{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":1105,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":1105,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"aec7402c8395","filters":{"topic":"Hydrology and Drought Analysis"}},"results":[{"id":"W2070337735","doi":"10.1016/s0022-1694(01)00594-7","title":"Power of the Mann–Kendall and Spearman's rho tests for detecting monotonic trends in hydrological series","year":2002,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":2072,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Statistics; Series (stratigraphy); Sample size determination; Skewness; Negative binomial distribution; Mathematics; Magnitude (astronomy); Monte Carlo method; Rank correlation; Statistical power; Percentile; Geology; Poisson distribution","retraction":null,"screen_n_in":null,"score":{"opus":0.01382803257843311,"gpt":0.2423900461355246,"spread":0.2285620135570915,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005223252,0.0001095075,0.0003212719,0.0001218793,0.00007878632,0.000005321041,0.0002377904,0.0001478916,0.001479112],"category_scores_gemma":[0.00009095124,0.00007087962,0.0001516563,0.0002438492,0.0003691035,0.0001451661,0.0001151611,0.0002773595,0.000007700774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003502469,"about_ca_system_score_gemma":0.000003085414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002599406,"about_ca_topic_score_gemma":0.0003714476,"domain_scores_codex":[0.9988913,0.0001346189,0.0004397254,0.0001565278,0.0001248964,0.0002529596],"domain_scores_gemma":[0.9993209,0.0001416074,0.0003430267,0.0001402972,0.000008952895,0.00004523244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006695685,0.000487828,0.9302777,0.00001189399,0.0001624313,0.0001714536,0.001918612,0.03045323,0.02563397,0.0002301306,0.001820522,0.008162654],"study_design_scores_gemma":[0.00430394,0.004287442,0.8783894,0.00002489927,0.0003187114,0.003398803,0.0001707146,0.05862591,0.00700232,0.02301341,0.01995732,0.0005071334],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936478,0.0002879122,0.00002335629,0.002775135,0.00008029855,0.0000499013,0.000001129334,0.000003438562,0.003130994],"genre_scores_gemma":[0.9989965,0.00004753948,0.0003054241,0.0002358152,0.00003507625,0.000002495149,1.417158e-7,0.000006329023,0.0003706756],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0518883,"threshold_uncertainty_score":0.9994337,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2129161539","doi":"10.1002/hyp.1095","title":"The influence of autocorrelation on the ability to detect trend in hydrological series","year":2002,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":2065,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Ministry of the Environment, Conservation and Parks; Environment and Climate Change Canada","funders":"","keywords":"Autocorrelation; Statistics; Series (stratigraphy); Autoregressive model; Statistic; Lag; Correlation; Trend analysis; Mathematics; Econometrics; Computer science; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.01699694926263079,"gpt":0.2300238143684853,"spread":0.2130268651058545,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007077905,0.0001569612,0.0002044049,0.00002910332,0.000302687,0.00001923088,0.0004949747,0.0001586311,0.001506075],"category_scores_gemma":[0.002031022,0.00007272275,0.00005946591,0.0008871504,0.0008687489,0.0001480731,0.0001626132,0.0002919163,0.0003784134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003854657,"about_ca_system_score_gemma":0.000004233176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003901052,"about_ca_topic_score_gemma":0.001009832,"domain_scores_codex":[0.9984004,0.0002602589,0.0003484495,0.0003857276,0.0002778748,0.0003272676],"domain_scores_gemma":[0.9983474,0.001141567,0.0001033748,0.0003332449,0.00001042245,0.00006401003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000517946,0.0004306422,0.2192398,0.00002165893,0.00002314769,0.00002352998,0.001182354,0.7655553,0.00499887,0.001086784,0.0009436043,0.005976377],"study_design_scores_gemma":[0.0005339073,0.002755407,0.8575381,0.00002638314,0.00005783956,0.00002805256,0.00008926551,0.02923037,0.004064798,0.0898935,0.01520168,0.0005806941],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9871677,0.00005667512,0.00002765807,0.007292546,0.00001231986,0.0002759653,0.000001790239,0.00003761951,0.005127703],"genre_scores_gemma":[0.998741,0.00004748049,0.00006616435,0.0008706108,0.00001220454,0.0001262726,7.010819e-7,0.000003980037,0.0001315558],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7363249,"threshold_uncertainty_score":0.9994067,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2098280243","doi":"10.1061/(asce)1084-0699(2007)12:4(347)","title":"Everything You Always Wanted to Know about Copula Modeling but Were Afraid to Ask","year":2007,"lang":"en","type":"article","venue":"Journal of Hydrologic Engineering","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":1617,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval; GDG Environnement; Institut National de la Recherche Scientifique","funders":"","keywords":"Copula (linguistics); Computer science; Ask price; Inference; Goodness of fit; Econometrics; Data mining; Mathematics; Machine learning; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.007275876483697187,"gpt":0.221739811612525,"spread":0.2144639351288278,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001728784,0.0002325525,0.000422661,0.0002875545,0.0001248296,0.00002921622,0.0004455475,0.0001759478,0.0003897614],"category_scores_gemma":[0.0003242081,0.0001978397,0.0001989921,0.0004919819,0.00002405667,0.0002818051,0.0001850947,0.000526341,0.0002321011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002195232,"about_ca_system_score_gemma":0.00001060567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005339395,"about_ca_topic_score_gemma":0.00003336761,"domain_scores_codex":[0.9980001,0.00003078481,0.0006881992,0.0002648116,0.0004173473,0.0005986915],"domain_scores_gemma":[0.9991004,0.00009183583,0.0001518108,0.0002237373,0.00002785818,0.0004042962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006023337,0.00003048388,0.005128993,0.000004477281,0.00004575395,0.0002187039,0.0004109607,0.9530121,0.03994041,0.00001384792,0.0002797982,0.0008542278],"study_design_scores_gemma":[0.0005392333,0.0004859154,0.003720596,0.000115542,0.0001296677,0.0004608225,0.0001561829,0.9775709,0.005574788,0.0002038052,0.01050801,0.0005345274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7648075,0.0002413009,0.2329503,0.000565826,0.0002803342,0.00007262236,0.000001234218,0.00004188096,0.001038945],"genre_scores_gemma":[0.9850043,0.00003638987,0.013778,0.0007469333,0.0002142907,0.000001955301,9.295975e-7,0.00002149163,0.000195704],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2201968,"threshold_uncertainty_score":0.8067668,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2171932819","doi":"10.1139/a11-013","title":"A review of drought indices","year":2011,"lang":"en","type":"review","venue":"Environmental Reviews","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":1333,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Environmental science; Climate change; Geography; Ecology; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.03828069343761698,"gpt":0.2992282135927478,"spread":0.2609475201551308,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001193515,0.0006950946,0.003778979,0.000083644,0.0000861215,0.000005054055,0.0009803852,0.0003556588,0.03238903],"category_scores_gemma":[0.00007084126,0.00049292,0.001780275,0.0004779965,0.0005302096,0.0001816248,0.0005435959,0.000477475,0.02376005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002139805,"about_ca_system_score_gemma":0.00001598793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003319999,"about_ca_topic_score_gemma":0.000008773787,"domain_scores_codex":[0.9959302,0.0007610244,0.00183087,0.000783262,0.0002869155,0.0004077186],"domain_scores_gemma":[0.9969111,0.00007814297,0.001707828,0.001125553,2.641926e-7,0.0001770766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[8.427965e-7,0.0001218721,0.00006055194,0.02813132,0.00008718154,0.000005413633,0.00002872711,1.576174e-8,4.531714e-7,0.0000041319,0.01369495,0.9578645],"study_design_scores_gemma":[0.00004437457,0.00004556182,0.000008136689,0.053431,0.002269197,0.00003570917,0.000001163359,2.892131e-7,9.662479e-7,0.0000274849,0.9437262,0.0004098897],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[8.632691e-7,0.9744751,0.00001177543,0.000007549513,0.00008807999,0.001319327,0.00002664411,0.00001627658,0.02405442],"genre_scores_gemma":[6.407612e-7,0.9961755,0.0004363112,0.0006715324,0.00005200621,0.0003057187,0.0002176398,0.00005452477,0.002086172],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9574547,"threshold_uncertainty_score":0.9997522,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1650177443","doi":"10.1029/2001wr000861","title":"Applicability of prewhitening to eliminate the influence of serial correlation on the Mann‐Kendall test","year":2002,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":864,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Autocorrelation; Autoregressive model; Statistics; Series (stratigraphy); Correlation; Mathematics; Null hypothesis; Null (SQL); Magnitude (astronomy); Sample size determination; Econometrics; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.02909005901394387,"gpt":0.2767385460376581,"spread":0.2476484870237143,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002075734,0.00007477917,0.0001153054,0.0000537099,0.0002744346,0.00001756495,0.0005894393,0.00006257427,0.001884152],"category_scores_gemma":[0.00031676,0.0000344104,0.00004884761,0.0004155944,0.000722059,0.00005545011,0.000395857,0.0002889351,0.0007195829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003551156,"about_ca_system_score_gemma":0.000001256584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00048632,"about_ca_topic_score_gemma":0.00009195315,"domain_scores_codex":[0.9982043,0.0003996346,0.0002237572,0.0002435217,0.0006242061,0.0003046],"domain_scores_gemma":[0.9987599,0.0006114405,0.00004128244,0.0005061136,0.00003094312,0.00005035146],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003786774,0.000376942,0.3398314,0.00003048684,0.00004425132,0.000004809228,0.04588897,0.4324456,0.1746295,0.00008618365,0.001550601,0.004732542],"study_design_scores_gemma":[0.0006299504,0.001558595,0.6918807,0.00006586252,0.00005511845,0.000006565416,0.001320039,0.06977395,0.1759672,0.002265128,0.05611824,0.0003586082],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988403,0.000005952945,0.00001413261,0.002708033,0.000006507778,0.0003142241,0.000003827764,0.000006635334,0.008537644],"genre_scores_gemma":[0.998167,0.000003486692,0.00002516729,0.00008494365,0.00002086261,0.00004158686,0.000001036757,0.000005597954,0.001650272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3626717,"threshold_uncertainty_score":0.9990283,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1624356652","doi":"10.1029/2003wr002456","title":"Multivariate hydrological frequency analysis using copulas","year":2004,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":703,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Hydro-Québec; Institut National de la Recherche Scientifique","funders":"","keywords":"Copula (linguistics); Multivariate statistics; Watershed; Range (aeronautics); Hydrology (agriculture); Marginal distribution; Frequency analysis; Econometrics; Multivariate analysis; Tail dependence; Mathematics; Statistics; Environmental science; Computer science; Geology; Random variable; Geotechnical engineering; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.05830340208704678,"gpt":0.3418552559602142,"spread":0.2835518538731674,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001709912,0.0001626306,0.0003033228,0.0003698592,0.0006583543,0.00007647349,0.0005826498,0.000206585,0.006162105],"category_scores_gemma":[0.00007227188,0.0001041091,0.000218161,0.001655886,0.0007911373,0.0001666405,0.0005948381,0.0005193428,0.003490519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002690647,"about_ca_system_score_gemma":0.000006725375,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01266979,"about_ca_topic_score_gemma":0.0009875514,"domain_scores_codex":[0.9967493,0.0005380387,0.0002882229,0.0006335278,0.0008459113,0.0009450054],"domain_scores_gemma":[0.9991544,0.00006340697,0.00003194665,0.000507831,0.000021935,0.0002204857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001162268,0.000355914,0.4365791,0.000006327605,0.0007120343,0.0004255934,0.008260466,0.4278816,0.1251045,0.0001318212,0.00005394136,0.000372516],"study_design_scores_gemma":[0.006846458,0.001699485,0.3710578,0.00004905894,0.002940276,0.0001615216,0.001301566,0.2752351,0.1336555,0.1481808,0.05549346,0.003379055],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907231,0.0000352696,0.001007174,0.0006121479,0.00001715561,0.0001305637,0.000002870627,0.00007195547,0.007399721],"genre_scores_gemma":[0.9973859,0.000005211432,0.001733113,0.000111434,0.00005685076,0.00001472329,0.00001401814,0.00001763162,0.0006610953],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1526465,"threshold_uncertainty_score":0.9972854,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2597689547","doi":"10.1007/s00382-017-3580-6","title":"Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables","year":2017,"lang":"en","type":"article","venue":"Climate Dynamics","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":668,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"Environment and Climate Change Canada","funders":"Environment and Climate Change Canada","keywords":"Univariate; Quantile; Multivariate statistics; Projection (relational algebra); Statistics; Climate model; Mathematics; Climate change; Algorithm; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.04379571741281445,"gpt":0.2770954280104966,"spread":0.2332997105976822,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0007495761,0.0001768036,0.0002941551,0.00004558425,0.001387789,0.0000419563,0.0002212512,0.0001841139,0.0001257884],"category_scores_gemma":[0.0001960186,0.0001705214,0.0001521959,0.0000936843,0.000287038,0.0006405127,0.0001291645,0.0001241695,0.00001646059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000146533,"about_ca_system_score_gemma":0.00001809235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000549732,"about_ca_topic_score_gemma":0.006620792,"domain_scores_codex":[0.9985552,0.00005873979,0.0004116279,0.0004415994,0.0001691585,0.0003636433],"domain_scores_gemma":[0.9987617,0.0001911665,0.0003239536,0.000578404,0.00005837514,0.00008647137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002747931,0.0002650063,0.1614337,0.00004162872,0.00003311274,4.436579e-7,0.0001849334,0.8338332,0.002018848,0.0007239585,0.00001109006,0.001179365],"study_design_scores_gemma":[0.0005889039,0.00007334319,0.06448182,0.00001235583,0.0001113316,0.000001859917,0.000039284,0.926431,0.0001984565,0.007870365,0.00002256848,0.0001687336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8254365,0.000001662795,0.1728046,0.00007708238,0.0002908242,0.0003707426,0.0003080445,0.00005097977,0.0006596287],"genre_scores_gemma":[0.9868364,0.000007187627,0.01264528,0.00003541552,0.00002138458,0.00002471926,0.0003535781,0.00001638004,0.00005960239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1614,"threshold_uncertainty_score":0.9999123,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1978954330","doi":"10.1198/0003130043277","title":"Bootstrap Methods for Developing Predictive Models","year":2004,"lang":"en","type":"article","venue":"The American Statistician","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":620,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; Institute for Clinical Evaluative Sciences; Sunnybrook Health Science Centre","funders":"","keywords":"Resampling; Feature selection; Model selection; Computer science; Statistics; Predictive modelling; Selection (genetic algorithm); Bootstrap aggregating; Variables; Machine learning; Econometrics; Artificial intelligence; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.03567676071956769,"gpt":0.3619012094975819,"spread":0.3262244487780143,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003553103,0.0001005357,0.0001815439,0.00002000263,0.0002474012,0.0000114421,0.0002238543,0.00001691408,0.0000697531],"category_scores_gemma":[0.0000680084,0.000071624,0.00004860015,0.0002776659,0.000711503,0.00007993823,0.00005986656,0.0000768824,0.00006205683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001201646,"about_ca_system_score_gemma":0.00002497679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001382654,"about_ca_topic_score_gemma":0.0002855738,"domain_scores_codex":[0.9991741,0.0001124416,0.0001448554,0.0002119835,0.00009485089,0.0002618085],"domain_scores_gemma":[0.9993488,0.000294053,0.0001205277,0.000182587,0.000007601126,0.00004644114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003372935,0.0001069763,0.001961472,0.000009118893,0.0003765118,0.00001325365,0.006941964,0.4291057,0.002251995,0.08609217,0.002073311,0.4707302],"study_design_scores_gemma":[0.0004403414,0.0003614759,0.0214465,0.000007424278,0.0002247071,0.000009034681,0.0008415794,0.1293499,0.001227286,0.8417782,0.003972767,0.0003407157],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0213008,0.00001497221,0.9730555,0.00115404,0.00003043284,0.0001637885,0.00002848215,0.00003192077,0.004220013],"genre_scores_gemma":[0.5878313,0.000009874397,0.410531,0.001470619,0.00002203521,0.00004431793,0.000005883106,0.000008767274,0.00007624539],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7556861,"threshold_uncertainty_score":0.2920742,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2137848529","doi":"10.1061/(asce)1084-0699(2006)11:2(150)","title":"Bivariate Flood Frequency Analysis Using the Copula Method","year":2006,"lang":"en","type":"article","venue":"Journal of Hydrologic Engineering","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":528,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Copula (linguistics); Bivariate analysis; Gumbel distribution; Flood myth; Marginal distribution; Statistics; Mathematics; Joint probability distribution; Hydrology (agriculture); Environmental science; Econometrics; Extreme value theory; Geology; Random variable; Geography; Geotechnical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.007560802340943761,"gpt":0.2352492715439167,"spread":0.2276884692029729,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001235906,0.0001486545,0.0003622282,0.0001908328,0.0001216652,0.00002470748,0.0003477991,0.0001042962,0.0008351281],"category_scores_gemma":[0.00007113515,0.00009275677,0.0003662579,0.001119291,0.0000527362,0.0001828129,0.00006900771,0.0003232367,0.00002320405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001049983,"about_ca_system_score_gemma":0.000007586581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004226092,"about_ca_topic_score_gemma":0.00005373279,"domain_scores_codex":[0.9986892,0.0001197969,0.0004928244,0.0001505342,0.0002737093,0.0002739481],"domain_scores_gemma":[0.9992871,0.0001339521,0.0002982631,0.0002089484,0.00001459828,0.00005716222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004525541,0.00002506537,0.05398331,0.000001228327,0.0003109615,0.00006258034,0.00003724806,0.925056,0.02028711,0.0001338779,0.00004842758,0.00004961857],"study_design_scores_gemma":[0.000266588,0.00007725835,0.03650863,0.000004029895,0.002100948,0.0002533463,0.00001642733,0.9564075,0.001335294,0.002241136,0.0005911843,0.0001976191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7815869,0.0002827893,0.2169434,0.0002316915,0.0001050597,0.00003220799,9.053046e-7,0.00002461975,0.00079246],"genre_scores_gemma":[0.9613416,0.00001091826,0.038383,0.00009497235,0.0001213189,9.441295e-7,8.65364e-7,0.00001078277,0.00003563254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1797547,"threshold_uncertainty_score":0.9144067,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2409717638","doi":"10.5194/hess-20-3631-2016","title":"Drought in a human-modified world: reframing drought definitions,understanding, and analysis approaches","year":2016,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":498,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Natural Environment Research Council; Sight Research UK","keywords":"Anthropocene; Water scarcity; Environmental science; Vulnerability (computing); Environmental resource management; Scarcity; Water resources; Cognitive reframing; Confusion; Ecology; Computer science; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.07418866965879552,"gpt":0.2548088791713914,"spread":0.1806202095125959,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001596028,0.0001974214,0.0004397774,0.0005936244,0.0008408376,0.00005527924,0.000217526,0.0001636221,0.000302977],"category_scores_gemma":[0.00002661776,0.0001335904,0.00008024727,0.001850517,0.002000764,0.0004179107,0.0001566923,0.0001261625,0.00004897334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006017418,"about_ca_system_score_gemma":0.00001051944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003326273,"about_ca_topic_score_gemma":0.007248141,"domain_scores_codex":[0.9977727,0.0003323892,0.000392492,0.0007691115,0.0002343899,0.000498927],"domain_scores_gemma":[0.9992126,0.000290421,0.0001527454,0.0002191942,0.00000331255,0.0001217733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002600411,0.00004750233,0.9135948,0.00001836861,0.0001366514,0.0000367877,0.0008308204,0.00200529,0.0004591607,0.08208942,0.00003002857,0.000725146],"study_design_scores_gemma":[0.005230119,0.001081381,0.5680467,0.0003269396,0.002296557,0.0004733377,0.006971741,0.3084484,0.0007357839,0.1014139,0.002239698,0.002735441],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9431323,0.0002675857,0.004965435,0.0008416427,0.00004328484,0.0001231052,0.000005415094,0.00004755164,0.05057365],"genre_scores_gemma":[0.998704,0.00004616979,0.000450698,0.0001175509,0.00002140421,0.00002503095,0.000003574847,0.000005104558,0.0006265359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3455481,"threshold_uncertainty_score":0.7371897,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2028257469","doi":"10.1016/j.jhydrol.2006.03.004","title":"Frequency analysis of a sequence of dependent and/or non-stationary hydro-meteorological observations: A review","year":2006,"lang":"en","type":"review","venue":"Journal of Hydrology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":469,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Institut National de la Recherche Scientifique; Ouranos","funders":"","keywords":"Frequency analysis; Quantile; Independence (probability theory); Econometrics; Wavelet; Kernel density estimation; Flood myth; Series (stratigraphy); Computer science; Context (archaeology); Statistics; Probability density function; Mathematics; Geography; Geology; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.06306844916843606,"gpt":0.3372464986632326,"spread":0.2741780494947965,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001730632,0.0003670841,0.004089926,0.000646658,0.00006387545,0.000004641197,0.0006724186,0.0004874304,0.001185528],"category_scores_gemma":[0.0003965409,0.000241416,0.001247396,0.002102071,0.0006313121,0.0002086626,0.0001843853,0.0005759762,0.00001661327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001252277,"about_ca_system_score_gemma":0.0001599955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002228375,"about_ca_topic_score_gemma":0.0002697207,"domain_scores_codex":[0.9953094,0.0007567579,0.002731462,0.0004020611,0.0005055614,0.0002947172],"domain_scores_gemma":[0.9947099,0.0007978947,0.003867412,0.0004232365,0.00008332127,0.0001182345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005152759,0.004045158,0.143626,0.05048287,0.04804677,0.004328341,0.0005797975,0.02758396,0.0009143115,0.0008197076,0.008383056,0.7106747],"study_design_scores_gemma":[0.002643305,0.006711089,0.02137825,0.02047526,0.3039027,0.006600731,0.00003472316,0.01010727,0.0000205628,0.009504553,0.6157911,0.002830493],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.007226576,0.9914704,0.0003775138,0.0002051441,0.00005853403,0.0002873922,0.00008016873,0.000006456568,0.0002878042],"genre_scores_gemma":[0.004748164,0.9921116,0.002566705,0.0003302686,0.0000347369,0.00002139357,0.00007692236,0.00001856152,0.00009164227],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7078443,"threshold_uncertainty_score":0.9997275,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1987213677","doi":"10.1016/j.jhydrol.2013.10.052","title":"Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models","year":2013,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":468,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Support vector machine; Autoregressive integrated moving average; Wavelet; Computer science; Term (time); Mean squared error; Regression; Data mining; Artificial intelligence; Machine learning; Statistics; Mathematics; Time series","retraction":null,"screen_n_in":null,"score":{"opus":0.02596509696072136,"gpt":0.2554579024287879,"spread":0.2294928054680666,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001348018,0.000188822,0.0004443854,0.0001324705,0.0001336974,0.00002742578,0.0003357776,0.0002393498,0.0006609909],"category_scores_gemma":[0.00005822011,0.000122378,0.0001045232,0.0003464035,0.0003290689,0.0006117094,0.0001730529,0.0007645713,0.00001773634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008923889,"about_ca_system_score_gemma":0.00001979584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004093334,"about_ca_topic_score_gemma":0.0007074412,"domain_scores_codex":[0.9978229,0.0005189314,0.000637359,0.0002474039,0.0002601415,0.00051327],"domain_scores_gemma":[0.9990678,0.0002356245,0.0004059492,0.0001855352,0.00001451006,0.00009054577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001079462,0.00009300368,0.8751687,0.000007705816,0.00002952615,0.0008740636,0.001950351,0.1171035,0.0006940656,0.00002699412,0.0006524819,0.003291661],"study_design_scores_gemma":[0.0008648306,0.0002569609,0.428623,0.00003672881,0.00005684505,0.001658844,0.00003530397,0.5633323,0.0000395764,0.004870708,0.00006048041,0.0001644743],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969119,0.0001149894,0.0003567857,0.00192426,0.0001431958,0.0001348987,5.890689e-7,0.000003519531,0.0004098886],"genre_scores_gemma":[0.9969549,0.00005175011,0.001325119,0.001437499,0.0001725937,0.000003406046,0.000002071151,0.00001206813,0.00004059072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4465457,"threshold_uncertainty_score":0.7237388,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1970969255","doi":"10.1175/2008jamc1979.1","title":"Development and Testing of Canada-Wide Interpolated Spatial Models of Daily Minimum–Maximum Temperature and Precipitation for 1961–2003","year":2008,"lang":"en","type":"article","venue":"Journal of Applied Meteorology and Climatology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":440,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Environment and Climate Change Canada; Natural Resources Canada; Canadian Forest Service","funders":"","keywords":"Precipitation; Environmental science; Interpolation (computer graphics); Snow; Multivariate interpolation; Mean radiant temperature; Longitude; Smoothing; Latitude; Mean squared error; Climatology; Spatial variability; Mathematics; Statistics; Meteorology; Geodesy; Climate change; Computer science; Geology; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.01180383016294999,"gpt":0.2089584970424434,"spread":0.1971546668794935,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003773992,0.0001168052,0.0004868035,0.00007599339,0.0001294822,0.000001779756,0.00007599503,0.0001945594,0.00001879797],"category_scores_gemma":[0.0001387171,0.00009735361,0.00002158602,0.000119815,0.0003978025,0.0000686256,0.00005799886,0.0001594758,1.385236e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002063615,"about_ca_system_score_gemma":0.0001255803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007292434,"about_ca_topic_score_gemma":0.009802104,"domain_scores_codex":[0.9989411,0.00004994954,0.0005617324,0.0001663723,0.00009288023,0.0001879651],"domain_scores_gemma":[0.998951,0.0003701726,0.0004824145,0.00006132567,0.00005759336,0.00007754963],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007268395,0.00040372,0.7456061,0.0003042277,0.00133453,0.0001397077,0.01619757,0.00518937,0.2051024,0.002728621,0.001743891,0.01398152],"study_design_scores_gemma":[0.02099663,0.005145813,0.6228295,0.0001408699,0.002276829,0.00725452,0.00438812,0.07116178,0.2011202,0.05761635,0.005278812,0.001790564],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979334,0.0002100538,0.001043443,0.0001806081,0.00005110224,0.0001123723,0.000004020317,0.000001985478,0.000463011],"genre_scores_gemma":[0.9790713,0.00005849589,0.02063816,0.0002007112,0.00000821456,0.000005662916,0.000003012606,0.000005465765,0.000009024734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1227766,"threshold_uncertainty_score":0.5469804,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3136807982","doi":"10.1002/wat2.1520","title":"Challenges in modeling and predicting floods and droughts: A review","year":2021,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Water","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":437,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Canmore Museum and Geoscience Centre; University of Saskatchewan","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Flood myth; Temporal scales; Process (computing); Warning system; Climate change; Computer science; Human systems engineering; Scale (ratio); Environmental resource management; Stakeholder engagement; Risk analysis (engineering); Environmental science; Geography; Business; Ecology","retraction":null,"screen_n_in":null,"score":{"opus":0.08231490697962998,"gpt":0.3496334617960412,"spread":0.2673185548164112,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002061493,0.0006924963,0.003653847,0.0001231676,0.0001813787,0.00003010854,0.0003653834,0.0003662345,0.0009793442],"category_scores_gemma":[0.00005330419,0.0004161373,0.0005271249,0.0002798582,0.0001798496,0.0003295526,0.003220441,0.0007226231,0.0003428713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001205913,"about_ca_system_score_gemma":0.00001188068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000120381,"about_ca_topic_score_gemma":0.0002089782,"domain_scores_codex":[0.9954482,0.000981422,0.001529622,0.001305211,0.0001917941,0.0005437227],"domain_scores_gemma":[0.9987879,0.00007526035,0.0002561904,0.0007108503,0.000007338189,0.0001624319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001476656,0.00004636882,0.00005338837,0.03627293,0.00007978969,0.000097748,0.0008001889,0.00001415762,2.511289e-7,0.000003619523,0.0005587224,0.9620714],"study_design_scores_gemma":[0.00008244957,0.00003779499,0.000001174635,0.1592759,0.001137252,0.0004091394,0.00003265553,0.0008620382,1.280825e-7,0.0002615072,0.837431,0.0004689619],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00005210728,0.9947269,0.00002476839,0.0006764128,0.0001006563,0.001032143,0.000005579748,0.00002593855,0.003355495],"genre_scores_gemma":[0.00004200853,0.9986591,0.0002130199,0.000171253,0.0001067409,0.0004276153,0.0001006941,0.00004995492,0.0002296624],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9616024,"threshold_uncertainty_score":0.9999339,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2108719872","doi":"10.1029/2005wr004545","title":"Generalized maximum likelihood estimators for the nonstationary generalized extreme value model","year":2007,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":427,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Ouranos; Environment and Climate Change Canada; Institut National de la Recherche Scientifique","funders":"","keywords":"Covariate; Quantile; Estimator; Generalized extreme value distribution; Statistics; Generalized linear model; Estimation theory; Mathematics; Maximum likelihood; Scale parameter; Restricted maximum likelihood; Extreme value theory; Econometrics; Applied mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.0587021527822258,"gpt":0.3290394790977633,"spread":0.2703373263155375,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004410648,0.0001746134,0.0001960288,0.0001611289,0.00109674,0.00008050297,0.0007166524,0.0001465199,0.001561657],"category_scores_gemma":[0.00007981022,0.00009921178,0.0001665193,0.0003907308,0.0006383296,0.0001434412,0.0004263417,0.0003162972,0.0006464078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001271874,"about_ca_system_score_gemma":0.00001420759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009740356,"about_ca_topic_score_gemma":0.0003686284,"domain_scores_codex":[0.9969223,0.0002651838,0.0003361971,0.0004896562,0.0008887491,0.001097915],"domain_scores_gemma":[0.9988094,0.000397713,0.00003724129,0.0005137487,0.00004376925,0.0001981427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002450766,0.0005033455,0.0451771,0.00005050812,0.0004951667,0.000128063,0.02252203,0.7331259,0.1239028,0.002633091,0.03877432,0.03023694],"study_design_scores_gemma":[0.001053179,0.00008286084,0.001361658,0.000004501203,0.00005207394,0.00001060643,0.0001628444,0.8472756,0.01208023,0.04885407,0.08880883,0.0002535919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9198173,0.0001944126,0.07080815,0.002804063,0.00004831281,0.0005746929,0.00001104462,0.00004967955,0.005692294],"genre_scores_gemma":[0.9713074,0.00004285189,0.02116628,0.0006306174,0.0001616112,0.0001845888,0.00004567027,0.00004213671,0.006418846],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1141496,"threshold_uncertainty_score":0.999351,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2155802078","doi":"10.1623/hysj.49.1.21.53996","title":"A comparison of the power of the <i>t</i> test, Mann-Kendall and bootstrap tests for trend detection / Une comparaison de la puissance des tests <i>t</i> de Student, de Mann-Kendall et du bootstrap pour la détection de tendance","year":2004,"lang":"fr","type":"article","venue":"Hydrological Sciences Journal","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":424,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Statistics; Mathematics; Gumbel distribution; Series (stratigraphy); Weibull distribution; Type I and type II errors; Extreme value theory; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.03758232570830809,"gpt":0.3544457557316829,"spread":0.3168634300233747,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.004941186,0.0003598263,0.0005281234,0.00008777415,0.001425255,0.0001711482,0.00107686,0.0004197357,0.0001225574],"category_scores_gemma":[0.0006559733,0.0002299359,0.0003842969,0.001020494,0.006760151,0.0003845654,0.0002318839,0.001007294,0.000003369568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000426838,"about_ca_system_score_gemma":0.0002189587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001149815,"about_ca_topic_score_gemma":0.005178689,"domain_scores_codex":[0.9957128,0.001388192,0.0007520759,0.00052703,0.0006161639,0.001003767],"domain_scores_gemma":[0.997008,0.001579613,0.0008449415,0.0002488322,0.00004216453,0.0002764652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009860143,0.001056131,0.7641137,0.00002470835,0.00004064018,0.00001738524,0.00176325,0.1335884,0.09694009,0.0007404156,0.0001392012,0.001477486],"study_design_scores_gemma":[0.001203153,0.001109424,0.9089137,0.0001561055,0.0002264095,0.00231599,0.0004024504,0.01728806,0.02235227,0.0439451,0.001828791,0.0002585955],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9786128,0.001864733,0.009839678,0.007052983,0.0001594686,0.0002752893,0.00002671286,0.00002259638,0.002145716],"genre_scores_gemma":[0.994574,0.0004982518,0.004038909,0.0006095147,0.0001070125,0.00002050883,3.751497e-7,0.00001414262,0.0001372668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1448,"threshold_uncertainty_score":0.9998748,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2402979397","doi":"10.5194/hess-21-1397-2017","title":"The European 2015 drought from a climatological perspective","year":2017,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":409,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Geopotential height; Peninsula; Climatology; Evapotranspiration; Anomaly (physics); Precipitation; Period (music); Atmospheric circulation; Environmental science; Geography; Physical geography; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.01568059261555106,"gpt":0.2573874187152672,"spread":0.2417068260997162,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.001904521,0.0001272175,0.0001994971,0.00001849788,0.004906212,0.0001894279,0.0007830101,0.0000904879,0.0002123912],"category_scores_gemma":[0.000116919,0.00007218084,0.00005984371,0.00007723519,0.004252397,0.0002678431,0.0004121407,0.0001538929,0.001180784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001378973,"about_ca_system_score_gemma":0.000008627029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001439356,"about_ca_topic_score_gemma":0.001846767,"domain_scores_codex":[0.998191,0.0005551677,0.0001862357,0.0005062909,0.0001892724,0.0003720289],"domain_scores_gemma":[0.9990751,0.0002130881,0.0001838308,0.0004223469,0.000007729921,0.00009796675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006509538,0.00004455132,0.9606471,0.000003335849,0.00008788297,0.000201,0.001568812,0.0004391947,0.0002616793,0.03061223,0.001682889,0.004386201],"study_design_scores_gemma":[0.0007019553,0.0003500456,0.9167218,0.00002027994,0.0001185861,0.0002688548,0.003666385,0.03979242,0.00009518914,0.01448329,0.02331815,0.0004630547],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7062497,0.0004342419,0.00007214706,0.003957425,0.0001959771,0.00006850357,0.000002986625,0.00003506315,0.2889839],"genre_scores_gemma":[0.9990658,0.0000600049,0.0001344282,0.000171356,0.00008744164,0.000005482111,6.850044e-7,0.000003338942,0.0004714747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2928161,"threshold_uncertainty_score":0.9995969,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3121430079","doi":"10.1029/2019rg000683","title":"Anthropogenic Drought: Definition, Challenges, and Opportunities","year":2021,"lang":"en","type":"article","venue":"Reviews of Geophysics","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":368,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal; Concordia University","funders":"National Marine Fisheries Service; Division of Civil, Mechanical and Manufacturing Innovation; National Aeronautics and Space Administration; National Oceanic and Atmospheric Administration; National Science Foundation","keywords":"Environmental science; Water resources; Environmental resource management; Climate change; Water balance; Natural (archaeology); Environmental planning; Ecology; Geography; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.08136603126582892,"gpt":0.2667276976568483,"spread":0.1853616663910194,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001945734,0.00009673026,0.0003243247,0.00001039009,0.00006528278,0.000004114359,0.00007567543,0.00004596763,0.001448617],"category_scores_gemma":[0.00002714248,0.00008623301,0.0001077971,0.00009937522,0.0002508163,0.0001242327,0.0001107607,0.0000611619,0.000256567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001050091,"about_ca_system_score_gemma":0.00001037835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001768785,"about_ca_topic_score_gemma":0.00003863905,"domain_scores_codex":[0.9992191,0.0001197743,0.0002355945,0.0001980155,0.0001041623,0.000123394],"domain_scores_gemma":[0.9995322,0.00003276329,0.0001099719,0.0002624658,0.00001148647,0.00005111974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001201859,0.000427095,0.003759941,0.0005182125,0.0001747753,0.00009404045,0.002011398,0.00001851814,0.002995693,0.07246447,0.002431564,0.9150923],"study_design_scores_gemma":[0.0004053324,0.0001298782,0.0249948,0.00013252,0.0004653628,0.00006553088,0.0004092053,0.0001276396,0.001997271,0.07729949,0.8934318,0.0005412021],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"review","genre_scores_codex":[0.4493605,0.3673526,0.0003479688,0.003817379,0.0001438289,0.0002797502,0.00001963216,0.00004272288,0.1786356],"genre_scores_gemma":[0.3659495,0.6318565,0.001079394,0.0005352679,0.00004464992,0.00001110686,0.00002114849,0.000008895104,0.0004935947],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.9145511,"threshold_uncertainty_score":0.9994642,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2161007207","doi":"10.2166/wst.2013.251","title":"Impacts of climate change on rainfall extremes and urban drainage systems: a review","year":2013,"lang":"en","type":"review","venue":"Water Science & Technology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":337,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Climate change; Environmental science; Drainage; Climatology; Hydrology (agriculture); Geology; Ecology; Oceanography; Geotechnical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02773997645357129,"gpt":0.2836263615477433,"spread":0.2558863850941721,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001416475,0.0004319028,0.001692103,0.0008429721,0.0002455177,0.0000386257,0.001302132,0.0004571032,0.0003421898],"category_scores_gemma":[0.0001042522,0.0002480598,0.0001818527,0.001915589,0.002965493,0.0003567534,0.001158337,0.0003736247,0.001052481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001574721,"about_ca_system_score_gemma":0.00002579539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001364834,"about_ca_topic_score_gemma":0.00001889201,"domain_scores_codex":[0.9969946,0.0001277395,0.0006721895,0.0009278567,0.0003782036,0.0008994616],"domain_scores_gemma":[0.9983886,0.00003744973,0.0003970026,0.001013431,0.0000213013,0.0001421847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002437828,0.00009317837,0.0008078385,0.01791589,0.0000530409,0.00005579434,0.0003756233,0.000001026829,0.0001335798,0.001886272,0.0006168621,0.9780585],"study_design_scores_gemma":[0.00008166067,0.0002288893,0.00001191034,0.01232959,0.0004435229,0.0001372589,0.00001954393,0.00004481512,0.00005882011,0.0002710778,0.9859408,0.0004320693],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0004070802,0.9934938,0.000005837463,0.0004388996,0.0001079804,0.001194624,0.00001030035,0.00009557125,0.004245942],"genre_scores_gemma":[0.004770389,0.9944002,0.00007438722,0.0002067344,0.00002372045,0.0003543451,0.000007640344,0.00002003612,0.0001425431],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.985324,"threshold_uncertainty_score":0.9999971,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2054655850","doi":"10.1175/2010jamc2376.1","title":"New Techniques for the Detection and Adjustment of Shifts in Daily Precipitation Data Series","year":2010,"lang":"en","type":"article","venue":"Journal of Applied Meteorology and Climatology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":323,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"York University; Environment and Climate Change Canada","funders":"","keywords":"Series (stratigraphy); Precipitation; Algorithm; Computer science; Gaussian; Time series; Mathematics; Statistics; Meteorology; Geology; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.01048077493526945,"gpt":0.2552729786600729,"spread":0.2447922037248034,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008151206,0.0000694171,0.0002390491,0.00006267699,0.00005884001,0.000002880529,0.0001745493,0.0001735824,0.00007415225],"category_scores_gemma":[0.00006634912,0.00004690954,0.00002186823,0.00007700502,0.0003210053,0.0001402933,0.0001093355,0.0002224182,8.574062e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005327618,"about_ca_system_score_gemma":0.00001165582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003043377,"about_ca_topic_score_gemma":0.002062258,"domain_scores_codex":[0.9993444,0.00004027386,0.000307531,0.0001393078,0.00005249551,0.0001159629],"domain_scores_gemma":[0.9992113,0.00032606,0.0002634925,0.0001566397,0.000008463427,0.00003406862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.004769316,0.0001675604,0.07001448,0.00004446934,0.0003014941,0.000008318541,0.002175555,0.0001187434,0.2916955,0.01448967,0.000648557,0.6155663],"study_design_scores_gemma":[0.004120335,0.002436736,0.7189018,0.00001173188,0.001260073,0.0009641785,0.001361754,0.003537546,0.0514519,0.1872501,0.02831182,0.0003920851],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989567,0.0003841059,0.007950573,0.001323699,0.0001452005,0.0001838059,0.00000300755,0.000004230848,0.000438323],"genre_scores_gemma":[0.9863915,0.0004550942,0.01294765,0.0001427502,0.00004132312,0.000009503018,0.000001932603,0.000003481159,0.000006723435],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6488873,"threshold_uncertainty_score":0.1912915,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1968113320","doi":"10.1002/joc.931","title":"Drought indices and their application to East Africa","year":2003,"lang":"en","type":"article","venue":"International Journal of Climatology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":279,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"University of East Anglia; U.S. Department of Commerce","keywords":"Precipitation; Climatology; Index (typography); Flood myth; Scale (ratio); Environmental science; Surface runoff; Geography; Meteorology; Computer science; Cartography; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.007808258442509557,"gpt":0.2469254599696608,"spread":0.2391172015271512,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002893357,0.00006630321,0.0001444057,0.00009586713,0.00003925474,0.00001324009,0.0002374058,0.00005718519,0.0005801221],"category_scores_gemma":[0.00009053673,0.00005031779,0.00004874945,0.0001037108,0.00009739515,0.0001363684,0.00005708446,0.00009925999,0.0001587729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003437036,"about_ca_system_score_gemma":0.000008525477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008248941,"about_ca_topic_score_gemma":0.00004209428,"domain_scores_codex":[0.9993103,0.00006988361,0.0002563084,0.0001089814,0.0001448986,0.0001096896],"domain_scores_gemma":[0.9995642,0.00007126061,0.0001922256,0.00006489608,0.00003092036,0.00007652584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002174371,0.0003001593,0.9421357,0.000003547779,0.0003767678,0.0001058818,0.004795988,0.001973637,0.0108427,0.01790741,0.003910164,0.0174306],"study_design_scores_gemma":[0.001715711,0.0003913884,0.07624107,0.0000225953,0.0001036998,0.004833521,0.001177003,0.001979647,0.006364358,0.06044273,0.8462963,0.000432004],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9588343,0.0001309731,0.01800859,0.003952459,0.0002337528,0.00004157161,0.000002612354,0.000005322907,0.01879041],"genre_scores_gemma":[0.998073,0.00003230727,0.001236093,0.0005482318,0.00003513776,0.000002753807,9.201106e-7,0.000003628276,0.00006788121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8658946,"threshold_uncertainty_score":0.635193,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2044337727","doi":"10.1080/02626660209493019","title":"Challenges in drought research: some perspectives and future directions","year":2002,"lang":"en","type":"article","venue":"Hydrological Sciences Journal","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":276,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Lakehead University","funders":"","keywords":"Duration (music); Linear regression; Regression; Variety (cybernetics); Environmental science; Economic shortage; Regression analysis; Water resources; Agriculture; Computer science; Environmental resource management; Climatology; Statistics; Geography; Mathematics; Ecology","retraction":null,"screen_n_in":null,"score":{"opus":0.1249241480688898,"gpt":0.3270172518226852,"spread":0.2020931037537953,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002627192,0.0001112118,0.0001738833,0.0001732519,0.001086529,0.00008344205,0.0003618143,0.0001420761,0.006557918],"category_scores_gemma":[0.0001438699,0.00007247666,0.00006123682,0.0008520059,0.002026911,0.000602679,0.0001806712,0.0007555552,0.0002603517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009092777,"about_ca_system_score_gemma":0.000004550601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002927182,"about_ca_topic_score_gemma":0.0002433565,"domain_scores_codex":[0.9977265,0.0005269327,0.000197309,0.0004689932,0.0005206954,0.0005595574],"domain_scores_gemma":[0.9994435,0.0001886879,0.00005359383,0.0001094463,0.00000953579,0.0001952808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001213757,0.002561425,0.3875066,0.000008964162,0.0000919579,0.001215997,0.0442382,0.004470308,0.001317584,0.04893726,0.00617432,0.503356],"study_design_scores_gemma":[0.0008724873,0.001768929,0.4930839,0.00001593339,0.00002761159,0.001769998,0.00852087,0.03003201,0.00003024912,0.1642807,0.2990147,0.0005826032],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8531877,0.02213801,0.000002900958,0.03978268,0.00009207044,0.00005541526,3.248632e-7,0.00001970239,0.08472123],"genre_scores_gemma":[0.9488324,0.05012351,0.0002573476,0.0001748848,0.0003630549,0.000005976381,6.067345e-8,0.000002679994,0.0002401198],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5027734,"threshold_uncertainty_score":0.9943502,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1969074413","doi":"10.1002/met.145","title":"Copula‐based drought severity‐duration‐frequency analysis in Iran","year":2009,"lang":"en","type":"article","venue":"Meteorological Applications","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":271,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Copula (linguistics); Univariate; Natural hazard; Joint probability distribution; Climatology; Statistics; Environmental science; Multivariate statistics; Physical geography; Econometrics; Mathematics; Geography; Meteorology; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.01541902447476346,"gpt":0.259831232854294,"spread":0.2444122083795306,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004061706,0.0001588566,0.0003160682,0.0001488801,0.000203006,0.00002086313,0.0003593881,0.0001860518,0.003528223],"category_scores_gemma":[0.00004508736,0.0001333244,0.0001964461,0.002981946,0.0001858361,0.0001300079,0.00003748648,0.0002067596,0.0008848598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001018697,"about_ca_system_score_gemma":0.000005811451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001712527,"about_ca_topic_score_gemma":0.0009875655,"domain_scores_codex":[0.9983777,0.0001545228,0.0004155212,0.00052139,0.0002168672,0.0003139832],"domain_scores_gemma":[0.9992118,0.00009742202,0.0001021116,0.0004676921,0.000007529386,0.0001134184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004009288,0.001127141,0.9415387,0.000002144451,0.0001223644,0.00001982584,0.0001038994,0.03095376,0.006409341,0.008318817,0.0005101531,0.01085376],"study_design_scores_gemma":[0.0002965278,0.00009263842,0.9193817,6.224874e-7,0.0003415323,0.000001799516,0.000008435292,0.02797369,0.0003019784,0.04538644,0.005953744,0.0002608219],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.762295,0.00008411914,0.1929508,0.008596473,0.00001505658,0.0006398821,0.00001911907,0.0002945848,0.03510491],"genre_scores_gemma":[0.9844518,0.000007924016,0.01236045,0.002665619,0.00002148187,0.0002082335,0.00009156168,0.000004915643,0.0001879997],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2221568,"threshold_uncertainty_score":0.9998931,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1649253606","doi":"10.1029/2006wr005275","title":"Metaelliptical copulas and their use in frequency analysis of multivariate hydrological data","year":2007,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":271,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"Ministry of Education, Recreation and Sports; Institut National de la Recherche Scientifique; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Copula (linguistics); Multivariate statistics; Goodness of fit; Statistics; Econometrics; Flood myth; Multivariate analysis; Hydrology (agriculture); Mathematics; Computer science; Geology; Geography; Geotechnical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.1079611552798857,"gpt":0.3553240683180759,"spread":0.2473629130381902,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005773907,0.0001237099,0.0003825936,0.0005089695,0.0001227177,0.00003642507,0.0006927436,0.0001678673,0.001139098],"category_scores_gemma":[0.0002451209,0.0000705803,0.00007041138,0.0013853,0.001021816,0.0002322094,0.001339229,0.0004192457,0.00009425233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000416556,"about_ca_system_score_gemma":0.000002388216,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01007728,"about_ca_topic_score_gemma":0.01198376,"domain_scores_codex":[0.9973078,0.0005939969,0.0003834325,0.0006108601,0.0004582351,0.0006456154],"domain_scores_gemma":[0.9983961,0.0006247699,0.00003065705,0.0007807996,0.0000146862,0.0001530095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001528348,0.0001922807,0.9635941,0.00000410167,0.0003556992,0.00007721426,0.003515468,0.001114968,0.03004788,0.00007586812,0.00002365316,0.0008459442],"study_design_scores_gemma":[0.0005474402,0.0001780488,0.8592379,0.000005952353,0.0002784036,0.000005711033,0.0003300098,0.1212677,0.006724466,0.002342914,0.008818754,0.0002627405],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976568,0.00006517316,0.0003661549,0.0001788167,0.000006117025,0.000107774,0.00001434924,0.00001404903,0.00159076],"genre_scores_gemma":[0.9988651,0.00002045334,0.0007695064,0.00004267572,0.00001270489,0.00000342592,0.00005360289,0.000008770314,0.0002237942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1201527,"threshold_uncertainty_score":0.999774,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1997127262","doi":"10.1061/(asce)wr.1943-5452.0000023","title":"Design Criteria of Urban Drainage Infrastructures under Climate Change","year":2009,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":247,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Climate change; Context (archaeology); Drainage; Flooding (psychology); Environmental science; Greenhouse gas; Computer science; Extreme weather; Adaptation (eye); Environmental resource management; Risk analysis (engineering); Business; Geography; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.01796378792433712,"gpt":0.249166499961205,"spread":0.2312027120368678,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004921125,0.0001013709,0.0001957369,0.000118338,0.00009261058,0.00002851167,0.0001516098,0.00004132455,0.0002149342],"category_scores_gemma":[0.000002201448,0.00006409187,0.00005193212,0.00006775494,0.00007282954,0.0001517548,0.0000936791,0.00008676854,0.000005550234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001583163,"about_ca_system_score_gemma":3.677911e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009541753,"about_ca_topic_score_gemma":5.772783e-7,"domain_scores_codex":[0.9991516,0.00007604541,0.0002693876,0.0001115511,0.0001856124,0.0002057472],"domain_scores_gemma":[0.999674,0.00001339898,0.0001530314,0.00009392969,0.000005338413,0.00006033849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00477239,0.001051686,0.2521265,0.0006393344,0.002329513,0.003847318,0.2818213,0.240315,0.06171216,0.001750184,0.05913695,0.09049769],"study_design_scores_gemma":[0.003037179,0.002675684,0.8811949,0.0004645533,0.00109314,0.0002956065,0.002937725,0.005692289,0.009287644,0.02535978,0.06714389,0.0008175416],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9890724,0.0003131989,0.003771954,0.0007098799,0.00005007016,0.00008734521,8.594363e-7,0.000008617228,0.005985617],"genre_scores_gemma":[0.9967536,0.00006746896,0.002405548,0.0005911309,0.00005750178,0.000001406733,0.000001002739,0.000004307754,0.0001180634],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6290686,"threshold_uncertainty_score":0.261359,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2919575991","doi":"10.1029/2018ef001091","title":"Adaptation to Future Water Shortages in the United States Caused by Population Growth and Climate Change","year":2019,"lang":"en","type":"article","venue":"Earth s Future","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":245,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Stantec (Canada)","funders":"Rocky Mountain Research Station; U.S. Army Corps of Engineers; U.S. Forest Service; U.S. Department of Energy; Department of Water Resources; Colorado State University","keywords":"Economic shortage; Climate change; Population growth; Water scarcity; Natural resource economics; Adaptation (eye); Agriculture; Population; Environmental science; Water resource management; Business; Environmental resource management; Geography; Economics; Ecology","retraction":null,"screen_n_in":null,"score":{"opus":0.007161359115776361,"gpt":0.2088142188048771,"spread":0.2016528596891008,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001799437,0.00009107351,0.00008964474,0.00005098536,0.00008523528,0.00002790472,0.00008026794,0.00008588069,0.0004985192],"category_scores_gemma":[0.00000154522,0.00005268052,0.00001825909,0.0002553888,0.00001691327,0.0002204766,0.00003412915,0.0001131525,0.0001848472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006828701,"about_ca_system_score_gemma":5.3756e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001020066,"about_ca_topic_score_gemma":0.0040003,"domain_scores_codex":[0.999289,0.00009698673,0.00009505855,0.0001881046,0.0001369798,0.0001938816],"domain_scores_gemma":[0.9998083,0.00001360617,0.00002093338,0.0001191199,0.000002970277,0.00003511464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004223714,0.00003241608,0.9612402,0.00001204549,0.00001124668,0.000006852745,0.03093372,0.00252104,0.001726024,0.000137732,0.0009558433,0.002380612],"study_design_scores_gemma":[0.000206922,0.00006666467,0.9684742,0.000003981238,0.0000170804,0.000001415888,0.003005142,0.003918638,0.0001980406,0.0001188639,0.02385497,0.0001340787],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927205,0.00005802272,0.00001434579,0.006721146,0.00008835475,0.0002209507,0.00001065368,0.00001412033,0.0001518547],"genre_scores_gemma":[0.996525,0.0001415624,0.0000560596,0.002652233,0.0001392118,0.00001489784,0.0004094646,0.000005710901,0.00005588686],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02792858,"threshold_uncertainty_score":0.5458436,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2015643136","doi":"10.1007/s00477-007-0194-2","title":"Drought characterization: a probabilistic approach","year":2007,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":245,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Probabilistic logic; Probability density function; Interval (graph theory); Joint probability distribution; Precipitation; Mathematics; Statistics; Series (stratigraphy); Probability distribution; Computational intelligence; Climatology; Econometrics; Environmental science; Computer science; Meteorology; Geography; Geology; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.01715356543102776,"gpt":0.3063217614230577,"spread":0.28916819599203,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002426648,0.0002034425,0.0002117704,0.0001107133,0.0007694935,0.00005389876,0.0002364051,0.0001279735,0.001437918],"category_scores_gemma":[0.00005970404,0.0001694984,0.0000541967,0.0003371396,0.001259857,0.0002228714,0.0004651509,0.0005995557,0.0003160437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003923183,"about_ca_system_score_gemma":0.00001666948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001228367,"about_ca_topic_score_gemma":0.00006978786,"domain_scores_codex":[0.9971545,0.0001956066,0.0003114124,0.0006556267,0.0009212727,0.0007615448],"domain_scores_gemma":[0.9989401,0.0002513942,0.00008946619,0.0003487827,0.000005573518,0.0003646968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004387565,0.003977417,0.8829283,0.00004736617,0.000319428,0.000117786,0.001899777,0.004405682,0.02719726,0.003332493,0.0003619385,0.07497386],"study_design_scores_gemma":[0.000814499,0.0006303106,0.9613689,0.000009796379,0.00007421442,0.00004269566,0.000596129,0.02751571,0.0001357571,0.00633103,0.002088656,0.0003923216],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8243795,0.00005250441,0.1646898,0.0001010298,0.00004196033,0.000507582,0.00002828481,0.00002705784,0.0101723],"genre_scores_gemma":[0.994859,0.0001852071,0.002970433,0.00004048172,0.00008294933,0.00007362724,0.0001164762,0.0000199773,0.001651805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1704796,"threshold_uncertainty_score":0.9994749,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3019828710","doi":"10.1111/gcb.15075","title":"Robust ecological drought projections for drylands in the 21st century","year":2020,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":239,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Arid; Temperate climate; Precipitation; Ecosystem; Environmental science; Climate change; Ecology; Water balance; Aridity index; Climatology; Physical geography; Geography; Biology; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.06979426688735431,"gpt":0.2791840008746024,"spread":0.2093897339872481,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001816492,0.00009651869,0.0001452193,0.00001037732,0.0001212994,0.000007292781,0.000267678,0.0001750604,0.0006132701],"category_scores_gemma":[0.00006703874,0.00006029782,0.00007692133,0.0004121248,0.0001717238,0.00005019321,0.0001041089,0.00009915334,0.0002020004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006491479,"about_ca_system_score_gemma":0.00000491983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002961088,"about_ca_topic_score_gemma":0.001401566,"domain_scores_codex":[0.9991059,0.0001305922,0.0001284378,0.000292702,0.00004816644,0.0002941939],"domain_scores_gemma":[0.9997491,0.00005590227,0.00003736083,0.0001046853,0.000003428127,0.00004953543],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009140631,0.0002225871,0.9787899,0.000005773424,0.0000295098,0.00001339046,0.001516501,0.00005620046,0.00005891747,0.006454078,0.006065163,0.006696628],"study_design_scores_gemma":[0.001122543,0.001173347,0.3920327,0.000002192682,0.00008603482,0.00003218572,0.001599475,0.005110526,0.000008548682,0.003866706,0.5946029,0.0003628892],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8976684,0.0004676258,0.000921724,0.04712444,0.0004872024,0.001437015,0.0002689064,0.000111698,0.05151304],"genre_scores_gemma":[0.9915634,0.00009230098,0.0002735857,0.00752766,0.0002544224,0.000204153,0.00006928004,0.000002296376,0.00001283648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5885378,"threshold_uncertainty_score":0.6714877,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2043236587","doi":"10.1016/j.aap.2013.09.006","title":"Freeway safety estimation using extreme value theory approaches: A comparative study","year":2013,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":239,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"China Scholarship Council","keywords":"Extreme value theory; Reliability (semiconductor); Crash; Statistics; Poison control; Econometrics; Computer science; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.06758968449134925,"gpt":0.2977227027727651,"spread":0.2301330182814159,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001340907,0.0002222039,0.0004531413,0.0002493045,0.0003584685,0.00008666488,0.0003043806,0.00009643992,0.01343159],"category_scores_gemma":[0.000037447,0.000196867,0.0004111817,0.001618189,0.0001019965,0.0009963481,0.0001914081,0.0001459462,0.00102813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002175255,"about_ca_system_score_gemma":0.000009048366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001803924,"about_ca_topic_score_gemma":0.005537015,"domain_scores_codex":[0.9971585,0.0009940009,0.0005704853,0.0005556303,0.0004486619,0.0002727587],"domain_scores_gemma":[0.9989553,0.00009520311,0.0003402967,0.0004992901,0.00002012029,0.00008977204],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002327238,0.0004937149,0.6700956,4.970346e-7,0.001399293,0.000001475069,0.001974547,0.3218598,0.0002472787,0.0001672377,0.00005006899,0.003687166],"study_design_scores_gemma":[0.0002798314,0.00005687997,0.4490549,0.000003094374,0.003835025,0.000001016998,0.001427723,0.5366771,0.0000920665,0.008378177,0.000007302831,0.0001868712],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7671757,0.0000378553,0.2299768,0.00003990312,0.0000225581,0.0005089411,1.256301e-7,0.00003724083,0.002200871],"genre_scores_gemma":[0.9943687,0.000002908247,0.004276339,0.00003205701,0.00002488108,0.0000750487,0.00007210711,0.00000929772,0.00113869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.227193,"threshold_uncertainty_score":0.9997497,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2963662460","doi":"10.1016/j.rse.2019.111290","title":"Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the Loess Plateau in China","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":237,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin; State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering; National Key Research and Development Program of China; Xi'an University of Technology; China Scholarship Council; China Institute of Water Resources and Hydropower Research; Ministry of Science and Technology of the People's Republic of China; Shaanxi Provincial Department of Water Resources; National Natural Science Foundation of China","keywords":"Normalized Difference Vegetation Index; Vegetation (pathology); Environmental science; Enhanced vegetation index; Physical geography; Desertification; Hydrology (agriculture); Climate change; Geography; Ecology; Geology; Vegetation Index","retraction":null,"screen_n_in":null,"score":{"opus":0.008643668674853194,"gpt":0.2452016267425079,"spread":0.2365579580676547,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008260651,0.0002079969,0.0004906997,0.00007595128,0.00005178794,0.000004965552,0.0002095056,0.0001361217,0.00009939399],"category_scores_gemma":[0.0001025563,0.0001665847,0.0001692018,0.0003081921,0.0003597891,0.00005833663,0.0001810895,0.0002263388,0.0000158316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003608328,"about_ca_system_score_gemma":0.00003652812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002760564,"about_ca_topic_score_gemma":0.0008657405,"domain_scores_codex":[0.9975353,0.0004195084,0.0007125576,0.000469988,0.0005948297,0.000267809],"domain_scores_gemma":[0.9985019,0.0001597787,0.0004424082,0.0008257503,0.000005988867,0.00006418276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001079131,0.0001371834,0.0205259,0.00008112092,0.00003059798,0.00000397067,0.0004519314,0.9021122,0.04507764,0.000003826527,0.000003803735,0.03146388],"study_design_scores_gemma":[0.0006922772,0.0001502189,0.3160361,0.0001754621,0.0000672585,0.000001793916,0.00002528105,0.6545135,0.02751533,0.0006182592,0.00004894949,0.0001555446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847998,0.000008263036,0.01300863,0.0003236152,0.0001429838,0.0006334906,0.000005875868,0.00000674311,0.001070572],"genre_scores_gemma":[0.9788593,0.000003385922,0.02101003,0.00004070071,0.00001540013,5.833289e-8,0.000007805924,0.00001446148,0.00004887584],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2955102,"threshold_uncertainty_score":0.6793128,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2087414266","doi":"10.1002/joc.1649","title":"Comparison of suitable drought indices for climate change impacts assessment over Australia towards resource management","year":2007,"lang":"en","type":"article","venue":"International Journal of Climatology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":233,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Environmental science; Climate change; Climatology; Greenhouse gas; Global warming; Water resources; Downscaling; Geography; Ecology","retraction":null,"screen_n_in":null,"score":{"opus":0.04653851565647919,"gpt":0.4145946055429147,"spread":0.3680560898864355,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001419137,0.0001255526,0.0004002994,0.0002325761,0.00005835444,0.00001732455,0.0005252283,0.0001303903,0.001234308],"category_scores_gemma":[0.00003323127,0.0001074581,0.0001953527,0.0001383467,0.0001554749,0.0002899299,0.0001884075,0.0001896136,0.00002331398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001605872,"about_ca_system_score_gemma":0.00000888487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008437923,"about_ca_topic_score_gemma":0.0001797884,"domain_scores_codex":[0.9980702,0.00006286987,0.0008375554,0.0001579879,0.0005191686,0.0003522398],"domain_scores_gemma":[0.9986237,0.0001671532,0.0009370602,0.0001150728,0.00005971354,0.00009730929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000466726,0.0003764208,0.9875847,0.00003163175,0.0004014387,0.0001153267,0.0005886998,0.0003753006,0.0004804253,0.003358602,0.002342252,0.003878476],"study_design_scores_gemma":[0.001974734,0.0004716485,0.9443243,0.0000659107,0.0002666213,0.0001976178,0.0005961002,0.0008873343,0.003564096,0.001856485,0.04559642,0.0001987795],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9842175,0.00008015814,0.003728355,0.001031641,0.000530403,0.0001395868,0.00002013078,0.000006218511,0.01024599],"genre_scores_gemma":[0.9948999,0.00007400187,0.004466448,0.0003057123,0.0001264071,0.000005151375,0.00001450217,0.00000831838,0.00009959716],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04326045,"threshold_uncertainty_score":0.9996787,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1964197214","doi":"10.1016/j.jhydrol.2008.02.011","title":"On the tails of extreme event distributions in hydrology","year":2008,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":214,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Hydro-Québec; Institut National de la Recherche Scientifique; Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Event (particle physics); Class (philosophy); Extreme value theory; Exponential function; Computer science; Mathematics; Hydrology (agriculture); Statistics; Statistical physics; Geology; Physics; Artificial intelligence; Mathematical analysis; Geotechnical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01826359332888292,"gpt":0.2345989302579022,"spread":0.2163353369290192,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007676974,0.0001042622,0.0003420642,0.0001128381,0.0001044551,0.000001320646,0.0003589773,0.0001376122,0.003059539],"category_scores_gemma":[0.0002300936,0.0000668771,0.0001808025,0.0003107684,0.0006387323,0.0000789315,0.00007679541,0.0004057685,0.0001354629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007546363,"about_ca_system_score_gemma":0.00002553565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007085711,"about_ca_topic_score_gemma":0.000200424,"domain_scores_codex":[0.9984897,0.0003365782,0.0005752844,0.0001336072,0.0002043612,0.0002604911],"domain_scores_gemma":[0.998934,0.0003754701,0.0003970151,0.0002185063,0.00001362048,0.00006137002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009720146,0.001854373,0.7610823,0.000005117224,0.0003404641,0.001073938,0.002676336,0.1814442,0.02268547,0.00733281,0.01969501,0.0008379501],"study_design_scores_gemma":[0.006296233,0.007439164,0.7019212,0.00004910866,0.0005066903,0.008074054,0.000268283,0.04148619,0.01533884,0.1826344,0.03510734,0.0008785095],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906139,0.00009175153,0.0005993478,0.005787375,0.00009850066,0.00005285484,0.000002728678,0.000002725139,0.002750766],"genre_scores_gemma":[0.999114,0.00007545733,0.00006029032,0.0005883443,0.0000344959,0.000002921283,0.000001286032,0.000004508425,0.0001187254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1753016,"threshold_uncertainty_score":0.9978518,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2768091302","doi":"10.1007/s00382-017-3987-0","title":"Comparison of various drought indices to monitor drought status in Pakistan","year":2017,"lang":"en","type":"article","venue":"Climate Dynamics","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":214,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"Pakistan Science Foundation","keywords":"Anomaly (physics); Precipitation; Index (typography); Evapotranspiration; Environmental science; Climatology; Decile; Geography; Statistics; Meteorology; Mathematics; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.01269587304552153,"gpt":0.3318080748344159,"spread":0.3191122017888944,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003316867,0.0001697285,0.0004034112,0.0000824352,0.0003308821,0.00006105406,0.0006061518,0.0001526346,0.0003589121],"category_scores_gemma":[0.00004431606,0.0001657427,0.00007496052,0.0002052876,0.0003207472,0.0002676942,0.0004645327,0.0001956204,0.0003272331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002620614,"about_ca_system_score_gemma":0.000008369234,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002378137,"about_ca_topic_score_gemma":0.02337235,"domain_scores_codex":[0.9983421,0.00005648476,0.0004458214,0.0003569814,0.0002413053,0.0005573365],"domain_scores_gemma":[0.9987726,0.00005559135,0.0003234577,0.0006978227,0.000008833878,0.0001417546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005067492,0.0001840767,0.9890993,0.00001454587,0.00001677011,0.00001859682,0.001246859,0.00514098,0.0002616793,0.0004323396,0.0000921597,0.003442026],"study_design_scores_gemma":[0.0004545363,0.0001262995,0.8693706,0.00002217273,0.000052246,0.000001688566,0.0005056434,0.1268555,0.0003226187,0.0007626736,0.001249767,0.0002762547],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9804291,0.00003580414,0.0003547998,0.000295865,0.0001657422,0.0001413136,0.00006876582,0.00002188306,0.01848677],"genre_scores_gemma":[0.998322,0.00006448238,0.001250775,0.00007320439,0.00001897561,0.00001216668,0.00003868205,0.00001552714,0.000204192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1217146,"threshold_uncertainty_score":0.9944485,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2049688455","doi":"10.1002/hyp.1185","title":"Bivariate frequency analysis: discussion of some useful concepts in hydrological application","year":2002,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":206,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba; Environment and Climate Change Canada","funders":"","keywords":"Bivariate analysis; Joint probability distribution; Univariate; Return period; Extreme value theory; Multivariate statistics; Storm; Econometrics; Flood myth; Joint (building); Marginal distribution; Multivariate normal distribution; Conditional probability distribution; Computer science; Statistics; Mathematics; Meteorology; Geography; Random variable","retraction":null,"screen_n_in":null,"score":{"opus":0.01734593144420721,"gpt":0.2549911298077926,"spread":0.2376451983635854,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004078814,0.0002347691,0.0005740238,0.0001658592,0.0001098523,0.00001259262,0.0004830397,0.0003420412,0.004885103],"category_scores_gemma":[0.0002914709,0.0001380653,0.000168607,0.002422975,0.0006384738,0.0003573913,0.0001816848,0.0002751071,0.0005575325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004982792,"about_ca_system_score_gemma":0.000005964548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001706495,"about_ca_topic_score_gemma":0.0003407445,"domain_scores_codex":[0.9976959,0.0002030675,0.0005969965,0.0007248008,0.0003483123,0.0004309376],"domain_scores_gemma":[0.9990765,0.0001621476,0.000251436,0.0003698396,0.00001594001,0.0001241664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005589646,0.0009542756,0.9515603,0.00002681217,0.0001243326,0.00003306063,0.0003786377,0.04119914,0.003579779,0.0005291016,0.0001017534,0.001456939],"study_design_scores_gemma":[0.001847815,0.00113401,0.5186945,0.00002486009,0.001275243,0.00001909635,0.00009856378,0.3181128,0.003130595,0.1523392,0.002084978,0.001238395],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915224,0.000409276,0.002009595,0.001528635,0.00001756038,0.0002080861,0.00000623351,0.0001153664,0.004182887],"genre_scores_gemma":[0.9986798,0.0001690842,0.0003248956,0.0005385242,0.00003027324,0.00008085538,0.00001909935,0.000009751924,0.0001477623],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4328657,"threshold_uncertainty_score":0.9960245,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1776911451","doi":"10.1029/2003wr002816","title":"Artificial neural network ensembles and their application in pooled flood frequency analysis","year":2004,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":205,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Computer science; Artificial intelligence; Generalization; Ensemble learning; Boosting (machine learning); Ensemble forecasting; Machine learning; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02093775302569431,"gpt":0.2774856757709578,"spread":0.2565479227452635,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001285795,0.0001140976,0.0002078148,0.0002395586,0.0003131372,0.00005933049,0.0002656271,0.0001103914,0.0002730322],"category_scores_gemma":[0.00001868877,0.00007487839,0.00007346772,0.001381589,0.0003701479,0.0001101264,0.0002482054,0.0003084134,0.0003049482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008089988,"about_ca_system_score_gemma":0.000003424347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005978371,"about_ca_topic_score_gemma":0.01595454,"domain_scores_codex":[0.9981064,0.0003158649,0.0002345082,0.0004414385,0.0003082592,0.0005935053],"domain_scores_gemma":[0.999498,0.00006165246,0.00002198962,0.0003064199,0.00001033741,0.0001015775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009069403,0.0001443224,0.6627213,0.000005805948,0.0001892486,0.00003303439,0.008884547,0.2795206,0.03997362,0.0001159957,0.00003091577,0.008289875],"study_design_scores_gemma":[0.001265248,0.0004322829,0.6087644,0.00002032447,0.0003984423,0.00002160078,0.001432669,0.1465902,0.0510767,0.1817396,0.007248901,0.001009583],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967672,0.0001247931,0.0003739752,0.001237546,0.000006870318,0.0001564447,0.000001593892,0.0000283022,0.001303324],"genre_scores_gemma":[0.9995549,0.00001507558,0.0001180099,0.00006926578,0.0000738328,0.00004439924,0.00001776768,0.00001074173,0.00009600135],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1816236,"threshold_uncertainty_score":0.9037549,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2066509844","doi":"10.1002/joc.781","title":"Regional streamflow trend detection with consideration of both temporal and spatial correlation","year":2002,"lang":"en","type":"article","venue":"International Journal of Climatology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":201,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Statistic; Streamflow; Environmental science; Climatology; Correlation; Statistics; Physical geography; Geography; Mathematics; Geology; Cartography; Drainage basin","retraction":null,"screen_n_in":null,"score":{"opus":0.01158497653605688,"gpt":0.2341619120251026,"spread":0.2225769354890457,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001349773,0.00006611053,0.0001591479,0.0001088295,0.00003840528,0.000008998593,0.00008000499,0.00007153631,0.001034052],"category_scores_gemma":[0.00003799302,0.00005280132,0.00004520699,0.00005896509,0.0002179672,0.0002198634,0.00002099571,0.0001097351,0.00001033307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003500772,"about_ca_system_score_gemma":0.000006092259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001850381,"about_ca_topic_score_gemma":0.002343858,"domain_scores_codex":[0.9991795,0.00006971703,0.0003352813,0.00009325812,0.0002502416,0.0000720378],"domain_scores_gemma":[0.9993387,0.00009829392,0.0004395228,0.00004542896,0.00004074255,0.00003728858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003800632,0.000133329,0.9772139,0.00000232142,0.0001612672,0.0000772854,0.000421373,0.003842352,0.002001021,0.0002010165,0.0004252005,0.01514085],"study_design_scores_gemma":[0.007162417,0.002262055,0.6477591,0.00008328594,0.0004203441,0.01322736,0.0003144033,0.3091401,0.005391815,0.006912772,0.00685198,0.000474397],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9801562,0.00005421037,0.01754041,0.0009765331,0.000165585,0.0000268431,0.000002525818,0.000003665386,0.00107401],"genre_scores_gemma":[0.9989316,0.00005796911,0.0007993578,0.0001085864,0.00005234063,7.418734e-7,0.000004779793,0.000003619214,0.00004104793],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3294548,"threshold_uncertainty_score":0.9998791,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3117937926","doi":"10.1016/j.scitotenv.2020.144232","title":"Multi-timescale assessment of propagation thresholds from meteorological to hydrological drought","year":2020,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":195,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Ministry of Environment","funders":"National Natural Science Foundation of China","keywords":"Streamflow; Precipitation; Environmental science; Drainage basin; Climatology; Hydrology (agriculture); Meteorology; Geology; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.01884599817472746,"gpt":0.2465334745401029,"spread":0.2276874763653755,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009409919,0.0001543002,0.0002573215,0.0000179526,0.0002768398,0.00001181439,0.001271909,0.00007157009,0.002452519],"category_scores_gemma":[0.0001083728,0.00007767486,0.0001510692,0.0005145367,0.002577867,0.0001440414,0.001456593,0.0001966325,0.0002109851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001057624,"about_ca_system_score_gemma":0.00001846557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001204794,"about_ca_topic_score_gemma":0.000002896607,"domain_scores_codex":[0.9977533,0.0001807021,0.0003820524,0.0004950808,0.0008877402,0.0003011028],"domain_scores_gemma":[0.9989786,0.00007573512,0.0002110299,0.0005763362,0.000004421477,0.0001538868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00003352198,0.0001800871,0.006204136,0.000001264139,0.00002008551,9.669113e-7,0.0005530349,0.4195023,0.5729765,0.00009872538,0.00006718349,0.0003621785],"study_design_scores_gemma":[0.0003958683,0.000533614,0.5340489,0.000005347055,0.0001295893,0.000003557291,0.0001048054,0.3500631,0.113482,0.0008704969,0.0001491202,0.0002136263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9871819,0.00001568556,0.002396051,0.008288885,0.00004942606,0.0003717218,0.00001121443,0.00001222537,0.001672905],"genre_scores_gemma":[0.9928874,0.000005687312,0.006512178,0.0003890892,0.00002204337,0.00002096607,0.000001356515,0.00000518231,0.0001561249],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5278447,"threshold_uncertainty_score":0.9984594,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2809051154","doi":"10.1016/j.jhydrol.2018.06.053","title":"Impacts of reservoir operations on multi-scale correlations between hydrological drought and meteorological drought","year":2018,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":193,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Ministry of Environment; Ministry of the Environment, Conservation and Parks","funders":"Chinese Academy of Sciences Key Project; National Natural Science Foundation of China","keywords":"Inflow; Outflow; Streamflow; Hydrology (agriculture); Environmental science; Precipitation; Structural basin; Drainage basin; Climatology; Scale (ratio); Geology; Meteorology; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.02530621822585296,"gpt":0.2896960088066945,"spread":0.2643897905808416,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001099931,0.000206497,0.0006414184,0.0001809619,0.0002899146,0.00001393805,0.0003847304,0.000412308,0.001776848],"category_scores_gemma":[0.000420592,0.0001462672,0.0001918313,0.0003894175,0.001475258,0.0002842613,0.00020928,0.0005673546,0.0001831034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005735041,"about_ca_system_score_gemma":0.00002604847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001016732,"about_ca_topic_score_gemma":0.0005740969,"domain_scores_codex":[0.9976799,0.0004857889,0.0008259496,0.0003165955,0.0003043514,0.0003874085],"domain_scores_gemma":[0.9985907,0.0004416067,0.0003758204,0.0002868863,0.00005948216,0.0002455087],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004828612,0.0005956244,0.9598458,0.000004208044,0.0002734277,0.00007637238,0.0009268136,0.01585575,0.01950715,0.0003249078,0.001576973,0.0005300742],"study_design_scores_gemma":[0.003845789,0.01219745,0.8955748,0.00002431627,0.0008047633,0.0007366835,0.00008427978,0.06270333,0.008550805,0.007191859,0.007751734,0.0005341883],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908251,0.00009152885,0.003633506,0.003315176,0.0001322624,0.0001048128,0.00001019286,0.00001343772,0.001874046],"genre_scores_gemma":[0.9931027,0.00005475137,0.005657097,0.000744521,0.0002564024,0.000003206632,0.000005591549,0.00001092721,0.0001648476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06427103,"threshold_uncertainty_score":0.9991357,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1978786685","doi":"10.1016/j.jhydrol.2009.06.050","title":"Trends in the timing and magnitude of floods in Canada","year":2009,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":190,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"Hydro-Québec; Institut National de la Recherche Scientifique; Natural Sciences and Engineering Research Council; Conestoga College","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Snowmelt; Magnitude (astronomy); Flood myth; Environmental science; Trend analysis; Climatology; Seasonality; Streamflow; Hydrology (agriculture); Physical geography; Flash flood; Geography; Drainage basin; Meteorology; Geology; Snow; Statistics; Cartography","retraction":null,"screen_n_in":null,"score":{"opus":0.007852324140084783,"gpt":0.2298806348816115,"spread":0.2220283107415267,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004805513,0.00004837637,0.0001825651,0.0001082839,0.00001607076,0.000001629133,0.0001565708,0.0000388372,0.0003502437],"category_scores_gemma":[0.00002215745,0.00003204633,0.00002839151,0.0002399392,0.00006205502,0.00006107312,0.00001711165,0.0001904755,0.000001058729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006061249,"about_ca_system_score_gemma":0.00002622745,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1332899,"about_ca_topic_score_gemma":0.7835495,"domain_scores_codex":[0.9992963,0.0001210636,0.0002760332,0.00006194969,0.0001150809,0.0001295776],"domain_scores_gemma":[0.9997146,0.00006384751,0.000127641,0.00006644921,0.000002489255,0.00002495617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007768736,0.00008833992,0.9499544,0.000001086478,0.00001514134,0.0004377293,0.001054975,0.02296783,0.001412037,0.00002995636,0.0007829945,0.02317784],"study_design_scores_gemma":[0.0004548836,0.0002213016,0.9934208,0.000002873677,0.00002449975,0.0002471406,0.00008627417,0.003679644,0.0001120104,0.001092079,0.0006156542,0.00004287205],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992987,0.0001270291,0.000005730068,0.003599021,0.00002198079,0.00001020268,2.537794e-7,3.125382e-7,0.003248486],"genre_scores_gemma":[0.9987864,0.00002705364,0.0001128257,0.00103845,0.00001290727,1.816134e-7,2.039686e-7,0.000001089696,0.00002083754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6502596,"threshold_uncertainty_score":0.8724816,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2061754679","doi":"10.1002/hyp.259","title":"A bivariate gamma distribution for use in multivariate flood frequency analysis","year":2001,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":188,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Bivariate analysis; Joint probability distribution; Flood myth; Multivariate statistics; Marginal distribution; Multivariate analysis; Statistics; Gamma distribution; 100-year flood; Return period; Distribution (mathematics); Hydrology (agriculture); Environmental science; Multivariate normal distribution; Mathematics; Geography; Geology; Random variable; Geotechnical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02693700887820406,"gpt":0.2629935128014266,"spread":0.2360565039232226,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004270616,0.0002113171,0.0003982237,0.0001005471,0.0001754326,0.00004473772,0.0002777621,0.0002466263,0.001275469],"category_scores_gemma":[0.0009186019,0.0001629193,0.0001741655,0.002532092,0.0002046266,0.0004396782,0.00009898102,0.0001673896,0.0001903582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008273611,"about_ca_system_score_gemma":0.00001377881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001309586,"about_ca_topic_score_gemma":0.004171647,"domain_scores_codex":[0.9981481,0.0001154205,0.000388314,0.0006442415,0.0001905164,0.0005133906],"domain_scores_gemma":[0.9991366,0.0003490571,0.0001255256,0.0002493109,0.00002568683,0.0001138712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001869415,0.0004142601,0.9701515,0.0000111244,0.0001581802,0.00005658636,0.00008551028,0.02761862,0.0005581459,0.0001773388,0.00008260885,0.0004992096],"study_design_scores_gemma":[0.001556673,0.0003602869,0.8041707,0.000008463292,0.001054272,0.00001557242,0.00001829851,0.1574748,0.0004205258,0.02929081,0.005000179,0.0006294958],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9402317,0.00006224278,0.05819552,0.0004515703,0.00002337,0.0002463031,0.00004264012,0.00012188,0.0006248072],"genre_scores_gemma":[0.9971378,0.00005021553,0.001940772,0.0003301202,0.00003248769,0.0001408262,0.0001939993,0.00001047918,0.0001632447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1659808,"threshold_uncertainty_score":0.9996375,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2108706807","doi":"10.1155/2012/794061","title":"Standard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Support Vector Regression","year":2012,"lang":"en","type":"article","venue":"Applied Computational Intelligence and Soft Computing","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":187,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Artificial neural network; Support vector machine; Computer science; Mean squared error; Wavelet; Artificial intelligence; Machine learning; Data mining; Regression; Algorithm; Precipitation; Statistics; Meteorology; Mathematics; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.02762651705106631,"gpt":0.268448673925309,"spread":0.2408221568742427,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006310298,0.0002657329,0.0002942577,0.0000665107,0.0006867228,0.00009370114,0.0001368854,0.0001416313,0.0001049496],"category_scores_gemma":[0.00003462775,0.000246275,0.00005550073,0.0003651169,0.0002684032,0.0003783962,0.0003124606,0.0003161364,0.000006098673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007300095,"about_ca_system_score_gemma":0.000009592713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004436149,"about_ca_topic_score_gemma":0.00001082285,"domain_scores_codex":[0.9981238,0.00009102228,0.0004660367,0.0004450787,0.0003153196,0.000558763],"domain_scores_gemma":[0.9988893,0.0005249102,0.0002512927,0.0001118843,0.00002818305,0.0001944345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004714556,0.00002072693,0.06747848,0.000006322173,0.00002106635,0.000003198002,0.0006633174,0.8444946,0.000008469291,0.0004517827,0.00003321243,0.08677165],"study_design_scores_gemma":[0.0001318425,0.00004850531,0.01589882,0.00001459934,0.00004023747,0.0000679065,0.0001609088,0.9818531,0.00002188101,0.001463956,0.00003587071,0.0002623728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4649129,0.0001365101,0.5344656,0.00003966534,0.0001261455,0.0001203012,0.000001148729,0.00003667135,0.0001610742],"genre_scores_gemma":[0.9899378,0.000009829682,0.009262287,0.0003810695,0.0003194986,0.000004586403,0.00005263677,0.00002095032,0.00001134978],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5252033,"threshold_uncertainty_score":0.9999989,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2085187101","doi":"10.1002/joc.924","title":"Comparison of techniques for detection of discontinuities in temperature series","year":2003,"lang":"en","type":"article","venue":"International Journal of Climatology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":187,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Ministère des Transports","funders":"","keywords":"Classification of discontinuities; Homogeneity (statistics); Series (stratigraphy); Homogeneous; Homogenization (climate); Computer science; Mathematics; Machine learning; Geology; Mathematical analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.009908129528780873,"gpt":0.3097388909590057,"spread":0.2998307614302248,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002748252,0.00004805918,0.0002562009,0.0001171369,0.00001221804,0.000003028009,0.0001474361,0.00007752074,0.0001284338],"category_scores_gemma":[0.0001549179,0.00003986394,0.00008725166,0.00006649371,0.0001632275,0.0001568947,0.00001834622,0.00009366133,8.417522e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002883712,"about_ca_system_score_gemma":0.00001008725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001751332,"about_ca_topic_score_gemma":0.0003586541,"domain_scores_codex":[0.9992127,0.00006527494,0.0004754144,0.00005526277,0.0001242557,0.00006711007],"domain_scores_gemma":[0.9993822,0.00009184118,0.0004133854,0.00004155932,0.0000583542,0.00001268909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003201737,0.0001660644,0.84141,0.000009344297,0.00007968944,0.000007305262,0.0006856027,0.0003952511,0.1526374,0.002846471,0.0001103247,0.001332405],"study_design_scores_gemma":[0.0005080659,0.0003589659,0.02523005,0.00003205656,0.00003495903,0.0002304131,0.000661827,0.000142583,0.9592676,0.009777598,0.003685644,0.00007026156],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969369,0.00008038366,0.001542261,0.0002992506,0.000171311,0.00003801217,0.000003948332,0.000001849348,0.0009261431],"genre_scores_gemma":[0.9979226,0.00003098133,0.001963346,0.00002456526,0.00001373931,0.000002660022,0.000001080505,0.000002618801,0.00003838841],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8161799,"threshold_uncertainty_score":0.1625604,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2045439988","doi":"10.1002/joc.1438","title":"On the critical values of the standard normal homogeneity test (SNHT)","year":2006,"lang":"en","type":"article","venue":"International Journal of Climatology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":178,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Hydro-Québec; Institut National de la Recherche Scientifique; Natural Sciences and Engineering Research Council of Canada; Ouranos","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Homogeneity (statistics); Statistics; Monte Carlo method; Homogenization (climate); Statistic; Sample size determination; Ranging; Test statistic; Statistical hypothesis testing; Econometrics; Mathematics; Climatology; Environmental science; Geography; Geology; Geodesy","retraction":null,"screen_n_in":null,"score":{"opus":0.006787883096213565,"gpt":0.2682566727925467,"spread":0.2614687896963331,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004471573,0.00006442914,0.0001516164,0.0000358764,0.00008320977,0.00001013052,0.0005992112,0.00005894525,0.001933715],"category_scores_gemma":[0.0006817283,0.00003374369,0.0001660675,0.00007821813,0.0006131575,0.00008066525,0.0001192246,0.0002027351,0.00004802414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004785038,"about_ca_system_score_gemma":0.0000201506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004830985,"about_ca_topic_score_gemma":0.0001104201,"domain_scores_codex":[0.9988565,0.0001369328,0.0003701776,0.00007518119,0.0004433049,0.0001178732],"domain_scores_gemma":[0.9985139,0.001037067,0.0002355248,0.0001174034,0.00007332629,0.00002273437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001663317,0.0002311926,0.9466887,0.0000013907,0.0000771395,0.00006141977,0.00007698935,0.00252806,0.001872782,0.03850854,0.009551677,0.0002357355],"study_design_scores_gemma":[0.00119446,0.000583093,0.6907504,0.0000482654,0.0002127385,0.00177471,0.0001128256,0.001973405,0.0353171,0.254843,0.01297239,0.0002176548],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9711823,0.00003164033,0.0005601948,0.01703264,0.000412492,0.0000224342,0.00001423937,0.000002248434,0.01074181],"genre_scores_gemma":[0.998933,0.00001041445,0.0001726242,0.0007220033,0.00008586563,7.130926e-7,5.914872e-7,0.000003103999,0.0000717522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2559384,"threshold_uncertainty_score":0.9989787,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2588503365","doi":"10.1002/2016gl072201","title":"Time‐varying extreme rainfall intensity‐duration‐frequency curves in a changing climate","year":2017,"lang":"en","type":"article","venue":"Geophysical Research Letters","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":177,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Precipitation; Environmental science; Climate change; Duration (music); Probabilistic logic; Markov chain; Climatology; Intensity (physics); Reliability (semiconductor); Markov chain Monte Carlo; Bayesian probability; Computer science; Meteorology; Econometrics; Statistics; Mathematics; Geology; Geography; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.04736813058221395,"gpt":0.3142758715262239,"spread":0.26690774094401,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001659757,0.000129217,0.0002303394,0.0002060112,0.0009964413,0.00009949323,0.0006578662,0.000059305,0.0009138051],"category_scores_gemma":[0.0006543125,0.0001187391,0.00009533604,0.0004789711,0.0009225251,0.0006141145,0.0008554124,0.0005326614,0.004239117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001432074,"about_ca_system_score_gemma":0.00001041683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001320727,"about_ca_topic_score_gemma":0.0001853662,"domain_scores_codex":[0.9974587,0.0002176908,0.0002084903,0.0004440093,0.0006132976,0.00105782],"domain_scores_gemma":[0.9989774,0.0001740404,0.00007446359,0.000622622,0.0000193863,0.0001320548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002035166,0.0003722921,0.1764172,0.0001025676,0.00009074683,0.0009889823,0.002725731,0.0005930638,0.7939253,0.0007346799,0.01721271,0.006633181],"study_design_scores_gemma":[0.002383727,0.0003202496,0.8363091,0.0008256635,0.00008530937,0.00002672171,0.0002323014,0.1343287,0.00733039,0.01297733,0.003538422,0.001642059],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.962066,0.00002360361,0.00005926414,0.02543934,0.00003928892,0.0001842605,0.000002554073,0.00002668765,0.01215899],"genre_scores_gemma":[0.9965,0.00005464152,0.0001837528,0.002486474,0.0001145476,0.00003518835,0.00001185046,0.00001172438,0.0006018568],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7865949,"threshold_uncertainty_score":0.9999995,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2166781611","doi":"10.1029/2006wr005142","title":"Flood frequency analysis at ungauged sites using artificial neural networks in canonical correlation analysis physiographic space","year":2007,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":176,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Canonical correlation; Jackknife resampling; Artificial neural network; Quantile; Generalization; Computer science; Ensemble forecasting; Artificial intelligence; Kriging; Machine learning; Mathematics; Data mining; Statistics; Estimator","retraction":null,"screen_n_in":null,"score":{"opus":0.03547735419306032,"gpt":0.3162222556207778,"spread":0.2807449014277175,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003704295,0.0002203357,0.0004961691,0.002061096,0.0006876488,0.0000895343,0.000441896,0.000287337,0.002348601],"category_scores_gemma":[0.00005589849,0.0001709925,0.0004956055,0.0114459,0.0006622568,0.0002062578,0.0004765003,0.0007640973,0.0001706457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003823657,"about_ca_system_score_gemma":0.00000560571,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0151916,"about_ca_topic_score_gemma":0.1321715,"domain_scores_codex":[0.9954478,0.000910131,0.0005602725,0.0007928478,0.001038231,0.001250686],"domain_scores_gemma":[0.9987896,0.0002829977,0.00008015297,0.0005672324,0.00003455739,0.0002454217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008425592,0.00006537417,0.5526459,0.00000120559,0.0004140995,0.00005199996,0.0006301478,0.4371266,0.008877932,0.000007669716,0.000006242355,0.00008854607],"study_design_scores_gemma":[0.0001307784,0.00003960536,0.3551444,0.000001600681,0.0007761772,0.000001476549,0.00009406658,0.6423044,0.00102175,0.0002275811,0.00008490474,0.0001732048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954181,0.0001095082,0.002930359,0.0001940555,0.00001980913,0.0001661293,0.000003156466,0.00004067147,0.001118224],"genre_scores_gemma":[0.9992476,0.000009113636,0.0001926029,0.00003659885,0.00008241298,0.00000799484,0.0001086208,0.00002086615,0.0002941845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2051778,"threshold_uncertainty_score":0.9985634,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1930398149","doi":"10.1111/j.1753-318x.2009.01020.x","title":"Bivariate flood frequency analysis. Part 2: a copula‐based approach with mixed marginal distributions","year":2009,"lang":"en","type":"article","venue":"Journal of Flood Risk Management","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":175,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Marginal distribution; Joint probability distribution; Nonparametric statistics; Mathematics; Copula (linguistics); Bivariate analysis; Statistics; Akaike information criterion; Flood myth; Bayesian information criterion; Parametric statistics; Flood mitigation; Gumbel distribution; Econometrics; Extreme value theory; Random variable","retraction":null,"screen_n_in":null,"score":{"opus":0.005563032460512135,"gpt":0.2058261053114277,"spread":0.2002630728509156,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009740344,0.0002411353,0.0004981032,0.0003132426,0.0002970237,0.00005933716,0.000453579,0.00008007771,0.001097887],"category_scores_gemma":[0.00002021313,0.0001728028,0.0004137005,0.001770333,0.0001257188,0.0002661346,0.00005646782,0.0003410274,0.00009804632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001833673,"about_ca_system_score_gemma":0.00001704864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001067756,"about_ca_topic_score_gemma":0.000152022,"domain_scores_codex":[0.9977297,0.0002430783,0.0006187523,0.0003395136,0.0006683923,0.000400532],"domain_scores_gemma":[0.9986617,0.00002664756,0.000634911,0.0004344453,0.00003316838,0.0002091271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0005285839,0.003845499,0.4644208,0.00002561348,0.008989325,0.0008587598,0.0001934502,0.4965983,0.00008893842,0.003602038,0.010316,0.0105327],"study_design_scores_gemma":[0.0039741,0.001296422,0.9254018,0.0000328483,0.0220122,0.00006278132,0.0002039827,0.03127974,0.0001587412,0.004939548,0.009963184,0.0006746529],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4916444,0.0002667289,0.4814479,0.001521265,0.0001380316,0.0003274098,0.00004770354,0.00006353835,0.02454303],"genre_scores_gemma":[0.9626575,0.0001183691,0.03670583,0.000209178,0.00006239877,0.000007125705,0.00003510224,0.00001003958,0.0001944797],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4710131,"threshold_uncertainty_score":0.9998152,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2171205625","doi":"10.1002/hyp.9233","title":"Data‐based analysis of bivariate copula tail dependence for drought duration and severity","year":2012,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":170,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Copula (linguistics); Bivariate analysis; Gumbel distribution; Skewness; Statistics; Tail dependence; Mathematics; Joint probability distribution; Econometrics; Marginal distribution; Multivariate statistics; Extreme value theory; Random variable","retraction":null,"screen_n_in":null,"score":{"opus":0.04182602628566671,"gpt":0.2906898955186665,"spread":0.2488638692329998,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000784113,0.0001338641,0.0003334865,0.0000672509,0.0001536768,0.00001434453,0.0003193304,0.0001538623,0.0009101656],"category_scores_gemma":[0.0006642832,0.0001009175,0.00006016508,0.0009593756,0.0003081553,0.0005329992,0.0001984861,0.00008289333,0.00002101897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001642013,"about_ca_system_score_gemma":0.00001272775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001143321,"about_ca_topic_score_gemma":0.000490232,"domain_scores_codex":[0.9986958,0.00008160694,0.0003280777,0.0004034301,0.0001991558,0.0002919349],"domain_scores_gemma":[0.9990191,0.0003187322,0.0001985829,0.0003369671,0.00002109596,0.0001055183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001556261,0.0003448515,0.9892949,0.00006980097,0.0002791704,0.000001760902,0.0001278883,0.007288465,0.001389993,0.0001274219,0.0002321803,0.0006879498],"study_design_scores_gemma":[0.0007006954,0.0002553431,0.3851923,0.000007273617,0.003792268,0.000006413673,0.00003376575,0.594147,0.004275465,0.003303895,0.007780838,0.0005047407],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9600393,0.0002829015,0.03833609,0.0003645197,0.00002371162,0.0001628157,0.0001088651,0.00003626773,0.0006455675],"genre_scores_gemma":[0.9956471,0.00003756753,0.003581051,0.0003921001,0.00002530127,0.00002711048,0.0002364088,0.000004660065,0.00004874559],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6041026,"threshold_uncertainty_score":0.9965675,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2054344391","doi":"10.1002/sim.3504","title":"Age‐ and size‐related reference ranges: A case study of spirometry through childhood and adulthood","year":2008,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":165,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Hospital for Sick Children","funders":"Medical Research Council; National Institute for Health and Care Research","keywords":"Skewness; Kurtosis; Statistics; Spirometry; Mathematics; Generalized additive model; Linear regression; Medicine; Internal medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.01751881853312985,"gpt":0.2850192669584043,"spread":0.2675004484252745,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002745934,0.0001175865,0.0003395978,0.00004162329,0.00009883197,0.000001533845,0.00006887259,0.00006867253,0.0006635091],"category_scores_gemma":[0.0003704878,0.00009223189,0.000006372407,0.0003735511,0.0007309882,0.00005245372,0.00008240667,0.000209652,0.000005634985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001633143,"about_ca_system_score_gemma":0.000005259558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004412549,"about_ca_topic_score_gemma":0.002898436,"domain_scores_codex":[0.9989222,0.0001132511,0.0003308748,0.0002739315,0.0002090918,0.0001506769],"domain_scores_gemma":[0.9992803,0.0003744899,0.00009492858,0.0001767484,0.000007780954,0.00006579891],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006067939,0.0009564372,0.8184949,0.00004118104,0.0001318392,0.02348729,0.1494661,0.00007442326,0.0003394989,0.0003243714,0.0008279659,0.005795313],"study_design_scores_gemma":[0.004955043,0.001625379,0.9700427,0.00004081815,0.0002095534,0.002097339,0.01448907,0.0004679362,0.00001554604,0.005794944,0.00006326279,0.0001983877],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974462,0.0002575044,0.0004995455,0.00006872196,0.00002461511,0.0001734752,0.0000157449,0.000008266806,0.001505899],"genre_scores_gemma":[0.9955213,0.0005028989,0.00376428,0.0001091421,0.000009760242,0.000005421128,0.000006277047,0.000006376947,0.00007451944],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1515478,"threshold_uncertainty_score":0.726496,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2502383460","doi":"10.1016/j.gloplacha.2016.07.008","title":"Changes of extreme drought and flood events in Iran","year":2016,"lang":"en","type":"article","venue":"Global and Planetary Change","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":162,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Flood myth; Arid; Magnitude (astronomy); Environmental science; Climate change; Climatology; Trend analysis; Water resources; Physical geography; Hydrology (agriculture); Geography; Geology; Statistics; Ecology","retraction":null,"screen_n_in":null,"score":{"opus":0.02921732854570379,"gpt":0.2101873971246803,"spread":0.1809700685789765,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000988113,0.00008149869,0.0001353394,0.00001831213,0.00002462661,0.00000115547,0.00006045134,0.00006345454,0.0007236137],"category_scores_gemma":[0.000004127325,0.00005361548,0.00001215617,0.00009918053,0.00009242869,0.00009208102,0.00006447203,0.00002432557,0.00005059976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001098224,"about_ca_system_score_gemma":7.590663e-7,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002059627,"about_ca_topic_score_gemma":0.02999747,"domain_scores_codex":[0.9994588,0.00003659144,0.00008692077,0.0001766582,0.000083193,0.0001577835],"domain_scores_gemma":[0.9998146,0.00002048759,0.00002996276,0.00007742113,7.593443e-7,0.00005672616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002757617,0.00001858859,0.9800739,0.000006334222,0.000009474273,0.00001124093,0.0001310091,6.42191e-7,0.0002708645,0.0000138835,0.0000821045,0.01935438],"study_design_scores_gemma":[0.0003776913,0.00007003124,0.9959363,0.0000172176,0.00001889786,0.000018733,0.00001405159,0.00006453357,0.00004751496,0.0009787616,0.002373,0.00008329021],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996285,0.0007381271,0.000005848432,0.001082501,0.00003250071,0.00006973356,0.000091878,0.000006523214,0.001687837],"genre_scores_gemma":[0.9989103,0.0006356848,0.00004541368,0.0002524104,0.00003156353,0.000003853679,0.00001677627,0.000001538788,0.000102452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02793784,"threshold_uncertainty_score":0.9877025,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1938666370","doi":"10.1111/j.1541-0064.2008.00211.x","title":"Dry times: hard lessons from the Canadian drought of 2001 and 2002","year":2008,"lang":"en","type":"article","venue":"Canadian Geographies / Géographies canadiennes","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":160,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true},"ca_institutions":"Impact; Environment and Climate Change Canada; University of Saskatchewan; Saskatchewan Research Council (Canada)","funders":"Agriculture and Agri-Food Canada; University of Manitoba; University of Saskatchewan","keywords":"Vulnerability (computing); Agriculture; Climate change; Phone; Environmental resource management; Environmental planning; Geography; Business; Natural resource economics; Political science; Agricultural economics; Economics; Ecology","retraction":null,"screen_n_in":null,"score":{"opus":0.01122368945989773,"gpt":0.1908443754758262,"spread":0.1796206860159285,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.0003082143,0.0003993255,0.0004700157,0.001177157,0.00209085,0.00005199186,0.000752768,0.0003050108,0.004766349],"category_scores_gemma":[0.0001064056,0.0003401686,0.0002723455,0.003848137,0.0057433,0.0002827874,0.00008557364,0.0003451272,0.0001157956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001653126,"about_ca_system_score_gemma":0.000308649,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.998541,"about_ca_topic_score_gemma":0.9999815,"domain_scores_codex":[0.9971046,0.0001359744,0.0004094744,0.000659661,0.0002854,0.001404818],"domain_scores_gemma":[0.9970406,0.0002122537,0.0001407314,0.0007685468,0.00004624876,0.001791641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000007306925,0.00001081813,0.895335,0.000003487656,0.0002081708,0.0001494926,0.001556524,0.00006472095,0.00001617968,0.0005579658,0.1006655,0.001424866],"study_design_scores_gemma":[0.0002297193,0.00005104998,0.7763972,0.00001578367,0.0001457317,0.0000349365,0.001285152,0.0001074779,0.0000213386,0.001273078,0.2199922,0.0004463424],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.970004,0.003104961,0.00000165936,0.007123812,0.0002358986,0.0002602821,0.0005764069,0.00003592328,0.01865712],"genre_scores_gemma":[0.9927378,0.002857952,0.0002657114,0.002145293,0.0000814111,0.00003712412,0.00008833745,0.00003434624,0.001751972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1193267,"threshold_uncertainty_score":0.999905,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}