environmental science; Water supply; Environmental resource management; Sustainability; Streamflow; Climate change; Water resources; Water resource management; Water security; Hydrology (agriculture); Drainage basin; Ecology; Geography; Environmental engineering","score_opus":0.00782832299033646,"score_gpt":0.18471764542367552,"score_spread":0.17688932243333907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025731018","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99384725,0.00002944636,0.00005838034,0.00062931504,0.000052223233,0.00043090773,0.0000058204378,0.000022234906,0.0049244454],"genre_scores_gemma":[0.9980866,0.00004450215,0.00035527887,0.0006017143,0.00001436684,0.000059269045,0.000026910973,0.0000116916135,0.0007996685],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99831045,0.00019730775,0.0003378963,0.00044448551,0.00026582912,0.00044403694],"domain_scores_gemma":[0.99952686,0.000034246794,0.00006859931,0.00032465108,0.0000055463124,0.000040080067],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00071191794,0.00015712873,0.00030706738,0.00015774592,0.00025448122,0.0001187507,0.00039407794,0.00005440033,0.0011636979],"category_scores_gemma":[0.0000051363536,0.000115331844,0.00010019654,0.0002836791,0.0002651777,0.00014533079,0.0006108008,0.00013519016,0.000023526994],"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":[0.00007249383,0.00019894257,0.97125846,0.000073746654,0.00063703506,0.0001796324,0.02160457,0.001794863,0.0000386464,0.0000022998718,0.0008671939,0.0032721183],"study_design_scores_gemma":[0.0007447888,0.00008044687,0.96490186,0.000040553572,0.00075868954,0.0000018747459,0.0045040236,0.0017101129,0.00036557773,0.00008161047,0.026587589,0.00022288502],"about_ca_topic_score_codex":0.018473016,"about_ca_topic_score_gemma":0.08943199,"teacher_disagreement_score":0.07095897,"about_ca_system_score_codex":0.00003094216,"about_ca_system_score_gemma":2.7962577e-7,"threshold_uncertainty_score":0.99974936},"labels":[],"label_agreement":null},{"id":"W2026936225","doi":"10.1007/s11269-012-0016-2","title":"Modelling the Potential Impacts of Climate Change on Snowpack in the North Saskatchewan River Watershed, Alberta","year":2012,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Snowpack; Snowmelt; Watershed; Environmental science; Climate change; Hydrometeorology; Snow; Precipitation; Hydrology (agriculture); Water resources; Drainage basin; Climatology; Geography; Geology; Meteorology; Ecology","score_opus":0.02524045913845663,"score_gpt":0.20158528269347553,"score_spread":0.1763448235550189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026936225","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99474114,0.0001984407,0.000040708343,0.0012150562,0.00013142008,0.00038361928,0.000012814398,0.0000074201675,0.0032693658],"genre_scores_gemma":[0.9984832,0.0002858643,0.00008991607,0.0007230684,0.00015915048,0.000008888583,0.00004670774,0.0000039999004,0.00019919775],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99884266,0.00007960947,0.00018931249,0.00013907201,0.00027242437,0.000476931],"domain_scores_gemma":[0.9995979,0.00004198617,0.000050757993,0.0002616916,0.000008602601,0.00003907391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003793149,0.00012713124,0.00011506811,0.000031509568,0.00022070635,0.00004223099,0.0003048005,0.000019997464,0.00019869326],"category_scores_gemma":[0.0000011539695,0.000053475105,0.00006150206,0.0001363565,0.00006664558,0.0001205577,0.000061219354,0.00008284092,0.000118056356],"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":[0.00010498756,0.00009097577,0.6665548,0.00007803948,0.0000840557,0.000009482745,0.16088413,0.16157551,7.768672e-7,0.000063972926,0.00033081384,0.010222462],"study_design_scores_gemma":[0.00025874778,0.000072532246,0.92269725,0.000023502906,0.000055117267,0.0000012284368,0.012409912,0.017721616,0.000007163851,0.00006519563,0.046549678,0.00013805932],"about_ca_topic_score_codex":0.023495603,"about_ca_topic_score_gemma":0.012079156,"teacher_disagreement_score":0.25614244,"about_ca_system_score_codex":0.000004417157,"about_ca_system_score_gemma":6.755297e-7,"threshold_uncertainty_score":0.983007},"labels":[],"label_agreement":null},{"id":"W2027656870","doi":"10.1007/s11269-014-0618-y","title":"A Virtual Water Assessment Methodology for Cropping Pattern Investigation","year":2014,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Virtual water; Cropping; Environmental science; Water use; Current (fluid); Agricultural engineering; Crop; Hydrogeology; Arid; Water resource management; Water resources; Water scarcity; Agriculture; Geography; Agronomy; Engineering; Geology; Ecology","score_opus":0.025542800237618,"score_gpt":0.2711868657725346,"score_spread":0.24564406553491658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027656870","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8841191,0.0000014466774,0.1085129,0.0011767066,0.0000981136,0.000648851,0.0000015333179,0.000052953237,0.0053883996],"genre_scores_gemma":[0.9862931,0.0000013941491,0.007644614,0.0013559515,0.000059070742,0.00018765921,0.00004484579,0.00002501757,0.004388385],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99808204,0.00031751688,0.00028328339,0.0004650344,0.00028372023,0.0005684156],"domain_scores_gemma":[0.9994136,0.00004316494,0.000042629614,0.00037502227,0.0000036535953,0.000121910816],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001556953,0.00019985558,0.00018867393,0.0000525065,0.00023664706,0.00007533781,0.0002758855,0.00006040926,0.0010584971],"category_scores_gemma":[0.000010099665,0.00012484414,0.00008814681,0.00003781815,0.00021195854,0.00017106881,0.00066309934,0.00008953215,0.00027041647],"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":[0.00021864875,0.00057835993,0.32179987,0.0005678345,0.00034278014,0.000030549603,0.043272194,0.024667362,0.18518293,0.0011394607,0.0037491187,0.4184509],"study_design_scores_gemma":[0.0021724037,0.0009310808,0.31863394,0.000028044678,0.00016118863,0.0000056118593,0.0028401613,0.011469637,0.078126036,0.015798235,0.5689035,0.0009301535],"about_ca_topic_score_codex":0.00021913057,"about_ca_topic_score_gemma":0.000027612263,"teacher_disagreement_score":0.5651544,"about_ca_system_score_codex":0.00030221112,"about_ca_system_score_gemma":5.5421737e-7,"threshold_uncertainty_score":0.9998547},"labels":[],"label_agreement":null},{"id":"W2027996677","doi":"10.1007/s11269-012-0143-9","title":"Comparison of Record-Extension Techniques for Water Quality Variables","year":2012,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"National Water Center, United Arab Emirates University; Helwan University; National Research Centre","keywords":"Monte Carlo method; Ordinary least squares; Statistics; Robustness (evolution); Econometrics; Standard error; Population; Population variance; Variance (accounting); Regression; Percentile; Mathematics","score_opus":0.05722857590310414,"score_gpt":0.3196002365147445,"score_spread":0.26237166061164036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027996677","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9803093,0.000009866208,0.004145831,0.00025693598,0.00010403844,0.00045705205,0.0000018921373,0.00011700684,0.0145980865],"genre_scores_gemma":[0.97663075,0.0000018624419,0.021570453,0.00017495485,0.000051308507,0.00005653902,0.000014414105,0.00001675074,0.0014829466],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984426,0.00009852066,0.00039476334,0.00027553496,0.00027053905,0.0005180544],"domain_scores_gemma":[0.9994797,0.00002903462,0.00007871671,0.00032912195,0.00000778944,0.00007565493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001307531,0.00014632673,0.0002466349,0.000040942115,0.00013310977,0.00002133496,0.00025054583,0.000067101915,0.0006980175],"category_scores_gemma":[0.000013010173,0.00008137806,0.0000745038,0.000051777457,0.00010178153,0.00010804356,0.00057389017,0.00006709293,0.00018871862],"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":[0.00063998124,0.0025066794,0.37395728,0.00079954806,0.0002064675,0.0000050551384,0.018753856,0.009460291,0.41698733,0.00083346234,0.01654797,0.15930207],"study_design_scores_gemma":[0.00020522674,0.00018769527,0.008931439,0.000032616,0.00005162741,8.4025925e-7,0.00008247937,0.0011483922,0.33245653,0.001440798,0.6552123,0.00025005484],"about_ca_topic_score_codex":0.00020767347,"about_ca_topic_score_gemma":0.0000057061916,"teacher_disagreement_score":0.63866436,"about_ca_system_score_codex":0.00006434592,"about_ca_system_score_gemma":1.7405736e-7,"threshold_uncertainty_score":0.7642802},"labels":[],"label_agreement":null},{"id":"W2028049166","doi":"10.1007/s11269-013-0372-6","title":"Multi-Objective Design Optimization of Branched Pipeline Systems with Analytical Assessment of Fire Flow Failure Probability","year":2013,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Queen's University","funders":"","keywords":"Sizing; Sorting; Multi-objective optimization; Probabilistic logic; Mathematical optimization; Flow (mathematics); Pareto principle; Optimization problem; Pipeline (software); Selection (genetic algorithm); Conditional probability; Pipeline transport; Reduction (mathematics); Fire protection; Computer science; Engineering; Mathematics; Algorithm; Environmental engineering; Civil engineering","score_opus":0.011764683683968815,"score_gpt":0.20059614183905514,"score_spread":0.1888314581550863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028049166","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05068664,0.00003115586,0.946548,0.000032693963,0.00006756333,0.001702298,0.000005439982,0.000103068116,0.0008231392],"genre_scores_gemma":[0.8603538,0.000005409652,0.1388907,0.0000026435964,0.000022794911,0.00016460467,0.000030580963,0.000029188333,0.0005002469],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868125,0.000097446224,0.00048169162,0.00023576134,0.0002831942,0.00022066929],"domain_scores_gemma":[0.99937034,0.000013454602,0.000082606864,0.000325376,0.0001561942,0.000052008432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027488804,0.00019254214,0.0003308394,0.00011224247,0.000031948555,0.000057024714,0.00014709929,0.0000654766,0.000050709892],"category_scores_gemma":[0.0000026859293,0.00012480174,0.00004666632,0.00016851263,0.00004189928,0.00016417968,0.000054776807,0.000067891684,0.000004594333],"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":[0.000012823674,0.00008120884,0.0007963411,0.00096993113,0.00017088011,0.0000016386883,0.00095238455,0.99641377,0.00008382922,0.000012178052,0.00032186814,0.00018313501],"study_design_scores_gemma":[0.00066489715,0.00008624025,0.001657997,0.00016042526,0.000070475915,0.0000011186044,0.0002840418,0.9959647,0.0007704862,0.0000051618986,0.0001777885,0.00015666039],"about_ca_topic_score_codex":0.00011625077,"about_ca_topic_score_gemma":0.000012906229,"teacher_disagreement_score":0.80966717,"about_ca_system_score_codex":0.000082330545,"about_ca_system_score_gemma":0.000003201811,"threshold_uncertainty_score":0.5089267},"labels":[],"label_agreement":null},{"id":"W2028925833","doi":"10.1007/s11269-014-0626-y","title":"Equidistance Quantile Matching Method for Updating IDFCurves under Climate Change","year":2014,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Climate variability and models","field":"Environmental Science","cited_by":124,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Canadian Water Network","keywords":"Downscaling; Quantile; Representative Concentration Pathways; Baseline (sea); Climate change; Environmental science; Precipitation; Climatology; GCM transcription factors; Matching (statistics); Projection (relational algebra); Climate model; Econometrics; Meteorology; Computer science; Statistics; General Circulation Model; Mathematics; Geography; Geology; Algorithm","score_opus":0.037945004334428316,"score_gpt":0.28667841891955054,"score_spread":0.24873341458512221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028925833","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54184484,0.000016364338,0.41770288,0.0015161703,0.00018441043,0.0010799642,0.000020456517,0.00017977643,0.03745515],"genre_scores_gemma":[0.9365243,0.000023298315,0.061010715,0.0013169423,0.00009885552,0.00030110928,0.000034101497,0.00003843746,0.00065226725],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982111,0.0001289761,0.00028981178,0.0005159058,0.00025510433,0.0005991286],"domain_scores_gemma":[0.99936557,0.000076928394,0.000076718905,0.00040319163,0.0000045014385,0.00007309587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017718947,0.00018513127,0.00019062223,0.00003963417,0.00031619833,0.00011581962,0.00032953807,0.000044673274,0.00043168495],"category_scores_gemma":[0.0000062136837,0.00013956639,0.00008665438,0.00007165903,0.00005641442,0.00023304576,0.00073331466,0.000065630986,0.00024388176],"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":[0.0008196738,0.0013126788,0.015226922,0.0045393878,0.00040974782,0.000021904967,0.07526593,0.21003297,0.038295493,0.42432073,0.0029530653,0.2268015],"study_design_scores_gemma":[0.0019787825,0.0002802008,0.008098583,0.00024207932,0.00020777705,0.0000044805233,0.0035891521,0.22471294,0.0037691225,0.12607794,0.6296475,0.0013914604],"about_ca_topic_score_codex":0.0002392619,"about_ca_topic_score_gemma":0.000107965396,"teacher_disagreement_score":0.62669444,"about_ca_system_score_codex":0.00007938473,"about_ca_system_score_gemma":2.2808682e-7,"threshold_uncertainty_score":0.5691352},"labels":[],"label_agreement":null},{"id":"W2028979056","doi":"10.1007/s11269-007-9178-8","title":"Riparian Forest Harvesting Effects on Maximum Water Temperatures in Wetland-sourced Headwater Streams from the Nicola River Watershed, British Columbia, Canada","year":2007,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Okanagan College; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Riparian zone; Hydrology (agriculture); River ecosystem; Environmental science; STREAMS; Wetland; Watershed; Lake ecosystem; Ecosystem; Ecology; Habitat; Geology; Biology","score_opus":0.0036976941235294454,"score_gpt":0.16456697275807997,"score_spread":0.16086927863455053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028979056","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98257226,0.000011557795,0.000017027623,0.0019536528,0.00031777305,0.0010824179,0.000009726039,0.00006601935,0.013969589],"genre_scores_gemma":[0.97546935,0.000011203486,0.00009467756,0.006038481,0.00011801628,0.0001233214,0.000111775145,0.000037921207,0.017995264],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99695534,0.00016934039,0.00042556724,0.0007599817,0.00052176666,0.0011680196],"domain_scores_gemma":[0.9992206,0.000115108065,0.000058492784,0.00048363875,0.0000065819636,0.000115566545],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007393011,0.00030567235,0.0003135152,0.000047675752,0.0007442552,0.00040072243,0.00064599834,0.00008973406,0.0009811241],"category_scores_gemma":[0.000011641181,0.00022269723,0.000070828864,0.00011965137,0.00023577761,0.00015979433,0.0011582508,0.00030858198,0.00022284451],"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":[0.00009048224,0.00011616009,0.91048557,0.00005353368,0.00018677422,0.0013378882,0.0029748105,0.00083798193,0.00017521129,0.0000031948464,0.079938754,0.0037996664],"study_design_scores_gemma":[0.0011380757,0.00007623624,0.77617455,0.000078562996,0.000055474902,0.000003298108,0.00053638394,0.000031128904,0.0009312895,0.00038112246,0.22025338,0.00034048222],"about_ca_topic_score_codex":0.866226,"about_ca_topic_score_gemma":0.99443656,"teacher_disagreement_score":0.14031462,"about_ca_system_score_codex":0.0003737633,"about_ca_system_score_gemma":0.00000214863,"threshold_uncertainty_score":0.9999321},"labels":[],"label_agreement":null},{"id":"W2030261925","doi":"10.1007/s11269-009-9490-6","title":"An Operational Model for Support of Integrated Watershed Management","year":2009,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; BGC Engineering (Canada)","funders":"","keywords":"Watershed; Watershed management; Environmental resource management; Climate change; Vulnerability (computing); Flooding (psychology); Environmental science; Computer science; Water resource management; Risk analysis (engineering); Business; Geology","score_opus":0.011398084918908308,"score_gpt":0.2294196294732269,"score_spread":0.21802154455431857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030261925","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79471236,0.000010399625,0.10138399,0.0046140607,0.00014000061,0.0026197287,0.00003322313,0.0002402488,0.09624596],"genre_scores_gemma":[0.96927714,0.000020722628,0.012021056,0.0021677949,0.000019041685,0.00014174049,0.00021866478,0.000016884844,0.016116926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99825805,0.000041168787,0.0003775343,0.00051803247,0.00032128338,0.00048390627],"domain_scores_gemma":[0.99943775,0.0000041790977,0.000058105004,0.00041186908,0.000011829871,0.00007626465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046496725,0.00024238812,0.00023191098,0.00012748748,0.00022402509,0.000040969928,0.0004956538,0.00005211464,0.00070549134],"category_scores_gemma":[0.0000010305646,0.00016762626,0.00009028045,0.0000994875,0.00013864653,0.0002487213,0.00027785607,0.0000585442,0.00015270709],"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":[0.0017244268,0.0020113622,0.00872165,0.000431296,0.0012956732,0.00013483014,0.023211023,0.8108953,0.00808814,0.016628182,0.07843065,0.048427444],"study_design_scores_gemma":[0.005729122,0.0020839807,0.019308927,0.000044752753,0.00075311057,0.0000045756733,0.001713536,0.34732848,0.017216185,0.030704262,0.57354385,0.0015692295],"about_ca_topic_score_codex":0.000019118936,"about_ca_topic_score_gemma":0.0000074243303,"teacher_disagreement_score":0.4951132,"about_ca_system_score_codex":0.000075038035,"about_ca_system_score_gemma":7.712833e-7,"threshold_uncertainty_score":0.77246356},"labels":[],"label_agreement":null},{"id":"W2030631428","doi":"10.1007/s11269-010-9635-7","title":"Temporal Variability of Annual Rainfall in the Macta and Tafna Catchments, Northwestern Algeria","year":2010,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":130,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Precipitation; Environmental science; Climatology; Trend analysis; Canonical correlation; Hydrology (agriculture); Geology; Geography; Meteorology","score_opus":0.0035287857686450153,"score_gpt":0.19899679633619044,"score_spread":0.19546801056754542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030631428","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9874859,0.0000037934612,0.00003737234,0.00075679366,0.000035308884,0.00021354151,0.000004826493,0.000009074913,0.011453419],"genre_scores_gemma":[0.99873334,0.00000407677,0.00023960877,0.00027642358,0.000015576577,0.000021005673,0.000012505371,0.0000047676817,0.0006927007],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99892634,0.00013677581,0.00022472496,0.00027910207,0.00020941248,0.00022363101],"domain_scores_gemma":[0.9995055,0.00002361433,0.00004905082,0.00038234636,0.0000025432096,0.00003693778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010778471,0.00011052135,0.00013561128,0.00004239955,0.00007097668,0.000020773903,0.00033914574,0.000050772855,0.0006826229],"category_scores_gemma":[0.0000034617403,0.00006376054,0.000038508504,0.000111697125,0.00025435677,0.00009437725,0.00038604165,0.00013706318,0.000058183472],"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":[0.000028829063,0.00012839888,0.9846282,0.000016460635,0.00003597553,0.000021088605,0.012057677,0.00011105464,0.0005485824,0.000042958767,0.00019421868,0.002186556],"study_design_scores_gemma":[0.00050113915,0.000056280882,0.8303986,0.0000037522514,0.000071531285,0.0000042481315,0.0007465091,0.00039039625,0.0002663386,0.001668066,0.16572396,0.00016915902],"about_ca_topic_score_codex":0.0024529914,"about_ca_topic_score_gemma":0.002979453,"teacher_disagreement_score":0.16552974,"about_ca_system_score_codex":0.000012715098,"about_ca_system_score_gemma":6.034721e-7,"threshold_uncertainty_score":0.74742424},"labels":[],"label_agreement":null},{"id":"W2030800917","doi":"10.1007/s11269-008-9250-z","title":"Methods for Integrating an Extensive Geodatabase with 3D Modeling and Data Management Tools for the Virttaankangas Artificial Recharge Project, Southwestern Finland","year":2008,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Geological Modeling and Analysis","field":"Earth and Planetary Sciences","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Regional Municipality of Waterloo","funders":"European Commission","keywords":"Groundwater recharge; Hydrogeology; Spatial database; Oracle; Computer science; Visualization; Groundwater model; Aquifer; Process (computing); Database; Groundwater; Data mining; Geology; Spatial analysis; Remote sensing; Software engineering","score_opus":0.15283083262020217,"score_gpt":0.3254914794126813,"score_spread":0.17266064679247914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030800917","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.241528,0.00024160958,0.7558966,0.00043042196,0.00005764417,0.0013113635,0.00020709922,0.00004732152,0.000279893],"genre_scores_gemma":[0.6006576,0.00016877192,0.39597404,0.00047584804,0.00019384231,0.000107504624,0.0016247284,0.000013944231,0.000783753],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998171,0.00013474496,0.0003047779,0.0007467972,0.00019671195,0.00044601664],"domain_scores_gemma":[0.9989325,0.00019376577,0.00007431252,0.0006716689,0.000055844426,0.000071926894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014577743,0.00022533968,0.00024483912,0.000111179004,0.0007678738,0.00028010292,0.0005413353,0.000039348917,0.000035859823],"category_scores_gemma":[0.00003129085,0.00010849209,0.000053700645,0.0001060257,0.00007294114,0.0003230005,0.00014073073,0.00010274159,0.000010177143],"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":[0.00072099036,0.000072435345,0.0027276173,0.00023729059,0.0005876379,0.00004838813,0.0040504825,0.17090052,0.000020107034,0.00012299133,0.00017571134,0.8203358],"study_design_scores_gemma":[0.00037735523,0.0002069499,0.00047047145,0.00003556178,0.00033187342,0.0000087453745,0.0036336107,0.96877277,0.000017840614,0.00039792407,0.025520822,0.00022606403],"about_ca_topic_score_codex":0.0008637977,"about_ca_topic_score_gemma":0.000869879,"teacher_disagreement_score":0.8201098,"about_ca_system_score_codex":0.0000033601575,"about_ca_system_score_gemma":0.0000035514452,"threshold_uncertainty_score":0.5905942},"labels":[],"label_agreement":null},{"id":"W2031250408","doi":"10.1007/s11269-014-0867-9","title":"Development of a Fuzzy-Boundary Interval Programming Method for Water Quality Management Under Uncertainty","year":2014,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"Hydrogeology; Fuzzy logic; Interval (graph theory); Water quality; Boundary (topology); Computer science; Quality (philosophy); Water resource management; Environmental science; Reliability engineering; Mathematical optimization; Operations research; Mathematics; Engineering; Artificial intelligence; Geotechnical engineering; Biology","score_opus":0.019173209906214066,"score_gpt":0.2594754524358051,"score_spread":0.24030224252959104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031250408","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19410719,0.000037399896,0.78609306,0.00016499397,0.00031722998,0.0017960748,0.0000039189995,0.000523816,0.01695633],"genre_scores_gemma":[0.5524798,0.000014495831,0.44098923,0.00018476478,0.00014035878,0.0007896746,0.00036463756,0.00014868066,0.004888375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99705374,0.000117942094,0.00095655513,0.00054563803,0.0004974761,0.0008286675],"domain_scores_gemma":[0.9991726,0.000024776344,0.00008914311,0.00055355683,0.00005416878,0.00010570632],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016919875,0.0004383397,0.00046634293,0.00040871193,0.00023116307,0.00021067614,0.00056205114,0.00008735953,0.00006296746],"category_scores_gemma":[0.0000021751264,0.00030695958,0.0001979522,0.00014662345,0.000059854145,0.00015513471,0.00055528467,0.000112888614,0.000057588208],"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":[0.00027034467,0.00025084967,0.00009921822,0.0068059736,0.0020010676,0.0000067565525,0.01803617,0.6088158,0.001458662,0.004198617,0.0006372504,0.35741928],"study_design_scores_gemma":[0.0017133129,0.00007835673,0.00032914802,0.00015857545,0.00022800245,7.9147895e-7,0.0017839059,0.049020577,0.014787099,0.0015156177,0.9296895,0.00069510646],"about_ca_topic_score_codex":0.0000135176615,"about_ca_topic_score_gemma":0.00002732052,"teacher_disagreement_score":0.92905223,"about_ca_system_score_codex":0.00015978367,"about_ca_system_score_gemma":0.0000013437533,"threshold_uncertainty_score":0.99993825},"labels":[],"label_agreement":null},{"id":"W2031921420","doi":"10.1007/s11269-015-0976-0","title":"Optimal Remediation Design of Unconfined Contaminated Aquifers Based on the Finite Element Method and a Modified Firefly Algorithm","year":2015,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Firefly algorithm; Environmental remediation; Optimal design; Finite element method; Aquifer; Mathematical optimization; Local optimum; Computer science; Metaheuristic; Groundwater remediation; Optimization problem; Genetic algorithm; Groundwater; Algorithm; Engineering; Contamination; Mathematics; Machine learning; Particle swarm optimization; Geotechnical engineering","score_opus":0.02713139073158324,"score_gpt":0.23342437796154267,"score_spread":0.20629298722995942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031921420","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10568221,0.00001247496,0.8896339,0.001409072,0.00006222799,0.000776465,0.0000042887227,0.000036423524,0.0023829604],"genre_scores_gemma":[0.9773934,0.00000553684,0.018497044,0.0005517167,0.000014736828,0.00016116102,0.000016435593,0.000013083153,0.003346878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984036,0.00027060419,0.0002687395,0.0002947463,0.0005193581,0.00024296001],"domain_scores_gemma":[0.9994733,0.00010629002,0.00008919392,0.00024382403,0.000022026103,0.00006538721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012811456,0.00016926945,0.00016753316,0.000071730974,0.00012100976,0.000048551112,0.000196636,0.000031340373,0.000118591815],"category_scores_gemma":[0.000015638812,0.00009799177,0.000033332824,0.00011813851,0.00010649012,0.000066925946,0.00023416783,0.000060491708,0.00004527444],"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":[0.00059696723,0.00040300764,0.0019747124,0.00006140105,0.0003274999,0.000045355595,0.025920602,0.6887132,0.0014404929,0.00020676552,0.0033503284,0.2769597],"study_design_scores_gemma":[0.0027690297,0.000665168,0.0061416994,0.00003216511,0.00013021228,7.9274696e-7,0.0024487427,0.93206346,0.010045428,0.00020030516,0.045184635,0.00031833383],"about_ca_topic_score_codex":0.0001754929,"about_ca_topic_score_gemma":0.000007879577,"teacher_disagreement_score":0.8717112,"about_ca_system_score_codex":0.00010213533,"about_ca_system_score_gemma":0.0000021557366,"threshold_uncertainty_score":0.39959884},"labels":[],"label_agreement":null},{"id":"W2035399681","doi":"10.1007/s11269-007-9211-y","title":"Transboundary Water Policies: Assessment, Comparison and Enhancement","year":2007,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Transboundary Water Resource Management","field":"Social Sciences","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; Wilfrid Laurier University; University of Waterloo","funders":"","keywords":"Treaty; Commission; Convention; Political science; Order (exchange); International law; Law; Environmental planning; Business; Geography","score_opus":0.0177630095510692,"score_gpt":0.3177587514695509,"score_spread":0.2999957419184817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035399681","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70579165,0.000116461786,0.013902626,0.005061533,0.0003340959,0.0012447907,0.0000031523844,0.0003077063,0.27323797],"genre_scores_gemma":[0.97814536,0.0001177692,0.0017159659,0.0010484662,0.00026174917,0.00007351239,0.000037281585,0.00005377878,0.018546136],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9952702,0.0002530651,0.0007669223,0.00074602256,0.0012387142,0.0017250903],"domain_scores_gemma":[0.99895835,0.00003614803,0.00007659461,0.0005205197,0.00004730382,0.00036107432],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.003331261,0.0004106203,0.000423956,0.0004854181,0.0016311347,0.0008245228,0.00070475833,0.00011555476,0.00087968784],"category_scores_gemma":[0.0000022263323,0.0002882098,0.00013557212,0.00020779479,0.0007525641,0.00028831125,0.0004064191,0.00027083931,0.00023976187],"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":[0.0003939643,0.0014062435,0.04216986,0.001022416,0.0013877612,0.0003308141,0.7874876,0.00022859199,0.005613044,0.03665148,0.004261075,0.119047105],"study_design_scores_gemma":[0.00089535356,0.00012157831,0.0066954657,0.000046210826,0.00012186412,0.0000012937284,0.020022927,0.00002072301,0.0040592602,0.000883911,0.9666539,0.00047752017],"about_ca_topic_score_codex":0.0019311829,"about_ca_topic_score_gemma":0.0012551075,"teacher_disagreement_score":0.9623928,"about_ca_system_score_codex":0.00033861812,"about_ca_system_score_gemma":0.000009152254,"threshold_uncertainty_score":0.999957},"labels":[],"label_agreement":null},{"id":"W2039501868","doi":"10.1007/s11269-010-9610-3","title":"An Integrated Simulation-Assessment Approach for Evaluating Health Risks of Groundwater Contamination Under Multiple Uncertainties","year":2010,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Latin hypercube sampling; Fuzzy logic; Risk assessment; Health risk assessment; Environmental science; Stochastic simulation; Computer science; Sampling (signal processing); Uncertainty analysis; Health risk; Hydrogeology; Monte Carlo method; Risk analysis (engineering); Reliability engineering; Data mining; Engineering; Statistics; Mathematics; Simulation; Environmental health; Business; Artificial intelligence","score_opus":0.05801378167307983,"score_gpt":0.3482566656989557,"score_spread":0.29024288402587584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039501868","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60107774,0.0000029932673,0.39738196,0.00012414141,0.00008473411,0.00077218143,0.000004895018,0.000039607992,0.0005117227],"genre_scores_gemma":[0.9738589,0.0000017154634,0.02338165,0.00017741116,0.000028205632,0.00029302645,0.00021778094,0.000022323355,0.0020189662],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814487,0.00014013214,0.0004540002,0.00043261622,0.00047496738,0.00035339632],"domain_scores_gemma":[0.99932,0.000057505596,0.00016691088,0.00032932614,0.00005867421,0.00006764068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011800495,0.00020406445,0.0002409555,0.00010369044,0.0003578762,0.000100321704,0.00027096333,0.000051880113,0.00019226877],"category_scores_gemma":[0.000007786083,0.00014182538,0.0000709272,0.000108339984,0.00014399257,0.00026843007,0.00018400581,0.000115541254,0.000012157372],"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":[0.0001507213,0.0011375182,0.122904375,0.00032162722,0.0002749298,9.773852e-7,0.01954767,0.53196347,0.012352631,0.0007439032,0.00022267432,0.31037953],"study_design_scores_gemma":[0.0019550936,0.0006894042,0.31267753,0.000019762449,0.00008053553,6.768485e-7,0.009625728,0.6418975,0.0018994681,0.00036551914,0.030399568,0.0003891946],"about_ca_topic_score_codex":0.0017064777,"about_ca_topic_score_gemma":0.00048886053,"teacher_disagreement_score":0.3740003,"about_ca_system_score_codex":0.00017029811,"about_ca_system_score_gemma":0.000003895189,"threshold_uncertainty_score":0.57834715},"labels":[],"label_agreement":null},{"id":"W2039516227","doi":"10.1007/s11269-007-9206-8","title":"An Inexact Two-stage Fuzzy-stochastic Programming Model for Water Resources Management","year":2007,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":96,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada; University of Regina","funders":"","keywords":"Stochastic programming; Mathematical optimization; Fuzzy logic; Computer science; Extension (predicate logic); Stage (stratigraphy); Probability distribution; Inflow; Operations research; Mathematics; Artificial intelligence","score_opus":0.01257434651241361,"score_gpt":0.22948216088457266,"score_spread":0.21690781437215906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039516227","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32718766,0.00007989206,0.64846975,0.00009518324,0.0003027095,0.0029427272,0.0000127155945,0.0014988572,0.01941049],"genre_scores_gemma":[0.95994765,0.000021638025,0.022013007,0.0001932156,0.00034879273,0.0005344145,0.0004283912,0.00028269453,0.016230177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955394,0.00004673289,0.00089552987,0.00093560887,0.00072477985,0.0018579105],"domain_scores_gemma":[0.9985191,0.000019458259,0.00008460932,0.0010090537,0.000059593123,0.00030814932],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012934601,0.00075524824,0.00046867694,0.000959688,0.0004333337,0.00064709317,0.0009906655,0.00013679374,0.000077712466],"category_scores_gemma":[0.0000019640315,0.0005613826,0.00022989305,0.00026841144,0.00009316176,0.0005225469,0.000477784,0.00022730915,0.00014599497],"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":[0.00022173376,0.00015872618,0.000063349704,0.0010042913,0.00044882132,0.000074587435,0.009392687,0.97095853,0.00040796513,0.0005351007,0.00032169305,0.016412536],"study_design_scores_gemma":[0.0023339586,0.00015499745,0.00008913343,0.00010643788,0.00034728623,0.0000025444776,0.0014999157,0.7701167,0.002494397,0.0005988856,0.22117664,0.0010791348],"about_ca_topic_score_codex":0.000022197255,"about_ca_topic_score_gemma":0.00004610745,"teacher_disagreement_score":0.63276,"about_ca_system_score_codex":0.00020650639,"about_ca_system_score_gemma":8.145227e-7,"threshold_uncertainty_score":0.99968374},"labels":[],"label_agreement":null},{"id":"W2041102947","doi":"10.1007/s11269-011-9934-7","title":"Water Efficiency and the Professional Plumbing Sector: How Capacity and Capability Influence Knowledge Acquisition and Innovation","year":2011,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Sustainable Building Design and Assessment","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Toronto Public Health","funders":"","keywords":"Context (archaeology); Retraining; Accreditation; Curriculum; Certification; Best practice; Water efficiency; Process (computing); Business; Professional development; Knowledge transfer; Public relations; Engineering; Knowledge management; Political science; Medical education; Computer science; Psychology; Pedagogy; Medicine","score_opus":0.015116384957463523,"score_gpt":0.207755411349427,"score_spread":0.19263902639196345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041102947","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9962007,0.00012584274,0.0012455927,0.00013746943,0.000079492835,0.00045560944,7.6665634e-7,0.000085894375,0.0016686254],"genre_scores_gemma":[0.999234,0.00001825681,0.00020187719,0.00004989008,0.000023964129,0.00007165938,0.0000029631117,0.0000120743,0.00038530907],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9992025,0.00007195696,0.00014786352,0.00021430817,0.00011756859,0.00024583444],"domain_scores_gemma":[0.9997493,0.00001713579,0.000017696178,0.00014669495,0.000037881684,0.00003128257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006583072,0.00013570415,0.000115021896,0.00013617257,0.00020173423,0.0001129617,0.0000792933,0.00003875893,0.00001521819],"category_scores_gemma":[0.0000032982605,0.00007361624,0.000011632857,0.00010334609,0.00017441949,0.0001606229,0.00019050567,0.00010112192,0.0000019639087],"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":[0.00088708586,0.00073822803,0.07981151,0.014875619,0.0010489097,0.00009141108,0.68450624,0.0045173774,0.08545225,0.063690916,0.0010237764,0.06335666],"study_design_scores_gemma":[0.015339902,0.00054738385,0.53564775,0.001215091,0.0006188867,0.000081089755,0.030109152,0.11305164,0.16675334,0.061218027,0.07206776,0.003349968],"about_ca_topic_score_codex":0.000027943082,"about_ca_topic_score_gemma":0.000002317439,"teacher_disagreement_score":0.6543971,"about_ca_system_score_codex":0.00004528392,"about_ca_system_score_gemma":0.0000012305911,"threshold_uncertainty_score":0.30019832},"labels":[],"label_agreement":null},{"id":"W2042150116","doi":"10.1007/s11269-008-9394-x","title":"Water Resources Management and Planning under Uncertainty: an Inexact Multistage Joint-Probabilistic Programming Method","year":2009,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Probabilistic logic; Mathematical optimization; Computer science; Context (archaeology); Linear programming; Stochastic programming; Interval (graph theory); Joint (building); Integer programming; Operations research; Algorithm; Engineering; Mathematics; Civil engineering; Artificial intelligence","score_opus":0.01828887237936539,"score_gpt":0.23829226079669857,"score_spread":0.2200033884173332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042150116","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8659941,0.00044697322,0.10809478,0.0005205841,0.00034841226,0.0027142155,0.00000711033,0.0021948845,0.01967894],"genre_scores_gemma":[0.9650303,0.000074809985,0.02788771,0.0004550889,0.00025442857,0.00017483319,0.00026443703,0.0001566564,0.005701728],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99634314,0.00016577818,0.00071738695,0.0009391115,0.00060192955,0.0012326587],"domain_scores_gemma":[0.99883884,0.000016853171,0.00007283955,0.0007686718,0.000039025286,0.00026373955],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007872179,0.00069845765,0.0005056125,0.00070439006,0.0003834521,0.0008602488,0.00053919136,0.00013559891,0.0001001733],"category_scores_gemma":[0.0000032294924,0.000499533,0.00012302873,0.00024812046,0.000085008694,0.00046698502,0.00044479396,0.00027977861,0.00006460206],"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":[0.00008852493,0.00012361599,0.00014242539,0.0007217909,0.000375327,0.00023123933,0.012312406,0.9386455,0.0003195589,0.00033956373,0.00031063874,0.046389416],"study_design_scores_gemma":[0.0031647678,0.0004714541,0.004469684,0.0004567611,0.00071233127,0.00002071992,0.006972461,0.46645457,0.0015995995,0.0027928094,0.51073176,0.002153099],"about_ca_topic_score_codex":0.000041078365,"about_ca_topic_score_gemma":0.000009879651,"teacher_disagreement_score":0.5104211,"about_ca_system_score_codex":0.00016036083,"about_ca_system_score_gemma":5.973155e-7,"threshold_uncertainty_score":0.9997456},"labels":[],"label_agreement":null},{"id":"W2042163009","doi":"10.1007/s11269-012-0183-1","title":"21st Century Drought Scenarios for the UK","year":2012,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Environment Research Council; Engineering and Physical Sciences Research Council; Met Office; Canadian Centre for Applied Research in Cancer Control","keywords":"Environmental science; Precipitation; Climate change; Water resources; Climatology; Dry season; Physical geography; Geography; Ecology; Biology; Meteorology; Geology","score_opus":0.008682774552670506,"score_gpt":0.2097380425622879,"score_spread":0.2010552680096174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042163009","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8585416,0.00058956654,0.0016823325,0.0036995087,0.00037918324,0.0010059417,0.0000045793577,0.00008746651,0.1340098],"genre_scores_gemma":[0.98359615,0.00008619017,0.00045919025,0.0012883923,0.0001576165,0.00012480735,0.000011576661,0.000012669411,0.014263432],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99881554,0.00004288446,0.00015476043,0.00021944821,0.0002244013,0.0005429697],"domain_scores_gemma":[0.9994746,0.000030685267,0.000035096164,0.00038072962,0.0000022440836,0.00007667859],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00052141893,0.00013210319,0.00010960081,0.000029854411,0.00038640943,0.00003765476,0.00038015316,0.00004540229,0.0036851966],"category_scores_gemma":[0.0000031527502,0.0000688695,0.00011128716,0.00010161209,0.00013328862,0.000108920736,0.00042688803,0.00007255683,0.0019296875],"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":[0.00041568908,0.0014643406,0.70759046,0.0002009192,0.0026675542,0.00003334002,0.05004887,0.03219254,0.0016715118,0.0050541135,0.099776715,0.09888397],"study_design_scores_gemma":[0.00022758951,0.000018441822,0.009816888,0.000002944912,0.00020209994,0.0000012964301,0.00048011699,0.00082271907,0.00043292757,0.00016080086,0.98769945,0.00013473503],"about_ca_topic_score_codex":0.0001035031,"about_ca_topic_score_gemma":0.000047717087,"teacher_disagreement_score":0.8879227,"about_ca_system_score_codex":0.000051159608,"about_ca_system_score_gemma":2.3124362e-7,"threshold_uncertainty_score":0.9988474},"labels":[],"label_agreement":null},{"id":"W2042225058","doi":"10.1007/s11269-012-0007-3","title":"Estimations of Evapotranspiration and Water Balance with Uncertainty over the Yukon River Basin","year":2012,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Evapotranspiration; Environmental science; Eddy covariance; Precipitation; Water balance; Hydrology (agriculture); Structural basin; Drainage basin; Confidence interval; Vegetation (pathology); Climatology; Ecosystem; Geography; Meteorology; Geology; Mathematics; Statistics; Ecology; Geomorphology","score_opus":0.00531995150617288,"score_gpt":0.18120123266676127,"score_spread":0.1758812811605884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042225058","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9908904,0.0000132249315,0.0016269198,0.00029186366,0.000023652361,0.00024417968,0.000006004852,0.000015706068,0.006888059],"genre_scores_gemma":[0.99811274,0.000010484537,0.00062552537,0.000121714584,0.000011756419,0.000023341847,0.000029091058,0.0000075140933,0.0010578535],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992553,0.000038087655,0.00012880703,0.00013216607,0.00021986388,0.00022575508],"domain_scores_gemma":[0.99973667,0.00000767437,0.000028115752,0.00018606625,0.00000336087,0.000038105503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002159143,0.00009730581,0.00007599,0.00002703005,0.00012276457,0.000026923039,0.00010946297,0.000023038345,0.00024685424],"category_scores_gemma":[4.3864574e-7,0.000042856936,0.000019809188,0.000052342763,0.00016589437,0.00018831492,0.000109251385,0.000049496946,0.00005669488],"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":[0.00011041392,0.00023736643,0.5575654,0.00011079989,0.00017190514,0.0000073454908,0.03202772,0.39642072,0.0038768789,0.0014949823,0.00027074962,0.007705744],"study_design_scores_gemma":[0.0011804426,0.00012981617,0.701063,0.00006616849,0.000283628,0.000016473332,0.00019906432,0.13317096,0.003988238,0.0010485965,0.15837015,0.0004834543],"about_ca_topic_score_codex":0.00023631935,"about_ca_topic_score_gemma":0.000038055805,"teacher_disagreement_score":0.26324975,"about_ca_system_score_codex":0.000032436714,"about_ca_system_score_gemma":2.701178e-7,"threshold_uncertainty_score":0.27028808},"labels":[],"label_agreement":null},{"id":"W2047655712","doi":"10.1007/s11269-010-9708-7","title":"A Multi-objective Linear Programming Model with Interval Parameters for Water Resources Allocation in Dalian City","year":2010,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Water resources; Interval (graph theory); Linear programming; Consumption (sociology); Natural resource; Environmental economics; Water quality; Computer science; Hydrogeology; Farm water; Water use; Operations research; Water supply; Water resource management; Water conservation; Environmental science; Environmental engineering; Engineering; Economics; Mathematics","score_opus":0.015506310838810405,"score_gpt":0.21896880241848546,"score_spread":0.20346249157967505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047655712","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.886753,0.0000095308915,0.10961213,0.00020247743,0.00013725497,0.0018869763,0.0000049016776,0.0004170828,0.0009766528],"genre_scores_gemma":[0.9055308,0.0000060722564,0.09114494,0.00007119603,0.00007177646,0.0007320572,0.00014622851,0.00010737802,0.002189533],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997875,0.000037034515,0.00044398571,0.00056216784,0.00030458334,0.0007772593],"domain_scores_gemma":[0.99933285,0.000012413779,0.000049913462,0.00045277333,0.0000502356,0.00010180234],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047436528,0.0004061144,0.00030342455,0.000489176,0.00014467703,0.00026499308,0.00044271824,0.000118923585,0.000018721248],"category_scores_gemma":[0.0000045708766,0.00027068483,0.00010162783,0.00017365905,0.000087340886,0.00031487996,0.00021742648,0.000293959,0.00002428396],"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":[0.0002578942,0.00016273183,0.0016223657,0.00047875303,0.00025031043,0.000017659211,0.024869991,0.9645867,0.001991168,0.000023209976,0.000068061796,0.0056711826],"study_design_scores_gemma":[0.0020593875,0.0001348612,0.00056721485,0.00009976989,0.00011212866,0.0000018371057,0.00093845854,0.93443793,0.0145455925,0.00009259421,0.046410564,0.00059966516],"about_ca_topic_score_codex":0.0000835925,"about_ca_topic_score_gemma":0.0004915185,"teacher_disagreement_score":0.046342503,"about_ca_system_score_codex":0.00008814801,"about_ca_system_score_gemma":0.00000116523,"threshold_uncertainty_score":0.99997455},"labels":[],"label_agreement":null},{"id":"W2050550668","doi":"10.1007/s11269-006-9143-y","title":"A Fuzzy Stochastic Dynamic Nash Game Analysis of Policies for Managing Water Allocation in a Reservoir System","year":2007,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Nash equilibrium; Stochastic programming; Fuzzy logic; Dynamic programming; Mathematical optimization; Sequential game; Operator (biology); Operations research; Game theory; Engineering; Mathematics; Mathematical economics; Artificial intelligence","score_opus":0.007265319918982517,"score_gpt":0.2142090367669017,"score_spread":0.20694371684791918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050550668","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80893475,0.00006934973,0.18146215,0.00011428952,0.000110474786,0.0010600985,0.000006405733,0.0002856222,0.007956871],"genre_scores_gemma":[0.9973349,0.000011294127,0.00084990886,0.000023891685,0.000032290154,0.00012800809,0.00023195389,0.00005908767,0.0013286578],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979004,0.000034857465,0.00068842113,0.00034661437,0.00033846626,0.00069118943],"domain_scores_gemma":[0.9993729,0.000023715815,0.00006360299,0.00043543027,0.000043346758,0.00006100307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091796747,0.00027413198,0.00041343062,0.0023175955,0.00006405044,0.000096694464,0.00037447928,0.00007069971,0.000011861842],"category_scores_gemma":[0.0000023979926,0.0002074349,0.00016892166,0.0007091337,0.000037295133,0.00015115041,0.00019976006,0.00008124579,0.00001578551],"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":[0.00008873096,0.000034249755,0.00027091484,0.0011886952,0.0009678939,0.000010720019,0.007984217,0.98724777,0.00063027407,0.00059324596,0.00002783628,0.00095544645],"study_design_scores_gemma":[0.0011457863,0.00006128616,0.004724992,0.00021350903,0.001205069,7.89893e-7,0.0037367602,0.98180157,0.0020701473,0.00039076633,0.004156525,0.0004927735],"about_ca_topic_score_codex":0.000102457445,"about_ca_topic_score_gemma":0.0003518837,"teacher_disagreement_score":0.18840018,"about_ca_system_score_codex":0.0002609057,"about_ca_system_score_gemma":5.560582e-7,"threshold_uncertainty_score":0.845895},"labels":[],"label_agreement":null},{"id":"W2051152583","doi":"10.1007/s11269-009-9542-y","title":"Assessing the Potential for Rainwater Harvesting","year":2009,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","cited_by":246,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Rainwater harvesting; Laundry; Environmental science; Flushing; Hydrology (agriculture); Water supply; Water storage; Water resource management; Environmental engineering; Engineering; Waste management; Medicine; Ecology","score_opus":0.01585853438128925,"score_gpt":0.2427388700702781,"score_spread":0.22688033568898885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051152583","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75057405,0.000027392776,0.0815452,0.012343211,0.00037498848,0.0020669408,0.000002956963,0.00035675388,0.15270849],"genre_scores_gemma":[0.95776993,0.0000019206273,0.005365624,0.0016673317,0.0001252926,0.000089757195,0.000016740583,0.000019932191,0.034943476],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983157,0.0000620671,0.00025519764,0.00040987082,0.0003591183,0.0005980689],"domain_scores_gemma":[0.99942493,0.000013004534,0.000058571626,0.00043745455,0.0000054855177,0.00006053877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059774565,0.00018917209,0.00011392653,0.000058285117,0.0007085281,0.0005114703,0.0005495052,0.00003072599,0.00046336275],"category_scores_gemma":[0.000004507075,0.00010833564,0.00010540137,0.000113271606,0.0001162229,0.00046700472,0.00045653147,0.00008378144,0.00048359676],"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":[0.00019809516,0.0012582137,0.012467967,0.00026817515,0.00079596264,0.00031189446,0.02267375,0.15515807,0.041661777,0.0052780984,0.28084287,0.47908515],"study_design_scores_gemma":[0.0006189839,0.00007741373,0.05559417,0.000018708406,0.00014285425,0.000004981654,0.0005379831,0.006050496,0.00091279566,0.0045872186,0.931109,0.00034537527],"about_ca_topic_score_codex":0.000055539167,"about_ca_topic_score_gemma":0.0000073477577,"teacher_disagreement_score":0.6502662,"about_ca_system_score_codex":0.000116776864,"about_ca_system_score_gemma":4.5356177e-7,"threshold_uncertainty_score":0.6215819},"labels":[],"label_agreement":null},{"id":"W2051806646","doi":"10.1007/s11269-014-0550-1","title":"Water Diversion Conflicts in China: A Hierarchical Perspective","year":2014,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":56,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; Centre for International Governance Innovation; University of Waterloo","funders":"National Development and Reform Commission","keywords":"China; Central government; Conflict resolution; Population; Government (linguistics); Local government; Conflict analysis; Hydrogeology; Operations research; Environmental planning; Environmental resource management; Computer science; Geography; Political science; Environmental science; Engineering; Public administration; Sociology; Law","score_opus":0.03028465346815418,"score_gpt":0.30653995068130035,"score_spread":0.2762552972131462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051806646","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8654223,0.0000063739963,0.0017916596,0.005659759,0.00006070442,0.00028467824,0.0000015359547,0.000044655175,0.12672836],"genre_scores_gemma":[0.98269874,0.0000025735171,0.000164745,0.00039696152,0.000055328237,0.00003918853,0.0000046811488,0.000009121746,0.016628686],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977504,0.00031495315,0.0003240757,0.0005548605,0.00067607156,0.00037966605],"domain_scores_gemma":[0.9991353,0.000086678374,0.00003694444,0.00060225365,0.000044438573,0.000094352596],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002167874,0.00014024414,0.00019436005,0.00034804994,0.00020050144,0.0001914658,0.0007832012,0.000036216836,0.0007787441],"category_scores_gemma":[0.000045337078,0.00007157216,0.00008012621,0.00020196423,0.00014209091,0.00011919141,0.00074743805,0.00014764328,0.002984823],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008445971,0.001153662,0.008947505,0.000057525496,0.0001741277,0.00017421266,0.26570112,0.0053667324,0.0074685724,0.63363147,0.005762524,0.07071795],"study_design_scores_gemma":[0.00073832547,0.000070080336,0.017830504,0.000014400177,0.000014026548,0.000002657642,0.0031719196,0.00093948946,0.0040346705,0.27807623,0.6949102,0.00019748238],"about_ca_topic_score_codex":0.000067974746,"about_ca_topic_score_gemma":0.0000137935585,"teacher_disagreement_score":0.68914765,"about_ca_system_score_codex":0.0000499807,"about_ca_system_score_gemma":7.4731145e-7,"threshold_uncertainty_score":0.99779147},"labels":[],"label_agreement":null},{"id":"W2052606781","doi":"10.1007/s11269-012-0254-3","title":"Introduction to the Special Issue on “Adaptation and Resilience of Water Systems to an Uncertain Changing Climate”","year":2013,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Economic and Social Research Council; Engineering and Physical Sciences Research Council; Canadian Centre for Applied Research in Cancer Control","keywords":"Resilience (materials science); Adaptation (eye); Environmental resource management; Climate change adaptation; Climate change; Environmental planning; Environmental science; Water resource management; Psychology; Geology; Oceanography","score_opus":0.00767884475066591,"score_gpt":0.18712200579127844,"score_spread":0.17944316104061253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052606781","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9773725,0.000019421788,0.007959703,0.0048818076,0.0008301368,0.00218933,0.0000029906462,0.00020222386,0.0065418407],"genre_scores_gemma":[0.99094975,0.000027461487,0.00074658456,0.00028652136,0.004050562,0.00026381118,0.000049698127,0.00004848665,0.003577105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99861956,0.00005518672,0.0002699662,0.00030778,0.0002822792,0.00046525785],"domain_scores_gemma":[0.99950695,0.000005098678,0.000022818487,0.00035406265,0.000031749507,0.00007930865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040099493,0.000179976,0.00014612167,0.00040270967,0.00016032296,0.0002542112,0.00023486867,0.00003069527,0.00012992264],"category_scores_gemma":[0.0000022917982,0.00010451493,0.000021427055,0.0001902469,0.000019465859,0.0002079114,0.00021368715,0.000057177975,0.00041884463],"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":[0.000030287847,0.000015500258,0.000029585663,0.00018399375,0.000029636876,0.0000016859058,0.018997492,0.96059734,0.0005672384,0.00013240663,0.006943264,0.012471555],"study_design_scores_gemma":[0.0002550551,0.00024798836,0.00063260226,0.0000819019,0.000042350523,0.0000011569869,0.008025692,0.18879552,0.006687847,0.000029636374,0.7948778,0.00032239268],"about_ca_topic_score_codex":0.000077006865,"about_ca_topic_score_gemma":0.00001477683,"teacher_disagreement_score":0.7879346,"about_ca_system_score_codex":0.00004918277,"about_ca_system_score_gemma":1.6795818e-7,"threshold_uncertainty_score":0.538354},"labels":[],"label_agreement":null},{"id":"W2054962612","doi":"10.1007/s11269-007-9186-8","title":"Interval-parameter Two-stage Stochastic Nonlinear Programming for Water Resources Management under Uncertainty","year":2007,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":86,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Interval (graph theory); Context (archaeology); Stochastic programming; Water resources; Computer science; Mathematical optimization; Nonlinear system; Function (biology); Stage (stratigraphy); Water supply; Scale (ratio); Resource allocation; Operations research; Environmental science; Mathematics; Environmental engineering","score_opus":0.01386017096388166,"score_gpt":0.23499249895166405,"score_spread":0.22113232798778237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054962612","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5463827,0.00012720356,0.43586174,0.00026736414,0.00068825047,0.0036369516,0.000017090717,0.0013575909,0.011661067],"genre_scores_gemma":[0.939893,0.000028901368,0.029307416,0.00045981124,0.0005509221,0.00064752554,0.00052818935,0.00034535455,0.028238857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9954756,0.000060038903,0.0009872302,0.00091965066,0.0007073886,0.0018500735],"domain_scores_gemma":[0.9986661,0.00005774079,0.000094604424,0.0008681112,0.00006886478,0.00024458714],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012534844,0.000774286,0.0005281613,0.0008742309,0.0003597371,0.0006078641,0.0008781939,0.00014244323,0.00020381901],"category_scores_gemma":[0.000004208812,0.0005530339,0.0003256579,0.00028940957,0.00013156452,0.00030495008,0.00074397435,0.00026832463,0.00025581862],"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":[0.00033892554,0.00017201729,0.000069054935,0.0013769255,0.0010894267,0.00009657473,0.0061357613,0.963305,0.00021860325,0.00033894912,0.00057579245,0.026282955],"study_design_scores_gemma":[0.0034816922,0.00022549766,0.0001538935,0.00024860125,0.0005306312,0.0000044998287,0.0031761457,0.2022255,0.003839817,0.00053587434,0.784229,0.0013488831],"about_ca_topic_score_codex":0.000039739432,"about_ca_topic_score_gemma":0.000064786684,"teacher_disagreement_score":0.78365314,"about_ca_system_score_codex":0.00028225628,"about_ca_system_score_gemma":7.96742e-7,"threshold_uncertainty_score":0.99969214},"labels":[],"label_agreement":null},{"id":"W2056993245","doi":"10.1007/s11269-011-9900-4","title":"Issues and Implications of Carbon-Abatement Discounting and Pricing for Drinking Water System Design in Canada","year":2011,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University; Utilities Kingston (Canada)","funders":"","keywords":"Greenhouse gas; Discounting; Carbon price; Electricity; Environmental economics; Carbon offset; Environmental science; Water use; Mains electricity; Natural resource economics; Water pricing; Economics; Social discount rate; Environmental engineering; Cost–benefit analysis; Water conservation; Water resources; Engineering; Finance; Ecology","score_opus":0.0750432996779414,"score_gpt":0.22194167907449755,"score_spread":0.14689837939655614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056993245","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99347687,0.00031609056,0.0005935068,0.00028452603,0.000057038575,0.0004755125,0.000017105895,0.000008562946,0.004770763],"genre_scores_gemma":[0.99867135,0.00012079246,0.0008910761,0.00004798552,0.000021891354,0.00011122214,0.000006567108,0.000015628915,0.00011346135],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989861,0.000007791205,0.00045235374,0.00025318633,0.00001592466,0.00028464227],"domain_scores_gemma":[0.99969417,0.000013438307,0.00009893008,0.00015569018,0.000006364609,0.00003138344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044606577,0.00010411806,0.00024621873,0.00015585475,0.00006564417,0.00003359537,0.000098483455,0.000020427262,0.000010842019],"category_scores_gemma":[0.0000018095363,0.00008730446,0.000018015771,0.000027348278,0.000019257146,0.00006439685,0.00016189637,0.000027884887,0.000001678873],"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":[0.00007064801,0.000064035616,0.884112,0.0024549663,0.0002740569,0.0000070101014,0.05721427,0.00053627696,0.00017314868,0.053181488,0.000056470726,0.0018556184],"study_design_scores_gemma":[0.007545927,0.00049599103,0.66413516,0.0014281594,0.00029258084,0.00002442647,0.059184227,0.07419255,0.042154122,0.07502934,0.071952686,0.0035648234],"about_ca_topic_score_codex":0.4365693,"about_ca_topic_score_gemma":0.107668646,"teacher_disagreement_score":0.32890067,"about_ca_system_score_codex":0.0001853273,"about_ca_system_score_gemma":0.0000019046853,"threshold_uncertainty_score":0.9086141},"labels":[],"label_agreement":null},{"id":"W2059837539","doi":"10.1007/s11269-012-0207-x","title":"Predicting Drought Magnitudes: A Parsimonious Model for Canadian Hydrological Droughts","year":2012,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Multiplicative function; Statistics; Standard deviation; Series (stratigraphy); Streamflow; Climatology; Drainage basin; Geography; Geology","score_opus":0.013911725847001002,"score_gpt":0.21725454966341531,"score_spread":0.2033428238164143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059837539","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9205293,0.0000628625,0.004354304,0.0008915333,0.00009499308,0.00056099624,0.000009543311,0.00009923892,0.07339723],"genre_scores_gemma":[0.98343134,0.000011054739,0.0024428868,0.0014451214,0.00012318768,0.00020548348,0.000037233665,0.00002377522,0.012279912],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977604,0.00006492389,0.00027788262,0.00044247124,0.00026863426,0.001185644],"domain_scores_gemma":[0.9991753,0.00002204781,0.00004958528,0.00036194263,0.000004689885,0.00038643382],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00061934185,0.00023437206,0.00021763738,0.00010529114,0.00053746655,0.000048846287,0.00041447242,0.00013610216,0.0011680096],"category_scores_gemma":[0.000007910936,0.00016674948,0.00014253728,0.0001395703,0.00014547409,0.00025137363,0.00032415226,0.00012928469,0.00088796404],"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":[0.00022764405,0.00071141747,0.64858115,0.00012654634,0.00076739106,0.00011838238,0.030648498,0.2942537,0.00028615037,0.0012032223,0.015874438,0.0072014574],"study_design_scores_gemma":[0.0007738252,0.000099922574,0.0074875364,0.000010782974,0.00039736586,0.000009251264,0.0002381335,0.5411444,0.00029952996,0.0035754347,0.4453912,0.0005726519],"about_ca_topic_score_codex":0.0072666225,"about_ca_topic_score_gemma":0.021438103,"teacher_disagreement_score":0.6410936,"about_ca_system_score_codex":0.00020742424,"about_ca_system_score_gemma":0.0000022049842,"threshold_uncertainty_score":0.99988997},"labels":[],"label_agreement":null},{"id":"W2060278017","doi":"10.1007/s11269-009-9418-1","title":"Adaptation to Climate Change in the Management of a Canadian Water-Resources System Exploited for Hydropower","year":2009,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":155,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Hydro-Québec; École de Technologie Supérieure; Université du Québec à Montréal; Natural Sciences and Engineering Research Council of Canada; Université du Québec; Polytechnique Montréal","funders":"","keywords":"Environmental science; Hydropower; Climate change; Context (archaeology); Representative Concentration Pathways; Greenhouse gas; Baseline (sea); Water resources; Climate model; Time horizon; Hydrology (agriculture); Climatology; Geography; Ecology; Oceanography; Geology","score_opus":0.01906247166074232,"score_gpt":0.20614914730772868,"score_spread":0.18708667564698636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060278017","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95298254,0.00014176957,0.0047945515,0.0018144457,0.00028477237,0.005516908,0.00002707975,0.00038957153,0.03404834],"genre_scores_gemma":[0.9963743,0.000097233875,0.0018046288,0.00038663676,0.00009509466,0.00072654785,0.00013526001,0.000054528882,0.0003257561],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99769884,0.000059387123,0.0005442087,0.00038682882,0.00040271983,0.0009080044],"domain_scores_gemma":[0.99928457,0.000007787216,0.000044201937,0.00050777226,0.000030853476,0.00012479018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006229714,0.00033798622,0.0002870124,0.0015439112,0.00015539984,0.00018119808,0.0006447891,0.000068159665,0.000022290154],"category_scores_gemma":[5.408129e-7,0.00021786468,0.00010453733,0.00061089254,0.000018929195,0.00019016663,0.00010997202,0.00008315396,0.00007509297],"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":[0.00035308354,0.00022660388,0.00034284766,0.0053393524,0.0005895308,0.00029945135,0.2048813,0.73337656,0.00032668552,0.0038515471,0.001120928,0.04929208],"study_design_scores_gemma":[0.0030474337,0.00053749874,0.006903688,0.0013719653,0.00048687757,0.000005377251,0.030200306,0.20844631,0.0025737926,0.00022016416,0.74486107,0.0013455382],"about_ca_topic_score_codex":0.0030041388,"about_ca_topic_score_gemma":0.0023634818,"teacher_disagreement_score":0.74374014,"about_ca_system_score_codex":0.00016947807,"about_ca_system_score_gemma":4.0908373e-7,"threshold_uncertainty_score":0.8884264},"labels":[],"label_agreement":null},{"id":"W2061022676","doi":"10.1007/s11269-013-0381-5","title":"Probability Analysis of Crop Water Stress Index: An Application of Double Bounded Density Function (DB-CDF)","year":2013,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Irrigation Practices and Water Management","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Probability density function; Irrigation; Mathematics; Saturation (graph theory); Water content; Soil science; Environmental science; Moment (physics); Randomness; Deficit irrigation; Hydrology (agriculture); Statistics; Irrigation management; Agronomy; Geotechnical engineering; Physics; Geology","score_opus":0.019770517616421908,"score_gpt":0.21323072430033016,"score_spread":0.19346020668390826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061022676","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99617434,0.0000048803813,0.0005736974,0.0005664745,0.00003945968,0.000857824,0.000006806577,0.00004533233,0.0017311637],"genre_scores_gemma":[0.99856853,0.000003493442,0.00009943833,0.00008551901,0.000033818706,0.00011250144,0.0003623293,0.000001568535,0.0007328195],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99838275,0.00010561594,0.00044884125,0.0004124917,0.0003811697,0.00026915016],"domain_scores_gemma":[0.99934095,0.000011406491,0.00018553715,0.00024492107,0.00015226369,0.00006490721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046023302,0.00015243053,0.0002571294,0.00008238559,0.00014812042,0.00012849942,0.00030500576,0.000059511643,0.0007921342],"category_scores_gemma":[0.0000010070597,0.000052587202,0.00012888656,0.0003430692,0.00007552718,0.00038285027,0.00027265283,0.000059088437,0.00004997894],"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":[0.0016586058,0.0040932293,0.49601623,0.0010191556,0.005571416,0.0000057526167,0.00769073,0.021155857,0.2633927,0.007133829,0.0003240791,0.19193843],"study_design_scores_gemma":[0.0005950093,0.00040651424,0.87732404,0.000015003121,0.0012784415,2.6110314e-7,0.0012519252,0.006374208,0.06702374,0.0076849395,0.037662715,0.0003832052],"about_ca_topic_score_codex":0.0058511286,"about_ca_topic_score_gemma":0.001068956,"teacher_disagreement_score":0.3813078,"about_ca_system_score_codex":0.000030324843,"about_ca_system_score_gemma":4.7485437e-7,"threshold_uncertainty_score":0.8845195},"labels":[],"label_agreement":null},{"id":"W2061840085","doi":"10.1007/s11269-009-9503-5","title":"Impacts of Accuracy and Resolution of Conventional and LiDAR Based DEMs on Parameters Used in Hydrologic Modeling","year":2009,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Soil erosion and sediment transport","field":"Agricultural and Biological Sciences","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of New Brunswick","funders":"","keywords":"Lidar; Digital elevation model; Watershed; Remote sensing; Environmental science; Hydrological modelling; Hydrology (agriculture); Elevation (ballistics); Hydrogeology; Geology; Geotechnical engineering","score_opus":0.02681534362544805,"score_gpt":0.2214881874575142,"score_spread":0.19467284383206615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061840085","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9988475,0.000046647718,0.000015659085,0.0007300224,0.0000098396895,0.00017415894,0.0000031091731,0.000011204635,0.00016190334],"genre_scores_gemma":[0.9996011,0.000037397425,0.000092110335,0.00022921803,0.000004969666,0.000003418486,0.000020960106,4.1363265e-7,0.000010366684],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99929875,0.00003882961,0.00020319501,0.00016378889,0.00016128604,0.00013412633],"domain_scores_gemma":[0.99984074,0.000033782875,0.000044998284,0.000034818153,0.000008549174,0.000037123904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023848773,0.000077511395,0.00012409592,0.000037320995,0.000033125634,0.000012130543,0.00006398633,0.00003621852,0.000019310864],"category_scores_gemma":[0.0000041904395,0.000029247654,0.000035192745,0.00006892161,0.000034910394,0.000040210227,0.000017067077,0.000039552175,6.594469e-7],"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":[0.0023406665,0.0014848772,0.42119905,0.000277719,0.00008971773,0.000043561115,0.0022996326,0.03807766,0.4787469,0.0011278506,0.000042677617,0.054269705],"study_design_scores_gemma":[0.0016069273,0.0013895612,0.9156439,0.00021468708,0.00003418278,7.927027e-7,0.000384771,0.060736306,0.017845742,0.0012757418,0.00065377477,0.00021364335],"about_ca_topic_score_codex":0.00015276478,"about_ca_topic_score_gemma":0.000030976273,"teacher_disagreement_score":0.49444482,"about_ca_system_score_codex":0.0000067647734,"about_ca_system_score_gemma":4.2842353e-7,"threshold_uncertainty_score":0.11926848},"labels":[],"label_agreement":null},{"id":"W2062903759","doi":"10.1007/s11269-012-9990-7","title":"Sustainable Management for Minimizing Land Subsidence of an Over-Pumped Volcanic Aquifer System: Tools for Policy Design","year":2012,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Groundwater and Isotope Geochemistry","field":"Earth and Planetary Sciences","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Geological Survey of Canada; Institut National de la Recherche Scientifique; Natural Resources Canada","funders":"","keywords":"Aquifer; Groundwater recharge; Subsidence; Hydrogeology; Groundwater; Environmental science; Water supply; Water pumping; Hydrology (agriculture); Water resource management; Geology; Environmental engineering; Geotechnical engineering; Geomorphology; Structural basin","score_opus":0.0219561953310349,"score_gpt":0.23009750560701195,"score_spread":0.20814131027597704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062903759","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9739238,0.0002629096,0.013067261,0.00012598741,0.0001852114,0.0027525378,0.00006498743,0.000090304646,0.00952699],"genre_scores_gemma":[0.985123,0.000015872476,0.005565132,0.00009546587,0.00026478196,0.00011101417,0.00022143722,0.000013915261,0.0085893925],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979846,0.000064310465,0.00035763113,0.00034784887,0.00027819647,0.00096744276],"domain_scores_gemma":[0.9992153,0.00008180184,0.00009655346,0.000390699,0.0000482467,0.00016741075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091869273,0.00022694771,0.00026268233,0.00017465984,0.00022900543,0.0002259795,0.00043931336,0.000058136015,0.000101528785],"category_scores_gemma":[0.000011435084,0.00016527108,0.00010415727,0.00012398453,0.000047547892,0.0006023293,0.000082224804,0.000041637748,0.000023112158],"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":[0.01501895,0.002425038,0.36044675,0.16533248,0.0069197044,0.00078792585,0.0648756,0.087352306,0.005595763,0.03182811,0.023074124,0.23634325],"study_design_scores_gemma":[0.012570648,0.0025457847,0.20970021,0.0010486953,0.0018131068,0.00006862071,0.054529965,0.03367771,0.06305291,0.0054788967,0.61190987,0.0036035816],"about_ca_topic_score_codex":0.0013266294,"about_ca_topic_score_gemma":0.00008625652,"teacher_disagreement_score":0.5888358,"about_ca_system_score_codex":0.000037362894,"about_ca_system_score_gemma":0.0000082119595,"threshold_uncertainty_score":0.6739559},"labels":[],"label_agreement":null},{"id":"W2063341969","doi":"10.1007/s11269-010-9668-y","title":"Selecting Model Parameter Sets from a Trade-off Surface Generated from the Non-Dominated Sorting Genetic Algorithm-II","year":2010,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Agriculture","keywords":"Parameter space; Mathematical optimization; Sorting; Genetic algorithm; Calibration; Set (abstract data type); Pareto principle; Solution set; Algorithm; Multi-objective optimization; Computer science; Selection (genetic algorithm); Surface (topology); Mathematics; Data mining; Statistics; Machine learning","score_opus":0.00820584903122235,"score_gpt":0.20393878794729825,"score_spread":0.1957329389160759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063341969","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9905529,0.000039408857,0.00388173,0.0019013558,0.00023278235,0.00064303365,0.000016396223,0.00012669452,0.002605681],"genre_scores_gemma":[0.97308636,0.00002992288,0.023335772,0.0014309511,0.000089781184,0.00006856958,0.000067354486,0.000044987224,0.0018462995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997399,0.00013184076,0.00044467542,0.00083951454,0.0004057317,0.00077923614],"domain_scores_gemma":[0.99907726,0.000074080126,0.00013201425,0.00061683543,0.0000059856375,0.000093839604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046387385,0.00038469408,0.00028959732,0.000037658487,0.0010863058,0.00013294189,0.0007430506,0.00012337323,0.0006372556],"category_scores_gemma":[0.000008698686,0.00024040912,0.00010272087,0.00017986397,0.0002570204,0.00014869882,0.0015930817,0.00042215327,0.00036969417],"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":[0.0002105761,0.0006038119,0.09662325,0.000034666868,0.0022230775,0.00032780142,0.066427715,0.6017906,0.078364916,0.000010746376,0.018379243,0.13500361],"study_design_scores_gemma":[0.001133161,0.000065414984,0.039641205,0.00001839272,0.00033774105,0.0000018439525,0.00053779007,0.92104185,0.010154098,0.0022198858,0.024218423,0.00063022337],"about_ca_topic_score_codex":0.0014887396,"about_ca_topic_score_gemma":0.00036274394,"teacher_disagreement_score":0.31925124,"about_ca_system_score_codex":0.000053616546,"about_ca_system_score_gemma":0.0000012631114,"threshold_uncertainty_score":0.98036},"labels":[],"label_agreement":null},{"id":"W2063429913","doi":"10.1007/s11269-006-9080-9","title":"Impacts of Snowmelt on Peak Flows in a Forest Watershed","year":2006,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"Deutscher Akademischer Austauschdienst","keywords":"Snowmelt; Snow; Hydrograph; Environmental science; Surface runoff; Hydrology (agriculture); Watershed; Precipitation; Hydrogeology; Climatology; Meteorology; Geology; Geography; Ecology","score_opus":0.005547760935246633,"score_gpt":0.19193949827365517,"score_spread":0.18639173733840853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063429913","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9042775,0.000011470862,0.000029860379,0.00086838607,0.000054254648,0.00038945922,0.0000013066602,0.000039555074,0.094328225],"genre_scores_gemma":[0.9956941,0.000014399395,0.0001488277,0.0002828931,0.000026371803,0.000051685267,0.000017291602,0.000016458105,0.0037479613],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998368,0.000065904744,0.00033722827,0.0003880458,0.000305851,0.0005349171],"domain_scores_gemma":[0.9995518,0.000011399539,0.000055961926,0.00033938207,0.0000025310571,0.0000389656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035558958,0.00021041174,0.00022645465,0.00017892712,0.00009269791,0.000020182953,0.00031694575,0.000048375234,0.00048425805],"category_scores_gemma":[0.0000021770898,0.00013959594,0.0000699401,0.00014891809,0.00014362663,0.0001018676,0.0005986213,0.00008772365,0.00073390274],"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":[0.00048112829,0.00092225056,0.8492252,0.00023651452,0.00016998264,0.0003683384,0.005647789,0.12531514,0.0027666762,0.0010116003,0.011687062,0.0021682587],"study_design_scores_gemma":[0.0019418314,0.0003121133,0.8341634,0.00006414751,0.00006522585,0.0000015100128,0.00033705943,0.0009857217,0.0056252675,0.0049615363,0.15110067,0.00044148794],"about_ca_topic_score_codex":0.001931976,"about_ca_topic_score_gemma":0.0021235049,"teacher_disagreement_score":0.13941361,"about_ca_system_score_codex":0.00009097555,"about_ca_system_score_gemma":2.7264312e-7,"threshold_uncertainty_score":0.94330794},"labels":[],"label_agreement":null},{"id":"W2063626233","doi":"10.1007/s11269-007-9204-x","title":"Characterization and Evaluation of Elevation Data Uncertainty in Water Resources Modeling with GIS","year":2007,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Regina","funders":"","keywords":"Digital elevation model; Elevation (ballistics); Raster graphics; Grid; Hydrological modelling; Monte Carlo method; Uncertainty analysis; Hydrogeology; Computer science; Remote sensing; Environmental science; Data mining; Simulation; Geology; Statistics; Geodesy; Mathematics","score_opus":0.028628718561962675,"score_gpt":0.24679492718258061,"score_spread":0.21816620862061795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063626233","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99044013,0.000011137158,0.0052978247,0.0004532061,0.000024469113,0.0005351223,0.000001980815,0.000021741249,0.0032143865],"genre_scores_gemma":[0.99908155,0.000025562715,0.00021383226,0.0001097128,0.000016699692,0.000022950459,0.00019535399,0.000010915635,0.00032341212],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983618,0.00009332241,0.00030228362,0.0004317605,0.00049594697,0.00031484006],"domain_scores_gemma":[0.99951726,0.000008025881,0.00005683185,0.00037633436,0.000013149101,0.000028416925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027828503,0.00013993101,0.00014245816,0.0001498622,0.00011702937,0.000025467085,0.00024786044,0.00003993823,0.0001408769],"category_scores_gemma":[0.000003978098,0.00008426785,0.000010496523,0.00011164286,0.000116673546,0.00030608274,0.0007931595,0.00006162047,0.000024272606],"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":[0.0009949424,0.00036802422,0.18728742,0.00030615352,0.0003588228,0.000040949697,0.059307393,0.6548281,0.029973557,0.00010822086,0.0000669822,0.06635948],"study_design_scores_gemma":[0.003310033,0.00023306864,0.24030365,0.0001384977,0.00047280572,0.0000030511287,0.003062795,0.7123273,0.007107263,0.00113153,0.031262524,0.0006474765],"about_ca_topic_score_codex":0.00034373958,"about_ca_topic_score_gemma":0.00051685167,"teacher_disagreement_score":0.065712005,"about_ca_system_score_codex":0.00006947851,"about_ca_system_score_gemma":3.876566e-7,"threshold_uncertainty_score":0.3436343},"labels":[],"label_agreement":null},{"id":"W2066816878","doi":"10.1007/s11269-011-9783-4","title":"Non-Cooperative Stability Definitions for Strategic Analysis of Generic Water Resources Conflicts","year":2011,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":151,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of California, Davis; Lunds Universitet","keywords":"Stability (learning theory); Futures studies; Game theory; Computer science; Management science; Nash equilibrium; Range (aeronautics); Outcome (game theory); Cooperative game theory; Mathematical economics; Operations research; Artificial intelligence; Economics; Mathematics; Machine learning; Engineering","score_opus":0.066488261343055,"score_gpt":0.21200264641939867,"score_spread":0.14551438507634368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066816878","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9330731,0.000053415795,0.010991496,0.00002763289,0.000103412014,0.001000859,0.00004655222,0.00023791014,0.054465603],"genre_scores_gemma":[0.9959301,0.00006014577,0.0022758937,0.00005333683,0.000043865988,0.00030402947,0.00042325683,0.00006156915,0.00084782374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979342,0.00005308494,0.00063707656,0.00046890706,0.0002955902,0.0006111307],"domain_scores_gemma":[0.999125,0.000016024005,0.000067062145,0.0005886239,0.000100063335,0.00010323572],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037287764,0.00036732087,0.00050962204,0.0007589234,0.0001711016,0.000112470545,0.00043732917,0.0000906696,0.00067777117],"category_scores_gemma":[0.0000015730378,0.0002524705,0.00028050464,0.0004930496,0.00011391397,0.0001779209,0.00020595982,0.00009522625,0.000047761987],"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":[0.00036323245,0.0005411709,0.0033446024,0.0017273101,0.0113908565,0.00003182544,0.13491641,0.8389502,0.00618926,0.0014545503,0.0003410012,0.00074956677],"study_design_scores_gemma":[0.0043095113,0.0008474319,0.011866743,0.00014206216,0.011442653,0.0000017878424,0.012284709,0.57836086,0.26878053,0.002527137,0.10670375,0.002732833],"about_ca_topic_score_codex":0.0000779242,"about_ca_topic_score_gemma":0.00006367656,"teacher_disagreement_score":0.26259124,"about_ca_system_score_codex":0.000058883204,"about_ca_system_score_gemma":9.328746e-7,"threshold_uncertainty_score":0.9999927},"labels":[],"label_agreement":null},{"id":"W2067344948","doi":"10.1007/s11269-009-9431-4","title":"Water Quality Monitoring and Modeling in Lake Kastoria, Using GIS. Assessment and Management of Pollution Sources","year":2009,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Eutrophication; Water quality; Environmental science; Nitrate; Pollution; Hydrology (agriculture); Thematic map; Geographic information system; Environmental monitoring; Environmental engineering; Geography; Nutrient; Ecology; Cartography; Geology","score_opus":0.057586624906369725,"score_gpt":0.31706091214883947,"score_spread":0.25947428724246974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067344948","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99388033,0.00006004126,0.0025094778,0.0006374084,0.00010295932,0.00043154618,0.0000043021864,0.00003509204,0.0023388164],"genre_scores_gemma":[0.9939827,0.00015031833,0.005340785,0.000073291616,0.000037384394,0.000015600122,0.0000067719307,0.000011920221,0.00038127217],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9977619,0.00018695439,0.0005716259,0.0004896375,0.0005149598,0.0004749292],"domain_scores_gemma":[0.9995246,0.000005352573,0.00008143635,0.00028527775,0.000005913376,0.00009744096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012197531,0.00023447975,0.0002705236,0.00014066356,0.00017954936,0.00009243379,0.00017848363,0.000061654144,0.000089584835],"category_scores_gemma":[5.422722e-7,0.00017012728,0.00004601092,0.00010752385,0.00009737574,0.00026140138,0.00052396295,0.000118056436,0.000009098506],"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":[0.00056226406,0.0017138448,0.26826206,0.0023345365,0.00061562494,0.00020359115,0.06374066,0.49475855,0.07349101,0.0050873044,0.00006564231,0.089164935],"study_design_scores_gemma":[0.0042579966,0.00033892132,0.8925146,0.0007318327,0.00028773258,0.000012242365,0.0074690664,0.040704545,0.019214712,0.006601151,0.026386576,0.0014806656],"about_ca_topic_score_codex":0.00052316475,"about_ca_topic_score_gemma":0.00006140662,"teacher_disagreement_score":0.6242525,"about_ca_system_score_codex":0.00020902941,"about_ca_system_score_gemma":9.514265e-7,"threshold_uncertainty_score":0.69375896},"labels":[],"label_agreement":null},{"id":"W2070375731","doi":"10.1007/s11269-014-0555-9","title":"Runoff Reduction Capabilities and Irrigation Requirements of Green Roofs","year":2014,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Surface runoff; Environmental science; Hydrology (agriculture); Irrigation; Hydrogeology; Arid; Low-impact development; Water resource management; Stormwater; Stormwater management; Geotechnical engineering; Geology; Ecology","score_opus":0.007957085948001905,"score_gpt":0.19076512714329572,"score_spread":0.1828080411952938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070375731","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9692072,0.000008204531,0.00024139954,0.0002397673,0.00006116066,0.00024556104,9.928688e-7,0.000028130353,0.029967556],"genre_scores_gemma":[0.9926899,0.0000044096305,0.00048403957,0.000036278987,0.000035053556,0.00002218366,0.000012762581,0.000008707236,0.006706659],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.999116,0.000058186888,0.00019373406,0.00022499193,0.00025015872,0.0001569259],"domain_scores_gemma":[0.99970555,0.000005201983,0.000047840163,0.00019982563,0.0000048100324,0.00003674978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030205122,0.0000879719,0.00008740507,0.000046376437,0.000074128824,0.000019209876,0.000090704,0.000026497588,0.0003142277],"category_scores_gemma":[0.0000028263987,0.00006659497,0.000020808504,0.00005216841,0.0001323459,0.00016567204,0.0001654627,0.000029878043,0.00007996123],"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":[0.00029699664,0.000524693,0.31811136,0.0015346301,0.00031268742,0.0000074582113,0.0948313,0.005971789,0.218405,0.0072048283,0.008252622,0.34454665],"study_design_scores_gemma":[0.00216578,0.00064553355,0.5808748,0.00017401212,0.00019950021,0.000011722549,0.0026898345,0.0040614414,0.13038363,0.06109896,0.21689942,0.00079533656],"about_ca_topic_score_codex":0.0006032491,"about_ca_topic_score_gemma":0.000052080184,"teacher_disagreement_score":0.3437513,"about_ca_system_score_codex":0.00006045826,"about_ca_system_score_gemma":2.4614866e-7,"threshold_uncertainty_score":0.3440573},"labels":[],"label_agreement":null},{"id":"W2071701179","doi":"10.1007/s11269-008-9311-3","title":"Interval-parameter Two-stage Stochastic Semi-infinite Programming: Application to Water Resources Management under Uncertainty","year":2008,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stochastic programming; Interval (graph theory); Mathematical optimization; Computer science; Probability distribution; Probability density function; Linear programming; Reliability (semiconductor); Random variable; Range (aeronautics); Dynamic programming; Operations research; Mathematics; Statistics; Engineering","score_opus":0.013385916162483205,"score_gpt":0.21641143662874204,"score_spread":0.20302552046625882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071701179","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6813714,0.00008267228,0.29512662,0.0005105798,0.00035378934,0.0034980886,0.0000108764325,0.001663422,0.017382532],"genre_scores_gemma":[0.96929586,0.000043887718,0.004397818,0.00061493274,0.00022527507,0.0011627296,0.00029690273,0.00021614411,0.02374643],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99568474,0.00010248339,0.0008750867,0.0010479636,0.00086712075,0.0014225969],"domain_scores_gemma":[0.99833363,0.000026079544,0.000084136555,0.0011766636,0.00006216148,0.00031731403],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004733839,0.0007866019,0.0005240517,0.0009486261,0.00042466333,0.00040820052,0.0009934355,0.0001254271,0.00024412616],"category_scores_gemma":[0.000002891292,0.00058146595,0.00023048691,0.00048182195,0.00013311824,0.00030012833,0.0010326221,0.0002849807,0.0013840458],"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":[0.00012490622,0.00012716337,0.00015199844,0.00048604058,0.0005627363,0.000094507435,0.009403263,0.9785155,0.00025286063,0.00017194873,0.0011589336,0.008950141],"study_design_scores_gemma":[0.002064443,0.00018822915,0.00061018876,0.0001849894,0.00032495297,0.000012219856,0.0015395558,0.16058959,0.0020563435,0.00029826156,0.8306502,0.0014810412],"about_ca_topic_score_codex":0.00008147591,"about_ca_topic_score_gemma":0.000033046173,"teacher_disagreement_score":0.82949126,"about_ca_system_score_codex":0.0003001517,"about_ca_system_score_gemma":0.0000010475217,"threshold_uncertainty_score":0.99966365},"labels":[],"label_agreement":null},{"id":"W2072043228","doi":"10.1007/s11269-009-9568-1","title":"Crisis in Urban Water Systems during the Reconstruction Period: A System Dynamics Analysis of Alternative Policies after the 2003 Earthquake in Bam-Iran","year":2010,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Vulnerability (computing); System dynamics; Natural hazard; Hydrogeology; Process (computing); Vulnerability assessment; Environmental science; Natural disaster; Water resources; Environmental planning; Environmental resource management; Civil engineering; Computer science; Risk analysis (engineering); Environmental economics; Water resource management; Business; Engineering; Computer security; Geography; Meteorology; Economics","score_opus":0.009517482448483718,"score_gpt":0.19670405982268063,"score_spread":0.18718657737419692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072043228","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99677783,0.000046767105,3.4548944e-7,0.0017694256,0.00021005883,0.00061111024,0.000054212673,0.000026595466,0.0005036493],"genre_scores_gemma":[0.99912983,0.000020372037,0.000002854627,0.00005138596,0.00012038834,0.00013019623,0.00003979251,0.0000020970137,0.0005030551],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983928,0.00018772575,0.00041028217,0.0002971576,0.0002932993,0.00041874126],"domain_scores_gemma":[0.9996015,0.000021930457,0.000102774466,0.00017525206,0.000055107408,0.000043447264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005082321,0.00019353902,0.00029333564,0.00013312438,0.00014858898,0.00021720525,0.00040689638,0.00007766628,0.00012092708],"category_scores_gemma":[0.0000048526026,0.000044428565,0.00011369128,0.0008534156,0.00008691207,0.00010346082,0.0002139057,0.00021404708,0.00001212944],"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":[0.00068326533,0.00038493535,0.77051944,0.00076096645,0.0019236056,0.00019086357,0.15106566,0.0056810966,0.062083893,0.0005332247,0.00022600447,0.0059470665],"study_design_scores_gemma":[0.00020778034,0.000034667468,0.92007667,0.00009022276,0.00017726712,0.000019077761,0.073095806,0.002217754,0.00089998165,0.0000072426456,0.00296278,0.00021075289],"about_ca_topic_score_codex":0.005296217,"about_ca_topic_score_gemma":0.041132003,"teacher_disagreement_score":0.14955725,"about_ca_system_score_codex":0.00010706188,"about_ca_system_score_gemma":4.346449e-7,"threshold_uncertainty_score":0.97636485},"labels":[],"label_agreement":null},{"id":"W2073105236","doi":"10.1007/s11269-012-0193-z","title":"Analytical Support for Integrated Water Resources Management: A New Method for Addressing Spatial and Temporal Variability","year":2012,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"System dynamics; Integrated water resources management; Watershed; Computer science; Process (computing); Decision support system; Hydrogeology; Environmental resource management; Function (biology); Water resources; Environmental science; Operations research; Engineering; Data mining; Ecology; Artificial intelligence","score_opus":0.02269163887589416,"score_gpt":0.26211190447125576,"score_spread":0.23942026559536161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073105236","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074087605,0.00009414096,0.905125,0.00044982115,0.0005128083,0.0034684702,0.000037958853,0.00072665664,0.015497487],"genre_scores_gemma":[0.8119135,0.000037941925,0.16124412,0.00032960743,0.00095151574,0.0007467552,0.0010420586,0.00026600558,0.023468474],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.996832,0.000118964635,0.0007042158,0.0006657569,0.0003739162,0.0013051382],"domain_scores_gemma":[0.9989668,0.000060916995,0.00006685929,0.00053444,0.000047506262,0.00032344714],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016724577,0.00055759115,0.000515243,0.00047083845,0.00029039188,0.00044716196,0.0004180684,0.00015874075,0.00024571922],"category_scores_gemma":[0.00001056175,0.00038883128,0.00020533515,0.00016839668,0.00007518197,0.0004146732,0.00045693156,0.00016054064,0.00003696404],"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":[0.0051097074,0.0016874012,0.035954546,0.02657069,0.012005805,0.00010640756,0.095724955,0.12724996,0.0017030995,0.008507683,0.08538687,0.5999929],"study_design_scores_gemma":[0.002139602,0.00011682711,0.0008363355,0.000079411206,0.00070121087,0.000003380689,0.0005000277,0.16551529,0.0020436526,0.0008489513,0.8265432,0.0006720781],"about_ca_topic_score_codex":0.000082229155,"about_ca_topic_score_gemma":0.000013736669,"teacher_disagreement_score":0.7438809,"about_ca_system_score_codex":0.00012281259,"about_ca_system_score_gemma":0.0000015320101,"threshold_uncertainty_score":0.99985635},"labels":[],"label_agreement":null},{"id":"W2073212003","doi":"10.1007/s11269-008-9326-9","title":"Time Evolution of an AMD-Affected River Chemical Makeup","year":2008,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Mine drainage and remediation techniques","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Rio Tinto; Ministerio de Ciencia y Tecnología","keywords":"Acid mine drainage; Hydrogeology; Drainage; Pyrite; Peninsula; Manganese; Drainage basin; Environmental science; Cadmium; Geology; Arsenic; Hydrology (agriculture); Environmental chemistry; Geochemistry; Ecology; Archaeology; Geography; Chemistry; Geotechnical engineering","score_opus":0.004591160018376152,"score_gpt":0.18389676062847185,"score_spread":0.1793056006100957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073212003","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.977073,0.00000716225,0.00047018178,0.000056691857,0.000014391685,0.00023714684,0.0000016064107,0.00010831876,0.022031479],"genre_scores_gemma":[0.99091566,0.000006090062,0.0022052114,0.000056058798,0.000029361407,0.000018239716,0.000025502182,0.000010517679,0.0067333826],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990817,0.000043904594,0.00016374516,0.00022289992,0.00029903383,0.00018872684],"domain_scores_gemma":[0.99964446,0.0000033769577,0.000046160592,0.000247188,0.0000052470295,0.000053569747],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013310541,0.00009853137,0.0001051814,0.00005641687,0.00006449645,0.000005501551,0.00021399789,0.000044604763,0.0015472671],"category_scores_gemma":[0.0000025243755,0.00007009764,0.000044774137,0.00010436494,0.00024284805,0.00011181501,0.00024887163,0.00004681086,0.0006407828],"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":[0.0000962843,0.0008337436,0.0024845912,0.000103268205,0.00009255997,0.00018071737,0.014733373,0.0006676233,0.94570565,0.00010758229,0.021498274,0.013496312],"study_design_scores_gemma":[0.0011170511,0.00032590734,0.046110593,0.000035023517,0.000076769546,0.000026316864,0.0002320781,0.0054357434,0.8079947,0.0015280559,0.1365057,0.00061202154],"about_ca_topic_score_codex":0.00009946546,"about_ca_topic_score_gemma":0.0000017830145,"teacher_disagreement_score":0.13771091,"about_ca_system_score_codex":0.000094769835,"about_ca_system_score_gemma":5.099953e-7,"threshold_uncertainty_score":0.99936545},"labels":[],"label_agreement":null},{"id":"W2076719588","doi":"10.1007/s11269-013-0283-6","title":"Evaluating Water Quality Failure Potential in Water Distribution Systems: A Fuzzy-TOPSIS-OWA-based Methodology","year":2013,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Water quality; TOPSIS; Quality (philosophy); Fuzzy logic; Environmental science; Computer science; Risk analysis (engineering); Operations research; Engineering; Business; Artificial intelligence","score_opus":0.08113647468878878,"score_gpt":0.323688614585279,"score_spread":0.24255213989649022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076719588","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9894575,0.000011647233,0.0042893915,0.0038351296,0.00031661722,0.0012445342,0.000006025545,0.00037126185,0.000467932],"genre_scores_gemma":[0.990937,0.0000016679587,0.0067581544,0.00008814612,0.00007203997,0.0006371182,0.00016241515,0.00003451563,0.0013089719],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99478245,0.0014403159,0.0008538581,0.000890552,0.0008143366,0.0012185004],"domain_scores_gemma":[0.99882555,0.000047571655,0.00009371309,0.00091446744,0.00002381913,0.000094874216],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003774415,0.0003875753,0.00043983746,0.00016004751,0.00023554465,0.0002801608,0.0008396143,0.00022378673,0.00078172586],"category_scores_gemma":[0.000032096395,0.00021725074,0.00012254226,0.00013622371,0.00027494368,0.000326765,0.001680476,0.0002986237,0.0028396307],"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":[0.00035454275,0.0008517221,0.0609901,0.0013672124,0.00035331084,0.00024466545,0.013725433,0.21430877,0.6853947,0.0004326068,0.0037666648,0.0182103],"study_design_scores_gemma":[0.0040047057,0.0005249706,0.09264894,0.00022864141,0.00021746247,0.000014952438,0.00829622,0.010826087,0.80632293,0.013029807,0.061693717,0.0021915457],"about_ca_topic_score_codex":0.006483145,"about_ca_topic_score_gemma":0.00007777102,"teacher_disagreement_score":0.20348269,"about_ca_system_score_codex":0.0005391239,"about_ca_system_score_gemma":0.0000012114871,"threshold_uncertainty_score":0.9979368},"labels":[],"label_agreement":null},{"id":"W2076787842","doi":"10.1007/s11269-014-0544-z","title":"Water Management and the Procedural Turn: Norms and Transitions in Alberta","year":2014,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Governance and Infrastructure","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Sustainability; Politics; Stakeholder; Sociotechnical system; Procedural justice; Political science; Order (exchange); Sociology; Environmental resource management; Public relations; Business; Management; Economics; Epistemology; Law","score_opus":0.003895931581942873,"score_gpt":0.1971580606383888,"score_spread":0.19326212905644594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076787842","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8704531,0.00004287051,0.000058869893,0.030028172,0.00008713675,0.0006933804,0.0000011470831,0.000027008417,0.09860827],"genre_scores_gemma":[0.9824996,0.000105976214,0.00009170539,0.0013300424,0.00009031675,0.00007155446,0.0000055143155,0.000010033536,0.01579526],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.998776,0.00011585048,0.00017744875,0.00026574853,0.00025696872,0.00040797482],"domain_scores_gemma":[0.9997304,0.000011650705,0.000023265258,0.0001678672,0.000009237109,0.000057559653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051073276,0.00013472691,0.00014507631,0.00007181368,0.00037508024,0.0002147881,0.00021747133,0.000042034924,0.000076247095],"category_scores_gemma":[0.0000026758585,0.00006230216,0.00003303157,0.000066866996,0.00035804685,0.00018480782,0.00017327756,0.000093280396,0.000027340886],"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":[0.0004144969,0.000108240834,0.008707901,0.00062701723,0.00029473088,0.000046779165,0.7138039,0.00025328033,0.0000889917,0.24333212,0.0040342575,0.028288279],"study_design_scores_gemma":[0.002023306,0.00002752185,0.01675879,0.000057702135,0.000060127564,0.0000023065745,0.0066319564,0.00014367503,0.00022953206,0.022925358,0.950913,0.00022668538],"about_ca_topic_score_codex":0.0012798482,"about_ca_topic_score_gemma":0.0027383289,"teacher_disagreement_score":0.9468788,"about_ca_system_score_codex":0.000025728421,"about_ca_system_score_gemma":8.233693e-7,"threshold_uncertainty_score":0.2884852},"labels":[],"label_agreement":null},{"id":"W2080579115","doi":"10.1007/s11269-009-9516-0","title":"Relaxing the Principle of Prior Appropriation: Stored Water and Sharing the Shortage in Alberta, Canada","year":2009,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"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 Lethbridge","funders":"","keywords":"Water scarcity; Appropriation; Water right; Economic shortage; Business; Water resource management; Water resources; Irrigation; Natural resource economics; Environmental planning; Environmental science; Agriculture; Geography; Economics","score_opus":0.0047726961968556315,"score_gpt":0.169333026534737,"score_spread":0.16456033033788137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080579115","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9893105,0.00008146546,0.0003751338,0.001201797,0.00008636578,0.00066666235,6.0953545e-7,0.000047048416,0.008230408],"genre_scores_gemma":[0.9968498,0.000029634799,0.00011181698,0.00012871462,0.000042993524,0.00003253009,0.000019362189,0.00002078901,0.0027643847],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988068,0.00003081935,0.00033073794,0.00021993942,0.0002707761,0.00034093787],"domain_scores_gemma":[0.9995309,0.000012947585,0.000031393225,0.00038374664,0.000010780903,0.000030233201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033513756,0.000173672,0.00014974129,0.00009492983,0.00013541957,0.00010552156,0.00038933675,0.000031174528,0.000024437606],"category_scores_gemma":[0.0000023580767,0.00008410214,0.000029630375,0.00010913468,0.00002866676,0.00010335242,0.00022048962,0.0001226766,0.0000030508486],"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":[0.00007295328,0.000053823973,0.011358487,0.0004908583,0.000278768,0.000043475982,0.034010883,0.939933,0.00043032697,0.0024698763,0.0005330283,0.0103245415],"study_design_scores_gemma":[0.001520682,0.000076639975,0.08138473,0.00018237477,0.00017386404,0.0000043280033,0.0016752961,0.251209,0.0084878905,0.0007456584,0.65377927,0.0007602575],"about_ca_topic_score_codex":0.010863646,"about_ca_topic_score_gemma":0.038941644,"teacher_disagreement_score":0.688724,"about_ca_system_score_codex":0.00008090867,"about_ca_system_score_gemma":0.0000013681091,"threshold_uncertainty_score":0.99572307},"labels":[],"label_agreement":null},{"id":"W2081516133","doi":"10.1007/s11269-008-9380-3","title":"A Two-Step Infinite α-Cuts Fuzzy Linear Programming Method in Determination of Optimal Allocation Strategies in Agricultural Irrigation Systems","year":2008,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Linear programming; Fuzzy logic; Mathematical optimization; Fuzzy number; Mathematics; Fuzzy set operations; Linear-fractional programming; Computer science; Fuzzy set; Artificial intelligence","score_opus":0.015241318940135213,"score_gpt":0.24995074344953497,"score_spread":0.23470942450939974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081516133","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.571615,0.00009271449,0.41193864,0.000039548042,0.000110912224,0.001351682,9.9883e-7,0.00025403537,0.014596505],"genre_scores_gemma":[0.84680134,0.000012317761,0.15285172,0.0000035141104,0.00002018059,0.00013442451,0.000032858232,0.00001615141,0.0001275176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988315,0.00006716634,0.00048754318,0.00016274948,0.00021428007,0.00023675634],"domain_scores_gemma":[0.9997292,0.00002131026,0.000059243346,0.00011962392,0.00003794632,0.00003266883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033159507,0.00014519691,0.00019561802,0.00028459643,0.000033608092,0.000060289338,0.000107781154,0.00005296913,0.000004211931],"category_scores_gemma":[0.000006149385,0.00011239749,0.000034621746,0.00028923986,0.000024972871,0.0002820844,0.0000449258,0.00009362026,0.000008187028],"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":[0.00001167564,0.00008534198,0.000110893954,0.0009125296,0.000022811697,0.000018961024,0.009197188,0.9675292,0.0006640171,0.0028442915,0.000004335795,0.018598793],"study_design_scores_gemma":[0.0009243064,0.000053835236,0.0010093638,0.00027994238,0.000023388144,0.0000105313065,0.0058739297,0.98831034,0.0013590328,0.00017012891,0.0017199549,0.0002652488],"about_ca_topic_score_codex":0.000066900284,"about_ca_topic_score_gemma":0.000032220218,"teacher_disagreement_score":0.27518633,"about_ca_system_score_codex":0.00007530617,"about_ca_system_score_gemma":0.0000020186847,"threshold_uncertainty_score":0.45834365},"labels":[],"label_agreement":null},{"id":"W2081925977","doi":"10.1007/s11269-014-0803-z","title":"Hydrological Impacts of Warmer and Wetter Climate in Troutlake and Sturgeon River Basins in Central Canada","year":2014,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba; Manitoba Hydro; Government of Manitoba","funders":"Manitoba Hydro; National Aeronautics and Space Administration","keywords":"Downscaling; Precipitation; Environmental science; Surface runoff; Climate change; Evapotranspiration; Climatology; Climate model; Hydrology (agriculture); Meteorology; Geography; Geology; Ecology","score_opus":0.0037391926426329843,"score_gpt":0.1709105158711206,"score_spread":0.16717132322848763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081925977","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9918124,0.000023556897,0.000009154521,0.0018726512,0.0000342597,0.00022573324,0.0000028799177,0.000007967651,0.0060113664],"genre_scores_gemma":[0.9983789,0.00010477148,0.0000734868,0.00126828,0.000007912809,0.0000130739145,0.0000031606473,0.00000619558,0.0001442564],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986611,0.00010011434,0.0002263037,0.0003233029,0.00016484444,0.0005243605],"domain_scores_gemma":[0.9997508,0.000018008566,0.0000365303,0.0001332663,0.0000010083025,0.000060393315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003549487,0.00014883581,0.00020938185,0.00007264946,0.000059413193,0.000011919396,0.00011405125,0.000038228554,0.00011188076],"category_scores_gemma":[0.0000036012684,0.00009943389,0.000015653884,0.000063563166,0.00023375817,0.00007688698,0.00063569465,0.00008800166,0.000008335999],"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":[0.000073673065,0.000049914317,0.9940963,0.000065224005,0.000023208591,0.00005771767,0.002436113,0.0010185617,0.00007817885,0.000044652304,0.00030737763,0.0017490708],"study_design_scores_gemma":[0.0007470004,0.000058682526,0.95405644,0.0000162848,0.00002154785,9.7514e-7,0.00013422882,0.0008715684,0.00012235143,0.0002319483,0.04360934,0.00012964156],"about_ca_topic_score_codex":0.048312075,"about_ca_topic_score_gemma":0.28864723,"teacher_disagreement_score":0.24033517,"about_ca_system_score_codex":0.000064034866,"about_ca_system_score_gemma":4.79836e-7,"threshold_uncertainty_score":0.9580253},"labels":[],"label_agreement":null},{"id":"W2082905016","doi":"10.1007/s11269-009-9438-x","title":"Streamflow Forecast and Reservoir Operation Performance Assessment Under Climate Change","year":2009,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":116,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Ansys (Canada)","funders":"Chinese Academy of Sciences","keywords":"Streamflow; Environmental science; Watershed; Climate change; Water resources; Hydrology (agriculture); Flood forecasting; Flood myth; Climatology; Water resource management; Computer science; Geology; Drainage basin; Geography; Ecology","score_opus":0.016736513119091845,"score_gpt":0.23304396315359185,"score_spread":0.2163074500345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082905016","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9305062,0.000024652689,0.00014962217,0.0054523675,0.00005980636,0.0005288757,0.0000014328035,0.00007324631,0.06320374],"genre_scores_gemma":[0.99512714,0.0004974957,0.0006966138,0.0018756982,0.000048457623,0.00008799594,0.000018173845,0.000010436213,0.0016379956],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986096,0.000051146708,0.00018824021,0.00041467085,0.00025376442,0.00048259323],"domain_scores_gemma":[0.9996352,0.000004566166,0.00003592015,0.00026022215,0.0000030808192,0.000060994214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036794285,0.00019431894,0.00014555552,0.00006904725,0.0004599727,0.000081306156,0.00019462383,0.000041691706,0.000330667],"category_scores_gemma":[4.6313107e-7,0.00013417249,0.000028701048,0.00007054755,0.000108614084,0.00037413475,0.0006365736,0.00008802685,0.0002627429],"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":[0.0005171791,0.0010826068,0.55468637,0.0005333536,0.0005089563,0.00022426287,0.02294148,0.053958163,0.0011233719,0.0067174523,0.007873293,0.3498335],"study_design_scores_gemma":[0.00096286787,0.00049978046,0.90291506,0.000043807537,0.00009441559,0.0000039653605,0.00052953255,0.01170644,0.00038917657,0.0013044525,0.081117675,0.00043285222],"about_ca_topic_score_codex":0.00003463211,"about_ca_topic_score_gemma":0.000033583357,"teacher_disagreement_score":0.34940064,"about_ca_system_score_codex":0.000074335316,"about_ca_system_score_gemma":2.0903931e-7,"threshold_uncertainty_score":0.5471395},"labels":[],"label_agreement":null},{"id":"W2083106240","doi":"10.1007/s11269-006-9069-4","title":"Planning water resources systems with interval stochastic dynamic programming","year":2006,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Interval (graph theory); Stochastic programming; Mathematical optimization; Dynamic programming; Computer science; Probability distribution; Mathematics; Statistics","score_opus":0.004951408562414451,"score_gpt":0.1777544996227613,"score_spread":0.17280309106034686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083106240","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88352287,0.00042529136,0.09662809,0.00009711147,0.00034606046,0.0013459384,0.000003725656,0.0016062352,0.016024692],"genre_scores_gemma":[0.9891719,0.000004325885,0.0011322494,0.000023263683,0.00019827463,0.00024881368,0.00020129186,0.00015712493,0.008862735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972984,0.00005687821,0.00053335406,0.00053466414,0.0005529978,0.0010237105],"domain_scores_gemma":[0.9993262,0.000010053651,0.000056674136,0.000479329,0.00003230406,0.00009543534],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002862018,0.0005136537,0.00036638352,0.0005137141,0.0002442687,0.0007627632,0.0004882561,0.000088758694,0.00004563335],"category_scores_gemma":[6.319861e-7,0.00031891704,0.00009117475,0.00019045842,0.000083112805,0.00027116283,0.00029709385,0.00020588569,0.00017592369],"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":[0.00007137214,0.0000481244,0.00036073642,0.0006053249,0.00025433837,0.00014247304,0.004333833,0.9922126,0.0001529238,0.000034578254,0.00034556055,0.0014381144],"study_design_scores_gemma":[0.0020842575,0.00026835306,0.0009972147,0.0008127464,0.0004211567,0.00003414605,0.0027893165,0.5141254,0.00091615756,0.00010804587,0.47588897,0.0015542564],"about_ca_topic_score_codex":0.00009496734,"about_ca_topic_score_gemma":0.000021104639,"teacher_disagreement_score":0.47808725,"about_ca_system_score_codex":0.00014436373,"about_ca_system_score_gemma":5.203464e-7,"threshold_uncertainty_score":0.99992627},"labels":[],"label_agreement":null},{"id":"W2083586118","doi":"10.1007/s11269-014-0549-7","title":"Mitigating Socio-Economic-Environmental Impacts During Drought Periods by Optimizing the Conjunctive Management of Water Resources","year":2014,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"University of Tehran; Islamic Azad University","keywords":"Conjunctive use; Aquifer; Environmental science; Water resource management; Water quality; Water resources; Water supply; Water use; Resource (disambiguation); Groundwater; Environmental engineering; Computer science; Engineering","score_opus":0.0032873553960391932,"score_gpt":0.16296085602121152,"score_spread":0.15967350062517233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083586118","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9639914,0.00023054266,0.0020612732,0.00029171922,0.0002325621,0.0008927669,0.000015667358,0.00031267668,0.031971414],"genre_scores_gemma":[0.9927617,0.00024051896,0.0011648182,0.00009253728,0.00015702334,0.00013419482,0.000134407,0.00013889627,0.00517592],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970992,0.00013558488,0.0007385684,0.00058547675,0.0004811835,0.00095995684],"domain_scores_gemma":[0.99901444,0.000022184602,0.00012807241,0.00069774396,0.000009588088,0.00012798292],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005891026,0.0005567872,0.00042994198,0.00021464373,0.00049151265,0.0003256699,0.00076499634,0.000100936406,0.0003612999],"category_scores_gemma":[8.711197e-7,0.000358935,0.00021017414,0.0000761695,0.00022059813,0.00033265929,0.000834919,0.00022651354,0.00023802843],"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":[0.0002685816,0.00021488636,0.0034763324,0.0032438622,0.0039090617,0.00006435669,0.068777524,0.8887438,0.019525073,0.000318055,0.0035578222,0.0079006525],"study_design_scores_gemma":[0.007512937,0.0003583288,0.0070732674,0.0007752756,0.0013849653,0.00001755332,0.033459447,0.13510928,0.20578972,0.00052753295,0.6047478,0.0032439195],"about_ca_topic_score_codex":0.000030202102,"about_ca_topic_score_gemma":0.0000024634835,"teacher_disagreement_score":0.7536345,"about_ca_system_score_codex":0.0002646255,"about_ca_system_score_gemma":4.502857e-7,"threshold_uncertainty_score":0.9998863},"labels":[],"label_agreement":null},{"id":"W2083679619","doi":"10.1007/s11269-012-0103-4","title":"Integrated Reservoir Management System for Flood Risk Assessment Under Climate Change","year":2012,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Climate change; Environmental science; Flood myth; Structural basin; Hydrogeology; Hydrology (agriculture); Drainage basin; Hydrological modelling; Water resource management; Climatology; Geology; Geography; Geomorphology; Geotechnical engineering; Oceanography","score_opus":0.01955406816719199,"score_gpt":0.223019296164352,"score_spread":0.20346522799716002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083679619","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55328685,0.001056728,0.22129613,0.0005542429,0.003702411,0.0107273655,0.00018149373,0.0051769814,0.20401779],"genre_scores_gemma":[0.97875303,0.00051159033,0.015446156,0.00011625276,0.00053333334,0.0020527253,0.00041466375,0.0001963303,0.0019758993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99693525,0.000112098765,0.00058460754,0.000474451,0.0004936292,0.0013999697],"domain_scores_gemma":[0.99892426,0.000017423254,0.00009960488,0.0007103252,0.000045392924,0.0002030078],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009494929,0.000528753,0.00037794944,0.00053017325,0.00035729932,0.00032440483,0.00054860284,0.00011085783,0.00007017198],"category_scores_gemma":[0.0000010529196,0.00040786286,0.00018676354,0.0003062158,0.00003610691,0.00054680684,0.00052473036,0.00019001652,0.00024113139],"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":[0.00072654645,0.0016705627,0.032588154,0.031147974,0.009494746,0.00015342455,0.020380614,0.7160095,0.00020331371,0.052269008,0.020056335,0.115299836],"study_design_scores_gemma":[0.0033576363,0.00013808557,0.01317803,0.00053002965,0.0012629336,0.0000042269926,0.0062080715,0.13900653,0.0009882974,0.00024519453,0.83367217,0.0014087926],"about_ca_topic_score_codex":0.000023926708,"about_ca_topic_score_gemma":0.000007890128,"teacher_disagreement_score":0.81361586,"about_ca_system_score_codex":0.0003902191,"about_ca_system_score_gemma":5.4561457e-7,"threshold_uncertainty_score":0.99983734},"labels":[],"label_agreement":null},{"id":"W2084074253","doi":"10.1007/s11269-009-9403-8","title":"Incorporating Virtual Water into Water Management: A British Columbia Example","year":2009,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Virtual water; Environmental science; Livestock; Agriculture; Water conservation; Water use; Irrigation; Farm water; Rainwater harvesting; Water resources; Water resource management; Agroforestry; Geography; Agronomy; Water scarcity; Ecology; Biology; Forestry","score_opus":0.007201184940873034,"score_gpt":0.17632785112282193,"score_spread":0.16912666618194888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084074253","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7296583,0.00002522546,0.00041605474,0.0009373524,0.00020502461,0.0006831055,0.0000055468063,0.00033833156,0.26773104],"genre_scores_gemma":[0.9233211,0.0000091007305,0.0015443943,0.0015085438,0.000113151276,0.00015079224,0.000093184724,0.000059981285,0.07319971],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9958189,0.00013611776,0.00063182716,0.0011591917,0.0008904397,0.0013635332],"domain_scores_gemma":[0.9989676,0.0000073358406,0.00006557506,0.0007495697,0.0000130269655,0.00019692114],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00056391465,0.0004077894,0.0003948049,0.00010348381,0.000974149,0.0008326778,0.0008745865,0.00009511166,0.0028362342],"category_scores_gemma":[0.000001355508,0.00033670425,0.00014428973,0.00016084083,0.00027162782,0.00045939971,0.002825579,0.00016683571,0.002222166],"study_design_candidate":"not_applicable","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":[0.00056582206,0.003976049,0.016144847,0.0009272567,0.0030517902,0.0119556235,0.08368779,0.011829298,0.07545361,0.0067706783,0.2068334,0.57880384],"study_design_scores_gemma":[0.0022503717,0.0007371516,0.015491709,0.00015246893,0.00024782188,0.000046459274,0.0022605187,0.00027645464,0.027749097,0.13980721,0.80922073,0.0017599884],"about_ca_topic_score_codex":0.01524475,"about_ca_topic_score_gemma":0.012144193,"teacher_disagreement_score":0.6023873,"about_ca_system_score_codex":0.00027388782,"about_ca_system_score_gemma":4.6854086e-7,"threshold_uncertainty_score":0.9999085},"labels":[],"label_agreement":null},{"id":"W2084512008","doi":"10.1007/s11269-012-0147-5","title":"A Systematic Review of Water Vulnerability Assessment Tools","year":2012,"lang":"en","type":"review","venue":"Water Resources Management","topic":"Child Nutrition and Water Access","field":"Nursing","cited_by":146,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Brock University","funders":"","keywords":"Vulnerability (computing); Context (archaeology); Vulnerability assessment; Environmental resource management; Adaptation (eye); Integrated water resources management; Environmental planning; Water resources; Corporate governance; Environmental science; Business; Computer science; Psychological resilience; Geography; Psychology; Ecology","score_opus":0.050476988510751906,"score_gpt":0.34099752124897587,"score_spread":0.290520532738224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084512008","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014097247,0.98509556,0.00002830367,0.00053044915,0.00068115024,0.008389581,0.000048745143,0.00013432262,0.005077813],"genre_scores_gemma":[0.00009072227,0.9952714,0.00013766774,0.00078433607,0.00023625967,0.001447595,0.00072791445,0.00011298023,0.0011911632],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.9934908,0.0014534126,0.002607461,0.0007346244,0.00088157004,0.0008321359],"domain_scores_gemma":[0.997364,0.00009744696,0.000558998,0.0017207445,0.000090635025,0.00016819258],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0025599357,0.0007949393,0.0041987053,0.00039321894,0.00015007526,0.00021902217,0.0010860296,0.00020262347,0.0007017014],"category_scores_gemma":[0.000016195947,0.00039313536,0.001272566,0.00020222226,0.000088024215,0.00024827244,0.00079527777,0.00043770875,0.00050281634],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007901493,0.00028109847,0.0000018381394,0.9673616,0.0005859858,0.000010264949,0.0002907384,1.5801236e-7,2.4662424e-7,0.000021432,0.00070520065,0.030733528],"study_design_scores_gemma":[0.00015746137,0.000030677213,0.0000015255725,0.44599232,0.0060009845,0.000011825558,0.000009061264,3.8844746e-7,0.00003383585,0.000027206315,0.5474118,0.00032293517],"about_ca_topic_score_codex":0.000015001609,"about_ca_topic_score_gemma":8.9174637e-7,"teacher_disagreement_score":0.54670656,"about_ca_system_score_codex":0.00034650113,"about_ca_system_score_gemma":0.000003426983,"threshold_uncertainty_score":0.99985206},"labels":[],"label_agreement":null},{"id":"W2085539475","doi":"10.1007/s11269-011-9909-8","title":"Determining the Main Factors in Declining the Urmia Lake Level by Using System Dynamics Modeling","year":2011,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":385,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Precipitation; Water level; Structural basin; Climate change; Hydrology (agriculture); Ecosystem; Water resources; Surface water; System dynamics; Quarter (Canadian coin); Water resource management; Physical geography; Environmental resource management; Geography; Ecology; Meteorology; Environmental engineering; Oceanography; Geology","score_opus":0.044364562976025534,"score_gpt":0.1981980432278346,"score_spread":0.15383348025180907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085539475","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90857476,0.000039528724,0.081528895,0.000023269822,0.00020588924,0.0005658304,0.000010630145,0.00024863597,0.00880257],"genre_scores_gemma":[0.9980791,0.000013737639,0.0011948186,0.000046484623,0.00004328644,0.0000384322,0.000057436886,0.00007873867,0.0004479428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830645,0.000078415906,0.00046924647,0.0002935584,0.00029152387,0.000560784],"domain_scores_gemma":[0.9994326,0.000019263236,0.000056039135,0.0004326294,0.0000143951465,0.000045047127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053184596,0.00030800473,0.00020282753,0.00016619114,0.0002791279,0.00021193066,0.00063919724,0.00006849851,0.00002001383],"category_scores_gemma":[0.0000022723702,0.00017205466,0.00009987044,0.00020553084,0.000041744814,0.00018219631,0.00034176552,0.00020824524,0.000013668336],"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":[0.000011999022,0.000020726442,0.011492287,0.00022162397,0.00014987904,0.000014519999,0.015737733,0.9704359,0.000013404541,0.00013156097,0.00009880136,0.0016715312],"study_design_scores_gemma":[0.00033626973,0.000010777469,0.0008603767,0.00010805768,0.00008079395,0.0000010369337,0.0055725975,0.9897791,0.00010714416,0.00002616027,0.002850099,0.00026756345],"about_ca_topic_score_codex":0.00012026285,"about_ca_topic_score_gemma":0.00035086993,"teacher_disagreement_score":0.089504376,"about_ca_system_score_codex":0.00018226723,"about_ca_system_score_gemma":9.198913e-7,"threshold_uncertainty_score":0.7016185},"labels":[],"label_agreement":null},{"id":"W2086148740","doi":"10.1007/s11269-014-0694-z","title":"Impacts and Adaptation to Climate Change Using a Reservoir Management Tool to a Northern Watershed: Application to Lièvre River Watershed, Quebec, Canada","year":2014,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Watershed; Environmental science; Climate change; Streamflow; Watershed management; Flooding (psychology); Hydrology (agriculture); Water resource management; Flood myth; Water resources; Environmental resource management; Drainage basin; Geography; Ecology; Geology","score_opus":0.01308319574861168,"score_gpt":0.2088035493470622,"score_spread":0.1957203535984505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086148740","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98085666,0.000008295841,0.0050194906,0.008832113,0.000107741034,0.0026851376,0.000007180638,0.00009109265,0.0023922983],"genre_scores_gemma":[0.98626083,0.000022006016,0.004165768,0.006903004,0.00008460646,0.0006703066,0.000030190433,0.000047205813,0.0018160883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99691814,0.00011048546,0.000415425,0.00095768913,0.0005677963,0.0010304388],"domain_scores_gemma":[0.9989417,0.000011570039,0.0000701951,0.00062503904,0.000015732936,0.00033573824],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006171281,0.00040108076,0.00029884127,0.00024210145,0.00048798372,0.00012591032,0.00047894794,0.00005497813,0.000081014165],"category_scores_gemma":[0.000006361352,0.00030827007,0.000044942393,0.00030006,0.00006719595,0.00024755605,0.0023923966,0.00008340027,0.0007295955],"study_design_candidate":"not_applicable","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":[0.0021073036,0.00046831247,0.19331636,0.0018887626,0.0011006503,0.0003844815,0.18999907,0.37700203,0.0029714275,0.0006116252,0.020402415,0.20974755],"study_design_scores_gemma":[0.0011191828,0.00030169552,0.17941238,0.000194022,0.00028626955,0.000003372557,0.003603014,0.009149898,0.00065906264,0.00027220504,0.80364317,0.0013557293],"about_ca_topic_score_codex":0.3033595,"about_ca_topic_score_gemma":0.673286,"teacher_disagreement_score":0.78324074,"about_ca_system_score_codex":0.00056730787,"about_ca_system_score_gemma":0.0000014372614,"threshold_uncertainty_score":0.99993694},"labels":[],"label_agreement":null},{"id":"W2088166615","doi":"10.1007/s11269-008-9294-0","title":"An Inexact Chance-constrained Quadratic Programming Model for Stream Water Quality Management","year":2008,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Mathematical optimization; Water quality; Quadratic programming; Computer science; Transformation (genetics); Nonlinear programming; Interval (graph theory); Quadratic equation; Hydrogeology; Quality (philosophy); Nonlinear system; Mathematics; Engineering","score_opus":0.02423675182781286,"score_gpt":0.23640679342027865,"score_spread":0.21217004159246577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088166615","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56447953,0.000050919196,0.41858396,0.00013692174,0.00021167104,0.0030334233,0.0000143460875,0.0014159058,0.0120733185],"genre_scores_gemma":[0.96604776,0.00008246301,0.02470557,0.00012623677,0.00014706852,0.0008944875,0.0004966806,0.00013610895,0.0073635913],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99702024,0.000060250874,0.00069948524,0.00066262676,0.0004862236,0.0010711811],"domain_scores_gemma":[0.99894905,0.000007676172,0.000058726313,0.000764838,0.00004380007,0.00017589655],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048753142,0.0005264333,0.0004097375,0.00041478768,0.00038961117,0.00024073328,0.0005951573,0.00009754299,0.00006781836],"category_scores_gemma":[0.0000012197421,0.00038029073,0.00017791569,0.00015176495,0.00009631837,0.00044682514,0.00021313548,0.00012764506,0.000081117214],"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":[0.000120052195,0.00026008394,0.00026257706,0.0016013001,0.00055885134,0.000061891194,0.011824174,0.9684005,0.00037403777,0.00069033337,0.00051598647,0.015330216],"study_design_scores_gemma":[0.0019691668,0.00013997793,0.000273226,0.00006830212,0.00019472656,0.0000037713796,0.0008390271,0.9378845,0.0035867211,0.0004599685,0.053672176,0.0009084503],"about_ca_topic_score_codex":0.000015603115,"about_ca_topic_score_gemma":0.000014639646,"teacher_disagreement_score":0.40156826,"about_ca_system_score_codex":0.000118579585,"about_ca_system_score_gemma":0.000001235347,"threshold_uncertainty_score":0.9998649},"labels":[],"label_agreement":null},{"id":"W2089245452","doi":"10.1007/s11269-013-0272-9","title":"A CFD Modeling Approach for Municipal Sewer System Design Optimization to Minimize Emissions into Receiving Water Body","year":2013,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computational fluid dynamics; Volume of fluid method; Turbulence; Environmental science; Hydrogeology; Marine engineering; Combined sewer; Flow (mathematics); Environmental engineering; Engineering; Stormwater; Geotechnical engineering; Meteorology; Mechanics; Surface runoff","score_opus":0.016331034851378424,"score_gpt":0.1965034849992662,"score_spread":0.18017245014788777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089245452","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018399011,0.000031027987,0.9742587,0.00009104987,0.00025068416,0.002361409,0.0000017074207,0.00050874427,0.0040976605],"genre_scores_gemma":[0.68798834,0.000005222558,0.30681756,0.000036823487,0.00013717511,0.0015448934,0.00009748842,0.00010089363,0.003271627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981938,0.00007290712,0.00050840946,0.00040809924,0.0002346046,0.000582192],"domain_scores_gemma":[0.9992793,0.000010830562,0.000025691685,0.00043918515,0.000082522805,0.00016242982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041384352,0.0003008552,0.00028518745,0.00027573114,0.00024054843,0.00033304014,0.0003186801,0.000106531996,0.000044561824],"category_scores_gemma":[0.00000393712,0.00019914714,0.00008288611,0.000108007465,0.000008502751,0.00025441695,0.00020022322,0.00007710201,0.00008089245],"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":[0.000017920207,0.000020188818,0.0000059986287,0.0006893247,0.00010329747,0.0000014957986,0.010449971,0.98654485,0.0006075487,0.000011891921,0.0013639481,0.00018358366],"study_design_scores_gemma":[0.00042952137,0.000027667424,0.0000025370575,0.00015787997,0.000059562622,0.000001956972,0.0018791389,0.99136853,0.002313361,0.000013104038,0.003409238,0.00033747224],"about_ca_topic_score_codex":0.00013713064,"about_ca_topic_score_gemma":0.0000020394864,"teacher_disagreement_score":0.66958934,"about_ca_system_score_codex":0.00016828593,"about_ca_system_score_gemma":0.0000010924452,"threshold_uncertainty_score":0.81209844},"labels":[],"label_agreement":null},{"id":"W2090560127","doi":"10.1023/b:warm.0000049145.37577.87","title":"Possible Regional Probability Distribution Type of Canadian Annual Streamflow by L-moments","year":2004,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Generalized extreme value distribution; Streamflow; Log-normal distribution; Tundra; Weibull distribution; Distribution (mathematics); Extreme value theory; Gumbel distribution; Physical geography; Arctic; Environmental science; Hydrology (agriculture); Geography; Geology; Mathematics; Statistics; Oceanography; Drainage basin; Cartography","score_opus":0.00957540485934347,"score_gpt":0.2028725339336579,"score_spread":0.19329712907431443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090560127","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9876629,0.000015554355,0.00010978538,0.00119903,0.000027458158,0.00017262643,0.00005224888,0.00001618194,0.010744228],"genre_scores_gemma":[0.99544865,0.000012299838,0.00019970821,0.00015293056,0.00000960451,0.000010545995,0.00026090725,0.0000052377354,0.0039001289],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989627,0.000035919267,0.00017673246,0.00027282714,0.00023812309,0.0003136824],"domain_scores_gemma":[0.9995872,0.000002676481,0.000037601345,0.00024004244,0.000008085504,0.0001243898],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020033805,0.00010445235,0.000111082336,0.00007502946,0.00012733285,0.000012262144,0.0002139878,0.000052746167,0.001061815],"category_scores_gemma":[0.0000026551597,0.00007855504,0.000047161477,0.000343994,0.00016202564,0.00010126599,0.00012926407,0.000056052868,0.0003626014],"study_design_candidate":"not_applicable","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":[0.0005994517,0.0023662439,0.664581,0.00023914294,0.0012146399,0.00020065795,0.017906254,0.15133597,0.0033429237,0.0031573884,0.14179923,0.0132570965],"study_design_scores_gemma":[0.0011860589,0.00029329772,0.12690075,0.000024821438,0.00017120171,0.0000039694,0.0003568548,0.00026530793,0.006041322,0.008089228,0.8562254,0.00044180933],"about_ca_topic_score_codex":0.07298642,"about_ca_topic_score_gemma":0.025093058,"teacher_disagreement_score":0.71442616,"about_ca_system_score_codex":0.00024763268,"about_ca_system_score_gemma":0.0000036457661,"threshold_uncertainty_score":0.99985135},"labels":[],"label_agreement":null},{"id":"W2092726758","doi":"10.1007/s11269-010-9612-1","title":"Integrated Reservoir Management System for Adaptation to Climate Change: The Nakdong River Basin in Korea","year":2010,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":86,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Climate change; Environmental science; Structural basin; Inflow; Hydrology (agriculture); Water resources; Hydrogeology; Drainage basin; Effects of global warming; Water resource management; Global warming; Geology; Meteorology; Geomorphology; Geography; Ecology","score_opus":0.016028456274610456,"score_gpt":0.20359649012315076,"score_spread":0.18756803384854032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092726758","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9340344,0.000059733287,0.027206738,0.0013678388,0.0012968143,0.0069508776,0.00003079013,0.0010003352,0.02805248],"genre_scores_gemma":[0.98648846,0.00005711322,0.009234153,0.00020230746,0.00020359445,0.002061282,0.00015139543,0.00010431211,0.0014973923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979735,0.00006028133,0.00045678578,0.00044027547,0.00034690127,0.00072225445],"domain_scores_gemma":[0.9992129,0.000019508245,0.000047668025,0.0005904074,0.00004093773,0.000088548724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076427095,0.00033851605,0.00024171629,0.0005815807,0.0002006012,0.00027392214,0.0006346091,0.00008145723,0.000026768166],"category_scores_gemma":[0.0000030970616,0.00022781389,0.00009097363,0.00045228988,0.00004029831,0.00023033956,0.00037320136,0.00019878263,0.00014696848],"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":[0.00061761314,0.00019270394,0.0018176404,0.005403258,0.00067460514,0.00018086692,0.07208534,0.76630235,0.0003713119,0.009805034,0.0051812967,0.13736798],"study_design_scores_gemma":[0.0015533547,0.00007077847,0.005407348,0.0003536411,0.00015636745,0.0000020097657,0.007823126,0.30384943,0.0008957861,0.00016805257,0.6791039,0.0006161836],"about_ca_topic_score_codex":0.00011332927,"about_ca_topic_score_gemma":0.00032634192,"teacher_disagreement_score":0.6739226,"about_ca_system_score_codex":0.00013906276,"about_ca_system_score_gemma":5.5154936e-7,"threshold_uncertainty_score":0.92899805},"labels":[],"label_agreement":null},{"id":"W2094495849","doi":"10.1007/s11269-011-9908-9","title":"Multi-Objective Sensitivity Analysis of a Fully Distributed Hydrologic Model WetSpa","year":2011,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Sensitivity (control systems); Evapotranspiration; Hydrogeology; Calibration; Snowmelt; Environmental science; Distributed element model; Watershed; Hydrological modelling; Hydrology (agriculture); Process (computing); Groundwater; Computer science; Mathematics; Meteorology; Statistics; Engineering; Snow; Geology; Geotechnical engineering; Machine learning; Climatology","score_opus":0.02032396465366768,"score_gpt":0.21212618705818626,"score_spread":0.19180222240451858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094495849","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9463014,0.0000066830344,0.032403607,0.00017755332,0.000032505603,0.00037836222,0.000025563057,0.00008814095,0.020586193],"genre_scores_gemma":[0.9949462,0.000016193331,0.0027254862,0.00024271577,0.00000442588,0.000047140657,0.000038074042,0.000010952741,0.0019688315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826694,0.00014872372,0.0002988165,0.000550356,0.00026250366,0.00047265255],"domain_scores_gemma":[0.9993637,0.000013709124,0.00010633447,0.00044345483,0.00000895741,0.00006380788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005703177,0.00023576409,0.0003962818,0.0002293823,0.00018365322,0.000010952169,0.00026652543,0.00006590739,0.0005812173],"category_scores_gemma":[0.0000054703123,0.00014711841,0.00019112066,0.00044237028,0.00036417902,0.00011734255,0.0012051694,0.000095761316,0.0002060354],"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":[0.00039449695,0.0010975159,0.26102498,0.00006317522,0.007118304,0.0002715265,0.026247011,0.7000451,0.0018415558,0.00014548338,0.0006134757,0.0011373468],"study_design_scores_gemma":[0.00082697003,0.00015639408,0.4009521,0.0000079279125,0.0032092873,0.0000011507148,0.0007464577,0.5867313,0.0030336685,0.0009378434,0.002908241,0.0004886263],"about_ca_topic_score_codex":0.0005632332,"about_ca_topic_score_gemma":0.00031211216,"teacher_disagreement_score":0.13992712,"about_ca_system_score_codex":0.000071233844,"about_ca_system_score_gemma":4.1238718e-7,"threshold_uncertainty_score":0.6363921},"labels":[],"label_agreement":null},{"id":"W2099726537","doi":"10.1007/s11269-015-0959-1","title":"Developing a Non-Discrete Dynamic Game Model and Corresponding Monthly Collocation Solution Considering Variability in Reservoir Inflow","year":2015,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Universiti Malaya; Imperial College London","keywords":"Inflow; Randomness; Variable (mathematics); Mathematical optimization; Basis (linear algebra); Computer science; Collocation (remote sensing); Hydrogeology; Mathematics; Statistics; Geology","score_opus":0.01743480391393302,"score_gpt":0.22039202987423395,"score_spread":0.20295722596030094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099726537","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8074746,0.000058666807,0.18557015,0.00023548718,0.00011696947,0.0007086679,0.0000024809772,0.00024310025,0.005589875],"genre_scores_gemma":[0.9867264,0.00003401622,0.012263781,0.000045614936,0.000020052037,0.000116103794,0.000059651953,0.00004710256,0.00068729225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998335,0.000089288085,0.00043284023,0.0003869522,0.00028718595,0.00046871442],"domain_scores_gemma":[0.9994873,0.000019826894,0.00004601101,0.00030936292,0.000037247224,0.00010025621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011150363,0.0002607145,0.00023554493,0.0004850194,0.00008325025,0.00020297895,0.00020300841,0.00008237316,0.000004054839],"category_scores_gemma":[0.000020846805,0.00024112406,0.00003074175,0.000281932,0.000046488432,0.0004000169,0.00037802468,0.00013964367,0.000015760013],"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":[0.000060319224,0.000011153013,0.0011903375,0.00029571826,0.000042492404,0.000014122825,0.0068170726,0.9901509,0.00021133399,0.00012502074,0.00007793351,0.0010035456],"study_design_scores_gemma":[0.0008782251,0.000019125198,0.0019086895,0.00013916146,0.000031498006,0.0000010235311,0.0005185328,0.9900164,0.0002826419,0.001926849,0.0039478084,0.0003300312],"about_ca_topic_score_codex":0.00005631015,"about_ca_topic_score_gemma":0.000129364,"teacher_disagreement_score":0.17925179,"about_ca_system_score_codex":0.00047068566,"about_ca_system_score_gemma":0.0000055608257,"threshold_uncertainty_score":0.98327535},"labels":[],"label_agreement":null},{"id":"W2112055273","doi":"10.1007/s11269-015-0983-1","title":"Nonpoint-Source Water Quality Management Under Uncertainty Through an Inexact Double-Sided Chance-Constrained Model","year":2015,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Nonpoint source pollution; Water quality; Constraint (computer-aided design); Environmental science; Quality (philosophy); Interval (graph theory); Computer science; Mathematics; Mathematical optimization; Ecology","score_opus":0.04816725346529931,"score_gpt":0.2591536873257121,"score_spread":0.21098643386041277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112055273","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.534944,0.00008425395,0.27394456,0.00083405245,0.0004864986,0.0022323932,0.0000130964245,0.0022627846,0.18519837],"genre_scores_gemma":[0.97098297,0.000101585785,0.00724209,0.00070431584,0.0002504557,0.00032322286,0.0005332028,0.00021506545,0.019647105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99555963,0.00014400191,0.0009455574,0.00096857134,0.0009919179,0.0013903173],"domain_scores_gemma":[0.9981841,0.000012581519,0.00009188078,0.0012685172,0.000087030276,0.0003559169],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010087731,0.0007968374,0.00058795174,0.0004259687,0.00025034274,0.0005139453,0.0009521325,0.00017167244,0.0001876297],"category_scores_gemma":[0.0000021618773,0.0005717773,0.00019649115,0.0002812806,0.00014529002,0.0010111773,0.00076550595,0.0002822427,0.00045053902],"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":[0.00029336553,0.00015103268,0.000048700967,0.00040232448,0.0005153734,0.00004269546,0.010698316,0.982636,0.00018829496,0.0019359124,0.0013358415,0.0017521812],"study_design_scores_gemma":[0.0074276775,0.00016034192,0.00009660341,0.00014349309,0.0003729689,0.00000595654,0.0051989504,0.8164815,0.0048076482,0.0063942755,0.15709366,0.0018169328],"about_ca_topic_score_codex":0.00016673584,"about_ca_topic_score_gemma":0.00005722732,"teacher_disagreement_score":0.43603897,"about_ca_system_score_codex":0.00036048642,"about_ca_system_score_gemma":0.000003156999,"threshold_uncertainty_score":0.99967337},"labels":[],"label_agreement":null},{"id":"W2113393819","doi":"10.1007/s11269-014-0586-2","title":"Simulation of Multiple Hydropower Reservoir Operations Using System Dynamics Approach","year":2014,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Hydropower; System dynamics; Causal loop diagram; Computer science; Process (computing); Reservoir simulation; Simulation modeling; Complex system; Reservoir engineering; Reservoir modeling; Operations research; Simulation; Industrial engineering; Engineering; Petroleum engineering; Geology; Mathematics","score_opus":0.011086066531574032,"score_gpt":0.19742324966543406,"score_spread":0.18633718313386002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113393819","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42167574,0.000014231093,0.5573326,0.00000880288,0.000101839214,0.00040412584,0.0000033069034,0.00026761988,0.020191744],"genre_scores_gemma":[0.98853725,0.0000027578446,0.010389049,0.00000939604,0.000070439004,0.000024356299,0.00014558306,0.000057872392,0.00076327246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869275,0.00006622108,0.0004000013,0.00024937803,0.00029298334,0.00029865978],"domain_scores_gemma":[0.99943,0.000015919566,0.000037067002,0.0004261899,0.00004008003,0.000050736384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002809789,0.00020716638,0.00021520439,0.00029371062,0.00012892298,0.00010781343,0.00027199535,0.00006240135,0.0000144158785],"category_scores_gemma":[0.0000046874816,0.00016925408,0.00007034198,0.00018790878,0.00003415936,0.00018452406,0.0001778356,0.00007395845,0.000022640184],"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":[0.000011041855,0.00002967708,0.0003770587,0.00068626215,0.000092946764,0.0000012621421,0.0009383408,0.99691737,0.00009254665,0.00047253273,0.000018554327,0.0003624345],"study_design_scores_gemma":[0.0004302429,0.000018901681,0.00008799225,0.00005913362,0.00007341161,4.236002e-7,0.00048533943,0.9907926,0.00041075682,0.000014490253,0.0074164537,0.00021028891],"about_ca_topic_score_codex":0.000044909262,"about_ca_topic_score_gemma":0.000014322193,"teacher_disagreement_score":0.5668615,"about_ca_system_score_codex":0.00014514552,"about_ca_system_score_gemma":4.782617e-7,"threshold_uncertainty_score":0.6901981},"labels":[],"label_agreement":null},{"id":"W2114675401","doi":"10.1007/s11269-010-9650-8","title":"Cross-Comparison of Climate Change Adaptation Strategies Across Large River Basins in Europe, Africa and Asia","year":2010,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":101,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"Climate change; Drainage basin; Structural basin; Water resources; Geography; Environmental resource management; Adaptation (eye); Climate change adaptation; Environmental science; Water resource management; Ecology; Geology","score_opus":0.04703029422530812,"score_gpt":0.29147226891474814,"score_spread":0.24444197468944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114675401","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.993682,0.00008352708,8.966143e-7,0.00045004088,0.0000809179,0.00038485145,0.00018030927,0.000049609942,0.0050878003],"genre_scores_gemma":[0.99929374,0.00015408214,0.000067473135,0.000047219582,0.00010178222,0.00003293213,0.00008485582,0.0000022216043,0.00021567043],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985661,0.000046333866,0.00027835547,0.00030800942,0.00022763784,0.0005735449],"domain_scores_gemma":[0.9996541,0.000022878712,0.0001055374,0.000080163925,0.00006432426,0.00007295739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036077586,0.00017556148,0.00020838474,0.000026107622,0.00016917905,0.00024958965,0.0002279787,0.000085397776,0.00016987104],"category_scores_gemma":[0.000008183231,0.000061522944,0.000034819903,0.0003120919,0.0000981846,0.0003145775,0.00037403757,0.00015865246,0.000027127155],"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":[0.0003732778,0.0013975627,0.4028805,0.00052159914,0.000076218865,0.00009607007,0.17572376,0.00007420414,0.20812233,0.002330274,0.00033177232,0.20807247],"study_design_scores_gemma":[0.0002915748,0.000120992765,0.8573452,0.00003647471,0.000010437674,0.000001607092,0.013303431,0.00017873327,0.0011756375,0.000062305626,0.12729864,0.0001749525],"about_ca_topic_score_codex":0.00015436616,"about_ca_topic_score_gemma":0.007051174,"teacher_disagreement_score":0.45446473,"about_ca_system_score_codex":0.000009988054,"about_ca_system_score_gemma":4.0091368e-7,"threshold_uncertainty_score":0.39347205},"labels":[],"label_agreement":null},{"id":"W2116208828","doi":"10.1007/s11269-012-9992-5","title":"River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia","year":2012,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":78,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Golder Associates (Canada)","funders":"Universiti Teknologi Petronas","keywords":"Conjugate gradient method; Algorithm; Gradient descent; Multilayer perceptron; Artificial neural network; Convergence (economics); Training (meteorology); Sediment; Levenberg–Marquardt algorithm; Hydrogeology; Computer science; Machine learning; Geology; Geomorphology; Geotechnical engineering; Meteorology; Geography","score_opus":0.044213029755571856,"score_gpt":0.2608464373813931,"score_spread":0.21663340762582128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116208828","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9969036,0.000012257882,0.0002579177,0.00004586558,0.00032503452,0.0009182418,0.000001435926,0.000118277334,0.001417373],"genre_scores_gemma":[0.9950045,7.551295e-7,0.0041717095,0.000116166455,0.00026891593,0.000047754307,0.0000058619985,0.000034345732,0.00034999763],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972306,0.00028582255,0.0004048452,0.00053495925,0.00048798925,0.0010557674],"domain_scores_gemma":[0.99940777,0.00001757403,0.00007449793,0.0003228584,0.0000036775361,0.00017361347],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010802455,0.00028644095,0.00023813003,0.00008808908,0.00029468446,0.00006900349,0.00022274531,0.00007452505,0.0009770043],"category_scores_gemma":[0.0000037218247,0.00021541925,0.000063921645,0.00021920512,0.00011819667,0.0002694354,0.00075068354,0.0002476772,0.00015213397],"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":[0.00003460717,0.00033836375,0.19520326,0.000007687965,0.000041189738,0.0014061161,0.04180709,0.7511434,0.00017706717,9.0723324e-7,0.000047455876,0.009792886],"study_design_scores_gemma":[0.0018689065,0.00041046806,0.11107545,0.000033894932,0.0001982867,0.0006055009,0.0053883186,0.8739723,0.000053654272,0.00005269042,0.00577594,0.00056456507],"about_ca_topic_score_codex":0.0019196022,"about_ca_topic_score_gemma":0.000090281035,"teacher_disagreement_score":0.12282895,"about_ca_system_score_codex":0.00044057192,"about_ca_system_score_gemma":7.672584e-7,"threshold_uncertainty_score":0.9999362},"labels":[],"label_agreement":null},{"id":"W2117023137","doi":"10.1007/s11269-014-0634-y","title":"Water Use Efficiency and Productivity of the Irrigation Districts in Southern Alberta","year":2014,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":64,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Lethbridge","funders":"University of Lethbridge; Alberta Water Research Institute","keywords":"Total factor productivity; Data envelopment analysis; Irrigation; Productivity; Malmquist index; Environmental science; Agricultural economics; Water resource management; Water use; Water-use efficiency; Precipitation; Economics; Hydrology (agriculture); Mathematics; Geography; Statistics; Meteorology; Engineering; Economic growth","score_opus":0.0276559046732602,"score_gpt":0.2635389187956307,"score_spread":0.23588301412237048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117023137","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99386925,0.000008470406,0.0011658177,0.0014472106,0.0000855533,0.00027456594,0.0000013158776,0.000009993265,0.0031378423],"genre_scores_gemma":[0.98968154,4.882405e-7,0.000054478438,0.000067366054,0.000019550876,0.000007818983,0.000001825305,0.000008081865,0.01015886],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99714583,0.00046632107,0.00051923795,0.000556953,0.0010157279,0.0002959445],"domain_scores_gemma":[0.9986518,0.00019560309,0.00012838843,0.00092307763,0.00006464741,0.000036474674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003243643,0.00013794201,0.00022089102,0.00029239993,0.00015619386,0.00023249266,0.00068048725,0.000035519035,0.000040842297],"category_scores_gemma":[0.0003554217,0.000057577214,0.000078354016,0.0005296366,0.00021424303,0.00016918573,0.000618522,0.00008515542,0.00010201131],"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":[0.00027937902,0.0016009317,0.5613579,0.00025652396,0.00018984573,0.000014108498,0.21843192,0.10634929,0.021621864,0.00553591,0.00058348663,0.08377885],"study_design_scores_gemma":[0.0028303708,0.000309552,0.5121087,0.00031673198,0.00038962925,0.000009427061,0.009059384,0.10044776,0.09708813,0.050301123,0.22559857,0.0015406383],"about_ca_topic_score_codex":0.001048019,"about_ca_topic_score_gemma":0.000624431,"teacher_disagreement_score":0.22501509,"about_ca_system_score_codex":0.000023259521,"about_ca_system_score_gemma":0.0000015382439,"threshold_uncertainty_score":0.23479305},"labels":[],"label_agreement":null},{"id":"W2121734101","doi":"10.1007/s11269-015-1083-y","title":"Geophysical Monitoring of Ground Surface Deformation Associated with a Confined Aquifer Storage and Recovery Operation","year":2015,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":14,"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":"Pacific Northwest National Laboratory; Canadian Space Agency; Jet Propulsion Laboratory; U.S. Department of Energy","keywords":"Aquifer; Geology; Hydrogeology; Groundwater; Interferometric synthetic aperture radar; Injection well; Water injection (oil production); Water well; Geotechnical engineering; Synthetic aperture radar; Geophysics; Petroleum engineering; Remote sensing","score_opus":0.010251538474724002,"score_gpt":0.19431742810867447,"score_spread":0.18406588963395046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121734101","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92838377,0.00004542368,0.06831546,0.000046653688,0.00002897366,0.00024898045,0.0000024300496,0.00015771798,0.0027706143],"genre_scores_gemma":[0.9808616,0.000017729175,0.018735887,0.0000069392563,0.000022735503,0.000021128575,0.000025528443,0.000017648164,0.000290801],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994589,0.000018251178,0.00014406416,0.000103346174,0.00015848238,0.000116933516],"domain_scores_gemma":[0.9997409,0.000012015052,0.000026116642,0.00014689557,0.000037312988,0.000036770063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015402392,0.00010048484,0.00011747449,0.00004487566,0.00003517251,0.000044772278,0.000063290754,0.00003640968,0.000004774908],"category_scores_gemma":[0.00000276519,0.00007056346,0.000013264528,0.00006873611,0.00002448729,0.00011384708,0.000036476864,0.000047398335,0.000005926268],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009105817,0.0014291705,0.022928245,0.0023544307,0.004378743,0.00006268391,0.085061245,0.09823707,0.03545917,0.007004122,0.0052476535,0.7369269],"study_design_scores_gemma":[0.004353842,0.00089393725,0.060157254,0.00084410346,0.00058414304,0.000021550008,0.0048174034,0.11093427,0.13462606,0.0024537223,0.6784126,0.0019011284],"about_ca_topic_score_codex":0.000047698875,"about_ca_topic_score_gemma":0.0000044004064,"teacher_disagreement_score":0.73502576,"about_ca_system_score_codex":0.000064253494,"about_ca_system_score_gemma":0.0000013653919,"threshold_uncertainty_score":0.28774944},"labels":[],"label_agreement":null},{"id":"W2132761196","doi":"10.1007/s11269-009-9500-8","title":"Behaviour and Performance of a Water Resource System in Québec (Canada) Under Adapted Operating Policies in a Climate Change Context","year":2009,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Hydro-Québec; École de Technologie Supérieure; Université du Québec à Montréal; Natural Sciences and Engineering Research Council of Canada; Université du Québec; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hydropower; Environmental science; Climate change; Context (archaeology); Representative Concentration Pathways; Climatology; Resource (disambiguation); Greenhouse gas; Water resources; Climate model; Time horizon; Hydrology (agriculture); Water resource management; Geography; Ecology; Computer science; Mathematics; Geology","score_opus":0.010269146351969391,"score_gpt":0.17807246157637988,"score_spread":0.1678033152244105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132761196","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9931027,0.00021896324,0.000029826808,0.00041897604,0.00003404963,0.0006294982,0.0000028448512,0.00011053852,0.005452561],"genre_scores_gemma":[0.99927086,0.00006414142,0.000076114935,0.00022550253,0.0000310336,0.00006502441,0.000021869446,0.00003365899,0.00021179473],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99832094,0.000048133013,0.0005114607,0.00026983905,0.00027535783,0.0005742725],"domain_scores_gemma":[0.9996454,0.000006229511,0.000038551658,0.00024117617,0.00001380158,0.00005483067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028213943,0.0002600979,0.00031991996,0.00041843788,0.000072713345,0.00007546802,0.00021446942,0.0000550774,0.0000112490625],"category_scores_gemma":[7.136518e-7,0.00019226337,0.000028339184,0.00016151345,0.000028287577,0.00017961371,0.0001717459,0.00012601657,0.000003966424],"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":[0.0004139316,0.00022953538,0.21504182,0.0046181493,0.00019291128,0.00029105417,0.07059829,0.6740587,0.0010378457,0.001067732,0.00030718118,0.032142855],"study_design_scores_gemma":[0.0046568867,0.00045421615,0.49821806,0.0023788498,0.00014466837,0.000014442945,0.020885443,0.4378108,0.009274337,0.000011772362,0.024633344,0.0015171808],"about_ca_topic_score_codex":0.05148377,"about_ca_topic_score_gemma":0.108632095,"teacher_disagreement_score":0.28317624,"about_ca_system_score_codex":0.00023560761,"about_ca_system_score_gemma":0.0000021504418,"threshold_uncertainty_score":0.9548325},"labels":[],"label_agreement":null},{"id":"W2138116293","doi":"10.1007/s11269-011-9792-3","title":"Monitoring Lake Simcoe Water Clarity Using Landsat-5 TM Images","year":2011,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada; University of Waterloo","funders":"","keywords":"Secchi disk; Environmental science; Thematic Mapper; Water quality; Remote sensing; Hydrology (agriculture); Eutrophication; CLARITY; Scale (ratio); Satellite imagery; Geology; Geography; Cartography; Ecology","score_opus":0.028942449127271837,"score_gpt":0.1968916659872239,"score_spread":0.16794921685995207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138116293","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87013274,0.00006016866,0.000084139996,0.00004269777,0.00036299505,0.00019577192,0.000015846588,0.000086620756,0.12901904],"genre_scores_gemma":[0.99407685,0.000023037484,0.00058381684,0.000060984126,0.00023767613,0.0000027624965,0.000052573174,0.00000812971,0.004954153],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985541,0.00006724126,0.0002462111,0.0003262543,0.0002683236,0.00053788675],"domain_scores_gemma":[0.9995397,0.000005949096,0.000034118646,0.00029270267,0.000017984717,0.00010958898],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003347702,0.00018874783,0.00017515563,0.0001150019,0.00021267646,0.00014470312,0.0003098526,0.000050867355,0.003874578],"category_scores_gemma":[9.897542e-7,0.000108934786,0.00007117978,0.00006047208,0.000033302338,0.00021055281,0.00016001596,0.00011610397,0.00073623424],"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":[0.00014122171,0.000056853987,0.9472651,0.00029165714,0.00017143626,0.0004643099,0.0046409434,0.00062948634,0.0001900733,0.000007651702,0.00023838812,0.045902904],"study_design_scores_gemma":[0.0007636387,0.0001872777,0.45893764,0.0001162405,0.00014391774,0.0000345144,0.0018171686,0.0026489752,0.018957745,0.0008883659,0.514685,0.00081951637],"about_ca_topic_score_codex":0.0061657494,"about_ca_topic_score_gemma":0.0019658797,"teacher_disagreement_score":0.5144466,"about_ca_system_score_codex":0.000004538046,"about_ca_system_score_gemma":0.0000012257958,"threshold_uncertainty_score":0.99703604},"labels":[],"label_agreement":null},{"id":"W2139092951","doi":"10.1007/s11269-011-9870-6","title":"Temporal Regionalization of 7-Day Low Flows in the St. Lawrence Watershed in Quebec (Canada)","year":2011,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Shore; Watershed; Precipitation; North Atlantic oscillation; STREAMS; Period (music); Climatology; Evapotranspiration; Hydrology (agriculture); Environmental science; Geography; Oceanography; Geology; Meteorology","score_opus":0.012581836921105531,"score_gpt":0.18520436147714864,"score_spread":0.1726225245560431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139092951","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96351683,0.00001100155,0.00018071265,0.001663029,0.00005801278,0.00045229698,0.0000011359157,0.0000135687615,0.034103416],"genre_scores_gemma":[0.995991,0.00001465378,0.00010101207,0.0007282022,0.000008412409,0.00006451995,0.000017104932,0.000007736538,0.0030673915],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99850106,0.00020407466,0.0003031545,0.00029664344,0.00036286403,0.0003321921],"domain_scores_gemma":[0.99961025,0.000013105622,0.000057841156,0.00029392002,0.0000030374388,0.000021872485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007048483,0.0001447032,0.00015322391,0.000086979686,0.00007212484,0.000012279832,0.0004889034,0.00003584125,0.0005014713],"category_scores_gemma":[0.0000031306315,0.00008612836,0.000028496008,0.0001922302,0.00015635003,0.0001331934,0.00039611646,0.00008521113,0.00003935029],"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":[0.00021137763,0.00047306085,0.9082785,0.0001774612,0.00009280892,0.00041285087,0.06705812,0.009828698,0.00008742672,0.0009284069,0.009652346,0.002798945],"study_design_scores_gemma":[0.0009741226,0.000072397765,0.82734895,0.000068971625,0.000034009016,8.95855e-7,0.003080275,0.0012038515,0.000835763,0.002293872,0.16371642,0.00037048932],"about_ca_topic_score_codex":0.44101557,"about_ca_topic_score_gemma":0.88407683,"teacher_disagreement_score":0.44306123,"about_ca_system_score_codex":0.00008495639,"about_ca_system_score_gemma":0.0000018338035,"threshold_uncertainty_score":0.56270677},"labels":[],"label_agreement":null},{"id":"W2139131480","doi":"10.1007/s11269-009-9452-z","title":"Influence of Trend on Short Duration Design Storms","year":2009,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":84,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; McGill University","funders":"","keywords":"Duration (music); Storm; Environmental science; Return period; Climatology; Climate change; Meteorology; Trend analysis; Atmospheric sciences; Statistics; Geography; Geology; Mathematics","score_opus":0.009034644614758522,"score_gpt":0.21081547172005044,"score_spread":0.2017808271052919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139131480","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.975065,0.000003605709,0.0009059558,0.00030602058,0.00000763796,0.00012348767,3.3546024e-7,0.000022937631,0.023565015],"genre_scores_gemma":[0.99717754,0.0000057263214,0.00037502294,0.00040626118,0.000009548873,0.0000071441173,0.0000045868424,0.0000034693667,0.0020107217],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99914116,0.00005064528,0.00018431988,0.00021317325,0.00023411842,0.0001766097],"domain_scores_gemma":[0.9996921,0.000006722046,0.00003097792,0.00023354834,0.0000015859157,0.000035097524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025292172,0.00009292917,0.000102741695,0.00006428168,0.00007467298,0.000011125555,0.00018505417,0.000033865486,0.0003645157],"category_scores_gemma":[0.0000019150668,0.000060573464,0.000041117546,0.00011603205,0.000064101805,0.000100171164,0.000060506612,0.000046001052,0.0002999096],"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":[0.00014774363,0.00020889717,0.013549915,0.000007645717,0.000069436726,0.00004050501,0.002944207,0.95417476,0.009767629,0.00014016092,0.0010259299,0.01792316],"study_design_scores_gemma":[0.0007495873,0.001339505,0.77334446,0.000039093222,0.0002817743,0.000004080912,0.00017587401,0.010200255,0.11094435,0.0047327173,0.09754825,0.0006400787],"about_ca_topic_score_codex":0.000023742972,"about_ca_topic_score_gemma":0.00001412283,"teacher_disagreement_score":0.9439745,"about_ca_system_score_codex":0.000038119273,"about_ca_system_score_gemma":2.0341913e-7,"threshold_uncertainty_score":0.39911914},"labels":[],"label_agreement":null},{"id":"W2140352621","doi":"10.1007/s11269-015-1001-3","title":"Combining Stochastic Weather Generation and Ensemble Weather Forecasts for Short-Term Streamflow Prediction","year":2015,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal; Université du Québec","funders":"National Oceanic and Atmospheric Administration","keywords":"Streamflow; Probabilistic logic; Forecast skill; Global Forecast System; Numerical weather prediction; Meteorology; Probabilistic forecasting; Environmental science; Forecast verification; Lead time; Quantitative precipitation forecast; Flood forecasting; Consensus forecast; Quantile; Ensemble forecasting; Computer science; Model output statistics; Reliability (semiconductor); Mathematics; Statistics; Artificial intelligence; Geography; Flood myth; Precipitation; Engineering","score_opus":0.02777726161370108,"score_gpt":0.22599071020518602,"score_spread":0.19821344859148493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140352621","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9637059,0.000034302207,0.025093732,0.00047592912,0.00020586103,0.00081639836,0.000004373653,0.00007991463,0.009583571],"genre_scores_gemma":[0.9922238,0.000010101202,0.00081105466,0.00015709484,0.000098446646,0.0002191335,0.000039083938,0.000022015067,0.0064192875],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99887705,0.000040827385,0.00017875984,0.00039091095,0.00017874969,0.0003336863],"domain_scores_gemma":[0.9996875,0.000009137067,0.000029112818,0.00018784078,0.000006421423,0.000079983896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040173394,0.00017442959,0.00013927656,0.000060320584,0.0002279628,0.000057293386,0.00011868416,0.00004847757,0.00006662854],"category_scores_gemma":[0.0000034553038,0.0001269923,0.000034594832,0.00004198914,0.000109093075,0.00015876515,0.00037180405,0.000048233716,0.00009432037],"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":[0.0015693202,0.0016315538,0.19844258,0.00059244863,0.003009519,0.00015635857,0.12046716,0.26217118,0.017669845,0.0033438373,0.115511425,0.27543476],"study_design_scores_gemma":[0.013151732,0.0037877364,0.039389595,0.0002037479,0.0018869049,0.00003839433,0.0065233386,0.4079132,0.009144219,0.017903991,0.4972601,0.0027970453],"about_ca_topic_score_codex":0.000019434707,"about_ca_topic_score_gemma":0.00004248602,"teacher_disagreement_score":0.38174868,"about_ca_system_score_codex":0.000065042084,"about_ca_system_score_gemma":3.9220427e-7,"threshold_uncertainty_score":0.5178595},"labels":[],"label_agreement":null},{"id":"W2147182341","doi":"10.1007/s11269-012-0117-y","title":"Developing Novel Approaches to Tracking Domestic Water Demand Under Uncertainty—A Reflection on the “Up Scaling” of Social Science Approaches in the United Kingdom","year":2012,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":98,"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":"Economic and Social Research Council; Engineering and Physical Sciences Research Council; Natural Environment Research Council; University of Southampton; Canadian Centre for Applied Research in Cancer Control","keywords":"Demand management; Futures contract; Climate change; Proxy (statistics); Supply and demand; Demand patterns; Demand forecasting; Water resources; Environmental economics; Population; Economics; Natural resource economics; Environmental resource management; Computer science; Microeconomics; Ecology; Sociology; Operations management","score_opus":0.19972920480737344,"score_gpt":0.27459063979448917,"score_spread":0.07486143498711573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147182341","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94525856,0.000010505302,0.045791905,0.0013849305,0.00018021275,0.000845514,7.629953e-7,0.00009944956,0.0064281435],"genre_scores_gemma":[0.99865973,0.0000021313772,0.0005725529,0.0003046121,0.00012309302,0.00012075697,0.000024864337,0.000036657806,0.00015559838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979484,0.00010493698,0.00037254672,0.00028901777,0.00056944677,0.00071562594],"domain_scores_gemma":[0.999544,0.00004027274,0.00004664049,0.00030316037,0.000020955822,0.00004492625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020295295,0.0002588594,0.0001850596,0.0007214772,0.00041332512,0.00026967152,0.0006508488,0.000055534238,0.000010646902],"category_scores_gemma":[0.000008396277,0.00012903598,0.00005716331,0.000903344,0.00014721384,0.00021913787,0.0002593462,0.0001831895,0.000018074206],"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":[0.000044296256,0.00007564124,0.00024958645,0.00019856988,0.00008081376,0.0000011177697,0.051883973,0.92220116,0.0005102899,0.023645276,0.000038180235,0.0010710715],"study_design_scores_gemma":[0.0047295303,0.00029175618,0.047029212,0.00096728053,0.00073567685,0.000020930598,0.06521482,0.57388973,0.08910078,0.0037190316,0.21078958,0.0035116775],"about_ca_topic_score_codex":0.000031837848,"about_ca_topic_score_gemma":0.000006647382,"teacher_disagreement_score":0.34831145,"about_ca_system_score_codex":0.00019953416,"about_ca_system_score_gemma":0.0000018116,"threshold_uncertainty_score":0.5261935},"labels":[],"label_agreement":null},{"id":"W2158729808","doi":"10.1007/s11269-012-0134-x","title":"Ground Water-Surface Water Interface (GWSWI) Modeling: Recent Advances and Future Challenges","year":2012,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Surface water; Environmental science; Groundwater; Hydrogeology; Biogeochemical cycle; Hydrology (agriculture); Environmental engineering; Environmental chemistry; Chemistry; Geology","score_opus":0.01833735671507539,"score_gpt":0.22483034152261094,"score_spread":0.20649298480753556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158729808","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9719798,0.006663116,0.001767275,0.00554353,0.00048432418,0.00039112868,0.0000014066467,0.00010959036,0.013059806],"genre_scores_gemma":[0.9791293,0.009308592,0.00031937272,0.00022270615,0.00022035645,0.000052282354,0.000013550171,0.000031938493,0.010701863],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977862,0.00009157605,0.00029965193,0.00051014725,0.00043683383,0.00087555475],"domain_scores_gemma":[0.9994646,0.0000053475537,0.000027526912,0.00034212033,0.000012456379,0.00014799999],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005070373,0.0003298647,0.00024036283,0.000047330646,0.0003350479,0.00011811582,0.00029152233,0.0000652531,0.00086851785],"category_scores_gemma":[5.5331225e-7,0.00017012274,0.000050353337,0.00003948827,0.000116044546,0.0006729493,0.0012980215,0.00012150401,0.0010672159],"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":[0.00022222103,0.0006783091,0.008253556,0.00067912816,0.00051000225,0.000029180519,0.22772641,0.0074199634,0.010507476,0.00028158975,0.00091029826,0.7427819],"study_design_scores_gemma":[0.00037763093,0.000056952395,0.0013228473,0.000013166075,0.00005172212,0.000006064293,0.0069790925,0.00035460675,0.009888381,0.00016302743,0.9804341,0.00035238403],"about_ca_topic_score_codex":0.000040635314,"about_ca_topic_score_gemma":0.000033556942,"teacher_disagreement_score":0.97952384,"about_ca_system_score_codex":0.00011151069,"about_ca_system_score_gemma":2.0307182e-7,"threshold_uncertainty_score":0.99971056},"labels":[],"label_agreement":null},{"id":"W2163429861","doi":"10.1007/s11269-014-0763-3","title":"Optimal Hydropower Generation Under Climate Change Conditions for a Northern Water Resources System","year":2014,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Rio Tinto (Canada); École de Technologie Supérieure; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hydropower; Climate change; Environmental science; Water resources; Flood myth; Electricity generation; Hydrology (agriculture); Water resource management; Spring (device); Power (physics); Engineering; Geology; Geography; Ecology","score_opus":0.014367769991714413,"score_gpt":0.20097221233402238,"score_spread":0.18660444234230797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163429861","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92243844,0.00006545081,0.05608695,0.00046068514,0.000610287,0.0019523571,0.00003073047,0.0012425147,0.017112564],"genre_scores_gemma":[0.9942746,0.000025777228,0.0011347058,0.00020883352,0.00085284875,0.00091581786,0.00075873174,0.0001531898,0.0016754993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99767536,0.00007841921,0.0004915583,0.00051488273,0.00035511074,0.00088469277],"domain_scores_gemma":[0.99924135,0.000012125965,0.00005530552,0.0005191388,0.00005345084,0.00011860562],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044940153,0.00042310948,0.00031728178,0.00037778876,0.00042581256,0.00039763865,0.00036876005,0.00011340854,0.00009715339],"category_scores_gemma":[0.000001218885,0.00029958563,0.00016161228,0.00010877788,0.000054537646,0.00030146047,0.00024272056,0.000104618804,0.00042745334],"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":[0.00005840569,0.000066912406,0.00023579723,0.0012468178,0.00035867965,0.000011919219,0.0086211,0.98393476,0.0014774343,0.0013031692,0.0011350636,0.0015499423],"study_design_scores_gemma":[0.0012606203,0.00011872496,0.00020621212,0.000111292204,0.0002527371,0.0000036475665,0.0008294048,0.66672134,0.00457228,0.0000796009,0.3251811,0.0006630654],"about_ca_topic_score_codex":0.000021458001,"about_ca_topic_score_gemma":0.0000481771,"teacher_disagreement_score":0.32404602,"about_ca_system_score_codex":0.00014532874,"about_ca_system_score_gemma":3.342752e-7,"threshold_uncertainty_score":0.99994564},"labels":[],"label_agreement":null},{"id":"W2165220437","doi":"10.1007/s11269-012-0122-1","title":"Erratum to: Forecasting Urban Water Demand Via Wavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy","year":2012,"lang":"en","type":"erratum","venue":"Water Resources Management","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Artificial neural network; Wavelet; Computer science; Filter (signal processing); Generalization; Daubechies wavelet; Noise (video); Haar wavelet; Wavelet transform; Artificial intelligence; Machine learning; Econometrics; Discrete wavelet transform; Mathematics","score_opus":0.02236027769712346,"score_gpt":0.2068371371714715,"score_spread":0.18447685947434805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165220437","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95148337,0.002392102,0.0030256265,0.000038030677,0.009477571,0.0012913827,0.00001872222,0.00042741562,0.031845763],"genre_scores_gemma":[0.9772135,0.000037473408,0.0007197799,0.000058514805,0.0020709962,0.000081901504,0.00013268318,0.00026262458,0.019422542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99616784,0.000114528055,0.0009457305,0.00071486045,0.0005297851,0.0015272609],"domain_scores_gemma":[0.9987625,0.000029905666,0.00012916469,0.00069295656,0.0000598783,0.0003256235],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007746831,0.00088574295,0.00092493324,0.00041819006,0.00036491675,0.00026740954,0.000430914,0.00032445492,0.00003462397],"category_scores_gemma":[0.0000036030751,0.00063887413,0.00017223193,0.00021161961,0.000063681335,0.0003252896,0.0011301477,0.00081777887,0.000007979355],"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":[0.00010160512,0.00022538149,0.0015216209,0.0048024715,0.001956229,0.0066789663,0.065816306,0.75443536,0.00013472863,0.000017352477,0.14600247,0.018307485],"study_design_scores_gemma":[0.0023556368,0.0008746343,0.00029559646,0.0028645932,0.0028688204,0.0026930494,0.0044240854,0.5916028,0.0008044794,0.0007011183,0.38538128,0.0051339013],"about_ca_topic_score_codex":0.00036381526,"about_ca_topic_score_gemma":0.00026882312,"teacher_disagreement_score":0.2393788,"about_ca_system_score_codex":0.0001040629,"about_ca_system_score_gemma":0.000002648966,"threshold_uncertainty_score":0.99960625},"labels":[],"label_agreement":null},{"id":"W2165618495","doi":"10.1007/s11269-006-2072-y","title":"Quantitative Assessment of Regional Rock Aquifers, South-Western Quebec, Canada","year":2006,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Geological Modeling and Analysis","field":"Earth and Planetary Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Institut National de la Recherche Scientifique; Geological Survey of Canada; Natural Resources Canada","funders":"","keywords":"Aquifer; Groundwater recharge; Groundwater; Hydrogeology; Drawdown (hydrology); Hydrology (agriculture); Environmental science; Depression-focused recharge; Geology; Groundwater flow; Sustainable yield; Water table; Water resource management","score_opus":0.017147267579041246,"score_gpt":0.2132594164142483,"score_spread":0.19611214883520706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165618495","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9878128,0.00015181949,0.0025725293,0.0011350263,0.0000646223,0.00010459563,0.000021471698,0.000023683437,0.00811341],"genre_scores_gemma":[0.9895761,0.000006986554,0.00082389324,0.00021769539,0.00004523069,0.0000016956312,0.00020360525,0.0000026036464,0.0091221845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986264,0.00007280055,0.00028324866,0.00027271008,0.00045237003,0.0002924849],"domain_scores_gemma":[0.99962956,0.000023124207,0.00008297117,0.00017592986,0.000031107687,0.000057288762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001614753,0.00013350487,0.00020037263,0.00008281096,0.00010347224,0.00004170499,0.0002317822,0.000027644137,0.0006215476],"category_scores_gemma":[0.0000013023432,0.00008364378,0.000070830225,0.00010243084,0.000029548188,0.00004574943,0.000035344932,0.00006838884,0.000046879763],"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":[0.000024827148,0.00002850769,0.44563332,0.00004884019,0.00011230249,0.000042091207,0.0002674362,0.55078477,0.000007671614,0.00014762186,0.0020803232,0.0008223102],"study_design_scores_gemma":[0.00028981082,0.00007927099,0.85241115,0.00003075694,0.00009210628,8.9903267e-7,0.0011300538,0.06833425,0.000027065746,0.00039645232,0.07695233,0.00025586988],"about_ca_topic_score_codex":0.868889,"about_ca_topic_score_gemma":0.94823366,"teacher_disagreement_score":0.48245052,"about_ca_system_score_codex":0.000012459718,"about_ca_system_score_gemma":0.000013827316,"threshold_uncertainty_score":0.680551},"labels":[],"label_agreement":null},{"id":"W2165884388","doi":"10.1007/s11269-015-0982-2","title":"Sustainability Evaluation of Surface Water Quality Management Options in Developing Countries: Multicriteria Analysis Using Fuzzy UTASTAR Method","year":2015,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"European Social Fund","keywords":"Sustainability; Ranking (information retrieval); Environmental economics; Quality (philosophy); Multiple-criteria decision analysis; Fuzzy logic; Developing country; Environmental resource management; Risk analysis (engineering); Computer science; Business; Environmental science; Operations research; Engineering; Economics","score_opus":0.3003390668515622,"score_gpt":0.5048816733350623,"score_spread":0.2045426064835001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165884388","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82207227,0.0000566699,0.17397791,0.0010794927,0.00023861064,0.0014216888,0.00001831756,0.00004303844,0.0010920269],"genre_scores_gemma":[0.85174817,0.000009221713,0.14740238,0.0001661661,0.000023146034,0.00005458941,0.000032587748,0.000024956691,0.00053877156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9851829,0.004422691,0.0026912172,0.0014225339,0.0053652036,0.0009154656],"domain_scores_gemma":[0.9947288,0.0003867818,0.0004399391,0.0019198563,0.0022806958,0.00024394003],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.06575531,0.0004075779,0.0009684702,0.0021241612,0.00022110133,0.00065214245,0.0013014335,0.00011244207,0.00046448738],"category_scores_gemma":[0.0008633565,0.00026711944,0.0002888636,0.0020868396,0.00015473593,0.00061493664,0.0018950658,0.00013773711,0.000093006296],"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":[0.00041971906,0.0002875962,0.013905226,0.00028669622,0.0009065268,0.00005880185,0.03527692,0.93303347,0.00046295166,0.0019836961,0.00020126482,0.0131771285],"study_design_scores_gemma":[0.006388472,0.00007789964,0.061734498,0.00019375929,0.0025109502,0.000004781553,0.074377306,0.6540137,0.004540469,0.072176754,0.12246149,0.0015199451],"about_ca_topic_score_codex":0.0007485271,"about_ca_topic_score_gemma":0.00021989402,"teacher_disagreement_score":0.2790198,"about_ca_system_score_codex":0.0015851246,"about_ca_system_score_gemma":0.000057772093,"threshold_uncertainty_score":0.9999781},"labels":[],"label_agreement":null},{"id":"W2170460777","doi":"10.1007/s11269-015-1103-y","title":"Modeling the Relationship between Catchment Attributes and In-stream Water Quality","year":2015,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Water quality; Land cover; Hydrology (agriculture); Hydrogeology; Drainage basin; Environmental science; Land use; Linear regression; Regression analysis; Mathematics; Statistics; Geology; Geography; Ecology; Cartography; Civil engineering; Engineering","score_opus":0.11121156760448567,"score_gpt":0.288806920926414,"score_spread":0.17759535332192836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170460777","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9930773,0.000017341968,0.00041043342,0.0028246925,0.00002770674,0.00024718844,0.0000011694905,0.000045809003,0.003348388],"genre_scores_gemma":[0.99837434,0.0000011568053,0.0003679831,0.0001878742,0.000029495262,0.000024824189,0.000013822102,0.000009083735,0.0009914202],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99851036,0.00018026147,0.00027775398,0.00031872463,0.00033817175,0.00037472005],"domain_scores_gemma":[0.9995631,0.000042918593,0.00002185451,0.00027420622,0.0000035883172,0.00009431386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015899268,0.00013025668,0.000126926,0.000033969573,0.00016203539,0.000075997166,0.00023849511,0.000044576394,0.000045261124],"category_scores_gemma":[0.000026150312,0.00006216325,0.000025388441,0.00007494532,0.00012455313,0.00008492362,0.0007987646,0.00013715937,0.0003233807],"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":[0.00001684975,0.000028441988,0.7866079,0.000014119148,0.000009936963,0.000007155173,0.004993511,0.20751946,0.000024230492,0.000044131317,0.00009393386,0.00064029265],"study_design_scores_gemma":[0.0016097152,0.00017093123,0.8663365,0.000086755965,0.000100816294,0.000005321959,0.0010101816,0.057609048,0.0011629985,0.029195715,0.041938383,0.00077359524],"about_ca_topic_score_codex":0.00087459007,"about_ca_topic_score_gemma":0.00007657966,"teacher_disagreement_score":0.1499104,"about_ca_system_score_codex":0.00013517869,"about_ca_system_score_gemma":4.5294806e-7,"threshold_uncertainty_score":0.41565126},"labels":[],"label_agreement":null},{"id":"W2220720087","doi":"10.1007/s11269-015-1166-9","title":"Comparison of the Characteristics (Frequency and Timing) of Drought and Wetness Indices of Annual Mean Water Levels in the Five North American Great Lakes","year":2015,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Climate variability and models","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Teleconnection; Precipitation; Environmental science; North Atlantic oscillation; Climatology; Watershed; Climate change; Pacific decadal oscillation; Period (music); Hydrology (agriculture); Physical geography; El Niño Southern Oscillation; Geology; Oceanography; Geography","score_opus":0.034115522953901155,"score_gpt":0.2585242225812878,"score_spread":0.22440869962738666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2220720087","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9983539,0.000010054756,0.000012695058,0.00025991225,0.000018047487,0.00026578072,0.00004105203,0.000003305383,0.0010352668],"genre_scores_gemma":[0.9997211,0.000008885736,0.00009828672,0.000045328346,0.0000056518224,0.000008646503,0.0000068391123,0.0000050051963,0.00010024258],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99893314,0.0001252475,0.0003090526,0.0001798019,0.00028629866,0.00016648995],"domain_scores_gemma":[0.9995537,0.000024777148,0.00013195573,0.0002505058,0.000009579464,0.000029490144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046512627,0.0001032243,0.00023169554,0.0000357636,0.000044504166,0.000014525334,0.00028095365,0.000018234055,0.000024837707],"category_scores_gemma":[0.00000510956,0.000047249316,0.00002251894,0.00009583949,0.00062293565,0.00008294384,0.00047605025,0.000061795305,0.0000016896709],"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":[0.000029973211,0.00012408529,0.84659964,0.00012451885,0.000022108885,0.0000018600974,0.15040004,0.00045451676,0.000464152,0.000027217118,0.0000278655,0.0017240411],"study_design_scores_gemma":[0.00032294216,0.000152534,0.9839447,0.000034812227,0.00006174155,0.0000013303074,0.009808609,0.0008301889,0.0028562758,0.0002844251,0.0015909146,0.00011149992],"about_ca_topic_score_codex":0.0011802206,"about_ca_topic_score_gemma":0.0011615461,"teacher_disagreement_score":0.14059143,"about_ca_system_score_codex":0.00001898596,"about_ca_system_score_gemma":9.016265e-7,"threshold_uncertainty_score":0.22952324},"labels":[],"label_agreement":null},{"id":"W2222740999","doi":"10.1007/s11269-016-1227-8","title":"Water Variability and the Economic Impacts on Small-Scale Farmers. A Farm Risk-Based Integrated Modelling Approach","year":2016,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"International Development Research Centre","keywords":"Agriculture; Scale (ratio); Context (archaeology); Water resources; Climate change; Small farm; Economic impact analysis; Water use; Natural resource economics; Business; Profit (economics); Water resource management; Environmental resource management; Environmental science; Economics; Geography","score_opus":0.008284409592778708,"score_gpt":0.163579049724356,"score_spread":0.1552946401315773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2222740999","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70762664,0.00002278238,0.27318284,0.00036360108,0.000125202,0.0009881716,0.000011691169,0.00037259873,0.017306471],"genre_scores_gemma":[0.9962213,0.00010867763,0.0019924857,0.00011427675,0.0000723393,0.00014679361,0.000046632744,0.000068848814,0.0012286479],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981658,0.00019710757,0.00038248644,0.00049182936,0.00017762076,0.00058519625],"domain_scores_gemma":[0.9992252,0.000049135775,0.00004815512,0.0005668616,0.000014808892,0.000095846786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010446474,0.00037071513,0.0002903884,0.00021092972,0.00019953153,0.00024726565,0.00036419494,0.00007904133,0.0000722673],"category_scores_gemma":[0.0000026238415,0.00015476214,0.00011216149,0.00006147071,0.00015596583,0.000091861926,0.00019583124,0.00015711071,0.00011383959],"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":[0.00028538593,0.000034059,0.00052082754,0.00015702515,0.0002178687,0.0000025355373,0.0021666025,0.98969316,0.000059278656,0.00021344055,0.00007556868,0.0065742573],"study_design_scores_gemma":[0.0033549152,0.000049922284,0.00014026408,0.00006109186,0.00017834608,6.877433e-7,0.00023050475,0.94190127,0.0031306234,0.0005588123,0.04996882,0.00042472756],"about_ca_topic_score_codex":0.00012123969,"about_ca_topic_score_gemma":0.00001297254,"teacher_disagreement_score":0.28859466,"about_ca_system_score_codex":0.00020602645,"about_ca_system_score_gemma":0.0000013395909,"threshold_uncertainty_score":0.63110167},"labels":[],"label_agreement":null},{"id":"W2274680160","doi":"10.1007/s11269-016-1254-5","title":"Water Security Assessment Indicators: The Rural Context","year":2016,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Child Nutrition and Water Access","field":"Nursing","cited_by":77,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"CLARITY; Context (archaeology); Prioritization; Environmental resource management; Process (computing); Computer science; Environmental planning; Selection (genetic algorithm); Field (mathematics); Water resources; Process management; Business; Environmental science; Geography; Ecology","score_opus":0.008200401564322864,"score_gpt":0.2478600668977396,"score_spread":0.23965966533341673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2274680160","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.932572,0.000042581265,0.00012736952,0.05453111,0.00063488615,0.0007234795,0.0000100287425,0.00018972506,0.011168796],"genre_scores_gemma":[0.9936183,0.000015622256,0.000024158508,0.0033502604,0.00032478615,0.000104202794,0.000024733912,0.000033996843,0.0025039394],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980433,0.00019575079,0.00034439258,0.00034760046,0.00045621602,0.00061272865],"domain_scores_gemma":[0.99921894,0.000028986417,0.000051194427,0.000575341,0.000024155732,0.00010138989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043865337,0.00024275598,0.00019531803,0.00019839426,0.00036064387,0.0002669525,0.00061071524,0.00005835915,0.0008668193],"category_scores_gemma":[0.0000019138724,0.00008733118,0.00013558294,0.0000840575,0.00016236407,0.00020628987,0.0004825521,0.00014155495,0.00065350445],"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":[0.0035412682,0.0033848325,0.16577621,0.0015381222,0.0023600992,0.00041489673,0.13407615,0.000023927998,0.021103501,0.008989146,0.25990805,0.3988838],"study_design_scores_gemma":[0.0019924212,0.00008656376,0.010487418,0.00011530688,0.000070097245,0.0000061628243,0.0007674791,0.000008797485,0.07562153,0.0035827737,0.90698564,0.00027583097],"about_ca_topic_score_codex":0.00006184549,"about_ca_topic_score_gemma":0.000015159566,"teacher_disagreement_score":0.64707756,"about_ca_system_score_codex":0.00012531607,"about_ca_system_score_gemma":7.4263403e-7,"threshold_uncertainty_score":0.94910634},"labels":[],"label_agreement":null},{"id":"W2297878978","doi":"10.1007/s11269-016-1241-x","title":"Prediction of Timing of Watermain Failure Using Gene Expression Models","year":2016,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Gene expression programming; Parametric statistics; Environmental science; Reliability engineering; Structural engineering; Engineering; Computer science; Statistics; Mathematics","score_opus":0.02261287429235681,"score_gpt":0.17884090729014646,"score_spread":0.15622803299778965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2297878978","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67452717,0.00003097187,0.32371676,0.000013915767,0.00010714454,0.0001828479,0.000012298792,0.000076120385,0.0013327461],"genre_scores_gemma":[0.99236536,0.000016509699,0.0069887377,0.0000017100286,0.000043808544,0.000009904959,0.000008955725,0.000024741503,0.0005402551],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992062,0.0000222781,0.00030905972,0.00012734144,0.00016991797,0.00016520964],"domain_scores_gemma":[0.9996914,0.0000030403712,0.000044338813,0.00021176322,0.000023049333,0.00002635828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012416391,0.00010580026,0.00014227132,0.00013971305,0.0000262182,0.000011101272,0.000107264306,0.00004495825,0.000025227153],"category_scores_gemma":[4.081928e-7,0.00005997516,0.00004047594,0.00004361897,0.00001659351,0.0001829631,0.000084466505,0.000020377523,0.00000302335],"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":[0.000009703171,0.00001117575,0.00018358079,0.0003468259,0.000049706156,0.000002312954,0.0018889711,0.62929744,0.3673885,0.000018359931,0.0002929343,0.00051048794],"study_design_scores_gemma":[0.0004161133,0.000019178366,0.000080579055,0.0004276982,0.00003121139,0.0000016753778,0.00009748283,0.19840892,0.79780966,0.00012729944,0.0024789318,0.000101233156],"about_ca_topic_score_codex":0.000012651943,"about_ca_topic_score_gemma":0.0000016869355,"teacher_disagreement_score":0.43088853,"about_ca_system_score_codex":0.00003376734,"about_ca_system_score_gemma":4.4335775e-7,"threshold_uncertainty_score":0.2445716},"labels":[],"label_agreement":null},{"id":"W2312854946","doi":"10.1007/s11269-016-1312-z","title":"Impact of Raw Water Quality and Climate Factors on the Variability of Drinking Water Quality in Small Systems","year":2016,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Water quality; Environmental science; Hydrogeology; Raw water; Quality (philosophy); Water resource management; Environmental engineering; Engineering; Ecology; Biology","score_opus":0.047694408337745416,"score_gpt":0.29345667351290455,"score_spread":0.24576226517515914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2312854946","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9958373,0.0000023814164,0.00018625794,0.0007781651,0.00007535513,0.00059902185,0.000025102348,0.0000223859,0.0024740633],"genre_scores_gemma":[0.9994645,0.000010477827,0.000025070502,0.000056251527,0.000011932711,0.000031091724,0.0000111473555,0.000012808629,0.00037668282],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9962125,0.0014027728,0.0009063867,0.00046038296,0.00043574037,0.0005822285],"domain_scores_gemma":[0.998916,0.00014684902,0.00015212632,0.00069443585,0.00001069777,0.000079943195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056008236,0.0002523784,0.00039896078,0.00007593583,0.0001124729,0.00004919653,0.00037331582,0.00007322395,0.00078265043],"category_scores_gemma":[0.000012829889,0.0000866337,0.00014334552,0.00006074747,0.00032710453,0.0001228786,0.00081509823,0.00009937929,0.000074609794],"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":[0.00023748557,0.00034374913,0.92362416,0.00030092723,0.000112492264,0.000002720285,0.013917771,0.0013991753,0.057951443,0.0015700848,0.00002171148,0.00051829073],"study_design_scores_gemma":[0.00071379606,0.00014270641,0.9171669,0.000100875455,0.000024338764,4.1935255e-7,0.00048382988,0.000032753134,0.07809327,0.0011598255,0.0018295306,0.00025175177],"about_ca_topic_score_codex":0.004360364,"about_ca_topic_score_gemma":0.00018072147,"teacher_disagreement_score":0.020141827,"about_ca_system_score_codex":0.0002551101,"about_ca_system_score_gemma":0.0000012001448,"threshold_uncertainty_score":0.8569473},"labels":[],"label_agreement":null},{"id":"W2334970001","doi":"10.1007/s11269-016-1308-8","title":"Risk Perceptions and Terror Management Theory: Assessing Public Responses to Urban Flooding in Toronto, Canada","year":2016,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Death Anxiety and Social Exclusion","field":"Psychology","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Mortality salience; Terror management theory; Risk perception; Flood myth; Flooding (psychology); Geography; Perception; Social psychology; Socioeconomics; Environmental planning; Environmental resource management; Psychology; Sociology; Environmental science","score_opus":0.01378506974520837,"score_gpt":0.27834542189115286,"score_spread":0.2645603521459445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2334970001","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89852893,0.00014773315,0.00072416477,0.0032140608,0.00033423203,0.00048928004,0.000013576587,0.00006313422,0.09648487],"genre_scores_gemma":[0.9593921,0.00007958804,0.0003270992,0.00073159917,0.00009177568,0.00013179542,0.0000049900214,0.000026587933,0.039214477],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.997864,0.00046907025,0.00027967233,0.00050247146,0.00028040074,0.00060438557],"domain_scores_gemma":[0.99931604,0.000052256295,0.00005127289,0.00039089666,0.000016226048,0.00017328325],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00095134974,0.00019803575,0.00018330116,0.00016118899,0.00037230534,0.00015203591,0.0002766222,0.00006067589,0.0015419375],"category_scores_gemma":[0.000016319469,0.00013037829,0.000044264612,0.000109455716,0.000053307464,0.00019777766,0.0004850338,0.00008141391,0.00008479907],"study_design_candidate":"design_other","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":[0.00083771563,0.00041320056,0.1483684,0.00015450253,0.0007010638,0.00067562243,0.084176525,0.000008153979,0.00036102638,0.046587132,0.021885572,0.69583106],"study_design_scores_gemma":[0.0009879294,0.0000566059,0.4668723,0.00010827724,0.00006328473,0.0000028710906,0.04064271,0.000004915215,0.0000095939995,0.000696865,0.49023697,0.00031767928],"about_ca_topic_score_codex":0.08872014,"about_ca_topic_score_gemma":0.2615737,"teacher_disagreement_score":0.69551337,"about_ca_system_score_codex":0.0005976437,"about_ca_system_score_gemma":0.000006929456,"threshold_uncertainty_score":0.9993708},"labels":[],"label_agreement":null},{"id":"W2336714909","doi":"10.1007/s11269-016-1329-3","title":"Annual and Seasonal Variations of Hydrological Processes Under Climate Change Scenarios in Two Sub-Catchments of a Complex Watershed","year":2016,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Biodiversity Monitoring Institute; University of Calgary","funders":"Alberta Innovates - Technology Futures; Climate Extremes; Alberta Environment and Parks","keywords":"Baseflow; Watershed; Environmental science; Hydrology (agriculture); Groundwater recharge; Climate change; Surface runoff; Drainage basin; Streamflow; Water balance; Evapotranspiration; Interflow; Water resources; Groundwater; Aquifer; Geology; Geography; Ecology","score_opus":0.021627014584953655,"score_gpt":0.24505090767529158,"score_spread":0.22342389309033794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2336714909","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9948869,0.000019012838,0.00020732601,0.0025604034,0.000021530923,0.00047750853,0.000027146107,0.000023365099,0.0017768241],"genre_scores_gemma":[0.99906904,0.000108851964,0.00021527828,0.00028352402,0.000012442443,0.00009783877,0.000009636326,0.000008958874,0.00019441287],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985714,0.00008858386,0.0003113807,0.0003655719,0.00025055942,0.00041248414],"domain_scores_gemma":[0.99964714,0.0000285847,0.00009097598,0.0001772164,0.00000926084,0.000046814657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036295867,0.00016814847,0.00024582303,0.00010724409,0.00008855849,0.0000067582905,0.00021686027,0.000034226912,0.00027399918],"category_scores_gemma":[0.0000047067583,0.00009447597,0.000031402604,0.00013542129,0.00041473057,0.00018652034,0.0010220131,0.000042185005,0.00005507657],"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":[0.00034727613,0.0005687365,0.97709477,0.00034589053,0.00020639886,0.000032379332,0.011249901,0.0011303312,0.0040545734,0.0005470258,0.00022121365,0.004201499],"study_design_scores_gemma":[0.002822317,0.00024799924,0.98504776,0.00010240167,0.00011194916,0.0000023191114,0.0006160287,0.0005200213,0.0024376619,0.0031150314,0.004659027,0.00031746825],"about_ca_topic_score_codex":0.00013916889,"about_ca_topic_score_gemma":0.00017311962,"teacher_disagreement_score":0.010633872,"about_ca_system_score_codex":0.000039665272,"about_ca_system_score_gemma":5.181955e-7,"threshold_uncertainty_score":0.38526183},"labels":[],"label_agreement":null},{"id":"W2339329431","doi":"10.1007/s11269-016-1322-x","title":"Incorporating Water Demand Management into a Cooperative Water Allocation Framework","year":2016,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Centre for International Governance Innovation; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Incentive; Demand management; Environmental economics; Water resources; Plan (archaeology); Cost sharing; Business; Economics; Microeconomics; Ecology","score_opus":0.005578820419604387,"score_gpt":0.18699780868073826,"score_spread":0.18141898826113387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2339329431","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56595564,0.00010249682,0.3795253,0.0021832113,0.000693701,0.002123101,0.0000038948924,0.0014958521,0.04791681],"genre_scores_gemma":[0.97961295,0.000104928746,0.00707819,0.0002593096,0.00022237534,0.00038032094,0.00012764188,0.0001356142,0.012078656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99712324,0.00010477237,0.0006387982,0.0006652813,0.0005321585,0.00093574025],"domain_scores_gemma":[0.99904746,0.000015330013,0.00004149179,0.00069323106,0.00005763874,0.00014483219],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00052295125,0.0005405437,0.00033447417,0.00047779153,0.00033789917,0.00036559495,0.00057663745,0.00013668934,0.0005269408],"category_scores_gemma":[0.000002301698,0.00026003568,0.00011178502,0.00016031599,0.00010233212,0.00050715613,0.00058036175,0.0001519922,0.0017268074],"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":[0.00060204405,0.0005839539,0.0022696003,0.0051057236,0.0056167757,0.00083631603,0.09526405,0.65212977,0.06225505,0.01597938,0.009996689,0.14936064],"study_design_scores_gemma":[0.0035197127,0.00023832572,0.0004484572,0.0009628369,0.0005301493,0.0000071540285,0.0023815248,0.019787772,0.36185464,0.013623587,0.5942215,0.0024243596],"about_ca_topic_score_codex":0.000012062418,"about_ca_topic_score_gemma":0.000011548265,"teacher_disagreement_score":0.632342,"about_ca_system_score_codex":0.00020905599,"about_ca_system_score_gemma":5.9377425e-7,"threshold_uncertainty_score":0.99998516},"labels":[],"label_agreement":null},{"id":"W2356635637","doi":"10.1007/s11269-016-1340-8","title":"Artificial Neural Network Rainfall-Discharge Model Assessment Under Rating Curve Uncertainty and Monthly Discharge Volume Predictions","year":2016,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Rating curve; Environmental science; Discharge; Streamflow; Extrapolation; Precipitation; Artificial neural network; Hydrology (agriculture); Statistics; Meteorology; Computer science; Mathematics; Drainage basin; Machine learning; Geology; Geography","score_opus":0.019688343854110543,"score_gpt":0.23627866566656558,"score_spread":0.21659032181245502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2356635637","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9647857,0.000010211199,0.021028383,0.0041633826,0.0001548029,0.0005167729,0.000023378409,0.00019883667,0.009118551],"genre_scores_gemma":[0.99128246,0.0000048315933,0.0023337423,0.00048659698,0.00014660027,0.000091012575,0.000020308174,0.000030260548,0.005604193],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99751294,0.00015049375,0.00040411743,0.0006710025,0.00047653125,0.0007849457],"domain_scores_gemma":[0.9993213,0.000034867284,0.00009741374,0.00035334163,0.0000073956676,0.0001857277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069316715,0.0002768088,0.00021412477,0.000042222877,0.0005988139,0.00014764996,0.00028808674,0.000075175376,0.0007746997],"category_scores_gemma":[0.000011198879,0.00015702247,0.00007757903,0.00012301757,0.00028707497,0.0002446248,0.0009372269,0.00016121147,0.00019298984],"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":[0.000028133798,0.000076403216,0.013886749,0.000010724723,0.000043364795,0.000007035385,0.0005279665,0.9778779,0.00087435235,0.0004506722,0.0021816383,0.0040350338],"study_design_scores_gemma":[0.00032682164,0.00010459548,0.013854773,0.000040163337,0.00005458421,0.0000021090038,0.000051894116,0.96604437,0.000034682776,0.005297431,0.013867172,0.00032143688],"about_ca_topic_score_codex":0.00012863352,"about_ca_topic_score_gemma":0.000086103675,"teacher_disagreement_score":0.026496774,"about_ca_system_score_codex":0.00019651532,"about_ca_system_score_gemma":0.0000019149907,"threshold_uncertainty_score":0.84824187},"labels":[],"label_agreement":null},{"id":"W2412379129","doi":"10.1007/s11269-016-1382-y","title":"Evaluating and Calibrating Reference Evapotranspiration Models Using Water Balance under Hyper-Arid Environment","year":2016,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Evapotranspiration; Water balance; Irrigation scheduling; Calibration; Crop coefficient; Environmental science; Arid; Penman–Monteith equation; Energy balance; Coefficient of determination; Irrigation; Air temperature; Hydrology (agriculture); Mathematics; Atmospheric sciences; Statistics; Meteorology; Soil science; Soil water; Agronomy; Ecology; Geography","score_opus":0.04336113405605582,"score_gpt":0.23691270101633796,"score_spread":0.19355156696028214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2412379129","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95781523,0.000018808834,0.037965,0.00033229322,0.000030268322,0.00028993987,0.000007267177,0.000044683853,0.003496509],"genre_scores_gemma":[0.9932495,0.000047987774,0.0046534054,0.00015097445,0.000019010864,0.000029295034,0.00001958231,0.000022081149,0.0018082022],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831146,0.00008714469,0.00028658364,0.00048481708,0.00041719177,0.00041279278],"domain_scores_gemma":[0.9995803,0.000011930103,0.000046450812,0.0002802887,0.0000031548186,0.000077823235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038816224,0.00019727809,0.00013260028,0.000045338486,0.00023860033,0.00008698724,0.00017178428,0.000058976362,0.00054630527],"category_scores_gemma":[7.3855216e-7,0.0001039148,0.000030138975,0.000030499215,0.000104874,0.0004243699,0.00038543378,0.0000699505,0.00015744712],"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":[0.000029436525,0.000043918153,0.0052006184,0.000029407263,0.000039738643,0.000009864834,0.0018186971,0.762202,0.21574955,0.00032782197,0.0000053212275,0.014543598],"study_design_scores_gemma":[0.0008835044,0.000092249706,0.001483209,0.00009242259,0.0000978333,0.000014762703,0.0000616275,0.9726134,0.010378369,0.007049503,0.006715972,0.00051715644],"about_ca_topic_score_codex":0.00010270993,"about_ca_topic_score_gemma":0.000008935682,"teacher_disagreement_score":0.21041137,"about_ca_system_score_codex":0.00015910271,"about_ca_system_score_gemma":7.610838e-7,"threshold_uncertainty_score":0.5981659},"labels":[],"label_agreement":null},{"id":"W2461311562","doi":"10.1007/s11269-016-1425-4","title":"An Efficient Method to Correct Under-Dispersion in Ensemble Streamflow Prediction of Inflow Volumes for Reservoir Optimization","year":2016,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Rio Tinto (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Streamflow; Hindcast; Environmental science; Surface runoff; Meteorology; Inflow; Flood forecasting; Hydropower; Hydrology (agriculture); Computer science; Geology; Drainage basin; Engineering; Geotechnical engineering","score_opus":0.011459791893271207,"score_gpt":0.2396875750967511,"score_spread":0.2282277832034799,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2461311562","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6036183,0.0000046007176,0.39283434,0.0009961174,0.000082943756,0.0007556497,0.00000831972,0.000039546758,0.0016601963],"genre_scores_gemma":[0.9738881,0.000015530031,0.023568168,0.0001641965,0.00001974951,0.00015648046,0.000017763825,0.000016520475,0.0021534914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985506,0.00013098316,0.0002802473,0.00045148475,0.00023847967,0.00034822657],"domain_scores_gemma":[0.99952954,0.00003442731,0.00005383276,0.00031142577,0.000009591276,0.00006117745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075106946,0.00015048272,0.00017384082,0.00018066332,0.00012669733,0.00001627135,0.00025228085,0.00005119826,0.00019138241],"category_scores_gemma":[0.000008105944,0.00009369791,0.000047052497,0.00015613022,0.00006766343,0.00013155179,0.00037646302,0.000031759908,0.000051594594],"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":[0.00023240785,0.00015910022,0.015168284,0.000037730588,0.000032154636,0.0000021561714,0.0018610161,0.9667464,0.0034315211,0.000036643098,0.00083206716,0.011460559],"study_design_scores_gemma":[0.006385278,0.0026303832,0.09747008,0.0003807022,0.0002947855,0.000001944135,0.0033647055,0.7382615,0.05661926,0.0018928255,0.091724925,0.000973607],"about_ca_topic_score_codex":0.00014524357,"about_ca_topic_score_gemma":0.000102831145,"teacher_disagreement_score":0.3702698,"about_ca_system_score_codex":0.00014450241,"about_ca_system_score_gemma":6.186953e-7,"threshold_uncertainty_score":0.38208902},"labels":[],"label_agreement":null},{"id":"W2466875295","doi":"10.1007/s11269-016-1370-2","title":"Optimization and Evaluation of Environmental Operations for Three Gorges Reservoir","year":2016,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"National Key Research and Development Program of China","keywords":"TOPSIS; Analytic hierarchy process; Three gorges; Water quality; Flood control; Computer science; Tributary; Multiple-criteria decision analysis; Environmental science; Water resources; Fuzzy logic; Flood myth; Operations research; Engineering; Artificial intelligence","score_opus":0.014563562264689647,"score_gpt":0.20308720199488026,"score_spread":0.1885236397301906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2466875295","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7902776,0.00017403335,0.20555434,0.00025909703,0.00008685196,0.0012845246,0.00002348786,0.00011194159,0.0022281012],"genre_scores_gemma":[0.99373776,0.00012292761,0.0050557447,0.000010045973,0.000043217195,0.00020996062,0.00006022682,0.00003281232,0.000727287],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990649,0.000029068813,0.00023702584,0.00019453956,0.00028800158,0.00018643735],"domain_scores_gemma":[0.9997041,0.000011343539,0.000021618534,0.0002056692,0.000023737863,0.00003352002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039601012,0.00013270213,0.00011146768,0.00015990889,0.000080471575,0.000046198198,0.00012145347,0.000035840134,0.00015600034],"category_scores_gemma":[0.00000346991,0.00008571034,0.000034872475,0.00004655989,0.000042849704,0.00020249233,0.00009888972,0.000018294746,0.000007989485],"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":[0.000015575168,0.000021926051,0.00044617156,0.000098248966,0.00010170475,2.5750094e-7,0.0005608963,0.9790319,0.0014986332,0.00012383607,0.0001487461,0.017952088],"study_design_scores_gemma":[0.0016615365,0.00006151505,0.0025182532,0.00005912987,0.00022253234,4.0888148e-7,0.00016395414,0.9758923,0.005449541,0.00038193076,0.013363234,0.00022564839],"about_ca_topic_score_codex":0.0000025921925,"about_ca_topic_score_gemma":0.000013642931,"teacher_disagreement_score":0.20346016,"about_ca_system_score_codex":0.00005882412,"about_ca_system_score_gemma":5.720801e-7,"threshold_uncertainty_score":0.3495166},"labels":[],"label_agreement":null},{"id":"W2507636127","doi":"10.1007/s11269-016-1469-5","title":"Regionalization of Tank Model Using Landscape Metrics of Catchments","year":2016,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Alexander von Humboldt-Stiftung","keywords":"Surface runoff; Environmental science; Hydrology (agriculture); Land cover; Infiltration (HVAC); Hydrogeology; Land use; Regression analysis; Statistics; Mathematics; Geology; Geography; Civil engineering; Meteorology; Geotechnical engineering; Engineering","score_opus":0.018589727223006967,"score_gpt":0.2235594100448032,"score_spread":0.20496968282179623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2507636127","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9552652,0.00002355199,0.02588688,0.00034941875,0.00003966385,0.00021206854,0.0000032974338,0.000018249051,0.018201657],"genre_scores_gemma":[0.99403197,0.00006874587,0.0015683842,0.00008101101,0.0000067326496,0.000008269498,0.0000030557178,0.000008564963,0.0042232694],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99901295,0.000033181426,0.00024295198,0.00021453139,0.00029392983,0.00020244015],"domain_scores_gemma":[0.9996328,0.000009678836,0.00010206699,0.00022274375,0.0000075319426,0.00002520044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024410982,0.00010325327,0.00014669096,0.0001241851,0.00006224959,0.000003791817,0.00020791832,0.000030125268,0.0002388126],"category_scores_gemma":[0.0000036908627,0.00005836024,0.000044486067,0.0001519037,0.00014766195,0.00010128378,0.0005507185,0.000017644737,0.000041883595],"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":[0.0002770477,0.0005587285,0.6258816,0.00041030295,0.00080730644,0.000025421627,0.007203057,0.32359228,0.022489514,0.0022368024,0.007970552,0.008547343],"study_design_scores_gemma":[0.015659925,0.0011823869,0.13961723,0.0010060259,0.002727143,0.000010844224,0.0025574206,0.24005574,0.25634447,0.083590865,0.25373146,0.003516505],"about_ca_topic_score_codex":0.00004372717,"about_ca_topic_score_gemma":0.000004057424,"teacher_disagreement_score":0.4862644,"about_ca_system_score_codex":0.000040494695,"about_ca_system_score_gemma":4.0517602e-7,"threshold_uncertainty_score":0.26148304},"labels":[],"label_agreement":null},{"id":"W2508860493","doi":"10.1007/s11269-016-1466-8","title":"Assessing an Enhanced Version of SWAT on Water Quantity and Quality Simulation in Regions with Seasonal Snow Cover","year":2016,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; Agriculture and Agri-Food Canada; University of New Brunswick","funders":"Agriculture and Agri-Food Canada","keywords":"Soil and Water Assessment Tool; Environmental science; SWAT model; Watershed; Snow; Hydrology (agriculture); Surface runoff; Streamflow; Meteorology; Computer science; Drainage basin; Geology; Geography; Ecology","score_opus":0.02367516256948427,"score_gpt":0.2722343616214289,"score_spread":0.24855919905194462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2508860493","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99008375,0.0000016292855,0.0033039323,0.000707708,0.000023283977,0.00022415555,0.0000012313831,0.000022266568,0.005632061],"genre_scores_gemma":[0.99815977,0.000012471988,0.00015096524,0.00015048569,0.00000826045,0.000011766505,0.000007447949,0.0000080373575,0.001490818],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998786,0.00013981701,0.00017917294,0.0003634568,0.00026508598,0.00026650654],"domain_scores_gemma":[0.999652,0.000035236957,0.00004685565,0.00022232902,0.0000047999765,0.00003875381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039866523,0.00013228453,0.00015135735,0.000069932445,0.00013689794,0.00002717661,0.000115204486,0.00003658987,0.00026685235],"category_scores_gemma":[0.000003856086,0.000062768435,0.000020761974,0.00005122574,0.00021742168,0.0004260738,0.0003196769,0.000045522258,0.000120121185],"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":[0.0021908658,0.0008648767,0.77731735,0.00023784048,0.00024349893,0.000055680142,0.0134286545,0.15292425,0.033596184,0.0007149182,0.00025894155,0.018166928],"study_design_scores_gemma":[0.0035395753,0.0006215888,0.9153273,0.00027179238,0.00008771499,4.8239457e-7,0.00091489666,0.0017200123,0.06003224,0.0010343239,0.015916727,0.0005333543],"about_ca_topic_score_codex":0.00013429722,"about_ca_topic_score_gemma":0.00013589388,"teacher_disagreement_score":0.15120424,"about_ca_system_score_codex":0.00006587785,"about_ca_system_score_gemma":3.1294735e-7,"threshold_uncertainty_score":0.29218462},"labels":[],"label_agreement":null},{"id":"W2518119950","doi":"10.1007/s11269-016-1489-1","title":"Probabilistic Prediction for Monthly Streamflow through Coupling Stepwise Cluster Analysis and Quantile Regression Methods","year":2016,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; University of Regina","funders":"Science and Engineering Research Board; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Quantile; Probabilistic logic; Statistics; Streamflow; Linear regression; Standard deviation; Quantile regression; Regression; Mathematics; Regression analysis; Stepwise regression; Prediction interval; Calibration; Probabilistic forecasting; Geography","score_opus":0.022614397117446207,"score_gpt":0.28180607966094123,"score_spread":0.259191682543495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2518119950","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89144576,0.000018232355,0.10566,0.000610069,0.000067899666,0.00062705064,0.000016379805,0.00010717106,0.0014474435],"genre_scores_gemma":[0.94519967,0.00001214824,0.05214262,0.0001257439,0.00003697727,0.00013098936,0.000017584118,0.000020625042,0.0023136232],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839497,0.00009160916,0.00028951935,0.0005963869,0.00025266537,0.00037487198],"domain_scores_gemma":[0.9993803,0.00011428097,0.00008407754,0.00033798552,0.000008210739,0.000075179996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082120235,0.00019166636,0.00023079356,0.00007100158,0.00022596968,0.000064863554,0.00018272037,0.000069398986,0.00025918114],"category_scores_gemma":[0.000042448653,0.0000912821,0.000102844504,0.00018568043,0.00017553946,0.00015927869,0.000445843,0.00004877159,0.000036244735],"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":[0.0008858184,0.0005114318,0.06897391,0.00041332358,0.001344725,0.000028315073,0.0065620434,0.7101805,0.013923924,0.00018342238,0.003218619,0.19377397],"study_design_scores_gemma":[0.0019595567,0.00056236464,0.024391083,0.00022191713,0.0017790465,0.0000024932083,0.000116458374,0.80096793,0.006080064,0.008160757,0.15512915,0.00062915985],"about_ca_topic_score_codex":0.00008862781,"about_ca_topic_score_gemma":0.000030394322,"teacher_disagreement_score":0.1931448,"about_ca_system_score_codex":0.00010813161,"about_ca_system_score_gemma":4.319178e-7,"threshold_uncertainty_score":0.3722376},"labels":[],"label_agreement":null},{"id":"W2537874370","doi":"10.1007/s11269-016-1526-0","title":"Granular Computing for Prediction of Scour Below Spillways","year":2016,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Dam Engineering and Safety","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Hydrogeology; Geology; Hydrology (agriculture); Geotechnical engineering; Engineering; Environmental science","score_opus":0.007190148567701634,"score_gpt":0.17450827989838433,"score_spread":0.16731813133068268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2537874370","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5603421,0.00008741883,0.43280286,0.00010538022,0.00041958227,0.00036561093,0.000050485713,0.00050041114,0.005326167],"genre_scores_gemma":[0.99704236,0.00002672417,0.0021783027,0.0000073429137,0.00009346106,0.00001787829,0.000012160505,0.00002994134,0.00059184444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993148,0.000005657727,0.0002005963,0.00012698266,0.000119531935,0.00023245037],"domain_scores_gemma":[0.99973977,0.000012037233,0.000014318683,0.00018536644,0.000014691335,0.000033795768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017689037,0.00010857394,0.00012223191,0.000095775504,0.000036051817,0.000011269998,0.00012235738,0.000029502116,0.000012135792],"category_scores_gemma":[0.0000020708753,0.000068028115,0.000060788618,0.000041415533,0.00001536739,0.00004414735,0.000047369187,0.000026115196,0.000016335536],"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":[0.00012031464,0.00014879243,0.008329887,0.0052515673,0.0008644417,0.000026239473,0.0050696526,0.62043154,0.07730171,0.00470377,0.010931502,0.26682055],"study_design_scores_gemma":[0.0034417089,0.00022590598,0.03291189,0.000870288,0.00017835476,0.000004571182,0.00018304863,0.14257114,0.08022034,0.0012274312,0.73753554,0.00062977703],"about_ca_topic_score_codex":0.0000018161601,"about_ca_topic_score_gemma":2.977224e-7,"teacher_disagreement_score":0.72660404,"about_ca_system_score_codex":0.000030713192,"about_ca_system_score_gemma":3.0294987e-7,"threshold_uncertainty_score":0.27741063},"labels":[],"label_agreement":null},{"id":"W2586499936","doi":"10.1007/s11269-017-1582-0","title":"Influence of Topography, Peak Demand, and Topology on Energy Use Patterns in four Small to Medium-Sized Systems in Ontario, Canada","year":2017,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Redundancy (engineering); Statistics; Efficient energy use; Energy (signal processing); Energy consumption; Environmental science; Mathematics; Simulation; Computer science; Engineering; Reliability engineering; Electrical engineering","score_opus":0.009798833935049668,"score_gpt":0.1720298473392747,"score_spread":0.16223101340422505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586499936","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9984702,0.000017845283,0.00021350024,0.00010368506,0.000187919,0.00025211106,0.0000033506747,0.000014552416,0.00073685806],"genre_scores_gemma":[0.99896353,0.00001726723,0.000047174923,0.000051150604,0.000015739679,0.000060364146,0.0000043908926,0.000014336754,0.00082602474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9990908,0.000037140464,0.00030100657,0.00019404668,0.00012928325,0.00024773055],"domain_scores_gemma":[0.99953204,0.000011355358,0.000042447995,0.0003449285,0.000013447294,0.00005579655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014009868,0.00013732615,0.00022838221,0.00022593651,0.000039901013,0.00008562299,0.00022947743,0.000045144083,0.0000047768604],"category_scores_gemma":[0.0000033824963,0.00010992505,0.000015925369,0.000032905442,0.000014461282,0.00007917991,0.00013695128,0.00006463984,5.8568315e-7],"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":[0.000028469021,0.000009770275,0.2797868,0.00020311998,0.000040308278,0.00011152524,0.0015724987,0.71782255,0.000060216018,0.00012243305,0.00011296839,0.00012931676],"study_design_scores_gemma":[0.0006898767,0.000046606998,0.97083145,0.0003232852,0.000010012705,0.0000022626573,0.00013270447,0.0028492592,0.0005091379,0.000011260188,0.024399284,0.00019486215],"about_ca_topic_score_codex":0.9731123,"about_ca_topic_score_gemma":0.9972999,"teacher_disagreement_score":0.71497333,"about_ca_system_score_codex":0.00013141551,"about_ca_system_score_gemma":0.0000051401225,"threshold_uncertainty_score":0.44826135},"labels":[],"label_agreement":null},{"id":"W2587682171","doi":"10.1007/s11269-017-1578-9","title":"Modelling Scenarios to Estimate the Potential Impact of Hydrological Standards on Nutrient Retention in the Tobacco Creek Watershed, Manitoba, Canada","year":2017,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Environment and Climate Change Canada; Government of Manitoba; Global Institute for Water Security","funders":"","keywords":"Environmental science; Hydrology (agriculture); Watershed; Water quality; Nutrient; Drainage; Water resource management; Ecology; Geology","score_opus":0.014622562789163943,"score_gpt":0.24411555538255433,"score_spread":0.2294929925933904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587682171","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9944705,0.0000037838497,0.00071489875,0.0015073604,0.00011090227,0.00067627046,0.000021364536,0.000012417219,0.0024825132],"genre_scores_gemma":[0.99934727,0.000008463774,0.00012002215,0.00021281885,0.00003210528,0.00004633252,0.000015252879,0.000012814086,0.00020491859],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99774444,0.000108208726,0.0002995986,0.00037999693,0.00097114436,0.0004966123],"domain_scores_gemma":[0.9989812,0.000011869093,0.00010337122,0.00081903883,0.000011676762,0.000072888026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008470027,0.00019710173,0.00017267528,0.000041028485,0.000534963,0.00027894197,0.0011554974,0.000040989195,0.000052429205],"category_scores_gemma":[0.0000066018943,0.00009203225,0.00010862052,0.00006608387,0.00013642869,0.00010347404,0.00067094906,0.00015285322,0.000025513848],"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":[0.0002785224,0.00010151521,0.10468535,0.000011677782,0.00003510712,0.000118616204,0.0013002688,0.8907484,0.00002260716,0.000027297103,0.0009930711,0.0016775903],"study_design_scores_gemma":[0.002664046,0.0013095635,0.51267874,0.00018569148,0.00018052106,0.000020692392,0.0022687633,0.43422192,0.0004255736,0.0082880985,0.036863897,0.0008924965],"about_ca_topic_score_codex":0.46391883,"about_ca_topic_score_gemma":0.09783803,"teacher_disagreement_score":0.45652646,"about_ca_system_score_codex":0.0005168049,"about_ca_system_score_gemma":0.0000051020165,"threshold_uncertainty_score":0.9186241},"labels":[],"label_agreement":null},{"id":"W2594822946","doi":"10.1007/s11269-017-1608-7","title":"An Integrated Approach of System Dynamics, Orthogonal Experimental Design and Inexact Optimization for Supporting Water Resources Management under Uncertainty","year":2017,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China; National Science Foundation","keywords":"Credibility; Context (archaeology); Stochastic programming; Computer science; Mathematical optimization; Variety (cybernetics); Interval (graph theory); System dynamics; Operations research; Stochastic optimization; Water resources; Risk analysis (engineering); Engineering; Mathematics; Business","score_opus":0.014002160361369139,"score_gpt":0.22459873395137858,"score_spread":0.21059657359000944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594822946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33871654,0.00004842569,0.6542198,0.000047374942,0.00015424579,0.0019080555,0.000016666345,0.00035897325,0.004529916],"genre_scores_gemma":[0.9516645,0.0000235505,0.046177644,0.000021442613,0.00007398915,0.00031552673,0.0007940711,0.00011872551,0.00081056834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974353,0.00010739572,0.0007122913,0.00064727175,0.00040091737,0.00069686706],"domain_scores_gemma":[0.9987849,0.0000123829495,0.00019708942,0.0008094699,0.00006277526,0.00013335787],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00083519175,0.0004849551,0.00044482388,0.00041022224,0.00055977335,0.0006694054,0.0007357547,0.00012014207,0.00003690779],"category_scores_gemma":[0.0000018003059,0.0003494641,0.000110762056,0.00007252354,0.0001457883,0.00049009774,0.00042643852,0.000114347575,0.0000049107784],"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":[0.00020609997,0.00011470892,0.00018854566,0.0015167983,0.00044694514,0.000012882683,0.0029619762,0.99128205,0.00018545866,0.0006571532,0.00006665943,0.0023607232],"study_design_scores_gemma":[0.0014652131,0.00015736526,0.0001631674,0.00015618885,0.00021671085,0.0000032820685,0.0064703366,0.9850244,0.0030311837,0.000045104018,0.0027735573,0.00049352023],"about_ca_topic_score_codex":0.000053873813,"about_ca_topic_score_gemma":0.000005885978,"teacher_disagreement_score":0.61294794,"about_ca_system_score_codex":0.00021613593,"about_ca_system_score_gemma":0.0000011560378,"threshold_uncertainty_score":0.99989575},"labels":[],"label_agreement":null},{"id":"W2601074364","doi":"10.1007/s11269-017-1611-z","title":"Improvement on the Existing Equations for Predicting Longitudinal Dispersion Coefficient","year":2017,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Particle swarm optimization; Hydrogeology; Dispersion (optics); Range (aeronautics); Applied mathematics; Mathematics; Mathematical optimization; Statistics; Computer science; Geotechnical engineering; Geology; Engineering; Physics","score_opus":0.03526346808981652,"score_gpt":0.2574473900651309,"score_spread":0.2221839219753144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2601074364","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8898563,0.000006313014,0.009076898,0.01070803,0.00030507988,0.0014850755,0.0000053694826,0.00007250811,0.088484466],"genre_scores_gemma":[0.9902559,0.0000065578793,0.000181086,0.00052385026,0.00007150045,0.00031348786,0.000007838627,0.000013683302,0.0086260745],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99859816,0.000029764842,0.00019755958,0.00042535114,0.00030392891,0.00044522854],"domain_scores_gemma":[0.9991418,0.00006222496,0.00011664096,0.0006326305,0.0000057530006,0.000040931394],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0007603905,0.0001696432,0.00011185841,0.00003501217,0.0033292153,0.00018438356,0.0006232726,0.000026729533,0.00025827743],"category_scores_gemma":[0.000040662337,0.00009318048,0.00007781588,0.000026083886,0.00025860124,0.00010064095,0.0013772018,0.00008105003,0.00030722897],"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":[0.0013942563,0.0025347506,0.40836394,0.0007838427,0.0026874407,0.0001851491,0.04586458,0.22838539,0.005047643,0.054072063,0.063858695,0.18682225],"study_design_scores_gemma":[0.0031675883,0.0013733904,0.12964557,0.00022497277,0.0005847559,0.0000014006058,0.0040149796,0.08866631,0.0072829756,0.0056422725,0.75825614,0.0011396696],"about_ca_topic_score_codex":0.00011197616,"about_ca_topic_score_gemma":0.00004451103,"teacher_disagreement_score":0.6943974,"about_ca_system_score_codex":0.00009079984,"about_ca_system_score_gemma":3.0172018e-7,"threshold_uncertainty_score":0.9979683},"labels":[],"label_agreement":null},{"id":"W2602776385","doi":"10.1007/s11269-017-1619-4","title":"Non-Stationary Frequency Analysis of Extreme Water Level: Application of Annual Maximum Series and Peak-over Threshold Approaches","year":2017,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Akaike information criterion; Generalized Pareto distribution; Generalized extreme value distribution; Mathematics; Statistics; Extreme value theory; Gumbel distribution; Range (aeronautics); Series (stratigraphy); Frequency analysis; Log-normal distribution; Time series; Econometrics; Geology","score_opus":0.025566079510268975,"score_gpt":0.2210418896730603,"score_spread":0.1954758101627913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602776385","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9854492,0.00001935496,0.0016616328,0.0003731791,0.000011996875,0.00018409069,0.000024973755,0.000013912558,0.012261675],"genre_scores_gemma":[0.9971547,0.000016644382,0.00082248746,0.00003503774,0.000010914919,0.000032256765,0.00007420805,0.000010861792,0.0018428962],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988282,0.000025852609,0.00029391175,0.00036083546,0.00027694908,0.00021429421],"domain_scores_gemma":[0.99917996,0.000004826406,0.00014424391,0.00062002346,0.000008850241,0.000042067844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003281473,0.0001397969,0.0002683863,0.00015729763,0.00022632975,0.000029096982,0.00038431524,0.000058704314,0.00040983502],"category_scores_gemma":[0.0000014697267,0.00009084324,0.000102081634,0.000106879015,0.00045095544,0.00033581708,0.0005567878,0.000049996335,0.00003800659],"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":[0.00013499899,0.00021896955,0.9554924,0.00012393607,0.0021197943,0.000015800846,0.012226626,0.013762154,0.008682726,0.00048805802,0.00019574798,0.0065388232],"study_design_scores_gemma":[0.00035459458,0.000069665075,0.96879447,0.000006274991,0.0014565726,9.1820345e-7,0.00063198194,0.009633936,0.009870086,0.004407609,0.004530814,0.00024305981],"about_ca_topic_score_codex":0.00069471856,"about_ca_topic_score_gemma":0.00041495444,"teacher_disagreement_score":0.013302122,"about_ca_system_score_codex":0.000020301824,"about_ca_system_score_gemma":4.8994235e-7,"threshold_uncertainty_score":0.4487406},"labels":[],"label_agreement":null},{"id":"W2616732649","doi":"10.1007/s11269-017-1704-8","title":"Bringing Future Climatic Change into Water Resources Management Practice Today","year":2017,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Downscaling; Environmental science; Climate change; Water resources; Environmental resource management; Water cycle; Flood myth; Water resource management; Water supply; Water quality; Hydrology (agriculture); Computer science; Environmental engineering; Engineering; Geography; Geology","score_opus":0.013105220518557996,"score_gpt":0.23995828089250906,"score_spread":0.22685306037395106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2616732649","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5494129,0.0001476502,0.00020343294,0.03460895,0.0008620986,0.0016921979,0.0000019599122,0.00029419846,0.4127766],"genre_scores_gemma":[0.96109813,0.00053699,0.0026241972,0.003936119,0.0005067609,0.00050151674,0.00002226919,0.000072288516,0.030701729],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961986,0.00018346818,0.00049556163,0.0010621545,0.00078527356,0.0012749238],"domain_scores_gemma":[0.9979315,0.000020102734,0.00021383923,0.0016620115,0.000011808242,0.0001607772],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0013805145,0.00053329347,0.0003992395,0.00019931971,0.002597098,0.00055905554,0.0016542089,0.00011569012,0.0013808149],"category_scores_gemma":[0.000009211385,0.00034389633,0.00015712707,0.00007881888,0.00045571124,0.0011308943,0.006704789,0.00026746668,0.005056061],"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":[0.0018689843,0.0025440063,0.08705527,0.0052330294,0.007938936,0.009197739,0.48550814,0.003806179,0.0012087038,0.004379925,0.05924403,0.33201507],"study_design_scores_gemma":[0.00087277096,0.00009153987,0.022839736,0.000085269356,0.00039426069,0.000007830848,0.0038132942,0.00026096104,0.0005460714,0.0011561211,0.96933967,0.00059245364],"about_ca_topic_score_codex":0.0005252455,"about_ca_topic_score_gemma":0.0001077841,"teacher_disagreement_score":0.91009563,"about_ca_system_score_codex":0.0001875353,"about_ca_system_score_gemma":2.3081384e-7,"threshold_uncertainty_score":0.9999013},"labels":[],"label_agreement":null},{"id":"W2620841105","doi":"10.1007/s11269-017-1718-2","title":"SWAT Setup with Long-Term Detailed Landuse and Management Records and Modification for a Micro-Watershed Influenced by Freeze-Thaw Cycles","year":2017,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of New Brunswick","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Soil and Water Assessment Tool; Watershed; Environmental science; SWAT model; Land use; Hydrology (agriculture); Universal Soil Loss Equation; Hydrogeology; Erosion; Water resource management; Computer science; Streamflow; Geology; Soil loss; Drainage basin; Cartography; Civil engineering; Geography; Engineering; Geotechnical engineering; Geomorphology","score_opus":0.011866557289270833,"score_gpt":0.22978223993714408,"score_spread":0.21791568264787325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2620841105","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9909998,0.00006502708,0.00086844625,0.0019200301,0.000048933183,0.0015086008,0.000014840471,0.00007124879,0.0045030643],"genre_scores_gemma":[0.988184,0.0005549987,0.0016453597,0.00035765124,0.000021780777,0.00048004795,0.0000631724,0.000031434407,0.008661557],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981893,0.000046554018,0.00025211228,0.0007537402,0.0002273379,0.00053096603],"domain_scores_gemma":[0.99908173,0.000012818857,0.00013966158,0.000655133,0.000007149993,0.00010349806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035560774,0.0003200639,0.0002603116,0.00008463319,0.0010227743,0.0003255125,0.00046988312,0.00006876357,0.00007423812],"category_scores_gemma":[0.000002667411,0.00021466202,0.00003811699,0.000033854125,0.00052845775,0.00036965415,0.0011868475,0.00007708886,0.00005845817],"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":[0.0005081024,0.00012526201,0.97878677,0.00045468478,0.0006262338,0.00006927396,0.0024671445,0.00013862232,0.0018457331,0.000057164612,0.003931166,0.010989838],"study_design_scores_gemma":[0.003294315,0.000247844,0.94851553,0.00008696443,0.0004423133,0.0000055779756,0.0003347874,0.00016445239,0.0041213604,0.0005875997,0.041617587,0.0005816641],"about_ca_topic_score_codex":0.00021719685,"about_ca_topic_score_gemma":0.00041034125,"teacher_disagreement_score":0.03768642,"about_ca_system_score_codex":0.00004473773,"about_ca_system_score_gemma":3.1899447e-7,"threshold_uncertainty_score":0.87536633},"labels":[],"label_agreement":null},{"id":"W274788335","doi":"10.1007/s11269-015-0978-y","title":"Plunging Flow Depth Estimation in a Stratified Dam Reservoir Using Neuro-Fuzzy Technique","year":2015,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Hydrogeology; Inflow; Flow (mathematics); Turbulence; Geology; Mean squared error; Entrainment (biomusicology); Arithmetic underflow; Nonlinear system; Mechanics; Geotechnical engineering; Mathematics; Statistics; Geometry; Computer science","score_opus":0.039796998330483155,"score_gpt":0.25927372660313497,"score_spread":0.2194767282726518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W274788335","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9612465,0.000004518172,0.011760245,0.0003371262,0.00005778949,0.0005631678,6.608006e-7,0.00014186911,0.025888154],"genre_scores_gemma":[0.9529242,5.6106575e-7,0.046383362,0.0002163374,0.00001708971,0.00004814314,0.000008804781,0.000021896849,0.00037962242],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982447,0.00014046249,0.00029405224,0.00043725347,0.00043074132,0.00045277117],"domain_scores_gemma":[0.9994704,0.000015733935,0.000056636283,0.0003398678,0.0000047340477,0.000112628215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008788606,0.000177211,0.00014922065,0.000120787576,0.000103315346,0.000106131505,0.00034295363,0.00006492192,0.000098161174],"category_scores_gemma":[0.000038476064,0.00013401655,0.000033249995,0.00027898164,0.00009250678,0.00019434454,0.0006411494,0.00016573115,0.00018359341],"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":[0.00003060387,0.00005619341,0.0038170645,0.000019594774,0.0000059862264,0.0001135782,0.001411728,0.98873335,0.00244512,0.0000146711145,0.0002606433,0.0030914934],"study_design_scores_gemma":[0.0004975168,0.00011286063,0.0032563054,0.000083015104,0.000022067872,0.000015797432,0.000082825565,0.97744167,0.0031143127,0.0040763305,0.010975146,0.00032214346],"about_ca_topic_score_codex":0.000542568,"about_ca_topic_score_gemma":0.00010985923,"teacher_disagreement_score":0.034623116,"about_ca_system_score_codex":0.0003242847,"about_ca_system_score_gemma":0.0000018127157,"threshold_uncertainty_score":0.54650366},"labels":[],"label_agreement":null},{"id":"W2750910385","doi":"10.1007/s11269-017-1801-8","title":"Best Management Practices Optimization at Watershed Scale: Incorporating Spatial Topology among Fields","year":2017,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Topology optimization; Mathematical optimization; Multi-objective optimization; Computer science; Genetic algorithm; Watershed; Pareto principle; Population; Topology (electrical circuits); Mathematics; Engineering; Machine learning","score_opus":0.01209018098539283,"score_gpt":0.21926704802742364,"score_spread":0.2071768670420308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2750910385","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64557755,0.000066524255,0.071528025,0.0008339142,0.0009440489,0.0014348348,0.0000048334095,0.000688221,0.27892205],"genre_scores_gemma":[0.9682285,0.00016284385,0.0076495134,0.000087201755,0.00026596192,0.0001655895,0.00018547976,0.00009312891,0.023161788],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977603,0.000069937545,0.0005101752,0.0005790373,0.00042811004,0.0006524611],"domain_scores_gemma":[0.99837893,0.000010220327,0.0003742357,0.0010775985,0.000035738267,0.00012329164],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036445702,0.00041645716,0.00031576498,0.0003134476,0.0009736758,0.0008401248,0.0009329629,0.0001515965,0.0003978427],"category_scores_gemma":[0.0000069640337,0.0003460354,0.000101424484,0.000075639175,0.00017220425,0.00069860223,0.0013377954,0.00017881677,0.00025031038],"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":[0.000092326176,0.000087669214,0.017273199,0.0007274156,0.00057168177,0.00020493906,0.0021293722,0.96821594,0.00005928463,0.00011288167,0.0017227557,0.00880255],"study_design_scores_gemma":[0.0032293247,0.0002180909,0.014494501,0.0002987192,0.00094398233,0.000010438091,0.0012956542,0.8294823,0.005783516,0.00040244698,0.14207853,0.0017624978],"about_ca_topic_score_codex":0.00029228255,"about_ca_topic_score_gemma":0.00038156006,"teacher_disagreement_score":0.32265097,"about_ca_system_score_codex":0.00014864608,"about_ca_system_score_gemma":6.6727216e-7,"threshold_uncertainty_score":0.99989915},"labels":[],"label_agreement":null},{"id":"W2761117300","doi":"10.1007/s11269-017-1827-y","title":"Boundary Judgments in Water Governance: Diagnosing Internal and External Factors that Matter in a Complex World","year":2017,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Social Sciences and Humanities Research Council of Canada; U.S. Environmental Protection Agency","keywords":"Corporate governance; Context (archaeology); Perspective (graphical); Set (abstract data type); Boundary (topology); Identification (biology); Face (sociological concept); Management science; Business; Process management; Political science; Computer science; Public relations; Economics; Sociology; Artificial intelligence; Mathematics; Geography; Social science","score_opus":0.01779596398547544,"score_gpt":0.2152516313815897,"score_spread":0.19745566739611425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2761117300","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97783643,0.00009782213,0.00037320363,0.00030401937,0.000228311,0.00042991413,0.0000029163482,0.00007433304,0.020653032],"genre_scores_gemma":[0.99378383,0.00010498652,0.00027521717,0.00014154437,0.000051275867,0.000051368028,0.000030068511,0.000056875862,0.005504852],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982808,0.00003578066,0.00035355144,0.0003921698,0.00030884295,0.00062881934],"domain_scores_gemma":[0.99941224,0.000007462348,0.00005800734,0.00044665512,0.0000054487045,0.0000701734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021395907,0.00032240845,0.0002761585,0.0003628442,0.00018908668,0.0009784835,0.00054860895,0.000046301724,0.00043667867],"category_scores_gemma":[0.0000011781339,0.0002305908,0.000052525935,0.000045504217,0.000089763285,0.0005484296,0.00077132805,0.00019656519,0.000097035576],"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":[0.00005830485,0.00006616037,0.9761531,0.00037690657,0.00008515761,0.0002309259,0.006038899,0.011928116,0.00029268922,0.000021249796,0.0009388237,0.0038096842],"study_design_scores_gemma":[0.0014021639,0.000015508846,0.9084206,0.00035522992,0.000024084346,0.0000016468994,0.00017893381,0.01006125,0.0024465013,0.00022983771,0.076441035,0.00042318122],"about_ca_topic_score_codex":0.00040558967,"about_ca_topic_score_gemma":0.0005342113,"teacher_disagreement_score":0.07550221,"about_ca_system_score_codex":0.00015596568,"about_ca_system_score_gemma":3.5941991e-7,"threshold_uncertainty_score":0.9435538},"labels":[],"label_agreement":null},{"id":"W2762046177","doi":"10.1007/s11269-017-1813-4","title":"Balancing Costs and Benefits in Selecting New Information: Efficient Monitoring Using Deterministic Hydro-economic Models","year":2017,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","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 British Columbia","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; AXA Research Fund","keywords":"Computer science; Operations research; Mathematical optimization; Value of information; Time horizon; Mathematics; Artificial intelligence","score_opus":0.01594519874965311,"score_gpt":0.20658629393154537,"score_spread":0.19064109518189226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2762046177","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98255104,0.00010087394,0.006540279,0.00003070681,0.00029268998,0.0003661721,0.0000012920663,0.00014664927,0.009970283],"genre_scores_gemma":[0.99794656,0.00005295988,0.0016583723,0.000011194781,0.00012510674,0.000014617469,0.000006627405,0.000031268253,0.00015327302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988374,0.000013022178,0.00035517907,0.00021552629,0.00015890165,0.00041993943],"domain_scores_gemma":[0.9994767,0.0000075212406,0.000080994716,0.0003398474,0.000008518475,0.00008643585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021353562,0.0002218636,0.00018879591,0.0003043159,0.0003183027,0.00081418135,0.0002992713,0.000049657076,0.000007248096],"category_scores_gemma":[0.0000030452418,0.0002087921,0.00003065056,0.000041301424,0.000021889471,0.00067995273,0.00036168122,0.0001017044,0.000024305726],"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":[0.000009026785,0.0000039579304,0.0042796456,0.00013083043,0.00003253866,0.00000808724,0.0024562564,0.978133,0.000020499614,0.000052765085,0.000009850792,0.014863547],"study_design_scores_gemma":[0.00064943626,0.0000117251075,0.004543795,0.00021539828,0.000034683846,0.0000029728985,0.00024181571,0.9917896,0.0006616207,0.00006115245,0.0015085824,0.00027921319],"about_ca_topic_score_codex":0.00024366075,"about_ca_topic_score_gemma":0.000023954577,"teacher_disagreement_score":0.015395526,"about_ca_system_score_codex":0.0002526819,"about_ca_system_score_gemma":0.0000017650617,"threshold_uncertainty_score":0.8514295},"labels":[],"label_agreement":null},{"id":"W2768082481","doi":"10.1007/s11269-017-1851-y","title":"Concept of Equivalent Reliability for Estimating the Design Flood under Non-stationary Conditions","year":2017,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; Central University Basic Research Fund of China; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Flood myth; Reliability (semiconductor); Engineering design process; Series (stratigraphy); Computer science; Return period; Hydrogeology; Probabilistic design; Estimation; Mathematical optimization; Reliability engineering; Environmental science; Mathematics; Engineering; Geology; Systems engineering; Geotechnical engineering","score_opus":0.022871022618246035,"score_gpt":0.2737777592540066,"score_spread":0.2509067366357606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768082481","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64892894,0.000008633319,0.33699045,0.0031805187,0.00010111178,0.00081750983,0.000013797386,0.000020459236,0.009938567],"genre_scores_gemma":[0.98044014,0.0000010876864,0.016755044,0.00019955111,0.00002608033,0.00011825824,0.000017526978,0.00000672137,0.0024356132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911594,0.00006881612,0.00020516942,0.00023251881,0.00017832368,0.00019924252],"domain_scores_gemma":[0.99920493,0.00007419022,0.00012160422,0.0005614025,0.000008053759,0.000029786699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005974289,0.00009074853,0.00011613422,0.000018302862,0.000804039,0.000040866922,0.0004585219,0.00003001661,0.000746268],"category_scores_gemma":[0.000018313049,0.000053566615,0.00008261107,0.000026500062,0.00047868604,0.00012078997,0.0003567508,0.000046745008,0.00008925953],"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":[0.000045973724,0.00013212144,0.0048250505,0.00004198087,0.00017483474,0.0000041017493,0.0035876355,0.98462665,0.0004297295,0.0003076759,0.0035196086,0.0023046422],"study_design_scores_gemma":[0.0022093416,0.00035354006,0.15371034,0.000059664675,0.00091795396,0.0000030376148,0.0014687183,0.7233744,0.014209809,0.07904739,0.024082327,0.00056343665],"about_ca_topic_score_codex":0.00012465769,"about_ca_topic_score_gemma":0.000013281659,"teacher_disagreement_score":0.33151117,"about_ca_system_score_codex":0.00003508665,"about_ca_system_score_gemma":0.0000010765256,"threshold_uncertainty_score":0.81711113},"labels":[],"label_agreement":null},{"id":"W2773430417","doi":"10.1007/s11269-017-1830-3","title":"Evaluating the Importance of Non-Unique Behavioural Parameter Sets on Surface Water Quality Variables under Climate Change Conditions in a Mesoscale Agricultural Watershed","year":2017,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Klima- und Energiefonds; Ministère de l'Éducation, du Loisir et du Sport Québec","keywords":"Soil and Water Assessment Tool; Environmental science; Climate change; Watershed; SWAT model; Equifinality; Streamflow; Climate model; Quantile; Climatology; Hydrology (agriculture); Statistics; Mathematics; Computer science; Geography; Ecology; Drainage basin","score_opus":0.0722823688064991,"score_gpt":0.3326376344370631,"score_spread":0.260355265630564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773430417","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9861264,0.000005024607,0.000012863659,0.0050989757,0.0001051595,0.0010493214,0.000012588771,0.000031094143,0.007558531],"genre_scores_gemma":[0.99744356,0.00002174796,0.0002889427,0.000753485,0.000017282853,0.00025395758,0.000049166832,0.000016224627,0.001155663],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99759054,0.00027794353,0.0004773335,0.000527569,0.00041293007,0.00071368006],"domain_scores_gemma":[0.99890333,0.00004304559,0.00018841162,0.0008046975,0.0000101607475,0.000050333238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016434685,0.00028422155,0.0003168199,0.000045753728,0.0008591943,0.00010548693,0.000712704,0.0000760943,0.00045365308],"category_scores_gemma":[0.000008233426,0.00013491063,0.000102968486,0.00004823045,0.00038758878,0.00031906995,0.0015302654,0.00018077593,0.00018820369],"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":[0.00026386205,0.00043040008,0.9489352,0.00016818785,0.00025609232,0.000054991262,0.021168377,0.01834625,0.009027593,0.0004048642,0.0005069231,0.000437226],"study_design_scores_gemma":[0.00084672024,0.0001628583,0.98841006,0.00005797138,0.00010317463,0.0000012205006,0.0009816056,0.0008431814,0.006441373,0.0015683171,0.0002868055,0.0002967173],"about_ca_topic_score_codex":0.0010514898,"about_ca_topic_score_gemma":0.0005563845,"teacher_disagreement_score":0.039474823,"about_ca_system_score_codex":0.00009837901,"about_ca_system_score_gemma":4.0897953e-7,"threshold_uncertainty_score":0.66083145},"labels":[],"label_agreement":null},{"id":"W2784313367","doi":"10.1007/s11269-017-1900-6","title":"Long-Term Planning of Water Systems in the Context of Climate Non-Stationarity with Deterministic and Stochastic Optimization","year":2018,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inflow; Stochastic programming; Context (archaeology); Hydropower; Environmental science; Stochastic modelling; Climate change; Term (time); Sampling (signal processing); Computer science; Mathematical optimization; Meteorology; Ecology; Mathematics; Statistics; Geography; Biology","score_opus":0.008724140843288512,"score_gpt":0.20487649738047709,"score_spread":0.19615235653718857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2784313367","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9244838,0.0000559085,0.072388634,0.000020022533,0.000065360415,0.0007140881,0.000004493716,0.000039813553,0.0022278773],"genre_scores_gemma":[0.9993735,0.00002053491,0.00036865077,0.000017452483,0.00003114633,0.000049775575,0.00005923115,0.000024626599,0.000055094566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989417,0.000042389212,0.00034797942,0.00017776465,0.00022752845,0.00026260852],"domain_scores_gemma":[0.99964917,0.000016769955,0.000058820373,0.00021644965,0.000036088568,0.000022698583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028017283,0.00016180016,0.00019783442,0.00022649817,0.00006609459,0.00007799993,0.00018728708,0.000033480206,0.000011576048],"category_scores_gemma":[0.0000011344584,0.00008981984,0.000019086512,0.000099166464,0.00011141685,0.000113449154,0.000096768556,0.00005492728,0.0000040179434],"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":[0.0000911831,0.000029768029,0.004641482,0.0010005919,0.00007563705,0.00001567234,0.019718034,0.97370166,0.000057287245,0.000044552955,0.000009648051,0.0006145146],"study_design_scores_gemma":[0.0016259195,0.000341172,0.018361596,0.0006392406,0.00018869013,0.000007718647,0.0037683572,0.97348136,0.00094193546,0.00001622724,0.0002925283,0.00033525794],"about_ca_topic_score_codex":0.000017435412,"about_ca_topic_score_gemma":0.000009940501,"teacher_disagreement_score":0.07488968,"about_ca_system_score_codex":0.000018250048,"about_ca_system_score_gemma":5.175553e-7,"threshold_uncertainty_score":0.36627468},"labels":[],"label_agreement":null},{"id":"W2793065876","doi":"10.1007/s11269-018-1906-8","title":"Regional Water Use Structure Optimization Under Multiple Uncertainties Based on Water Resources Vulnerability Analysis","year":2018,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"National Natural Science Foundation of China","keywords":"Water resources; Vulnerability (computing); Hydrogeology; Water use; Vulnerability assessment; Environmental science; Scenario analysis; Water resource management; Computer science; Integrated water resources management; Environmental resource management; Environmental economics; Business; Engineering; Psychological resilience","score_opus":0.014257796075213434,"score_gpt":0.20250454663833534,"score_spread":0.1882467505631219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793065876","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8956221,0.000014921956,0.098578595,0.000714877,0.0003224534,0.00087819336,0.000024194262,0.00086246186,0.00298221],"genre_scores_gemma":[0.9902794,0.000010739227,0.0035111387,0.00068725407,0.00030622873,0.000071904215,0.0012108836,0.00011827966,0.0038042066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99647117,0.00020490422,0.0006359836,0.00087231473,0.0008072719,0.0010083702],"domain_scores_gemma":[0.99856156,0.0000380669,0.000055340803,0.001064116,0.00012564186,0.0001552992],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00041128832,0.0006346492,0.0004742343,0.0011401289,0.00050595397,0.00072262355,0.00058024394,0.00018847709,0.0016843408],"category_scores_gemma":[0.000005756077,0.00037368838,0.00027860136,0.00045909453,0.00023628084,0.0004344297,0.00031562912,0.00024124603,0.00017197376],"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":[0.00019074377,0.00007071333,0.0023907153,0.0001460843,0.00090106134,0.00001076077,0.004433576,0.9904333,0.00035846417,0.000013710994,0.0008075681,0.00024335223],"study_design_scores_gemma":[0.0010066404,0.000118405624,0.0019797767,0.000037186062,0.00064590876,5.6552943e-7,0.0003089523,0.87996244,0.01498296,0.00013962855,0.10010793,0.00070960855],"about_ca_topic_score_codex":0.000118756754,"about_ca_topic_score_gemma":0.00012492666,"teacher_disagreement_score":0.11047082,"about_ca_system_score_codex":0.0002072461,"about_ca_system_score_gemma":0.0000010258321,"threshold_uncertainty_score":0.9998715},"labels":[],"label_agreement":null},{"id":"W2793171933","doi":"10.1007/s11269-018-1935-3","title":"Regionalizing Flood Magnitudes using Landscape Structural Patterns of Catchments","year":2018,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Geological Survey and Mineral Exploration of Iran","keywords":"Hydrology (agriculture); Land cover; Flood myth; Land use; Landscape ecology; Drainage basin; Context (archaeology); Physical geography; Environmental science; Geology; Geography; Cartography; Ecology","score_opus":0.014035420691442677,"score_gpt":0.23411578351050935,"score_spread":0.22008036281906668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793171933","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97981274,0.000016321312,0.00031432224,0.00020487045,0.0001341163,0.0002238513,0.0000025939469,0.000038258193,0.019252913],"genre_scores_gemma":[0.99557227,0.000012267017,0.00078385614,0.00027984387,0.00007012683,0.0000106802845,0.0000091498705,0.000013810098,0.0032480075],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986981,0.000039811894,0.00023567538,0.00034120822,0.00029973377,0.00038542596],"domain_scores_gemma":[0.99959433,0.0000053881145,0.00007158621,0.00027758174,0.000007427793,0.00004368873],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018023457,0.00017231885,0.00016641368,0.0000800815,0.00025730542,0.000023709106,0.00031683673,0.000034361266,0.0016441403],"category_scores_gemma":[0.0000011911408,0.00011971445,0.000053954594,0.00008061904,0.00021747833,0.00012562414,0.0010493262,0.000050965908,0.00022368666],"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":[0.000089214926,0.00007167842,0.9862931,0.00014135335,0.00037052145,0.000046996134,0.0073559857,0.0017099751,0.0011853367,0.00020392679,0.00090032455,0.0016316152],"study_design_scores_gemma":[0.0027150193,0.00051992474,0.8340139,0.00019767963,0.00059399294,0.000017596398,0.0025873873,0.0043985313,0.018476957,0.0056466954,0.12969293,0.0011394222],"about_ca_topic_score_codex":0.0004255177,"about_ca_topic_score_gemma":0.00008078832,"teacher_disagreement_score":0.1522792,"about_ca_system_score_codex":0.00003849373,"about_ca_system_score_gemma":2.699627e-7,"threshold_uncertainty_score":0.9992685},"labels":[],"label_agreement":null},{"id":"W2794439414","doi":"10.1007/s11269-018-1961-1","title":"Method for Extended Period Simulation of Water Distribution Networks with Pressure Driven Demands","year":2018,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Snapshot (computer storage); Throttle; Flow control valve; Benchmark (surveying); Programmer; Simulation; Real-time computing; Control engineering; Automotive engineering; Engineering; Embedded system; Operating system","score_opus":0.005942184055577788,"score_gpt":0.21294689929746433,"score_spread":0.20700471524188654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794439414","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02755338,0.000029434217,0.9706017,0.000031793737,0.000112114314,0.0006058994,0.000010161341,0.0001190984,0.00093642826],"genre_scores_gemma":[0.99060744,0.00000231358,0.0072256597,0.000007235825,0.00017725636,0.00008196491,0.00023620548,0.00003163795,0.0016302685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991738,0.00002872265,0.00022830228,0.00018165275,0.00013223337,0.00025524286],"domain_scores_gemma":[0.99966115,0.000006163989,0.000029408851,0.00020094545,0.00007094762,0.00003139001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018572394,0.0001399276,0.0001634093,0.000054205102,0.00008473563,0.000053136464,0.00010397175,0.000058479203,0.000028386454],"category_scores_gemma":[5.793218e-7,0.000081048165,0.000041856318,0.000050264345,0.000024381246,0.00009618298,0.00004963509,0.00003382701,0.0000046406917],"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":[0.000073211086,0.000013247199,0.000090838024,0.00025901638,0.0001777253,9.989715e-7,0.0019116127,0.9957866,0.00014212771,0.000041050964,0.0003851659,0.0011184079],"study_design_scores_gemma":[0.00048982387,0.00012021467,0.00032459782,0.000047313333,0.000115638024,7.840042e-7,0.00006251393,0.87335914,0.007044053,0.000025831936,0.1182793,0.00013077892],"about_ca_topic_score_codex":0.000007784497,"about_ca_topic_score_gemma":0.000015860434,"teacher_disagreement_score":0.96337605,"about_ca_system_score_codex":0.00002109331,"about_ca_system_score_gemma":3.7444255e-7,"threshold_uncertainty_score":0.3305048},"labels":[],"label_agreement":null},{"id":"W2802634149","doi":"10.1007/s11269-018-1983-8","title":"Integrating Social Dimensions into Flood Cost Forecasting","year":2018,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; McMaster University","funders":"","keywords":"Hydrogeology; Flood myth; Water resource management; Computer science; Environmental science; Geography; Engineering; Geotechnical engineering; Archaeology","score_opus":0.01581180621877038,"score_gpt":0.23991223407902604,"score_spread":0.22410042786025566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802634149","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72225964,0.000008520931,0.0045336396,0.0009399287,0.0003043095,0.00073514093,0.0000010992762,0.0001776645,0.27104005],"genre_scores_gemma":[0.9804354,0.0000054560787,0.010351997,0.000559791,0.00027178877,0.00010776579,0.000016728398,0.000032285632,0.008218805],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980977,0.000065440654,0.00029559332,0.0004947813,0.00046713356,0.00057934306],"domain_scores_gemma":[0.9995301,0.00001050199,0.0000729829,0.00028799736,0.000010103063,0.000088357694],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00042826738,0.00023437904,0.00016315274,0.00008993528,0.00089936994,0.00015108546,0.00042012564,0.000044582317,0.0018940923],"category_scores_gemma":[0.0000048519296,0.00016533477,0.000081676786,0.00021011368,0.00024165549,0.00020063746,0.001709637,0.000118742006,0.0018451501],"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":[0.00021410907,0.0010087207,0.031084284,0.00027158274,0.0007936134,0.00031071465,0.103085205,0.0015414661,0.010512937,0.0070986687,0.1398059,0.7042728],"study_design_scores_gemma":[0.00082596927,0.0001987044,0.005413774,0.000047229503,0.00013637857,0.000002625194,0.0043301694,0.00909427,0.002764808,0.0024019082,0.97424644,0.000537732],"about_ca_topic_score_codex":0.0006130314,"about_ca_topic_score_gemma":0.00061548245,"teacher_disagreement_score":0.8344405,"about_ca_system_score_codex":0.00017257206,"about_ca_system_score_gemma":8.181033e-7,"threshold_uncertainty_score":0.9990183},"labels":[],"label_agreement":null},{"id":"W2810186820","doi":"10.1007/s11269-018-1988-3","title":"Transient Investigation of the Critical Abstraction Rates in Coastal Aquifers: Numerical and Experimental Study","year":2018,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Groundwater and Isotope Geochemistry","field":"Earth and Planetary Sciences","cited_by":29,"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":"Queen's University; Queen's University Belfast","keywords":"Aquifer; Hydrogeology; Hydraulic conductivity; Aquifer test; Geology; Volumetric flow rate; Soil science; Environmental science; Aquifer properties; Geotechnical engineering; Mechanics; Hydrology (agriculture); Groundwater; Groundwater recharge; Physics","score_opus":0.013437324065951392,"score_gpt":0.2325696101226548,"score_spread":0.21913228605670343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810186820","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99698603,0.000024964078,0.000013485229,0.00018420078,0.000117629075,0.00021907309,0.0000031886568,0.000008349885,0.0024430826],"genre_scores_gemma":[0.9997376,8.5078545e-7,0.000049278184,0.000055291403,0.000046479567,0.000003645423,0.00000846577,0.000001898512,0.00009645352],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992395,0.00006267468,0.0001696346,0.0001811921,0.00018987684,0.00015714628],"domain_scores_gemma":[0.99980855,0.000012287612,0.000018048408,0.00011023319,0.000010331001,0.000040557923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016692183,0.00008002688,0.000082242375,0.000035353296,0.000085647574,0.000047992587,0.00010532046,0.00001956391,0.00030321834],"category_scores_gemma":[0.0000022421355,0.000046348145,0.000018900724,0.00006564184,0.00018845666,0.00009786035,0.000035030953,0.0000558228,0.000015864272],"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":[0.00011939884,0.00015955363,0.9765076,0.00005454338,0.000023691216,0.000011137122,0.018602831,0.00011658845,0.0032507025,0.0000059551658,0.0000680835,0.0010798979],"study_design_scores_gemma":[0.00039709796,0.00031223666,0.93746835,0.000019626894,0.000014503581,0.000003859082,0.008215957,0.0006276808,0.051798902,0.00010563906,0.00094884634,0.000087269356],"about_ca_topic_score_codex":0.0009892265,"about_ca_topic_score_gemma":0.00032639518,"teacher_disagreement_score":0.048548203,"about_ca_system_score_codex":0.0000033457393,"about_ca_system_score_gemma":0.0000014475986,"threshold_uncertainty_score":0.33200282},"labels":[],"label_agreement":null},{"id":"W2885824106","doi":"10.1007/s11269-018-2035-0","title":"Water Resources and Farmland Management in the Songhua River Watershed under Interval and Fuzzy Uncertainties","year":2018,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; Jane ja Aatos Erkon Säätiö","keywords":"Watershed; Agriculture; Environmental science; Water resource management; Water resources; Business; Water scarcity; Scarcity; Irrigation; Water quality; Natural resource economics; Environmental planning; Environmental resource management; Geography; Economics; Ecology; Computer science","score_opus":0.010309857452487263,"score_gpt":0.19627556963984655,"score_spread":0.18596571218735927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885824106","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9647351,0.00021993868,0.0008568234,0.00097090553,0.00016792274,0.0008515623,0.0000024152669,0.00022838174,0.03196694],"genre_scores_gemma":[0.99359804,0.00030669867,0.00053899846,0.00053173606,0.00016844607,0.000106167754,0.00004537898,0.00006467426,0.0046398295],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.997746,0.00012270671,0.00042107675,0.0005397545,0.00040264783,0.0007678327],"domain_scores_gemma":[0.9993887,0.000014516156,0.000031176136,0.0004651112,0.000021085103,0.0000793871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058470486,0.00043226365,0.00028688266,0.00047881374,0.00026711752,0.0005622426,0.0004932646,0.000083371924,0.00008114476],"category_scores_gemma":[8.2738325e-7,0.00023982076,0.000059272326,0.0001608573,0.00036021444,0.00025696628,0.00072452304,0.00017311079,0.000101517035],"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":[0.0014904583,0.00059825706,0.024636157,0.006320351,0.0050571263,0.0011157892,0.75608456,0.09259328,0.0012596536,0.0057666027,0.019533083,0.08554469],"study_design_scores_gemma":[0.0037776753,0.00032534744,0.032052815,0.00025255894,0.00047711824,0.000023423283,0.021759445,0.017631661,0.0021220585,0.006516547,0.9136179,0.0014434623],"about_ca_topic_score_codex":0.00011204261,"about_ca_topic_score_gemma":0.00009657094,"teacher_disagreement_score":0.8940848,"about_ca_system_score_codex":0.000055832268,"about_ca_system_score_gemma":2.5283674e-7,"threshold_uncertainty_score":0.97796065},"labels":[],"label_agreement":null},{"id":"W2887632787","doi":"10.1007/s11269-018-2081-7","title":"Game Theory Analysis of the Virtual Water Strategy","year":2018,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Museum of Nature","funders":"Guizhou Science and Technology Department","keywords":"Incentive; Virtual water; Environmental economics; Computer science; Game theory; Process (computing); Sustainability; Flexibility (engineering); Psychological intervention; Water resources; Operations research; Management science; Risk analysis (engineering); Knowledge management; Microeconomics; Economics; Business; Water scarcity; Engineering; Psychology","score_opus":0.005980494127211137,"score_gpt":0.1812194345511034,"score_spread":0.17523894042389226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887632787","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.921533,0.000027755954,0.006789381,0.000057768917,0.00022506206,0.00033516128,0.000004923397,0.00020687393,0.07082009],"genre_scores_gemma":[0.9883944,0.000013120821,0.000054564425,0.00008470048,0.00009614507,0.00002113782,0.000044662134,0.000036468107,0.01125485],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985684,0.00008574238,0.00034450015,0.0002508895,0.00033439757,0.00041604173],"domain_scores_gemma":[0.9992745,0.00000772376,0.00003398811,0.00060742453,0.000032972413,0.000043426706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035347123,0.00021819223,0.00023961626,0.00043050555,0.000102091006,0.000100710575,0.0005724404,0.00005195609,0.00058996654],"category_scores_gemma":[9.614947e-7,0.0001106864,0.00019395478,0.00045710438,0.0001568804,0.00010616815,0.00037720072,0.00008144324,0.0001557235],"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":[0.00008103711,0.000073777126,0.0015985686,0.00018820749,0.0054744864,0.000009065384,0.015041603,0.96105576,0.0017198165,0.002817281,0.0008134339,0.011126983],"study_design_scores_gemma":[0.0016714724,0.00034449185,0.022220515,0.00012220595,0.0068868636,0.0000014508429,0.0027088972,0.28350395,0.18729052,0.0031403531,0.49073797,0.0013712958],"about_ca_topic_score_codex":0.000011252457,"about_ca_topic_score_gemma":0.000018274271,"teacher_disagreement_score":0.6775518,"about_ca_system_score_codex":0.000034517412,"about_ca_system_score_gemma":4.1312035e-7,"threshold_uncertainty_score":0.645972},"labels":[],"label_agreement":null},{"id":"W2887904734","doi":"10.1007/s11269-018-2073-7","title":"An Entropy-Based Approach to Fuzzy Multi-objective Optimization of Reservoir Water Quality Monitoring Networks Considering Uncertainties","year":2018,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"University of Mohaghegh Ardabili","keywords":"Fuzzy logic; Mathematical optimization; Entropy (arrow of time); Data mining; Minimax; Water quality; Computer science; Multi-objective optimization; Mathematics; Artificial intelligence; Ecology","score_opus":0.02385447026182454,"score_gpt":0.24489924566989651,"score_spread":0.22104477540807196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887904734","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40419787,0.000027782293,0.58900356,0.000036278365,0.0002869112,0.0008119379,0.000002987199,0.0004447978,0.005187848],"genre_scores_gemma":[0.9394059,0.000011630012,0.05955985,0.000040776486,0.0002701947,0.00012144067,0.00010744501,0.000086812805,0.00039593293],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976629,0.00015516928,0.0005836879,0.0005154188,0.00040628764,0.0006765344],"domain_scores_gemma":[0.99900395,0.000013018205,0.00006452957,0.0006499952,0.00012826551,0.00014023653],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005696333,0.00036646242,0.00034917198,0.00045777977,0.00021449549,0.00022770421,0.00046885127,0.00009916168,0.0000403107],"category_scores_gemma":[0.000005185741,0.00027513312,0.00009009586,0.00025525616,0.00010895998,0.00029048827,0.00026968043,0.00012890344,0.000024325818],"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":[0.00011694472,0.00010559276,0.0016957265,0.0003065657,0.00017016382,0.0000029712492,0.0073974226,0.98876023,0.000996959,0.00004728742,0.000057014924,0.00034309708],"study_design_scores_gemma":[0.0009225522,0.00013532759,0.0008990167,0.00009175448,0.000080530095,3.1020923e-7,0.0017118056,0.94506,0.046987418,0.0000292306,0.0035915228,0.0004904957],"about_ca_topic_score_codex":0.00009848705,"about_ca_topic_score_gemma":0.000008683214,"teacher_disagreement_score":0.53520805,"about_ca_system_score_codex":0.00013167664,"about_ca_system_score_gemma":0.0000013489448,"threshold_uncertainty_score":0.9999701},"labels":[],"label_agreement":null},{"id":"W2888276012","doi":"10.1007/s11269-018-2092-4","title":"Drinking Water Source Monitoring Using Early Warning Systems Based on Data Mining Techniques","year":2018,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thales (Canada); Université Laval","funders":"Mitacs; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Turbidity; Environmental science; Water quality; Warning system; Percentile; Early warning system; Hydrology (agriculture); Statistics; Computer science; Engineering; Mathematics; Geology","score_opus":0.06038004253882832,"score_gpt":0.2769738702565887,"score_spread":0.2165938277177604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888276012","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9866029,0.0000090611575,0.0077867443,0.00016993623,0.0004983468,0.00048595798,0.0000018676891,0.0011654565,0.003279704],"genre_scores_gemma":[0.9754185,0.000002074484,0.022613177,0.00004520486,0.00049769675,0.000049427323,0.000014177264,0.000079204234,0.0012805364],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9964643,0.0001704783,0.00047056843,0.0010424906,0.0008706346,0.0009815051],"domain_scores_gemma":[0.9977821,0.000030131887,0.000109205845,0.0019754048,0.000013869258,0.00008929843],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013777242,0.0003867345,0.00027784682,0.00026549047,0.00064030534,0.0004833337,0.0019452809,0.00013642178,0.000073730596],"category_scores_gemma":[0.000014740421,0.00026312086,0.000052918782,0.0001818912,0.00031480147,0.00041773837,0.0044525466,0.00024093347,0.00045303613],"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":[0.00028124708,0.00040252195,0.6745824,0.00051628763,0.00037063428,0.00036774017,0.030884277,0.050296083,0.15729398,0.000023305021,0.001268967,0.08371252],"study_design_scores_gemma":[0.0005374074,0.0004239534,0.0068503143,0.0010783229,0.00013559558,0.000009050565,0.0024370702,0.048377357,0.69821376,0.00008616829,0.24058037,0.0012706531],"about_ca_topic_score_codex":0.00092504575,"about_ca_topic_score_gemma":0.0000038691187,"teacher_disagreement_score":0.6677321,"about_ca_system_score_codex":0.00035446603,"about_ca_system_score_gemma":0.0000010584313,"threshold_uncertainty_score":0.9999821},"labels":[],"label_agreement":null},{"id":"W2889670723","doi":"10.1007/s11269-018-2107-1","title":"Surface Water Quantity for Drinking Water during Low Flows - Sensitivity Assessment Solely from Climate Data","year":2018,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Climate change; Water supply; Climate model; Sensitivity (control systems); Water resources; Surface water; Streamflow; Water use; Hydrology (agriculture); Hydrogeology; Water resource management; Climatology; Econometrics; Drainage basin; Geography; Environmental engineering; Mathematics; Geology; Engineering","score_opus":0.02145028700255952,"score_gpt":0.2547262287822159,"score_spread":0.23327594177965638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889670723","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9879335,0.000005573573,0.003680639,0.001369031,0.00046237497,0.0009605781,0.00005091245,0.00019470367,0.005342676],"genre_scores_gemma":[0.99299175,0.00003212464,0.0037875057,0.0005105479,0.00022811114,0.00005399609,0.00047570607,0.000051471336,0.0018688064],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99608296,0.00020889704,0.00045288075,0.0013336662,0.0004854179,0.0014361977],"domain_scores_gemma":[0.9984274,0.00002916937,0.000058390968,0.0013640864,0.000014364976,0.0001065798],"candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001699553,0.00043183027,0.00040622088,0.00007101953,0.0012929117,0.00020564062,0.0008342392,0.000097451644,0.0012303985],"category_scores_gemma":[0.0000031714787,0.0002526373,0.00010559002,0.00005488896,0.00034479654,0.0005758112,0.008092847,0.00016384988,0.002465915],"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":[0.0021608488,0.0017840655,0.46180078,0.0013521614,0.004247297,0.0011093596,0.039711256,0.054123517,0.4171661,0.00019321912,0.011828595,0.004522792],"study_design_scores_gemma":[0.0050912052,0.00041323467,0.12569654,0.0001922465,0.000997803,0.0000082811885,0.0011744712,0.076458104,0.35650483,0.0021257547,0.4288001,0.002537425],"about_ca_topic_score_codex":0.00076443603,"about_ca_topic_score_gemma":0.0013464725,"teacher_disagreement_score":0.4169715,"about_ca_system_score_codex":0.00015614911,"about_ca_system_score_gemma":6.4624004e-7,"threshold_uncertainty_score":0.9999926},"labels":[],"label_agreement":null},{"id":"W2890873614","doi":"10.1007/s11269-018-2099-x","title":"Enhancement of Model Reliability by Integrating Prediction Interval Optimization into Hydrogeological Modeling","year":2018,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"University of Calgary","keywords":"Hydrogeology; Perturbation (astronomy); Interval (graph theory); Computer science; Reliability (semiconductor); Data mining; Mathematical optimization; Statistics; Mathematics; Geology","score_opus":0.01417174759341163,"score_gpt":0.2251945242782452,"score_spread":0.21102277668483357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890873614","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59030807,0.0000029317928,0.40337387,0.00013205699,0.000039258197,0.00018006536,0.0000013542247,0.000059794736,0.005902604],"genre_scores_gemma":[0.94974655,0.000004686928,0.049522106,0.00014269781,0.000026514728,0.00003229576,0.000025183928,0.000012456408,0.00048753683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820405,0.0000922685,0.00047464413,0.00051061745,0.00039680686,0.00032163778],"domain_scores_gemma":[0.9995021,0.000008858938,0.00008906166,0.00031186102,0.000019565416,0.000068551686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007139728,0.00017450003,0.00017537131,0.00003997429,0.00018125732,0.00003105909,0.0003165728,0.0000799157,0.000853102],"category_scores_gemma":[0.000028934686,0.00011697243,0.00006460926,0.00012084189,0.00031880033,0.00013989802,0.00076760346,0.000116583644,0.0000858682],"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":[0.000054260432,0.00013253739,0.0007889571,0.00002164772,0.000014137809,7.6085064e-7,0.001959285,0.9888319,0.004892848,0.000011866135,0.00022727756,0.0030645598],"study_design_scores_gemma":[0.00017375943,0.00029524576,0.000011453747,0.000035070803,0.000023078122,5.619875e-7,0.0000517711,0.9887758,0.008295033,0.0013805398,0.00083532726,0.00012236534],"about_ca_topic_score_codex":0.00018641709,"about_ca_topic_score_gemma":0.0000072032567,"teacher_disagreement_score":0.35943845,"about_ca_system_score_codex":0.00018917887,"about_ca_system_score_gemma":8.1230803e-7,"threshold_uncertainty_score":0.93408686},"labels":[],"label_agreement":null},{"id":"W2906839783","doi":"10.1007/s11269-018-2165-4","title":"Groundwater Vulnerability Modeling to Assess Seawater Intrusion: a Methodological Comparison with Geospatial Interpolation","year":2019,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Groundwater and Isotope Geochemistry","field":"Earth and Planetary Sciences","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Aquifer; Geospatial analysis; Groundwater; Kriging; Hydrogeology; Vulnerability (computing); Environmental science; Water resource management; Vulnerability assessment; Saltwater intrusion; Hydrology (agriculture); Geographic information system; Geology; Remote sensing; Computer science; Statistics; Mathematics","score_opus":0.06450712874443634,"score_gpt":0.27830199030760827,"score_spread":0.21379486156317193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906839783","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96196663,0.000011412785,0.028365524,0.0003337177,0.0002344927,0.0006147202,0.000004154196,0.00009123469,0.008378121],"genre_scores_gemma":[0.98866457,0.0000014194593,0.008913984,0.0003657527,0.00010696033,0.0000131269435,0.000196228,0.000008692055,0.0017292678],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977409,0.00024067903,0.0003711648,0.0006451371,0.00045710502,0.00054499396],"domain_scores_gemma":[0.9992834,0.000034958997,0.00004345225,0.0004348304,0.000044403623,0.00015900096],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008538395,0.00026629362,0.00033611318,0.00010389149,0.00015593883,0.000294352,0.00039151104,0.000079555786,0.005372587],"category_scores_gemma":[0.0000039819524,0.0001448714,0.000067040106,0.000113516675,0.000035555775,0.00023004125,0.00017911436,0.00020454866,0.0011886357],"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":[0.0011153909,0.00012044428,0.83652735,0.00026837765,0.00014380508,0.000037395203,0.0055746953,0.14032072,0.0010357349,0.000015306985,0.00013724758,0.01470352],"study_design_scores_gemma":[0.0019468415,0.0015928957,0.15960258,0.00017798085,0.00014776406,0.00003512742,0.0030852328,0.7600051,0.008953829,0.0012467043,0.061733346,0.0014725733],"about_ca_topic_score_codex":0.0017345569,"about_ca_topic_score_gemma":0.00054808543,"teacher_disagreement_score":0.67692477,"about_ca_system_score_codex":0.000017066202,"about_ca_system_score_gemma":0.0000026554512,"threshold_uncertainty_score":0.999589},"labels":[],"label_agreement":null},{"id":"W2907073661","doi":"10.1007/s11269-018-2182-3","title":"A Combinatorial Procedure to Determine the Full Range of Potential Operating Scenarios for a Dam System","year":2019,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Component (thermodynamics); Computer science; Range (aeronautics); Monte Carlo method; Set (abstract data type); Reliability engineering; Representation (politics); State (computer science); Engineering; Algorithm; Mathematics; Statistics","score_opus":0.0057746863937723995,"score_gpt":0.17883437389038723,"score_spread":0.17305968749661482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907073661","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9789913,0.000038430913,0.009530713,0.00019078958,0.0007591983,0.0036134599,0.000007809111,0.00026554667,0.0066027096],"genre_scores_gemma":[0.99713546,0.000002437175,0.00095627306,0.000048077585,0.00019801121,0.0003038307,0.000023710258,0.000058820904,0.0012733819],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986517,0.000030082234,0.00036869082,0.00027458457,0.00029344123,0.00038150832],"domain_scores_gemma":[0.99946606,0.000011296427,0.000048096903,0.00037858513,0.00004322358,0.000052745072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030825046,0.00022212113,0.0002485292,0.00016626448,0.0001100071,0.000155546,0.00044409366,0.000049655366,0.000030722418],"category_scores_gemma":[0.0000029288874,0.00014182217,0.000099592144,0.00014570598,0.00001583196,0.00009541672,0.00024926517,0.000069237954,0.000067692075],"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":[0.00021058625,0.000047496018,0.0003321779,0.0038747457,0.00029804648,0.00000963295,0.0056487415,0.9833557,0.0029549857,0.00075734744,0.0011507533,0.0013598053],"study_design_scores_gemma":[0.0038380425,0.00046100936,0.0003676566,0.00048295624,0.0002927142,0.000005942928,0.0022502723,0.894565,0.0048778225,0.000056889752,0.092176534,0.000625186],"about_ca_topic_score_codex":0.000008233189,"about_ca_topic_score_gemma":0.00000382891,"teacher_disagreement_score":0.09102578,"about_ca_system_score_codex":0.000059187816,"about_ca_system_score_gemma":0.0000010772035,"threshold_uncertainty_score":0.57833403},"labels":[],"label_agreement":null},{"id":"W2915703925","doi":"10.1007/s11269-019-02211-0","title":"Analyzing the Impact of Impervious Area Disconnection on Urban Runoff Control Using an Analytical Probabilistic Model","year":2019,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Fundamental Research Funds for the Central Universities; Major Science and Technology Program for Water Pollution Control and Treatment; China Scholarship Council; National Natural Science Foundation of China","keywords":"Impervious surface; Surface runoff; Low-impact development; Environmental science; Stormwater; Hydrology (agriculture); Drainage; Stormwater management; Engineering; Geotechnical engineering; Ecology","score_opus":0.02280426639641989,"score_gpt":0.2479160718558679,"score_spread":0.225111805459448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2915703925","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98336834,0.0000059031695,0.0065482375,0.00010573573,0.000049189028,0.0010089916,0.000009156554,0.000055729215,0.008848703],"genre_scores_gemma":[0.99784195,0.0000016285177,0.00016257426,0.00005767669,0.000027167514,0.0000298631,0.000010693066,0.000028464869,0.0018400012],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99810386,0.0001230661,0.00035388026,0.00051570847,0.00039760736,0.00050587335],"domain_scores_gemma":[0.9989135,0.000025235102,0.00011154578,0.00084237766,0.000009656569,0.00009765322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004679888,0.0002591776,0.000250632,0.00014223933,0.00020902377,0.00009845563,0.00050646043,0.00004290745,0.0006463408],"category_scores_gemma":[0.000005785349,0.00014293178,0.00019903565,0.00023134713,0.00016701347,0.0002655454,0.00037448513,0.00012857514,0.00019456369],"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":[0.00008795106,0.0002566799,0.04303667,0.000020836307,0.00019020918,0.000003677552,0.0016221263,0.9527098,0.0012993513,0.00019827663,0.0003629927,0.00021143643],"study_design_scores_gemma":[0.00061941857,0.000291483,0.034458995,0.000023792394,0.00022231745,0.0000014243572,0.00022363309,0.9628396,0.000065617394,0.0007118502,0.00030635664,0.00023553043],"about_ca_topic_score_codex":0.0008655063,"about_ca_topic_score_gemma":0.00002761409,"teacher_disagreement_score":0.014473576,"about_ca_system_score_codex":0.0004995597,"about_ca_system_score_gemma":0.00000259355,"threshold_uncertainty_score":0.7076978},"labels":[],"label_agreement":null},{"id":"W2946770115","doi":"10.1007/s11269-019-02277-w","title":"Mixed General Extreme Value Distribution for Estimation of Future Precipitation Quantiles Using a Weighted Ensemble - Case Study of the Lim River Basin (Serbia)","year":2019,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Climate variability and models","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Science and Engineering Research Board; Natural Sciences and Engineering Research Council of Canada","keywords":"Quantile; Precipitation; Environmental science; Return period; Climatology; Structural basin; Climate change; Drainage basin; Generalized extreme value distribution; Extreme value theory; Statistics; Meteorology; Mathematics; Geography; Geology; Flood myth","score_opus":0.033103112542875734,"score_gpt":0.24396952336727745,"score_spread":0.21086641082440172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946770115","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99036825,0.0000031442287,0.007662539,0.00006488052,0.00016259796,0.0016183392,0.000024606526,0.000011868063,0.000083784835],"genre_scores_gemma":[0.99629074,0.0000014397373,0.0034358732,0.00001349293,0.000017238302,0.00003133986,0.000036532827,0.000009123664,0.00016422858],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988889,0.00013995012,0.00029182262,0.00025313464,0.00026529777,0.00016090182],"domain_scores_gemma":[0.9994724,0.000024135818,0.00013991512,0.000329011,0.0000137613715,0.00002081237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039937548,0.00010732762,0.00014364786,0.000028545195,0.00011603856,0.000018962972,0.00014818764,0.000038733568,0.00006226173],"category_scores_gemma":[0.0000039159163,0.00007040295,0.00006814991,0.000107941494,0.000052021318,0.00013456386,0.00023645682,0.000035729507,0.0000057396655],"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":[0.00023667057,0.0011803093,0.037031837,0.00042556436,0.00011083102,0.000009535413,0.029732479,0.90888435,0.01567058,0.0006215836,0.00011950886,0.0059767826],"study_design_scores_gemma":[0.0014634234,0.00029882503,0.043831833,0.00005529906,0.0002483179,0.000009253254,0.0073451484,0.9377984,0.0064373096,0.0010568437,0.0012612074,0.0001941434],"about_ca_topic_score_codex":0.0013068753,"about_ca_topic_score_gemma":0.000100097306,"teacher_disagreement_score":0.02891408,"about_ca_system_score_codex":0.00009907677,"about_ca_system_score_gemma":0.0000012191334,"threshold_uncertainty_score":0.2870949},"labels":[],"label_agreement":null},{"id":"W2956086524","doi":"10.1007/s11269-019-02319-3","title":"Representing Local Dynamics of Water Resource Systems through a Data-Driven Emulation Approach","year":2019,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Concordia University","funders":"Concordia University; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Ministère de l'Éducation, du Loisir et du Sport Québec","keywords":"Emulation; Resource (disambiguation); Environmental science; Computer science; Hydrogeology; Set (abstract data type); Environmental resource management; Water resources; Irrigation; Hydrology (agriculture); Ecology; Engineering","score_opus":0.016810129158892382,"score_gpt":0.22050760271418382,"score_spread":0.20369747355529144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2956086524","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7647828,0.000029002733,0.025257817,0.0005507871,0.00017017912,0.0011729063,0.000009413186,0.000113078895,0.20791402],"genre_scores_gemma":[0.9852359,0.000010475164,0.0009526661,0.00011630056,0.000033974808,0.000036522357,0.00040447124,0.000028386774,0.013181256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974733,0.0001578365,0.00046506678,0.0008209828,0.00051279675,0.00056999264],"domain_scores_gemma":[0.99853534,0.000015562566,0.00010690165,0.0012950904,0.000006783463,0.000040331557],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000680466,0.00024120846,0.00032603994,0.000079370315,0.00018487997,0.000055491335,0.0009060822,0.00008010505,0.00032613418],"category_scores_gemma":[0.0000022202162,0.00015213514,0.00006360419,0.00011641586,0.00025349882,0.00037108632,0.0043375776,0.00012937369,0.0008687252],"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":[0.00011382668,0.00019977373,0.04760913,0.0005440507,0.00042973153,0.000025195503,0.007950427,0.9380507,0.0002269702,0.000774273,0.0029000232,0.0011758805],"study_design_scores_gemma":[0.0010066573,0.00009339851,0.0045300387,0.00005995288,0.00021547666,0.0000052186806,0.0048657223,0.6917812,0.00042666745,0.0004928905,0.2960338,0.0004889763],"about_ca_topic_score_codex":0.00048309646,"about_ca_topic_score_gemma":0.00001690888,"teacher_disagreement_score":0.29313377,"about_ca_system_score_codex":0.00010621652,"about_ca_system_score_gemma":3.4787317e-7,"threshold_uncertainty_score":0.9999092},"labels":[],"label_agreement":null},{"id":"W2969357546","doi":"10.1007/s11269-019-02346-0","title":"Lake Water-Level fluctuations forecasting using Minimax Probability Machine Regression, Relevance Vector Machine, Gaussian Process Regression, and Extreme Learning Machine","year":2019,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":98,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Extreme learning machine; Relevance vector machine; Machine learning; Computer science; Artificial intelligence; Perceptron; Artificial neural network; Overfitting; Gaussian process; Support vector machine; Kriging; Gaussian","score_opus":0.03978697126735987,"score_gpt":0.24472581049632128,"score_spread":0.20493883922896142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969357546","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9913653,0.00010349241,0.0010134369,0.00081847527,0.00014806293,0.0010693886,0.000020010792,0.0002303819,0.005231421],"genre_scores_gemma":[0.98251027,0.000013652097,0.009284894,0.00016135049,0.00005549911,0.000053481155,0.00011582113,0.00007325805,0.0077317758],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995882,0.00030447432,0.00070132513,0.001293278,0.0008077756,0.0010111488],"domain_scores_gemma":[0.99876106,0.000068601985,0.0002461671,0.00064934575,0.000029605328,0.00024524648],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011823059,0.0005661184,0.00047467145,0.00014032784,0.00086160615,0.00019137224,0.00054828427,0.00014644799,0.0035964926],"category_scores_gemma":[0.00012560181,0.000317752,0.00010138981,0.0002959764,0.00030419204,0.0003484803,0.0013633138,0.0005324111,0.00038686953],"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":[0.0008668477,0.00085492147,0.47649223,0.0021627264,0.00025349483,0.00032787755,0.021347107,0.37175134,0.049347483,0.00007114467,0.00033799218,0.07618684],"study_design_scores_gemma":[0.0023503874,0.0005081003,0.03350684,0.0012422767,0.00021249446,0.00015186741,0.00023155966,0.8716389,0.007072094,0.0034405258,0.0779135,0.0017314793],"about_ca_topic_score_codex":0.0002193327,"about_ca_topic_score_gemma":0.00022295663,"teacher_disagreement_score":0.49988753,"about_ca_system_score_codex":0.0001850266,"about_ca_system_score_gemma":0.0000031289178,"threshold_uncertainty_score":0.99992746},"labels":[],"label_agreement":null},{"id":"W2969545205","doi":"10.1007/s11269-019-02339-z","title":"Probabilistic Event Based Rainfall-Runoff Modeling Using Copula Functions","year":2019,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Copula (linguistics); Bivariate analysis; Joint probability distribution; Probabilistic logic; Multivariate statistics; Econometrics; Statistics; Marginal distribution; Mathematics; Hydrogeology; Computer science; Random variable; Geology","score_opus":0.013224093509508551,"score_gpt":0.21259587021854096,"score_spread":0.19937177670903242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969545205","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95979714,0.000014191399,0.011090864,0.00064691534,0.0002595911,0.00090727257,0.000002224766,0.00012146522,0.02716036],"genre_scores_gemma":[0.98926973,0.000003282997,0.00067402807,0.0006890577,0.000029350325,0.00006925299,0.000023190949,0.00002464523,0.009217431],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818194,0.00008030052,0.0002884522,0.0005545245,0.000351548,0.0005432139],"domain_scores_gemma":[0.99940926,0.000010330288,0.00004725035,0.0004593771,0.000005766806,0.000068044654],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00042223593,0.00023391462,0.00019798294,0.000103582686,0.00032706623,0.00005087614,0.0002973688,0.00005057596,0.0024253267],"category_scores_gemma":[0.000003098935,0.00017158977,0.00009703768,0.00013695171,0.000098642646,0.00013805943,0.00076100155,0.00010764535,0.0023538892],"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":[0.00004364134,0.00009253037,0.011430951,0.000078124925,0.000090818394,0.000015488204,0.0005832973,0.98660094,0.00022461847,0.000049926373,0.00051432446,0.00027531513],"study_design_scores_gemma":[0.0008380634,0.00008637669,0.0016987086,0.000040426803,0.00016477618,0.0000014252153,0.00028350946,0.8987318,0.00007871806,0.0007477125,0.09694138,0.00038710464],"about_ca_topic_score_codex":0.00020166143,"about_ca_topic_score_gemma":0.000028495982,"teacher_disagreement_score":0.09642705,"about_ca_system_score_codex":0.00018171678,"about_ca_system_score_gemma":9.876029e-7,"threshold_uncertainty_score":0.9984866},"labels":[],"label_agreement":null},{"id":"W2980597459","doi":"10.1007/s11269-019-02356-y","title":"Advancing Freshwater Lake Level Forecast Using King’s Castle Optimization with Training Sample Adaption and Adaptive Neuro-Fuzzy Inference System","year":2019,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Adaptive neuro fuzzy inference system; Training (meteorology); Sample (material); Inference system; Fuzzy inference system; Hydrogeology; Inference; Artificial intelligence; Fuzzy logic; Computer science; Environmental science; Machine learning; Hydrology (agriculture); Operations research; Fuzzy control system; Engineering; Meteorology; Geography; Geotechnical engineering","score_opus":0.038975203698990546,"score_gpt":0.2197270873361447,"score_spread":0.18075188363715416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980597459","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.915834,0.000002871826,0.07947974,0.00006271931,0.00007681923,0.00049599016,0.00001616805,0.00011312102,0.0039185784],"genre_scores_gemma":[0.9230976,0.0000011577944,0.07641202,0.00011356041,0.00002642741,0.000016892025,0.000022515289,0.000034058736,0.00027578144],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812746,0.000089610585,0.0002510785,0.00057845516,0.00037757668,0.0005758311],"domain_scores_gemma":[0.99947244,0.00004644794,0.00010966411,0.00025136909,0.000011774171,0.00010832873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037007476,0.0002452517,0.00021987772,0.00008481502,0.00023489662,0.00012774473,0.00017983011,0.00005852574,0.00038702166],"category_scores_gemma":[0.000012399641,0.00016740317,0.000030533185,0.00014585306,0.000110593355,0.00029520487,0.00043914898,0.00013155605,0.00007271778],"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":[0.000073305884,0.00001708896,0.008158164,0.0000532662,0.000022102247,0.000028062253,0.0028053408,0.9859432,0.00052649266,0.00004597787,0.00000935867,0.0023176407],"study_design_scores_gemma":[0.0005935697,0.0003173524,0.0020994302,0.00023575338,0.00005935176,0.000040380346,0.00086683594,0.9886467,0.0002418734,0.0000894073,0.0064416444,0.00036768639],"about_ca_topic_score_codex":0.0006337903,"about_ca_topic_score_gemma":0.0009977105,"teacher_disagreement_score":0.007263597,"about_ca_system_score_codex":0.00017640622,"about_ca_system_score_gemma":0.0000020507434,"threshold_uncertainty_score":0.6826503},"labels":[],"label_agreement":null},{"id":"W2990986634","doi":"10.1007/s11269-019-02412-7","title":"A Modified Muskingum Flow Routing Model for Flood Wave Propagation during River Ice Thawing-Breakup Period","year":2019,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Breakup; Hydrograph; Hydrology (agriculture); Inflow; Flood myth; Routing (electronic design automation); Geology; Environmental science; Channel (broadcasting); Flow (mathematics); Outflow; Geotechnical engineering; Oceanography; Mechanics; Geography; Engineering","score_opus":0.011149928309544312,"score_gpt":0.1821760486470251,"score_spread":0.1710261203374808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990986634","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9709539,0.000008778343,0.017252397,0.0002494603,0.00019117011,0.00089555734,0.00002496636,0.00008464514,0.010339137],"genre_scores_gemma":[0.9849885,0.0000057937536,0.0064847176,0.00014961234,0.0001098771,0.000012239997,0.00016089888,0.000013606394,0.008074727],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983998,0.000032718992,0.0002600815,0.00043919386,0.0003200306,0.0005481992],"domain_scores_gemma":[0.99952877,0.000018101138,0.00007488038,0.00027076606,0.00003145488,0.00007603679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030707647,0.00020809004,0.00018803072,0.00009957501,0.00035458218,0.00013347986,0.00022142408,0.000057125584,0.0002098475],"category_scores_gemma":[0.0000050497083,0.00014996243,0.00010165964,0.00006944894,0.000045329423,0.00023897874,0.000083962776,0.000110601264,0.00016865841],"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":[0.00034157405,0.000061068575,0.027598623,0.00074358925,0.0001992195,0.000040979856,0.03706222,0.9168027,0.00007540603,0.00034247653,0.000023018245,0.01670911],"study_design_scores_gemma":[0.00080558576,0.000056408495,0.01623582,0.0000613639,0.000064778134,0.000008457773,0.0012279266,0.9797918,0.00005373415,0.000492135,0.00093965663,0.00026228902],"about_ca_topic_score_codex":0.00014588662,"about_ca_topic_score_gemma":0.00005925585,"teacher_disagreement_score":0.06298913,"about_ca_system_score_codex":0.000020018966,"about_ca_system_score_gemma":0.000005382862,"threshold_uncertainty_score":0.61152905},"labels":[],"label_agreement":null},{"id":"W2996955932","doi":"10.1007/s11269-019-02448-9","title":"Quantitative Assessment of Agricultural Practices on Farmland Evapotranspiration Using EddyCovariance Method and Numerical Modelling","year":2020,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Evapotranspiration; Irrigation; Environmental science; Sowing; Eddy covariance; Hydrology (agriculture); Hydrogeology; Agriculture; Drainage; Mathematics; Agricultural engineering; Agronomy; Geography; Ecology","score_opus":0.04898496509914524,"score_gpt":0.2998653431581031,"score_spread":0.25088037805895785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996955932","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58137,0.0000070429887,0.41496202,0.00036180142,0.000012911434,0.00016358358,0.0000042729457,0.000011928815,0.0031064376],"genre_scores_gemma":[0.90911806,0.0000123094205,0.09064572,0.00012410777,0.000010468083,0.000006805904,0.000013069667,0.000006376278,0.000063061605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990206,0.00010193237,0.0001940865,0.0002750906,0.00026762593,0.00014065986],"domain_scores_gemma":[0.99968904,0.000024578534,0.00014767623,0.00008068695,0.000005447873,0.00005255765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020364317,0.000111842106,0.00012947565,0.000023259998,0.00008700441,0.000048800233,0.00009724652,0.000027979235,0.000042523418],"category_scores_gemma":[0.0000023687455,0.00007215252,0.000029053283,0.0001013808,0.0000312349,0.00017384329,0.00009826679,0.000086293774,0.000008956443],"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":[0.00003177775,0.00003225944,0.0021750885,0.000031901323,0.000038945353,0.0000043802784,0.001854904,0.9892398,0.005589614,0.00062039745,0.000003128407,0.00037781103],"study_design_scores_gemma":[0.00022102437,0.00014887219,0.0048347125,0.000018801646,0.00006815844,0.0000020912935,0.0001414292,0.9919552,0.0005718843,0.00013358414,0.0017825855,0.00012162161],"about_ca_topic_score_codex":0.00012642155,"about_ca_topic_score_gemma":0.0000024481228,"teacher_disagreement_score":0.32774806,"about_ca_system_score_codex":0.000038126873,"about_ca_system_score_gemma":7.611969e-7,"threshold_uncertainty_score":0.29422945},"labels":[],"label_agreement":null},{"id":"W3004550468","doi":"10.1007/s11269-020-02488-6","title":"Crow Algorithm for Irrigation Management: A Case Study","year":2020,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Particle swarm optimization; Algorithm; Genetic algorithm; Mean squared error; Index (typography); Reliability (semiconductor); Mathematical optimization; Mathematics; Computer science; Statistics","score_opus":0.01430229749932637,"score_gpt":0.20455456370593866,"score_spread":0.19025226620661229,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004550468","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3582791,0.00013206845,0.5792413,0.0005949743,0.00063115935,0.008360782,0.000023913204,0.002499452,0.050237227],"genre_scores_gemma":[0.9733428,0.000022842321,0.021483408,0.0004023354,0.00040730822,0.00084242696,0.00012753133,0.00015243929,0.003218959],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982629,0.000038422826,0.00040478128,0.0004883273,0.00030925745,0.00049635145],"domain_scores_gemma":[0.9994513,0.000007838863,0.000039831528,0.00033580876,0.000026027408,0.00013922704],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020508234,0.0003317099,0.00023837606,0.00022711698,0.00021445853,0.00029734802,0.0003365121,0.00004623017,0.00008865273],"category_scores_gemma":[9.0154316e-7,0.00028065272,0.000105606305,0.00026224824,0.00002298072,0.00021993738,0.000274985,0.00009142313,0.00013928376],"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":[0.00013136455,0.00055448513,0.00047140714,0.0031276008,0.0032743334,0.009160766,0.058506854,0.39331657,0.000057384616,0.00043532444,0.017063849,0.51390004],"study_design_scores_gemma":[0.003230562,0.0002968747,0.00009295481,0.000031770225,0.00051741867,0.00002591796,0.011444263,0.6023588,0.00040461315,0.00022173708,0.38068452,0.00069051364],"about_ca_topic_score_codex":0.000014578536,"about_ca_topic_score_gemma":0.000005607419,"teacher_disagreement_score":0.6150636,"about_ca_system_score_codex":0.000053895317,"about_ca_system_score_gemma":3.2278493e-7,"threshold_uncertainty_score":0.99996454},"labels":[],"label_agreement":null},{"id":"W3017533883","doi":"10.1007/s11269-020-02529-0","title":"Machine Learning and Water Economy: a New Approach to Predicting Dams Water Sales Revenue","year":2020,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Revenue; Hydrogeology; Business; Finance; Engineering","score_opus":0.011377119728096881,"score_gpt":0.18264644767377866,"score_spread":0.17126932794568178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017533883","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7887399,0.00045406894,0.057393983,0.0051904293,0.0002628082,0.0025092396,0.000012822367,0.002023589,0.14341314],"genre_scores_gemma":[0.97936594,0.000073763425,0.0032900507,0.0006683725,0.00040970062,0.00007806762,0.00033738685,0.00013588868,0.015640829],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982642,0.00005024175,0.000345614,0.0005231224,0.00018147563,0.0006353569],"domain_scores_gemma":[0.99948126,0.000004685358,0.000019330893,0.0002153631,0.000010774805,0.00026859218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023059055,0.00033457286,0.00027806096,0.0001908937,0.00017536028,0.00036747666,0.00029594763,0.00006121087,0.00012783603],"category_scores_gemma":[0.0000025706595,0.00021409767,0.00006246876,0.00008609105,0.00002334406,0.00021077694,0.0006767897,0.00020276038,0.000599484],"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":[0.00012588952,0.000046621873,0.0044097677,0.0017756654,0.0005965406,0.000054231477,0.07739964,0.8968766,0.0021241244,0.00007939409,0.0065367124,0.009974846],"study_design_scores_gemma":[0.0006264247,0.00005750873,0.000072458126,0.00003671435,0.0000906054,0.000002345112,0.00040397936,0.14368577,0.0049859956,0.000077682875,0.8495578,0.00040270496],"about_ca_topic_score_codex":0.000030673113,"about_ca_topic_score_gemma":0.0000037174593,"teacher_disagreement_score":0.8430211,"about_ca_system_score_codex":0.000041875035,"about_ca_system_score_gemma":4.8496435e-7,"threshold_uncertainty_score":0.873065},"labels":[],"label_agreement":null},{"id":"W3034624793","doi":"10.1007/s11269-020-02511-w","title":"A Neighbourhood-Level Analysis of the Impact of Common Urban Forms on Energy Use in Drinking Water Distribution Systems","year":2020,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Engineering and Physical Sciences Research Council; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Embodied energy; Neighbourhood (mathematics); Network topology; Topology (electrical circuits); Population; Per capita; Environmental science; Population density; Urban density; Geography; Environmental engineering; Transport engineering; Civil engineering; Engineering; Computer science; Urban planning; Mathematics; Ecology; Demography; Sociology; Computer network","score_opus":0.014750368647946483,"score_gpt":0.19662906939133806,"score_spread":0.18187870074339157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034624793","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9891951,0.000016276412,0.01006871,0.000027022092,0.000036262343,0.000095646574,0.000018608014,0.00003983969,0.0005025023],"genre_scores_gemma":[0.9996908,0.000008345483,0.000014502467,0.000017509348,0.000012668416,0.000014542077,0.0001607287,0.0000145315735,0.000066401604],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991962,0.00003362804,0.00028973646,0.00012475188,0.00016971203,0.00018602537],"domain_scores_gemma":[0.99967235,0.000007807828,0.000043297372,0.00023660826,0.000011733575,0.000028177232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008503325,0.00012393216,0.00022966342,0.0001434675,0.000030264233,0.000035219316,0.00019378481,0.00004285513,0.000013942534],"category_scores_gemma":[0.0000012006602,0.000063799926,0.00015401775,0.00035965114,0.000014189068,0.000076314514,0.000104560764,0.000060886334,4.0814686e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025848369,0.000029208419,0.015515221,0.000049303544,0.00058954355,0.0000016712448,0.00084090646,0.98140466,0.0003658889,0.0008894796,0.00005543559,0.000232834],"study_design_scores_gemma":[0.00029939273,0.00005816592,0.054530516,0.00008122218,0.0002212146,1.8358996e-7,0.00006518509,0.9330136,0.010358998,0.00003115761,0.0011951457,0.00014523024],"about_ca_topic_score_codex":0.0008679743,"about_ca_topic_score_gemma":0.0000767649,"teacher_disagreement_score":0.04839107,"about_ca_system_score_codex":0.00007923104,"about_ca_system_score_gemma":6.108269e-7,"threshold_uncertainty_score":0.26016855},"labels":[],"label_agreement":null},{"id":"W3035967586","doi":"10.1007/s11269-020-02571-y","title":"Two-Phase Risk Hedging Rules for Informing Conservation of Flood Resources in Reservoir Operation Considering Inflow Forecast Uncertainty","year":2020,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Flood myth; Inflow; Water scarcity; Environmental science; Water storage; Water resource management; Economic shortage; Water resources; Risk analysis (engineering); Business; Engineering; Geography","score_opus":0.01830073954078933,"score_gpt":0.22619069757497634,"score_spread":0.207889958034187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035967586","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97244877,0.00011102817,0.02276311,0.00044537196,0.000085911204,0.0013512314,0.00002879066,0.00026921608,0.0024965836],"genre_scores_gemma":[0.9907198,0.000070858165,0.008285976,0.00019332425,0.0001276634,0.00017881098,0.0002497203,0.00006661901,0.00010720782],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980166,0.00006481265,0.000795523,0.00035445497,0.00030107502,0.00046753837],"domain_scores_gemma":[0.9993918,0.00005072785,0.00013709192,0.00027531764,0.000053254087,0.00009179227],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005115921,0.00030712405,0.0003613644,0.00039665442,0.00015780318,0.0001934004,0.00031502458,0.00007024248,0.000033021704],"category_scores_gemma":[0.000037060363,0.0002730667,0.00010021303,0.00025876364,0.000054107968,0.0004612901,0.0002414431,0.00015584985,0.000013601124],"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":[0.00023106458,0.000036269343,0.0031217535,0.0009693107,0.00014378091,0.000010250165,0.014035499,0.9731938,0.0006270399,0.000154137,0.00022746836,0.0072495867],"study_design_scores_gemma":[0.0042515006,0.00015074505,0.0003715237,0.00018386425,0.00009939952,6.975846e-7,0.002292891,0.9224208,0.0061787115,0.00023234468,0.06343936,0.00037813385],"about_ca_topic_score_codex":0.0001642025,"about_ca_topic_score_gemma":0.0001452883,"teacher_disagreement_score":0.063211896,"about_ca_system_score_codex":0.00008222087,"about_ca_system_score_gemma":0.0000025860836,"threshold_uncertainty_score":0.99997216},"labels":[],"label_agreement":null},{"id":"W3040992378","doi":"10.1007/s11269-020-02608-2","title":"Robust Subsampling ANOVA Methods for Sensitivity Analysis of Water Resource and Environmental Models","year":2020,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"National Key Scientific Instrument and Equipment Development Projects of China","keywords":"Sobol sequence; Analysis of variance; Variance-based sensitivity analysis; Sensitivity (control systems); Statistics; Variance (accounting); Estimator; Analysis of covariance; One-way analysis of variance; Computer science; Mathematics; Engineering; Monte Carlo method","score_opus":0.14291948233882412,"score_gpt":0.31839800984716465,"score_spread":0.17547852750834053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040992378","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13811906,0.00007892181,0.8604102,0.0005239703,0.000028201379,0.00034999585,0.00002822381,0.000039170907,0.00042225426],"genre_scores_gemma":[0.8978137,0.000006510461,0.1014187,0.00021939364,0.00003545103,0.000019823297,0.00004369904,0.000020078121,0.00042268712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99757415,0.00026118002,0.0005887901,0.0006997322,0.00051051914,0.0003656158],"domain_scores_gemma":[0.99887097,0.0004107098,0.00008697687,0.00046054478,0.000028058,0.00014275062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00351296,0.0002068119,0.0005363931,0.00036959484,0.00013088375,0.00012542262,0.00033052478,0.00006167969,0.000059910817],"category_scores_gemma":[0.00007278307,0.0001185497,0.00021591269,0.00033143442,0.00011957812,0.00012784633,0.00053609826,0.000079441525,0.000007291887],"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":[0.00010691554,0.000038203183,0.00021176737,0.00005914254,0.0006753531,0.000008550407,0.0061530136,0.9733042,0.00746918,0.0004152624,0.00016273119,0.0113956835],"study_design_scores_gemma":[0.00032022342,0.000060842078,0.00030037604,0.000007383091,0.0006978077,0.0000010904776,0.0011795239,0.9609613,0.0074044787,0.0027354462,0.026113017,0.00021852493],"about_ca_topic_score_codex":0.0000083480845,"about_ca_topic_score_gemma":0.0000011139422,"teacher_disagreement_score":0.7596946,"about_ca_system_score_codex":0.000025495563,"about_ca_system_score_gemma":0.0000010906887,"threshold_uncertainty_score":0.48343164},"labels":[],"label_agreement":null},{"id":"W3047702563","doi":"10.1007/s11269-020-02644-y","title":"A Comparative Study of Linear Stochastic with Nonlinear Daily River Discharge Forecast Models","year":2020,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Université Laval","funders":"","keywords":"Discharge; Flood myth; Nonlinear system; Time series; Environmental science; Meteorology; Statistics; Series (stratigraphy); Computer science; Mathematics; Geology; Drainage basin; Geography","score_opus":0.04212654556029443,"score_gpt":0.23882159028264954,"score_spread":0.1966950447223551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047702563","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9871531,0.000003284452,0.007855132,0.00023583724,0.000015149489,0.0008321208,0.0000071358227,0.00007101006,0.003827234],"genre_scores_gemma":[0.9957039,3.2555053e-7,0.0036355061,0.00020795266,0.000030307587,0.000035530666,0.000008858633,0.00002050861,0.00035714614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825686,0.000079113015,0.00028178064,0.0005045836,0.0005293396,0.00034830227],"domain_scores_gemma":[0.9994726,0.000014283238,0.000092160015,0.00027105885,0.000009169954,0.00014069385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014370702,0.00022256174,0.00031653963,0.000035247565,0.00014076928,0.000025709538,0.00039252918,0.000028540224,0.0003169431],"category_scores_gemma":[0.0000028848408,0.00013041818,0.00004269993,0.00020937319,0.00023656107,0.00012988856,0.0007388216,0.0001358839,0.00031148273],"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":[0.0002774493,0.0005257802,0.0014541053,0.000024561898,0.00010544505,0.000034801138,0.056219626,0.94078064,0.00013965598,0.000008650681,0.00011906002,0.00031022538],"study_design_scores_gemma":[0.0021309198,0.0029775817,0.0017427115,0.00004092106,0.00017230964,0.0000028154461,0.002231903,0.98610455,0.0003707422,0.00017419424,0.0036338135,0.00041754975],"about_ca_topic_score_codex":0.0001562124,"about_ca_topic_score_gemma":0.00003262364,"teacher_disagreement_score":0.053987723,"about_ca_system_score_codex":0.00003640456,"about_ca_system_score_gemma":7.4712364e-7,"threshold_uncertainty_score":0.5318299},"labels":[],"label_agreement":null},{"id":"W3049522560","doi":"10.1007/s11269-020-02645-x","title":"Proper Sizing of Infiltration Trenches Using Closed-Form Analytical Equations","year":2020,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; CIMA+ (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Infiltration (HVAC); Stormwater; Surface runoff; Trench; Sizing; Environmental science; Hydrogeology; Low-impact development; Hydrology (agriculture); Geology; Geotechnical engineering; Stormwater management; Meteorology; Materials science","score_opus":0.048730872812916604,"score_gpt":0.2329710115464645,"score_spread":0.1842401387335479,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3049522560","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92812806,0.000012201987,0.024594402,0.0015022698,0.000042406187,0.000578318,0.000003996123,0.0000966142,0.045041725],"genre_scores_gemma":[0.99510604,0.0000028417185,0.0030272105,0.0003108877,0.000044300214,0.000019945253,0.000017585824,0.000016622695,0.0014545645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850184,0.000045704735,0.0003599721,0.000337487,0.0004431227,0.00031188742],"domain_scores_gemma":[0.9995496,0.000010078058,0.00007791652,0.00025757457,0.0000072148086,0.00009757985],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018805214,0.0001542638,0.00015354004,0.000079820114,0.00015876483,0.000058115653,0.00029016138,0.00003292619,0.0011236734],"category_scores_gemma":[0.0000097076745,0.00011540573,0.000077154466,0.0002907603,0.00014678169,0.00031175793,0.00056026597,0.00007519753,0.00039960645],"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":[0.0002947841,0.0017003672,0.22499813,0.0013052521,0.0017126293,0.00015380705,0.15329294,0.41498363,0.120076254,0.015102351,0.014246437,0.052133437],"study_design_scores_gemma":[0.0017494272,0.00038942002,0.043051552,0.000096237025,0.0008895886,0.0000026500852,0.0032677958,0.51496905,0.016939474,0.001292027,0.41615367,0.0011991091],"about_ca_topic_score_codex":0.00016602267,"about_ca_topic_score_gemma":0.000014643006,"teacher_disagreement_score":0.40190724,"about_ca_system_score_codex":0.00012682859,"about_ca_system_score_gemma":0.0000013368294,"threshold_uncertainty_score":0.9997894},"labels":[],"label_agreement":null},{"id":"W3091860730","doi":"10.1007/s11269-020-02690-6","title":"Comparing Two Hydro-Economic Approaches for Multi-Objective Agricultural Water Resources Planning","year":2020,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Water resources; Flexibility (engineering); Status quo; Agriculture; Scenario analysis; Computer science; Water balance; Robustness (evolution); Environmental economics; Environmental resource management; Risk analysis (engineering); Business; Engineering; Environmental science; Economics","score_opus":0.04960339370876613,"score_gpt":0.21723380714648582,"score_spread":0.1676304134377197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091860730","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9409346,0.00016290849,0.030907363,0.00041487467,0.00025093852,0.0016005686,0.000009552779,0.0010512322,0.024667991],"genre_scores_gemma":[0.99349135,0.0000075880216,0.00376462,0.00013925694,0.0004644936,0.00027936389,0.0002587112,0.00010936256,0.0014852457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977658,0.00005008662,0.00049788394,0.0006370333,0.00022414679,0.000825071],"domain_scores_gemma":[0.9994713,0.000013153881,0.000056667366,0.00027269154,0.000015665271,0.00017052605],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021438912,0.00048732932,0.00043445165,0.00020105643,0.00027850812,0.00043513862,0.00055325544,0.0000747358,0.000049264283],"category_scores_gemma":[0.0000018889738,0.00032656884,0.00018530885,0.00009081452,0.00005451916,0.0003194352,0.0004576066,0.00018067264,0.00024436202],"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":[0.00009216242,0.000027508846,0.0010257164,0.00043552413,0.0004642637,0.000012130417,0.032253347,0.9635812,0.00040097284,0.00007590766,0.0008967765,0.0007344899],"study_design_scores_gemma":[0.002455461,0.00008660352,0.0014088419,0.00006383123,0.00019340967,0.0000019240717,0.0036358607,0.8343841,0.01053561,0.000052648767,0.14639191,0.0007898215],"about_ca_topic_score_codex":0.000023519291,"about_ca_topic_score_gemma":0.000009640527,"teacher_disagreement_score":0.14549513,"about_ca_system_score_codex":0.00013703377,"about_ca_system_score_gemma":5.0411086e-7,"threshold_uncertainty_score":0.99991864},"labels":[],"label_agreement":null},{"id":"W3096046034","doi":"10.1007/s11269-020-02700-7","title":"Trends in Demand of Urban Surface Water Extractions and in Situ Use Functions","year":2020,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Urban Stormwater Management Solutions","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":"Deltares","keywords":"Recreation; Environmental planning; Sustainability; Urban planning; Water use; Water supply; Environmental science; Water resources; Demand management; Environmental resource management; Surface water; Business; Stormwater; Water resource management; Environmental engineering; Civil engineering; Engineering; Surface runoff; Economics; Ecology","score_opus":0.020024251551304682,"score_gpt":0.20428577268920617,"score_spread":0.18426152113790148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096046034","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9830443,0.000019883224,0.0003686321,0.0017408498,0.000041010368,0.00026534483,0.0000044844346,0.000043109885,0.014472342],"genre_scores_gemma":[0.9877141,0.000013894169,0.00035917634,0.00018004257,0.000012978815,0.000025142517,0.000025995152,0.000017139602,0.011651528],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99851006,0.000076261254,0.00034258942,0.00043335417,0.0002436296,0.00039408475],"domain_scores_gemma":[0.99960166,0.000011473304,0.000035883502,0.00025420013,0.0000027838898,0.00009398109],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020025417,0.00016955043,0.00017899855,0.0002047616,0.00008383583,0.000056873807,0.0001849177,0.00004062757,0.0009658894],"category_scores_gemma":[0.000002324312,0.00012440156,0.000046323705,0.0003375925,0.00012866795,0.00041605538,0.0006381714,0.00012428139,0.00025048183],"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":[0.00017169642,0.0005054191,0.8510542,0.00010865397,0.00011425545,0.00012426001,0.02573431,0.06649706,0.03846728,0.000068813446,0.015218869,0.0019352162],"study_design_scores_gemma":[0.0010481491,0.000081794155,0.65007955,0.000022105361,0.000073959745,0.0000015864382,0.0008661762,0.0016420496,0.0033130413,0.000057953843,0.34248272,0.00033092426],"about_ca_topic_score_codex":0.00059140404,"about_ca_topic_score_gemma":0.0010055423,"teacher_disagreement_score":0.32726383,"about_ca_system_score_codex":0.00010329702,"about_ca_system_score_gemma":3.1327775e-7,"threshold_uncertainty_score":0.99994737},"labels":[],"label_agreement":null},{"id":"W3124425045","doi":"10.1007/s11269-020-02753-8","title":"Water Use and Climate Stressors in a Multiuser River Basin Setting: Who Benefits from Adaptation?","year":2021,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; International Development Research Centre","keywords":"Adaptation (eye); Interdependence; Futures studies; Agriculture; Structural basin; Environmental resource management; Climate change; Business; Environmental planning; Natural resource economics; Environmental economics; Water resource management; Environmental science; Computer science; Economics; Geography; Ecology","score_opus":0.011914588966618171,"score_gpt":0.17989313791384795,"score_spread":0.16797854894722977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124425045","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99669904,0.00019192355,0.0006055536,0.0002544614,0.00011340781,0.00035351352,0.000026527636,0.00022664746,0.0015289045],"genre_scores_gemma":[0.99344456,0.00051286584,0.0036158587,0.00019675388,0.00006190978,0.000055011835,0.00042260234,0.0000786892,0.0016117175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982235,0.00007939053,0.0003761631,0.00047258486,0.00026907725,0.00057931937],"domain_scores_gemma":[0.9994899,0.00002157933,0.000025932817,0.00035063524,0.000030911557,0.000081043334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016171133,0.0002953995,0.00025015074,0.0002647063,0.00010250409,0.00042963753,0.00016741425,0.00007646183,0.00015695929],"category_scores_gemma":[0.0000036549939,0.00022591205,0.000054373413,0.00013108972,0.000036804777,0.00044868415,0.0004477397,0.00012914333,0.000088540866],"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":[0.000056796453,0.000080326834,0.03454624,0.0003430594,0.00027249992,0.0003053711,0.03149041,0.9234315,0.0003248075,0.000104694795,0.00042652932,0.0086177755],"study_design_scores_gemma":[0.0043202937,0.000043416916,0.1928893,0.0006298475,0.00029142536,0.0000033463414,0.003497688,0.55104226,0.024369424,0.00028190087,0.22118028,0.0014507978],"about_ca_topic_score_codex":0.00018378421,"about_ca_topic_score_gemma":0.00027793084,"teacher_disagreement_score":0.3723892,"about_ca_system_score_codex":0.000053796106,"about_ca_system_score_gemma":5.4689787e-7,"threshold_uncertainty_score":0.9212426},"labels":[],"label_agreement":null},{"id":"W3158561926","doi":"10.1007/s11269-021-02806-6","title":"Solving Hydropower Unit Commitment Problem Using a Novel Sequential Mixed Integer Linear Programming Approach","year":2021,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Mathematical optimization; Integer programming; Computer science; Linear programming; Power system simulation; Integer (computer science); Computation; Convergence (economics); Nonlinear programming; Nonlinear system; Hydropower; Cascade; Mathematics; Power (physics); Algorithm; Electric power system; Engineering","score_opus":0.02606669531143445,"score_gpt":0.22304989082962481,"score_spread":0.19698319551819038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158561926","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21904431,0.0002689993,0.73274624,0.00011774016,0.0005185621,0.0015307835,0.0000068587783,0.0010857146,0.0446808],"genre_scores_gemma":[0.7714521,0.00004480315,0.22002247,0.000113188275,0.00033189126,0.00019157806,0.00047659932,0.0002326314,0.0071346797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99751306,0.00007369815,0.0005363024,0.0005659146,0.00049967243,0.0008113408],"domain_scores_gemma":[0.9991846,0.000007158305,0.00006702214,0.0005467584,0.00006725278,0.00012722611],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035073824,0.0004427434,0.00033342416,0.00031100874,0.00024964495,0.00045307344,0.0003899427,0.00010331594,0.00015632616],"category_scores_gemma":[0.0000022760237,0.0003757784,0.00015885576,0.00039612394,0.00006224306,0.0002602189,0.00074797025,0.0002500991,0.00006265777],"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":[0.000022324306,0.00027448463,0.000265823,0.00094522064,0.00066983723,0.000095426534,0.0038765674,0.98863024,0.0018881878,0.00032824854,0.00022883734,0.0027747846],"study_design_scores_gemma":[0.0010038529,0.0000286419,0.000030931467,0.00014810081,0.00023290885,0.000015403733,0.0012964535,0.6164453,0.004776833,0.000040273044,0.37539786,0.0005834484],"about_ca_topic_score_codex":0.00003387179,"about_ca_topic_score_gemma":0.000011435967,"teacher_disagreement_score":0.55240786,"about_ca_system_score_codex":0.00016983988,"about_ca_system_score_gemma":0.0000034790467,"threshold_uncertainty_score":0.9998694},"labels":[],"label_agreement":null},{"id":"W3161837918","doi":"10.1007/s11269-021-02948-7","title":"Assessing Optimal Digital Elevation Model Selection for Active River Area Delineation Across Broad Regions","year":2021,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Sediment Transport Processes","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nature Conservancy of Canada; Dalhousie University","funders":"","keywords":"Digital elevation model; Smoothing; Lidar; Elevation (ballistics); Riparian zone; Computer science; Remote sensing; Geographic information system; Data mining; Environmental science; Geography; Mathematics; Computer vision","score_opus":0.021582447001036422,"score_gpt":0.2609304974179533,"score_spread":0.2393480504169169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161837918","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6528209,0.0000046328128,0.3449549,0.00028612188,0.000024388703,0.00017844261,0.0000067664023,0.00004703492,0.0016768186],"genre_scores_gemma":[0.989932,0.000009168428,0.0044291383,0.00021861278,0.000034214838,0.000083272134,0.00029759982,0.000013977838,0.0049820254],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989155,0.000015380625,0.00018174315,0.00038109883,0.0002030891,0.00030322245],"domain_scores_gemma":[0.9997531,0.000011759068,0.000050437207,0.000118300064,0.000024727402,0.00004164763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000120092125,0.000127892,0.00009793943,0.00002731568,0.00036768653,0.00016064034,0.00010428338,0.00006426136,0.00014347238],"category_scores_gemma":[0.000005552058,0.000107685235,0.000056834473,0.00012276872,0.00008008401,0.0009400012,0.00009264609,0.00007048449,0.00004508897],"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":[0.00016798466,0.0002781857,0.0104150325,0.0000663033,0.00012536654,0.000013310759,0.0086851055,0.92685896,0.004923052,0.00010347433,0.0006352583,0.047727946],"study_design_scores_gemma":[0.0016115794,0.00012071469,0.012397806,0.000047494956,0.00018677254,0.000010796102,0.0020466386,0.80800205,0.051484767,0.0027141303,0.120781496,0.00059572706],"about_ca_topic_score_codex":0.000013059436,"about_ca_topic_score_gemma":0.00003067629,"teacher_disagreement_score":0.34052578,"about_ca_system_score_codex":0.00009676608,"about_ca_system_score_gemma":0.0000028202812,"threshold_uncertainty_score":0.43912762},"labels":[],"label_agreement":null},{"id":"W3200896511","doi":"10.1007/s11269-021-02963-8","title":"Implicit Finite-Volume Scheme to Solve Coupled Saint-Venant and Darcy–Forchheimer Equations for Modeling Flow Through Porous Structures","year":2021,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Computational Fluid Dynamics and Aerodynamics","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Finite volume method; Flow (mathematics); Mechanics; Open-channel flow; Porous medium; Darcy's law; Laminar flow; Nonlinear system; Mathematics; Applied mathematics; Geology; Geometry; Geotechnical engineering; Porosity; Physics","score_opus":0.013046968813789951,"score_gpt":0.22503818930176703,"score_spread":0.21199122048797708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200896511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30429742,0.00016384356,0.6944231,0.00036702535,0.000115969284,0.0003256768,0.000044830515,0.00008784861,0.00017433189],"genre_scores_gemma":[0.86340505,0.000042429147,0.13499126,0.0002963589,0.00009859265,0.00011030838,0.0003992711,0.00006364321,0.000593064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988079,0.000007351834,0.00028876748,0.00032789566,0.00019712356,0.00037096834],"domain_scores_gemma":[0.99955064,0.00004111116,0.000015482481,0.00024079837,0.00007632407,0.00007566004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000107441265,0.00020066382,0.00019128266,0.0000832954,0.00016739254,0.00021871901,0.00013764563,0.000044329383,0.000024885274],"category_scores_gemma":[0.000012004673,0.00017431447,0.00006747072,0.00010614026,0.000013392439,0.000101725345,0.00023139539,0.00008069133,0.000010807873],"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":[0.000010170678,0.00001246116,0.000011459047,0.00011329844,0.0001657358,0.000010927353,0.00094930606,0.97981995,0.0011483064,0.01515071,0.00006906197,0.002538624],"study_design_scores_gemma":[0.00035818433,0.000023466699,0.00006787419,0.000028636463,0.000050896655,0.0000026154505,0.00019156598,0.97964966,0.00007038606,0.011416334,0.007890665,0.00024969204],"about_ca_topic_score_codex":0.000048709102,"about_ca_topic_score_gemma":0.00006152329,"teacher_disagreement_score":0.5594318,"about_ca_system_score_codex":0.00008812573,"about_ca_system_score_gemma":0.0000043285477,"threshold_uncertainty_score":0.7108338},"labels":[],"label_agreement":null},{"id":"W4205735987","doi":"10.1007/s11269-021-03034-8","title":"Optimal Operational Scheduling of Pumps to Improve the Performance of Water Distribution Networks","year":2022,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Jacobs (Canada)","funders":"","keywords":"Particle swarm optimization; MATLAB; Reliability engineering; Computer science; Reliability (semiconductor); Software; Leakage (economics); Scheduling (production processes); Simulation; Real-time computing; Power (physics); Engineering; Algorithm","score_opus":0.00412070599879554,"score_gpt":0.16565421262630708,"score_spread":0.16153350662751154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205735987","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96136224,0.000030928335,0.037429962,0.00012199579,0.00025598388,0.0003520534,0.0000142565095,0.00003339886,0.00039917865],"genre_scores_gemma":[0.9984246,0.0000054554694,0.00047218511,0.0000219902,0.00006363588,0.00011954329,0.00014339696,0.000014533287,0.00073467504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999188,0.000027833694,0.00026446037,0.00011532822,0.00021497431,0.00018938872],"domain_scores_gemma":[0.99974716,0.000003089966,0.000021068852,0.00018294991,0.000024711106,0.00002101625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030632186,0.000089444584,0.000104280116,0.000047120797,0.00013794895,0.000028425922,0.00019676205,0.000015869879,0.00007289323],"category_scores_gemma":[4.3694294e-7,0.00005185317,0.000035835055,0.000075364296,0.000013953249,0.000062962135,0.0002712286,0.00007271248,0.0000055653272],"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":[0.00002023815,0.000012137909,0.00017665513,0.000088620756,0.000048446513,6.6975156e-7,0.0014915483,0.9965262,0.0010920159,0.00005552574,0.00021778433,0.00027018538],"study_design_scores_gemma":[0.00019286906,0.00008594301,0.00042608913,0.000016356098,0.000020337522,0.0000011595282,0.0002685703,0.93013036,0.03746043,0.0000016385188,0.031295933,0.00010033113],"about_ca_topic_score_codex":0.000019517896,"about_ca_topic_score_gemma":0.0000015030957,"teacher_disagreement_score":0.066395834,"about_ca_system_score_codex":0.00004763114,"about_ca_system_score_gemma":6.8097285e-7,"threshold_uncertainty_score":0.21145108},"labels":[],"label_agreement":null},{"id":"W4210351980","doi":"10.1007/s11269-021-02985-2","title":"Headwater-to-consumer Drinking Water Security Assessment Framework and Associated Indicators for Small Communities in High-income Countries","year":2022,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Child Nutrition and Water Access","field":"Nursing","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Business; Water security; Hydrogeology; Environmental planning; Natural resource economics; Environmental resource management; Economics; Environmental science; Engineering; Water resources; Ecology","score_opus":0.010958247970629527,"score_gpt":0.25646508430979065,"score_spread":0.24550683633916112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210351980","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98895305,0.000065424916,0.00018300494,0.008170079,0.00054562814,0.0015754822,0.00007027968,0.00016559698,0.0002714233],"genre_scores_gemma":[0.9948214,0.000016969892,0.0007139475,0.002986659,0.00006562835,0.00078240415,0.0003527059,0.000062450825,0.00019780881],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973402,0.0004365692,0.0005369887,0.00043738543,0.00044513732,0.00080371834],"domain_scores_gemma":[0.9992198,0.00012142839,0.00008294132,0.00042809846,0.000031459902,0.000116325056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011789332,0.00031577877,0.0004198597,0.0008399561,0.0009753708,0.0004310979,0.0005435994,0.00008591877,0.00026246617],"category_scores_gemma":[0.000005061554,0.0002449377,0.000082932056,0.00021150586,0.00010243975,0.0001323764,0.0013756006,0.0004885419,0.00001169114],"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":[0.0016381224,0.0010400235,0.8494752,0.0013591868,0.0005665745,0.00009204038,0.1395539,0.001166929,0.0000662353,0.0020673308,0.0013447565,0.0016296827],"study_design_scores_gemma":[0.007347598,0.00083786127,0.16922317,0.0007354718,0.00027537794,0.000009612146,0.0081078,0.00051911955,0.00704411,0.03435435,0.7700615,0.0014840026],"about_ca_topic_score_codex":0.0007549902,"about_ca_topic_score_gemma":0.00043173452,"teacher_disagreement_score":0.76871675,"about_ca_system_score_codex":0.00042239146,"about_ca_system_score_gemma":0.000002074593,"threshold_uncertainty_score":0.99882686},"labels":[],"label_agreement":null},{"id":"W4226549634","doi":"10.1007/s11269-021-02997-y","title":"Application of Artificial Neural Networks to Project Reference Evapotranspiration Under Climate Change Scenarios","year":2022,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; Dalhousie University; University of Prince Edward Island","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Evapotranspiration; Climate change; Artificial neural network; Environmental science; Hydrogeology; Environmental resource management; Hydrology (agriculture); Climatology; Computer science; Ecology; Geology; Artificial intelligence; Oceanography","score_opus":0.023444393552998107,"score_gpt":0.22757694356132066,"score_spread":0.20413255000832256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226549634","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.981828,0.000007531231,0.01320982,0.00037450233,0.000071564435,0.0012119572,0.000026344293,0.000053478314,0.003216825],"genre_scores_gemma":[0.9985821,0.0000054400953,0.00022551446,0.00034172152,0.000023992226,0.000521415,0.00013708051,0.0000122890015,0.00015040238],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988064,0.00006812803,0.00023116391,0.00029148287,0.00033542892,0.00026737447],"domain_scores_gemma":[0.9996584,0.000003825752,0.0000596677,0.00023918397,0.00000381938,0.000035111516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002599954,0.0001071131,0.00009469369,0.00008549879,0.00022102898,0.000028320565,0.00024190304,0.000023492083,0.00016088429],"category_scores_gemma":[2.6762905e-7,0.000088082845,0.00003284635,0.00023663114,0.000028542434,0.00008695197,0.00045556083,0.000096532895,0.000043697983],"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":[0.0000645606,0.00007307688,0.0033136515,0.000014724404,0.00000983767,0.0000031700808,0.0015373173,0.9677984,0.001007659,0.0005384859,0.000019481495,0.025619632],"study_design_scores_gemma":[0.0001467608,0.00013610638,0.007624119,0.000004691442,0.000036297737,0.0000024274452,0.00015207712,0.96559614,0.00009810495,0.0001853403,0.025829393,0.00018853399],"about_ca_topic_score_codex":0.0003730318,"about_ca_topic_score_gemma":0.00013441371,"teacher_disagreement_score":0.025809912,"about_ca_system_score_codex":0.0001053054,"about_ca_system_score_gemma":3.819546e-7,"threshold_uncertainty_score":0.35919142},"labels":[],"label_agreement":null},{"id":"W4281663838","doi":"10.1007/s11269-022-03167-4","title":"Lambert W-function Solution for Uniform Flow Depth Problem","year":2022,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Sports Dynamics and Biomechanics","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Dimensionless quantity; Mathematics; Flow (mathematics); Mathematical analysis; Logarithm; Surface finish; Series (stratigraphy); Power function; Viscosity; Function (biology); Mechanics; Geometry; Physics; Geology; Materials science; Thermodynamics","score_opus":0.0062884376625258025,"score_gpt":0.1711871349869451,"score_spread":0.1648986973244193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281663838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30838227,0.00018197259,0.6472502,0.0008198517,0.0025762452,0.003164238,0.00007092998,0.0015627632,0.03599153],"genre_scores_gemma":[0.9873238,0.000018746638,0.0056843446,0.0001030929,0.00008742527,0.0005418711,0.00033834,0.00005191071,0.0058504255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99925643,0.0000057518973,0.00014636769,0.0001571429,0.0001707081,0.0002636212],"domain_scores_gemma":[0.99979144,0.0000019084619,0.00001608927,0.00015339184,0.000009309216,0.000027852833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020031292,0.00010689987,0.00007807509,0.00011773874,0.00017969987,0.00003746234,0.0001180213,0.00002117266,0.000098093995],"category_scores_gemma":[1.5988206e-7,0.00008887135,0.000055607175,0.000091678674,0.000004724622,0.000043513402,0.00014677546,0.00006332946,0.000013373592],"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":[0.0002745232,0.00017010442,0.00017275156,0.00085051253,0.0005084465,0.000030231755,0.0023569996,0.7396849,0.0026848265,0.005881475,0.01375311,0.23363213],"study_design_scores_gemma":[0.00021526177,0.00005410886,0.0000825616,0.0000035627638,0.00002933214,0.0000015559768,0.00008456923,0.4521051,0.00012044263,0.0009781016,0.5462185,0.000106848885],"about_ca_topic_score_codex":0.000009046652,"about_ca_topic_score_gemma":0.000026896101,"teacher_disagreement_score":0.67894155,"about_ca_system_score_codex":0.00014335294,"about_ca_system_score_gemma":6.964608e-7,"threshold_uncertainty_score":0.36240682},"labels":[],"label_agreement":null},{"id":"W4281717260","doi":"10.1007/s11269-022-03194-1","title":"An Integrated Extreme Rainfall Modeling Tool (SDExtreme) for Climate Change Impacts and Adaptation","year":2022,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Climate variability and models","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":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Geological Survey","keywords":"Downscaling; Climate change; Environmental science; Climatology; Precipitation; Context (archaeology); Climate model; Storm; Rain gauge; Generalized extreme value distribution; Extreme value theory; Meteorology; Geography; Statistics; Mathematics; Geology","score_opus":0.06727808316721517,"score_gpt":0.24449184862853782,"score_spread":0.17721376546132264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281717260","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9833482,0.000028787887,0.0135834515,0.0004647172,0.000069957496,0.0012092624,0.000048978174,0.00009943401,0.001147194],"genre_scores_gemma":[0.9936269,0.000057908586,0.004818648,0.0005341803,0.00002961611,0.00057306094,0.00015041449,0.00002582748,0.00018346081],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844956,0.00009423503,0.00026794514,0.0004638218,0.00028096265,0.0004434512],"domain_scores_gemma":[0.99955565,0.000012561612,0.000050551764,0.00029196325,0.000006279358,0.00008302018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009680903,0.00016491966,0.0001337415,0.000074251846,0.0004685158,0.00009990011,0.00022449752,0.00002714712,0.00069701375],"category_scores_gemma":[0.000003938333,0.00013307229,0.00004620738,0.00009401399,0.000039343115,0.00031719895,0.0006019803,0.00008342736,0.00001794803],"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":[0.00067099556,0.00049375155,0.0032847468,0.0003524532,0.00006577169,0.000021460137,0.05248793,0.8405634,0.0046386835,0.0008951747,0.00018203293,0.096343614],"study_design_scores_gemma":[0.0005897613,0.00019035248,0.00043860433,0.0000125105635,0.000040972292,0.0000022858837,0.0025771027,0.955561,0.00003988471,0.0015648133,0.038754456,0.00022825197],"about_ca_topic_score_codex":0.000447273,"about_ca_topic_score_gemma":0.00013730407,"teacher_disagreement_score":0.11499761,"about_ca_system_score_codex":0.00017209434,"about_ca_system_score_gemma":0.000001021509,"threshold_uncertainty_score":0.76318115},"labels":[],"label_agreement":null},{"id":"W4283380451","doi":"10.1007/s11269-022-03206-0","title":"A multi-weight fuzzy Methodological Framework for Allocating Coalition Payoffs of Joint Water Environment Governance in Transboundary River Basins","year":2022,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Transboundary Water Resource Management","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Shapley value; Fuzzy logic; Mathematical optimization; Stochastic game; Operations research; Computer science; Corporate governance; Structural basin; Environmental economics; Game theory; Microeconomics; Economics; Mathematics; Geology; Artificial intelligence","score_opus":0.07284038001206143,"score_gpt":0.2992184050661329,"score_spread":0.22637802505407145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283380451","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8013622,0.00034134326,0.16870247,0.014677794,0.0006693441,0.0063008373,0.0002537769,0.00026230767,0.007429948],"genre_scores_gemma":[0.92230695,0.00011973621,0.07271827,0.0006421841,0.000107930515,0.0012306937,0.000090443194,0.000056225224,0.0027275742],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99460983,0.0014334944,0.00085292524,0.0008343033,0.0011988365,0.0010706286],"domain_scores_gemma":[0.99904346,0.00012223044,0.0001763837,0.0005115712,0.000021575397,0.0001247727],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004240122,0.000342695,0.00051801937,0.00023422757,0.0010750644,0.00012228552,0.0008331946,0.00010530262,0.0012868067],"category_scores_gemma":[0.000020701154,0.0002602373,0.00029392054,0.00019342678,0.000559287,0.00015826238,0.00063545594,0.00037180004,0.000031916385],"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":[0.0016572649,0.004029026,0.008605328,0.0018062422,0.0011000353,0.0003211747,0.79330003,0.053771734,0.005954043,0.08740977,0.0008879964,0.041157342],"study_design_scores_gemma":[0.0017936883,0.00028775827,0.006318054,0.00008166518,0.00012037142,0.0000011961281,0.009499148,0.00029876022,0.002309225,0.014734352,0.96405286,0.0005029133],"about_ca_topic_score_codex":0.0012905021,"about_ca_topic_score_gemma":0.00026491177,"teacher_disagreement_score":0.96316487,"about_ca_system_score_codex":0.0006808326,"about_ca_system_score_gemma":0.000010296595,"threshold_uncertainty_score":0.999985},"labels":[],"label_agreement":null},{"id":"W4288685189","doi":"10.1007/s11269-022-03132-1","title":"Digital Corporate Social Responsibility Reporting in the Water Industry","year":2022,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Corporate Social Responsibility Reporting","field":"Business, Management and Accounting","cited_by":9,"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":"Fundação para a Ciência e a Tecnologia; Canadian Intensive Care Foundation","keywords":"Corporate social responsibility; Business; Accounting; Portuguese; Social responsibility; Social media; Marketing; Public relations; Political science","score_opus":0.04993178362192528,"score_gpt":0.25508209943315036,"score_spread":0.20515031581122506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288685189","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9404589,0.000008413354,0.000009880031,0.005184898,0.00025615795,0.0009382939,0.0000037368766,0.00017843969,0.05296126],"genre_scores_gemma":[0.9886674,1.9471206e-7,0.000013925834,0.002801739,0.0005675043,0.00024432066,0.00010936788,0.000048519687,0.00754702],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9950758,0.00021065208,0.0018156876,0.0007948802,0.0012234494,0.0008794917],"domain_scores_gemma":[0.99760246,0.00004717396,0.0015478536,0.00069518574,0.00008969728,0.00001763888],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0097251255,0.00032149922,0.0003789255,0.00046759588,0.0014297394,0.0011771306,0.0008390976,0.000105641724,0.0007621508],"category_scores_gemma":[0.0002902844,0.0001824402,0.00022742024,0.0008629072,0.0001497198,0.0007770568,0.0027674376,0.0009946981,0.00015714322],"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":[0.00380843,0.0024723841,0.78176486,0.0013972059,0.00045692845,0.013524886,0.04423264,0.0041541765,0.0031274322,0.008180726,0.010830149,0.12605016],"study_design_scores_gemma":[0.00097512384,0.000035845074,0.098912574,0.000021923855,0.00009605117,0.000033358894,0.026137618,0.0004301164,0.00015910374,0.02811166,0.8443309,0.0007557214],"about_ca_topic_score_codex":0.00021514676,"about_ca_topic_score_gemma":0.000048811584,"teacher_disagreement_score":0.83350074,"about_ca_system_score_codex":0.00025140765,"about_ca_system_score_gemma":0.0000140061165,"threshold_uncertainty_score":0.99987024},"labels":[],"label_agreement":null},{"id":"W4303022718","doi":"10.1007/s11269-022-03347-2","title":"Comparison of the Performance of a Surrogate Based Gaussian Process, NSGA2 and PSO Multi-objective Optimization of the Operation and Fuzzy Structural Reliability of Water Distribution System: Case Study for the City of Asmara, Eritrea","year":2022,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Particle swarm optimization; Multi-objective optimization; Mathematical optimization; Harmony search; Surrogate model; Optimization problem; Computer science; Schedule; Maximization; Mathematics","score_opus":0.010665671976446465,"score_gpt":0.2267563975768062,"score_spread":0.21609072560035975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4303022718","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99389344,0.00003004096,0.0040487484,0.000026537173,0.000082467435,0.0018129988,0.000082361184,0.000009394606,0.000013995393],"genre_scores_gemma":[0.99968004,0.0000012270078,0.00015418936,8.4819106e-7,0.000005018189,0.00010360462,0.000027584221,0.0000091310585,0.000018355826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989336,0.00014313364,0.00047009785,0.00013601582,0.0002198512,0.000097315686],"domain_scores_gemma":[0.9994467,0.000017746024,0.00017654957,0.00025661,0.00009198013,0.000010445487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000505123,0.00010674268,0.0002330166,0.000035170568,0.0001900136,0.0000130401995,0.00014159089,0.000022060563,0.0000026865534],"category_scores_gemma":[0.0000037942432,0.00004873594,0.000042225904,0.000093667804,0.00008064546,0.000071604125,0.00014921004,0.000054974622,9.682541e-9],"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":[0.000079916674,0.000060894832,0.083319284,0.0019123375,0.000060415907,2.2091332e-7,0.011370781,0.90294695,0.00019458144,0.0000046441364,0.0000023594196,0.000047623224],"study_design_scores_gemma":[0.00083561416,0.00016938578,0.037871324,0.00006532914,0.00014551266,0.000003187229,0.008893844,0.9149164,0.03702502,0.0000013027736,0.000010771001,0.00006232386],"about_ca_topic_score_codex":0.00042967687,"about_ca_topic_score_gemma":0.00010665283,"teacher_disagreement_score":0.045447964,"about_ca_system_score_codex":0.000042592274,"about_ca_system_score_gemma":0.000002966293,"threshold_uncertainty_score":0.1987394},"labels":[],"label_agreement":null},{"id":"W4303986819","doi":"10.1007/s11269-022-03321-y","title":"Nonparametric Approach to Copula Estimation in Compounding The Joint Impact of Storm Surge and Rainfall Events in Coastal Flood Analysis","year":2022,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Copula (linguistics); Joint probability distribution; Nonparametric statistics; Kernel density estimation; Marginal distribution; Econometrics; Estimator; Flood myth; Parametric statistics; Statistics; Storm surge; Mathematics; Storm; Computer science; Random variable; Meteorology; Geography","score_opus":0.01151674894713656,"score_gpt":0.23221238031129599,"score_spread":0.22069563136415943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4303986819","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9962799,0.000011227196,0.0010864245,0.00010037565,0.000010219331,0.0003094689,0.0000042582724,0.0000060989373,0.0021920262],"genre_scores_gemma":[0.99924153,0.0000025048487,0.0004260711,0.000051902178,0.0000020980362,0.000062278785,0.00003605558,0.0000053750136,0.00017220412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986048,0.00024226082,0.0002950677,0.00027641558,0.00033598096,0.00024548091],"domain_scores_gemma":[0.9996404,0.00002328819,0.000069842055,0.00022318629,0.0000016668776,0.000041631938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013465277,0.00010802832,0.00024532655,0.0006569093,0.0001314625,0.000013761833,0.00021670584,0.000018757311,0.00027510946],"category_scores_gemma":[0.000006835191,0.000071148206,0.00010635206,0.001760741,0.00005100964,0.000064907574,0.00076358544,0.00011257146,0.000010326944],"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":[0.00002582978,0.000116530915,0.48630026,0.0000050255835,0.00013355451,0.000003971309,0.0032234672,0.5098605,0.000030811985,0.0000057829657,0.000018807244,0.00027547282],"study_design_scores_gemma":[0.00033351724,0.000056017918,0.8315568,0.0000021465519,0.00014814419,0.000001314685,0.00057781464,0.16685753,0.000015196544,0.00013018952,0.0002274876,0.00009381794],"about_ca_topic_score_codex":0.0060784947,"about_ca_topic_score_gemma":0.0013693087,"teacher_disagreement_score":0.34525657,"about_ca_system_score_codex":0.00021997692,"about_ca_system_score_gemma":9.510485e-7,"threshold_uncertainty_score":0.9188906},"labels":[],"label_agreement":null},{"id":"W4308264404","doi":"10.1007/s11269-022-03364-1","title":"Correction to: A New Approach for Dam Safety Assessment Using the Extended Cloud Model","year":2022,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Advanced Decision-Making Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"University of Northern British Columbia","keywords":"Cloud computing; Hydrogeology; Computer science; Environmental science; Engineering; Geotechnical engineering; Operating system","score_opus":0.03769912554697759,"score_gpt":0.3181308650226458,"score_spread":0.28043173947566824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308264404","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00078750716,0.000011056759,0.99138874,0.00072938896,0.0006402955,0.001430299,0.0000027291278,0.0003509275,0.0046590758],"genre_scores_gemma":[0.07703693,0.0000022416152,0.9129381,0.00094353163,0.00008775632,0.00040276424,0.0000062747454,0.000025743879,0.008556655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997916,0.00011601605,0.00029971928,0.00065063155,0.00063362037,0.00038400522],"domain_scores_gemma":[0.9986335,0.00006974649,0.00009446904,0.0010995085,0.00003171646,0.00007102649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010171238,0.00017655084,0.00015971648,0.0001827988,0.0008092206,0.00023111078,0.0016927599,0.00001752707,0.000013832504],"category_scores_gemma":[0.000011935165,0.00011981252,0.00010121139,0.0003296531,0.000017484768,0.0001689969,0.0033089647,0.00017676606,0.000003163529],"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":[0.00005871134,0.00008791164,0.0000026848566,0.000010516981,0.000032737524,0.000004621438,0.002224697,0.88544303,0.0001894552,0.008150305,0.018331463,0.08546386],"study_design_scores_gemma":[0.0002010973,0.000078116354,0.000017166727,0.000009737409,0.000016278946,0.000008291041,0.00023404209,0.8191352,0.00018938155,0.027158737,0.15278912,0.00016286789],"about_ca_topic_score_codex":0.00003183554,"about_ca_topic_score_gemma":0.0000013773509,"teacher_disagreement_score":0.13445766,"about_ca_system_score_codex":0.0004193507,"about_ca_system_score_gemma":0.00001546441,"threshold_uncertainty_score":0.6223952},"labels":[],"label_agreement":null},{"id":"W4321435046","doi":"10.1007/s11269-023-03474-4","title":"Climate Change Impacts on Water Resources and Sustainable Water Management Strategies in North America","year":2023,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":117,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; Concordia University","funders":"","keywords":"Overexploitation; Water resources; Climate change; Water scarcity; Surface runoff; Environmental science; Population; Water resource management; Snowmelt; Watershed management; Watershed; Geography; Environmental resource management; Ecology","score_opus":0.011100541755404474,"score_gpt":0.2169137427159924,"score_spread":0.20581320096058792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321435046","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92394453,0.000027005579,0.000007768794,0.0035097126,0.00008703105,0.0012387398,0.000003913736,0.00026514987,0.07091612],"genre_scores_gemma":[0.9858022,0.0008617304,0.000044784687,0.001375258,0.000056028483,0.00055787753,0.00008503375,0.00005159132,0.011165548],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9954133,0.00016277465,0.0004228687,0.0009587827,0.00051936146,0.0025229258],"domain_scores_gemma":[0.99923754,0.00001515068,0.00004724727,0.0005546457,0.0000060889965,0.00013930265],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008964366,0.00049021724,0.00037885364,0.0005670739,0.00070527487,0.0002543524,0.0005019674,0.00007584118,0.0005330064],"category_scores_gemma":[0.0000014456659,0.00027728072,0.0000772229,0.0003685974,0.0003568678,0.0005077158,0.0044868174,0.00019663342,0.004190505],"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":[0.0031057263,0.0018251266,0.24567759,0.0067348587,0.002087709,0.023300797,0.5497429,0.07596125,0.0009071658,0.0031414446,0.021231646,0.0662838],"study_design_scores_gemma":[0.0018261515,0.0004800562,0.20904243,0.000094389645,0.00015622603,0.0000034048321,0.028065149,0.00038610172,0.0013348263,0.0028612793,0.7547316,0.001018381],"about_ca_topic_score_codex":0.00038931126,"about_ca_topic_score_gemma":0.00026967356,"teacher_disagreement_score":0.73349994,"about_ca_system_score_codex":0.000141313,"about_ca_system_score_gemma":2.330966e-7,"threshold_uncertainty_score":0.99996793},"labels":[],"label_agreement":null},{"id":"W4322421709","doi":"10.1007/s11269-023-03448-6","title":"Trivariate Probabilistic Assessments of the Compound Flooding Events Using the 3-D Fully Nested Archimedean (FNA) Copula in the Semiparametric Distribution Setting","year":2023,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Copula (linguistics); Parametric statistics; Nonparametric statistics; Marginal distribution; Joint probability distribution; Econometrics; Multivariate statistics; Mathematics; Statistics; Computer science; Random variable","score_opus":0.02124455263298615,"score_gpt":0.27216203008273093,"score_spread":0.2509174774497448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322421709","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9969144,0.0000071530776,0.00041487862,0.00089170213,0.000070651105,0.00065256044,0.000008033968,0.000023153183,0.0010174728],"genre_scores_gemma":[0.99936575,0.000003585303,0.000074787094,0.00010833172,0.000018579633,0.000039745442,0.000048329697,0.000009122219,0.00033179307],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976345,0.00073257263,0.00036004983,0.00029650505,0.00057473476,0.00040166487],"domain_scores_gemma":[0.99920285,0.0001319381,0.00014922298,0.00048640164,0.0000051214047,0.000024488892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021957317,0.00014897945,0.00016685607,0.00008662852,0.00048509962,0.000055007797,0.00080153986,0.000041661482,0.000059379738],"category_scores_gemma":[0.00003680011,0.0000680825,0.00010277829,0.0014633907,0.00015473466,0.00007156001,0.0007757743,0.00019218281,0.000068036206],"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":[0.000067115696,0.00032027412,0.50358343,0.00010898745,0.00023926947,0.00004041217,0.007015481,0.4843543,0.0013118751,0.00019839602,0.000419458,0.002341012],"study_design_scores_gemma":[0.0007010265,0.000055242763,0.80753547,0.00008210039,0.00039096814,0.00000897897,0.0015741652,0.17799011,0.00027888158,0.004256854,0.0068854755,0.00024071905],"about_ca_topic_score_codex":0.00036836322,"about_ca_topic_score_gemma":0.00013486306,"teacher_disagreement_score":0.30636418,"about_ca_system_score_codex":0.00013868156,"about_ca_system_score_gemma":0.0000021895655,"threshold_uncertainty_score":0.3731043},"labels":[],"label_agreement":null},{"id":"W4322490148","doi":"10.1007/s11269-023-03464-6","title":"Synergetic Water Demand and Sustainable Supply Strategies in GCC Countries: Data-driven Recommendations","year":2023,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","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":"Ontario Tech University","funders":"Qatar University","keywords":"Greywater; Wastewater; Environmental science; Water resource management; Water resources; Water supply; Groundwater; Integrated water resources management; Reuse; Business; Environmental engineering; Waste management; Engineering","score_opus":0.010544317032213962,"score_gpt":0.2159370101131123,"score_spread":0.20539269308089836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322490148","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96826816,0.00022832425,0.0037960003,0.0027291914,0.0002565569,0.0013638432,0.000027726128,0.0012782179,0.022051957],"genre_scores_gemma":[0.98460996,0.0011212671,0.00046675478,0.00007551256,0.000063487176,0.00013238292,0.0013078278,0.00007035761,0.012152457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99809736,0.0000574411,0.00035733666,0.00042754808,0.00022741855,0.0008329118],"domain_scores_gemma":[0.9993357,0.000016539974,0.00001962093,0.00054087245,0.00002016859,0.00006710268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005636452,0.0002541528,0.00021397785,0.0006435235,0.00019338829,0.0005736985,0.00046395836,0.00005859506,0.00019591096],"category_scores_gemma":[0.0000021334947,0.00018578509,0.00002411512,0.00028061855,0.000054692315,0.0007161725,0.0010493186,0.000112984686,0.0002566187],"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":[0.000036614616,0.000043302574,0.002778135,0.0018783965,0.00028204703,0.00034557428,0.01583551,0.95123225,0.00009186943,0.0020559097,0.024219343,0.0012010146],"study_design_scores_gemma":[0.00081836287,0.00003197015,0.0016334087,0.00008835307,0.000080238446,0.000001726653,0.009095669,0.20438108,0.00033981208,0.0011525376,0.781929,0.0004478484],"about_ca_topic_score_codex":0.00006142887,"about_ca_topic_score_gemma":0.00006184347,"teacher_disagreement_score":0.7577097,"about_ca_system_score_codex":0.00006616987,"about_ca_system_score_gemma":0.0000014052007,"threshold_uncertainty_score":0.75760955},"labels":[],"label_agreement":null},{"id":"W4360985209","doi":"10.1007/s11269-023-03501-4","title":"Evaluation Climate Change Impacts on Water Resources Over the Upper Reach of the Yellow River Basin","year":2023,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"National Natural Science Foundation of China","keywords":"Streamflow; Environmental science; Precipitation; Climate change; Climatology; Water resources; Drainage basin; Climate model; Hydrology (agriculture); Meteorology; Geography; Geology","score_opus":0.024061005109285872,"score_gpt":0.24420986746662482,"score_spread":0.22014886235733894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360985209","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95435905,0.000017948405,0.0000012438965,0.008516812,0.00023136692,0.0011377757,0.000005512673,0.000073271265,0.035656992],"genre_scores_gemma":[0.99296725,0.00012305765,0.000008449753,0.0019230685,0.00007778496,0.00022548565,0.000013259711,0.000025711137,0.0046359506],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99704725,0.0004347517,0.00028606385,0.00045600353,0.0010702247,0.0007056985],"domain_scores_gemma":[0.9990926,0.000031366268,0.00008323332,0.00073963776,0.000010644457,0.00004252758],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0026108285,0.00024982783,0.00018871832,0.000117400334,0.00060210517,0.000047576046,0.00066359073,0.000062082014,0.0010253186],"category_scores_gemma":[0.000010505562,0.00010206968,0.00014035497,0.00024940775,0.00047247426,0.0001447319,0.0020684397,0.00014338664,0.002188127],"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":[0.000800789,0.0006917326,0.457954,0.000498939,0.0019460808,0.00011660397,0.34882563,0.03168376,0.0029314328,0.00044916174,0.09963626,0.054465633],"study_design_scores_gemma":[0.0005995725,0.000092506154,0.6752748,0.00006436221,0.0002751818,6.1807805e-7,0.0006833066,0.0011928207,0.0033955385,0.00094255566,0.31726226,0.00021651141],"about_ca_topic_score_codex":0.0003067166,"about_ca_topic_score_gemma":0.000062416046,"teacher_disagreement_score":0.34814233,"about_ca_system_score_codex":0.00010748824,"about_ca_system_score_gemma":3.750701e-7,"threshold_uncertainty_score":0.9998879},"labels":[],"label_agreement":null},{"id":"W4378472646","doi":"10.1007/s11269-023-03528-7","title":"Improving Hybrid Models for Precipitation Forecasting by Combining Nonlinear Machine Learning Methods","year":2023,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrological Forecasting Using AI","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":"University of British Columbia","funders":"","keywords":"Gene expression programming; Genetic programming; Computer science; Support vector machine; Nonlinear system; Mean squared error; Machine learning; Data pre-processing; Artificial intelligence; Data mining; Statistics; Mathematics","score_opus":0.03711088554043078,"score_gpt":0.27218122880898227,"score_spread":0.23507034326855147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378472646","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78583896,0.000017625563,0.20903614,0.00026097256,0.00012531731,0.0006271759,0.000009749932,0.0004974924,0.0035865973],"genre_scores_gemma":[0.83837247,0.0000062626796,0.15477629,0.00019201644,0.00005303452,0.00017739831,0.00026463246,0.000072236195,0.0060856645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790025,0.00017483131,0.00035048608,0.0005794,0.0003179798,0.00067703583],"domain_scores_gemma":[0.9994211,0.0001463461,0.00012219758,0.00020834984,0.000009003521,0.00009299865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019581895,0.00022019757,0.00020305428,0.00009790932,0.00051181036,0.00013081051,0.0003196203,0.000047322337,0.00009970446],"category_scores_gemma":[0.0000898235,0.0001705716,0.0000893432,0.00022391444,0.00007784634,0.0002025333,0.0008031819,0.0001815018,0.00016644283],"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":[0.00004206586,0.000036307985,0.00042588165,0.00007948257,0.000034773064,0.000012203212,0.0020829877,0.8149241,0.0050935796,0.000011142783,0.00062803004,0.17662942],"study_design_scores_gemma":[0.00040848268,0.00016516022,0.000019910454,0.000023805957,0.000036028592,0.000002632892,0.00010985608,0.94525856,0.0037116858,0.0017183914,0.048317052,0.00022844863],"about_ca_topic_score_codex":0.0001542704,"about_ca_topic_score_gemma":0.0000039507377,"teacher_disagreement_score":0.17640097,"about_ca_system_score_codex":0.00010714518,"about_ca_system_score_gemma":6.472943e-7,"threshold_uncertainty_score":0.6955708},"labels":[],"label_agreement":null},{"id":"W4385951829","doi":"10.1007/s11269-023-03558-1","title":"Development of a Comprehensive Water Simulation Model for Water, Food, and Energy Nexus Analysis in Basin Scale","year":2023,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Nexus (standard); Hydrogeology; Computer science; Groundwater; Scale (ratio); Data mining; Operations research; Environmental economics; Hydrology (agriculture); Engineering; Geography; Economics","score_opus":0.025365922156172307,"score_gpt":0.22839278822325532,"score_spread":0.203026866067083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385951829","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9885633,0.000017280094,0.008392621,0.00022695353,0.000030940915,0.00023331819,0.000011828026,0.00005850468,0.002465268],"genre_scores_gemma":[0.9935047,0.0000072760995,0.0023131103,0.00011226944,0.000010109118,0.00017833013,0.00012017566,0.000025826563,0.0037281888],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979786,0.000049841325,0.00047124576,0.00053896906,0.0003633197,0.00059797947],"domain_scores_gemma":[0.9995888,0.000021004375,0.00004429238,0.00026698448,0.000014621763,0.00006432179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002570137,0.0002425855,0.00036149405,0.00039820117,0.0001917515,0.00003427645,0.00021288947,0.00005298537,0.000045351833],"category_scores_gemma":[8.095742e-7,0.00014694137,0.0000916751,0.00028258964,0.00010399818,0.00012095019,0.0011974939,0.00003458894,0.00003211041],"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":[0.00010689413,0.00009414884,0.0009647028,0.00010107779,0.0005053952,0.000006708344,0.024735304,0.9596396,0.009218448,0.00010965984,0.00006863114,0.0044494197],"study_design_scores_gemma":[0.0012943833,0.00011049595,0.0074641407,0.000029125977,0.0002978936,3.4500033e-7,0.0020780177,0.82775,0.09899717,0.0070883418,0.054371446,0.00051860395],"about_ca_topic_score_codex":0.0002385747,"about_ca_topic_score_gemma":0.0013792826,"teacher_disagreement_score":0.13188957,"about_ca_system_score_codex":0.0001124238,"about_ca_system_score_gemma":7.526523e-7,"threshold_uncertainty_score":0.5992095},"labels":[],"label_agreement":null},{"id":"W4386760161","doi":"10.1007/s11269-023-03613-x","title":"Hybrid Iterative and Tree-Based Machine Learning Algorithms for Lake Water Level Forecasting","year":2023,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Random forest; Algorithm; Computer science; Decision tree; Autoregressive model; Machine learning; Ensemble learning; Artificial intelligence; Tree (set theory); Time series; Ensemble forecasting; Data mining; Mathematics; Statistics","score_opus":0.052421606748173304,"score_gpt":0.2409556969541197,"score_spread":0.1885340902059464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386760161","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9890913,0.00000647152,0.006196117,0.0009733133,0.000073900024,0.0005287068,0.000024212055,0.00027652894,0.0028294798],"genre_scores_gemma":[0.9828499,0.0000028468494,0.005654448,0.00036386494,0.000054996468,0.00012683899,0.00019086715,0.000042380565,0.010713837],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981567,0.000081059145,0.00024439496,0.00055052753,0.0002872727,0.00067999086],"domain_scores_gemma":[0.9996239,0.00005822756,0.00004343652,0.00016993043,0.0000066459215,0.00009784768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006081897,0.0002311095,0.0001903163,0.0001036903,0.0004772169,0.00014203659,0.00020560926,0.000038564398,0.00042828408],"category_scores_gemma":[0.000021786505,0.0001389068,0.000069735,0.00010135296,0.00014310233,0.0000943935,0.00061153964,0.00013681976,0.00036367215],"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":[0.00037401955,0.00021271865,0.021886805,0.00040860893,0.00023263924,0.0006425595,0.010886607,0.59543246,0.008053227,0.000033294727,0.0026656624,0.35917142],"study_design_scores_gemma":[0.0008518272,0.00024786967,0.0015529847,0.000035622354,0.00003794638,0.000004545471,0.000033894026,0.6915291,0.009292399,0.00050920964,0.2956002,0.0003044233],"about_ca_topic_score_codex":0.00005098668,"about_ca_topic_score_gemma":0.000053273834,"teacher_disagreement_score":0.358867,"about_ca_system_score_codex":0.00004947444,"about_ca_system_score_gemma":4.2575562e-7,"threshold_uncertainty_score":0.56644547},"labels":[],"label_agreement":null},{"id":"W4386807364","doi":"10.1007/s11269-023-03591-0","title":"Flood Subsidence Susceptibility Mapping using Elastic-net Classifier: New Approach","year":2023,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Topographic Wetness Index; Flood myth; Gradient boosting; Terrain; Hydrogeology; Artificial intelligence; Computer science; Data mining; Geology; Random forest; Remote sensing; Environmental science; Cartography; Digital elevation model; Geography; Geotechnical engineering","score_opus":0.037756045077046815,"score_gpt":0.2467247965782213,"score_spread":0.2089687515011745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386807364","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88643557,0.00001771069,0.021245882,0.0005165186,0.00027752196,0.0010290206,0.000002494885,0.0004889597,0.089986295],"genre_scores_gemma":[0.9347982,0.00006530794,0.017102389,0.0002457729,0.00014416686,0.000060570663,0.000068148605,0.00005688049,0.047458578],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967922,0.0001156158,0.00041587546,0.0009541969,0.0008172634,0.000904879],"domain_scores_gemma":[0.9989032,0.000019496263,0.00008506931,0.000779905,0.000005266582,0.0002070512],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008668447,0.0003260393,0.00025267416,0.0002071895,0.00034078426,0.00021802011,0.0007283531,0.00006953404,0.0016437107],"category_scores_gemma":[0.000006518334,0.0002528113,0.00012102249,0.0006806305,0.00014628713,0.00030929563,0.0021073795,0.0001641874,0.005363302],"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":[0.00023875339,0.0012035902,0.24937363,0.0012799095,0.0010642399,0.00064848847,0.02240985,0.43945092,0.022264661,0.0021162187,0.19301012,0.06693962],"study_design_scores_gemma":[0.0015535728,0.000098009594,0.15411149,0.00008656529,0.00025463838,0.0000062076283,0.0050418833,0.111678004,0.0009995459,0.002469385,0.7225333,0.0011674098],"about_ca_topic_score_codex":0.00080145575,"about_ca_topic_score_gemma":0.00012544284,"teacher_disagreement_score":0.5295232,"about_ca_system_score_codex":0.00026022637,"about_ca_system_score_gemma":0.0000032788926,"threshold_uncertainty_score":0.99999243},"labels":[],"label_agreement":null},{"id":"W4388199254","doi":"10.1007/s11269-023-03631-9","title":"Regional Groundwater Flow Modeling Using Improved Isogeometric Analysis: Application and Implications in Unconfined Aquifer Systems","year":2023,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue; Institut National de la Recherche Scientifique; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; McGill University","keywords":"Isogeometric analysis; Aquifer; Hydrogeology; Applied mathematics; Benchmark (surveying); Computer science; Boundary (topology); Mathematical optimization; Partial differential equation; Flow (mathematics); Groundwater flow; Mathematics; Geometry; Groundwater; Geology; Mathematical analysis; Geotechnical engineering; Finite element method; Engineering; Structural engineering","score_opus":0.01852588607649777,"score_gpt":0.240634076388972,"score_spread":0.22210819031247422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388199254","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4113333,0.00012784595,0.5874606,0.00008893448,0.000013996856,0.00033675437,0.0000022335967,0.0004793772,0.00015700514],"genre_scores_gemma":[0.9951154,0.00013426706,0.0040244944,0.000027375472,0.000032201242,0.00033356942,0.00010606885,0.000033990673,0.00019264211],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986904,0.000026402751,0.00039718376,0.00037367296,0.0001666014,0.00034571445],"domain_scores_gemma":[0.99949515,0.0000138939995,0.00003277204,0.00037083254,0.000028115875,0.000059210717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024242271,0.00018359027,0.00028163253,0.0017086965,0.00007929697,0.00009034969,0.00017806682,0.00005659093,0.0000055064793],"category_scores_gemma":[0.0000019121712,0.00014870346,0.00007930425,0.0026507678,0.000021356438,0.00012676152,0.00013863514,0.00009255505,0.00001723615],"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":[0.0000046239393,0.000015358804,0.0008060202,0.000085644744,0.00032086053,0.0000021952035,0.00021099715,0.9918597,0.0030409743,0.00008197619,0.000015906253,0.0035557752],"study_design_scores_gemma":[0.0001340066,0.000007668673,0.001109725,0.00001306592,0.00022417198,8.644331e-7,0.00014550776,0.99475265,0.000111824,0.0005609699,0.002741878,0.00019765414],"about_ca_topic_score_codex":0.0002461797,"about_ca_topic_score_gemma":0.000031731084,"teacher_disagreement_score":0.5837821,"about_ca_system_score_codex":0.0001309549,"about_ca_system_score_gemma":6.9145875e-7,"threshold_uncertainty_score":0.6063951},"labels":[],"label_agreement":null},{"id":"W4389618019","doi":"10.1007/s11269-023-03695-7","title":"Analyzing the Subsurface Consequences of Dam Removal on Groundwater Storage and Hydrologic Niches in a Mountain Meadow Ecosystem","year":2023,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hydrology (agriculture); Environmental science; Groundwater; Baseflow; Water table; MODFLOW; Dam removal; Ecosystem; Groundwater flow; Aquifer; Geology; Streamflow; Ecology; Geography; Drainage basin; Sediment","score_opus":0.015177772397112899,"score_gpt":0.21783122324766854,"score_spread":0.20265345085055564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389618019","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9882427,0.000056763474,0.000022064396,0.0031795015,0.000057180132,0.00046400665,0.0000019190784,0.00006457116,0.007911333],"genre_scores_gemma":[0.99704,0.00013953345,0.00004132396,0.00021797753,0.000010195799,0.000058992755,0.0000050230983,0.000011422812,0.0024755285],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998283,0.00025666607,0.00030396282,0.00044784832,0.0002545097,0.00045396748],"domain_scores_gemma":[0.99950826,0.00006180585,0.00007480537,0.00032057604,0.0000026148762,0.000031932002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014098918,0.00019852316,0.00024485812,0.00016230244,0.00023079831,0.000041615844,0.00037013262,0.000048889517,0.00010714261],"category_scores_gemma":[0.0000056756626,0.00010908744,0.000045923443,0.0002891193,0.00042397474,0.00009965295,0.0008579169,0.00011801474,0.00027839298],"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":[0.0004986506,0.00029900728,0.7383492,0.00052708905,0.0006962924,0.0014755967,0.030553324,0.21470672,0.0054963008,0.0014560067,0.0026465969,0.0032951874],"study_design_scores_gemma":[0.004911856,0.001760184,0.64806247,0.00047737942,0.00060226995,0.00005577495,0.02107121,0.035224844,0.017847983,0.022878004,0.24482498,0.0022830374],"about_ca_topic_score_codex":0.00034322133,"about_ca_topic_score_gemma":0.0003143092,"teacher_disagreement_score":0.24217838,"about_ca_system_score_codex":0.00006898759,"about_ca_system_score_gemma":4.5188597e-7,"threshold_uncertainty_score":0.4448457},"labels":[],"label_agreement":null},{"id":"W4389950029","doi":"10.1007/s11269-023-03698-4","title":"A Set Pair Analysis Method for Assessing and Forecasting Water Conflict Risk in Transboundary River Basins","year":2023,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Hydrogeology; Water resource management; Environmental science; Set (abstract data type); Hydrology (agriculture); Geology; Operations research; Computer science; Engineering; Geotechnical engineering","score_opus":0.0269639952558059,"score_gpt":0.249968509327777,"score_spread":0.2230045140719711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389950029","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7936038,0.00004406984,0.2030966,0.00019459696,0.00007753416,0.00069948455,0.00002603416,0.00047919792,0.0017787009],"genre_scores_gemma":[0.98093134,0.00009105594,0.015921846,0.000072302086,0.00006803583,0.00021330653,0.0004328276,0.00008840213,0.0021808706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981092,0.000114297865,0.00043932258,0.0004466494,0.00023225982,0.00065823575],"domain_scores_gemma":[0.9995568,0.000056090605,0.000039179304,0.0002597934,0.00001867861,0.0000694767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011608497,0.00028621295,0.00036853872,0.0011658223,0.00021878793,0.00038947607,0.00019413502,0.000073161,0.000048895778],"category_scores_gemma":[0.0000042759825,0.00020803609,0.0001585413,0.00055861764,0.00004332459,0.00021990156,0.00017221743,0.00012755023,0.000027189823],"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":[0.000042388132,0.00001548643,0.013794974,0.00069475267,0.0012205348,0.000049879356,0.026723715,0.9326881,0.0001222846,0.000017917404,0.00042428856,0.024205642],"study_design_scores_gemma":[0.0010114999,0.000021199145,0.012966571,0.000039407005,0.0006723725,6.556518e-7,0.001038139,0.78998524,0.0008301698,0.00021103022,0.19285603,0.0003676926],"about_ca_topic_score_codex":0.00009117776,"about_ca_topic_score_gemma":0.00007835581,"teacher_disagreement_score":0.19243173,"about_ca_system_score_codex":0.00005687407,"about_ca_system_score_gemma":4.828757e-7,"threshold_uncertainty_score":0.84834653},"labels":[],"label_agreement":null},{"id":"W4391600100","doi":"10.1007/s11269-024-03747-6","title":"Hydropower Reservoir Optimization with Solar Generation-Changed Energy Prices in California","year":2024,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":8,"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":"Aksaray Üniversitesi; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; Canada Excellence Research Chairs, Government of Canada","keywords":"Hydropower; Environmental science; Evening; Revenue; Photovoltaic system; Electricity generation; Solar energy; Morning; Meteorology; Hydrology (agriculture); Environmental engineering; Engineering; Geography; Business","score_opus":0.006955397806853696,"score_gpt":0.17498804190633402,"score_spread":0.16803264409948032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391600100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21975984,0.0016756727,0.7235419,0.0009907412,0.0006047055,0.0010624949,0.000015192028,0.001857503,0.050491925],"genre_scores_gemma":[0.98660237,0.00047880554,0.0072498806,0.00012950264,0.00029437538,0.00022997195,0.00046028203,0.00014812093,0.004406671],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984996,0.00003117334,0.0003069013,0.000398466,0.0003383103,0.0004256036],"domain_scores_gemma":[0.99960184,0.000006577872,0.000019182486,0.0002937489,0.000017919523,0.000060751816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020588128,0.00027432712,0.00017225137,0.0006218038,0.00008585475,0.0004585749,0.00023446234,0.00006708384,0.0002495511],"category_scores_gemma":[8.323558e-7,0.00019558784,0.00004374825,0.00043781314,0.00002897243,0.0003201707,0.00012907063,0.00011111232,0.00009658155],"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":[0.000018072113,0.000023618006,0.000092222705,0.00028609973,0.00013145416,0.00012691178,0.0015736858,0.9943831,0.000091039714,0.0001867477,0.0018651441,0.0012219255],"study_design_scores_gemma":[0.0002543281,0.000030423167,0.00002314317,0.000092680726,0.00004060433,0.0000015180673,0.000092240734,0.7152521,0.0010401162,0.000037810652,0.2828838,0.00025125986],"about_ca_topic_score_codex":0.000055568777,"about_ca_topic_score_gemma":0.00015574569,"teacher_disagreement_score":0.76684254,"about_ca_system_score_codex":0.00012353163,"about_ca_system_score_gemma":0.0000014214118,"threshold_uncertainty_score":0.79758406},"labels":[],"label_agreement":null},{"id":"W4392290913","doi":"10.1007/s11269-024-03803-1","title":"Developing Extended and Unscented Kalman Filter-Based Neural Networks to Predict Cluster-Induced Roughness in Gravel Bed Rivers","year":2024,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Sediment Transport Processes","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Mean squared error; Mean absolute percentage error; Artificial neural network; Extended Kalman filter; Kalman filter; Correlation coefficient; Statistics; Mathematics; Engineering; Computer science; Artificial intelligence","score_opus":0.012606850804917104,"score_gpt":0.22349443923732273,"score_spread":0.21088758843240563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392290913","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9886463,0.000048434173,0.007479525,0.002086432,0.00018320192,0.00058314984,0.0000023510559,0.00013741804,0.0008331418],"genre_scores_gemma":[0.9976875,0.0000123000755,0.0005361225,0.0012935757,0.00002734499,0.00008200951,0.000035652065,0.000022802837,0.00030265885],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982736,0.00006941348,0.0002782235,0.0006114543,0.00025338412,0.0005139362],"domain_scores_gemma":[0.99965435,0.000020623218,0.000020723397,0.00019209996,0.0000031408529,0.00010906843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030652512,0.00023295911,0.00017886439,0.00016826436,0.00012928343,0.0000870223,0.00028261004,0.00007658232,0.00022335177],"category_scores_gemma":[0.0000017500431,0.00018022227,0.000038607075,0.0003163283,0.00008566611,0.00016687039,0.00026062335,0.00016600857,0.000060043327],"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":[0.0014796599,0.0005501406,0.21182506,0.0016561323,0.00047075425,0.003056671,0.025494993,0.61429775,0.0025707148,0.00039284758,0.0013910431,0.13681424],"study_design_scores_gemma":[0.0037769715,0.0005646641,0.28802946,0.0007271891,0.00023304301,0.000015980671,0.0006284696,0.6097909,0.006470555,0.0008150976,0.08742991,0.0015177209],"about_ca_topic_score_codex":0.00014325038,"about_ca_topic_score_gemma":0.0002636615,"teacher_disagreement_score":0.13529651,"about_ca_system_score_codex":0.000109116176,"about_ca_system_score_gemma":0.000002073252,"threshold_uncertainty_score":0.7349251},"labels":[],"label_agreement":null},{"id":"W4392620822","doi":"10.1007/s11269-024-03811-1","title":"Generalized Structure of Group Method of Data Handling: Novel Technique for Flash Flood Forecasting","year":2024,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Flash flood; Flood myth; Statistics; Upstream (networking); Reliability (semiconductor); Environmental science; Sample (material); Meteorology; Hydrology (agriculture); Computer science; Mathematics; Operations research; Econometrics; Engineering; Geography; Telecommunications; Geotechnical engineering","score_opus":0.04077882566026494,"score_gpt":0.277461666495292,"score_spread":0.23668284083502708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392620822","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18849027,0.00016978022,0.80298907,0.00057366095,0.00024784173,0.0020342532,0.0001838842,0.00011496021,0.0051962966],"genre_scores_gemma":[0.714082,0.000023394026,0.2837652,0.000091962,0.000051750398,0.00008648073,0.0001407918,0.000030082885,0.0017283176],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985193,0.000044812266,0.0003625697,0.0005375399,0.00022417832,0.00031162618],"domain_scores_gemma":[0.9993283,0.00004278898,0.00007376003,0.0005207924,0.0000056007625,0.000028762855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000728722,0.000182426,0.0002655771,0.000118900556,0.000105271814,0.000025870879,0.0006544426,0.000061307815,0.00018257821],"category_scores_gemma":[0.00000745087,0.00012366509,0.00007511175,0.00014548164,0.00013345173,0.00017880875,0.001997248,0.00007638365,0.000008384913],"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":[0.0005629794,0.0004927321,0.0041595018,0.006980709,0.0036063257,0.000069808884,0.0116950385,0.033970222,0.8442942,0.0067734094,0.015167901,0.07222718],"study_design_scores_gemma":[0.0016762663,0.00033212287,0.0005566423,0.0002899643,0.0009325437,0.00001190197,0.0003096875,0.06286409,0.23829518,0.01088956,0.68325114,0.00059088925],"about_ca_topic_score_codex":0.000081365855,"about_ca_topic_score_gemma":0.000042271706,"teacher_disagreement_score":0.66808325,"about_ca_system_score_codex":0.000026013951,"about_ca_system_score_gemma":6.155392e-7,"threshold_uncertainty_score":0.5042916},"labels":[],"label_agreement":null},{"id":"W4392650245","doi":"10.1007/s11269-024-03813-z","title":"D-vine Copula Quantile Regression for a Multidimensional Water Expenditures Analysis: Social and Regional Impacts","year":2024,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Université Mohammed V de Rabat","keywords":"Vine copula; Quantile; Copula (linguistics); Econometrics; Quantile regression; Regression analysis; Flexibility (engineering); Variables; Statistics; Economics; Mathematics","score_opus":0.015083488076816677,"score_gpt":0.2450378057648399,"score_spread":0.22995431768802324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392650245","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97858137,0.0015084094,0.014698839,0.0010692267,0.00039186975,0.0009901732,0.000025673642,0.0009022107,0.0018322059],"genre_scores_gemma":[0.991909,0.00010087764,0.0018754012,0.00011251286,0.00031454678,0.00016036765,0.00073706167,0.00007753771,0.0047126696],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.998318,0.00003400418,0.00032965204,0.00045728416,0.00033887557,0.0005221585],"domain_scores_gemma":[0.9996601,0.000017331597,0.000020411353,0.0001963389,0.000017979291,0.000087883775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000259064,0.0003223742,0.00029217187,0.0006589824,0.00023986402,0.000279307,0.00014853319,0.000089718,0.00014379986],"category_scores_gemma":[8.131144e-7,0.00019246811,0.00020151999,0.0001518328,0.0000505105,0.00020776496,0.0001981528,0.00010660493,0.00004673985],"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":[0.0013106165,0.0004818282,0.0024910015,0.012937988,0.02465298,0.00079316343,0.09336494,0.53251165,0.027190758,0.0094148265,0.26663467,0.028215587],"study_design_scores_gemma":[0.00073481846,0.00005116204,0.0012134708,0.00012503543,0.0009579843,0.0000034709565,0.0003983653,0.2305166,0.004715911,0.00020433836,0.7606077,0.0004711343],"about_ca_topic_score_codex":0.0000166867,"about_ca_topic_score_gemma":0.000014216351,"teacher_disagreement_score":0.49397305,"about_ca_system_score_codex":0.00005578867,"about_ca_system_score_gemma":7.450657e-7,"threshold_uncertainty_score":0.7848622},"labels":[],"label_agreement":null},{"id":"W4392986276","doi":"10.1007/s11269-024-03820-0","title":"Investigating the Impacts of Seasonal Temperature Variations on the Hysteresis Response of Groundwater in the Aquitard in a Plain Reservoir area","year":2024,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Aquifer; Groundwater; Hydrogeology; Thermal diffusivity; Geology; Environmental science; Hydrology (agriculture); Aquifer properties; Soil science; Climate change; Geotechnical engineering; Groundwater recharge","score_opus":0.015119954501005655,"score_gpt":0.2251366234486357,"score_spread":0.21001666894763005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392986276","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.967829,0.000048156682,0.000006454136,0.02796839,0.000040942385,0.0005953508,0.0000042793426,0.000013379606,0.003494059],"genre_scores_gemma":[0.99787307,0.000013096921,0.00003098535,0.0010970305,0.0000126275945,0.0000968135,0.0000044169265,0.000009983294,0.0008620037],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99784535,0.00083904905,0.00031517542,0.00027023483,0.0004146301,0.00031555485],"domain_scores_gemma":[0.9990822,0.00042428097,0.000050078943,0.00042199812,0.0000032439286,0.000018161802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033368538,0.00015792034,0.00014606272,0.00012608887,0.00020809528,0.000072889394,0.0006195378,0.00004342774,0.00014639336],"category_scores_gemma":[0.000047415164,0.00006159595,0.00006332927,0.0004140078,0.00034316385,0.000100195095,0.0006028311,0.00023765258,0.00004314927],"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":[0.001514455,0.0008820279,0.27235705,0.0009901021,0.0010615705,0.00043191254,0.5875624,0.073176384,0.01445549,0.0116209015,0.03486923,0.0010785225],"study_design_scores_gemma":[0.0008018221,0.00043669547,0.930225,0.00076526514,0.00014933012,0.000003931858,0.017206155,0.004313065,0.0034141918,0.01013248,0.03219185,0.00036020376],"about_ca_topic_score_codex":0.0004993234,"about_ca_topic_score_gemma":0.00063117663,"teacher_disagreement_score":0.65786797,"about_ca_system_score_codex":0.00007376502,"about_ca_system_score_gemma":0.0000019647077,"threshold_uncertainty_score":0.251181},"labels":[],"label_agreement":null},{"id":"W4393431291","doi":"10.1007/s11269-023-03732-5","title":"Enhancing Equitable Water Distribution in Agriculture: A Novel Optimal Framework for Irrigation Equity Index Improvement Under Diverse Adaptation Strategies","year":2024,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute on Governance","funders":"Ministry of Science and Technology of the People's Republic of China; Chengdu University of Information Technology","keywords":"Equity (law); Adaptation (eye); Irrigation; Index (typography); Agriculture; Hydrogeology; Water resource management; Distribution (mathematics); Environmental science; Business; Environmental resource management; Agricultural engineering; Natural resource economics; Economics; Mathematics; Geography; Computer science; Agronomy; Engineering; Biology; Political science","score_opus":0.016964044576147707,"score_gpt":0.23688081749720682,"score_spread":0.21991677292105913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393431291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14948301,0.00012370384,0.84694743,0.00017779254,0.00043257483,0.0011307768,0.000024420913,0.000459874,0.0012204237],"genre_scores_gemma":[0.99251324,0.000028247805,0.0047563,0.000042934793,0.00019070132,0.00041171355,0.0011017399,0.00005491206,0.0009002175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787253,0.000020045472,0.0004683309,0.00047037986,0.00038227197,0.000786434],"domain_scores_gemma":[0.9996288,0.000019126868,0.000031171636,0.00022143002,0.000036160603,0.0000633023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049657485,0.00033287914,0.0002093605,0.00025748293,0.00017007356,0.00089942693,0.00024849921,0.00013735093,0.00005905334],"category_scores_gemma":[0.0000024613144,0.00023078726,0.00010360061,0.00024574006,0.00002898788,0.00074435864,0.00036170197,0.00019324312,0.00005327207],"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":[0.00004833151,0.00005631533,0.0000101288515,0.0010980833,0.00017879647,0.000009118641,0.005320547,0.9711933,0.007389368,0.010397551,0.00023682178,0.004061614],"study_design_scores_gemma":[0.001424298,0.00019029216,0.00039558235,0.00065857614,0.00025936725,0.0000014957856,0.012257017,0.87059164,0.04627099,0.016602814,0.050384518,0.0009633917],"about_ca_topic_score_codex":0.00007481689,"about_ca_topic_score_gemma":0.00009072516,"teacher_disagreement_score":0.8430302,"about_ca_system_score_codex":0.00042276003,"about_ca_system_score_gemma":0.000002959388,"threshold_uncertainty_score":0.9411231},"labels":[],"label_agreement":null},{"id":"W4394574276","doi":"10.1007/s11269-024-03844-6","title":"Investigating the Economic Impacts of Inter-Basin Water Transfer Projects with a Water- Embedded Multi - Regional Computable General Equilibrium Approach","year":2024,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computable general equilibrium; Water transfer; Hydrogeology; Structural basin; Water resource management; Environmental science; Economics; Natural resource economics; Geology; Hydrology (agriculture); Microeconomics; Geotechnical engineering; Geomorphology","score_opus":0.018254706084414287,"score_gpt":0.20267555430409362,"score_spread":0.18442084821967933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394574276","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.966586,0.000107561114,0.027210578,0.00033863256,0.00017711996,0.0010687319,0.0000063130424,0.00048251168,0.0040225806],"genre_scores_gemma":[0.99275494,0.00001450418,0.003853663,0.00010337682,0.00017105314,0.00013591652,0.00023528184,0.00012559995,0.0026056664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792534,0.00008401353,0.0004915413,0.0004820614,0.00027294704,0.00074407837],"domain_scores_gemma":[0.99944264,0.000010218556,0.00001789559,0.00041960826,0.00001848636,0.0000911314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041510182,0.0004153375,0.00032525172,0.0003329433,0.00010309677,0.00047651585,0.00047100725,0.00007021313,0.000068952315],"category_scores_gemma":[4.1454953e-7,0.00019223125,0.00012899328,0.000116211166,0.00014711294,0.00033601516,0.00027363712,0.00020483976,0.00008019681],"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":[0.000043842778,0.000043362095,0.0001656276,0.0018814993,0.0006896646,0.000022884642,0.030110262,0.9592729,0.005947808,0.00012035514,0.0010622543,0.0006395431],"study_design_scores_gemma":[0.00084623724,0.0001046403,0.000045623634,0.00024765788,0.00018467348,0.000012891515,0.00076938444,0.885641,0.07250629,0.00006266914,0.03912649,0.000452455],"about_ca_topic_score_codex":0.00012631455,"about_ca_topic_score_gemma":0.000012047997,"teacher_disagreement_score":0.07363191,"about_ca_system_score_codex":0.00013984284,"about_ca_system_score_gemma":0.00000390403,"threshold_uncertainty_score":0.78389627},"labels":[],"label_agreement":null},{"id":"W4398243568","doi":"10.1007/s11269-024-03892-y","title":"Impact of Pressure on the Deterioration of Drinking Water Distribution Networks","year":2024,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Hydrogeology; Environmental science; Distribution (mathematics); Water resource management; Water pressure; Hydrology (agriculture); Environmental engineering; Geology; Geotechnical engineering; Mathematics","score_opus":0.006208640020536222,"score_gpt":0.1923820423803597,"score_spread":0.18617340235982346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398243568","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.943874,0.00020754372,0.052227423,0.000056546654,0.00029606762,0.00038928218,0.000010006889,0.00013427594,0.0028048574],"genre_scores_gemma":[0.9991988,0.00001688823,0.00001631985,0.0000025191753,0.000066755645,0.000020961665,0.00008139982,0.000018066343,0.00057829474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993624,0.000028817645,0.0002152327,0.00010469083,0.00013363549,0.00015519123],"domain_scores_gemma":[0.9997647,0.0000066786524,0.00001534208,0.00018418173,0.000015254329,0.000013889215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018368589,0.000103470506,0.000101782636,0.000055706452,0.000031532138,0.000073915304,0.00010216135,0.00003622555,0.000045173172],"category_scores_gemma":[4.7311224e-7,0.000046657464,0.00007589559,0.000063487874,0.000014294102,0.00007094655,0.000049635713,0.00005615265,0.000010117335],"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":[0.00001136585,0.000008597525,0.00023461942,0.0002765906,0.00023495957,0.0000026554915,0.0016950326,0.9940021,0.0010764571,0.00022984429,0.0012574114,0.00097038044],"study_design_scores_gemma":[0.00017165161,0.00014588753,0.004819774,0.00048094828,0.00013720422,0.0000015132784,0.00007072734,0.9123726,0.02790035,0.00011474487,0.05358958,0.00019500194],"about_ca_topic_score_codex":0.000025193087,"about_ca_topic_score_gemma":0.0000047153862,"teacher_disagreement_score":0.08162947,"about_ca_system_score_codex":0.0000310992,"about_ca_system_score_gemma":3.9414533e-7,"threshold_uncertainty_score":0.19026361},"labels":[],"label_agreement":null},{"id":"W4399505898","doi":"10.1007/s11269-024-03907-8","title":"A New Data-Driven Model to Predict Monthly Runoff at Watershed Scale: Insights from Deep Learning Method Applied in Data-Driven Model","year":2024,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Hebei Province","keywords":"Watershed; Scale (ratio); Hydrogeology; Surface runoff; Hydrological modelling; Hydrology (agriculture); Environmental science; Data mining; Computer science; Machine learning; Geology; Climatology; Cartography; Geography; Geotechnical engineering; Ecology","score_opus":0.035478637529650775,"score_gpt":0.259951244124569,"score_spread":0.22447260659491822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399505898","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81541926,0.00005252567,0.16735807,0.0008044331,0.000097448974,0.000945531,0.0001007408,0.0005012414,0.0147207575],"genre_scores_gemma":[0.77962214,0.000009386986,0.21204975,0.00043337356,0.00008206831,0.00006104612,0.0012230786,0.00007945393,0.0064397003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956316,0.00013755988,0.0005530243,0.0020759478,0.0008076938,0.0007941929],"domain_scores_gemma":[0.9977497,0.000054657554,0.000058831265,0.0018027687,0.0000037436585,0.0003302878],"candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00055308075,0.00043598886,0.00041890348,0.00019848364,0.00022769246,0.0003038758,0.0024849786,0.00014071575,0.00044882923],"category_scores_gemma":[0.000013620168,0.00031010428,0.00005653731,0.000338262,0.00009016685,0.0004355612,0.01186626,0.00040229026,0.0015707359],"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":[0.0001092997,0.000051346797,0.00023007096,0.000034273802,0.000080553946,0.00009824973,0.00819031,0.9716692,0.0035465737,0.000019111636,0.0027083342,0.013262696],"study_design_scores_gemma":[0.00040256736,0.000038910446,0.00031033022,0.000071794726,0.00011770254,0.0000010760829,0.0000576122,0.94713354,0.0002649682,0.0023830214,0.048810527,0.00040795555],"about_ca_topic_score_codex":0.0012160266,"about_ca_topic_score_gemma":0.001138817,"teacher_disagreement_score":0.046102192,"about_ca_system_score_codex":0.00041241208,"about_ca_system_score_gemma":0.000003901924,"threshold_uncertainty_score":0.9999351},"labels":[],"label_agreement":null},{"id":"W4400090304","doi":"10.1007/s11269-024-03921-w","title":"Sustainable Water Resources Management through Disaggregated Multi-Region Virtual Water Flow and Interaction Analysis","year":2024,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","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":"McMaster University; University of Regina","funders":"","keywords":"Virtual water; Water resources; Water scarcity; Environmental science; Inflow; Environmental economics; Hydrogeology; Integrated water resources management; Resource (disambiguation); Computer science; Environmental resource management; Water resource management; Business; Engineering; Economics; Geology","score_opus":0.008485755222858845,"score_gpt":0.20648670337516498,"score_spread":0.19800094815230612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400090304","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8362706,0.00087402953,0.12877515,0.0013270745,0.0008941903,0.0021565359,0.000009213679,0.002930656,0.026762558],"genre_scores_gemma":[0.92722905,0.00051603874,0.0013890564,0.00015925978,0.00019113201,0.00025410845,0.00054027006,0.00017413295,0.069546945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961481,0.00012284984,0.00070194283,0.0010299255,0.0005570041,0.0014401827],"domain_scores_gemma":[0.99903905,0.000015219629,0.000034512334,0.00068522536,0.00004591366,0.00018007457],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00047010148,0.000708069,0.00050882087,0.0014079348,0.00042132725,0.0015572575,0.00047156823,0.0001531791,0.0003489935],"category_scores_gemma":[0.0000017626476,0.000435546,0.000285454,0.0006276628,0.00011689197,0.0010518285,0.0010759123,0.00029790562,0.00043246956],"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":[0.00022275993,0.00019351134,0.00035060925,0.0036552832,0.00944557,0.001902998,0.07122752,0.8917626,0.00043317451,0.0008002847,0.00415012,0.015855532],"study_design_scores_gemma":[0.00087553466,0.00008320684,0.00016535565,0.0001745005,0.0020165262,0.000009135243,0.0057229176,0.3434222,0.0061384616,0.000287663,0.64027435,0.0008301586],"about_ca_topic_score_codex":0.00012777098,"about_ca_topic_score_gemma":0.000028403374,"teacher_disagreement_score":0.6361242,"about_ca_system_score_codex":0.00025626185,"about_ca_system_score_gemma":6.3023197e-7,"threshold_uncertainty_score":0.9998096},"labels":[],"label_agreement":null},{"id":"W4400368299","doi":"10.1007/s11269-024-03923-8","title":"Prediction of Monthly Flow Regimes Using the Distance-Based Method Nested with Model Swapping","year":2024,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nested set model; Hydrogeology; Flow (mathematics); Geology; Environmental science; Computer science; Mathematics; Data mining; Geotechnical engineering; Geometry","score_opus":0.01994126474582528,"score_gpt":0.22351185829832243,"score_spread":0.20357059355249715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400368299","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26749867,0.00016699177,0.69765824,0.002246131,0.00012918869,0.0008230222,0.000013828406,0.00020895446,0.03125497],"genre_scores_gemma":[0.97863084,0.000010888177,0.016911795,0.00022779012,0.000022634445,0.000045421304,0.000011327993,0.00002154403,0.0041177734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876434,0.00008526659,0.00020052705,0.00036416747,0.00030446408,0.0002812254],"domain_scores_gemma":[0.9995962,0.0000205275,0.00004148307,0.00031141035,0.00000437057,0.000026044558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004669348,0.0001660531,0.00014445692,0.00007470411,0.00024075994,0.00005186244,0.0002339145,0.000030936073,0.000086576554],"category_scores_gemma":[0.0000012447668,0.00008470005,0.000056106557,0.00019301118,0.0002133632,0.00013812343,0.00029513007,0.00008887536,0.000026997744],"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":[0.0000681829,0.000029370265,0.0018346786,0.00014949367,0.00019517903,0.000025910653,0.0039288453,0.99129087,0.0002670938,0.00012574649,0.00088391907,0.0012007345],"study_design_scores_gemma":[0.00022380508,0.00004978452,0.00083321007,0.00010216529,0.00019715208,8.3515016e-7,0.0004278356,0.9609762,0.0010223746,0.00065086526,0.035399575,0.00011623787],"about_ca_topic_score_codex":0.00009035931,"about_ca_topic_score_gemma":0.00001964017,"teacher_disagreement_score":0.71113217,"about_ca_system_score_codex":0.00007263801,"about_ca_system_score_gemma":0.0000011906867,"threshold_uncertainty_score":0.34539676},"labels":[],"label_agreement":null},{"id":"W4400592982","doi":"10.1007/s11269-024-03919-4","title":"A Framework for Quantifying Stormwater Control Measures’ Hydrologic Performance with Analytical Stochastic Models","year":2024,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Stormwater; Hydrogeology; Stormwater management; Environmental science; Hydrological modelling; Hydrology (agriculture); Surface runoff; Computer science; Water resource management; Geology; Geotechnical engineering; Climatology","score_opus":0.04392157067256024,"score_gpt":0.24784367375215743,"score_spread":0.20392210307959718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400592982","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26179138,0.000086498854,0.72963023,0.0010893573,0.00017127991,0.0013261314,0.000008923512,0.00036618352,0.0055300053],"genre_scores_gemma":[0.98836756,0.000008527783,0.005603832,0.00040745098,0.000086378794,0.0005533792,0.000015010732,0.000064265594,0.004893586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99721116,0.000055926732,0.00033728208,0.0008309155,0.00066495116,0.00089975196],"domain_scores_gemma":[0.99921685,0.000055481953,0.000040614206,0.0005378108,0.00001056185,0.00013866977],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006073218,0.00035933332,0.00027670653,0.00019762716,0.00034990394,0.00029562207,0.00050840795,0.00008681165,0.0004227705],"category_scores_gemma":[0.000004281643,0.00022118558,0.00013063669,0.00025617215,0.0002371171,0.00046437213,0.0003737646,0.00022953,0.0008581143],"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":[0.0005556987,0.00020895092,0.0031251872,0.00047711187,0.0009320567,0.0001184085,0.0045094187,0.9676375,0.00011473513,0.012771238,0.0034964664,0.006053235],"study_design_scores_gemma":[0.0007788151,0.00036722654,0.0010209995,0.0001789504,0.00057188777,0.000009239853,0.00014673689,0.88642,0.000056075278,0.0064738165,0.10340242,0.0005738191],"about_ca_topic_score_codex":0.00004034633,"about_ca_topic_score_gemma":0.000017306307,"teacher_disagreement_score":0.7265762,"about_ca_system_score_codex":0.0002899,"about_ca_system_score_gemma":0.0000020947002,"threshold_uncertainty_score":0.99991983},"labels":[],"label_agreement":null},{"id":"W4402751381","doi":"10.1007/s11269-024-03985-8","title":"Distributed Nonconvex Optimization for Control of Water Networks with Time-coupling Constraints","year":2024,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; Bristol Water; Imperial College London; Royal Academy of Engineering","keywords":"Convergence (economics); Mathematical optimization; Computer science; Optimization problem; Nonlinear system; Set (abstract data type); Benchmarking; Scale (ratio); Coupling (piping); Hydrogeology; Mathematics; Engineering","score_opus":0.00352064870132687,"score_gpt":0.16487091908563084,"score_spread":0.16135027038430397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402751381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010385185,0.00012610546,0.9871335,0.0000638539,0.00020727653,0.0007367889,0.00003228177,0.00029375937,0.0010212233],"genre_scores_gemma":[0.99616814,0.000011256309,0.002383099,0.000012053221,0.00010029517,0.00010956655,0.00038566306,0.000052998763,0.00077692885],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999096,0.000008820756,0.00028329197,0.00019648585,0.00012402111,0.00029138412],"domain_scores_gemma":[0.99974126,0.000012757131,0.000016740436,0.00015249054,0.00003791922,0.00003880668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018989515,0.0001651335,0.00019643284,0.00010732137,0.000045791283,0.0001245619,0.000100672136,0.000056347082,0.0001095621],"category_scores_gemma":[4.940586e-7,0.00009681026,0.000053196552,0.000070339185,0.000038143422,0.00010133075,0.000026849422,0.000050775998,0.000017420793],"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":[0.000031577292,0.000008322129,0.000026522699,0.0005998443,0.00028490493,0.000009360695,0.00047406196,0.9975032,0.000101509686,0.00003581514,0.00056110864,0.00036376048],"study_design_scores_gemma":[0.00064602884,0.00004779557,0.0000075680346,0.00021188254,0.00011129821,0.0000023042048,0.000047538157,0.9837026,0.0023197131,0.0000071122686,0.012735088,0.00016106814],"about_ca_topic_score_codex":0.0000033840186,"about_ca_topic_score_gemma":0.0000017033736,"teacher_disagreement_score":0.985783,"about_ca_system_score_codex":0.00003833568,"about_ca_system_score_gemma":9.331559e-7,"threshold_uncertainty_score":0.39478078},"labels":[],"label_agreement":null},{"id":"W4404753959","doi":"10.1007/s11269-024-04037-x","title":"Assessing the Impact of Rainfall Nowcasts on an Encoder-Decoder LSTM Model for Short-Term Flash Flood Prediction","year":2024,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Flash flood; Term (time); Flash (photography); Nowcasting; Flood myth; Computer science; Meteorology; Environmental science; Climatology; Geology; Geography","score_opus":0.042608970350337415,"score_gpt":0.3200303655802326,"score_spread":0.27742139522989523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404753959","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9760956,0.000009504566,0.014378008,0.00018481392,0.00010768711,0.0005507711,0.000017066572,0.0001471165,0.008509408],"genre_scores_gemma":[0.9965297,0.000003282686,0.0016117028,0.000087117296,0.000080799044,0.000067076566,0.000028486184,0.000034087483,0.0015577667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998337,0.00007885273,0.00029320185,0.00049498264,0.0003798824,0.00041608547],"domain_scores_gemma":[0.99944067,0.000042640928,0.000036132962,0.00039470426,0.0000063865914,0.00007944632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078706146,0.0002118995,0.00015581903,0.00007402375,0.0002015324,0.00028483174,0.0003690289,0.00006663107,0.00018447357],"category_scores_gemma":[0.000008273055,0.000109625435,0.00016437878,0.00011484545,0.00014197842,0.00029179748,0.00029800317,0.00013848412,0.00008633697],"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":[0.000032779135,0.00013901765,0.0017351778,0.000054486736,0.00008894539,0.000010511184,0.004068634,0.96724015,0.0035254352,0.000021451051,0.00086255046,0.02222087],"study_design_scores_gemma":[0.00015044886,0.00032979535,0.0062506655,0.00007176874,0.00006565026,0.0000037886168,0.000030254072,0.9887709,0.0006157745,0.0010480257,0.0025188995,0.0001440072],"about_ca_topic_score_codex":0.000039724076,"about_ca_topic_score_gemma":0.000012036911,"teacher_disagreement_score":0.022076864,"about_ca_system_score_codex":0.00017844085,"about_ca_system_score_gemma":0.0000024488666,"threshold_uncertainty_score":0.44703954},"labels":[],"label_agreement":null},{"id":"W4404858319","doi":"10.1007/s11269-024-04050-0","title":"Evolution and Periodicity of Watershed-scale Runoff: Insight from the Spatiotemporal Variety in Intensive Anthropogenic Activities Region","year":2024,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Hebei Province","keywords":"Watershed; Surface runoff; Scale (ratio); Environmental science; Hydrogeology; Variety (cybernetics); Hydrology (agriculture); Ecology; Geography; Geology; Cartography; Computer science; Biology","score_opus":0.008655655443094552,"score_gpt":0.19944169063141928,"score_spread":0.19078603518832474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404858319","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9927033,0.00031796558,0.00067657506,0.0034540547,0.00013983267,0.00034057652,0.000003992465,0.00004368023,0.0023199974],"genre_scores_gemma":[0.998654,0.00019419871,0.000068092166,0.00026730314,0.000030524996,0.000030443702,0.000014416148,0.000010922002,0.0007301113],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99882406,0.00011383506,0.00022101244,0.00039211896,0.00019600872,0.0002529364],"domain_scores_gemma":[0.99967736,0.000028400826,0.000034818124,0.0002310129,0.000004225343,0.00002416036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023907605,0.00016210665,0.00017414315,0.00007331785,0.0001837154,0.00005328542,0.00017902459,0.000051409686,0.00015665263],"category_scores_gemma":[0.000002811127,0.00009276418,0.000050861938,0.00012757008,0.00068943744,0.00021529307,0.0008088844,0.00012741193,0.000048172267],"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":[0.0003545167,0.00015042952,0.8131722,0.00027789077,0.0005050282,0.00023592403,0.16564873,0.0022787515,0.002208432,0.0005242715,0.0059153126,0.008728496],"study_design_scores_gemma":[0.00069714553,0.00018037525,0.8382456,0.00017641234,0.00022050687,0.000005471786,0.014070709,0.0046196533,0.004033157,0.004995445,0.13234174,0.00041382725],"about_ca_topic_score_codex":0.0033255438,"about_ca_topic_score_gemma":0.00071336684,"teacher_disagreement_score":0.15157802,"about_ca_system_score_codex":0.00010110621,"about_ca_system_score_gemma":8.327532e-7,"threshold_uncertainty_score":0.50272495},"labels":[],"label_agreement":null},{"id":"W4406934403","doi":"10.1007/s11269-024-04078-2","title":"Reallocating Shared Groundwater Resources Using a Participatory Two- level Weighted Bankruptcy Framework","year":2025,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Hydrogeology; Bankruptcy; Groundwater; Citizen journalism; Groundwater resources; Business; Water resource management; Environmental science; Environmental economics; Environmental resource management; Computer science; Geology; Aquifer; Economics; Finance; Geotechnical engineering","score_opus":0.1381075411341681,"score_gpt":0.385644720885507,"score_spread":0.2475371797513389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406934403","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8474214,0.00022275565,0.11894331,0.001110138,0.00058644556,0.0006237041,0.000008400317,0.00019765645,0.030886175],"genre_scores_gemma":[0.9477393,0.000048988335,0.027948264,0.0011024802,0.00019907345,0.00008066385,0.000022748756,0.000040593583,0.022817908],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9950195,0.0004859134,0.0012449949,0.0010260929,0.0014137882,0.0008097094],"domain_scores_gemma":[0.99781626,0.00024212434,0.00027862674,0.0012972843,0.00021018255,0.00015554218],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0028322728,0.00036849175,0.00046610917,0.00096908247,0.0006999969,0.001292191,0.0012423221,0.00014551486,0.00069017283],"category_scores_gemma":[0.00014059433,0.00024285968,0.00018956492,0.001213363,0.00015234809,0.00044052725,0.00095235376,0.00025621743,0.00035793506],"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":[0.0014457351,0.0015792188,0.21089467,0.00043002793,0.0021570928,0.0007098144,0.14196156,0.17838266,0.0019035616,0.033137295,0.02935348,0.39804488],"study_design_scores_gemma":[0.0015650055,0.0000950513,0.020335149,0.0005345396,0.0003323227,0.000006485431,0.005704531,0.1052477,0.002124169,0.06871359,0.79436934,0.00097213197],"about_ca_topic_score_codex":0.00038465162,"about_ca_topic_score_gemma":0.00004821102,"teacher_disagreement_score":0.76501584,"about_ca_system_score_codex":0.0001243758,"about_ca_system_score_gemma":0.000013286852,"threshold_uncertainty_score":0.99974453},"labels":[],"label_agreement":null},{"id":"W4408663586","doi":"10.1007/s11269-025-04160-3","title":"Laboratory-Validated Model of Air Discharge at User Connections under Intermittent Water Supply","year":2025,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and Optimization","field":"Engineering","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":"Università degli Studi di Perugia","keywords":"Hydrogeology; Environmental science; Water discharge; Water supply; Hydrology (agriculture); Environmental engineering; Water resource management; Engineering; Geotechnical engineering","score_opus":0.006885201086736449,"score_gpt":0.19080753278777504,"score_spread":0.18392233170103858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408663586","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92520094,0.000052608597,0.061809026,0.0006826306,0.0005075107,0.0005438896,0.000043805394,0.00033574886,0.010823869],"genre_scores_gemma":[0.96295094,0.000014976485,0.0003181671,0.00011851145,0.000022132283,0.00008015502,0.00009559435,0.000034171393,0.036365375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99893624,0.000032006013,0.00035383817,0.00022427173,0.0001483966,0.0003052252],"domain_scores_gemma":[0.9995309,0.000003706609,0.000020421243,0.0003568098,0.000045754285,0.00004239943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012302112,0.00018957551,0.00020276099,0.00023691286,0.00009101229,0.000046083707,0.00018926304,0.00006186407,0.00010632119],"category_scores_gemma":[5.437277e-7,0.0001226521,0.00006688342,0.00011157566,0.000027727287,0.00011617321,0.0002582911,0.00006653031,0.000085007734],"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":[0.000018933537,0.000043468695,0.000556853,0.00044323172,0.00032357924,0.0000031138316,0.001974695,0.9739965,0.010649711,0.0010219564,0.010919148,0.00004877727],"study_design_scores_gemma":[0.001211171,0.000031231808,0.0009929454,0.00024027732,0.0001827088,9.911665e-7,0.00053095503,0.2663261,0.46401757,0.00026161727,0.26573226,0.00047217467],"about_ca_topic_score_codex":0.000024790335,"about_ca_topic_score_gemma":0.000060008395,"teacher_disagreement_score":0.70767045,"about_ca_system_score_codex":0.000110233654,"about_ca_system_score_gemma":0.0000014139781,"threshold_uncertainty_score":0.50016075},"labels":[],"label_agreement":null},{"id":"W4409707942","doi":"10.1007/s11269-025-04215-5","title":"Enhancing Rainfall-Runoff Simulation in Data-Poor Watersheds: Integrating Remote Sensing and Hybrid Decomposition for Hydrologic Modelling","year":2025,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"Korea Environmental Industry and Technology Institute; Ministry of Education, India; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Ministry of Environment; National Research Foundation","keywords":"Hydrogeology; Surface runoff; Environmental science; Hydrology (agriculture); Hydrological modelling; Decomposition; Remote sensing; Geology; Geotechnical engineering; Climatology","score_opus":0.01761388160037719,"score_gpt":0.2636367824654965,"score_spread":0.2460229008651193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409707942","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5317031,0.00002793696,0.46395892,0.0011130663,0.00007329977,0.00060660613,0.0000019173597,0.000055715213,0.0024594273],"genre_scores_gemma":[0.9772405,0.000031461466,0.02086882,0.00084022793,0.000022672526,0.000008600005,0.00008282724,0.000013159698,0.00089174486],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982952,0.000091562266,0.00037542166,0.00066150463,0.00013460286,0.00044170886],"domain_scores_gemma":[0.99949634,0.000075460055,0.000059783353,0.00033342748,0.000005607362,0.000029364626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096786453,0.00021066584,0.00022227515,0.00018818585,0.000359558,0.00008193469,0.00025587296,0.0000480111,0.000018364708],"category_scores_gemma":[0.000010975933,0.0001613019,0.000032522174,0.000106914486,0.00009850497,0.00025551344,0.0012332755,0.000107781,0.000020628982],"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":[0.00016441212,0.000028814475,0.0011643979,0.00018522193,0.00011294479,0.00003042729,0.0024439886,0.9646386,0.0012684704,0.00003440813,0.00016520591,0.029763099],"study_design_scores_gemma":[0.00059439306,0.00003115104,0.00014649966,0.00009835374,0.00007870278,8.6139477e-7,0.00032292496,0.97456443,0.0016639122,0.00499205,0.017321838,0.00018485913],"about_ca_topic_score_codex":0.00044746147,"about_ca_topic_score_gemma":0.00033223888,"teacher_disagreement_score":0.44553736,"about_ca_system_score_codex":0.00010365377,"about_ca_system_score_gemma":6.0112694e-7,"threshold_uncertainty_score":0.65777004},"labels":[],"label_agreement":null},{"id":"W4409816973","doi":"10.1007/s11269-025-04207-5","title":"Survey of Wildfire Effects on the Peak Flow Characteristics","year":2025,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Environment and Climate Change Canada","keywords":"Hydrogeology; Environmental science; Hydrology (agriculture); Flow (mathematics); Geology; Physical geography; Geography; Geotechnical engineering; Mathematics","score_opus":0.005560888534341396,"score_gpt":0.1902420765250794,"score_spread":0.18468118799073802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409816973","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97094077,0.000014454533,0.00007504294,0.0005675857,0.00029350942,0.00070040644,0.0000064108626,0.000037640704,0.027364166],"genre_scores_gemma":[0.9940338,0.0000055343717,0.00002975187,0.00053947186,0.000018148085,0.00006195217,0.000014493549,0.000013679202,0.0052831615],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985227,0.0003463735,0.00023429928,0.0002897606,0.0003321465,0.00027470654],"domain_scores_gemma":[0.9991153,0.00019640232,0.000064800384,0.00058676896,0.0000038057967,0.00003295682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000988839,0.00016149423,0.00019135719,0.000053421416,0.00011937319,0.00004596744,0.00050513714,0.0000367277,0.00019112304],"category_scores_gemma":[0.000039002276,0.00008961346,0.00005072619,0.00019833422,0.0000814836,0.000036259782,0.0004957023,0.00009240125,0.0007221553],"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":[0.00039705756,0.000680347,0.51303,0.0016796022,0.0006977536,0.00013385747,0.0047178906,0.00093267736,0.004476866,0.00033183664,0.06313577,0.4097863],"study_design_scores_gemma":[0.00021927767,0.00008164112,0.91381997,0.00014632578,0.00002801167,2.2960583e-7,0.000019767003,0.0040286984,0.0043308577,0.0000348466,0.07716915,0.00012124039],"about_ca_topic_score_codex":0.00082405924,"about_ca_topic_score_gemma":0.00015307426,"teacher_disagreement_score":0.40966508,"about_ca_system_score_codex":0.00008592279,"about_ca_system_score_gemma":7.2773366e-7,"threshold_uncertainty_score":0.92820865},"labels":[],"label_agreement":null},{"id":"W4413041311","doi":"10.1007/s11269-025-04223-5","title":"Linking Water Technologies with Water Practices: Case Studies from 11 Countries","year":2025,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of Queensland","keywords":"Business; China; Water supply; Water resources; Developing country; Natural resource economics; Emerging technologies; Resource (disambiguation); Water use; Homogeneous; Water scarcity; Environmental planning; Environmental science; Environmental resource management; Economic growth; Environmental engineering; Geography; Economics","score_opus":0.015749215715359147,"score_gpt":0.23936890497356828,"score_spread":0.22361968925820913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413041311","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95200807,0.00044770187,0.000258364,0.008730626,0.0002528116,0.0005537667,0.00001297553,0.00065388373,0.0370818],"genre_scores_gemma":[0.9840686,0.00010465077,0.0011033303,0.0007130119,0.000042216943,0.00028442338,0.000026612957,0.000038056183,0.01361911],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970631,0.000102391394,0.00042301082,0.0009242797,0.0005291048,0.00095809577],"domain_scores_gemma":[0.9989663,0.000059577953,0.00011038978,0.0007942424,0.000027955477,0.000041539515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039933002,0.00048060354,0.0004308682,0.00019079375,0.00083929696,0.00022979392,0.000607645,0.00010377714,0.00025058212],"category_scores_gemma":[0.000009876616,0.00021628305,0.00006907002,0.00014524993,0.00073739653,0.00043279093,0.0040177386,0.0001774404,0.0005417211],"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":[0.0038371147,0.0029059513,0.2211773,0.0051924433,0.028149683,0.11220808,0.42502326,0.019447858,0.035260312,0.017256716,0.058243837,0.07129747],"study_design_scores_gemma":[0.000818273,0.00013800382,0.00021472556,0.00020445618,0.00046065293,0.000069645874,0.029366197,0.000051552914,0.23842946,0.01902051,0.7106212,0.00060534006],"about_ca_topic_score_codex":0.0016570464,"about_ca_topic_score_gemma":0.0031084497,"teacher_disagreement_score":0.65237737,"about_ca_system_score_codex":0.0002567779,"about_ca_system_score_gemma":0.0000011601328,"threshold_uncertainty_score":0.8819767},"labels":[],"label_agreement":null},{"id":"W600162158","doi":"10.1007/s11269-015-1046-3","title":"A Real-time Flood Monitoring Index Based on Daily Effective Precipitation and its Application to Brisbane and Lockyer Valley Flood Events","year":2015,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Flood myth; Flood warning; Precipitation; Return period; Hydrology (agriculture); Environmental science; Geography; Meteorology; Geology; Geotechnical engineering","score_opus":0.00622995848006249,"score_gpt":0.22531211702538403,"score_spread":0.21908215854532154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W600162158","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9865778,0.000013946293,0.0017501301,0.0004287574,0.00007656367,0.0021193442,0.0000018742521,0.00008804875,0.008943526],"genre_scores_gemma":[0.99575514,0.000025781617,0.0012355043,0.0001052732,0.000057662324,0.00051756256,0.000015277035,0.000028262157,0.0022595255],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981984,0.00010604741,0.00019981313,0.00060192996,0.0005659171,0.00032787453],"domain_scores_gemma":[0.9994173,0.000019779112,0.00005950537,0.00028661898,0.000012157188,0.00020463423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005155963,0.00022970983,0.00016462579,0.00014008771,0.00013191008,0.00008055464,0.00018662587,0.000046620054,0.000034290035],"category_scores_gemma":[0.000006018607,0.0001849242,0.000025032852,0.00015203744,0.000023868566,0.00018206438,0.00054496154,0.00007131769,0.00045027758],"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":[0.00183883,0.0019895814,0.35188842,0.0006701295,0.0006607892,0.00008132175,0.028951067,0.30987036,0.021235043,0.00018100589,0.005594802,0.27703866],"study_design_scores_gemma":[0.0041241287,0.0011304196,0.88959026,0.00015892074,0.0002274291,0.0000012884678,0.000828075,0.06964767,0.0070832693,0.00050063024,0.025941735,0.0007661839],"about_ca_topic_score_codex":0.00029612993,"about_ca_topic_score_gemma":0.00001973837,"teacher_disagreement_score":0.53770185,"about_ca_system_score_codex":0.00018743008,"about_ca_system_score_gemma":0.0000011174924,"threshold_uncertainty_score":0.754099},"labels":[],"label_agreement":null},{"id":"W68706397","doi":"10.1007/s11269-014-0752-6","title":"Leak Size, Detectability and Test Conditions in Pressurized Pipe Systems","year":2014,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Università degli Studi di Perugia; Ministero dell’Istruzione, dell’Università e della Ricerca","keywords":"Leak; Leak detection; Benchmarking; Steady state (chemistry); Transient (computer programming); Hydrogeology; Environmental science; Nuclear engineering; Computer science; Reliability engineering; Mechanics; Engineering; Chemistry; Geotechnical engineering; Physics; Environmental engineering","score_opus":0.003506955429397738,"score_gpt":0.16398003555666973,"score_spread":0.160473080127272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W68706397","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97769743,0.00012549179,0.003097928,0.00005453373,0.00024173992,0.00068081747,0.0000073410147,0.00028470118,0.017810024],"genre_scores_gemma":[0.99743956,0.0000112457,0.00011503965,0.000010978367,0.0000485879,0.00011481585,0.000009185452,0.000020888725,0.0022296838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991908,0.00005208061,0.00024366469,0.00018788016,0.00010878114,0.0002168046],"domain_scores_gemma":[0.9996475,0.000054978453,0.000017043712,0.00022381566,0.000012090512,0.000044575485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026117772,0.00012491229,0.00016425627,0.000086210726,0.00004997337,0.00011841302,0.000093190974,0.000041983763,0.000019474872],"category_scores_gemma":[0.000011825527,0.00009598545,0.000018995375,0.00006447107,0.0000229887,0.00009283129,0.000067081106,0.000057084766,0.000025027846],"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":[0.000032156582,0.00018068316,0.051745933,0.0053466964,0.00021541891,0.00003394482,0.008683341,0.9239887,0.004763547,0.00062486745,0.0030637227,0.0013209716],"study_design_scores_gemma":[0.003101027,0.00012779557,0.1072143,0.00039350727,0.00009831747,0.000010275709,0.000597398,0.4527197,0.0041996543,0.00033193873,0.43034288,0.0008632171],"about_ca_topic_score_codex":0.000095087446,"about_ca_topic_score_gemma":0.00016774575,"teacher_disagreement_score":0.47126904,"about_ca_system_score_codex":0.000029856707,"about_ca_system_score_gemma":2.7653277e-7,"threshold_uncertainty_score":0.39141732},"labels":[],"label_agreement":null},{"id":"W7117448537","doi":"10.1007/s11269-025-04425-x","title":"Ensemble Machine Learning-Based Feature Selection for Flood Susceptibility Mapping Under Climate and Land Use Change Scenarios","year":2025,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Random forest; Feature selection; Flood myth; Climate change; Robustness (evolution); Land cover; Land use; Feature (linguistics); Redundancy (engineering)","score_opus":0.016415310894309636,"score_gpt":0.23255940762345553,"score_spread":0.2161440967291459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117448537","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9713463,0.00008481303,0.01994604,0.0032907932,0.00015847178,0.0024549,0.000008009723,0.00021274423,0.0024979103],"genre_scores_gemma":[0.98612773,0.00007639877,0.0033861804,0.0009477227,0.000035390054,0.00028093724,0.00007940028,0.0000220515,0.009044181],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99849343,0.0000818183,0.00017775346,0.00055518985,0.00021633464,0.00047548657],"domain_scores_gemma":[0.99962884,0.00002574566,0.000053999924,0.00022557769,0.000008406765,0.000057421064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046194484,0.00022180534,0.00017667928,0.00013786304,0.00042857495,0.00020760803,0.0001537339,0.000063626896,0.00012035292],"category_scores_gemma":[0.0000043039327,0.00016697959,0.000065188084,0.00018588529,0.000060152353,0.00020156406,0.00050874596,0.00013356475,0.000029516787],"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":[0.0002815531,0.00037088036,0.9483957,0.00086331664,0.0002219458,0.0000075050425,0.0011702365,0.026631523,0.0013659346,0.00019431331,0.0028600513,0.017637016],"study_design_scores_gemma":[0.0023443466,0.00022752139,0.28383717,0.00009058395,0.0002242896,6.5459693e-7,0.0003200222,0.14259236,0.000990893,0.00035028896,0.56858516,0.00043675033],"about_ca_topic_score_codex":0.0007918576,"about_ca_topic_score_gemma":0.0025117374,"teacher_disagreement_score":0.6645586,"about_ca_system_score_codex":0.00014361495,"about_ca_system_score_gemma":9.2501375e-7,"threshold_uncertainty_score":0.680923},"labels":[],"label_agreement":null},{"id":"W7117459661","doi":"10.1007/s11269-025-04437-7","title":"A Novel Index for Integrative Drought Assessment in Agricultural Reservoirs","year":2025,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Hydrometeorology; Water resources; Agriculture; Watershed; Hydrology (agriculture); Precipitation; Climate change; Index (typography); Resource (disambiguation)","score_opus":0.007014598569546431,"score_gpt":0.2459772466036237,"score_spread":0.23896264803407727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117459661","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7592561,0.000016281443,0.019841278,0.0044230055,0.00008909861,0.0007509254,0.0000039657066,0.00004155984,0.21557774],"genre_scores_gemma":[0.96231204,0.0000049363607,0.0023399962,0.0005356689,0.000015352272,0.00030265254,0.000027515542,0.0000059656522,0.034455888],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986964,0.000055059652,0.0002649704,0.00042468714,0.00019036517,0.00036855912],"domain_scores_gemma":[0.99964833,0.000028448758,0.00003958862,0.000240769,0.0000063506245,0.000036510894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003873404,0.00016894664,0.00020047334,0.00013870778,0.00014851692,0.000049227616,0.00037037503,0.000068492474,0.00036308833],"category_scores_gemma":[0.0000055891296,0.00009964762,0.00010075072,0.00038260315,0.00010503796,0.00013198223,0.0005117676,0.0001488505,0.00007139256],"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":[0.0011238091,0.0034848258,0.6020083,0.00049813837,0.0024955065,0.00015661342,0.038832672,0.22294986,0.016152313,0.025277913,0.060292542,0.02672753],"study_design_scores_gemma":[0.0030511648,0.00013922447,0.5064097,0.00010249915,0.00020663095,0.0000016521002,0.005232157,0.021209866,0.002144846,0.010346899,0.45059627,0.00055911706],"about_ca_topic_score_codex":0.00048285598,"about_ca_topic_score_gemma":0.001901487,"teacher_disagreement_score":0.39030373,"about_ca_system_score_codex":0.0002442096,"about_ca_system_score_gemma":0.0000015572275,"threshold_uncertainty_score":0.4063512},"labels":[],"label_agreement":null}]}