{"meta":{"query_hash":"2532ea0072d1","filters":{"venue":"Spatial Statistics"},"cohort_total":20,"direct_labels_cover":0,"predictions_cover":20,"exported":20,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/2532ea0072d1","api":"https://metacan.xera.ac/api/v1/cohort?venue=Spatial+Statistics"},"results":[{"id":"W2556888534","doi":"10.1016/j.spasta.2016.11.001","title":"A valid parametric test of significance for the average<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si24.gif\" display=\"inline\" overflow=\"scroll\"><mml:msup><mml:mrow><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mstyle mathvariant=\"normal\"><mml:mi>2</mml:mi></mml:mstyle></mml:mrow></mml:msup></mml:math>in redundancy analysis with spatial data","year":2016,"lang":"lv","type":"article","venue":"Spatial Statistics","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heteroscedasticity; Statistics; Mathematics; Parametric statistics; Spatial analysis; Autocorrelation; Algorithm; Computer science; Applied mathematics","score_opus":0.019283306726473666,"score_gpt":0.24552138566783077,"score_spread":0.2262380789413571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2556888534","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22692552,0.0017968646,0.62173694,0.0037402832,0.0031734367,0.0017930575,0.02650688,0.0056407154,0.10868638],"genre_scores_gemma":[0.81321484,0.0003572113,0.15642712,0.0011984295,0.0006488504,0.004706995,0.008084903,0.0022228195,0.013138853],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95743006,0.017792763,0.002618986,0.008633456,0.011321941,0.0022028962],"domain_scores_gemma":[0.7971003,0.1590002,0.007822795,0.024833137,0.0091617135,0.002081882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023419667,0.0013070742,0.0024542396,0.0035866275,0.0019692369,0.0033294098,0.0026427135,0.0034245702,0.050689507],"category_scores_gemma":[0.1723207,0.00056693354,0.0029610307,0.0034808728,0.004059191,0.00460943,0.003111723,0.0055466387,0.008394151],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0063295653,0.002909259,0.16766158,0.0048887236,0.0046895533,0.0043637967,0.0057769963,0.010386496,0.032750193,0.14156938,0.16007107,0.45860347],"study_design_scores_gemma":[0.0010517553,0.012749047,0.34570888,0.0022493268,0.002210633,0.013129814,0.009240436,0.08526798,0.046445332,0.27850178,0.20264016,0.0008048345],"about_ca_topic_score_codex":0.0008353238,"about_ca_topic_score_gemma":0.0008336345,"teacher_disagreement_score":0.050689507,"about_ca_system_score_codex":0.0009803626,"about_ca_system_score_gemma":0.002396676,"threshold_uncertainty_score":0.1695733},"labels":[],"label_agreement":null},{"id":"W2620778781","doi":"10.1016/j.spasta.2017.05.001","title":"A local-EM algorithm for spatio-temporal disease mapping with aggregated data","year":2017,"lang":"en","type":"article","venue":"Spatial Statistics","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University; Cancer Care Nova Scotia; University of Toronto; St. Michael's Hospital; Institute for Clinical Evaluative Sciences","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Expectation–maximization algorithm; Smoothing; Point process; Algorithm; Population; Poisson distribution; Computer science; Boundary (topology); Covariate; A priori and a posteriori; Maximization; Constraint (computer-aided design); Statistics; Econometrics; Mathematics; Data mining; Mathematical optimization; Maximum likelihood; Demography","score_opus":0.07654553392609349,"score_gpt":0.26679679845312254,"score_spread":0.19025126452702906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2620778781","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015193316,0.000117592084,0.9971398,0.00012786455,0.000025348336,0.000045489058,0.00012982165,0.00058139046,0.0003133478],"genre_scores_gemma":[0.037260402,0.00018863709,0.9580172,0.00018166866,0.00006043451,0.00042388737,0.0012283567,0.00030927348,0.0023301726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807286,0.0010237257,0.00013494537,0.000459868,0.00022736021,0.00008127532],"domain_scores_gemma":[0.9955112,0.0030625218,0.00027454662,0.0005473228,0.000491941,0.00011260615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055328817,0.0011032788,0.0015743716,0.0015525772,0.0008904278,0.0015575658,0.0032958775,0.0019788407,0.005073941],"category_scores_gemma":[0.016957467,0.0012598154,0.0022090813,0.0027669603,0.00084363815,0.002005451,0.003079564,0.0030077642,0.0025925264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021376848,0.00012408826,0.0041043744,0.00023286039,0.0004939815,0.00021521488,0.00033818814,0.62609917,0.0013268852,0.042469703,0.015001856,0.3093799],"study_design_scores_gemma":[0.000027413656,0.000019255202,0.00028733112,0.000020664516,0.000020167889,0.00006563062,0.000035694728,0.96902776,0.00040146598,0.026470616,0.0036076366,0.000016342954],"about_ca_topic_score_codex":0.01110108,"about_ca_topic_score_gemma":0.01434371,"teacher_disagreement_score":0.01110108,"about_ca_system_score_codex":0.0011763424,"about_ca_system_score_gemma":0.0023464486,"threshold_uncertainty_score":0.029260993},"labels":[],"label_agreement":null},{"id":"W2767161482","doi":"10.1016/j.spasta.2017.09.003","title":"Accounting for covariate information in the scale component of spatio-temporal mixing models","year":2017,"lang":"en","type":"article","venue":"Spatial Statistics","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Covariate; Gaussian process; Kurtosis; Covariance; Mathematics; Scale (ratio); Econometrics; Bayesian inference; Covariance function; Statistics; Bayesian probability; Transformation (genetics); Statistical physics; Gaussian; Computer science; Geography","score_opus":0.027744171750023503,"score_gpt":0.2619215363355759,"score_spread":0.2341773645855524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767161482","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04445242,0.0002522149,0.9535579,0.00040458178,0.000058750793,0.00003288036,0.00034178025,0.00038256953,0.00051679206],"genre_scores_gemma":[0.8443535,0.00077170244,0.14780922,0.00022161513,0.00021626063,0.00020852278,0.0011941685,0.00035480154,0.004870226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9964773,0.0022179612,0.00019491104,0.0006212633,0.00027140373,0.00021708469],"domain_scores_gemma":[0.9720928,0.020137344,0.0018515277,0.004864094,0.00077316206,0.00028100226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012089173,0.0009840248,0.0010749933,0.0008535295,0.00070999475,0.001810803,0.0019133933,0.0019250547,0.0023872126],"category_scores_gemma":[0.055064008,0.0009260367,0.0013714078,0.0023282475,0.0011648546,0.0039499556,0.0018159965,0.0019834144,0.0004954493],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038069545,0.00015198416,0.03958325,0.00018286848,0.0007373364,0.00040074624,0.00042135047,0.67127705,0.005567465,0.17497689,0.0028182212,0.103502266],"study_design_scores_gemma":[0.000028437607,0.000049924078,0.0048498865,0.000017132736,0.00010027983,0.00006757666,0.000026154888,0.9363633,0.000804869,0.056197554,0.001459923,0.000034955112],"about_ca_topic_score_codex":0.013054531,"about_ca_topic_score_gemma":0.014456416,"teacher_disagreement_score":0.013054531,"about_ca_system_score_codex":0.0008667281,"about_ca_system_score_gemma":0.0024252385,"threshold_uncertainty_score":0.063934445},"labels":[],"label_agreement":null},{"id":"W2908690106","doi":"10.1016/j.spasta.2020.100437","title":"Projections of determinantal point processes","year":2020,"lang":"en","type":"preprint","venue":"Spatial Statistics","topic":"Point processes and geometric inequalities","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Cardinality (data modeling); Determinantal point process; Projection (relational algebra); Mathematics; Point process; Bounded function; Kernel (algebra); Combinatorics; Point (geometry); Function (biology); Discrete mathematics; Physics; Mathematical analysis; Geometry; Algorithm; Quantum mechanics; Computer science; Random matrix","score_opus":0.10723140090067394,"score_gpt":0.3623869134029158,"score_spread":0.25515551250224183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908690106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21509962,0.0023796347,0.7354867,0.003533857,0.0003644523,0.00006594997,0.0009290885,0.00026922242,0.041871548],"genre_scores_gemma":[0.91512644,0.0033276286,0.05122063,0.0005520286,0.000987708,0.00014772594,0.0009677424,0.00026019546,0.027409913],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99786395,0.0009097974,0.00007734682,0.00037438577,0.00054769206,0.00022691561],"domain_scores_gemma":[0.98780257,0.0073960307,0.0014173945,0.00060007395,0.0016372498,0.0011466333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00351257,0.001510535,0.0015437123,0.0035689673,0.0010292389,0.0054698377,0.001608062,0.0015319473,0.009373988],"category_scores_gemma":[0.016889004,0.00092310185,0.0012044166,0.0024070803,0.004567705,0.0072825975,0.0037372804,0.003792657,0.0008626197],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016300975,0.00001173683,0.00020103379,0.000019759389,0.000008909848,0.000023709343,0.00005665256,0.002681697,0.00019871649,0.99462694,0.000494416,0.0016601414],"study_design_scores_gemma":[0.000007829775,0.000009320674,0.0002722498,0.000010388577,0.0000049694286,0.00004331514,0.000032664288,0.031313375,0.0001355803,0.96726483,0.0008944976,0.000010877753],"about_ca_topic_score_codex":0.0024009552,"about_ca_topic_score_gemma":0.0014911622,"teacher_disagreement_score":0.009373988,"about_ca_system_score_codex":0.002276634,"about_ca_system_score_gemma":0.001776861,"threshold_uncertainty_score":0.031359136},"labels":[],"label_agreement":null},{"id":"W2972237095","doi":"10.1016/j.spasta.2019.100385","title":"Simulation of decorrelated factors in presence of secondary data","year":2019,"lang":"en","type":"article","venue":"Spatial Statistics","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Multivariate statistics; Geostatistics; Multivariate analysis; Gaussian; Variable (mathematics); Computer science; Multivariate normal distribution; Variables; Data mining; Statistics; Algorithm; Mathematics; Machine learning; Spatial variability; Chemistry","score_opus":0.02016158448990196,"score_gpt":0.2668493752540915,"score_spread":0.24668779076418956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972237095","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56764936,0.00015724456,0.42542967,0.00038136527,0.00013634896,0.000108148284,0.0006514782,0.00071289303,0.0047734967],"genre_scores_gemma":[0.9599129,0.000039302275,0.037938908,0.000046210484,0.000019193383,0.00006670779,0.00037596351,0.000080725054,0.0015199604],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928516,0.00030749923,0.00003250093,0.00012238079,0.00012267409,0.00012971478],"domain_scores_gemma":[0.98187286,0.015313178,0.0005950361,0.00071402825,0.0011119556,0.00039290416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019720288,0.00060127437,0.00080380857,0.0006651918,0.00047001994,0.0010032343,0.0013230591,0.0017011572,0.00364873],"category_scores_gemma":[0.0155883515,0.0006175726,0.0008848959,0.00080751017,0.0010195432,0.0008011356,0.0009627999,0.0010661259,0.00025039865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007266428,0.000027234351,0.0009840952,0.000010566518,0.000009394148,0.00005251221,0.00002305003,0.9950671,0.00024528758,0.0024988572,0.00012174225,0.0008874667],"study_design_scores_gemma":[0.0000057319858,0.0000057688767,0.000068861766,8.493349e-7,0.0000014003459,0.000004439967,0.000002947429,0.9992822,0.00015823147,0.0004371762,0.000030441315,0.0000018964612],"about_ca_topic_score_codex":0.018218689,"about_ca_topic_score_gemma":0.010093959,"teacher_disagreement_score":0.018218689,"about_ca_system_score_codex":0.0008951234,"about_ca_system_score_gemma":0.0011721903,"threshold_uncertainty_score":0.03622532},"labels":[],"label_agreement":null},{"id":"W3004413721","doi":"10.1016/j.spasta.2020.100409","title":"Approximately optimal spatial design: How good is it?","year":2020,"lang":"en","type":"article","venue":"Spatial Statistics","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Western Canada Research Grid; Compute Canada","keywords":"Probabilistic logic; Human health; Quality (philosophy); Work (physics); Association (psychology); Optimization problem; Environmental quality","score_opus":0.042971335230506766,"score_gpt":0.248148063917712,"score_spread":0.2051767286872052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004413721","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008764002,0.002526331,0.976339,0.009037985,0.0003072065,0.00009119391,0.00020117755,0.00029225444,0.0024409543],"genre_scores_gemma":[0.29967165,0.0024789101,0.6899391,0.004341167,0.00088477996,0.0004040374,0.00029738736,0.00026860792,0.0017143437],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.961344,0.03241447,0.0010870693,0.0023476612,0.0024013298,0.0004054455],"domain_scores_gemma":[0.834386,0.14624348,0.0029792767,0.009842604,0.0056136237,0.00093500974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032093506,0.0010654036,0.004081195,0.00096994557,0.0007511403,0.002448619,0.0020538226,0.0038563763,0.0045088124],"category_scores_gemma":[0.17696685,0.0012405579,0.001185002,0.0014265105,0.0039698635,0.0058309897,0.0016596462,0.0027464987,0.0007697146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014470242,0.0004721774,0.011151386,0.0012066322,0.0010630644,0.00012141272,0.00036533698,0.29227984,0.0007622486,0.29811135,0.022074923,0.37094465],"study_design_scores_gemma":[0.0005980118,0.000573547,0.001950268,0.00029195758,0.0002659151,0.00017720404,0.00017487415,0.34744206,0.00077757327,0.63592285,0.011746604,0.00007902835],"about_ca_topic_score_codex":0.0036634684,"about_ca_topic_score_gemma":0.0040763044,"teacher_disagreement_score":0.032093506,"about_ca_system_score_codex":0.0018388474,"about_ca_system_score_gemma":0.0040518222,"threshold_uncertainty_score":0.16972876},"labels":[],"label_agreement":null},{"id":"W3097422813","doi":"10.1016/j.spasta.2020.100480","title":"Population-weighted exposure to air pollution and COVID-19 incidence in Germany","year":2020,"lang":"en","type":"article","venue":"Spatial Statistics","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Centre for Global Health Research; St. Michael's Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Air pollution; 2019-20 coronavirus outbreak; Population; Incidence (geometry); Environmental science; Environmental health; Biology; Medicine; Virology; Mathematics; Outbreak; Ecology; Internal medicine; Disease; Infectious disease (medical specialty)","score_opus":0.03655574252130881,"score_gpt":0.3219191452030815,"score_spread":0.2853634026817727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097422813","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9855003,0.00043223362,0.00039533852,0.0000994583,0.0000147217315,0.0000060394013,0.012246537,0.00004085183,0.0012646063],"genre_scores_gemma":[0.9935381,0.00026030207,0.00009900855,0.000015245845,0.0000062327977,0.000008212211,0.0055509033,0.000004140657,0.00051784015],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99951375,0.00008357431,0.00008091096,0.00012650916,0.000068611815,0.00012659181],"domain_scores_gemma":[0.99930716,0.00012876368,0.00029500312,0.00009024147,0.00010484859,0.00007409001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054119824,0.00021294256,0.0002470446,0.0019479054,0.00014152413,0.0007180825,0.00033267814,0.00025646182,0.0017030541],"category_scores_gemma":[0.001299696,0.0002597653,0.0005763777,0.002672987,0.00018525154,0.00037907148,0.0006080858,0.00023172155,0.00034414002],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015764455,0.000029573483,0.9864356,0.000044786837,0.0005237822,0.000113365335,0.00014678946,0.0030599916,0.000298233,0.0005759012,0.0020329405,0.0065814625],"study_design_scores_gemma":[0.0000034793502,0.00002053787,0.99753076,0.0000075055195,0.000069286034,0.000056264187,0.00013875225,0.0010200213,0.00010439545,0.000053439762,0.000988104,0.0000074509758],"about_ca_topic_score_codex":0.10441713,"about_ca_topic_score_gemma":0.073792204,"teacher_disagreement_score":0.10441713,"about_ca_system_score_codex":0.0010226074,"about_ca_system_score_gemma":0.0007382692,"threshold_uncertainty_score":0.20761871},"labels":[],"label_agreement":null},{"id":"W3160435793","doi":"10.1016/j.spasta.2021.100509","title":"The root-Gaussian Cox process and a generalized EMS algorithm","year":2021,"lang":"en","type":"article","venue":"Spatial Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Centre for Global Health Research; St. Michael's Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Algorithm; Gaussian process; Gaussian; Process (computing); Plasmodium falciparum; Computer science; Square root; Cox process; Mathematics; Statistics; Data mining; Malaria; Artificial intelligence; Biology","score_opus":0.02917966577671349,"score_gpt":0.3594704571164984,"score_spread":0.3302907913397849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160435793","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002255911,0.0001108693,0.99684286,0.0001919772,0.000031240346,0.00002805547,0.000045551253,0.000064588814,0.00042903447],"genre_scores_gemma":[0.16079213,0.0008548106,0.8289294,0.00025669983,0.00024618165,0.00042066505,0.00044077626,0.00015129367,0.007908088],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9956546,0.0027429555,0.00014265273,0.0005854189,0.0006790301,0.00019532263],"domain_scores_gemma":[0.99071175,0.0063375914,0.00064027554,0.00092221744,0.0011412762,0.00024698995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008884123,0.0007202753,0.0012366953,0.0015369422,0.0005835192,0.0014010526,0.0027400309,0.0014632521,0.004158058],"category_scores_gemma":[0.019399917,0.0007575676,0.0015614905,0.001764222,0.00209624,0.0020098018,0.0021198522,0.0023445997,0.00087555783],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011035837,0.00004016391,0.0028186818,0.00010415551,0.00014852396,0.00018766738,0.0001622966,0.332635,0.0010388821,0.58760446,0.002998383,0.07215154],"study_design_scores_gemma":[0.000028868792,0.00004450496,0.00044267066,0.000016614276,0.00002483498,0.00007069325,0.000023604125,0.8585153,0.00038129275,0.13654938,0.0038697068,0.00003251962],"about_ca_topic_score_codex":0.0054272725,"about_ca_topic_score_gemma":0.0055271913,"teacher_disagreement_score":0.008884123,"about_ca_system_score_codex":0.0010398151,"about_ca_system_score_gemma":0.0027663368,"threshold_uncertainty_score":0.046984255},"labels":[],"label_agreement":null},{"id":"W3182828618","doi":"10.1016/j.spasta.2021.100526","title":"Spatial-temporal generalized additive model for modeling COVID-19 mortality risk in Toronto, Canada","year":2021,"lang":"en","type":"article","venue":"Spatial Statistics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Generalized additive model; Statistics; Econometrics; Coronavirus disease 2019 (COVID-19); Computer science; Spline (mechanical); Sample (material); Range (aeronautics); Population; Mathematics; Demography; Medicine","score_opus":0.22699306763800617,"score_gpt":0.43469519178743443,"score_spread":0.20770212414942826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3182828618","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.708833,0.006031213,0.22014569,0.009499299,0.0006013615,0.00042500964,0.041047134,0.00089711533,0.01252016],"genre_scores_gemma":[0.95078385,0.0021054104,0.019197766,0.00021401775,0.00009726897,0.00021741167,0.007843483,0.00009397229,0.01944693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850047,0.0005109433,0.00008423641,0.00028077493,0.00017462565,0.0004489347],"domain_scores_gemma":[0.9967518,0.0012644666,0.00036325344,0.00019942551,0.0011731769,0.0002479579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029774876,0.0010360344,0.0012728628,0.0014444531,0.0016532156,0.0021343415,0.0028083446,0.0011743181,0.004694639],"category_scores_gemma":[0.00800569,0.000728648,0.0014320817,0.0030651984,0.0012432886,0.0007684434,0.0013925213,0.0016552316,0.00046118585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049188285,0.00014766361,0.14257719,0.00021998014,0.0008364368,0.0007491178,0.00087440625,0.72194624,0.0006715174,0.07457911,0.025470758,0.031435803],"study_design_scores_gemma":[0.00006720636,0.00004602949,0.034737688,0.00011020808,0.00029618584,0.00009917862,0.000775149,0.9433379,0.00016367735,0.011637496,0.008648714,0.000080662256],"about_ca_topic_score_codex":0.98268396,"about_ca_topic_score_gemma":0.97488594,"teacher_disagreement_score":0.019323487,"about_ca_system_score_codex":0.019323487,"about_ca_system_score_gemma":0.027331814,"threshold_uncertainty_score":0.14020234},"labels":[],"label_agreement":null},{"id":"W3201715906","doi":"10.1016/j.spasta.2022.100651","title":"A convolution type model for the intensity of spatial point processes applied to eye-movement data","year":2022,"lang":"en","type":"article","venue":"Spatial Statistics","topic":"Point processes and geometric inequalities","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Fondo Nacional de Desarrollo Científico y Tecnológico; Natural Sciences and Engineering Research Council of Canada; Agencia Nacional de Investigación y Desarrollo; Universidad Técnica Federico Santa María","keywords":"Convolution (computer science); Truncation (statistics); Mathematics; Poisson distribution; Covariate; Point process; Function (biology); Series (stratigraphy); Fourier series; Applied mathematics; Algorithm; Statistics; Computer science; Mathematical analysis; Artificial intelligence","score_opus":0.13568752498043302,"score_gpt":0.35609148364791493,"score_spread":0.22040395866748191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201715906","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00881202,0.00024172729,0.98932785,0.00042004604,0.000056043427,0.00002756194,0.00014455355,0.00010022657,0.0008698627],"genre_scores_gemma":[0.72142684,0.0024249759,0.2520386,0.00081519707,0.0007484547,0.0005885601,0.0010707058,0.00039158424,0.020495078],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996382,0.0013617361,0.00022663653,0.00077730836,0.0007385013,0.0005138223],"domain_scores_gemma":[0.9849722,0.0113373855,0.0009520405,0.001052724,0.0013150655,0.00037061237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010909502,0.001382885,0.0022077942,0.0020598392,0.0006948506,0.0032604034,0.0040661036,0.0037789752,0.003599498],"category_scores_gemma":[0.026854822,0.0012093189,0.0025872532,0.0032556031,0.0033084215,0.0051645376,0.0025961527,0.004184387,0.0007367037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014423233,0.000083552535,0.002217808,0.0001727986,0.00012602395,0.00019571032,0.0002787234,0.24708636,0.0047554686,0.7133949,0.0019011698,0.02964331],"study_design_scores_gemma":[0.00001150041,0.000028267046,0.0005280194,0.000018874696,0.000028260334,0.00006995514,0.000013514723,0.90750843,0.00044312584,0.090771474,0.0005524905,0.000026164915],"about_ca_topic_score_codex":0.013870481,"about_ca_topic_score_gemma":0.007832401,"teacher_disagreement_score":0.013870481,"about_ca_system_score_codex":0.0032168038,"about_ca_system_score_gemma":0.003014611,"threshold_uncertainty_score":0.057695627},"labels":[],"label_agreement":null},{"id":"W3202730044","doi":"10.1016/j.spasta.2021.100540","title":"Capturing spatial dependence of COVID-19 case counts with cellphone mobility data","year":2021,"lang":"en","type":"article","venue":"Spatial Statistics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Centre for Global Health Research; St. Michael's Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Ministerio de Ciencia e Innovación","keywords":"Leverage (statistics); Coronavirus disease 2019 (COVID-19); Spatial dependence; Markov chain; Spatial analysis; Markov chain Monte Carlo; Computer science; Statistics; Adjacency list; Econometrics; Mathematics; Monte Carlo method; Infectious disease (medical specialty); Medicine; Algorithm","score_opus":0.2867526967347605,"score_gpt":0.43611570708045483,"score_spread":0.14936301034569432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202730044","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71367335,0.0006447589,0.27255788,0.001534648,0.00017251262,0.00012694749,0.007676688,0.0006213378,0.002991878],"genre_scores_gemma":[0.97479916,0.00018770505,0.018183151,0.00009837429,0.00009997026,0.00006267791,0.0052588563,0.000031771255,0.0012782051],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99758995,0.0012327833,0.00012389255,0.00044194274,0.00030353604,0.00030789882],"domain_scores_gemma":[0.98178387,0.012311485,0.0019353606,0.0023712355,0.0011603624,0.00043776483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006281062,0.0005987692,0.0008829849,0.0024459546,0.00048421862,0.001283388,0.0016704346,0.001146652,0.0018754158],"category_scores_gemma":[0.039355893,0.00066914066,0.0009623551,0.00310003,0.00079793914,0.0014107923,0.0015952816,0.0011930304,0.00045241546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037261448,0.00023733635,0.5653792,0.0001442337,0.0004360987,0.00045760037,0.00059866154,0.31349936,0.0017187514,0.036021743,0.009747777,0.07138673],"study_design_scores_gemma":[0.000020308638,0.000078730496,0.05149033,0.000037171078,0.00006550871,0.00017343908,0.00038585326,0.92447686,0.00053446955,0.02051012,0.0022015723,0.00002562617],"about_ca_topic_score_codex":0.02956867,"about_ca_topic_score_gemma":0.034047544,"teacher_disagreement_score":0.02956867,"about_ca_system_score_codex":0.00088685803,"about_ca_system_score_gemma":0.0012496826,"threshold_uncertainty_score":0.058793128},"labels":[],"label_agreement":null},{"id":"W34249608","doi":"10.1016/j.spasta.2021.100526","title":"Utlakningsförsök med vitsenap och oljerättika som eftersådda fånggrödor","year":2011,"lang":"en","type":"article","venue":"Spatial Statistics","topic":"Agricultural Science and Fertilization","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Medicine; Gynecology","score_opus":0.03369513887091127,"score_gpt":0.19985741953130468,"score_spread":0.1661622806603934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W34249608","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2907897,0.00881414,0.6074208,0.011214852,0.001091395,0.0006175651,0.020381304,0.0032860425,0.056384146],"genre_scores_gemma":[0.72272474,0.006747117,0.15354373,0.00046644034,0.0002580499,0.00034477073,0.010081882,0.0005328467,0.10530049],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987802,0.0003076723,0.00008138335,0.0002590564,0.00040775625,0.00016389217],"domain_scores_gemma":[0.9990351,0.00042687342,0.00011198097,0.00011163422,0.00023704267,0.000077401055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030437561,0.00082036154,0.00088212756,0.0013773587,0.001010257,0.0034489299,0.0014686112,0.0009718362,0.010608314],"category_scores_gemma":[0.0044398247,0.00041780915,0.0013188507,0.0013102943,0.00081831403,0.0010665405,0.001386244,0.0012005732,0.0026706972],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005543472,0.00034577795,0.086699985,0.00073035,0.00055379403,0.0011042436,0.0034158612,0.37556309,0.00251927,0.0763892,0.031026488,0.42109767],"study_design_scores_gemma":[0.00012118576,0.00036725932,0.06423292,0.0007629468,0.0004632302,0.00056970754,0.0029988058,0.62010974,0.0041928636,0.076503575,0.22936994,0.00030776398],"about_ca_topic_score_codex":0.29232764,"about_ca_topic_score_gemma":0.33577362,"teacher_disagreement_score":0.29232764,"about_ca_system_score_codex":0.0045511466,"about_ca_system_score_gemma":0.007294894,"threshold_uncertainty_score":0.5812522},"labels":[],"label_agreement":null},{"id":"W4206512956","doi":"10.1016/j.spasta.2022.100593","title":"Bayesian disease mapping: Past, present, and future","year":2022,"lang":"en","type":"article","venue":"Spatial Statistics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":53,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bayesian probability; Data science; Disease; Pandemic; Spatial epidemiology; Population; Public health; Geography; Computer science; Coronavirus disease 2019 (COVID-19); Medicine; Epidemiology; Artificial intelligence; Infectious disease (medical specialty); Environmental health","score_opus":0.010517728311528784,"score_gpt":0.25360348064191796,"score_spread":0.24308575233038918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206512956","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025667634,0.31039324,0.3393879,0.30986953,0.001274251,0.000062517225,0.0016504902,0.00071114796,0.010983215],"genre_scores_gemma":[0.5425194,0.25380516,0.18242294,0.010275727,0.0059284074,0.0001837115,0.0013479231,0.00015287589,0.0033639143],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9961804,0.0025360454,0.00014293812,0.00046230404,0.0005474035,0.00013081435],"domain_scores_gemma":[0.9480953,0.040690824,0.0020519271,0.0020850298,0.0050790287,0.001997944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023222951,0.00080726465,0.0016456172,0.002474282,0.00042738428,0.0039985324,0.0018126838,0.0024733276,0.0030807017],"category_scores_gemma":[0.043084834,0.0006986024,0.0006460019,0.0030883776,0.004438352,0.0071352897,0.0018511007,0.0030606976,0.0005065181],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019909594,0.0002215324,0.028306492,0.0012697022,0.00025304983,0.00008105115,0.00045006585,0.02427834,0.0004293793,0.18261045,0.0292482,0.7326526],"study_design_scores_gemma":[0.000048032318,0.00012590224,0.00967808,0.0016151983,0.00011856343,0.0002608673,0.00071563345,0.10431196,0.00046477487,0.79721063,0.08534834,0.00010203462],"about_ca_topic_score_codex":0.0120180575,"about_ca_topic_score_gemma":0.016588705,"teacher_disagreement_score":0.023222951,"about_ca_system_score_codex":0.0028464196,"about_ca_system_score_gemma":0.0045378306,"threshold_uncertainty_score":0.122816205},"labels":[],"label_agreement":null},{"id":"W4210929552","doi":"10.1016/j.spasta.2022.100617","title":"Computation-free nonparametric testing for local spatial association with application to the US and Canadian electorate","year":2022,"lang":"en","type":"article","venue":"Spatial Statistics","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nonparametric statistics; Permutation (music); Spatial analysis; Parametric statistics; Resampling; Computer science; Statistical hypothesis testing; Econometrics; Statistics; Computation; Spatial econometrics; Triangle inequality; Omnibus test; Multiple comparisons problem; Mathematics; Algorithm; Discrete mathematics","score_opus":0.017113661838344063,"score_gpt":0.21039215885334897,"score_spread":0.1932784970150049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210929552","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0322324,0.00024767203,0.96248585,0.0006217754,0.000065633314,0.00017126168,0.0006268693,0.0021346807,0.001413833],"genre_scores_gemma":[0.5517474,0.00030515075,0.43936405,0.00022621259,0.00021015601,0.0005691175,0.0017410445,0.00080007844,0.0050368058],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9808833,0.012208196,0.0008457834,0.002208142,0.0029485908,0.00090596254],"domain_scores_gemma":[0.7029429,0.25703257,0.0054157334,0.024581667,0.008160803,0.0018663114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030345578,0.0010473257,0.0024247838,0.0035337952,0.0031253803,0.0025253596,0.006455218,0.0017207806,0.008943033],"category_scores_gemma":[0.19146802,0.0009225466,0.0028812455,0.006977963,0.0055122194,0.00267724,0.004283192,0.0037566307,0.0009346918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00093852595,0.00035316855,0.051776454,0.00037042645,0.00091992255,0.0015077438,0.0014099177,0.26297346,0.0021951678,0.23526506,0.017074639,0.42521545],"study_design_scores_gemma":[0.00017141819,0.00011422529,0.009422626,0.00004658085,0.00012084147,0.00031860967,0.00028182156,0.8372953,0.0010223335,0.14733638,0.0037915083,0.000078435165],"about_ca_topic_score_codex":0.20540366,"about_ca_topic_score_gemma":0.22547036,"teacher_disagreement_score":0.7945963,"about_ca_system_score_codex":0.0038531208,"about_ca_system_score_gemma":0.0142842755,"threshold_uncertainty_score":0.40841615},"labels":[],"label_agreement":null},{"id":"W4317568652","doi":"10.1016/j.spasta.2023.100726","title":"Adaptive Gaussian Markov random field spatiotemporal models for infectious disease mapping and forecasting","year":2023,"lang":"en","type":"article","venue":"Spatial Statistics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Poisson distribution; Autoregressive model; Conditional independence; Bayesian probability; Gaussian; Count data; Random field; Context (archaeology); Econometrics; Artificial intelligence; Statistics; Mathematics; Geography","score_opus":0.24400451631992848,"score_gpt":0.3832864950063597,"score_spread":0.13928197868643122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317568652","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011074373,0.00089098996,0.9853736,0.0011410759,0.00012172914,0.000029626086,0.00034241675,0.00021349954,0.00081261765],"genre_scores_gemma":[0.7519049,0.004208791,0.22493555,0.0005000659,0.0008325975,0.0004430672,0.0019981796,0.00022244976,0.014954392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982986,0.0009718922,0.00009419322,0.00032230228,0.00018393222,0.00012913925],"domain_scores_gemma":[0.9859439,0.011149918,0.0011432059,0.0006371158,0.0008846295,0.00024114802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047814175,0.00093945814,0.0020596206,0.0017337401,0.00059391244,0.0015481837,0.00324707,0.0024224783,0.003093069],"category_scores_gemma":[0.021983718,0.0010271829,0.0014015851,0.0028138368,0.001445885,0.0031505523,0.0016720345,0.0028323033,0.00057602645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003545894,0.000028168979,0.00084198074,0.00004670147,0.000050844395,0.00003187267,0.000051552714,0.8984929,0.00015402441,0.08685957,0.0014730017,0.011934058],"study_design_scores_gemma":[0.0000041325966,0.000004411696,0.000085862324,0.000004010212,0.0000052561845,0.0000046697423,0.0000046185964,0.9682834,0.000020751235,0.031325173,0.0002520697,0.000005538232],"about_ca_topic_score_codex":0.028982649,"about_ca_topic_score_gemma":0.016782602,"teacher_disagreement_score":0.028982649,"about_ca_system_score_codex":0.0021725788,"about_ca_system_score_gemma":0.002056882,"threshold_uncertainty_score":0.057627857},"labels":[],"label_agreement":null},{"id":"W4324144221","doi":"10.1016/j.spasta.2023.100729","title":"Analyzing COVID-19 data in the Canadian province of Manitoba: A new approach","year":2023,"lang":"en","type":"article","venue":"Spatial Statistics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary; University of Manitoba","funders":"Research Manitoba","keywords":"Homogeneous; Inference; Coronavirus disease 2019 (COVID-19); Epidemic model; Population; Geography; Estimation; Econometrics; Statistics; Demography; Computer science; Infectious disease (medical specialty); Mathematics; Medicine; Disease; Artificial intelligence","score_opus":0.4288658686534325,"score_gpt":0.44720516412892364,"score_spread":0.018339295475491124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324144221","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5486412,0.0053713713,0.36426538,0.009188592,0.000254246,0.00086776627,0.047946908,0.0010465713,0.022417976],"genre_scores_gemma":[0.85447174,0.0021500955,0.12605013,0.0003393899,0.0001042626,0.00018728401,0.01169908,0.00010914317,0.0048887925],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9967121,0.000891326,0.00019258095,0.0005351356,0.0011627827,0.00050604873],"domain_scores_gemma":[0.9897642,0.0051567056,0.0006907703,0.0008730468,0.0031405431,0.00037466828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051313746,0.0007144616,0.0008404772,0.008372163,0.002466522,0.0034349489,0.0023159129,0.0005177216,0.0022770392],"category_scores_gemma":[0.029145809,0.00040173254,0.0011599322,0.01581442,0.0016737763,0.0009159244,0.0019547392,0.0011608951,0.00020562591],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022068717,0.00015077443,0.5658212,0.00055483764,0.0011125879,0.0007405413,0.0022044433,0.1102533,0.0017590597,0.1167428,0.023375819,0.17706396],"study_design_scores_gemma":[0.00008264679,0.000105382234,0.502735,0.00022184772,0.00051501853,0.00036234976,0.009460194,0.35883853,0.0011946537,0.07721078,0.04914662,0.0001269431],"about_ca_topic_score_codex":0.9854689,"about_ca_topic_score_gemma":0.98834604,"teacher_disagreement_score":0.022247007,"about_ca_system_score_codex":0.022247007,"about_ca_system_score_gemma":0.049649816,"threshold_uncertainty_score":0.16141409},"labels":[],"label_agreement":null},{"id":"W4388088673","doi":"10.1016/j.spasta.2023.100788","title":"Review of Sujit Sahu’s “Bayesian modeling of spatio-temporal data with R”","year":2023,"lang":"en","type":"article","venue":"Spatial Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Bayesian probability; Computer science; Artificial intelligence","score_opus":0.2010256945709597,"score_gpt":0.4472928259600928,"score_spread":0.2462671313891331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388088673","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00089993357,0.7902506,0.16382563,0.029982394,0.008924084,0.000021343976,0.0003854226,0.000209415,0.005501118],"genre_scores_gemma":[0.02387538,0.857495,0.07447367,0.016552137,0.021254977,0.00009127628,0.0006080323,0.0003272608,0.0053222],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99703133,0.0016132016,0.00022352733,0.00043880084,0.0006154648,0.000077655524],"domain_scores_gemma":[0.9869336,0.0094430875,0.00037831327,0.0005937609,0.0024257961,0.0002254219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008111643,0.0010273429,0.0019442387,0.0029350552,0.0004674617,0.0019761513,0.002104363,0.0022959067,0.0016749272],"category_scores_gemma":[0.02457271,0.0007504645,0.0009884047,0.0057909596,0.0020742707,0.0031469022,0.0011316997,0.0034504756,0.0013893322],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086033295,0.000048619986,0.0006490683,0.0029065982,0.00029753827,0.00021580805,0.00028786878,0.010313631,0.0005163586,0.33658904,0.24172519,0.40636432],"study_design_scores_gemma":[0.000022514192,0.000064542546,0.0010041909,0.0015152047,0.0001827941,0.0005514287,0.00008095929,0.013412785,0.00067007955,0.123378575,0.85900426,0.000112579975],"about_ca_topic_score_codex":0.0084301,"about_ca_topic_score_gemma":0.0066399775,"teacher_disagreement_score":0.0084301,"about_ca_system_score_codex":0.002070862,"about_ca_system_score_gemma":0.0035831903,"threshold_uncertainty_score":0.042898953},"labels":[],"label_agreement":null},{"id":"W4388535002","doi":"10.1016/j.spasta.2023.100792","title":"A spatial model with vaccinations for COVID-19 in South Africa","year":2023,"lang":"en","type":"article","venue":"Spatial Statistics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Foundation; University of Pretoria; International Development Research Centre","keywords":"Pandemic; Outbreak; Government (linguistics); Vaccination; Geography; Vulnerability (computing); Spatial epidemiology; Coronavirus disease 2019 (COVID-19); Public health; Psychological intervention; Environmental health; Transmission (telecommunications); Disease; Computer science; Medicine; Infectious disease (medical specialty); Virology; Epidemiology","score_opus":0.30888337565906654,"score_gpt":0.43922832828170644,"score_spread":0.1303449526226399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388535002","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6254209,0.0046094405,0.28479382,0.015453899,0.000538146,0.0003440182,0.0038252107,0.0002985025,0.06471598],"genre_scores_gemma":[0.94934297,0.0014781564,0.010175224,0.00031431275,0.00011648452,0.00020719421,0.00045833745,0.000054991448,0.03785236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994905,0.0002263057,0.000020711332,0.00009786583,0.00003207592,0.00013251416],"domain_scores_gemma":[0.9988426,0.0006308046,0.0002298774,0.00002988482,0.00013989066,0.00012683225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000924971,0.00085886585,0.0011094597,0.0010292659,0.00097472104,0.0020520086,0.0019408284,0.0025194352,0.008276206],"category_scores_gemma":[0.003815132,0.00064875215,0.001283797,0.001000625,0.0015435603,0.0018650806,0.002264739,0.0013686306,0.00070908596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001552584,0.00006572657,0.0048228153,0.00009847004,0.0000585898,0.00089177635,0.0003617934,0.86095905,0.00079085893,0.12631439,0.002148048,0.0033332135],"study_design_scores_gemma":[0.000076980534,0.00006312511,0.000933716,0.00003278084,0.000034343808,0.00012835137,0.00023021825,0.97784936,0.00007556089,0.017862309,0.002688334,0.000024874076],"about_ca_topic_score_codex":0.073490724,"about_ca_topic_score_gemma":0.026840702,"teacher_disagreement_score":0.073490724,"about_ca_system_score_codex":0.0028536888,"about_ca_system_score_gemma":0.00169163,"threshold_uncertainty_score":0.14612597},"labels":[],"label_agreement":null},{"id":"W4403926102","doi":"10.1016/j.spasta.2024.100863","title":"Spatio-temporal data fusion for the analysis of in situ and remote sensing data using the INLA-SPDE approach","year":2024,"lang":"en","type":"article","venue":"Spatial Statistics","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Fusion; Sensor fusion; In situ; Data mining; Artificial intelligence; Geography","score_opus":0.07544103840889467,"score_gpt":0.3152046620988042,"score_spread":0.23976362368990956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403926102","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056373687,0.00006422005,0.9935865,0.000120433375,0.000015750396,0.000015538611,0.000103456434,0.00016939684,0.00028742777],"genre_scores_gemma":[0.4050726,0.00033007775,0.5913566,0.00023618844,0.000088812914,0.00027695645,0.0009478022,0.00011523834,0.0015758426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988355,0.0004863733,0.0000906619,0.00025021625,0.0002490471,0.000088239256],"domain_scores_gemma":[0.99776244,0.0011895463,0.00030021448,0.00024077197,0.0004293211,0.00007770565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002926163,0.0006563763,0.0010006889,0.0012211221,0.00045089162,0.001146646,0.0018145449,0.0010500562,0.00119602],"category_scores_gemma":[0.0063166474,0.00079345383,0.001292373,0.0015181921,0.0006524956,0.0018698322,0.0019469219,0.0015075943,0.00046013502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007094826,0.00008361184,0.0024645731,0.00010511486,0.00016333692,0.00007946435,0.0001251259,0.9067209,0.0034924226,0.019237708,0.0009632617,0.0664936],"study_design_scores_gemma":[0.00000224992,0.00001114947,0.00019821514,0.0000033433578,0.0000065867307,0.0000100604275,0.000007222376,0.99547935,0.00033930817,0.0035674733,0.00036839317,0.00000663486],"about_ca_topic_score_codex":0.010484203,"about_ca_topic_score_gemma":0.010790312,"teacher_disagreement_score":0.010484203,"about_ca_system_score_codex":0.00086068455,"about_ca_system_score_gemma":0.0017546169,"threshold_uncertainty_score":0.020846307},"labels":[],"label_agreement":null},{"id":"W4417212914","doi":"10.1016/j.spasta.2025.100948","title":"A heavy-tailed model for multivariate spatial processes","year":2025,"lang":"en","type":"article","venue":"Spatial Statistics","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; McGill University Health Centre","funders":"Fonds de recherche du Québec – Nature et technologies; Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Multivariate statistics; Multivariate analysis; Covariate; Variance (accounting); Multivariate normal distribution","score_opus":0.04363018229941034,"score_gpt":0.2704233268816329,"score_spread":0.22679314458222255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417212914","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008846547,0.00017873982,0.987004,0.00073439063,0.000057788813,0.00007373573,0.00086920185,0.00040393084,0.0018317896],"genre_scores_gemma":[0.5780152,0.001969174,0.37714723,0.0009097149,0.00047965016,0.0014432474,0.004640462,0.00045417345,0.034941167],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9955525,0.0020845414,0.00023675672,0.0011167604,0.00056844566,0.00044092952],"domain_scores_gemma":[0.9876155,0.008652652,0.0012288933,0.0011411917,0.0011232126,0.00023853625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010065498,0.0014462342,0.0018226859,0.0022308433,0.0009678256,0.0031210883,0.0042094714,0.0030132902,0.011149921],"category_scores_gemma":[0.021901872,0.0011241838,0.002861466,0.0038658255,0.0035246643,0.00450361,0.002485409,0.004430659,0.002216047],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007218819,0.000056501594,0.0044216346,0.00011395549,0.000108429645,0.00021709621,0.00031425885,0.35799202,0.0006023619,0.60996807,0.004584283,0.021549148],"study_design_scores_gemma":[0.000027754462,0.000033560875,0.0011908063,0.000027151791,0.000033056476,0.00009603726,0.000056735524,0.8224091,0.00018821895,0.1720254,0.003876676,0.00003564029],"about_ca_topic_score_codex":0.025995031,"about_ca_topic_score_gemma":0.018637473,"teacher_disagreement_score":0.025995031,"about_ca_system_score_codex":0.0024355403,"about_ca_system_score_gemma":0.0027141839,"threshold_uncertainty_score":0.053232074},"labels":[],"label_agreement":null}]}