{"meta":{"query_hash":"7a047e194645","filters":{"topic":"Data-Driven Disease Surveillance"},"cohort_total":1315,"direct_labels_cover":11,"predictions_cover":1315,"exported":1315,"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/7a047e194645","api":"https://metacan.xera.ac/api/v1/cohort?topic=Data-Driven+Disease+Surveillance"},"results":[{"id":"W100668801","doi":"10.1016/s0168-7069(06)16013-9","title":"Surveillance for Newly Emerging Viruses","year":2006,"lang":"en","type":"review","venue":"Perspectives in medical virology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Disease surveillance; Outbreak; Public health surveillance; Epidemiological surveillance; Public health; Population; Epidemiologic Surveillance; Medicine; Health surveillance; Environmental health; Disease; Data science; Computer science; Epidemiology; Virology; Pathology","score_opus":0.05749373095080195,"score_gpt":0.42727747593044646,"score_spread":0.3697837449796445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W100668801","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":[0.00010698082,0.9973775,0.00047548936,0.00065818394,0.0005638868,0.000007587259,0.000013320563,0.000010751887,0.00078628253],"genre_scores_gemma":[0.00129841,0.99598145,0.0008292047,0.00072701374,0.00058330566,0.000011463996,0.000036071982,0.0000015712963,0.00053154293],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99935657,0.00016165034,0.0000912286,0.0000969148,0.00024145374,0.000052169005],"domain_scores_gemma":[0.9978599,0.0012176515,0.00029784563,0.00006881086,0.00045439063,0.00010132616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00256328,0.0011540304,0.0017405959,0.0026298172,0.0002546483,0.0014608136,0.0009019803,0.0013281937,0.0018310518],"category_scores_gemma":[0.0029437614,0.00035513702,0.000603169,0.0016510503,0.0007846372,0.0021776978,0.0010057392,0.0017207141,0.0009333351],"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":[0.00003499069,0.000028886338,0.00029951782,0.006712292,0.000053184143,0.00015481461,0.00004810142,0.0002380949,0.0007810018,0.0019088798,0.018329607,0.97141063],"study_design_scores_gemma":[0.00002350501,0.00013883009,0.0012513251,0.008326894,0.00017889932,0.0015686438,0.0001291489,0.0003121473,0.0010075553,0.00258043,0.9844604,0.000022210375],"about_ca_topic_score_codex":0.0016753816,"about_ca_topic_score_gemma":0.002707635,"teacher_disagreement_score":0.0026298172,"about_ca_system_score_codex":0.0010371355,"about_ca_system_score_gemma":0.0021271412,"threshold_uncertainty_score":0.013556123},"labels":[],"label_agreement":null},{"id":"W107663614","doi":"10.1197/jamia.m2799","title":"Understanding Detection Performance in Public Health Surveillance: Modeling Aberrancy-detection Algorithms","year":2008,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"U.S. National Library of Medicine; Centers for Disease Control and Prevention; National Institutes of Health","keywords":"Computer science; Artificial intelligence; Public health; Algorithm; Data mining; Medicine; Nursing","score_opus":0.07856225860169881,"score_gpt":0.2978317227193995,"score_spread":0.2192694641177007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W107663614","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":[0.266171,0.00024406398,0.7291741,0.0007309609,0.000032705604,0.00037076283,0.00032186104,0.0011839732,0.0017706072],"genre_scores_gemma":[0.80732226,0.00011816544,0.19125673,0.000112628295,0.000021619311,0.000306429,0.000324827,0.00008586133,0.00045142323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9930854,0.0030667358,0.0005080859,0.0014707498,0.0014316317,0.00043740007],"domain_scores_gemma":[0.94445527,0.041701857,0.0059366524,0.0032329177,0.00413148,0.00054184644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01273603,0.0014828397,0.0008213064,0.002496095,0.00056234613,0.002780199,0.0026169368,0.0016831725,0.0011039649],"category_scores_gemma":[0.0719076,0.0007052718,0.0015178971,0.0015250427,0.0020859772,0.0039781625,0.001835424,0.001585525,0.0002651848],"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":[0.0002173379,0.00020791104,0.018492721,0.000109484645,0.000083740095,0.0000393495,0.00037228488,0.9471242,0.0018271037,0.009507929,0.00042988994,0.02158796],"study_design_scores_gemma":[0.000013482546,0.000059130653,0.0013726921,0.00000939653,0.0000151123695,0.000013841886,0.000024328181,0.9933007,0.0008626274,0.004165302,0.00015326196,0.000010127925],"about_ca_topic_score_codex":0.024723109,"about_ca_topic_score_gemma":0.009498064,"teacher_disagreement_score":0.024723109,"about_ca_system_score_codex":0.005788632,"about_ca_system_score_gemma":0.0035545304,"threshold_uncertainty_score":0.067355394},"labels":[],"label_agreement":null},{"id":"W125066202","doi":"10.1155/2007/386481","title":"Contributing to Communicable Diseases Intelligence Management in Canada: CACMID Meeting, March 2007, Halifax, Nova Scotia","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Infectious Diseases and Medical Microbiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada","funders":"","keywords":"Public health; Agency (philosophy); Nova scotia; Business; Public health surveillance; Public relations; Public health informatics; Political science; International health; Health policy; Medicine; Geography; Sociology","score_opus":0.008089856744326673,"score_gpt":0.25866321582635476,"score_spread":0.2505733590820281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W125066202","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053915795,0.080116555,0.0034329605,0.67829716,0.022683714,0.0015030348,0.010935718,0.0004870325,0.14862813],"genre_scores_gemma":[0.19658597,0.040251933,0.009702338,0.041339908,0.0031731369,0.000496177,0.009051765,0.0002888995,0.6991099],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99651426,0.000272002,0.000105297535,0.00030371,0.0015307091,0.0012741258],"domain_scores_gemma":[0.9918156,0.00023073907,0.00015232984,0.00007527035,0.0036918584,0.0040342147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005392646,0.0009241016,0.00044557953,0.0015465866,0.011493605,0.0068632476,0.0019916669,0.0034272678,0.017909518],"category_scores_gemma":[0.0044961534,0.0007324161,0.00036445784,0.0016788666,0.0020457066,0.0013511968,0.0027802864,0.002569549,0.0024172566],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011494507,0.00008095784,0.011478608,0.00028025507,0.000021894219,0.00074884656,0.0029518693,0.00046670152,0.0010676793,0.0026372033,0.9317286,0.048422433],"study_design_scores_gemma":[0.00001975669,0.000019327703,0.029566763,0.00028663347,0.000011358552,0.00009409501,0.004722408,0.00035860256,0.0002697247,0.00032599378,0.9642901,0.0000353503],"about_ca_topic_score_codex":0.98719317,"about_ca_topic_score_gemma":0.99672925,"teacher_disagreement_score":0.11077976,"about_ca_system_score_codex":0.11077976,"about_ca_system_score_gemma":0.24239653,"threshold_uncertainty_score":0.8037672},"labels":[],"label_agreement":null},{"id":"W14461158","doi":"10.1017/cbo9780511542534.012","title":"Infectious diseases in the historical archives: a modeling approach","year":2002,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; History","score_opus":0.031135934581650464,"score_gpt":0.20379519659040773,"score_spread":0.17265926200875728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W14461158","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":[0.032388855,0.04535703,0.55030376,0.04592499,0.0012225244,0.00025814766,0.0028494569,0.00040976043,0.32128552],"genre_scores_gemma":[0.5779171,0.081586,0.20794557,0.0028754622,0.002311914,0.00092886266,0.0016731403,0.00026233678,0.12449968],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999448,0.00032413562,0.000021589298,0.00007259781,0.00009410529,0.000039536597],"domain_scores_gemma":[0.99870026,0.0009185964,0.00010289832,0.0000808482,0.000114987604,0.00008235525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015938377,0.0007533385,0.00061141927,0.0023558785,0.0010548321,0.0043306756,0.002298443,0.0018344895,0.006151362],"category_scores_gemma":[0.0031913677,0.0004819478,0.0007848062,0.0028731674,0.0014605646,0.0036157044,0.0018352144,0.0021375266,0.0008751558],"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":[0.000008312721,0.000040016483,0.0013119818,0.000114835995,0.000039102404,0.00014328895,0.0007899363,0.041568857,0.00007929962,0.9243672,0.011658107,0.019879105],"study_design_scores_gemma":[0.000017174274,0.000038444618,0.0010000141,0.00032678206,0.000066533365,0.00026028653,0.0007770527,0.14280114,0.00012351229,0.6845109,0.17002992,0.000048202703],"about_ca_topic_score_codex":0.016555125,"about_ca_topic_score_gemma":0.017592262,"teacher_disagreement_score":0.016555125,"about_ca_system_score_codex":0.0027854524,"about_ca_system_score_gemma":0.0024065548,"threshold_uncertainty_score":0.03291756},"labels":[],"label_agreement":null},{"id":"W1493150479","doi":"10.1007/3-540-44842-x_4","title":"Testing Differences between Case and Control Point Patterns Using Nearest Neighbour Distances and Bootstrapping","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre; McGill University","funders":"","keywords":"Bootstrapping (finance); Nearest neighbour; Computer science; Wilcoxon signed-rank test; Statistics; Null hypothesis; Point pattern analysis; Point (geometry); Control point; Artificial intelligence; Pattern recognition (psychology); Data mining; Mathematics; Common spatial pattern; Econometrics; Mann–Whitney U test","score_opus":0.039131520312830535,"score_gpt":0.27310831292405513,"score_spread":0.2339767926112246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1493150479","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":[0.76334393,0.00068257545,0.23056139,0.0003138748,0.00026977155,0.00044419206,0.0018817264,0.00041967697,0.0020827423],"genre_scores_gemma":[0.9231236,0.00011723183,0.07291042,0.0000834247,0.00005411073,0.0003909677,0.0025774369,0.00008791083,0.00065486005],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9235201,0.04990875,0.005651176,0.011598894,0.008274069,0.0010470776],"domain_scores_gemma":[0.74362314,0.22789307,0.0052867373,0.016996881,0.0050645825,0.0011356456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.044809956,0.0011190312,0.002186006,0.003186775,0.0011624541,0.0018189715,0.004805776,0.0026949535,0.003964693],"category_scores_gemma":[0.16408683,0.00072220055,0.0035243393,0.0033550013,0.0019270923,0.0023701007,0.0014287988,0.0023011512,0.00095210056],"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":[0.006740104,0.0015096323,0.72387904,0.00081032404,0.010528218,0.0014652901,0.0016424422,0.013308041,0.010213032,0.0046364004,0.0029407837,0.2223267],"study_design_scores_gemma":[0.0010018816,0.0062162844,0.52059066,0.00027329748,0.00408684,0.005181653,0.0036481947,0.4112683,0.012191046,0.029655024,0.0056417543,0.00024510405],"about_ca_topic_score_codex":0.0020021154,"about_ca_topic_score_gemma":0.0018475113,"teacher_disagreement_score":0.044809956,"about_ca_system_score_codex":0.00047996955,"about_ca_system_score_gemma":0.0006745067,"threshold_uncertainty_score":0.2369805},"labels":[],"label_agreement":null},{"id":"W1498603666","doi":"10.4000/cybergeo.21123","title":"The Canadian Geospatial Data Infrastructure and health mapping","year":2011,"lang":"en","type":"article","venue":"Cybergeo","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Political science; Humanities; Geography; Library science; Computer science; Art","score_opus":0.09424217054713215,"score_gpt":0.3001348291790438,"score_spread":0.2058926586319117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1498603666","genre_codex":"other","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":[0.011114256,0.009714489,0.040358618,0.056830328,0.002087829,0.001024771,0.23489724,0.0055674464,0.63840497],"genre_scores_gemma":[0.27510384,0.031534385,0.2101962,0.008492117,0.00059754093,0.0015125797,0.2150614,0.0025540136,0.2549478],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961274,0.0004638861,0.00016856451,0.0003847431,0.0023016394,0.00055363023],"domain_scores_gemma":[0.9887466,0.0012640177,0.00032937853,0.0010845263,0.007450315,0.0011252441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031730642,0.00092846976,0.0005341409,0.00748708,0.0043346495,0.006717817,0.002313666,0.0011542899,0.026533857],"category_scores_gemma":[0.015129875,0.00058119325,0.0009125403,0.017426655,0.0016233973,0.0024346206,0.004085409,0.0012234831,0.0042767157],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008073613,0.000031790816,0.014794039,0.0009196376,0.00008327903,0.00020564185,0.0013764754,0.0038808894,0.00080514565,0.121109575,0.65321916,0.20349358],"study_design_scores_gemma":[0.000012078405,0.000006171841,0.014288286,0.00031356455,0.000023390005,0.000054340206,0.00090946804,0.0027153953,0.00033563367,0.005871623,0.97541785,0.000052354193],"about_ca_topic_score_codex":0.98594147,"about_ca_topic_score_gemma":0.9856699,"teacher_disagreement_score":0.05099786,"about_ca_system_score_codex":0.05099786,"about_ca_system_score_gemma":0.14992037,"threshold_uncertainty_score":0.3700171},"labels":[],"label_agreement":null},{"id":"W1501638453","doi":"10.1002/9781118630013.ch14","title":"West Nile Virus Mosquito Abundance Modeling Using Nonstationary Spatiotemporal Geostatistics","year":2014,"lang":"en","type":"other","venue":"Wiley series in probability and statistics","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Public Health Ontario; Queen's University","funders":"","keywords":"West Nile virus; Geostatistics; Abundance (ecology); Environmental science; Geography; Ecology; Biology; Virus; Virology; Mathematics; Spatial variability; Statistics","score_opus":0.02466875318127473,"score_gpt":0.28147539235335794,"score_spread":0.25680663917208324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1501638453","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":[0.027557552,0.00049793196,0.9653986,0.00060419546,0.000085221945,0.000048516158,0.0012577943,0.0007337842,0.0038164572],"genre_scores_gemma":[0.71291053,0.002135458,0.26642612,0.00025499277,0.00029535624,0.00019057702,0.0036308237,0.00032718063,0.013828995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997478,0.00010192714,0.000022045855,0.00005281389,0.00005994908,0.000015479452],"domain_scores_gemma":[0.9987465,0.00084320316,0.00013137716,0.00011054882,0.00014104514,0.00002728307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008359322,0.00039402946,0.00040082596,0.0005333107,0.00016620527,0.0006453232,0.0007179279,0.0004058743,0.0023608138],"category_scores_gemma":[0.004153645,0.00031603864,0.0006477349,0.0006856472,0.00019956693,0.0006854237,0.0006034778,0.0005492022,0.00065314164],"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":[0.000053211774,0.00007019688,0.0073792557,0.00010280017,0.00013458825,0.00012354068,0.000051999203,0.83062184,0.00096351,0.025037996,0.008930365,0.1265307],"study_design_scores_gemma":[0.0000019313507,0.0000033692027,0.00022816462,0.0000035084352,0.0000044557914,0.000010318957,0.0000028658183,0.99477094,0.000106129875,0.0042013247,0.0006644837,0.0000024858516],"about_ca_topic_score_codex":0.012774249,"about_ca_topic_score_gemma":0.011974473,"teacher_disagreement_score":0.012774249,"about_ca_system_score_codex":0.0003370026,"about_ca_system_score_gemma":0.00077511393,"threshold_uncertainty_score":0.025399804},"labels":[],"label_agreement":null},{"id":"W1511972862","doi":"10.24908/ss.v12i2.4750","title":"Gaps in the gaze: Informatic practice and the work of public health surveillance","year":2014,"lang":"en","type":"article","venue":"Surveillance & Society","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":32,"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":"Social Sciences and Humanities Research Council of Canada","keywords":"Ethos; Situated; Gaze; Big data; Sociology; Everyday life; Field (mathematics); Process (computing); Public relations; Epistemology; Computer science; Data science; Political science; Artificial intelligence; Law","score_opus":0.017492906447889166,"score_gpt":0.28916537712727824,"score_spread":0.2716724706793891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1511972862","genre_codex":"commentary","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":[0.23404898,0.035625234,0.04241449,0.37309632,0.001061377,0.0002739893,0.00014826165,0.00023183125,0.31309953],"genre_scores_gemma":[0.9853805,0.0040518795,0.0032421728,0.0035211649,0.00015088881,0.00010009187,0.000028277562,0.000066489265,0.0034584543],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.8648252,0.11526004,0.002761528,0.0052217487,0.007799472,0.004132012],"domain_scores_gemma":[0.82162523,0.15396719,0.007029009,0.007966357,0.006433817,0.0029783437],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.10335598,0.0010104877,0.001273021,0.009819455,0.042231403,0.029931005,0.0040792054,0.008999175,0.004396144],"category_scores_gemma":[0.09685593,0.0011481856,0.0006785312,0.0103267245,0.19021314,0.03756334,0.02649003,0.010586055,0.00065726484],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012558461,0.000008443309,0.00082619936,0.00010519344,0.000005163083,0.00014626155,0.872735,0.00008052349,0.00007955122,0.11761731,0.0011631399,0.007220579],"study_design_scores_gemma":[0.000015393873,0.000022602057,0.0008908857,0.00088329596,0.000012350606,0.000178205,0.80209404,0.0003341895,0.000204765,0.08695833,0.10836835,0.00003764483],"about_ca_topic_score_codex":0.1663389,"about_ca_topic_score_gemma":0.105875164,"teacher_disagreement_score":0.9577686,"about_ca_system_score_codex":0.055425297,"about_ca_system_score_gemma":0.04615008,"threshold_uncertainty_score":0.5466052},"labels":[],"label_agreement":null},{"id":"W1515908476","doi":"10.22230/cjc.2014v39n1a2789","title":"Exploring Health Communication: Language in Action","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Communication","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Centre for Addiction and Mental Health","funders":"","keywords":"Action (physics); Health communication; Communication; Linguistics; Computer science; Sociology; Psychology; Philosophy; Physics","score_opus":0.1210020686159554,"score_gpt":0.33990746567676455,"score_spread":0.21890539706080914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1515908476","genre_codex":"commentary","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":[0.070443735,0.024950946,0.042808462,0.55014104,0.0025945005,0.0002571596,0.0006240367,0.00033706182,0.30784306],"genre_scores_gemma":[0.91807044,0.012277395,0.020846125,0.033782844,0.0010670648,0.0005033535,0.000302622,0.00024676757,0.012903378],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.96997535,0.025591856,0.00044800516,0.0008947577,0.0018494399,0.0012406934],"domain_scores_gemma":[0.9540542,0.039584532,0.0018441955,0.0013133835,0.0017713396,0.0014323639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02331434,0.0009080531,0.0007282083,0.004366408,0.014172192,0.024234556,0.0027483408,0.008068046,0.01045895],"category_scores_gemma":[0.024077393,0.00052541477,0.00072073535,0.0046103667,0.060083773,0.0198816,0.012416035,0.0068626176,0.0011939928],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027498292,0.000024823641,0.0013513279,0.00044970884,0.000021281277,0.00068731175,0.70025295,0.00022241766,0.00047816822,0.25485024,0.017259255,0.024374954],"study_design_scores_gemma":[0.000018447628,0.000026899825,0.001458792,0.0015334494,0.000015170439,0.0005367548,0.5923443,0.0006221366,0.0005177297,0.1803781,0.2224888,0.00005947056],"about_ca_topic_score_codex":0.037227932,"about_ca_topic_score_gemma":0.02919673,"teacher_disagreement_score":0.9627721,"about_ca_system_score_codex":0.012804218,"about_ca_system_score_gemma":0.015076515,"threshold_uncertainty_score":0.12329948},"labels":[],"label_agreement":null},{"id":"W1521422465","doi":"10.1002/9781118543504.ch37","title":"The Global Public Health Intelligence Network","year":2013,"lang":"en","type":"other","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada","funders":"Health Canada; National Institutes of Health","keywords":"Public health; Agency (philosophy); Situation awareness; The Internet; Business; Human health; Public health surveillance; Environmental health; Computer science; Internet privacy; Data science; Engineering; Medicine; World Wide Web; Sociology","score_opus":0.03483557350674691,"score_gpt":0.3192686085771189,"score_spread":0.284433035070372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1521422465","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008040326,0.0054311394,0.0024376232,0.019663326,0.0012556985,0.00029492908,0.045571405,0.0018998455,0.92264205],"genre_scores_gemma":[0.026999485,0.027596887,0.013512572,0.010627457,0.0014351883,0.00078437466,0.13064642,0.0010190271,0.78737867],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99808526,0.00028899667,0.00010345122,0.0002505601,0.0010108489,0.00026094413],"domain_scores_gemma":[0.9949221,0.0005209146,0.0002982158,0.00043809143,0.0028612884,0.0009594299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002333861,0.00092261913,0.0004629066,0.004412991,0.0014533665,0.00446082,0.0011224498,0.0015808986,0.11582853],"category_scores_gemma":[0.007243804,0.00024467192,0.00031327875,0.005872402,0.00065518694,0.00269344,0.0028224264,0.0014814698,0.060903385],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015697504,0.000013205269,0.00097331766,0.00010453807,0.0000054398643,0.00003245085,0.000041242092,0.00008029205,0.000057543908,0.007982936,0.88500273,0.105690576],"study_design_scores_gemma":[0.00000273923,0.000002424889,0.0008568667,0.00008295889,0.0000024442297,0.000022391907,0.000034182383,0.00009534524,0.00003876557,0.0008434768,0.9980155,0.0000029409152],"about_ca_topic_score_codex":0.063097216,"about_ca_topic_score_gemma":0.049727175,"teacher_disagreement_score":0.11582853,"about_ca_system_score_codex":0.0042459457,"about_ca_system_score_gemma":0.0169017,"threshold_uncertainty_score":0.38748503},"labels":[],"label_agreement":null},{"id":"W1524948628","doi":"","title":"The future of health informatics and electronic health records: a look at the Canadian surveillance systems","year":2015,"lang":"en","type":"article","venue":"Journals & Books Hosting (International Knowledge Sharing Platform)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Health informatics; Public health informatics; Public health; Informatics; Public health surveillance; Government (linguistics); Disease surveillance; Consistency (knowledge bases); Business; Health care; Warning system; Information system; Computer science; Medicine; Computer security; Health promotion; Environmental health; HRHIS; Engineering; Political science; Nursing; Telecommunications","score_opus":0.04303219846888477,"score_gpt":0.32583357429197696,"score_spread":0.28280137582309217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1524948628","genre_codex":"commentary","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":[0.008527749,0.16171773,0.0034160933,0.66366196,0.003533963,0.00015305408,0.0012572974,0.0002337498,0.15749845],"genre_scores_gemma":[0.3039587,0.51990205,0.02808254,0.07228812,0.0039774794,0.00020490013,0.0019803403,0.00032112093,0.06928475],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9764681,0.0037805163,0.0009795323,0.0010643582,0.0144261215,0.0032814431],"domain_scores_gemma":[0.9512437,0.008788475,0.0015337445,0.0012080051,0.030722892,0.006503208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017808609,0.00092486094,0.00085282297,0.011757231,0.0124328965,0.020237494,0.004037577,0.0042422037,0.008409199],"category_scores_gemma":[0.028827887,0.0007499169,0.00075941865,0.020345941,0.014809028,0.011822107,0.003975299,0.0044044997,0.0009337648],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.000115239156,0.00012264511,0.011414363,0.001488771,0.00005978021,0.00045901502,0.010548198,0.001037804,0.00054918096,0.4330343,0.25100026,0.2901705],"study_design_scores_gemma":[0.000024645833,0.00005106667,0.018440034,0.0018102195,0.000035667796,0.00018972918,0.0071440265,0.0010594983,0.00018616635,0.015542933,0.95536596,0.0001501162],"about_ca_topic_score_codex":0.9911377,"about_ca_topic_score_gemma":0.98816675,"teacher_disagreement_score":0.7927383,"about_ca_system_score_codex":0.20726168,"about_ca_system_score_gemma":0.3071291,"threshold_uncertainty_score":0.9194641},"labels":[],"label_agreement":null},{"id":"W1541213972","doi":"","title":"A Bayesian network model for analysis of detection performance in surveillance systems.","year":2009,"lang":"en","type":"article","venue":"PubMed","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Identification (biology); Bayesian network; Bayesian probability; Data mining; Machine learning; Sensitivity (control systems); Artificial intelligence; Data science; Engineering","score_opus":0.022289003628709402,"score_gpt":0.24673126479835134,"score_spread":0.22444226116964194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1541213972","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":[0.005583413,0.0013970885,0.98925346,0.0009767803,0.00006968455,0.00015913878,0.00059404067,0.00022727287,0.001739267],"genre_scores_gemma":[0.45652938,0.004463604,0.5235915,0.0007240817,0.0005627302,0.0024864483,0.0026001171,0.00018665443,0.008855493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9918172,0.0050963885,0.00037515696,0.0010707752,0.0013068115,0.0003336573],"domain_scores_gemma":[0.9530498,0.041254073,0.0025216842,0.0010196451,0.0016569102,0.0004978416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019790975,0.0022532274,0.002750863,0.0047677183,0.0009977135,0.0028592532,0.004636597,0.0048557813,0.004224517],"category_scores_gemma":[0.07095072,0.0014944439,0.0020175462,0.004240681,0.0030571553,0.006373981,0.002079388,0.0035006546,0.0010754132],"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":[0.00011577849,0.000058273625,0.0016918256,0.00017398503,0.00015120622,0.0001124839,0.00015593819,0.8261559,0.00028920852,0.14590539,0.0017589049,0.023431046],"study_design_scores_gemma":[0.0000201581,0.000026917289,0.0003589732,0.000030322743,0.000030792355,0.000037258218,0.000012429257,0.92822653,0.00006545491,0.07007573,0.0010966758,0.000018846124],"about_ca_topic_score_codex":0.019656716,"about_ca_topic_score_gemma":0.013929934,"teacher_disagreement_score":0.019790975,"about_ca_system_score_codex":0.004980341,"about_ca_system_score_gemma":0.0021870637,"threshold_uncertainty_score":0.104665935},"labels":[],"label_agreement":null},{"id":"W1543878856","doi":"10.1111/zph.12012","title":"Spatio‐Temporal Scan Statistics for the Detection of Outbreaks Involving Common Molecular Subtypes: Using Human Cases of <i>Escherichia coli</i> O157:H7 Provincial PFGE Pattern 8 (National Designation ECXAI.0001) in Alberta as an Example","year":2012,"lang":"en","type":"article","venue":"Zoonoses and Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Provincial Laboratory of Public Health; Alberta Health; University of Calgary; University of Guelph","funders":"","keywords":"Pulsed-field gel electrophoresis; Outbreak; Biology; Virology; Genetics; Genotype","score_opus":0.07653537945280169,"score_gpt":0.3380146549039405,"score_spread":0.2614792754511388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1543878856","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":[0.9763821,0.00039031333,0.020798815,0.0001827637,0.000013701567,0.000050844956,0.0012405126,0.00016337352,0.0007776838],"genre_scores_gemma":[0.99011713,0.00008457588,0.008715738,0.000017788729,0.000010912001,0.00003588997,0.0008471956,0.000010568662,0.0001603363],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989188,0.00040753366,0.000075880605,0.00021699257,0.0002994666,0.0000813743],"domain_scores_gemma":[0.98859555,0.007943853,0.001977021,0.0004626757,0.0006917174,0.00032917655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002963384,0.00023985672,0.00031143334,0.0031611188,0.00028130953,0.00059128134,0.00060870417,0.00050041627,0.0007101328],"category_scores_gemma":[0.013969517,0.00017098768,0.00048204584,0.002906004,0.0007088775,0.00050900195,0.0004366498,0.00037024202,0.000091233065],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069255556,0.000047830057,0.94083023,0.0000650704,0.00023096934,0.00032045605,0.00026353236,0.026503326,0.0036191654,0.00112872,0.0005513668,0.0257468],"study_design_scores_gemma":[0.00004304002,0.00039646644,0.73224455,0.000028884851,0.00012335909,0.0006998846,0.00087528967,0.2607575,0.0014953892,0.0019473248,0.0013428233,0.000045593875],"about_ca_topic_score_codex":0.078900814,"about_ca_topic_score_gemma":0.08009661,"teacher_disagreement_score":0.9210992,"about_ca_system_score_codex":0.0011287599,"about_ca_system_score_gemma":0.0017498095,"threshold_uncertainty_score":0.15688312},"labels":[],"label_agreement":null},{"id":"W1551484630","doi":"10.1371/journal.pone.0014118","title":"Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak","year":2010,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":58,"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; University Health Network","funders":"Canadian Institutes of Health Research","keywords":"Social media; Misinformation; Content analysis; Pandemic; Descriptive statistics; Upload; Government (linguistics); Internet privacy; Exploratory analysis; Sentiment analysis; Public health; Microblogging; Computer science; World Wide Web; Medicine; Coronavirus disease 2019 (COVID-19); Computer security; Data science; Sociology; Statistics","score_opus":0.07192709722855671,"score_gpt":0.27390656610617253,"score_spread":0.2019794688776158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1551484630","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":[0.99672,0.00006917673,0.00041970235,0.00023790516,0.000015306052,0.00005641406,0.0017489784,0.000023298513,0.0007092163],"genre_scores_gemma":[0.99438995,0.00013548006,0.0021751537,0.000090363545,0.000045762325,0.0000985724,0.0025407798,0.000014917987,0.00050897],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995097,0.00016638938,0.000057716847,0.00008323132,0.000111425536,0.00007161575],"domain_scores_gemma":[0.997009,0.0013658975,0.0007722419,0.00014316592,0.0005577774,0.00015197002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084992364,0.0001884674,0.00021043092,0.002206369,0.0004950874,0.00078714744,0.00024037457,0.00036789,0.00064486574],"category_scores_gemma":[0.004529715,0.00012433415,0.0002321698,0.0018097295,0.0002743733,0.0011673488,0.0007959127,0.00032432145,0.0002634849],"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":[0.0007627736,0.00025001407,0.8850369,0.0004253298,0.00010259949,0.00067749934,0.021245416,0.001051562,0.014494025,0.0004886605,0.0059175477,0.06954765],"study_design_scores_gemma":[0.000008165625,0.00012417289,0.9713902,0.000060407536,0.000043076358,0.00028472676,0.015534809,0.0065030935,0.0021693523,0.00028070438,0.0035708614,0.00003036439],"about_ca_topic_score_codex":0.004558039,"about_ca_topic_score_gemma":0.005994724,"teacher_disagreement_score":0.004558039,"about_ca_system_score_codex":0.00050511415,"about_ca_system_score_gemma":0.00030291794,"threshold_uncertainty_score":0.009063005},"labels":[],"label_agreement":null},{"id":"W1562109682","doi":"10.1002/9780470986424.ch12","title":"The Cost of Secure and Reliable Sensing","year":2008,"lang":"en","type":"other","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Key (lock); Cluster (spacecraft); Node (physics); Computer science; Computer network; Data mining; Distributed computing; Engineering; Computer security","score_opus":0.00988026519702076,"score_gpt":0.25311310100164774,"score_spread":0.24323283580462698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1562109682","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":[0.08615639,0.0037057854,0.8073335,0.015017882,0.0008161441,0.00024368176,0.0007843207,0.00053085125,0.085411474],"genre_scores_gemma":[0.9310687,0.0021709178,0.045912616,0.00036454023,0.0002401932,0.0001421051,0.00016748194,0.00015803758,0.019775368],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974927,0.0006483263,0.00008494302,0.0003109007,0.0011126926,0.00035053588],"domain_scores_gemma":[0.9886685,0.008477126,0.00048303793,0.0012688804,0.00089482905,0.00020772417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002334936,0.00076604926,0.0008908933,0.0006646753,0.0009839502,0.0032981592,0.0019196001,0.001814196,0.012023471],"category_scores_gemma":[0.019936739,0.00052601536,0.0006153255,0.0012035823,0.0018522111,0.007318811,0.001919348,0.0021681509,0.0010945512],"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":[0.00026064858,0.000048903188,0.0012774004,0.0002222938,0.000055144814,0.00024782188,0.00014127645,0.279313,0.0050947755,0.6383371,0.006830673,0.06817098],"study_design_scores_gemma":[0.00002567102,0.000093231276,0.0011682269,0.0000691872,0.00005092241,0.0007431997,0.00022109647,0.7697941,0.0029009266,0.21638311,0.008507222,0.000043141597],"about_ca_topic_score_codex":0.002158946,"about_ca_topic_score_gemma":0.0018525822,"teacher_disagreement_score":0.012023471,"about_ca_system_score_codex":0.002970555,"about_ca_system_score_gemma":0.0019034666,"threshold_uncertainty_score":0.040222526},"labels":[],"label_agreement":null},{"id":"W1564023430","doi":"","title":"Opt-out prenatal HIV testing in Newfoundland and Labrador.","year":2002,"lang":"en","type":"letter","venue":"PubMed","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Kingston Health Sciences Centre","funders":"","keywords":"Human immunodeficiency virus (HIV); Prenatal screening; Opt-out; HIV screening; Genealogy; Medicine; Family medicine; Data science; Computer science; Pregnancy; History; Prenatal diagnosis; World Wide Web; Biology; Genetics; Men who have sex with men","score_opus":0.03936363735657032,"score_gpt":0.2319441601881756,"score_spread":0.19258052283160526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1564023430","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039738916,0.0048708203,0.00017445622,0.93163735,0.0030788332,0.000038693935,0.0007187571,0.000035219135,0.019707022],"genre_scores_gemma":[0.2840881,0.005759197,0.00063348963,0.6753191,0.004782572,0.00010343475,0.00032748934,0.000057428857,0.028929237],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99748576,0.0005898693,0.00016384157,0.00024472692,0.00039597304,0.0011198295],"domain_scores_gemma":[0.9953349,0.0020561826,0.00058449607,0.00009409953,0.0006622115,0.0012681735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015437205,0.00027130925,0.0004111022,0.00044413665,0.004602843,0.0010839914,0.0011052154,0.008209142,0.003962786],"category_scores_gemma":[0.008479637,0.0003894811,0.00030469286,0.0008951413,0.0014814374,0.001009297,0.0008228253,0.0074108527,0.00048006448],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003547601,0.00009078216,0.10330283,0.00024174408,0.000041123403,0.039031718,0.004667249,0.00026067966,0.0007032749,0.00446282,0.8067915,0.040051635],"study_design_scores_gemma":[0.0002645309,0.000244587,0.4315534,0.0021306283,0.00016919796,0.01868293,0.02234677,0.0015818788,0.0011551311,0.0036268502,0.5180167,0.00022737845],"about_ca_topic_score_codex":0.84425235,"about_ca_topic_score_gemma":0.94317365,"teacher_disagreement_score":0.15574765,"about_ca_system_score_codex":0.019421054,"about_ca_system_score_gemma":0.01778368,"threshold_uncertainty_score":0.3133297},"labels":[],"label_agreement":null},{"id":"W156809530","doi":"10.1017/s1481803500009817","title":"Implementation of an automated, real-time public health surveillance system linking emergency departments and health units: rationale and methodology","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Emergency Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kingston Health Sciences Centre; Queen's University","funders":"","keywords":"Public health; Public health surveillance; Medicine; Medical emergency; Disease surveillance; Agency (philosophy); Health care; Nursing","score_opus":0.2715266312547512,"score_gpt":0.44365422374702307,"score_spread":0.17212759249227189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W156809530","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":[0.10949814,0.00018755283,0.859949,0.0015172312,0.00017545633,0.020721665,0.0022757668,0.0020934236,0.00358167],"genre_scores_gemma":[0.25565562,0.000094670686,0.73366153,0.00032279655,0.000079256475,0.007956877,0.0014564317,0.00005587867,0.00071691663],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9806918,0.007891593,0.002482866,0.003339819,0.004874188,0.000719685],"domain_scores_gemma":[0.9576465,0.017096167,0.0048492895,0.0041507054,0.014800475,0.0014568833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02239653,0.0008885458,0.0007231806,0.0024847076,0.001037338,0.0035026046,0.0024939575,0.0017553329,0.0015495555],"category_scores_gemma":[0.043143664,0.0009998527,0.00082458195,0.0018215812,0.001243744,0.0020854701,0.0024106747,0.0015123909,0.00059986534],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001748472,0.0070397225,0.3213938,0.0028852415,0.0008532613,0.00080510514,0.003697844,0.06982974,0.029568387,0.03071159,0.00819737,0.5232696],"study_design_scores_gemma":[0.001989541,0.0050609447,0.09167983,0.0008541582,0.0009118631,0.0008710723,0.0026353896,0.74993914,0.08314166,0.017504387,0.044980254,0.00043175853],"about_ca_topic_score_codex":0.0067146965,"about_ca_topic_score_gemma":0.005026229,"teacher_disagreement_score":0.02239653,"about_ca_system_score_codex":0.002424981,"about_ca_system_score_gemma":0.013566277,"threshold_uncertainty_score":0.118445635},"labels":[],"label_agreement":null},{"id":"W1573322658","doi":"10.15353/joci.v9i2.3166","title":"Capturing Qualitative Spatial Data to Understand Social Epidemiology in Public Health","year":2012,"lang":"en","type":"article","venue":"The Journal of Community Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Public health; Geographic information system; Citizen journalism; Information and Communications Technology; GIS and public health; Data science; Qualitative research; Public relations; Social media; Health equity; Qualitative property; Sociology; Geography; Political science; Medicine; Computer science; Cartography; Social science; World Wide Web","score_opus":0.5107024726176533,"score_gpt":0.4887622466786148,"score_spread":0.02194022593903855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1573322658","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":[0.21405533,0.0044396017,0.7177965,0.0272686,0.00030358793,0.002002051,0.0036912223,0.00025814658,0.03018495],"genre_scores_gemma":[0.7586972,0.001956948,0.23436622,0.0009264055,0.00006139155,0.0021588209,0.00084930466,0.000039902894,0.00094377936],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9625837,0.034109026,0.0009793729,0.00067541446,0.0011645135,0.0004879918],"domain_scores_gemma":[0.87734705,0.10948711,0.004563094,0.004996422,0.0029397195,0.0006666237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.038053878,0.0006513368,0.00068297365,0.0068697254,0.0027774784,0.0057318253,0.0016279055,0.001231387,0.0028033333],"category_scores_gemma":[0.06624834,0.0007617652,0.00077266886,0.007458108,0.00687945,0.0073987087,0.0070786136,0.0013501967,0.00023372054],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008271092,0.0003273181,0.07481758,0.0027205818,0.00020165548,0.0007770202,0.17509192,0.026554564,0.0014876506,0.5906272,0.0045764167,0.122735314],"study_design_scores_gemma":[0.00004773465,0.00013335931,0.016302492,0.0023971885,0.00007354462,0.0003539527,0.2362492,0.035271008,0.0015257137,0.6474132,0.060146224,0.000086316926],"about_ca_topic_score_codex":0.02013287,"about_ca_topic_score_gemma":0.021965617,"teacher_disagreement_score":0.038053878,"about_ca_system_score_codex":0.0066430634,"about_ca_system_score_gemma":0.010612032,"threshold_uncertainty_score":0.20125061},"labels":[],"label_agreement":null},{"id":"W1577774937","doi":"10.1080/19475683.2011.625979","title":"Spatial and temporal aberration detection methods for disease outbreaks in syndromic surveillance systems","year":2011,"lang":"en","type":"article","venue":"Annals of GIS","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Leeds, Grenville & Lanark District Health Unit; Queen's University","funders":"","keywords":"Disease surveillance; Outbreak; Infectious disease (medical specialty); Public health surveillance; Public health; Computer science; Data quality; Notifiable disease; Data mining; Geographic information system; Data science; Medicine; Cartography; Geography; Disease; Pathology; Business","score_opus":0.08568741259797402,"score_gpt":0.37210251606485456,"score_spread":0.28641510346688054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1577774937","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":[0.011216139,0.00029331798,0.9874521,0.00018880937,0.000030028312,0.00005691241,0.00008118895,0.0003731505,0.00030835794],"genre_scores_gemma":[0.2164359,0.0005400048,0.78146505,0.00009573995,0.000086694025,0.00018471139,0.0002880693,0.00006878032,0.00083503727],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99700814,0.001189696,0.00036129745,0.00035400857,0.0009897894,0.00009706123],"domain_scores_gemma":[0.9812712,0.011813028,0.0024424721,0.0014315114,0.00283802,0.0002037793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057177604,0.00073332613,0.00073855807,0.0030713156,0.00052843103,0.0013267872,0.0010917978,0.00073309307,0.0010440587],"category_scores_gemma":[0.031221107,0.0003699575,0.0008061178,0.0026157398,0.0008641412,0.002084245,0.0013478403,0.0011262782,0.00037991587],"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":[0.00048723025,0.00018083889,0.03742611,0.0004659893,0.0002964051,0.000352937,0.0011287566,0.19818625,0.018831354,0.0572887,0.0041728048,0.6811827],"study_design_scores_gemma":[0.000028630706,0.000100081095,0.005413458,0.00003786371,0.00004434017,0.00044008266,0.00018001287,0.96232384,0.006656099,0.020980034,0.003744304,0.000051261606],"about_ca_topic_score_codex":0.0025800904,"about_ca_topic_score_gemma":0.002220015,"teacher_disagreement_score":0.0057177604,"about_ca_system_score_codex":0.0010545086,"about_ca_system_score_gemma":0.0011276213,"threshold_uncertainty_score":0.030238748},"labels":[],"label_agreement":null},{"id":"W1579800467","doi":"10.17269/cjph.105.4884","title":"What is the importance of postal codes for health research? Re: (Fuller and Shareck) Canada Post community mailboxes: Implications for health research","year":2014,"lang":"en","type":"letter","venue":"Canadian Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Université de Montréal; Centre Hospitalier de l’Université de Montréal; Institute of Population and Public Health","funders":"","keywords":"Political science","score_opus":0.32262026002576377,"score_gpt":0.4639511720296355,"score_spread":0.1413309120038717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1579800467","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015764375,0.00019194421,0.000023542641,0.9964701,0.0020355454,0.0000058090845,0.00004934874,0.0000087924445,0.0010573246],"genre_scores_gemma":[0.0021386778,0.00039063834,0.00018459014,0.98887616,0.0040980927,0.000026725518,0.000031117193,0.000025010428,0.0042290054],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9771746,0.0052922694,0.0031930334,0.002131468,0.008432888,0.0037756374],"domain_scores_gemma":[0.756539,0.10487914,0.012435589,0.0049177166,0.08404601,0.037182476],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.025398657,0.00059890933,0.0014722197,0.0022836265,0.014240388,0.009670463,0.0048006107,0.06914685,0.024088848],"category_scores_gemma":[0.19616371,0.0013340578,0.0012472977,0.0023106665,0.010438366,0.008343196,0.0037320713,0.048181426,0.009003956],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019116214,0.000016083963,0.0014051398,0.000034428456,0.000008161262,0.0002200795,0.00035703287,0.000018042669,0.000053411935,0.0017346446,0.9896319,0.0065019587],"study_design_scores_gemma":[0.00017377718,0.00005409742,0.010417423,0.0019135709,0.0000804132,0.0010231882,0.006525837,0.00040204855,0.0002900962,0.0085386485,0.9703016,0.00027929188],"about_ca_topic_score_codex":0.618327,"about_ca_topic_score_gemma":0.7838413,"teacher_disagreement_score":0.9746013,"about_ca_system_score_codex":0.0396607,"about_ca_system_score_gemma":0.10318519,"threshold_uncertainty_score":0.76784146},"labels":[],"label_agreement":null},{"id":"W1596222673","doi":"","title":"Infodemiology: tracking flu-related searches on the web for syndromic surveillance.","year":2006,"lang":"en","type":"article","venue":"PubMed","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":420,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; University Health Network","funders":"","keywords":"The Internet; Public health; Public health surveillance; Medicine; Outbreak; Environmental health; Epidemiology; Tracking (education); Warrant; Population; Medical emergency; Internet privacy; Computer science; Business; World Wide Web; Psychology; Pathology","score_opus":0.042694257181334856,"score_gpt":0.2593866065522838,"score_spread":0.21669234937094894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1596222673","genre_codex":"dataset","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":[0.23941821,0.026273716,0.024405014,0.008405233,0.000857063,0.003348487,0.63250834,0.02324478,0.041539196],"genre_scores_gemma":[0.4751469,0.015812894,0.19173285,0.0022973618,0.0007231162,0.004098413,0.2970925,0.0004812456,0.012614721],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9993069,0.00025415502,0.00016221755,0.00010621487,0.00014453557,0.00002597784],"domain_scores_gemma":[0.9934411,0.0035882643,0.0012717693,0.00041261574,0.00088596245,0.00040028337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018872736,0.0005286759,0.0005338661,0.008908083,0.00023396332,0.0010576653,0.0006353808,0.00045133132,0.006877903],"category_scores_gemma":[0.009645725,0.00026912458,0.0003948919,0.0044300035,0.00017542244,0.0014117284,0.0006999189,0.000255066,0.0027001617],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011363704,0.00025360976,0.12786706,0.0073761344,0.00041558789,0.00044578378,0.00056166254,0.00055596104,0.0033740625,0.001945548,0.16809776,0.6879705],"study_design_scores_gemma":[0.0010806103,0.0020234892,0.56797165,0.0048811203,0.0014709621,0.0037682273,0.001997538,0.023128368,0.01547743,0.0066756653,0.37127605,0.00024892084],"about_ca_topic_score_codex":0.0049800077,"about_ca_topic_score_gemma":0.011850932,"teacher_disagreement_score":0.008908083,"about_ca_system_score_codex":0.00043380843,"about_ca_system_score_gemma":0.001090523,"threshold_uncertainty_score":0.023008883},"labels":[],"label_agreement":null},{"id":"W1606017774","doi":"10.1002/9781118630013.ch9","title":"Cloud Computing–Enabled Cluster Detection Using a Flexibly Shaped Scan Statistic for Real‐Time Syndromic Surveillance","year":2014,"lang":"en","type":"other","venue":"Wiley series in probability and statistics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"","keywords":"Scan statistic; Cloud computing; Leverage (statistics); Computer science; Statistic; Cluster (spacecraft); Spatial analysis; Data mining; Data science; Artificial intelligence; Geography; Statistics; Remote sensing; Mathematics; Operating system","score_opus":0.014952867141197956,"score_gpt":0.2739576444206593,"score_spread":0.2590047772794613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1606017774","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":[0.41680732,0.00072531425,0.5576915,0.002304889,0.0001848994,0.00038071803,0.004128456,0.0061223265,0.011654558],"genre_scores_gemma":[0.8728316,0.00014660621,0.124353684,0.00015542936,0.00004971204,0.00008946692,0.0011812072,0.00012034177,0.0010720555],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988889,0.00028729404,0.00006322507,0.00022130014,0.00038194997,0.00015728368],"domain_scores_gemma":[0.99664634,0.0013154255,0.000471345,0.0005522018,0.0007541113,0.0002605488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015249096,0.00043071105,0.00044597386,0.0014660897,0.0007344431,0.0011885207,0.0010729741,0.00036488217,0.0015214359],"category_scores_gemma":[0.007438864,0.00022168086,0.0004447308,0.0020399524,0.0005746342,0.0008097112,0.0013257741,0.00047751484,0.0003674273],"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":[0.0013823067,0.00032239885,0.21990383,0.0002541234,0.00022579775,0.00088193046,0.00060448574,0.33691266,0.03190496,0.024634093,0.0244083,0.35856518],"study_design_scores_gemma":[0.0000302551,0.00004468413,0.013546588,0.000017439183,0.000020116375,0.00014580165,0.00015786762,0.9709781,0.0053076753,0.006312609,0.0034055207,0.0000334008],"about_ca_topic_score_codex":0.08097472,"about_ca_topic_score_gemma":0.10178679,"teacher_disagreement_score":0.08097472,"about_ca_system_score_codex":0.0021877903,"about_ca_system_score_gemma":0.0036634472,"threshold_uncertainty_score":0.16100681},"labels":[],"label_agreement":null},{"id":"W1642305810","doi":"10.18697/ajfand.55.11145","title":"Experience with digital entry of national iodine survey data in Senegal","year":2012,"lang":"en","type":"article","venue":"African Journal of Food Agriculture Nutrition and Development","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nutrition International; Health Canada","funders":"Government of Canada","keywords":"Data collection; Global Positioning System; Data quality; Mobile device; Computer science; Sample (material); Enthusiasm; Automatic identification and data capture; Data science; Operations management; Engineering; Psychology; Statistics; World Wide Web; Telecommunications","score_opus":0.0386853683779913,"score_gpt":0.2696170753180934,"score_spread":0.23093170694010212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1642305810","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":[0.9424015,0.0021023906,0.015945403,0.009053185,0.00023338919,0.0015232726,0.0015165483,0.0009772875,0.02624717],"genre_scores_gemma":[0.90921545,0.005851781,0.061405566,0.0022155142,0.00024231916,0.0011661252,0.0021844734,0.0005627742,0.017155949],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9859854,0.009461761,0.0011548687,0.0009191265,0.0015691676,0.0009097465],"domain_scores_gemma":[0.9606273,0.025640665,0.0020037396,0.0059402427,0.004078338,0.0017096294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019604322,0.0010300276,0.00062675425,0.0020743713,0.0027449697,0.0038470638,0.0022783775,0.0009905886,0.008423629],"category_scores_gemma":[0.043813553,0.0006681822,0.0006607239,0.005804926,0.0026316189,0.004147047,0.0060013393,0.0013656607,0.0024766594],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017940016,0.0010011395,0.0991968,0.0015764171,0.00019023575,0.008724312,0.2488167,0.002036791,0.014334025,0.001595669,0.0133726485,0.60736126],"study_design_scores_gemma":[0.0004935129,0.0046150517,0.15773647,0.0022324054,0.00047911206,0.009544892,0.2831669,0.0052048424,0.02426137,0.0022081102,0.50936306,0.00069440866],"about_ca_topic_score_codex":0.024063125,"about_ca_topic_score_gemma":0.021598997,"teacher_disagreement_score":0.024063125,"about_ca_system_score_codex":0.0021970302,"about_ca_system_score_gemma":0.0036334433,"threshold_uncertainty_score":0.10367882},"labels":[],"label_agreement":null},{"id":"W1701318927","doi":"10.1186/1471-2458-14-1302","title":"A focus group study of enteric disease case investigation: successful techniques utilized and barriers experienced from the perspective of expert disease investigators","year":2014,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University; Public Health Ontario; University of Toronto","funders":"","keywords":"Medicine; Focus group; Interview; Public health; Misinformation; Qualitative research; Disease; Medical education; Family medicine; Nursing; Pathology; Computer security","score_opus":0.03745689364864783,"score_gpt":0.32901915880352156,"score_spread":0.2915622651548737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1701318927","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":[0.9947614,0.00022826601,0.0014855856,0.0008986859,0.000038631213,0.0009249086,0.000045772304,0.000012688528,0.0016040109],"genre_scores_gemma":[0.9896487,0.00065627095,0.0037391926,0.0015073747,0.000053059837,0.0024165332,0.000078776844,0.000019523755,0.0018805545],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.97319645,0.020941611,0.00085663615,0.0009864694,0.0012210597,0.002797806],"domain_scores_gemma":[0.9335437,0.050576836,0.0036779242,0.0011771669,0.0073320386,0.0036923506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040107053,0.0009695916,0.0009356639,0.0025428738,0.013168318,0.0026627597,0.0028204822,0.003386346,0.0033357148],"category_scores_gemma":[0.05095295,0.0011819877,0.00046437883,0.001615026,0.0065163164,0.0042295083,0.0059822435,0.0027126318,0.00060355576],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051181556,0.00021080153,0.0038759161,0.00012391945,0.0000023598657,0.00063845236,0.99168396,0.0000111747895,0.0005275789,0.0001323381,0.00029806132,0.0024442857],"study_design_scores_gemma":[0.000026832584,0.00045060745,0.0036073583,0.0001239145,0.000005847401,0.00023407802,0.9921571,0.00005390396,0.00026779473,0.00010238398,0.0029550882,0.0000151571285],"about_ca_topic_score_codex":0.015214055,"about_ca_topic_score_gemma":0.020719044,"teacher_disagreement_score":0.040107053,"about_ca_system_score_codex":0.0061343377,"about_ca_system_score_gemma":0.0077409786,"threshold_uncertainty_score":0.21210891},"labels":[],"label_agreement":null},{"id":"W1707012735","doi":"","title":"Openness is key in fight against disease outbreaks.","year":2006,"lang":"en","type":"article","venue":"PubMed","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Outbreak; Public health; Communicable disease; Preparedness; Medicine; Political science; Law; Virology; Nursing","score_opus":0.012657462066368785,"score_gpt":0.22095016462867778,"score_spread":0.208292702562309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1707012735","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052190423,0.03396492,0.0070971716,0.66621655,0.035405073,0.00011449897,0.00044237942,0.0011243019,0.25041598],"genre_scores_gemma":[0.303888,0.063097835,0.010271376,0.41181698,0.032140955,0.0002960987,0.0006913359,0.0009053887,0.17689201],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9926287,0.002701031,0.00034959754,0.00071790314,0.0022882835,0.001314574],"domain_scores_gemma":[0.97519976,0.0058608144,0.002346943,0.001726808,0.0031717306,0.011694017],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.009500783,0.0008001423,0.00073567586,0.0009363952,0.0047429707,0.012192324,0.0014351857,0.0048621055,0.040985163],"category_scores_gemma":[0.023237802,0.00038235408,0.0006637976,0.0007079786,0.008692172,0.01521079,0.010604475,0.00853611,0.016006729],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014068426,0.00012603246,0.003223252,0.0010959359,0.00009362588,0.00046614226,0.006281957,0.00020101896,0.0013419829,0.062090054,0.7070732,0.21786611],"study_design_scores_gemma":[0.000017903689,0.000078664,0.0015182685,0.0005664969,0.000021633405,0.00034689618,0.0031219318,0.0000809268,0.00022076764,0.038358226,0.9556311,0.00003726534],"about_ca_topic_score_codex":0.0014987834,"about_ca_topic_score_gemma":0.0015604954,"teacher_disagreement_score":0.99856484,"about_ca_system_score_codex":0.0015847031,"about_ca_system_score_gemma":0.0061698426,"threshold_uncertainty_score":0.13710904},"labels":[],"label_agreement":null},{"id":"W1711791871","doi":"","title":"S ARS Unmasked: Risk Communication of Pandemics and Influenza in Canada (review)","year":2011,"lang":"en","type":"article","venue":"Labour / Le Travail","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Risk communication; Influenza pandemic; Virology; Coronavirus disease 2019 (COVID-19); Pandemic influenza; Medicine; Environmental health; Disease; Infectious disease (medical specialty); Internal medicine","score_opus":0.02119790843497012,"score_gpt":0.24923333676610596,"score_spread":0.22803542833113583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1711791871","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":[0.000996247,0.99323094,0.00019055276,0.0037153296,0.00036976257,0.000048064325,0.00021483203,0.000014051397,0.0012201216],"genre_scores_gemma":[0.016886396,0.97985244,0.00069411396,0.0015015678,0.00031738495,0.000038546073,0.00019931706,0.0000039950773,0.00050627626],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966709,0.00088289817,0.00044886852,0.00018937275,0.0015326318,0.0002752631],"domain_scores_gemma":[0.98402137,0.007718395,0.00300139,0.00021705677,0.0042698854,0.0007719772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006090457,0.0011948956,0.0027053463,0.0034264028,0.0011507061,0.0033167277,0.0020887146,0.00248984,0.003533436],"category_scores_gemma":[0.024453722,0.00053470227,0.0026587825,0.0042244187,0.0010128046,0.001797515,0.0013135382,0.0025209433,0.00031673783],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037236768,0.00006277529,0.0058923694,0.05874489,0.0022486465,0.00027203799,0.0007992561,0.0010329637,0.00014660017,0.0010829518,0.037286457,0.89205873],"study_design_scores_gemma":[0.0010511004,0.00071771775,0.08833716,0.33711067,0.0138858175,0.0023789634,0.0036912702,0.0021021245,0.001147384,0.0027850603,0.54643136,0.00036134076],"about_ca_topic_score_codex":0.74336,"about_ca_topic_score_gemma":0.84721273,"teacher_disagreement_score":0.25664002,"about_ca_system_score_codex":0.014265396,"about_ca_system_score_gemma":0.06492148,"threshold_uncertainty_score":0.5163028},"labels":[],"label_agreement":null},{"id":"W172539987","doi":"","title":"Using hierarchical mixture of experts model for fusion of outbreak detection methods.","year":2013,"lang":"en","type":"article","venue":"PubMed","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Polytechnique Montréal","funders":"","keywords":"Outbreak; Computer science; Hierarchical database model; Probabilistic logic; Data mining; Mixture model; Machine learning; Artificial intelligence; Medicine; Virology","score_opus":0.06782357633968157,"score_gpt":0.3366343658955035,"score_spread":0.26881078955582194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W172539987","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":[0.0128399925,0.00045295432,0.98516023,0.00021421436,0.00006224209,0.00011681171,0.0001374745,0.00044663905,0.00056939287],"genre_scores_gemma":[0.47979364,0.0004913675,0.5152625,0.0003976192,0.00018002724,0.0003799377,0.00085002324,0.000105306965,0.0025394985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944701,0.0025475004,0.00035091577,0.0010743779,0.001238592,0.00031853883],"domain_scores_gemma":[0.98481005,0.010963777,0.00083428225,0.000770122,0.0022567636,0.00036511486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012557396,0.0018362219,0.0019277353,0.002712131,0.0006451277,0.001591604,0.0025560607,0.0026208002,0.0013553906],"category_scores_gemma":[0.025520796,0.0010407165,0.003214809,0.0014462363,0.00077464466,0.002961184,0.0018470637,0.0028165553,0.00077634363],"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":[0.00060050195,0.00025145386,0.005813405,0.00030065997,0.0007235227,0.00021722868,0.0003758402,0.78896683,0.0050472827,0.009107073,0.0026769561,0.18591921],"study_design_scores_gemma":[0.000010321059,0.00004561162,0.00040838766,0.000015192823,0.000045840305,0.000040973624,0.000016090416,0.9929128,0.0014386054,0.004510434,0.0005351892,0.000020533493],"about_ca_topic_score_codex":0.008262946,"about_ca_topic_score_gemma":0.006952297,"teacher_disagreement_score":0.012557396,"about_ca_system_score_codex":0.001629128,"about_ca_system_score_gemma":0.0013410297,"threshold_uncertainty_score":0.06641066},"labels":[],"label_agreement":null},{"id":"W1750027614","doi":"10.1309/ajcp4j1xkdvjiugs","title":"Use of Geospatial Mapping to Determine Suitable Locations for Patient Service Centers for Phlebotomy Services","year":2015,"lang":"en","type":"article","venue":"American Journal of Clinical Pathology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary; CARE Canada; Calgary Laboratory Services","funders":"","keywords":"Phlebotomy; Geospatial analysis; Service (business); Geographic information system; Population; Medicine; Geography; Census; Medical emergency; Business; Cartography; Environmental health; Surgery; Marketing","score_opus":0.13853471205762047,"score_gpt":0.3993728136005621,"score_spread":0.2608381015429416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1750027614","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":[0.519038,0.0007059868,0.44195428,0.0012925988,0.0000698001,0.0011682058,0.017025745,0.0024576092,0.016287778],"genre_scores_gemma":[0.65999055,0.00024083372,0.33574837,0.000032260934,0.000010732177,0.00029816365,0.0030302317,0.000056285324,0.0005926297],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99885964,0.0005474731,0.00008200636,0.00019179573,0.000260984,0.000058054902],"domain_scores_gemma":[0.99609923,0.0019864666,0.0006562334,0.00042589527,0.00071926054,0.00011296089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019045306,0.00065177557,0.0003320372,0.0054074028,0.000623132,0.0017296382,0.0006578834,0.00023246132,0.0014938953],"category_scores_gemma":[0.009531665,0.00026930988,0.00046240212,0.0049119573,0.00026279848,0.00057900907,0.0012973753,0.00033241534,0.0002753269],"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":[0.00027655656,0.00027769327,0.48954397,0.00080631516,0.0005473126,0.00050525105,0.0032169665,0.07369363,0.0061518997,0.011320213,0.009383341,0.40427685],"study_design_scores_gemma":[0.00017310404,0.00024386091,0.36686194,0.0003330651,0.00042261856,0.0007877157,0.00703865,0.5597348,0.01019868,0.017262314,0.03674915,0.0001941158],"about_ca_topic_score_codex":0.048094235,"about_ca_topic_score_gemma":0.09989924,"teacher_disagreement_score":0.048094235,"about_ca_system_score_codex":0.0011416423,"about_ca_system_score_gemma":0.0032537375,"threshold_uncertainty_score":0.09562862},"labels":[],"label_agreement":null},{"id":"W1792215712","doi":"10.1111/j.1467-9566.2012.01536.x","title":"Public health intelligence and the detection of potential pandemics","year":2012,"lang":"en","type":"article","venue":"Sociology of Health & Illness","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Centers for Disease Control and Prevention; U.S. Department of Defense","keywords":"Pandemic; Public health; Sociology; Public relations; Medical sociology; Coronavirus disease 2019 (COVID-19); Social science; Political science; Medicine","score_opus":0.06332433873228155,"score_gpt":0.33833160136188717,"score_spread":0.2750072626296056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1792215712","genre_codex":"other","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":[0.1718348,0.04015705,0.11142618,0.31624955,0.0020679706,0.00019975442,0.0007703419,0.00046798645,0.3568264],"genre_scores_gemma":[0.9739318,0.007883288,0.012650124,0.0023533066,0.0005861618,0.00005446936,0.0001440788,0.00002196867,0.0023747433],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9952349,0.002950236,0.00022805364,0.00056415674,0.0008004585,0.00022225556],"domain_scores_gemma":[0.953352,0.03798287,0.0046922285,0.0016684616,0.0014878864,0.000816545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0085359495,0.0004067214,0.00040767415,0.005436882,0.0019915123,0.008004982,0.0008593541,0.003248992,0.0040644705],"category_scores_gemma":[0.037546296,0.00041655623,0.00034913624,0.0031357508,0.011902375,0.014363732,0.0048181177,0.0029087604,0.00040811038],"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":[0.000086041655,0.000078042045,0.021551492,0.00040696686,0.000063458385,0.0004868768,0.014061937,0.004922487,0.0003091855,0.8371302,0.00955328,0.11135003],"study_design_scores_gemma":[0.000024353954,0.000054331562,0.008114061,0.0005724618,0.000033432825,0.00054176396,0.015944244,0.008960683,0.0004519563,0.8883629,0.07688423,0.00005556857],"about_ca_topic_score_codex":0.0023443913,"about_ca_topic_score_gemma":0.0015095097,"teacher_disagreement_score":0.0085359495,"about_ca_system_score_codex":0.0022385665,"about_ca_system_score_gemma":0.0024143218,"threshold_uncertainty_score":0.04514295},"labels":[],"label_agreement":null},{"id":"W1794526355","doi":"10.2196/publichealth.4991","title":"The US National Tuberculosis Surveillance System: A Descriptive Assessment of the Completeness and Consistency of Data Reported from 2008 to 2012","year":2015,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Disease surveillance; Tuberculosis; Completeness (order theory); Electronic data; Under-reporting; Descriptive statistics; Pediatrics; Family medicine; Environmental health; Medical emergency; Disease; Database; Statistics; Computer science; Pathology","score_opus":0.12230776998889663,"score_gpt":0.3685469035371118,"score_spread":0.24623913354821514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1794526355","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78873813,0.002064384,0.0025245163,0.00074772595,0.00006632636,0.0005521365,0.19942811,0.00018301528,0.0056956895],"genre_scores_gemma":[0.8900755,0.0009182185,0.0031879612,0.00025310347,0.000043689102,0.0009233494,0.10413492,0.000037941285,0.00042521878],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98988056,0.0024989387,0.0038805811,0.0008137181,0.0025351343,0.00039109107],"domain_scores_gemma":[0.96573365,0.0071104555,0.01493357,0.0019040174,0.009674407,0.0006438856],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.011689346,0.0003185369,0.000512338,0.007370265,0.00041094646,0.0008362214,0.0008510619,0.00033556938,0.0007432593],"category_scores_gemma":[0.02664975,0.00031985063,0.0009380736,0.009507983,0.0003595774,0.0010487764,0.0009480435,0.0005408236,0.00023447226],"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":[0.00008184561,0.000023265013,0.9888609,0.00016508329,0.00017536595,0.000021616785,0.00028459626,0.00033630242,0.000074838135,0.00009851483,0.004120766,0.005756834],"study_design_scores_gemma":[0.0000069074463,0.00005132158,0.99417984,0.00015061151,0.000048060672,0.0001200411,0.0004892644,0.00053670653,0.0001444017,0.000037898357,0.004223644,0.00001135124],"about_ca_topic_score_codex":0.02525838,"about_ca_topic_score_gemma":0.020974813,"teacher_disagreement_score":0.98831064,"about_ca_system_score_codex":0.0017000522,"about_ca_system_score_gemma":0.0024200547,"threshold_uncertainty_score":0.06181991},"labels":[],"label_agreement":null},{"id":"W1829360400","doi":"10.1111/cag.12091","title":"Geocoding for public health research: Empirical comparison of two geocoding services applied to Canadian cities","year":2014,"lang":"en","type":"article","venue":"Canadian Geographies / Géographies canadiennes","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Toronto; University of Saskatchewan","funders":"Canadian Institutes of Health Research; University of Saskatchewan","keywords":"Geocoding; Computer science; Geography; Information retrieval; Cartography","score_opus":0.11780826672720802,"score_gpt":0.3582039647026375,"score_spread":0.24039569797542948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1829360400","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":[0.950228,0.002611323,0.018435795,0.0041528377,0.00016359368,0.0015459085,0.00960604,0.0002129405,0.013043572],"genre_scores_gemma":[0.9806126,0.0008212885,0.013495379,0.00023419716,0.000019262558,0.00047280145,0.0036103523,0.00005020874,0.00068395],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.95184857,0.020586537,0.0026049425,0.0032194704,0.018685574,0.003054987],"domain_scores_gemma":[0.8199682,0.080911815,0.01741336,0.010655328,0.06605854,0.004992704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0442446,0.00066010543,0.00067496713,0.00802533,0.003433782,0.0037966608,0.0027975913,0.0007977103,0.0012030613],"category_scores_gemma":[0.28139278,0.0005130009,0.0009884834,0.028667096,0.0031783038,0.0020539213,0.005680033,0.001256946,0.00019149337],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003576193,0.00015453532,0.87937623,0.00091662415,0.0004923018,0.000109662644,0.011837351,0.008308727,0.00032582518,0.011703939,0.00845034,0.07796686],"study_design_scores_gemma":[0.00006549284,0.00012444986,0.9434119,0.0006125809,0.00031276664,0.00009288882,0.012566503,0.027420327,0.001062812,0.0020022602,0.0122109605,0.000116981755],"about_ca_topic_score_codex":0.9769767,"about_ca_topic_score_gemma":0.9757495,"teacher_disagreement_score":0.06254265,"about_ca_system_score_codex":0.06254265,"about_ca_system_score_gemma":0.08892238,"threshold_uncertainty_score":0.45378077},"labels":[],"label_agreement":null},{"id":"W1851061130","doi":"10.3978/j.issn.2223-4683.2015.06.08","title":"Interstitial cystitis/painful bladder syndrome: the influence of modern diagnostic criteria on epidemiology and on Internet search activity by the public.","year":2015,"lang":"en","type":"review","venue":"PubMed","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Interstitial cystitis; Nocturia; Medicine; Epidemiology; Population; Public health; Demography; Internal medicine; Urinary system; Environmental health; Pathology","score_opus":0.10226025083026179,"score_gpt":0.36318705024938674,"score_spread":0.26092679941912494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1851061130","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":[0.17198564,0.80162424,0.0012694856,0.009883304,0.0007966692,0.00018885538,0.0054938938,0.00005268502,0.008705337],"genre_scores_gemma":[0.7250356,0.26006138,0.003221833,0.0037166749,0.0013435447,0.0002784845,0.005454948,0.000036892936,0.0008506718],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9958947,0.0015118925,0.0009477699,0.00040196255,0.0010255197,0.00021828621],"domain_scores_gemma":[0.9813085,0.007366772,0.007969137,0.00036792693,0.0022734217,0.00071420195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004009956,0.00027946074,0.00068479264,0.004392595,0.00039097582,0.0014012854,0.0006158715,0.0007658741,0.0017659342],"category_scores_gemma":[0.017606055,0.00023316499,0.00076053286,0.0063762036,0.0010507136,0.0025823456,0.0011557763,0.0008227771,0.00025265652],"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":[0.000408266,0.00006602219,0.825609,0.012150859,0.0006518568,0.0008953997,0.0014519019,0.00016693176,0.00038515823,0.0021204422,0.013876835,0.14221728],"study_design_scores_gemma":[0.000030109108,0.00009291538,0.9627568,0.008054266,0.00041538445,0.0045241993,0.0014745784,0.00033539665,0.000103287624,0.0009057101,0.02125612,0.000051150688],"about_ca_topic_score_codex":0.0144896135,"about_ca_topic_score_gemma":0.025140105,"teacher_disagreement_score":0.0144896135,"about_ca_system_score_codex":0.0010227935,"about_ca_system_score_gemma":0.0020159849,"threshold_uncertainty_score":0.02881056},"labels":[],"label_agreement":null},{"id":"W1856685400","doi":"10.1111/1469-0691.12273","title":"An overview of Internet biosurveillance","year":2013,"lang":"en","type":"review","venue":"Clinical Microbiology and Infection","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":75,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Agency of Canada","funders":"","keywords":"The Internet; Public health; Public sector; Internet privacy; Disease surveillance; Public health surveillance; Government (linguistics); Scope (computer science); Data science; Business; Computer science; Medicine; World Wide Web; Political science; Pathology","score_opus":0.15542103942687638,"score_gpt":0.45965510776053,"score_spread":0.30423406833365363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1856685400","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":[0.00005776652,0.9988913,0.00017729009,0.0002374547,0.000135744,0.000008399574,0.000032746488,0.000006763293,0.0004525215],"genre_scores_gemma":[0.0007405128,0.9979693,0.00047989213,0.0003575029,0.00020610064,0.000011763364,0.000057371868,0.0000023924035,0.00017516407],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99866605,0.00036623597,0.00033002064,0.00018009995,0.00037879767,0.00007881209],"domain_scores_gemma":[0.99323857,0.004758825,0.0007805528,0.00009961791,0.00094734307,0.0001750688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003440969,0.0014630363,0.0023710353,0.0066283285,0.0003325748,0.0022006114,0.0015374735,0.0019856007,0.005066314],"category_scores_gemma":[0.006957009,0.00061447045,0.0018330992,0.0046499935,0.00064626825,0.003152221,0.0014475535,0.0020934478,0.0014406027],"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":[0.00007952047,0.000059635717,0.0004442814,0.051594213,0.00021485603,0.000109003835,0.000054699918,0.00034534885,0.0003754304,0.001832079,0.015760724,0.92913026],"study_design_scores_gemma":[0.00006633898,0.0002673196,0.0032196655,0.082105316,0.0015523555,0.001464845,0.00021187215,0.00065588625,0.00075537455,0.0022516064,0.90736943,0.000079890764],"about_ca_topic_score_codex":0.0034650136,"about_ca_topic_score_gemma":0.0054848255,"teacher_disagreement_score":0.0066283285,"about_ca_system_score_codex":0.0014517468,"about_ca_system_score_gemma":0.0032064999,"threshold_uncertainty_score":0.018197834},"labels":[],"label_agreement":null},{"id":"W1873786287","doi":"10.1111/j.1467-9671.2011.01247.x","title":"Exploring the Boundaries of Web Map Services: The Example of the Online Injury Atlas for Ontario","year":2011,"lang":"en","type":"article","venue":"Transactions in GIS","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"St. Michael's Hospital; University of Toronto; Toronto Metropolitan University","funders":"","keywords":"Geospatial analysis; License; Confidentiality; Geocoding; Business; Government (linguistics); Data science; Public health; Private sector; Geography; Computer security; Cartography; Computer science; Political science; Medicine; Pathology","score_opus":0.10399464239806876,"score_gpt":0.2768480566348227,"score_spread":0.17285341423675393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1873786287","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":[0.7173464,0.0005974574,0.034253232,0.018821247,0.00028205127,0.0006213051,0.021853657,0.005630285,0.20059446],"genre_scores_gemma":[0.93622667,0.000506407,0.032235745,0.00025225087,0.00004173581,0.00009383718,0.008718672,0.0003637385,0.021560954],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9992004,0.0001563439,0.00003226311,0.000074274656,0.0003532847,0.0001834611],"domain_scores_gemma":[0.9971825,0.0006938068,0.0001048659,0.0004158476,0.0010708204,0.00053221994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010440402,0.00022334226,0.00018387256,0.001178946,0.003596144,0.002692878,0.00069113995,0.0006323692,0.0042801606],"category_scores_gemma":[0.003225943,0.00017188938,0.0003011103,0.004242961,0.0017337102,0.0014294377,0.0015572588,0.00039825562,0.00055262295],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00088306784,0.00024873155,0.1710854,0.0008951394,0.00012138554,0.0078307325,0.055659376,0.048251513,0.015560243,0.158007,0.2559321,0.28552526],"study_design_scores_gemma":[0.000067650195,0.00007883708,0.084471956,0.00015131055,0.000059587837,0.0005173784,0.023495939,0.06623419,0.0050062845,0.009886727,0.8098902,0.00013992404],"about_ca_topic_score_codex":0.9367125,"about_ca_topic_score_gemma":0.9631278,"teacher_disagreement_score":0.0632875,"about_ca_system_score_codex":0.015394688,"about_ca_system_score_gemma":0.015808202,"threshold_uncertainty_score":0.12732035},"labels":[],"label_agreement":null},{"id":"W1903591517","doi":"10.5210/ojphi.v7i1.5849","title":"Integrated Disease Surveillance to Reduce Data Fragmentation – An Application to Malaria Control","year":2015,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"McGill University; Bill and Melinda Gates Foundation","keywords":"Malaria; Cornerstone; Data sharing; Computer science; Data integration; Fragmentation (computing); Disease surveillance; Data science; Infectious disease (medical specialty); Psychological intervention; Automatic identification and data capture; Disease control; Process management; Risk analysis (engineering); Medicine; Disease; Data mining; Environmental health; Geography; Business","score_opus":0.11857222770888903,"score_gpt":0.41030081397800766,"score_spread":0.29172858626911863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1903591517","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":[0.07782328,0.0015908628,0.8792629,0.0059885764,0.00023530629,0.0012969844,0.0038653982,0.023784565,0.006152089],"genre_scores_gemma":[0.27590767,0.000723877,0.71626735,0.0004668658,0.00008267679,0.00035549302,0.0046007093,0.00035562666,0.0012397387],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9956722,0.00184604,0.0005222523,0.0007995324,0.0009809168,0.00017903971],"domain_scores_gemma":[0.98696417,0.0054610875,0.0012357392,0.0037647558,0.0017839791,0.00079023896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010335129,0.0007685221,0.00086170045,0.004417294,0.0010405891,0.0027522855,0.0020174417,0.00085332047,0.0023652797],"category_scores_gemma":[0.020606143,0.0005629939,0.0010623519,0.005329687,0.0009038235,0.0038138202,0.0059665525,0.00107756,0.00049814896],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059949287,0.00074606383,0.036974054,0.00070840167,0.00053404935,0.0004277913,0.0035182808,0.025272626,0.012453534,0.017117904,0.017430242,0.88421756],"study_design_scores_gemma":[0.0005294225,0.0007577727,0.04547236,0.0006962044,0.0005696003,0.0009722491,0.0039207297,0.666747,0.035440486,0.10389489,0.14076592,0.00023332374],"about_ca_topic_score_codex":0.008026662,"about_ca_topic_score_gemma":0.0056793494,"teacher_disagreement_score":0.010335129,"about_ca_system_score_codex":0.0009659906,"about_ca_system_score_gemma":0.0024612176,"threshold_uncertainty_score":0.054658055},"labels":[],"label_agreement":null},{"id":"W1904900966","doi":"10.24908/ss.v13i3/4.6014","title":"Presentation of the Surveillance Studies Network Outstanding Achievement Award","year":2015,"lang":"en","type":"article","venue":"Surveillance & Society","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Presentation (obstetrics); Sociology; Library science; Computer science; Medicine","score_opus":0.057278862130261204,"score_gpt":0.33743202715386533,"score_spread":0.2801531650236041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1904900966","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014354729,0.0035318828,0.0014681686,0.27266085,0.5871799,0.0007212406,0.00383787,0.00029054828,0.12887418],"genre_scores_gemma":[0.009894335,0.0032673674,0.0013865985,0.047796447,0.104011215,0.00054943474,0.0049144207,0.00047268256,0.82770747],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9911198,0.0011325766,0.0002657521,0.0006359711,0.0055055846,0.0013403986],"domain_scores_gemma":[0.9801875,0.0008061767,0.00031833877,0.00037837605,0.0080973385,0.010212427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013165301,0.0011088564,0.0009874536,0.0014531153,0.0029936314,0.008282111,0.0017204357,0.004694365,0.1595557],"category_scores_gemma":[0.017412845,0.00037813865,0.00096512213,0.0008119011,0.0006975742,0.0020494831,0.006315331,0.006916731,0.07611279],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021165226,0.0000105485005,0.00004688788,0.000010763374,0.0000018765742,0.000018837816,0.000008779383,0.000014006875,0.000046348247,0.00038189677,0.99629825,0.0031406258],"study_design_scores_gemma":[0.000031858955,0.000045033554,0.0009172081,0.000057900103,0.000004451817,0.00003857107,0.00010304676,0.00009767545,0.00008786948,0.00073522376,0.9978668,0.000014367572],"about_ca_topic_score_codex":0.0037790039,"about_ca_topic_score_gemma":0.014008975,"teacher_disagreement_score":0.1595557,"about_ca_system_score_codex":0.004970485,"about_ca_system_score_gemma":0.011755719,"threshold_uncertainty_score":0.533767},"labels":[],"label_agreement":null},{"id":"W190773545","doi":"10.1177/070674370705200704","title":"Geographical Variation in the Prevalence of Problematic Substance Use in Canada","year":2007,"lang":"en","type":"article","venue":"The Canadian Journal of Psychiatry","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Toronto; Centre for Addiction and Mental Health","funders":"","keywords":"Variation (astronomy); Regional variation; Demography; Psychology; Geography; Medicine; Sociology; Political science","score_opus":0.013408136125368137,"score_gpt":0.23415511595959665,"score_spread":0.2207469798342285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W190773545","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":[0.9859089,0.0019462486,0.00061482,0.00083106797,0.000020367275,0.000056109315,0.007291194,0.00003881985,0.0032924328],"genre_scores_gemma":[0.9974623,0.00049662584,0.00038078587,0.000037943573,0.0000030945391,0.000011353236,0.0013011548,0.0000050640133,0.00030162398],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99849725,0.0001944707,0.000114054994,0.00026761484,0.0006285278,0.00029821615],"domain_scores_gemma":[0.99690706,0.0003476366,0.00058202137,0.00010763581,0.0016944871,0.00036129754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011108462,0.0002390492,0.00041677093,0.0025274998,0.0017112846,0.0010243405,0.0011594297,0.00022643653,0.0015114624],"category_scores_gemma":[0.005251072,0.00023973668,0.00056099676,0.0061806794,0.0008619895,0.00027947727,0.00107345,0.00034516567,0.00009308165],"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":[0.00006812708,0.000010802094,0.986099,0.000084340514,0.00017551538,0.00008464824,0.00081178354,0.0009282444,0.0002158172,0.0003547969,0.0014162827,0.009750622],"study_design_scores_gemma":[0.0000042049187,0.0000063373986,0.9982141,0.000029932055,0.000024768206,0.00003212068,0.0004256139,0.0005425849,0.000037928417,0.000036878515,0.00063701015,0.000008539646],"about_ca_topic_score_codex":0.9961825,"about_ca_topic_score_gemma":0.99666435,"teacher_disagreement_score":0.027056629,"about_ca_system_score_codex":0.027056629,"about_ca_system_score_gemma":0.02243185,"threshold_uncertainty_score":0.19631046},"labels":[],"label_agreement":null},{"id":"W1917810517","doi":"10.1007/978-3-642-31037-9_15","title":"Biometric Monitoring as a Persuasive Technology: Ensuring Patients Visit Health Centers in India’s Slums","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Attendance; Slum; Medicine; Schedule; Biometrics; Developing country; Medical emergency; mHealth; Family medicine; Environmental health; Computer security; Nursing; Computer science; Economic growth; Population","score_opus":0.01485278164047654,"score_gpt":0.2821481940210346,"score_spread":0.26729541238055804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1917810517","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":[0.62593395,0.023427693,0.009101483,0.097775556,0.0019986052,0.00012132909,0.00040308802,0.00052972493,0.2407085],"genre_scores_gemma":[0.9713283,0.005225169,0.0041356864,0.0021764976,0.00017904963,0.000028940138,0.000061009494,0.000026461401,0.016838895],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99946195,0.00023836564,0.000022967064,0.00003975379,0.00010849003,0.00012841324],"domain_scores_gemma":[0.99906033,0.0005730036,0.00010877468,0.000025434578,0.000089324785,0.0001431598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073693675,0.00025215762,0.00012724975,0.000563721,0.0014567004,0.0023313265,0.00077152695,0.0009713874,0.0035512787],"category_scores_gemma":[0.0017884797,0.00016790823,0.00014712533,0.00085538375,0.0010376999,0.0012810291,0.0011773249,0.0013974234,0.0007060711],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016922658,0.00030097234,0.081266396,0.0011779106,0.000035199868,0.0027522082,0.14293033,0.0006893322,0.007609429,0.028845055,0.08681282,0.6474112],"study_design_scores_gemma":[0.00003195372,0.0007319985,0.17443645,0.0013494131,0.00012802899,0.005795709,0.3185617,0.003425515,0.007953236,0.011044289,0.4763635,0.00017803992],"about_ca_topic_score_codex":0.009476748,"about_ca_topic_score_gemma":0.019732391,"teacher_disagreement_score":0.009476748,"about_ca_system_score_codex":0.0014336435,"about_ca_system_score_gemma":0.0023448754,"threshold_uncertainty_score":0.018843174},"labels":[],"label_agreement":null},{"id":"W1926133158","doi":"10.29173/cjs6393","title":"S. Harris Ali and Roger Keil, eds., Networked Disease: Emerging Infections in the Global City","year":2009,"lang":"en","type":"article","venue":"The Canadian Journal of Sociology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sociology; Media studies","score_opus":0.01684569623380201,"score_gpt":0.29341725124819396,"score_spread":0.27657155501439196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1926133158","genre_codex":"review","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007603535,0.89312685,0.0029339774,0.0818993,0.0073212394,0.00001885931,0.0001523162,0.000071023955,0.013716157],"genre_scores_gemma":[0.004937507,0.9829071,0.0014930939,0.0015948792,0.0032378288,0.000013371593,0.00006794503,0.000017298997,0.0057310346],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990785,0.00029372665,0.000074203934,0.000101174344,0.00039160994,0.00006073451],"domain_scores_gemma":[0.9960001,0.0025481638,0.00030588038,0.0000894967,0.0004912691,0.0005650704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026381488,0.0013872782,0.0011938246,0.0034325225,0.0013031332,0.0055795154,0.0010098529,0.0027325142,0.006891997],"category_scores_gemma":[0.0036584348,0.0010174768,0.00051048683,0.004382842,0.0021986775,0.006793824,0.0019422116,0.002983948,0.004006895],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031559895,0.00003473357,0.0029862532,0.001385992,0.000034741523,0.00014254334,0.0010600094,0.0012718893,0.00009271086,0.016071454,0.5596354,0.41725266],"study_design_scores_gemma":[0.00001415565,0.00003811586,0.0055606063,0.003921019,0.0000613049,0.0010025628,0.0027921635,0.0022566884,0.0002171239,0.0282334,0.9558361,0.00006661289],"about_ca_topic_score_codex":0.021203948,"about_ca_topic_score_gemma":0.04848185,"teacher_disagreement_score":0.021203948,"about_ca_system_score_codex":0.0022282645,"about_ca_system_score_gemma":0.005065965,"threshold_uncertainty_score":0.042161047},"labels":[],"label_agreement":null},{"id":"W1954768048","doi":"","title":"Determining use of preventive health care in Ontario: comparison of rates of 3 maneuvers in administrative and survey data.","year":2009,"lang":"en","type":"article","venue":"PubMed","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Sunnybrook Health Science Centre","funders":"","keywords":"Medicine; Socioeconomic status; Mammography; Demography; Health care; Vaccination; Gerontology; Environmental health; Family medicine; Population; Breast cancer; Cancer","score_opus":0.191412707925232,"score_gpt":0.3902845542575508,"score_spread":0.19887184633231883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1954768048","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":[0.95970184,0.0026374336,0.0006609392,0.0009869614,0.000031175023,0.00017180161,0.03118211,0.00004809249,0.0045797382],"genre_scores_gemma":[0.98860127,0.0007411624,0.0004445238,0.00010509803,0.000010732335,0.000064396525,0.009467975,0.000010391853,0.0005545129],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9957397,0.00050813355,0.00047473187,0.0004889439,0.0021737597,0.00061478623],"domain_scores_gemma":[0.9864846,0.0011716496,0.006498685,0.000597696,0.004316624,0.00093066023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035907028,0.00039670867,0.00046297107,0.0029462732,0.0010811918,0.0010845925,0.0013883197,0.0004790862,0.0013224197],"category_scores_gemma":[0.013319288,0.00056971837,0.0008332617,0.008863841,0.00061170897,0.00064939825,0.0012931789,0.00042560964,0.00023509435],"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":[0.000038258273,0.000005734061,0.99777526,0.0000442874,0.00006877577,0.000010716202,0.00021291693,0.00005932552,0.0000309651,0.000030337838,0.0005002635,0.0012231474],"study_design_scores_gemma":[0.0000041029925,0.000007879116,0.9991986,0.000016034646,0.000018501125,0.000008819387,0.00015215787,0.00012593789,0.000018725244,0.0000053321974,0.00044059756,0.000003393759],"about_ca_topic_score_codex":0.9810038,"about_ca_topic_score_gemma":0.9857999,"teacher_disagreement_score":0.023421865,"about_ca_system_score_codex":0.023421865,"about_ca_system_score_gemma":0.018354123,"threshold_uncertainty_score":0.16993833},"labels":[],"label_agreement":null},{"id":"W1966700133","doi":"10.3390/ijgi3030868","title":"GIS and Public Health","year":2014,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Geospatial analysis; Public health; Data science; Bridge (graph theory); Geographic information system; GIS and public health; GIS applications; Computer science; Dimension (graph theory); Geography; Medicine; Cartography","score_opus":0.011459378681967041,"score_gpt":0.28721808811851646,"score_spread":0.2757587094365494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966700133","genre_codex":"review","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":[0.0012397589,0.3665387,0.014928046,0.3221421,0.14364481,0.00009337532,0.0034414537,0.0010654754,0.1469063],"genre_scores_gemma":[0.05076517,0.4756892,0.018905675,0.055719938,0.23292479,0.00021695124,0.0055523715,0.0017205429,0.1585054],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9958211,0.0018552401,0.0003012604,0.00046264243,0.0013370224,0.00022266764],"domain_scores_gemma":[0.9847541,0.010787691,0.0006065915,0.001094699,0.001909455,0.0008474076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045079133,0.0012042744,0.0016549802,0.006735193,0.0015014239,0.010968714,0.0015233749,0.0041810307,0.06428745],"category_scores_gemma":[0.015153604,0.00065118476,0.001172134,0.008159369,0.004477415,0.007223771,0.0043685604,0.005467309,0.009723006],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013836252,0.000016333903,0.00063689385,0.00048514793,0.00004236757,0.00005607371,0.00043896522,0.00048367685,0.00007418606,0.08032888,0.8287562,0.08866738],"study_design_scores_gemma":[0.0000028524375,0.000008274127,0.00036968666,0.00037016557,0.00000765339,0.00007078096,0.0003446059,0.00018366383,0.000028417844,0.035534576,0.9630703,0.000009036733],"about_ca_topic_score_codex":0.0053498438,"about_ca_topic_score_gemma":0.006563133,"teacher_disagreement_score":0.06428745,"about_ca_system_score_codex":0.003815768,"about_ca_system_score_gemma":0.0045096814,"threshold_uncertainty_score":0.21506298},"labels":[],"label_agreement":null},{"id":"W1967348488","doi":"10.14778/1920841.1920978","title":"Identifying, attributing and describing spatial bursts","year":2010,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Terabyte; Scalability; Computer science; Task (project management); Social media; Information retrieval; Scale (ratio); Data science; Data mining; World Wide Web; Database; Geography; Cartography","score_opus":0.02560125426761936,"score_gpt":0.2604515705919254,"score_spread":0.23485031632430606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967348488","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":[0.56061107,0.0017956778,0.41492668,0.0011028193,0.00015167073,0.000483767,0.013524048,0.0039744154,0.00342991],"genre_scores_gemma":[0.7557122,0.0011262156,0.22802484,0.00011832237,0.00022331596,0.0003144527,0.01270484,0.00023282455,0.0015429313],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985782,0.00021419562,0.0002527905,0.0003922191,0.0004315046,0.00013104346],"domain_scores_gemma":[0.9922272,0.0036201493,0.0016951385,0.0011117477,0.0010618027,0.00028400344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015688691,0.0010439517,0.0007646883,0.010933805,0.0006846615,0.0024888192,0.0011712332,0.0010503023,0.0005028618],"category_scores_gemma":[0.008214758,0.00050459825,0.00058493525,0.007631849,0.0005804858,0.0033582156,0.0019518195,0.000779626,0.00045903237],"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":[0.00067109044,0.00046537773,0.450899,0.00077632855,0.0002982401,0.0018250559,0.0044110394,0.058928996,0.028732048,0.011775994,0.012183026,0.42903388],"study_design_scores_gemma":[0.00005269243,0.00024012124,0.13560632,0.00015390902,0.00021343955,0.0015735744,0.0057515935,0.76936734,0.027125329,0.032432683,0.02735558,0.00012744621],"about_ca_topic_score_codex":0.006783954,"about_ca_topic_score_gemma":0.0102327345,"teacher_disagreement_score":0.010933805,"about_ca_system_score_codex":0.0008888782,"about_ca_system_score_gemma":0.0011145805,"threshold_uncertainty_score":0.013488948},"labels":[],"label_agreement":null},{"id":"W1968335419","doi":"10.1186/1471-2458-13-16","title":"Ascertainment of chronic diseases using population health data: a comparison of health administrative data and patient self-report","year":2013,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":244,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Ottawa Hospital; Institute for Clinical Evaluative Sciences; Élisabeth Bruyère Hospital","funders":"Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences","keywords":"Medicine; Stroke (engine); Myocardial infarction; Population; Concordance; Biostatistics; COPD; Epidemiology; Heart failure; Cohen's kappa; Diabetes mellitus; Public health; Emergency medicine; Physical therapy; Internal medicine; Environmental health; Pathology","score_opus":0.18250451586455765,"score_gpt":0.44371119297809974,"score_spread":0.26120667711354206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968335419","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98162293,0.002560473,0.0076792333,0.00050894526,0.000053717024,0.00039383682,0.0040285657,0.000036173027,0.00311607],"genre_scores_gemma":[0.9953271,0.0004356673,0.0020596734,0.00011916912,0.000025185733,0.00013775009,0.0017534593,0.000006249764,0.00013576717],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9708152,0.015338026,0.0018282054,0.0021419562,0.009128391,0.0007483473],"domain_scores_gemma":[0.9569191,0.020966113,0.0098700635,0.0033495477,0.008297573,0.0005976165],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03416019,0.00029437165,0.0006028696,0.0030536028,0.0005146521,0.0017641388,0.00087501697,0.0005509174,0.00048076143],"category_scores_gemma":[0.078459285,0.0002386641,0.000639986,0.0047175544,0.0007718248,0.0008047148,0.0012827629,0.0004423169,0.00010943447],"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":[0.00014489667,0.000022285267,0.99028975,0.00016075288,0.00045168202,0.000020835658,0.0006718708,0.00024660942,0.0001162419,0.00011687695,0.00023590034,0.0075224075],"study_design_scores_gemma":[0.000024679075,0.000082521,0.9969657,0.000080455335,0.00014191035,0.000042720745,0.000466452,0.001076597,0.0001383805,0.0000913906,0.0008788932,0.000010256929],"about_ca_topic_score_codex":0.21027723,"about_ca_topic_score_gemma":0.20452413,"teacher_disagreement_score":0.9658398,"about_ca_system_score_codex":0.00368064,"about_ca_system_score_gemma":0.003775668,"threshold_uncertainty_score":0.41810656},"labels":[],"label_agreement":null},{"id":"W1968744685","doi":"10.1186/1753-6561-2-s3-s6","title":"Visualization techniques and graphical user interfaces in syndromic surveillance systems. Summary from the Disease Surveillance Workshop, Sept. 11–12, 2007; Bangkok, Thailand","year":2008,"lang":"en","type":"article","venue":"BMC Proceedings","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kingston Health Sciences Centre; Queen's University","funders":"Johns Hopkins University; U.S. Department of Defense","keywords":"Visualization; Disease surveillance; Computer science; Asset (computer security); Graphical user interface; Data science; Data visualization; Data mining; Medicine; Human–computer interaction; Disease; Computer security; Pathology","score_opus":0.019860000818331933,"score_gpt":0.26584740796736184,"score_spread":0.2459874071490299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968744685","genre_codex":"methods","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010094952,0.014506159,0.90479445,0.0058196154,0.001074516,0.00045735465,0.0027645808,0.05025303,0.01023542],"genre_scores_gemma":[0.07380489,0.020922974,0.86528444,0.0013923637,0.0011034416,0.0013554977,0.007359922,0.0069604977,0.021815982],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99764,0.001365551,0.00026839142,0.00019811321,0.00044291603,0.000085072956],"domain_scores_gemma":[0.9846978,0.01239535,0.00030544872,0.00059806067,0.0015899112,0.00041352087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007846162,0.0022323085,0.00079805584,0.0017796357,0.00047469497,0.004460274,0.0018894902,0.0014205024,0.033797648],"category_scores_gemma":[0.018628566,0.001408908,0.0010400629,0.0019652701,0.0007643514,0.0051842947,0.0020118828,0.002327128,0.0056889243],"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":[0.0011058822,0.0002483974,0.0018436679,0.00244739,0.00022833045,0.0007022316,0.003128069,0.0062928363,0.012432037,0.014332022,0.30669898,0.6505402],"study_design_scores_gemma":[0.00059302856,0.000732245,0.0066462075,0.0032411828,0.00045765337,0.002176877,0.0014820136,0.1294745,0.021187272,0.047926225,0.7857338,0.00034897076],"about_ca_topic_score_codex":0.0016569116,"about_ca_topic_score_gemma":0.0016509672,"teacher_disagreement_score":0.033797648,"about_ca_system_score_codex":0.00048229474,"about_ca_system_score_gemma":0.00039566663,"threshold_uncertainty_score":0.11306441},"labels":[],"label_agreement":null},{"id":"W1970322322","doi":"10.12927/hcq.2011.22641","title":"Influenza Immunization Data: Can We Make Order Out of Chaos?","year":2011,"lang":"en","type":"article","venue":"Healthcare Quarterly","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario","funders":"","keywords":"Immunization; CHAOS (operating system); Order (exchange); Best practice; Medicine; Virology; Computer science; Business; Political science; Immunology; Computer security; Immune system; Law","score_opus":0.0899818188699059,"score_gpt":0.3496397347605576,"score_spread":0.2596579158906517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970322322","genre_codex":"commentary","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":[0.0025484795,0.018544002,0.011421537,0.9553342,0.0054057455,0.00005539647,0.0022049278,0.00025329832,0.004232402],"genre_scores_gemma":[0.30602735,0.14154057,0.14726159,0.34774327,0.038655505,0.0007072231,0.010074996,0.0012183046,0.0067712744],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.96832407,0.018186083,0.0030328594,0.002188244,0.0071783704,0.0010903623],"domain_scores_gemma":[0.8041784,0.12662432,0.009377171,0.020296028,0.031299863,0.008224116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08233276,0.0012457299,0.002356653,0.0068889074,0.003828248,0.017954882,0.0044308575,0.0056703812,0.0064220815],"category_scores_gemma":[0.27639174,0.001577715,0.0012601658,0.0073354687,0.010160964,0.04625669,0.009343479,0.01572123,0.0034422171],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014416431,0.00007780775,0.030020189,0.0015211089,0.00036663548,0.0001883308,0.0034267658,0.0021184226,0.00022390894,0.105419725,0.57545835,0.28103465],"study_design_scores_gemma":[0.000059205213,0.00006547248,0.007548867,0.0037272023,0.000105268264,0.00019531601,0.006634952,0.004447,0.00022703319,0.44939733,0.5274175,0.0001748507],"about_ca_topic_score_codex":0.016960504,"about_ca_topic_score_gemma":0.021530135,"teacher_disagreement_score":0.08233276,"about_ca_system_score_codex":0.004590777,"about_ca_system_score_gemma":0.013982514,"threshold_uncertainty_score":0.43542248},"labels":[],"label_agreement":null},{"id":"W1970849216","doi":"10.1016/j.vaccine.2010.09.016","title":"Time and motion study to compare electronic and hybrid data collection systems during the pandemic (H1N1) 2009 influenza vaccination campaign","year":2010,"lang":"en","type":"article","venue":"Vaccine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto General Hospital; University of British Columbia; University of Toronto; Public Health Ontario; Public Health Agency of Canada","funders":"Canadian Institutes of Health Research","keywords":"Data collection; Vaccination; Pandemic; H1n1 pandemic; Coronavirus disease 2019 (COVID-19); Electronic data; Sample (material); Electronic systems; Immunization; Data collection system; Computer science; Medical emergency; Medicine; Statistics; Virology; Database; Infectious disease (medical specialty); Engineering; Immunology; Mathematics","score_opus":0.015040477167096696,"score_gpt":0.28542555463302566,"score_spread":0.27038507746592894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970849216","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":[0.9983537,0.00022386885,0.00025200922,0.000055359873,0.000027033226,0.00010239097,0.00072246214,0.000003432202,0.00025978504],"genre_scores_gemma":[0.9985273,0.000059978713,0.00017846479,0.00006194795,0.000018896624,0.00016734433,0.00069156586,0.000003340192,0.00029114512],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99628913,0.0022945162,0.00036040912,0.00033629304,0.00033089783,0.00038874047],"domain_scores_gemma":[0.9859848,0.0076896725,0.0033445272,0.00082733366,0.0012234419,0.0009301847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060624504,0.0004958178,0.00067505107,0.0009411981,0.00051307323,0.00075075205,0.00066893623,0.0008807481,0.0021456021],"category_scores_gemma":[0.0195599,0.00034952155,0.0014164095,0.0011355209,0.00032267638,0.0008929691,0.00084185466,0.0007244854,0.00027606552],"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":[0.068665,0.0038996155,0.8950045,0.00036034707,0.0061051114,0.0001147211,0.00091398804,0.0019105109,0.0011777052,0.00038032007,0.0008838313,0.020584295],"study_design_scores_gemma":[0.0019865492,0.01768006,0.96989316,0.00006818767,0.0018445803,0.00008938098,0.0012027761,0.0055737393,0.0005518376,0.000110936824,0.00095730607,0.000041429597],"about_ca_topic_score_codex":0.017423976,"about_ca_topic_score_gemma":0.01091864,"teacher_disagreement_score":0.017423976,"about_ca_system_score_codex":0.0012510599,"about_ca_system_score_gemma":0.0009393973,"threshold_uncertainty_score":0.03464514},"labels":[],"label_agreement":null},{"id":"W1971021044","doi":"10.1016/j.healthplace.2005.09.012","title":"Mapping health on the Internet: A new tool for environmental justice and public health research","year":2006,"lang":"en","type":"article","venue":"Health & Place","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":56,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Joseph’s Healthcare Hamilton; York University; McMaster University; McMaster University Medical Centre","funders":"Health Canada; Minnesota Department of Natural Resources","keywords":"Public participation GIS; Environmental justice; GIS and public health; Geographic information system; Public health; The Internet; Traditional knowledge GIS; GIS Day; Public relations; Environmental health; World Wide Web; Environmental planning; Environmental resource management; Geography; Computer science; Political science; Medicine; Remote sensing; Nursing","score_opus":0.13838044549995315,"score_gpt":0.38726098500434164,"score_spread":0.2488805395043885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971021044","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":[0.044882245,0.0036926924,0.738352,0.02068293,0.0011369629,0.0013027149,0.07843096,0.051998023,0.059521608],"genre_scores_gemma":[0.2951078,0.0032009545,0.6430624,0.0022434518,0.00081029197,0.0015377798,0.043610387,0.001821043,0.0086058965],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953578,0.0021746124,0.00060251827,0.00053803157,0.0012041525,0.00012285612],"domain_scores_gemma":[0.9366417,0.041899204,0.0026356375,0.011973785,0.0041799257,0.0026696953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008251169,0.0010196888,0.0014548282,0.015881361,0.0010719054,0.007995135,0.0020637028,0.0019606554,0.011310713],"category_scores_gemma":[0.03707562,0.00069821574,0.00092300907,0.013216914,0.00081165164,0.012654025,0.004790209,0.0019315666,0.0028465914],"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":[0.00036166978,0.0005164333,0.032452285,0.0013587625,0.00041136995,0.0003806325,0.0028165423,0.0043234867,0.0021620598,0.044523034,0.10689671,0.80379707],"study_design_scores_gemma":[0.0002589852,0.00033161728,0.030813264,0.0014052967,0.0007249317,0.0011923556,0.0068491735,0.09064752,0.008481828,0.2723621,0.5864916,0.00044124713],"about_ca_topic_score_codex":0.006279777,"about_ca_topic_score_gemma":0.0064193406,"teacher_disagreement_score":0.015881361,"about_ca_system_score_codex":0.0010239913,"about_ca_system_score_gemma":0.0021063266,"threshold_uncertainty_score":0.043636918},"labels":[],"label_agreement":null},{"id":"W1974165922","doi":"10.6064/2012/875253","title":"The Past, Present, and Future of Public Health Surveillance","year":2012,"lang":"en","type":"review","venue":"Scientifica","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":184,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Public health; Public health surveillance; Dichotomy; Public relations; Political science; Environmental health; Medicine; Pathology","score_opus":0.10269315139656598,"score_gpt":0.3664852955211397,"score_spread":0.2637921441245737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974165922","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":[0.000056809513,0.9955512,0.00025081003,0.0031002925,0.00030328947,0.000002657495,0.000009239302,0.0000051450406,0.0007205694],"genre_scores_gemma":[0.0020246245,0.9955954,0.00045167404,0.0012093915,0.0005696663,0.000008029852,0.0000142729095,0.0000019335341,0.00012502528],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99301517,0.003519837,0.0008505611,0.0007318812,0.0016152408,0.00026728943],"domain_scores_gemma":[0.97445154,0.02014288,0.0015876518,0.000628827,0.0027181774,0.00047093557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008982427,0.0008120156,0.0016144802,0.005088175,0.000902589,0.0049220175,0.0015848888,0.0037316,0.0022771952],"category_scores_gemma":[0.013952639,0.00056726433,0.00086236815,0.007396678,0.004801203,0.008958048,0.0016853581,0.0039399313,0.0007433022],"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":[0.0000567392,0.00005528911,0.0008453081,0.030846953,0.00013502814,0.00013042707,0.0007101243,0.00068140996,0.0002743131,0.071034856,0.031692814,0.8635368],"study_design_scores_gemma":[0.000012509279,0.00007555459,0.0018128969,0.033841074,0.00012782605,0.0007516621,0.0009078478,0.0003530128,0.00024237468,0.03612381,0.92568964,0.000061865074],"about_ca_topic_score_codex":0.00612922,"about_ca_topic_score_gemma":0.005081522,"teacher_disagreement_score":0.008982427,"about_ca_system_score_codex":0.0045877774,"about_ca_system_score_gemma":0.009604956,"threshold_uncertainty_score":0.047504246},"labels":[],"label_agreement":null},{"id":"W1975706048","doi":"10.1016/j.sste.2009.12.001","title":"Review of methods for space–time disease surveillance","year":2010,"lang":"en","type":"review","venue":"Spatial and Spatio-temporal Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":152,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Child and Family Research Institute; University of Victoria","funders":"","keywords":"Variety (cybernetics); Disease surveillance; Scope (computer science); Context (archaeology); Computer science; Data science; Suite; Focus (optics); Risk analysis (engineering); Disease; Medicine; Artificial intelligence; Geography","score_opus":0.09295426642670605,"score_gpt":0.4570538062112592,"score_spread":0.3640995397845531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975706048","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":[0.000067317626,0.99567705,0.0027778891,0.00043275024,0.0002922764,0.000024257304,0.00012111442,0.000022544602,0.0005848643],"genre_scores_gemma":[0.00058181054,0.9940686,0.004517642,0.00021164061,0.00025895608,0.000028957471,0.00012171128,0.000004440454,0.00020638535],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980767,0.0005927294,0.0003879461,0.0002885731,0.0005908258,0.000063199994],"domain_scores_gemma":[0.99069726,0.0068481094,0.0006085473,0.0002627306,0.0014605357,0.00012283723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048907516,0.00186708,0.0030623495,0.005498038,0.00034486453,0.0017175317,0.002601502,0.001428664,0.0030008145],"category_scores_gemma":[0.012323186,0.00071739056,0.001559665,0.006681262,0.00093321595,0.00244384,0.0013460149,0.0015711347,0.0015020195],"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":[0.00003163327,0.000043405907,0.00038080534,0.024157051,0.00021632283,0.00005849822,0.000045764522,0.00086989003,0.0002496099,0.004019527,0.021403031,0.94852453],"study_design_scores_gemma":[0.00003488472,0.000097088305,0.002317371,0.024398351,0.0008318087,0.0009030456,0.00012076165,0.0015859309,0.00065677235,0.011078701,0.9578907,0.00008446721],"about_ca_topic_score_codex":0.006881838,"about_ca_topic_score_gemma":0.008371558,"teacher_disagreement_score":0.006881838,"about_ca_system_score_codex":0.001632806,"about_ca_system_score_gemma":0.0043608416,"threshold_uncertainty_score":0.025865078},"labels":[],"label_agreement":null},{"id":"W1978419880","doi":"10.1016/j.ijid.2014.03.660","title":"Enumeration for health and government census data: Consensus or controversy?","year":2014,"lang":"en","type":"article","venue":"International Journal of Infectious Diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Census; Population; Quarter (Canadian coin); Numbering; Government (linguistics); Geography; De facto; Enumeration; Demography; Data collection; Statistics; Medicine; Environmental health; Computer science; Political science; Sociology; Mathematics","score_opus":0.02200331207563559,"score_gpt":0.345873300409302,"score_spread":0.32386998833366637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978419880","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037100723,0.058066174,0.023044394,0.8765991,0.023718912,0.0003222678,0.0008874629,0.00022729093,0.013424261],"genre_scores_gemma":[0.19276534,0.11564253,0.14001364,0.48721746,0.048124332,0.0026445189,0.0046518594,0.0015374263,0.0074029216],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.64756703,0.22697328,0.04132256,0.01748555,0.062027816,0.0046237432],"domain_scores_gemma":[0.33184752,0.47206056,0.025772315,0.043204322,0.11933752,0.0077778087],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.43540525,0.0011554764,0.003413658,0.011516278,0.0047882292,0.014328818,0.015621767,0.010675834,0.0063103116],"category_scores_gemma":[0.6239158,0.0017905077,0.002284649,0.019640515,0.01927331,0.026768636,0.0131157525,0.0201612,0.0046127345],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001788436,0.00014306103,0.014664546,0.005867771,0.00035875652,0.0003527559,0.012149213,0.000541037,0.00014116082,0.14457695,0.43641105,0.38461477],"study_design_scores_gemma":[0.00011806894,0.000100952646,0.016484266,0.04841835,0.0001506,0.00087934936,0.021974057,0.0027483772,0.00047463315,0.13798063,0.77039146,0.00027926496],"about_ca_topic_score_codex":0.011094874,"about_ca_topic_score_gemma":0.0065786964,"teacher_disagreement_score":0.56459475,"about_ca_system_score_codex":0.007855098,"about_ca_system_score_gemma":0.018176766,"threshold_uncertainty_score":0.69624585},"labels":[],"label_agreement":null},{"id":"W1980317104","doi":"10.1109/congress.2009.29","title":"Research Data in Google Earth: How Do We Protect Privacy and Meet Ethical Obligations?","year":2009,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Confidentiality; Identification (biology); Computer science; Information privacy; Globe; Obligation; Global Positioning System; Internet privacy; Data science; Computer security; Political science; Telecommunications; Law","score_opus":0.14544730427790173,"score_gpt":0.4292136328594787,"score_spread":0.283766328581577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980317104","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028571077,0.0038025114,0.026422579,0.94650567,0.001872775,0.00023776384,0.0005373545,0.00015562004,0.017608594],"genre_scores_gemma":[0.27329612,0.01736385,0.16183832,0.51882064,0.0044022156,0.0027476733,0.0024038996,0.0008831202,0.018244114],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.6700961,0.22015326,0.01360989,0.009458668,0.074820854,0.011861215],"domain_scores_gemma":[0.4500811,0.34366992,0.017909056,0.071759865,0.09777756,0.018802468],"candidate_categories":["metaresearch","research_integrity"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.32758886,0.0011553289,0.0029784234,0.0071455385,0.022667136,0.037695862,0.010000919,0.015881727,0.0039479095],"category_scores_gemma":[0.41962123,0.00179357,0.0018442579,0.01551473,0.080361396,0.037970368,0.01746868,0.02803994,0.002563841],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013678489,0.000069640635,0.005311544,0.00070701534,0.00011948522,0.00052955374,0.071523115,0.0008529266,0.00047348,0.61523396,0.2210016,0.0840409],"study_design_scores_gemma":[0.00009090244,0.000043859935,0.0032975504,0.0026017374,0.000063730986,0.00045645604,0.04254819,0.0012484486,0.0009092216,0.2581182,0.69033235,0.00028947034],"about_ca_topic_score_codex":0.52931637,"about_ca_topic_score_gemma":0.44223145,"teacher_disagreement_score":0.9841183,"about_ca_system_score_codex":0.040575046,"about_ca_system_score_gemma":0.17050965,"threshold_uncertainty_score":0.94691116},"labels":[],"label_agreement":null},{"id":"W1980378234","doi":"10.1080/10106049.2010.496496","title":"Geomasking sensitive health data and privacy protection: an evaluation using an E911 database","year":2010,"lang":"en","type":"article","venue":"Geocarto International","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":83,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"National Institute of Environmental Health Sciences; National Institute of Allergy and Infectious Diseases; National Institutes of Health","keywords":"Anonymity; k-anonymity; Geography; Census; Population; Computer science; Privacy protection; Database; Statistics; Data mining; Internet privacy; Computer security; Mathematics; Demography","score_opus":0.13148936986654516,"score_gpt":0.41046787902246284,"score_spread":0.2789785091559177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980378234","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":[0.983084,0.0008931676,0.009878219,0.0006227043,0.000070710885,0.0006691906,0.0011388458,0.0010006615,0.002642428],"genre_scores_gemma":[0.9687384,0.00038653714,0.027592685,0.0001918925,0.000029545803,0.00016516961,0.002241792,0.00004025889,0.0006137094],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98505336,0.007194782,0.0013404128,0.0013209033,0.004626557,0.0004639954],"domain_scores_gemma":[0.96432376,0.02419841,0.0016197889,0.0053446414,0.003723618,0.0007897512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016345179,0.00071533537,0.00092781894,0.0012142232,0.0009831754,0.0021191824,0.0017703057,0.0013394163,0.0005892096],"category_scores_gemma":[0.03769022,0.0002604731,0.00047004546,0.001746506,0.0008613039,0.0026552193,0.0017735646,0.00067888794,0.00017780498],"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":[0.028975274,0.006054147,0.1787508,0.0026814498,0.001679281,0.0016021321,0.002725628,0.1288465,0.036609337,0.009056619,0.022884302,0.5801347],"study_design_scores_gemma":[0.0030918785,0.011195117,0.1412426,0.00026827835,0.00074075937,0.0034583965,0.002173375,0.7661547,0.05225078,0.0029373167,0.016256584,0.0002302473],"about_ca_topic_score_codex":0.011066679,"about_ca_topic_score_gemma":0.005304408,"teacher_disagreement_score":0.016345179,"about_ca_system_score_codex":0.0013757605,"about_ca_system_score_gemma":0.001179297,"threshold_uncertainty_score":0.08644259},"labels":[],"label_agreement":null},{"id":"W1980409581","doi":"10.1186/1476-072x-9-28","title":"An exact test to detect geographic aggregations of events","year":2010,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Alberta","funders":"Canadian Institutes of Health Research; Alberta Heritage Foundation for Medical Research; Fondation pour la Recherche Médicale","keywords":"Population; Cluster (spacecraft); Statistical hypothesis testing; Health geography; Computer science; Identification (biology); Aggregate (composite); Statistics; Data mining; Geography; Econometrics; Mathematics; Public health; Demography; Medicine; Ecology","score_opus":0.014011580888847103,"score_gpt":0.3548019587715671,"score_spread":0.34079037788272004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980409581","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":[0.19857338,0.00041841675,0.79079676,0.0008420085,0.00026077716,0.00068989146,0.0017924396,0.00088165863,0.005744659],"genre_scores_gemma":[0.8022427,0.000084519175,0.19419578,0.00028538884,0.00013915787,0.0009572781,0.0011405379,0.00006525464,0.00088938855],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9730663,0.015404316,0.0017759053,0.0040021553,0.005081425,0.00066985504],"domain_scores_gemma":[0.73360723,0.24072309,0.01005899,0.009871288,0.0043572313,0.0013822053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02136528,0.00059583253,0.0015282269,0.0034409661,0.0008051954,0.0010810143,0.002654314,0.0018858775,0.006890806],"category_scores_gemma":[0.159998,0.00038424187,0.001203388,0.0027509348,0.0027918958,0.0022970615,0.0024528564,0.0018487058,0.00055470853],"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":[0.0030479925,0.00059729035,0.43902403,0.0009675767,0.002593618,0.0016837962,0.001332491,0.057476055,0.00302285,0.09150845,0.011247915,0.38749802],"study_design_scores_gemma":[0.00079591526,0.0025911555,0.09037911,0.0002705745,0.0004855455,0.0034030827,0.00079276075,0.73069036,0.0059673865,0.15106371,0.013377617,0.00018282792],"about_ca_topic_score_codex":0.0014737697,"about_ca_topic_score_gemma":0.0008215381,"teacher_disagreement_score":0.02136528,"about_ca_system_score_codex":0.0009205267,"about_ca_system_score_gemma":0.0015292873,"threshold_uncertainty_score":0.11299175},"labels":[],"label_agreement":null},{"id":"W1984510198","doi":"10.1186/1476-072x-7-8","title":"Online GIS services for mapping and sharing disease information","year":2008,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":117,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of New Brunswick","funders":"Natural Resources Canada; U.S. Geological Survey; University of Southern Maine","keywords":"Health geography; Health informatics; Geographic information system; Geospatial analysis; GIS and public health; Public health; Computer science; World Wide Web; Geography; Data science; Cartography; Medicine; Health policy; International health; Pathology","score_opus":0.033170650355586105,"score_gpt":0.3308298480443543,"score_spread":0.2976591976887682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984510198","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":[0.07461731,0.0006319377,0.7429077,0.0033289348,0.00024509814,0.0015440163,0.0072256876,0.0636762,0.10582312],"genre_scores_gemma":[0.45677203,0.00097169087,0.5050111,0.00079766306,0.00009496684,0.00088015443,0.013193135,0.001791768,0.020487554],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998021,0.00071714143,0.00018433442,0.00015234931,0.00074486114,0.00018028666],"domain_scores_gemma":[0.9959422,0.001331707,0.0002590589,0.0009757687,0.0011207373,0.00037054124],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002921103,0.0006889307,0.00026922338,0.0023075347,0.00094406615,0.0023200058,0.0018530258,0.0011012182,0.0100764455],"category_scores_gemma":[0.004624458,0.00025648111,0.00067370816,0.003146075,0.00075054105,0.002401058,0.002603397,0.000594708,0.0037999444],"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":[0.0007646563,0.00089922565,0.029916843,0.0015168126,0.00027522456,0.002264496,0.006615032,0.045160614,0.02373657,0.19636138,0.14862302,0.54386616],"study_design_scores_gemma":[0.00018665177,0.00015890285,0.008103841,0.00024428565,0.00013520358,0.0012254431,0.0024377354,0.20176332,0.025793876,0.06815733,0.69166434,0.00012917344],"about_ca_topic_score_codex":0.023228247,"about_ca_topic_score_gemma":0.018699836,"teacher_disagreement_score":0.99768,"about_ca_system_score_codex":0.0018582508,"about_ca_system_score_gemma":0.0032542509,"threshold_uncertainty_score":0.04618609},"labels":[],"label_agreement":null},{"id":"W1984926150","doi":"10.2196/jmir.1157","title":"Infodemiology and Infoveillance: Framework for an Emerging Set of Public Health Informatics Methods to Analyze Search, Communication and Publication Behavior on the Internet","year":2009,"lang":"en","type":"editorial","venue":"Journal of Medical Internet Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1412,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University Health Network","funders":"","keywords":"The Internet; Computer science; Social media; Health informatics; Public health; Public health informatics; Public health surveillance; Population; Data science; World Wide Web; Internet privacy; Health policy; Medicine; Environmental health; HRHIS","score_opus":0.19569045247440497,"score_gpt":0.563388056423884,"score_spread":0.36769760394947903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984926150","genre_codex":"methods","genre_gemma":"editorial","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017628114,0.0054967343,0.98386085,0.003531884,0.00021771145,0.00035792994,0.0010585602,0.0004999864,0.0032135053],"genre_scores_gemma":[0.051504657,0.0065473733,0.9347232,0.0012799167,0.0010622547,0.0019479704,0.0016339876,0.00019112004,0.0011094977],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97119564,0.01911036,0.0024748496,0.003211303,0.00359169,0.00041623643],"domain_scores_gemma":[0.9342919,0.05126813,0.0037882198,0.0058700913,0.003608063,0.001173613],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.034725163,0.00317083,0.0034478714,0.024079736,0.0014347504,0.012564676,0.005612431,0.0041771294,0.002312493],"category_scores_gemma":[0.0521719,0.0015416573,0.0044122785,0.01393334,0.011514817,0.015963031,0.00650512,0.0053479234,0.0010197563],"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":[0.00005308895,0.00016865552,0.005958518,0.00096083863,0.00031610578,0.00022410405,0.0013898055,0.009991271,0.00048199343,0.8872974,0.004683817,0.08847448],"study_design_scores_gemma":[0.000041082414,0.00013387442,0.003026998,0.0007412752,0.00013178181,0.0003918024,0.0008920482,0.107307844,0.00073184853,0.8152485,0.07119634,0.00015645193],"about_ca_topic_score_codex":0.009223181,"about_ca_topic_score_gemma":0.0036171991,"teacher_disagreement_score":0.9652748,"about_ca_system_score_codex":0.0041622934,"about_ca_system_score_gemma":0.0055993795,"threshold_uncertainty_score":0.18364638},"labels":[],"label_agreement":null},{"id":"W1985042097","doi":"10.4018/jmdem.2011040101","title":"A Real-Time 3D Visualization Framework for Multimedia Data Management, Simulation, and Prediction","year":2011,"lang":"en","type":"article","venue":"International Journal of Multimedia Data Engineering and Management","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Government of Alberta; University of Alberta","funders":"","keywords":"Geospatial analysis; Computer science; Visualization; Geographic information system; Data science; Data visualization; Geovisualization; Event (particle physics); Data mining; Information visualization; Cartography; Geography","score_opus":0.0459136978597085,"score_gpt":0.3332574645773859,"score_spread":0.2873437667176774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985042097","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":[0.0018266551,0.00017537334,0.98917806,0.00022573555,0.000057845005,0.00010720449,0.000555355,0.0063904203,0.001483278],"genre_scores_gemma":[0.0653124,0.00063844095,0.9289914,0.00014589391,0.00006241902,0.00042480184,0.0017406687,0.0007168757,0.001967059],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954575,0.000107792745,0.00004564032,0.00005602148,0.0002094475,0.000035339413],"domain_scores_gemma":[0.9992072,0.0002818058,0.00006307962,0.000123843,0.00022127548,0.00010268267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011795273,0.0010537364,0.00068781874,0.0015620101,0.0005962339,0.0025926298,0.0019559853,0.0011789746,0.006406112],"category_scores_gemma":[0.00228113,0.00058881246,0.0013167936,0.00097391347,0.0005557837,0.0014571034,0.0020481192,0.0013571925,0.0010355883],"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":[0.0005431439,0.00035692815,0.002903357,0.00088758534,0.00027074508,0.0010664292,0.0017740165,0.29796904,0.048260633,0.09021464,0.066112906,0.48964053],"study_design_scores_gemma":[0.000078965706,0.00008362694,0.00060421205,0.00012010921,0.0000486799,0.0003450847,0.00011612997,0.87523365,0.010053443,0.023226695,0.08998494,0.000104522616],"about_ca_topic_score_codex":0.0083794,"about_ca_topic_score_gemma":0.009925804,"teacher_disagreement_score":0.0083794,"about_ca_system_score_codex":0.00062341953,"about_ca_system_score_gemma":0.0011272748,"threshold_uncertainty_score":0.021430552},"labels":[],"label_agreement":null},{"id":"W1985369819","doi":"10.1002/sim.2403","title":"A workflow spatial scan statistic","year":2006,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Scan statistic; Workflow; Statistic; Computer science; Completeness (order theory); Data mining; Statistics; Mathematics; Database","score_opus":0.010736343537186738,"score_gpt":0.3030262739491112,"score_spread":0.29228993041192447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985369819","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":[0.009808766,0.00012801168,0.9840577,0.0008779378,0.00018202183,0.0001631,0.0012425228,0.0016736358,0.0018663654],"genre_scores_gemma":[0.24345203,0.00026658564,0.74762136,0.0009723035,0.0003322981,0.0007570856,0.0035066162,0.00051164214,0.0025800793],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98841953,0.0062118033,0.00075556803,0.0015491057,0.0025724063,0.00049154286],"domain_scores_gemma":[0.95643103,0.025281563,0.0023360923,0.007054159,0.007720355,0.0011768969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013918666,0.00073726627,0.001296187,0.0024562445,0.000590173,0.0022413395,0.0035935652,0.0016050922,0.0053279554],"category_scores_gemma":[0.052796517,0.00051133835,0.0014853708,0.0038475173,0.001537767,0.0027608108,0.0021198813,0.0023119552,0.0014345618],"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":[0.0015643386,0.00032203356,0.0499307,0.0004004403,0.00041356278,0.00070137077,0.0003343021,0.19968858,0.0052255755,0.22965279,0.036798414,0.47496793],"study_design_scores_gemma":[0.0003118714,0.00036549923,0.004001651,0.00007266689,0.00011014845,0.0008284526,0.00013318777,0.8040456,0.0042248424,0.1605981,0.025206612,0.00010140075],"about_ca_topic_score_codex":0.0059261736,"about_ca_topic_score_gemma":0.004795076,"teacher_disagreement_score":0.013918666,"about_ca_system_score_codex":0.0013916409,"about_ca_system_score_gemma":0.0064873532,"threshold_uncertainty_score":0.07360983},"labels":[],"label_agreement":null},{"id":"W1985925012","doi":"10.1186/1471-2458-11-17","title":"Accuracy of syndrome definitions based on diagnoses in physician claims","year":2011,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University; McGill University Health Centre","funders":"Canadian Institutes of Health Research; McGill University Health Centre; McGill University; U.S. Department of Defense","keywords":"Medicine; Medical diagnosis; Diagnosis code; Biostatistics; Emergency department; Epidemiology; Family medicine; Pediatrics; Emergency medicine; Internal medicine; Pathology; Population","score_opus":0.14606178403281,"score_gpt":0.33834113284572986,"score_spread":0.19227934881291986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985925012","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":[0.9862316,0.0021798096,0.0069420794,0.0003372945,0.00006999065,0.000104703824,0.0016822086,0.00009244143,0.002359878],"genre_scores_gemma":[0.9976744,0.00013353884,0.0014180527,0.0000399416,0.00002293819,0.000012606535,0.0006456799,0.000004538954,0.00004823821],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.969494,0.016811816,0.003906879,0.0029889192,0.0059294607,0.0008688249],"domain_scores_gemma":[0.82234263,0.108995244,0.04561926,0.008713952,0.012898606,0.0014303081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021009358,0.0005666445,0.00061931513,0.0038189192,0.00038035854,0.0018823382,0.0010047987,0.00078365713,0.00070498994],"category_scores_gemma":[0.14208888,0.00035975102,0.00084362086,0.0016372621,0.0010781314,0.0012171975,0.0013936874,0.0006784442,0.00024396981],"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":[0.0002456444,0.000018958886,0.99027234,0.00007561096,0.00013058734,0.000031186693,0.00018852772,0.0006901291,0.00020246336,0.00006868539,0.00024474738,0.007831135],"study_design_scores_gemma":[0.000040081984,0.00021256313,0.9823682,0.00019454244,0.00013369737,0.00057334744,0.00024498763,0.013992551,0.0011468546,0.00038570169,0.00068309065,0.000024370727],"about_ca_topic_score_codex":0.008566844,"about_ca_topic_score_gemma":0.007992716,"teacher_disagreement_score":0.021009358,"about_ca_system_score_codex":0.001028445,"about_ca_system_score_gemma":0.0007594979,"threshold_uncertainty_score":0.111109436},"labels":[],"label_agreement":null},{"id":"W1987457291","doi":"10.1016/j.prevetmed.2014.04.001","title":"Syndromic surveillance using laboratory test requests: A practical guide informed by experience with two systems","year":2014,"lang":"en","type":"article","venue":"Preventive Veterinary Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; University of Prince Edward Island","funders":"Jordbruksverket","keywords":"Computer science; Software; Test (biology); Disease surveillance; Identification (biology); Warning system; Set (abstract data type); Epidemiological surveillance; Data science; Data mining; Disease; Medicine; Epidemiology; Pathology; Telecommunications","score_opus":0.03405825521272772,"score_gpt":0.375763750105562,"score_spread":0.3417054948928343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987457291","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":[0.008943446,0.004666907,0.82222295,0.09852119,0.001236823,0.007839546,0.001292228,0.009203507,0.046073403],"genre_scores_gemma":[0.028954795,0.003653741,0.9471505,0.006399698,0.00047801397,0.003572661,0.00051371154,0.0006637517,0.008613061],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9811395,0.010862004,0.0031140721,0.0012177583,0.0029179784,0.00074871955],"domain_scores_gemma":[0.96664685,0.018855851,0.0018169892,0.0021488585,0.007070196,0.0034611882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.038581904,0.0035157928,0.0023071712,0.004972729,0.00394631,0.009766747,0.0065817414,0.009629336,0.016526347],"category_scores_gemma":[0.042729452,0.0018912381,0.0017221244,0.0017797203,0.0032002907,0.009483254,0.00968244,0.00858398,0.014821731],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000376829,0.0021830718,0.021632124,0.0022903609,0.00020086394,0.009206585,0.01768047,0.012663637,0.006463899,0.030427104,0.25522295,0.64165217],"study_design_scores_gemma":[0.0007746607,0.002139136,0.017600048,0.012042376,0.00022441526,0.021874072,0.037119724,0.044379976,0.007575713,0.18257709,0.67268306,0.0010096626],"about_ca_topic_score_codex":0.006175983,"about_ca_topic_score_gemma":0.015249259,"teacher_disagreement_score":0.038581904,"about_ca_system_score_codex":0.0035519134,"about_ca_system_score_gemma":0.0120534785,"threshold_uncertainty_score":0.20404309},"labels":[],"label_agreement":null},{"id":"W1988370432","doi":"10.1016/j.jbi.2006.09.003","title":"Outbreak detection through automated surveillance: A review of the determinants of detection","year":2006,"lang":"en","type":"review","venue":"Journal of Biomedical Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":191,"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":"Canada Research Chairs","keywords":"Outbreak; Disease surveillance; Public health surveillance; Medicine; Representativeness heuristic; Warning system; Epidemiologic Surveillance; Public health; Medical emergency; Environmental health; Computer science; Data mining; Virology; Pathology; Statistics; Telecommunications","score_opus":0.027953561723803977,"score_gpt":0.3590521525335417,"score_spread":0.33109859080973775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988370432","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":[0.000634499,0.9943645,0.0027130472,0.0011320827,0.00020232557,0.000020167896,0.00009843489,0.0000306998,0.0008043087],"genre_scores_gemma":[0.007237682,0.98717284,0.004491826,0.00036689878,0.00036975788,0.000026130518,0.000119737364,0.000008373995,0.00020675959],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99735236,0.0008033796,0.00044658,0.0004182607,0.00089162687,0.0000878029],"domain_scores_gemma":[0.9506897,0.03918925,0.003007908,0.0006996303,0.0060616573,0.0003519234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008866426,0.001368613,0.003159501,0.0052533764,0.00026999376,0.0022432383,0.0023547434,0.0013776348,0.0016683509],"category_scores_gemma":[0.022599416,0.00073223043,0.0011807875,0.005352171,0.00081142824,0.0026274896,0.0007907847,0.0014431415,0.0007295739],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005903004,0.00007599911,0.003299982,0.011699386,0.00030346346,0.000070721035,0.000056389417,0.0014663951,0.0002686848,0.0021506872,0.006975813,0.9735734],"study_design_scores_gemma":[0.000117402014,0.0010134194,0.029792612,0.05364493,0.0036291906,0.004184569,0.0006608368,0.013203357,0.0070151524,0.018535009,0.8678588,0.00034461892],"about_ca_topic_score_codex":0.0046516843,"about_ca_topic_score_gemma":0.0055908635,"teacher_disagreement_score":0.008866426,"about_ca_system_score_codex":0.001754293,"about_ca_system_score_gemma":0.003582591,"threshold_uncertainty_score":0.046890736},"labels":[],"label_agreement":null},{"id":"W1990729254","doi":"10.1371/journal.pone.0057252","title":"Evaluation of Epidemic Intelligence Systems Integrated in the Early Alerting and Reporting Project for the Detection of A/H5N1 Influenza Events","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":91,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Agency of Canada","funders":"European Food Safety Authority; Public Health Agency of Canada; European Centre for Disease Prevention and Control; Georgetown University; Centers for Disease Control and Prevention; National Institute of Informatics; Helsingin Yliopisto","keywords":"Influenza A virus subtype H5N1; Representativeness heuristic; Disease surveillance; Medicine; Computer science; Data science; Psychology; Public health; Pathology; Virology; Social psychology","score_opus":0.2637975367211539,"score_gpt":0.37812208779432016,"score_spread":0.11432455107316625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990729254","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":[0.9945775,0.00012877784,0.0033533648,0.000099026154,0.000015720629,0.0005773479,0.00010477236,0.000164478,0.0009789641],"genre_scores_gemma":[0.9879445,0.000105564846,0.010832001,0.000046598336,0.00001160679,0.0002899167,0.00033692832,0.000012342069,0.00042057762],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9892647,0.0067969505,0.0010174392,0.00073772,0.0018315897,0.00035148414],"domain_scores_gemma":[0.97974616,0.012588738,0.0013940604,0.00090817636,0.004351724,0.0010110851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020947766,0.00090193126,0.00069078564,0.0009799906,0.00046404358,0.0016666704,0.0009831983,0.0010231974,0.0010776503],"category_scores_gemma":[0.03617361,0.000442888,0.00076136354,0.000634334,0.00046995273,0.0014002742,0.0013873291,0.0006376836,0.00021520411],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.05317145,0.034192786,0.22468378,0.00374832,0.003193167,0.0008081025,0.00581242,0.14234869,0.041635353,0.0014907072,0.002521613,0.48639357],"study_design_scores_gemma":[0.0041727494,0.17573208,0.32711044,0.00039682625,0.0032293405,0.0004338182,0.0038115778,0.4402085,0.037135657,0.0007845547,0.0067294263,0.0002550648],"about_ca_topic_score_codex":0.004883709,"about_ca_topic_score_gemma":0.002079957,"teacher_disagreement_score":0.020947766,"about_ca_system_score_codex":0.0012120699,"about_ca_system_score_gemma":0.0015126509,"threshold_uncertainty_score":0.110783696},"labels":[],"label_agreement":null},{"id":"W1992750851","doi":"10.1155/2014/424138","title":"Disparities in Spatial Prevalence of Feline Retroviruses due to Data Aggregation: A Case of the Modifiable Areal Unit Problem","year":2014,"lang":"en","type":"article","venue":"Journal of Veterinary Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"OVC Pet Trust","keywords":"Spatial ecology; Spatial analysis; Geography; Spatial epidemiology; Poisson regression; Cluster analysis; Scale (ratio); Spatial distribution; Poisson distribution; Cluster (spacecraft); Cartography; Statistics; Medicine; Computer science; Epidemiology; Mathematics; Biology; Environmental health; Ecology; Remote sensing; Internal medicine; Population","score_opus":0.08611546381469304,"score_gpt":0.35351158240016545,"score_spread":0.2673961185854724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992750851","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":[0.9218048,0.0022646806,0.06454142,0.0056591723,0.00016351278,0.00028657267,0.000883367,0.00009942164,0.0042970693],"genre_scores_gemma":[0.9931941,0.00010047337,0.0061760633,0.00020601881,0.00003723099,0.000047536312,0.00010841529,0.0000098477285,0.00012039924],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9477277,0.03707142,0.002608541,0.0066432273,0.0045039877,0.0014450864],"domain_scores_gemma":[0.85693425,0.08528622,0.03139126,0.021604192,0.0038203585,0.00096370763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039796535,0.00033248033,0.00077661296,0.0018144811,0.0012741454,0.0014290552,0.001539775,0.0010044962,0.0011352277],"category_scores_gemma":[0.13564385,0.00041577208,0.0010221067,0.0035904876,0.003227857,0.0022401891,0.003478196,0.0014354433,0.00006739812],"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":[0.00009759473,0.000030705098,0.9517664,0.0001073707,0.00046035842,0.0003746198,0.0027437399,0.0017224392,0.0001658155,0.009507098,0.0007598729,0.032264058],"study_design_scores_gemma":[0.00003333745,0.00022425342,0.9330035,0.00023016857,0.00046119033,0.0011793339,0.005097087,0.023745557,0.0007503369,0.029471282,0.0057220054,0.00008196354],"about_ca_topic_score_codex":0.020742606,"about_ca_topic_score_gemma":0.01803281,"teacher_disagreement_score":0.039796535,"about_ca_system_score_codex":0.001562278,"about_ca_system_score_gemma":0.0015210182,"threshold_uncertainty_score":0.21046668},"labels":[],"label_agreement":null},{"id":"W1993180632","doi":"10.3138/carto.43.1.3","title":"Visualizing and Analysing Public-Health Data Using Value-by-Area Cartograms: Toward a New Synthetic Framework","year":2008,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Public health; Situated; Data science; Visualization; Cartography; Geography; Computer science; Data mining; Artificial intelligence; Medicine","score_opus":0.1034883399732306,"score_gpt":0.37042510948294366,"score_spread":0.26693676950971307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993180632","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":[0.028543336,0.0010775386,0.95143765,0.0039807404,0.00008243449,0.00018399017,0.0015298373,0.0011378065,0.012026655],"genre_scores_gemma":[0.22832724,0.0009238952,0.76875025,0.00011276115,0.000052236854,0.00020811852,0.0009303988,0.0001977931,0.0004973346],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99629045,0.002626295,0.00021797992,0.00029650555,0.00046746692,0.00010136853],"domain_scores_gemma":[0.98428637,0.010824325,0.001079995,0.0019357657,0.0014882028,0.0003854143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007924341,0.00094993954,0.00072918186,0.01313796,0.0011531744,0.012203171,0.0017119641,0.00082895986,0.002692028],"category_scores_gemma":[0.025472924,0.0005860042,0.0011783952,0.010857223,0.005316324,0.00813449,0.0028971473,0.0012552518,0.00038357178],"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":[0.000052435487,0.0000440729,0.0081399195,0.00041976103,0.00009379605,0.00017315423,0.008697916,0.032433063,0.0009320459,0.852311,0.0042154943,0.09248739],"study_design_scores_gemma":[0.000032324315,0.00004725807,0.0049984828,0.00052739773,0.000082022525,0.00028598635,0.01263207,0.15252666,0.0012946143,0.7461987,0.08125828,0.00011617764],"about_ca_topic_score_codex":0.010689254,"about_ca_topic_score_gemma":0.009310263,"teacher_disagreement_score":0.01313796,"about_ca_system_score_codex":0.0021460413,"about_ca_system_score_gemma":0.0026694199,"threshold_uncertainty_score":0.041908383},"labels":[],"label_agreement":null},{"id":"W1997506516","doi":"10.1016/j.sste.2012.02.009","title":"Spatio-temporal assessment of food safety risks in Canadian food distribution systems using GIS","year":2012,"lang":"en","type":"article","venue":"Spatial and Spatio-temporal Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada; Université de Moncton; Agriculture and Agri-Food Canada","funders":"Defence Research and Development Canada","keywords":"Geographic information system; Scale (ratio); Computer science; Event (particle physics); Visualization; Product (mathematics); Distribution (mathematics); Food safety; Index (typography); Risk analysis (engineering); Environmental resource management; Data science; Data mining; Geography; Cartography; Business; Environmental science","score_opus":0.08274500168597523,"score_gpt":0.3591589759409353,"score_spread":0.27641397425496006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997506516","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":[0.96068156,0.0010670827,0.01014988,0.00071714754,0.000015832939,0.00023260787,0.019293524,0.00025237887,0.0075899754],"genre_scores_gemma":[0.9812617,0.00058439636,0.012414491,0.000025722,0.0000040378854,0.00005900112,0.0046723047,0.000009180535,0.0009690404],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99913174,0.00012404592,0.00006837609,0.00009849942,0.00043338584,0.00014392915],"domain_scores_gemma":[0.99813443,0.00041219458,0.00030508585,0.00006973365,0.0009713423,0.00010731276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011827957,0.00046100956,0.00026721953,0.005571576,0.0009020714,0.0013906587,0.0008081325,0.00023448904,0.0010240922],"category_scores_gemma":[0.00427261,0.0002452322,0.0006448618,0.008400262,0.00036641082,0.00048449956,0.0007852434,0.00025525695,0.00009388322],"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":[0.00016887754,0.00006480207,0.883065,0.00030667326,0.00036827207,0.00022924143,0.0011514035,0.05423742,0.0015252557,0.0038507672,0.003485977,0.051546186],"study_design_scores_gemma":[0.000016518097,0.000049775837,0.84171116,0.00011238488,0.00022865048,0.000112997666,0.0028979147,0.14519776,0.00088617223,0.0011156831,0.007618289,0.000052668503],"about_ca_topic_score_codex":0.98460126,"about_ca_topic_score_gemma":0.9869709,"teacher_disagreement_score":0.019958222,"about_ca_system_score_codex":0.019958222,"about_ca_system_score_gemma":0.017149216,"threshold_uncertainty_score":0.1448077},"labels":[],"label_agreement":null},{"id":"W1997912974","doi":"10.1016/j.swevo.2012.02.001","title":"A new PSO-optimized geometry of spatial and spatio-temporal scan statistics for disease outbreak detection","year":2012,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Statistics; Scan statistic; Outbreak; Pattern recognition (psychology); Artificial intelligence; Mathematics; Medicine; Virology","score_opus":0.012641377421825062,"score_gpt":0.267918497261658,"score_spread":0.2552771198398329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997912974","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":[0.010156195,0.00011529782,0.9879551,0.00014044027,0.00006282112,0.00004062768,0.000118046104,0.00035608513,0.0010554463],"genre_scores_gemma":[0.31064472,0.00017516573,0.6862469,0.00016493129,0.00010792573,0.00021836262,0.0005814034,0.00027768832,0.0015829637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948907,0.00013687162,0.000038291182,0.00012304714,0.00016747645,0.000045161647],"domain_scores_gemma":[0.99850047,0.0007135936,0.00012921616,0.00013290775,0.0004241698,0.000099556404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082275865,0.0008256127,0.0017006245,0.0013071414,0.00041364663,0.0010969255,0.0018659934,0.0013645256,0.0017682676],"category_scores_gemma":[0.005371348,0.0008051919,0.0011911104,0.0012197661,0.00067457417,0.0012186203,0.0012146368,0.00096379983,0.0003403547],"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":[0.00006158924,0.000042856176,0.0009544571,0.00004175803,0.000047233872,0.000048137554,0.000033418433,0.95103276,0.0023385717,0.0068223947,0.0013173223,0.037259493],"study_design_scores_gemma":[0.0000035723724,0.000006105262,0.00006111936,0.000001253796,0.0000018054764,0.0000068242603,0.0000015118416,0.99911505,0.0001059902,0.00058108533,0.00011346206,0.0000022236964],"about_ca_topic_score_codex":0.010630602,"about_ca_topic_score_gemma":0.007104618,"teacher_disagreement_score":0.010630602,"about_ca_system_score_codex":0.0010950404,"about_ca_system_score_gemma":0.0018065433,"threshold_uncertainty_score":0.021137476},"labels":[],"label_agreement":null},{"id":"W1998840937","doi":"10.5210/ojphi.v3i1.3506","title":"Use of a Web Forum and an Online Questionnaire in the Detection and Investigation of an Outbreak","year":2011,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Vancouver Coastal Health; BC Centre for Disease Control; Public Health Agency of Canada","funders":"","keywords":"Outbreak; The Internet; Public health; Web application; Population; Web 2.0; World Wide Web; Internet privacy; Medicine; Computer science; Data science; Environmental health; Pathology","score_opus":0.13410500332514533,"score_gpt":0.3449675164132252,"score_spread":0.21086251308807988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998840937","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":[0.94819415,0.00047812943,0.033192955,0.0023795096,0.00039212743,0.004411158,0.0005837377,0.0009258021,0.009442519],"genre_scores_gemma":[0.89850146,0.00035654107,0.09337475,0.0009305658,0.00019481822,0.0044123055,0.00025655256,0.00007331731,0.0018997622],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9628532,0.030796558,0.0021668533,0.0012990894,0.0020795085,0.000804871],"domain_scores_gemma":[0.7970487,0.17663191,0.0087867575,0.005688196,0.0073466217,0.0044978475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.044869944,0.0008218615,0.0005916009,0.004495436,0.0010755289,0.0015947311,0.00061087287,0.001699621,0.0029146578],"category_scores_gemma":[0.090779774,0.0004493814,0.0005730262,0.0013810298,0.0008136752,0.0026889585,0.0025929543,0.0009679381,0.0007600518],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0045722583,0.011789806,0.22157355,0.002200906,0.00022322766,0.004107542,0.054818723,0.0014009929,0.017155321,0.0018458837,0.0059199394,0.67439187],"study_design_scores_gemma":[0.0019515329,0.04466955,0.68165696,0.0042101606,0.0007920229,0.012245743,0.07566751,0.03908951,0.04542156,0.009151956,0.08381437,0.0013291371],"about_ca_topic_score_codex":0.00048989913,"about_ca_topic_score_gemma":0.0007656811,"teacher_disagreement_score":0.044869944,"about_ca_system_score_codex":0.00058551284,"about_ca_system_score_gemma":0.0011379572,"threshold_uncertainty_score":0.23729777},"labels":[],"label_agreement":null},{"id":"W1999168261","doi":"10.3138/cjccj.2012.e13","title":"Exploring Hotspots of Drug Offences in Toronto: A Comparison of Four Local Spatial Cluster Detection Methods","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénale","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Scan statistic; Statistic; Contiguity; Spatial analysis; Geography; Cluster (spacecraft); Euclidean distance; Downtown; Cartography; Statistics; Computer science; Mathematics; Artificial intelligence; Remote sensing","score_opus":0.1756424763747665,"score_gpt":0.36728001526063647,"score_spread":0.19163753888586998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999168261","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":[0.97554046,0.00104037,0.016085038,0.0003157054,0.000017240538,0.00023997064,0.0026116846,0.0001980482,0.0039515924],"genre_scores_gemma":[0.98000115,0.00036129612,0.017371753,0.00001912303,0.0000054445663,0.000112723326,0.0014320998,0.000013610683,0.00068273954],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99887985,0.000476727,0.00009611245,0.00019624036,0.00023788944,0.000113119684],"domain_scores_gemma":[0.99410534,0.0033019285,0.0009885494,0.0002769484,0.00099848,0.00032869092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023646085,0.0004939305,0.0004313485,0.0052512735,0.00093315553,0.0012657281,0.00073141203,0.000291272,0.0011068522],"category_scores_gemma":[0.010311847,0.00019033617,0.0006916391,0.005079114,0.00062510534,0.0004788125,0.0013767456,0.00027491795,0.0001567302],"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":[0.00053924223,0.000065484666,0.87242454,0.0005771641,0.0004351311,0.00036156952,0.007665248,0.015031651,0.0014297296,0.0016966392,0.0020197679,0.097753756],"study_design_scores_gemma":[0.000038288654,0.0001505751,0.9233928,0.00021343764,0.00027629946,0.00022477773,0.013393636,0.057490963,0.0012787209,0.00081880094,0.0026305472,0.00009132024],"about_ca_topic_score_codex":0.5594471,"about_ca_topic_score_gemma":0.7242664,"teacher_disagreement_score":0.4405529,"about_ca_system_score_codex":0.003459374,"about_ca_system_score_gemma":0.0043285745,"threshold_uncertainty_score":0.8862948},"labels":[],"label_agreement":null},{"id":"W2001272536","doi":"10.1016/s1473-3099(03)00645-5","title":"West Nile virus warning for Canada","year":2003,"lang":"en","type":"article","venue":"The Lancet Infectious Diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"West Nile virus; Geography; Virology; Virus; Biology","score_opus":0.016336889011157582,"score_gpt":0.26869084320329506,"score_spread":0.25235395419213746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001272536","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021036038,0.009883375,0.0013036155,0.8004012,0.01559057,0.00033737003,0.028266618,0.00067263783,0.12250847],"genre_scores_gemma":[0.30995438,0.019311193,0.007738778,0.30171806,0.004619024,0.00030642273,0.0123928785,0.0003465053,0.3436128],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983034,0.0001291421,0.000074105716,0.000089672016,0.00089856377,0.00050503114],"domain_scores_gemma":[0.9890587,0.0010397594,0.00030902197,0.00014475132,0.0064556436,0.002992196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016906332,0.00047676414,0.00042989332,0.0018342441,0.0033270544,0.0036054421,0.001311064,0.00513644,0.015450664],"category_scores_gemma":[0.012724385,0.00035647227,0.00061083445,0.0017711323,0.00088410877,0.0009027773,0.0010618824,0.0040009716,0.0012798468],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008256956,0.000024654726,0.0067882445,0.0001400659,0.000023229723,0.00013312543,0.00017081486,0.00050123286,0.00027796545,0.0069375886,0.95861983,0.02630072],"study_design_scores_gemma":[0.00018248998,0.000054481257,0.037211414,0.00040650772,0.00007465897,0.000081519436,0.0008571281,0.002070646,0.0005256255,0.0039746794,0.9544751,0.00008567403],"about_ca_topic_score_codex":0.993248,"about_ca_topic_score_gemma":0.99558794,"teacher_disagreement_score":0.045410257,"about_ca_system_score_codex":0.045410257,"about_ca_system_score_gemma":0.29555732,"threshold_uncertainty_score":0.32947606},"labels":[],"label_agreement":null},{"id":"W2004521315","doi":"10.1186/1472-6947-13-138","title":"Determination of French influenza outbreaks periods between 1985 and 2011 through a web-based Delphi method","year":2013,"lang":"en","type":"article","venue":"BMC Medical Informatics and Decision Making","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":17,"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":"Bundesamt für Gesundheit; Public Health Agency; Public Health Agency of Canada","keywords":"Delphi method; Incidence (geometry); Influenza season; Outbreak; Seasonal influenza; Delphi; Gold standard (test); Medicine; Demography; Statistics; Computer science; Operations research; Geography; Virology; Coronavirus disease 2019 (COVID-19); Vaccination; Mathematics; Influenza vaccine; Disease; Internal medicine","score_opus":0.04523677015746864,"score_gpt":0.376174587444692,"score_spread":0.3309378172872234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004521315","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":[0.8884267,0.00041087007,0.08131449,0.0010400631,0.000059901064,0.014024065,0.0013734354,0.00019693693,0.013153398],"genre_scores_gemma":[0.888485,0.00027055724,0.09289476,0.00035907672,0.000034995162,0.015455262,0.0006711047,0.000020150765,0.0018091113],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.98056936,0.014556615,0.0013290313,0.0013068309,0.0013105626,0.0009274798],"domain_scores_gemma":[0.97592795,0.014803463,0.00180354,0.000957099,0.005765211,0.0007427689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02578483,0.000959214,0.00053823896,0.006586252,0.0013464169,0.0012788129,0.00089701155,0.0009876813,0.006496445],"category_scores_gemma":[0.026296385,0.00038679226,0.0010938408,0.0018391073,0.00087942317,0.00094542984,0.002535783,0.0005522646,0.0009206724],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004584711,0.0013819361,0.2561602,0.004386917,0.00048656145,0.0019321464,0.16517478,0.016649643,0.021534072,0.006845756,0.008657384,0.51220596],"study_design_scores_gemma":[0.000817651,0.007903782,0.53320324,0.003275658,0.0005453372,0.0028391136,0.25844878,0.10075406,0.023596419,0.016452964,0.05150194,0.0006610003],"about_ca_topic_score_codex":0.0042100027,"about_ca_topic_score_gemma":0.0044049514,"teacher_disagreement_score":0.02578483,"about_ca_system_score_codex":0.0029213699,"about_ca_system_score_gemma":0.0036149241,"threshold_uncertainty_score":0.13636488},"labels":[],"label_agreement":null},{"id":"W2005105600","doi":"10.1097/00063110-200402000-00002","title":"Real-time syndrome surveillance in Ontario, Canada: the potential use of emergency departments and Telehealth","year":2004,"lang":"en","type":"review","venue":"European Journal of Emergency Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Sudbury Regional Hospital","funders":"","keywords":"Public health; Medicine; Public health surveillance; Outbreak; Medical emergency; Triage; Disease surveillance; Environmental health; Telehealth; Health care; Infection control; Population; Telemedicine; Intensive care medicine; Nursing; Virology","score_opus":0.0585807465351725,"score_gpt":0.3194257464567847,"score_spread":0.2608449999216122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005105600","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":[0.049545527,0.9069741,0.00059965526,0.017634723,0.00050054456,0.0001585881,0.005170658,0.000049403105,0.019366767],"genre_scores_gemma":[0.33371747,0.65225494,0.0028707755,0.0041649668,0.00028480138,0.0001299857,0.002486491,0.000023725921,0.004066798],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977099,0.00038690164,0.00031222182,0.00017725072,0.0011084119,0.00030540748],"domain_scores_gemma":[0.98951614,0.0022703651,0.0017772058,0.00011019915,0.005607393,0.0007187391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020097902,0.0003816645,0.00050409924,0.00402754,0.0010690428,0.0025032035,0.0013964542,0.0007502786,0.0032525116],"category_scores_gemma":[0.007008994,0.0002573944,0.00080076855,0.009114188,0.001023312,0.0010502181,0.00068036915,0.00051262544,0.00018561311],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082456344,0.00015474636,0.18345176,0.09794855,0.0009996638,0.0039001284,0.005438806,0.0019370442,0.002377827,0.0038753392,0.05150635,0.6475852],"study_design_scores_gemma":[0.00029284964,0.00041031014,0.6164337,0.050297055,0.002399644,0.002284272,0.008587405,0.001558609,0.0011395896,0.00060467067,0.31579667,0.00019535702],"about_ca_topic_score_codex":0.9870891,"about_ca_topic_score_gemma":0.9921733,"teacher_disagreement_score":0.036108527,"about_ca_system_score_codex":0.036108527,"about_ca_system_score_gemma":0.12895617,"threshold_uncertainty_score":0.2619869},"labels":[],"label_agreement":null},{"id":"W2005149773","doi":"10.1186/1472-6947-10-18","title":"A method for managing re-identification risk from small geographic areas in Canada","year":2010,"lang":"en","type":"article","venue":"BMC Medical Informatics and Decision Making","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada; Ottawa Hospital; Children's Hospital of Eastern Ontario; University of Ottawa","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Public Health Agency; Public Health Agency of Canada","keywords":"Uniqueness; Logistic regression; Identifier; Computer science; Identification (biology); Population; Equivalence (formal languages); Statistics; Data mining; Econometrics; Mathematics; Medicine; Environmental health","score_opus":0.021785889915245,"score_gpt":0.3200426985456615,"score_spread":0.2982568086304165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005149773","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":[0.09395947,0.0018807013,0.8590536,0.0038917637,0.00031039843,0.0017091879,0.014012527,0.0068159164,0.018366382],"genre_scores_gemma":[0.34448245,0.0005386556,0.638065,0.00036052128,0.00006394238,0.0006127739,0.00537054,0.00029832133,0.010207787],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99596304,0.0005168865,0.00030938245,0.0010343419,0.0016496236,0.00052671914],"domain_scores_gemma":[0.9914579,0.0018552038,0.0007192446,0.0010658605,0.004525695,0.00037617926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055179517,0.0008649519,0.0009224654,0.0057400023,0.003728605,0.0030455436,0.0037718443,0.0009768687,0.0035480624],"category_scores_gemma":[0.025512276,0.0004885035,0.0013878524,0.0072969585,0.001036612,0.0015007722,0.0027653845,0.0015246993,0.0006920999],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033997642,0.000117802265,0.16873471,0.0003379773,0.00047581192,0.0005989343,0.0019304744,0.07793247,0.0018915357,0.038069267,0.056089174,0.6534817],"study_design_scores_gemma":[0.00010362875,0.000050400206,0.05782249,0.00019781428,0.0002469443,0.0005212314,0.0019340273,0.83799726,0.005196476,0.031633418,0.06408113,0.00021527198],"about_ca_topic_score_codex":0.9188331,"about_ca_topic_score_gemma":0.94246364,"teacher_disagreement_score":0.08116692,"about_ca_system_score_codex":0.018849958,"about_ca_system_score_gemma":0.044671487,"threshold_uncertainty_score":0.16328979},"labels":[],"label_agreement":null},{"id":"W2005774269","doi":"10.1177/1075547004267026","title":"Print Media Coverage of Antibiotic Resistance","year":2004,"lang":"en","type":"article","venue":"Science Communication","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Antibiotic resistance; Newspaper; Antibiotics; Streptococcus pneumoniae; Resistance (ecology); Quarter (Canadian coin); Medical prescription; Microbiology; Medicine; Biology; Advertising; Business; Geography","score_opus":0.0181651944834075,"score_gpt":0.2990411755113154,"score_spread":0.28087598102790795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005774269","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":[0.72782654,0.029203571,0.0010200992,0.010803528,0.0011316759,0.00036985314,0.09455821,0.00023877507,0.13484776],"genre_scores_gemma":[0.9256975,0.030836398,0.0018094613,0.0029573722,0.002804246,0.00032415934,0.017029852,0.00011815934,0.018422836],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9963898,0.0005248139,0.00046735775,0.00031592502,0.001994359,0.00030758322],"domain_scores_gemma":[0.9160423,0.040168714,0.018961163,0.0010151869,0.022218542,0.0015940985],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0023549988,0.00039658975,0.000439164,0.021570178,0.0011863947,0.003136967,0.00043944208,0.00043501865,0.007876681],"category_scores_gemma":[0.025682952,0.00016797788,0.00028078782,0.020535719,0.0008226928,0.0013489068,0.00084329175,0.00056112726,0.0009820196],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009344104,0.0002812915,0.43992588,0.014154756,0.0004284326,0.0026846079,0.025031472,0.00043182174,0.006405023,0.0025501729,0.09769067,0.40948153],"study_design_scores_gemma":[0.000037718793,0.0002009076,0.80780786,0.0028809176,0.0006701871,0.0012776244,0.014073464,0.0008923764,0.0062499563,0.0004075872,0.16542313,0.000078209734],"about_ca_topic_score_codex":0.094062395,"about_ca_topic_score_gemma":0.10897299,"teacher_disagreement_score":0.9988136,"about_ca_system_score_codex":0.003373706,"about_ca_system_score_gemma":0.0033796802,"threshold_uncertainty_score":0.18702978},"labels":[],"label_agreement":null},{"id":"W2007025273","doi":"10.1016/s2214-109x(14)70356-0","title":"Role of big data in the early detection of Ebola and other emerging infectious diseases","year":2014,"lang":"en","type":"letter","venue":"The Lancet Global Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public health; Disease surveillance; Public health surveillance; Outbreak; Global health; The Internet; Pandemic; Government (linguistics); Social media; Medicine; Infectious disease (medical specialty); Environmental health; Internet privacy; Disease; Political science; Computer science; World Wide Web; Virology; Coronavirus disease 2019 (COVID-19)","score_opus":0.03827597620856053,"score_gpt":0.33619501522892936,"score_spread":0.2979190390203688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007025273","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024414549,0.39214385,0.05184644,0.42520368,0.014319795,0.0007888403,0.04190665,0.0027291947,0.046646915],"genre_scores_gemma":[0.43400368,0.2888737,0.15111494,0.06147087,0.022880375,0.0011385098,0.0339847,0.0008089675,0.0057242983],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9814456,0.009918106,0.0014416728,0.0017484488,0.0048825326,0.00056364643],"domain_scores_gemma":[0.8223764,0.1298226,0.0105670905,0.012156582,0.0188078,0.0062695653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033942524,0.0014639519,0.0021005804,0.013087059,0.00152712,0.010967627,0.003097545,0.004385446,0.005270893],"category_scores_gemma":[0.08519525,0.00087236386,0.0014829688,0.014194995,0.002675771,0.015932404,0.006849062,0.006356203,0.0018585153],"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":[0.0007602866,0.00020384438,0.06573626,0.010240067,0.0011901091,0.00072021136,0.0013485729,0.007904648,0.0016505562,0.06444065,0.28301072,0.5627941],"study_design_scores_gemma":[0.00014995535,0.00029766996,0.050142717,0.018320499,0.00066188193,0.001024445,0.003837641,0.047796458,0.0034480367,0.27408212,0.59977365,0.00046491154],"about_ca_topic_score_codex":0.008352164,"about_ca_topic_score_gemma":0.009824306,"teacher_disagreement_score":0.033942524,"about_ca_system_score_codex":0.0029464273,"about_ca_system_score_gemma":0.005175734,"threshold_uncertainty_score":0.17950737},"labels":[],"label_agreement":null},{"id":"W2008164289","doi":"10.1016/s1473-3099(14)70953-3","title":"Digital surveillance for enhanced detection and response to outbreaks","year":2014,"lang":"en","type":"article","venue":"The Lancet Infectious Diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":56,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Michael's Hospital; Ottawa Hospital; Simon Fraser University; St. Paul's Hospital; University of Ottawa; University of British Columbia","funders":"U.S. National Library of Medicine; Bill and Melinda Gates Foundation; National Institute of Allergy and Infectious Diseases; Canadian Institutes of Health Research; National Institutes of Health; Wellcome Trust","keywords":"Outbreak; Computer science; Environmental health; Virology; Medicine","score_opus":0.010164590397987816,"score_gpt":0.26941216494443987,"score_spread":0.25924757454645203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008164289","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16933022,0.039023485,0.5016484,0.08816477,0.009655886,0.0013289443,0.03453162,0.017531764,0.13878483],"genre_scores_gemma":[0.84809685,0.009882403,0.11797042,0.0056848614,0.0023476952,0.00037635874,0.008558422,0.0002317934,0.006851189],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982487,0.00080482045,0.000163797,0.0002491392,0.0004450095,0.00008854371],"domain_scores_gemma":[0.9799029,0.012199135,0.0019613965,0.0030235376,0.0023393498,0.00057362544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040863696,0.0006243075,0.0004786019,0.0031523216,0.00025129554,0.0027419378,0.0011884883,0.0009223717,0.005675104],"category_scores_gemma":[0.019256191,0.00030042583,0.0004363733,0.0015394877,0.0004148006,0.0026794178,0.0020441231,0.0010237714,0.0013538238],"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":[0.00028255762,0.00022249615,0.037971914,0.0013487695,0.00021936778,0.00011575512,0.00037486316,0.010850559,0.004953376,0.024824975,0.09205251,0.8267828],"study_design_scores_gemma":[0.00030846664,0.0006125033,0.06375667,0.0025164373,0.0005904535,0.0009564663,0.0022327218,0.22068186,0.035474166,0.124057874,0.5485879,0.00022444701],"about_ca_topic_score_codex":0.0014866833,"about_ca_topic_score_gemma":0.0017622438,"teacher_disagreement_score":0.005675104,"about_ca_system_score_codex":0.00072724745,"about_ca_system_score_gemma":0.00091186014,"threshold_uncertainty_score":0.021611094},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"medium"}],"label_agreement":"split"},{"id":"W2008565433","doi":"10.3390/ijgi3031039","title":"Geographical Variation of Incidence of Chronic Obstructive Pulmonary Disease in Manitoba, Canada","year":2014,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Manitoba Health Research Council","keywords":"Scan statistic; Confounding; Statistic; Cluster (spacecraft); Geography; Population; Incidence (geometry); Pulmonary disease; Statistics; Medicine; Cartography; Environmental health; Computer science; Mathematics","score_opus":0.004476759938024196,"score_gpt":0.22764713022023672,"score_spread":0.22317037028221254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008565433","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":[0.98369455,0.0024201777,0.00069478655,0.0009032032,0.00003152056,0.00009325856,0.0087398775,0.000044967197,0.0033775612],"genre_scores_gemma":[0.99480695,0.00090772455,0.00083498226,0.00010491652,0.000008369282,0.00002940744,0.0025146059,0.0000069456883,0.0007859733],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990972,0.00013005182,0.000071265444,0.00015817519,0.00034164407,0.00020163231],"domain_scores_gemma":[0.9973131,0.00022430683,0.0004342541,0.00009552743,0.0015619535,0.00037080896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088554923,0.00033659628,0.00038349867,0.0029122045,0.0015451079,0.0011069915,0.0011890099,0.00022634976,0.0011228155],"category_scores_gemma":[0.0039183116,0.00025629828,0.00050754315,0.007829901,0.0006533225,0.00025152328,0.0008558999,0.0003952889,0.00011655066],"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":[0.000023531182,0.000009214267,0.99374783,0.000041484353,0.00007844184,0.00004537705,0.00034337892,0.00047936564,0.00020318516,0.00017717444,0.00090166694,0.0039493507],"study_design_scores_gemma":[0.0000041267895,0.000007684605,0.99765056,0.000031750027,0.000034184646,0.000037554233,0.0005932034,0.00068194326,0.000056540066,0.000033248398,0.0008607033,0.0000084767435],"about_ca_topic_score_codex":0.9968659,"about_ca_topic_score_gemma":0.9980736,"teacher_disagreement_score":0.025368989,"about_ca_system_score_codex":0.025368989,"about_ca_system_score_gemma":0.038495425,"threshold_uncertainty_score":0.18406576},"labels":[],"label_agreement":null},{"id":"W2009730143","doi":"10.3357/asem.3023.2011","title":"Airport Quarantine Inspection, Follow-Up Observation, and the Prevention of Pandemic Influenza","year":2011,"lang":"en","type":"article","venue":"Aviation Space and Environmental Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarantine; Pandemic; Population; Coronavirus disease 2019 (COVID-19); Geography; Influenza A virus subtype H5N1; Environmental health; Medicine; Socioeconomics; Business; Infectious disease (medical specialty); Virology; Disease","score_opus":0.03672813749321952,"score_gpt":0.2616809523432959,"score_spread":0.2249528148500764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009730143","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":[0.95840263,0.031750355,0.0011910127,0.0034126714,0.00027844872,0.00008067357,0.0003644175,0.0000854916,0.0044342857],"genre_scores_gemma":[0.9956475,0.0031826016,0.0005468222,0.00018947513,0.00010417033,0.000020242313,0.000113648595,0.000001893557,0.00019353566],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995096,0.00027336937,0.000034167835,0.000053426254,0.000051996973,0.00007751303],"domain_scores_gemma":[0.9981369,0.0004038084,0.0008565648,0.000055270837,0.00018336182,0.00036406884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011127356,0.0002238202,0.00025944653,0.00069802674,0.00028210858,0.00033949592,0.00040342155,0.00044878764,0.0012319026],"category_scores_gemma":[0.003734467,0.00009900137,0.00030480066,0.00036331755,0.00019743372,0.00033306406,0.0003445034,0.00039152973,0.00008421146],"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":[0.0007502128,0.0010369018,0.8670468,0.00047616693,0.00019567895,0.00029192443,0.0002315295,0.0006505542,0.00039054808,0.0001409131,0.0024673033,0.12632148],"study_design_scores_gemma":[0.00005221144,0.00091320835,0.99488777,0.00036481212,0.00014316532,0.00020448372,0.0003071424,0.0013011895,0.00016445135,0.00014163512,0.0015079712,0.000011962134],"about_ca_topic_score_codex":0.0075248936,"about_ca_topic_score_gemma":0.007674621,"teacher_disagreement_score":0.0075248936,"about_ca_system_score_codex":0.00040858574,"about_ca_system_score_gemma":0.00079512806,"threshold_uncertainty_score":0.014962196},"labels":[],"label_agreement":null},{"id":"W2011201996","doi":"10.1016/j.cmpb.2013.01.007","title":"A new surveillance and spatio-temporal visualization tool SIMID: SIMulation of Infectious Diseases using random networks and GIS","year":2013,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario; University of Waterloo","funders":"","keywords":"Randomness; Visualization; Computer science; Infectious disease (medical specialty); Outbreak; Population; Disease surveillance; Data mining; Data science; Disease; Virology; Statistics; Medicine; Mathematics; Environmental health","score_opus":0.031512296695163255,"score_gpt":0.3616154893259346,"score_spread":0.33010319263077137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011201996","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":[0.026049031,0.0002188897,0.9407627,0.000730994,0.00017877853,0.00015715355,0.003910824,0.023095248,0.0048964187],"genre_scores_gemma":[0.27381787,0.00067275576,0.71349347,0.00035796332,0.00012485994,0.0007412095,0.0040739104,0.0029327415,0.003785284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995647,0.00017692655,0.000043354863,0.00006601301,0.00012053371,0.000028370976],"domain_scores_gemma":[0.99756795,0.001738968,0.00013869087,0.00016036448,0.00022331174,0.00017064752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014532843,0.0012000855,0.0011403468,0.0013872174,0.0005150796,0.0016129292,0.0022002042,0.0013127351,0.010689914],"category_scores_gemma":[0.0047381874,0.0007733341,0.0013448859,0.0011028082,0.00048604523,0.0014320756,0.001429497,0.0014542498,0.0010933273],"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":[0.000184761,0.00015510185,0.004715955,0.0003571419,0.00023490365,0.00024568557,0.00032820334,0.92096704,0.0023680541,0.020951098,0.012695987,0.03679608],"study_design_scores_gemma":[0.000039007016,0.000011686849,0.000115694624,0.0000114564555,0.000016102185,0.000037889593,0.0000126976465,0.99036485,0.0005568733,0.004219849,0.004602449,0.00001139788],"about_ca_topic_score_codex":0.008713192,"about_ca_topic_score_gemma":0.009219112,"teacher_disagreement_score":0.010689914,"about_ca_system_score_codex":0.00067727134,"about_ca_system_score_gemma":0.001307167,"threshold_uncertainty_score":0.035761297},"labels":[],"label_agreement":null},{"id":"W2011403054","doi":"10.1186/1471-2458-11-s2-s8","title":"Antimicrobial resistance surveillance in the AFHSC-GEIS network","year":2011,"lang":"en","type":"review","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":48,"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":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Defense","keywords":"Medicine; Public health; Center of excellence; Disease surveillance; Public health surveillance; International Health Regulations; Health care; Environmental health; Infectious disease (medical specialty); Disease; Nursing; Economic growth; Political science; Coronavirus disease 2019 (COVID-19)","score_opus":0.11742596460794572,"score_gpt":0.3685095523813728,"score_spread":0.2510835877734271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011403054","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":[0.0001230727,0.9981669,0.000119384735,0.00052497105,0.00009979222,0.000019100298,0.00006885206,0.0000072257117,0.00087052974],"genre_scores_gemma":[0.0020907216,0.9965178,0.00058092,0.00028955948,0.00011257894,0.000036083213,0.00017255265,0.0000016113889,0.00019817626],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9981369,0.0006425109,0.00032253662,0.00018120848,0.0006337845,0.000083078405],"domain_scores_gemma":[0.9964259,0.0012817325,0.0006898562,0.00007444643,0.001383798,0.00014411613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040580463,0.00077944074,0.0013837729,0.004418668,0.00021147725,0.0010324843,0.001635806,0.0010800445,0.0018602171],"category_scores_gemma":[0.0073531596,0.00031741254,0.0009763309,0.004705205,0.0002904739,0.0014805575,0.0007560563,0.0010397824,0.00083785405],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044286437,0.00003656268,0.0009153097,0.022893097,0.0001704254,0.00007562992,0.00007450171,0.00024117799,0.00017433053,0.0014596056,0.030643733,0.94327134],"study_design_scores_gemma":[0.000054033888,0.00020992833,0.007630322,0.052672908,0.0005654039,0.0010929295,0.00014974584,0.0003459607,0.00061442127,0.0009947318,0.93563646,0.000033073495],"about_ca_topic_score_codex":0.0077667106,"about_ca_topic_score_gemma":0.009011371,"teacher_disagreement_score":0.0077667106,"about_ca_system_score_codex":0.0022485114,"about_ca_system_score_gemma":0.0053998325,"threshold_uncertainty_score":0.021461248},"labels":[],"label_agreement":null},{"id":"W2015157245","doi":"10.2196/mhealth.2859","title":"The Schisto Track: A System for Gathering and Monitoring Epidemiological Surveys by Connecting Geographical Information Systems in Real Time","year":2014,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundação Oswaldo Cruz","keywords":"Track (disk drive); Information system; Computer science; Epidemiology; Data collection; Geography; Data science; Medicine; Statistics; Engineering; Mathematics","score_opus":0.036226930572127725,"score_gpt":0.34800196991652277,"score_spread":0.31177503934439504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015157245","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":[0.23798005,0.0020564965,0.50681883,0.0013130918,0.0005161634,0.0058097993,0.03984577,0.17807333,0.027586484],"genre_scores_gemma":[0.7019843,0.0016696793,0.24219853,0.0006473491,0.00025008435,0.0033159796,0.030064637,0.0017357061,0.018133828],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993017,0.00019722455,0.00010793079,0.00014308927,0.00019674064,0.00005327636],"domain_scores_gemma":[0.9980253,0.0006999755,0.00029394805,0.00036156815,0.00035362315,0.00026556954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013621126,0.00079883274,0.0005397193,0.0021920393,0.00039067946,0.00087347464,0.00068610534,0.0004673227,0.0067261616],"category_scores_gemma":[0.003889646,0.0003602997,0.00045688174,0.0010147913,0.00030393622,0.0011364266,0.001773698,0.0003717207,0.0023012552],"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":[0.003964614,0.00073100993,0.117384136,0.0046418477,0.0008146199,0.0021828776,0.0045969808,0.005032298,0.07386253,0.0071867714,0.1336531,0.64594924],"study_design_scores_gemma":[0.0013374995,0.0036389837,0.35963774,0.0014022617,0.0010707902,0.006226182,0.0020969054,0.14716205,0.06050829,0.007495877,0.40850884,0.000914641],"about_ca_topic_score_codex":0.003373092,"about_ca_topic_score_gemma":0.003295793,"teacher_disagreement_score":0.0067261616,"about_ca_system_score_codex":0.00026209734,"about_ca_system_score_gemma":0.0009461783,"threshold_uncertainty_score":0.02250129},"labels":[],"label_agreement":null},{"id":"W2015725890","doi":"10.1016/j.sste.2010.03.004","title":"On the effect of diagnostic misclassification bias on the observed spatial pattern in regional count data – A case study using West Nile virus mortality data from Ontario, 2005","year":2010,"lang":"en","type":"article","venue":"Spatial and Spatio-temporal Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"National Institute of Environmental Health Sciences; Ontario Ministry of Health and Long-Term Care","keywords":"Statistic; Statistics; Scan statistic; Context (archaeology); Count data; Epidemiology; Spatial epidemiology; Spatial analysis; Public health; Sample (material); Geography; West Nile virus; Demography; Medicine; Mathematics; Pathology","score_opus":0.2901290533570771,"score_gpt":0.3812476395885589,"score_spread":0.09111858623148178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015725890","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9898623,0.002019023,0.0039642183,0.0011459667,0.00005421163,0.00010029936,0.0011242438,0.000028635412,0.0017011927],"genre_scores_gemma":[0.9976943,0.00015464086,0.00145099,0.00012652163,0.000016692235,0.000018325822,0.00028052146,0.00001072212,0.00024728704],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9712371,0.020378135,0.001967386,0.0023461343,0.0030943302,0.0009769646],"domain_scores_gemma":[0.8258301,0.13666433,0.016441774,0.010811288,0.009546779,0.00070571457],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.037994977,0.00041539597,0.00069238036,0.001242599,0.000894086,0.0016567657,0.0021060028,0.0014745966,0.0010340525],"category_scores_gemma":[0.13352722,0.0005776553,0.0018107599,0.002108302,0.0019025021,0.0013066069,0.0009048329,0.0010413517,0.00015070933],"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":[0.0005643678,0.000049621307,0.98167795,0.000078973426,0.0007027577,0.00057871477,0.00089631643,0.0016833853,0.00017834843,0.000491092,0.0008629307,0.01223549],"study_design_scores_gemma":[0.000073774456,0.00019359468,0.9756162,0.0001450538,0.0015009303,0.0013553364,0.0012291741,0.016700959,0.00068469543,0.00077234604,0.001676893,0.000050945146],"about_ca_topic_score_codex":0.32603103,"about_ca_topic_score_gemma":0.33472478,"teacher_disagreement_score":0.962005,"about_ca_system_score_codex":0.005050944,"about_ca_system_score_gemma":0.003076832,"threshold_uncertainty_score":0.6482667},"labels":[],"label_agreement":null},{"id":"W2016577928","doi":"10.1111/j.1541-0064.2002.tb00737.x","title":"Conceptual and practical issues in the detection of local disease clusters: a study of mortality in Hamilton, Ontario","year":2002,"lang":"en","type":"article","venue":"Canadian Geographies / Géographies canadiennes","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Ottawa; Health Canada; McMaster University","funders":"Health Canada; Centers for Disease Control and Prevention","keywords":"Geocoding; Geography; Public health; Cartography; Geographic information system; Cluster (spacecraft); Regional science; Statistics; Operations research; Data science; Computer science; Medicine; Mathematics; Pathology","score_opus":0.022186421806888075,"score_gpt":0.2528475060794441,"score_spread":0.23066108427255605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016577928","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":[0.9756032,0.0025074205,0.0022249827,0.0073530064,0.000039835813,0.00039118598,0.004189011,0.00003404973,0.0076571982],"genre_scores_gemma":[0.992444,0.0013075733,0.0017283818,0.0006108866,0.000024625973,0.00020838124,0.00095008616,0.000013809546,0.0027122514],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9966344,0.00087698875,0.00022977161,0.00039849326,0.000986843,0.00087356515],"domain_scores_gemma":[0.9927362,0.00204319,0.0015700058,0.0003083345,0.002399083,0.00094320305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027747604,0.0003597898,0.0005845493,0.002243075,0.0058559757,0.0016251156,0.002231096,0.000534844,0.0013806196],"category_scores_gemma":[0.012781141,0.00032418265,0.0005587557,0.007612275,0.0030481988,0.0008882865,0.0023670257,0.000627705,0.00013405253],"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":[0.00013609008,0.000053237858,0.9065987,0.00080180774,0.0001273177,0.0008744391,0.039768353,0.001868405,0.00056563393,0.0075128987,0.0095847165,0.032108374],"study_design_scores_gemma":[0.000016425443,0.000042973785,0.96669275,0.00025513538,0.00005504152,0.00009849807,0.024556777,0.0011592963,0.0001218568,0.0008446189,0.0061148647,0.000041667114],"about_ca_topic_score_codex":0.99696004,"about_ca_topic_score_gemma":0.99856144,"teacher_disagreement_score":0.07972721,"about_ca_system_score_codex":0.07972721,"about_ca_system_score_gemma":0.060709104,"threshold_uncertainty_score":0.5784641},"labels":[],"label_agreement":null},{"id":"W2017288354","doi":"10.4081/gh.2012.132","title":"Geocoding-protected health information using online services may compromise patient privacy - Comments on “Evaluation of the positional difference between two common geocoding methods” by Duncan et al.","year":2012,"lang":"en","type":"letter","venue":"Geospatial health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Centre for Disease Control","funders":"","keywords":"Geocoding; Compromise; Computer science; Health information; Internet privacy; Geography; Environmental health; Information retrieval; Data mining; Medicine; Remote sensing; Health care; Sociology; Political science","score_opus":0.08484164203921446,"score_gpt":0.4257448200529706,"score_spread":0.34090317801375614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017288354","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038913608,0.00016910088,0.000116193296,0.99484986,0.0022576079,0.000008559401,0.000072278555,0.00002363782,0.0021136554],"genre_scores_gemma":[0.0022996035,0.00019172147,0.0002343429,0.9912934,0.0020887742,0.00002578154,0.000026845017,0.000014042856,0.0038255027],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9883591,0.0046306006,0.0013293742,0.0012736386,0.0028827873,0.0015245043],"domain_scores_gemma":[0.9636669,0.024260046,0.0018715378,0.00083724334,0.006641793,0.002722396],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.011779025,0.00076097145,0.0008782675,0.0006904194,0.008226257,0.00475615,0.0026391414,0.066325955,0.009914835],"category_scores_gemma":[0.052560758,0.00087783585,0.0015190989,0.0011182736,0.0037598065,0.0059975716,0.0028862485,0.0426226,0.006657891],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036991343,0.000023237817,0.0008224984,0.000034965964,0.000009718546,0.0007273879,0.0005869835,0.000049756836,0.00018062879,0.003424496,0.98718137,0.0069220304],"study_design_scores_gemma":[0.00009259309,0.0001125286,0.0019673356,0.00044866672,0.00003607802,0.0017959432,0.0035485993,0.00053225085,0.0006344072,0.009241353,0.98147905,0.00011116289],"about_ca_topic_score_codex":0.03679942,"about_ca_topic_score_gemma":0.064794786,"teacher_disagreement_score":0.988221,"about_ca_system_score_codex":0.0054083774,"about_ca_system_score_gemma":0.010922811,"threshold_uncertainty_score":0.07317048},"labels":[],"label_agreement":null},{"id":"W2018430452","doi":"10.1016/j.prevetmed.2012.10.010","title":"Retrospective time series analysis of veterinary laboratory data: Preparing a historical baseline for cluster detection in syndromic surveillance","year":2012,"lang":"en","type":"article","venue":"Preventive Veterinary Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture, Food and Rural Affairs; University of Guelph; University of Prince Edward Island","funders":"","keywords":"Percentile; Count data; Poisson regression; Medicine; Disease surveillance; Retrospective cohort study; Outbreak; Poisson distribution; Cluster (spacecraft); Disease monitoring; Statistics; Pediatrics; Epidemiology; Disease; Environmental health; Computer science; Surgery; Pathology; Mathematics; Population","score_opus":0.03631175839270411,"score_gpt":0.3287234919419029,"score_spread":0.2924117335491988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018430452","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":[0.92414975,0.0005750888,0.05980679,0.00043343008,0.0001255109,0.0005368083,0.012681065,0.00024247529,0.0014491059],"genre_scores_gemma":[0.957461,0.0001836603,0.028760055,0.00005810113,0.000041760366,0.00030453762,0.012972934,0.000021876016,0.00019614182],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9955356,0.0020259265,0.00079137605,0.0006267917,0.0007931074,0.00022723146],"domain_scores_gemma":[0.9865109,0.0052223955,0.0029135295,0.0019781746,0.0029187412,0.00045619337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009812333,0.00038218056,0.0006413406,0.002667817,0.0003660399,0.0010096994,0.0008458368,0.00046224103,0.0006739814],"category_scores_gemma":[0.021854222,0.00026367666,0.00043781765,0.003115269,0.00023080272,0.0008148304,0.0006944117,0.00059002056,0.00021796049],"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":[0.0005403168,0.00023685046,0.94022703,0.00017838372,0.00025666613,0.00017546145,0.00033850892,0.006647503,0.0024125273,0.0011705168,0.002680528,0.045135733],"study_design_scores_gemma":[0.00003614779,0.0008043923,0.9458298,0.00008591672,0.00017759895,0.00032351643,0.0008644919,0.04190243,0.0032494732,0.001197104,0.005497498,0.00003167915],"about_ca_topic_score_codex":0.00854585,"about_ca_topic_score_gemma":0.008068046,"teacher_disagreement_score":0.009812333,"about_ca_system_score_codex":0.0006782312,"about_ca_system_score_gemma":0.001316867,"threshold_uncertainty_score":0.051893234},"labels":[],"label_agreement":null},{"id":"W2019432915","doi":"10.1016/j.amepre.2011.02.006","title":"Infodemiology and Infoveillance","year":2011,"lang":"en","type":"article","venue":"American Journal of Preventive Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":431,"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 Health Network","funders":"","keywords":"Health informatics; The Internet; Public health informatics; Informatics; Public health; Health information; World Wide Web; Analytics; Data science; Computer science; Internet privacy; Health policy; Medicine; Political science; HRHIS; Health care; Nursing","score_opus":0.02292560272081214,"score_gpt":0.2937874217530999,"score_spread":0.2708618190322878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019432915","genre_codex":"commentary","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":[0.19349568,0.15275742,0.18311803,0.22609521,0.008381246,0.00027618418,0.018183561,0.0015817756,0.21611091],"genre_scores_gemma":[0.9538698,0.017679796,0.013280827,0.0048966887,0.0028687038,0.00004979814,0.002225344,0.00004648337,0.005082455],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99571216,0.0025305115,0.0003052087,0.0003654518,0.00094121776,0.00014540149],"domain_scores_gemma":[0.97204113,0.017639006,0.0045568724,0.0021749688,0.002684992,0.00090308243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005471573,0.00033653757,0.00046966725,0.0024885184,0.00027238255,0.0036716994,0.0004447688,0.0008449003,0.0029894786],"category_scores_gemma":[0.033293545,0.00017039281,0.00041772376,0.0017274743,0.0009789137,0.0030096008,0.0011422575,0.00096389535,0.00052939367],"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":[0.00018010613,0.00013404584,0.2115882,0.0008898355,0.0003215706,0.00026230092,0.00044783394,0.0037769026,0.00072443456,0.18827808,0.06438783,0.5290089],"study_design_scores_gemma":[0.00004878914,0.00032113248,0.20832002,0.0015103603,0.00035172363,0.0035273759,0.0018769804,0.044222992,0.0045150192,0.37299964,0.3621806,0.00012542971],"about_ca_topic_score_codex":0.0020855207,"about_ca_topic_score_gemma":0.0013200175,"teacher_disagreement_score":0.005471573,"about_ca_system_score_codex":0.0007231595,"about_ca_system_score_gemma":0.0009019055,"threshold_uncertainty_score":0.028936803},"labels":[],"label_agreement":null},{"id":"W2020578868","doi":"10.5539/gjhs.v4n5p63","title":"Application of Moran’s Test with an Empirical Bayesian Rate to Leading Health Care Problems in Taiwan in a 7-Year Period (2002–2008)","year":2012,"lang":"en","type":"article","venue":"Global Journal of Health Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Demography; Spatial analysis; Geography; Logistic regression; Statistics; Cluster (spacecraft); Cartography; Population; Spatial distribution; Cluster analysis; Environmental health; Medicine; Mathematics; Computer science","score_opus":0.021633988518003574,"score_gpt":0.3685107729113889,"score_spread":0.3468767843933853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020578868","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":[0.99086034,0.0002644424,0.0067615113,0.00042126878,0.00003564536,0.00005565053,0.00032010503,0.000033469045,0.0012475023],"genre_scores_gemma":[0.9976671,0.00004396431,0.001794596,0.000018662695,0.000023629282,0.000041986263,0.0002721482,0.0000053903277,0.00013247375],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9939209,0.004086444,0.00030857974,0.0007828871,0.0006073199,0.0002938939],"domain_scores_gemma":[0.97950304,0.013154244,0.004142602,0.0010384395,0.0016647974,0.00049685704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009320587,0.00025567372,0.00040749094,0.0024510135,0.00046581056,0.0007401913,0.0008749794,0.00056514586,0.0013409678],"category_scores_gemma":[0.039627567,0.00018089575,0.0013402177,0.001917971,0.000603142,0.0007722489,0.0011715111,0.0007548501,0.00011603569],"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":[0.00014164169,0.000039215436,0.97812,0.000042194857,0.00034287348,0.0002555071,0.00043720973,0.004696003,0.000078043515,0.000854331,0.00065324316,0.014339678],"study_design_scores_gemma":[0.000019539215,0.00026454957,0.9030097,0.00004019711,0.00013288084,0.00024160811,0.0017077078,0.09177245,0.00018964398,0.0015632028,0.0010312265,0.000027179798],"about_ca_topic_score_codex":0.021881992,"about_ca_topic_score_gemma":0.01163884,"teacher_disagreement_score":0.021881992,"about_ca_system_score_codex":0.0009931963,"about_ca_system_score_gemma":0.0011833558,"threshold_uncertainty_score":0.049292564},"labels":[],"label_agreement":null},{"id":"W2020766884","doi":"10.1186/1476-072x-12-14","title":"Detecting activity locations from raw GPS data: a novel kernel-based algorithm","year":2013,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":132,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Fonds de Recherche du Québec - Santé","keywords":"Global Positioning System; Computer science; Algorithm; Context (archaeology); Noise (video); Kernel (algebra); Data mining; Artificial intelligence; Mathematics; Geography","score_opus":0.04901334359892456,"score_gpt":0.35786773501273433,"score_spread":0.30885439141380977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020766884","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":[0.04152166,0.000198321,0.956569,0.00013033998,0.000040354378,0.00006536154,0.0000958321,0.00096013054,0.00041900398],"genre_scores_gemma":[0.3844009,0.00018230075,0.6129942,0.00008264469,0.000055613225,0.00014924258,0.0005588746,0.000093497176,0.0014827577],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896514,0.00018290036,0.00011477803,0.00033698173,0.00030018477,0.00010001728],"domain_scores_gemma":[0.9972893,0.0012494115,0.00025386698,0.00027508422,0.00084143743,0.00009091459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013899688,0.0006756435,0.0011428398,0.0012887936,0.00043015365,0.001117977,0.0018141813,0.0011046986,0.000875801],"category_scores_gemma":[0.0063449973,0.00037432517,0.00073440984,0.0011692629,0.00047692735,0.0014694185,0.001038584,0.0010326235,0.00071583764],"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":[0.00072393526,0.00050553406,0.014731081,0.00016536392,0.0002731598,0.00014930326,0.00020313395,0.36500543,0.015293587,0.003952236,0.002549233,0.5964479],"study_design_scores_gemma":[0.00001955797,0.000043352953,0.0011343941,0.000003992964,0.000016252892,0.000055374127,0.000014757233,0.99584705,0.001748857,0.00064547744,0.0004612896,0.0000096673475],"about_ca_topic_score_codex":0.00785641,"about_ca_topic_score_gemma":0.0042458144,"teacher_disagreement_score":0.00785641,"about_ca_system_score_codex":0.00070151925,"about_ca_system_score_gemma":0.001273617,"threshold_uncertainty_score":0.015621364},"labels":[],"label_agreement":null},{"id":"W2021874750","doi":"10.3138/6338-8m7x-4h12-30t9","title":"The Utility of Exploratory Spatial Data Analysis in the Study of Tuberculosis Incidences in an Urban Canadian Population","year":2004,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Vancouver General Hospital; Simon Fraser University","funders":"","keywords":"Public health; Tuberculosis; Confidentiality; Epidemiology; Geography; Population; Geographic information system; Data collection; Environmental health; Data science; Medicine; Computer science; Cartography; Statistics; Pathology","score_opus":0.027162721484232936,"score_gpt":0.3344506482119448,"score_spread":0.3072879267277119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021874750","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":[0.75201124,0.0016973836,0.20835894,0.002382794,0.00006058123,0.0018316006,0.00499572,0.00069497677,0.027966775],"genre_scores_gemma":[0.87720793,0.0007521722,0.12006856,0.000070932365,0.000015060934,0.00031107842,0.0007527011,0.000032424236,0.00078914076],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9955798,0.0024736035,0.00022319042,0.0002659686,0.0012773168,0.00018015997],"domain_scores_gemma":[0.9817053,0.013386927,0.0010841223,0.0010078233,0.0025296304,0.0002861087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00742947,0.00050512544,0.00044391,0.005794882,0.0021098966,0.0024203956,0.000647385,0.0002512764,0.0009360794],"category_scores_gemma":[0.039200447,0.00023433572,0.0007155198,0.010582309,0.0017709285,0.00061917916,0.0015845033,0.0005169792,0.00012198495],"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":[0.0001824972,0.0001428425,0.5956721,0.00061978603,0.0004119625,0.0008172314,0.012896656,0.03201337,0.0021022065,0.027787684,0.0047302414,0.3226235],"study_design_scores_gemma":[0.000057626017,0.00024867235,0.7860741,0.0003538198,0.00022016678,0.0008817218,0.02696828,0.123779446,0.0024744188,0.028993296,0.029759,0.00018942873],"about_ca_topic_score_codex":0.7959686,"about_ca_topic_score_gemma":0.8373601,"teacher_disagreement_score":0.20403141,"about_ca_system_score_codex":0.0052698385,"about_ca_system_score_gemma":0.01674775,"threshold_uncertainty_score":0.41046596},"labels":[],"label_agreement":null},{"id":"W2023771054","doi":"10.1111/j.1541-0420.2005.00503.x","title":"Spatial Event Cluster Detection Using a Compound Poisson Distribution","year":2006,"lang":"en","type":"article","venue":"Biometrics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Heritage Foundation for Medical Research","keywords":"Poisson distribution; Geography; Cluster (spacecraft); Event (particle physics); Population; Poisson regression; Distribution (mathematics); Cartography; Disease surveillance; Computer science; Statistics; Disease; Medicine; Environmental health; Mathematics","score_opus":0.019553953257069916,"score_gpt":0.2865288886447513,"score_spread":0.26697493538768136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023771054","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":[0.03826125,0.0000861109,0.9594867,0.00024153969,0.000032154287,0.00023038208,0.000489501,0.000623912,0.00054840336],"genre_scores_gemma":[0.5652907,0.00015602072,0.4312119,0.00012182419,0.00006666737,0.00043853655,0.0012934053,0.00006394668,0.0013570617],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958698,0.0017071981,0.00030009542,0.0010488848,0.000867888,0.00020606384],"domain_scores_gemma":[0.98589104,0.009702442,0.0013813676,0.0011843033,0.0016225422,0.00021821108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00775735,0.0004581882,0.0009448666,0.002655362,0.0007336648,0.0014632047,0.0021356957,0.00086229184,0.0014538219],"category_scores_gemma":[0.022695696,0.00044771325,0.0012035255,0.0022775484,0.00070269185,0.0011991868,0.001771119,0.00090970326,0.00031765716],"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":[0.0005379729,0.000285429,0.22501072,0.00037990062,0.00067338895,0.0010610407,0.0009518122,0.3199449,0.0044236807,0.065365285,0.010255073,0.37111074],"study_design_scores_gemma":[0.000026047033,0.000039612358,0.0063761286,0.00001511369,0.000030401145,0.00022334381,0.0000894101,0.9768446,0.0012619401,0.013296288,0.0017704603,0.000026660398],"about_ca_topic_score_codex":0.0107494425,"about_ca_topic_score_gemma":0.0072107846,"teacher_disagreement_score":0.0107494425,"about_ca_system_score_codex":0.0011959574,"about_ca_system_score_gemma":0.00180926,"threshold_uncertainty_score":0.04102528},"labels":[],"label_agreement":null},{"id":"W2026220531","doi":"10.3390/ijgi3020481","title":"Web GIS-Based Public Health Surveillance Systems: A Systematic Review","year":2014,"lang":"en","type":"review","venue":"ISPRS International Journal of Geo-Information","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Wuhan University","keywords":"Metadata; Public health; World Wide Web; Geographic information system; GIS and public health; Web standards; Disease surveillance; Social media; Citizen journalism; Web application; Computer science; Volunteered geographic information; Web mapping; Public participation GIS; Data science; The Internet; Geography; Medicine; Cartography","score_opus":0.03285923038692668,"score_gpt":0.3585451237153916,"score_spread":0.3256858933284649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026220531","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":[0.00030456006,0.9987747,0.00012726878,0.00019952614,0.000052476345,0.00009746586,0.00017851393,0.0000063816888,0.00025913405],"genre_scores_gemma":[0.002906597,0.996079,0.0005410889,0.00012957897,0.00003002442,0.00012596998,0.00013869733,0.0000024847182,0.000046560643],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9951208,0.0016710475,0.0017043148,0.00032920056,0.0010505342,0.00012402747],"domain_scores_gemma":[0.97258884,0.020548224,0.003003515,0.00036017798,0.003175277,0.00032399717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068172133,0.0012809157,0.0040084636,0.014361146,0.00057450985,0.0021377616,0.0020549637,0.0014326962,0.0050994917],"category_scores_gemma":[0.03111202,0.0008692477,0.0036917548,0.016725965,0.0007130325,0.0028866413,0.0014488044,0.00096534606,0.000515432],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009623247,0.000050479535,0.0009972283,0.7310893,0.0020037307,0.00011898853,0.0003451677,0.00021464496,0.00012377535,0.0006236926,0.0056706276,0.25866607],"study_design_scores_gemma":[0.000099334975,0.00023870595,0.005738504,0.86741894,0.013390268,0.00066542614,0.0007076989,0.00022566649,0.00030910812,0.00066234946,0.11048663,0.000057517213],"about_ca_topic_score_codex":0.011775146,"about_ca_topic_score_gemma":0.02428422,"teacher_disagreement_score":0.014361146,"about_ca_system_score_codex":0.0028167858,"about_ca_system_score_gemma":0.014968767,"threshold_uncertainty_score":0.03605324},"labels":[],"label_agreement":null},{"id":"W2026378886","doi":"10.1007/s00477-007-0166-6","title":"Towards generic real-time mapping algorithms for environmental monitoring and emergency detection","year":2007,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computational intelligence; Computer science; Algorithm; Data mining; Artificial intelligence","score_opus":0.03680605556599701,"score_gpt":0.3628839576820044,"score_spread":0.32607790211600735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026378886","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":[0.0019332516,0.000051055435,0.99703515,0.000035436104,0.00001322974,0.000018321662,0.000034828077,0.0007628805,0.00011589414],"genre_scores_gemma":[0.075854085,0.00015704238,0.9225341,0.00009426196,0.000036837377,0.00013749281,0.00036627337,0.00020172451,0.0006181473],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987073,0.00036383528,0.00014165198,0.00033565096,0.0003393608,0.00011223894],"domain_scores_gemma":[0.9961732,0.0014837104,0.00032404801,0.00072804984,0.0011649742,0.00012602862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038200535,0.0013106912,0.0015804681,0.001382351,0.00052870816,0.0019060622,0.0030904117,0.0020380188,0.0014553788],"category_scores_gemma":[0.011401869,0.0008913399,0.0016039875,0.0014314641,0.0007737258,0.0024425732,0.002067797,0.0019355319,0.00091475353],"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":[0.00018589734,0.0001319699,0.0019081691,0.00018519061,0.00012721083,0.00007855865,0.00014382608,0.68215346,0.008046643,0.019642068,0.0033289345,0.28406814],"study_design_scores_gemma":[0.000011295478,0.000014454879,0.00014012963,0.0000067969345,0.00001304406,0.000027737402,0.000012571391,0.9891657,0.0018941485,0.0075644,0.0011432447,0.000006443345],"about_ca_topic_score_codex":0.0036989755,"about_ca_topic_score_gemma":0.004214415,"teacher_disagreement_score":0.0038200535,"about_ca_system_score_codex":0.0012080001,"about_ca_system_score_gemma":0.0015243255,"threshold_uncertainty_score":0.020202577},"labels":[],"label_agreement":null},{"id":"W2026433105","doi":"10.4104/pcrj.2011.00098","title":"Agreement between self-report and medical records on signs and symptoms of respiratory illness","year":2012,"lang":"en","type":"article","venue":"Primary Care Respiratory Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; St. Joseph’s Healthcare Hamilton; McMaster University","funders":"Canadian Institutes of Health Research","keywords":"Chills; Sore throat; Medicine; Medical record; Nose; Throat; Headaches; Physical therapy; Pediatrics; Internal medicine; Surgery","score_opus":0.019868427988661286,"score_gpt":0.2933993566084632,"score_spread":0.27353092861980194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026433105","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":[0.98015964,0.00031547796,0.005226331,0.00032885582,0.00011123166,0.0004099728,0.0078145685,0.00009982416,0.0055341315],"genre_scores_gemma":[0.99210703,0.00008124057,0.001918083,0.00015594582,0.000027122227,0.00018993423,0.0049315845,0.000010735599,0.0005784935],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.97819376,0.010943187,0.0038500282,0.0022938857,0.0039221984,0.0007970656],"domain_scores_gemma":[0.8774242,0.07981483,0.015498795,0.008677809,0.017210018,0.0013743414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023653649,0.0003207539,0.00076025754,0.002136646,0.0004082001,0.0014820745,0.00085975707,0.00090847624,0.0014783484],"category_scores_gemma":[0.07198035,0.00035906612,0.0009529282,0.0010868557,0.000491492,0.0011591491,0.0018043967,0.0007701196,0.0007564797],"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":[0.00020097652,0.00007257558,0.9929724,0.00007903184,0.00018099698,0.000024835386,0.0006875802,0.00019566221,0.00032045465,0.000052337433,0.00057228695,0.00464083],"study_design_scores_gemma":[0.000031073178,0.00023327634,0.9935135,0.000050314004,0.0001111736,0.00015243702,0.001003543,0.0025276465,0.0010012612,0.00014643445,0.0012096822,0.000019699019],"about_ca_topic_score_codex":0.003482941,"about_ca_topic_score_gemma":0.0037128946,"teacher_disagreement_score":0.023653649,"about_ca_system_score_codex":0.0005331008,"about_ca_system_score_gemma":0.00057374104,"threshold_uncertainty_score":0.12509388},"labels":[],"label_agreement":null},{"id":"W2027005163","doi":"10.1186/1472-6947-7-15","title":"Automated real time constant-specificity surveillance for disease outbreaks","year":2007,"lang":"en","type":"article","venue":"BMC Medical Informatics and Decision Making","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"U.S. National Library of Medicine","keywords":"Variance (accounting); Statistics; Constant (computer programming); Autoregressive model; Wavelet; Computer science; Medicine; Econometrics; Data mining; Artificial intelligence; Mathematics","score_opus":0.02452610080771238,"score_gpt":0.3411901748177787,"score_spread":0.3166640740100663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027005163","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":[0.5041319,0.00025606173,0.490876,0.00032865326,0.000025057858,0.00011964597,0.00039945447,0.002604625,0.0012586246],"genre_scores_gemma":[0.9355241,0.000045464665,0.06391013,0.000041906944,0.000011481221,0.000042083084,0.00024054063,0.00001963768,0.00016455873],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811304,0.0011474921,0.00008997972,0.00027798035,0.00030350298,0.00006800543],"domain_scores_gemma":[0.9911732,0.0054503214,0.0014893601,0.0008079445,0.00091603625,0.00016311521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030765396,0.000463269,0.00042850882,0.001085964,0.00013462563,0.0006555254,0.00080479187,0.00052018545,0.0005865985],"category_scores_gemma":[0.012002271,0.00021573498,0.0003262597,0.0005262618,0.00022693862,0.0006401635,0.00057573774,0.0003305193,0.00023097884],"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":[0.0010643123,0.0006578876,0.3070199,0.00030775083,0.00030645618,0.00028850883,0.00050534995,0.21592794,0.020737499,0.002356521,0.0038367403,0.44699112],"study_design_scores_gemma":[0.000047631725,0.00024511403,0.041197214,0.000024011088,0.0000545749,0.0002955746,0.00007903445,0.9465233,0.008673943,0.0020129776,0.0008121549,0.000034485965],"about_ca_topic_score_codex":0.0013642814,"about_ca_topic_score_gemma":0.0014860881,"teacher_disagreement_score":0.0030765396,"about_ca_system_score_codex":0.0004171141,"about_ca_system_score_gemma":0.00053169637,"threshold_uncertainty_score":0.016270459},"labels":[],"label_agreement":null},{"id":"W2027288560","doi":"10.1097/opx.0b013e3182120514","title":"Waterloo Eye Study: Data Abstraction and Population Representation","year":2011,"lang":"en","type":"article","venue":"Optometry and Vision Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Representation (politics); Abstraction; Computer science; Population; Optometry; Medicine; Political science; Environmental health","score_opus":0.07392387330951153,"score_gpt":0.49160215443401556,"score_spread":0.417678281124504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027288560","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":[0.8683742,0.004316749,0.0078021563,0.0021320481,0.00011911232,0.011789851,0.091504775,0.00019063344,0.013770396],"genre_scores_gemma":[0.92307925,0.0013638327,0.0076667657,0.00083083176,0.00011964879,0.011265746,0.05245548,0.00008520208,0.003133271],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.96118736,0.011852223,0.008002381,0.002721798,0.014846312,0.0013899542],"domain_scores_gemma":[0.963134,0.006924998,0.011464236,0.0039538126,0.013334057,0.0011889111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019548217,0.000437763,0.0008069132,0.0059406916,0.0012613402,0.0020577055,0.0016251118,0.00044966338,0.0030112953],"category_scores_gemma":[0.058289804,0.00069018616,0.00078642543,0.012937335,0.0008928482,0.0014699076,0.0021170054,0.00050646527,0.00053054036],"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":[0.00049949496,0.000049146925,0.9740276,0.00048002644,0.00019403924,0.00008558266,0.0013814567,0.00011137976,0.0002033731,0.00033748613,0.007930819,0.014699551],"study_design_scores_gemma":[0.00012428382,0.00009173487,0.98743314,0.00030085107,0.000074692885,0.0000860833,0.0008410495,0.00035713133,0.00016025876,0.00009073049,0.010419131,0.00002092425],"about_ca_topic_score_codex":0.3902558,"about_ca_topic_score_gemma":0.39290547,"teacher_disagreement_score":0.3902558,"about_ca_system_score_codex":0.008024631,"about_ca_system_score_gemma":0.013128084,"threshold_uncertainty_score":0.77596855},"labels":[],"label_agreement":null},{"id":"W2029156575","doi":"10.2196/jmir.2998","title":"Guess Who’s Not Coming to Dinner? Evaluating Online Restaurant Reservations for Disease Surveillance","year":2014,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"U.S. National Library of Medicine; Intelligence Advanced Research Projects Activity","keywords":"Disease surveillance; Internet privacy; Computer science; Advertising; Disease; Medicine; Business","score_opus":0.20212847466361558,"score_gpt":0.5173822551237164,"score_spread":0.3152537804601008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029156575","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":[0.98972803,0.001022353,0.0015177693,0.00049843494,0.00007788491,0.000572028,0.0028803423,0.00012787442,0.0035752535],"genre_scores_gemma":[0.9888052,0.00047434855,0.007053111,0.00014624019,0.000052600946,0.000268408,0.0027967182,0.000011018609,0.00039235942],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99517363,0.0029737789,0.00050788367,0.00040160355,0.0007279605,0.00021525101],"domain_scores_gemma":[0.96514755,0.02259167,0.0051520593,0.0008610123,0.004247181,0.0020005729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010043902,0.00052621216,0.00074785546,0.0016367532,0.00043357137,0.0019845124,0.0006701132,0.00066858064,0.001727202],"category_scores_gemma":[0.03256187,0.00017790045,0.0010857261,0.001132262,0.0002060249,0.0014179271,0.0005709872,0.0005477577,0.0006892587],"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":[0.0018457592,0.0008120977,0.934615,0.00060539163,0.00037739836,0.00007256691,0.0005306024,0.0015825685,0.0005208803,0.00009442206,0.0023305775,0.056612816],"study_design_scores_gemma":[0.0001413994,0.005447169,0.9459671,0.00047690637,0.00072764,0.00020603454,0.004238264,0.035639986,0.0018269236,0.0002520378,0.0049855947,0.000090932845],"about_ca_topic_score_codex":0.009294066,"about_ca_topic_score_gemma":0.015137871,"teacher_disagreement_score":0.010043902,"about_ca_system_score_codex":0.0008284036,"about_ca_system_score_gemma":0.0008378584,"threshold_uncertainty_score":0.05311787},"labels":[],"label_agreement":null},{"id":"W2030246929","doi":"10.1007/s10742-009-0048-0","title":"Comparison of cluster detection using patients and events of medically treated self-inflicted injuries in Alberta, Canada","year":2009,"lang":"en","type":"article","venue":"Health Services and Outcomes Research Methodology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Context (archaeology); Cluster analysis; Epidemiology; Psychological intervention; Cluster (spacecraft); Medicine; Public health; Health care; Health services research; Population; Intervention (counseling); Medical emergency; Demography; Environmental health; Geography; Computer science; Nursing; Pathology","score_opus":0.11969533947296526,"score_gpt":0.5064610241919099,"score_spread":0.38676568471894457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030246929","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":[0.99060386,0.0005529642,0.0013458751,0.00029382404,0.000044346158,0.00021364435,0.00528299,0.000043941003,0.001618524],"genre_scores_gemma":[0.9926837,0.00033346796,0.0015391143,0.000095860014,0.0000138822925,0.000071099406,0.0040673777,0.000014681961,0.0011809451],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.994402,0.001730985,0.00041114894,0.00063923985,0.0017566669,0.0010599813],"domain_scores_gemma":[0.98779637,0.0034173739,0.001304796,0.0005820327,0.0056710783,0.0012282699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006044831,0.000773661,0.0009540238,0.0030393677,0.0018459959,0.001965345,0.0033554938,0.0008975887,0.0013457143],"category_scores_gemma":[0.020327225,0.0006179349,0.0013487918,0.0048200823,0.00080801966,0.0005596589,0.0014989275,0.000796785,0.00014973387],"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":[0.00092859543,0.000115084455,0.9814301,0.0000804115,0.0004489692,0.000058168927,0.00055096793,0.004088299,0.00019755484,0.000345327,0.0018655554,0.009890987],"study_design_scores_gemma":[0.00008807442,0.00012825658,0.9893885,0.000049706603,0.00021438049,0.000048478898,0.0012188053,0.0077244425,0.00024505114,0.00008005299,0.0007771714,0.00003708639],"about_ca_topic_score_codex":0.99408984,"about_ca_topic_score_gemma":0.9939464,"teacher_disagreement_score":0.025575306,"about_ca_system_score_codex":0.025575306,"about_ca_system_score_gemma":0.040917598,"threshold_uncertainty_score":0.18556273},"labels":[],"label_agreement":null},{"id":"W2030411766","doi":"10.1080/14999010903358763","title":"Invited Commentary: The Role of GIS in Forensic Mental Health: Challenges and Opportunities","year":2009,"lang":"en","type":"article","venue":"International Journal of Forensic Mental Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Mental health; Forensic science; Psychology; Applied psychology; Psychiatry; Geography; Archaeology","score_opus":0.045546972801900065,"score_gpt":0.3330249637068481,"score_spread":0.287477990904948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030411766","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009566795,0.0007366188,0.0000747546,0.96714103,0.031594854,0.000007029382,0.000044312812,0.000012697237,0.0002931177],"genre_scores_gemma":[0.0021241584,0.0015943835,0.00040104898,0.9325697,0.060126007,0.000041630712,0.000033419776,0.000041037772,0.0030686406],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9932949,0.0025464622,0.0007240732,0.0009195121,0.0020210282,0.00049397047],"domain_scores_gemma":[0.92806715,0.04819239,0.00231294,0.0007759272,0.016639506,0.0040121144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01118354,0.001383929,0.0017933564,0.0013380043,0.005589278,0.0056618354,0.0046205814,0.054632265,0.008314175],"category_scores_gemma":[0.06858826,0.0010509389,0.0018692939,0.001788977,0.0046071615,0.0071870484,0.0027044397,0.04833357,0.0038043747],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018559345,0.000005257142,0.00007639398,0.00008457204,0.000007771472,0.0002472808,0.00030055566,0.00003605091,0.000052541327,0.000564501,0.99644154,0.0021649543],"study_design_scores_gemma":[0.00006561826,0.000046876386,0.00088920264,0.0010374587,0.000056747267,0.0011937764,0.0031794019,0.00047676725,0.0002713814,0.00547549,0.9871837,0.00012358284],"about_ca_topic_score_codex":0.018301474,"about_ca_topic_score_gemma":0.02190576,"teacher_disagreement_score":0.054632265,"about_ca_system_score_codex":0.0050891335,"about_ca_system_score_gemma":0.008886441,"threshold_uncertainty_score":0.059144974},"labels":[],"label_agreement":null},{"id":"W2032230207","doi":"10.1186/1471-2458-12-929","title":"The use of syndromic surveillance for decision-making during the H1N1 pandemic: A qualitative study","year":2012,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa; Kingston Health Sciences Centre; McMaster University; University of Toronto; Public Health Ontario","funders":"Institute of Population and Public Health; Canadian Institutes of Health Research","keywords":"Medicine; Biostatistics; Pandemic; Public health; Qualitative research; Epidemiology; H1n1 pandemic; Public health surveillance; Environmental health; Coronavirus disease 2019 (COVID-19); Medical emergency; Infectious disease (medical specialty); Disease; Pathology","score_opus":0.15542566293116208,"score_gpt":0.43867117432452374,"score_spread":0.2832455113933616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032230207","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":[0.99090624,0.00042711984,0.0012483014,0.0022335302,0.00002944938,0.0002714193,0.00019087447,0.000012810399,0.0046803495],"genre_scores_gemma":[0.99625814,0.000658843,0.0006045184,0.00039504617,0.000009870632,0.00020966784,0.000053684762,0.0000125918805,0.0017977584],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99031687,0.006513978,0.00032123522,0.00046670923,0.00092058827,0.001460654],"domain_scores_gemma":[0.97766167,0.014837003,0.0020369173,0.00045292138,0.0024194205,0.0025921739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014575193,0.0004732327,0.0005937254,0.0013607221,0.011934697,0.0034238785,0.0018003741,0.0011144222,0.0024687566],"category_scores_gemma":[0.016679963,0.0006746674,0.00033917325,0.0019685782,0.010492903,0.0024021582,0.004297104,0.0015268539,0.00020349098],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017835779,0.000013391315,0.003705092,0.000091546026,0.0000017422603,0.00032933085,0.9932492,0.000019809755,0.00030580338,0.00027627376,0.00024871217,0.0017413927],"study_design_scores_gemma":[0.0000025242105,0.000019385094,0.0020079864,0.00010686722,0.0000020715368,0.00007108484,0.9939534,0.0000595781,0.00007741255,0.000060643823,0.0036319369,0.0000071186773],"about_ca_topic_score_codex":0.3541539,"about_ca_topic_score_gemma":0.48427793,"teacher_disagreement_score":0.3541539,"about_ca_system_score_codex":0.02754673,"about_ca_system_score_gemma":0.027423827,"threshold_uncertainty_score":0.704185},"labels":[],"label_agreement":null},{"id":"W2032553307","doi":"10.1016/j.ijmedinf.2008.12.006","title":"Differences between notifiable and administrative health information in the spatial–temporal surveillance of enteric infections","year":2009,"lang":"en","type":"article","venue":"International Journal of Medical Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Health; McMaster University","funders":"","keywords":"Enteric fever; Enteric virus; Environmental health; Medicine; Typhoid fever; Virology","score_opus":0.0325170990452322,"score_gpt":0.35751765779742,"score_spread":0.3250005587521878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032553307","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":[0.9966576,0.00054029963,0.0009922583,0.00045767036,0.000024124862,0.000013666432,0.00072612864,0.0000066874863,0.0005815561],"genre_scores_gemma":[0.9991654,0.00007276683,0.0003515276,0.000029272685,0.000009463673,0.0000041210988,0.0003192006,0.0000015735078,0.000046823363],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98917025,0.006860124,0.0011756676,0.0009036467,0.0014361646,0.0004542339],"domain_scores_gemma":[0.94629806,0.042866938,0.0054539954,0.0015996442,0.003071301,0.00070998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018519113,0.0002028227,0.00031003033,0.0014053696,0.00024206821,0.0016227376,0.00066827296,0.0005939855,0.00055680407],"category_scores_gemma":[0.07022158,0.0003037902,0.00077613874,0.0029567175,0.0004883575,0.0016258452,0.0008781496,0.0009008654,0.000093426315],"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":[0.00040720653,0.00005610933,0.9903674,0.00003879236,0.0002408334,0.000026413029,0.00035129202,0.0016025385,0.00025826614,0.00035711698,0.00015031939,0.006143606],"study_design_scores_gemma":[0.00001892182,0.00021139388,0.9827,0.000036249483,0.00022722268,0.00011382754,0.0009077067,0.0139217265,0.00063656084,0.00062839844,0.0005834073,0.00001472297],"about_ca_topic_score_codex":0.022054803,"about_ca_topic_score_gemma":0.022391018,"teacher_disagreement_score":0.022054803,"about_ca_system_score_codex":0.00091383094,"about_ca_system_score_gemma":0.0009378028,"threshold_uncertainty_score":0.09793961},"labels":[],"label_agreement":null},{"id":"W2034270607","doi":"10.1371/journal.pone.0109209","title":"Improving Google Flu Trends Estimates for the United States through Transformation","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":23,"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 Alberta","funders":"China Scholarship Council; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Demography; Seasonal influenza; Influenza season; Medicine; Disease control; Statistics; Incidence (geometry); Geography; Coronavirus disease 2019 (COVID-19); Mathematics; Environmental health; Virology; Vaccination; Disease; Internal medicine; Influenza vaccine","score_opus":0.044260501127408036,"score_gpt":0.2750779780420251,"score_spread":0.23081747691461707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034270607","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":[0.3707896,0.0073394873,0.24903186,0.007848653,0.0019388484,0.0010112349,0.26974893,0.025815504,0.066475786],"genre_scores_gemma":[0.7275282,0.0024242462,0.09710959,0.00069084816,0.0004005885,0.0007060882,0.16445737,0.0017062518,0.0049767946],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99777645,0.0005987968,0.00031522897,0.00042729135,0.0007408889,0.00014131442],"domain_scores_gemma":[0.9958573,0.000922832,0.000586903,0.00057535374,0.0019872196,0.00007044626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032239533,0.00079015654,0.00068487885,0.005088047,0.0003249581,0.0020769876,0.0006305841,0.00043880224,0.0030622198],"category_scores_gemma":[0.024147557,0.00040248292,0.001544035,0.006611953,0.00026465664,0.0021628046,0.0013965513,0.00096832047,0.0026181815],"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":[0.00021895698,0.00014164454,0.33348772,0.00083319814,0.000817902,0.00033464734,0.0009384978,0.08513476,0.001018193,0.020054517,0.19409798,0.36292198],"study_design_scores_gemma":[0.00012247133,0.00018997393,0.29594013,0.0005809106,0.00036880828,0.0005200569,0.0014370602,0.38371456,0.002549954,0.018530743,0.29580045,0.0002449589],"about_ca_topic_score_codex":0.09891885,"about_ca_topic_score_gemma":0.053678807,"teacher_disagreement_score":0.09891885,"about_ca_system_score_codex":0.001333088,"about_ca_system_score_gemma":0.0018188208,"threshold_uncertainty_score":0.19668615},"labels":[],"label_agreement":null},{"id":"W2035432485","doi":"10.1111/j.1538-4632.2001.tb00434.x","title":"An Approximation for the Rank Adjacency Statistic for Spatial Clustering with Sparse Data","year":2001,"lang":"en","type":"article","venue":"Geographical Analysis","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Statistic; Adjacency list; Cluster analysis; Mathematics; Rank (graph theory); Statistics; Combinatorics","score_opus":0.03476071642325056,"score_gpt":0.31290417715023267,"score_spread":0.2781434607269821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035432485","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":[0.011945487,0.0003370517,0.9860096,0.0001731453,0.000048195707,0.000054775057,0.0002014208,0.00034464608,0.0008856187],"genre_scores_gemma":[0.29855853,0.0006987577,0.69744533,0.00023393938,0.00016831567,0.00040760863,0.0010781839,0.00022960128,0.0011797809],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9951887,0.0027736698,0.00028287328,0.0005337476,0.0010233156,0.00019772632],"domain_scores_gemma":[0.97069734,0.02243024,0.0018608063,0.0027315952,0.0020005885,0.00027941007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008396259,0.00057516346,0.0010895863,0.004944088,0.0007881754,0.0017672282,0.0016718785,0.0013180382,0.0018253347],"category_scores_gemma":[0.073186554,0.00039447972,0.0010062802,0.004299672,0.0016832221,0.002398471,0.0018105876,0.0017513352,0.00097290764],"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":[0.0002650459,0.000105911204,0.030594764,0.00049392396,0.0003519203,0.00051642186,0.0006863141,0.3562149,0.0053976886,0.35363355,0.010331388,0.24140824],"study_design_scores_gemma":[0.000032357762,0.00011368662,0.006092768,0.000074325864,0.000042819393,0.0006126787,0.00017263227,0.78577775,0.0017125764,0.19598359,0.009287801,0.00009696619],"about_ca_topic_score_codex":0.0028797006,"about_ca_topic_score_gemma":0.002587139,"teacher_disagreement_score":0.008396259,"about_ca_system_score_codex":0.0011745666,"about_ca_system_score_gemma":0.0013814331,"threshold_uncertainty_score":0.04440415},"labels":[],"label_agreement":null},{"id":"W2036483307","doi":"10.2196/resprot.3277","title":"Cameras for Public Health Surveillance: A Methods Protocol for Crowdsourced Annotation of Point-of-Sale Photographs","year":2014,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Crowdsourcing; Annotation; Protocol (science); Usability; Zoom; Information retrieval; Quality (philosophy); Process (computing); Reliability (semiconductor); Data science; Artificial intelligence; World Wide Web; Human–computer interaction","score_opus":0.3976258438809228,"score_gpt":0.6361401561526883,"score_spread":0.23851431227176545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036483307","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010382342,0.0007761499,0.3546092,0.0031715012,0.0016107172,0.51766557,0.046344373,0.010693173,0.05474702],"genre_scores_gemma":[0.00890438,0.00053898524,0.2544188,0.0012880111,0.00017725368,0.70971876,0.010237727,0.0010507407,0.01366535],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9777478,0.010680114,0.0034174572,0.0027608112,0.004229392,0.0011644779],"domain_scores_gemma":[0.96112096,0.012634538,0.0021453216,0.011442473,0.010919786,0.0017369683],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.032680888,0.0021012041,0.0011544664,0.0037297213,0.004676426,0.0025042456,0.0036325452,0.0042582224,0.08815167],"category_scores_gemma":[0.036587093,0.0021171304,0.0014871901,0.0022358638,0.0027708865,0.0020364618,0.0051168646,0.003927376,0.04193129],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0066948854,0.0028466126,0.0046181893,0.008664557,0.00016366805,0.00265977,0.013903312,0.004897564,0.062208477,0.030688765,0.48683906,0.3758151],"study_design_scores_gemma":[0.0013857695,0.0011698531,0.006458458,0.0022367437,0.000066934954,0.00059025426,0.0020519749,0.003425976,0.019432267,0.009991547,0.952854,0.00033632282],"about_ca_topic_score_codex":0.0050630746,"about_ca_topic_score_gemma":0.0117121795,"teacher_disagreement_score":0.96731913,"about_ca_system_score_codex":0.0037280472,"about_ca_system_score_gemma":0.0112383235,"threshold_uncertainty_score":0.29489672},"labels":[],"label_agreement":null},{"id":"W2037491558","doi":"10.1007/s13171-012-0012-x","title":"A new estimator for the number of species in a population","year":2012,"lang":"en","type":"article","venue":"Sankhya A","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Dalhousie University","funders":"","keywords":"Estimator; Statistics; Confidence interval; Mathematics; Bias of an estimator; Minimum-variance unbiased estimator; Bayes estimator; Population; Dirichlet distribution; Consistent estimator; Variance (accounting); Applied mathematics","score_opus":0.027680383201362218,"score_gpt":0.33579473417464833,"score_spread":0.3081143509732861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037491558","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":[0.018834535,0.0008180326,0.9781149,0.0003165422,0.0001503596,0.00006498118,0.0008518585,0.00028464937,0.0005641118],"genre_scores_gemma":[0.39854965,0.0011330817,0.5923459,0.0004326321,0.00062422367,0.00048643321,0.003990232,0.000074746524,0.002363183],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99677795,0.0011438635,0.00025451768,0.0010582362,0.0006409597,0.00012451905],"domain_scores_gemma":[0.97910327,0.0150783,0.001703709,0.0015441496,0.0021745847,0.00039601617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004648361,0.0006986321,0.0016561111,0.003532875,0.0003471669,0.0020165911,0.0022390916,0.0014800057,0.0014284492],"category_scores_gemma":[0.026309412,0.00051214977,0.00085970346,0.001540895,0.0009369659,0.0029718818,0.0016937315,0.0017210991,0.0005389346],"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":[0.0004958681,0.0005376533,0.21540293,0.0010708645,0.0012757926,0.00032414545,0.00040281352,0.18990667,0.01606963,0.07033128,0.010246627,0.49393564],"study_design_scores_gemma":[0.00007545314,0.00020634755,0.020054545,0.000103127495,0.0001234988,0.00076519506,0.00009934903,0.92801356,0.0035471793,0.040152527,0.0067759687,0.00008332037],"about_ca_topic_score_codex":0.0015036516,"about_ca_topic_score_gemma":0.0017691677,"teacher_disagreement_score":0.004648361,"about_ca_system_score_codex":0.0007033994,"about_ca_system_score_gemma":0.0009536585,"threshold_uncertainty_score":0.02458316},"labels":[],"label_agreement":null},{"id":"W2041377702","doi":"10.1145/1754239.1754271","title":"A method for evaluating marketer re-identification risk","year":2010,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Agricultural Research Institute of Ontario","funders":"","keywords":"Metric (unit); Matching (statistics); Identification (biology); Computer science; Health records; Data mining; Health care; Business; Marketing; Statistics; Mathematics","score_opus":0.03474971224900748,"score_gpt":0.42025682692688554,"score_spread":0.38550711467787807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041377702","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":[0.070777595,0.0020863279,0.9171649,0.0007975007,0.00023778781,0.0011082268,0.0024732589,0.0015645863,0.0037897616],"genre_scores_gemma":[0.44546282,0.00053746364,0.5486848,0.00018134408,0.0002134963,0.0009495432,0.0023361936,0.00013818909,0.0014963157],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97399443,0.009399908,0.0031247414,0.0024409494,0.010527679,0.0005122662],"domain_scores_gemma":[0.8825431,0.07783633,0.0155438185,0.010116581,0.012715764,0.0012444016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026169822,0.002108259,0.002155539,0.00965348,0.00094548037,0.0030974553,0.0026709375,0.0030006133,0.0014762249],"category_scores_gemma":[0.1175589,0.0004884397,0.0013363048,0.005072766,0.001313752,0.004133917,0.0026233455,0.0023676844,0.00047974035],"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":[0.00092686055,0.0007112547,0.108099245,0.0012736265,0.0016440413,0.000410823,0.000636195,0.35350063,0.009762924,0.05977289,0.013294618,0.44996697],"study_design_scores_gemma":[0.00007697944,0.0008005432,0.017241765,0.00010616398,0.00016423549,0.00092854706,0.0002039049,0.93905246,0.007939282,0.027510736,0.0057876427,0.00018784546],"about_ca_topic_score_codex":0.0024453145,"about_ca_topic_score_gemma":0.0023862044,"teacher_disagreement_score":0.026169822,"about_ca_system_score_codex":0.002537354,"about_ca_system_score_gemma":0.0020812398,"threshold_uncertainty_score":0.13840091},"labels":[],"label_agreement":null},{"id":"W2041992473","doi":"10.1016/j.healthplace.2011.07.009","title":"Spatial aberration vs. geographical substance: Representing place in public health surveillance","year":2011,"lang":"en","type":"article","venue":"Health & Place","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Public health; Public health surveillance; Health geography; Public relations; Public space; Environmental health; Health promotion; Political science; International health; Medicine","score_opus":0.060851791958168845,"score_gpt":0.3151026103634171,"score_spread":0.2542508184052482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041992473","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":[0.47964293,0.0013480758,0.49526563,0.0056833182,0.00026830903,0.0002008921,0.008097157,0.00062085065,0.008872821],"genre_scores_gemma":[0.9420294,0.00027719061,0.05591415,0.000097079115,0.000042757627,0.0000436542,0.0011489324,0.000022871496,0.00042395378],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974739,0.001224634,0.00028470144,0.00033461823,0.000490084,0.00019206869],"domain_scores_gemma":[0.98888457,0.005443291,0.0020239095,0.0016289036,0.0017372973,0.00028202735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034287225,0.00034155612,0.00057373225,0.0027816154,0.00047429535,0.0029931804,0.0007979874,0.0005928108,0.0014356956],"category_scores_gemma":[0.026177898,0.00027146138,0.00078507396,0.005732124,0.0015210515,0.0034033856,0.003050531,0.00085919123,0.00025440345],"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":[0.0008712083,0.0000962677,0.5934435,0.00039199102,0.0002504086,0.00021921535,0.003302884,0.09256879,0.003299789,0.060187403,0.0034935914,0.24187496],"study_design_scores_gemma":[0.00014219616,0.0004489489,0.24942519,0.00026552076,0.00041292328,0.0006018825,0.0072430363,0.5866063,0.0061334996,0.12175058,0.026766634,0.00020329354],"about_ca_topic_score_codex":0.047359534,"about_ca_topic_score_gemma":0.049583763,"teacher_disagreement_score":0.047359534,"about_ca_system_score_codex":0.0014651874,"about_ca_system_score_gemma":0.0017981242,"threshold_uncertainty_score":0.09416771},"labels":[],"label_agreement":null},{"id":"W2042174736","doi":"10.1371/journal.pone.0017381","title":"Nonparametric Evaluation of Dynamic Disease Risk: A Spatio-Temporal Kernel Approach","year":2011,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; McMaster University; Queen's University","funders":"","keywords":"Kernel (algebra); Nonparametric statistics; Kernel density estimation; Computer science; Dimension (graph theory); Kernel method; Machine learning; Artificial intelligence; Data mining; Statistics; Mathematics; Support vector machine","score_opus":0.0961482991572639,"score_gpt":0.2830366739602166,"score_spread":0.18688837480295267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042174736","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":[0.023034958,0.00028671924,0.9759134,0.00021922769,0.0000151828735,0.000018472223,0.000069452726,0.00007418729,0.00036843822],"genre_scores_gemma":[0.8132524,0.0005224557,0.18454394,0.000067323956,0.000067293535,0.00008557914,0.00024862008,0.000033914912,0.0011784546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842596,0.0009507608,0.00009423454,0.00021346337,0.00024262197,0.00007302801],"domain_scores_gemma":[0.9922998,0.005497105,0.00071946107,0.00074161595,0.0006086014,0.0001334193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00471084,0.00046104516,0.0008798958,0.0016697063,0.00023037138,0.0012371805,0.0013938142,0.0010362694,0.0007914523],"category_scores_gemma":[0.019493302,0.00033619313,0.0007424881,0.0014634541,0.0007991989,0.00219507,0.001177713,0.0009850092,0.00013799897],"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":[0.00016213217,0.000114379895,0.014661362,0.00014476689,0.00031928474,0.00018851513,0.00015661067,0.7927325,0.0022989982,0.079840526,0.000897081,0.108483866],"study_design_scores_gemma":[0.00000505692,0.000016032036,0.0013840887,0.0000070131423,0.000012371337,0.000046772857,0.000017499437,0.98571473,0.00028146402,0.012191968,0.00031127932,0.000011815378],"about_ca_topic_score_codex":0.003416386,"about_ca_topic_score_gemma":0.002141288,"teacher_disagreement_score":0.00471084,"about_ca_system_score_codex":0.000726392,"about_ca_system_score_gemma":0.0007690027,"threshold_uncertainty_score":0.024913609},"labels":[],"label_agreement":null},{"id":"W2042271503","doi":"10.1186/1476-072x-6-17","title":"Using GIS-based methods of multicriteria analysis to construct socio-economic deprivation indices","year":2007,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":115,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Geographic information system; Health geography; Index (typography); Social deprivation; Socioeconomic status; Geocoding; Geography; Construct (python library); Variable (mathematics); Population; Econometrics; Statistics; Computer science; Public health; Mathematics; Cartography; Medicine; Environmental health; Health policy; Economics","score_opus":0.06077023611313951,"score_gpt":0.4817446342364156,"score_spread":0.4209743981232761,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042271503","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":[0.020736095,0.00017887098,0.9739509,0.00018660368,0.00004020406,0.00064784416,0.0012415006,0.0006930725,0.0023249348],"genre_scores_gemma":[0.09896328,0.00014076813,0.89850426,0.00003018839,0.000024254416,0.0009676066,0.00093400414,0.00009372253,0.00034196107],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9900746,0.0065436554,0.0008909106,0.0009850861,0.0013253152,0.00018053505],"domain_scores_gemma":[0.97544235,0.015991334,0.0029213717,0.0022777,0.0031719143,0.0001953335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008208082,0.0016819839,0.0011737953,0.012745237,0.00096387515,0.0035428081,0.0013432839,0.00048495864,0.0028947874],"category_scores_gemma":[0.045296654,0.00060376286,0.0020857628,0.013039102,0.0010032902,0.0016236255,0.003280461,0.0010630884,0.0005993965],"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":[0.00020061813,0.00025269177,0.10537415,0.0015904866,0.002103257,0.00048649585,0.006923322,0.18641494,0.0038031747,0.096630305,0.00701082,0.5892097],"study_design_scores_gemma":[0.00008466009,0.000244863,0.05352525,0.00036058875,0.0003221799,0.00042360276,0.00397245,0.7555174,0.0041713202,0.15355548,0.027605198,0.00021699083],"about_ca_topic_score_codex":0.0138657475,"about_ca_topic_score_gemma":0.012249673,"teacher_disagreement_score":0.0138657475,"about_ca_system_score_codex":0.0018242646,"about_ca_system_score_gemma":0.0021946484,"threshold_uncertainty_score":0.04340899},"labels":[],"label_agreement":null},{"id":"W2043177955","doi":"10.1016/j.sste.2010.03.002","title":"Residential address errors in public health surveillance data: A description and analysis of the impact on geocoding","year":2010,"lang":"en","type":"article","venue":"Spatial and Spatio-temporal Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":29,"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 Health Centre; McGill University","funders":"Canada Research Chairs","keywords":"Geocoding; Public health; Public health surveillance; Environmental health; Disease surveillance; Psychological intervention; Geography; Medicine; Cartography","score_opus":0.11103321313569306,"score_gpt":0.3793860011355259,"score_spread":0.26835278799983286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043177955","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":[0.82595444,0.0056647873,0.14388101,0.004693627,0.00018305173,0.0003603552,0.012461842,0.00021553693,0.006585289],"genre_scores_gemma":[0.9710015,0.0022592933,0.021947268,0.00016878986,0.000094120755,0.00007609841,0.0035148528,0.00004327513,0.0008947634],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9817452,0.009296198,0.0015273421,0.0013837971,0.0055640144,0.00048342542],"domain_scores_gemma":[0.7530422,0.20764819,0.015577717,0.0100238,0.013305972,0.0004020457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015321405,0.0003528057,0.0004996484,0.004665995,0.0004445611,0.0020936201,0.0010574149,0.0008730312,0.0011116655],"category_scores_gemma":[0.11323698,0.0003709002,0.0013146057,0.011476693,0.0011752918,0.0028580667,0.0017010714,0.001030921,0.00016172104],"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":[0.0002200381,0.00008523756,0.8289748,0.00039180007,0.00034023303,0.00039078135,0.0007070197,0.05625311,0.00039131296,0.017048562,0.0021277377,0.09306939],"study_design_scores_gemma":[0.00003195694,0.00015555964,0.57946855,0.00048112776,0.0007639284,0.0014588097,0.0023127366,0.37512085,0.0033664564,0.02574327,0.010989336,0.00010744219],"about_ca_topic_score_codex":0.029286178,"about_ca_topic_score_gemma":0.026896268,"teacher_disagreement_score":0.029286178,"about_ca_system_score_codex":0.0021106838,"about_ca_system_score_gemma":0.0019528852,"threshold_uncertainty_score":0.08102828},"labels":[],"label_agreement":null},{"id":"W2043706468","doi":"10.1016/j.trstmh.2008.11.012","title":"Nonparametric spatial analysis to detect high-risk regions for schistosomiasis in Guichi, China","year":2008,"lang":"en","type":"article","venue":"Transactions of the Royal Society of Tropical Medicine and Hygiene","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Schistosomiasis; Multivariate statistics; Environmental health; Nonparametric statistics; Epidemiology; Relative risk; Risk analysis (engineering); Geography; Computer science; Statistics; Medicine; Immunology; Mathematics; Machine learning; Confidence interval; Pathology","score_opus":0.015648428844078167,"score_gpt":0.26170730223163935,"score_spread":0.2460588733875612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043706468","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":[0.95968294,0.00018760042,0.039186474,0.00012495846,0.0000058593528,0.000031469142,0.00017997433,0.000075613534,0.0005250805],"genre_scores_gemma":[0.9951395,0.00002551365,0.0046487325,0.0000038063095,0.0000020997875,0.000010862521,0.000112319496,0.0000022490767,0.000054818724],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989881,0.0006319731,0.000058481888,0.00012689199,0.00012700057,0.00006756035],"domain_scores_gemma":[0.9949626,0.0034398083,0.00065069494,0.00039017445,0.00044470892,0.000112084366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029689416,0.00022879512,0.00028815333,0.0015687504,0.0003080143,0.0005029346,0.000372564,0.00021576996,0.0003499393],"category_scores_gemma":[0.0122500695,0.00012707939,0.0004192806,0.0011775829,0.0004658734,0.00035687638,0.0005802612,0.00024274054,0.000038214275],"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":[0.00029884535,0.00006813114,0.82978225,0.00010288035,0.00024783207,0.0004902169,0.00050752785,0.081393406,0.0047273226,0.0035010227,0.0007313209,0.07814926],"study_design_scores_gemma":[0.000018927276,0.00008322975,0.53209984,0.000013082238,0.00006061998,0.00017126877,0.0004327048,0.46229059,0.0012180042,0.002932817,0.0006511534,0.000027754935],"about_ca_topic_score_codex":0.028327957,"about_ca_topic_score_gemma":0.023508402,"teacher_disagreement_score":0.028327957,"about_ca_system_score_codex":0.0006224061,"about_ca_system_score_gemma":0.0007528723,"threshold_uncertainty_score":0.05632609},"labels":[],"label_agreement":null},{"id":"W2047515884","doi":"10.1136/injuryprev-2012-040580b.25","title":"ANALYSIS OF INJURY TRENDS—SHOW ME THE ELBOW!","year":2012,"lang":"en","type":"article","venue":"Injury Prevention","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada","funders":"","keywords":"Poison control; Statistical software; Computer science; Statistics; Medicine; Medical emergency; Data science; Mathematics","score_opus":0.017507765240168182,"score_gpt":0.3365438557911298,"score_spread":0.3190360905509616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047515884","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":[0.33129638,0.015065971,0.41641182,0.045740616,0.004448875,0.0027450735,0.14465784,0.010057341,0.029576039],"genre_scores_gemma":[0.7265198,0.0041508395,0.21095689,0.005269724,0.0017325738,0.003028039,0.037604604,0.0020149103,0.008722566],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98937714,0.0054280516,0.001058027,0.0015012992,0.0022435908,0.00039187752],"domain_scores_gemma":[0.95570654,0.022914663,0.008699399,0.004542756,0.0073258486,0.0008108403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018420352,0.00083113246,0.0009791137,0.0055828765,0.000565873,0.002450164,0.0016276496,0.0008758858,0.014203458],"category_scores_gemma":[0.084702015,0.00041477018,0.0021384738,0.0077731553,0.00089478097,0.002394019,0.0019355428,0.0018730726,0.0027891528],"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":[0.0010809076,0.00021843443,0.43030718,0.0038117282,0.0032648337,0.00034996148,0.0024685585,0.006213358,0.0016028971,0.011071877,0.16105565,0.37855467],"study_design_scores_gemma":[0.00027884255,0.0019049597,0.67897147,0.004054671,0.0013497621,0.0008498542,0.0055895317,0.035401627,0.0043780115,0.036142036,0.23077057,0.0003086298],"about_ca_topic_score_codex":0.024114218,"about_ca_topic_score_gemma":0.017638117,"teacher_disagreement_score":0.024114218,"about_ca_system_score_codex":0.0014064977,"about_ca_system_score_gemma":0.001986941,"threshold_uncertainty_score":0.097417295},"labels":[],"label_agreement":null},{"id":"W2051644535","doi":"10.1016/j.psychres.2014.07.033","title":"Spatial analysis of suicide mortality in Québec: Spatial clustering and area factor correlates","year":2014,"lang":"en","type":"review","venue":"Psychiatry Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Sherbrooke; Institut National de la Recherche Scientifique; Hôpital Charles-Le Moyne; Quebec Rehabilitation Research Network","funders":"","keywords":"Geography; Poison control; Suicide prevention; Cluster analysis; Injury prevention; Demography; Rural area; Human factors and ergonomics; Occupational safety and health; Spatial analysis; Suicide methods; Environmental health; Suicide rates; Cartography; Medicine; Statistics; Sociology; Remote sensing","score_opus":0.13308457803799262,"score_gpt":0.4540750875154672,"score_spread":0.3209905094774746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051644535","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":[0.002478357,0.9943013,0.00060151354,0.000914501,0.00010440617,0.00002140355,0.00082766026,0.000017585036,0.00073315273],"genre_scores_gemma":[0.021595826,0.97468555,0.0019697307,0.00030075494,0.00016289396,0.000024694886,0.0008670466,0.0000057690863,0.0003876208],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99925977,0.00013959012,0.00008865411,0.00014575153,0.00030854807,0.00005776138],"domain_scores_gemma":[0.99598384,0.0013780806,0.00064325624,0.00012356268,0.0017338312,0.00013748965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024908313,0.0010351104,0.0019061491,0.0061081536,0.00047125277,0.0017334908,0.0016988146,0.000648322,0.0015043033],"category_scores_gemma":[0.0061131003,0.00033391963,0.0019926196,0.011507884,0.00062561827,0.00073679286,0.00065140444,0.0011010655,0.00021700311],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008910417,0.00004673203,0.05591233,0.013472966,0.0023814717,0.000094773044,0.0002202346,0.0016419373,0.0001633099,0.0016278457,0.017287351,0.907062],"study_design_scores_gemma":[0.000088687724,0.00021445318,0.55107766,0.0504797,0.009139004,0.0011372791,0.0019741256,0.007438746,0.000736812,0.007209653,0.3701727,0.0003312211],"about_ca_topic_score_codex":0.6663249,"about_ca_topic_score_gemma":0.778615,"teacher_disagreement_score":0.3336751,"about_ca_system_score_codex":0.0063577686,"about_ca_system_score_gemma":0.01494611,"threshold_uncertainty_score":0.6712803},"labels":[],"label_agreement":null},{"id":"W2052218589","doi":"10.1080/07060660809507492","title":"Comment analyser la structure spatiale et modéliser le développement spatio-temporel des épiphyties?","year":2008,"lang":"fr","type":"article","venue":"Canadian Journal of Plant Pathology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Cellular automaton; Data mining; Context (archaeology); Dimension (graph theory); Data science; Cartography; Geography; Mathematics; Artificial intelligence","score_opus":0.03493102360832191,"score_gpt":0.2650404176786682,"score_spread":0.2301093940703463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052218589","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":[0.51743245,0.0038804838,0.41552714,0.032588087,0.00068378524,0.0001056698,0.013079228,0.002122287,0.014580793],"genre_scores_gemma":[0.92149544,0.0015872904,0.06644768,0.0008417403,0.00017964533,0.00011573946,0.0033985795,0.00024282123,0.0056911157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932086,0.00018900707,0.000042242333,0.00028157217,0.00009210046,0.000074215626],"domain_scores_gemma":[0.9946162,0.003649389,0.0005152484,0.00046084225,0.0005922944,0.0001660539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031796019,0.00056913623,0.0006112276,0.0009171714,0.00053425424,0.0027848156,0.0017319708,0.0021141819,0.0036231484],"category_scores_gemma":[0.013918541,0.00042132402,0.0015445793,0.001498279,0.00086558424,0.003937103,0.0008147384,0.0012977781,0.00059573655],"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":[0.00016599397,0.000073819436,0.14165232,0.00043259852,0.00031010783,0.00028435714,0.0005613279,0.7624369,0.0053605773,0.04288503,0.005827618,0.0400093],"study_design_scores_gemma":[0.000029145871,0.00004271881,0.026323697,0.00011930222,0.00011837142,0.00013106296,0.0007276743,0.91919494,0.0027859726,0.0373301,0.013140371,0.000056582277],"about_ca_topic_score_codex":0.13966604,"about_ca_topic_score_gemma":0.1134453,"teacher_disagreement_score":0.13966604,"about_ca_system_score_codex":0.0023788093,"about_ca_system_score_gemma":0.003156835,"threshold_uncertainty_score":0.27770615},"labels":[],"label_agreement":null},{"id":"W2052560952","doi":"10.1007/s10654-009-9364-5","title":"Predictors of locating children participants in epidemiological studies 20 years after last contact: internet resources and longitudinal research","year":2009,"lang":"en","type":"article","venue":"European Journal of Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"The Internet; Medicine; Longitudinal study; Public health; Socioeconomic status; Epidemiology; Telephone number; Cohort study; Sample (material); Cohort; Demography; Family medicine; Environmental health; Population; World Wide Web; Pathology","score_opus":0.17301329867312928,"score_gpt":0.4180390214887329,"score_spread":0.2450257228156036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052560952","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99736804,0.000660808,0.00007161923,0.00030511335,0.000012932816,0.000014172015,0.0011495699,0.0000044519434,0.00041324532],"genre_scores_gemma":[0.99801517,0.0004240061,0.00017699915,0.00007864581,0.000020246802,0.000025391451,0.0009808813,0.0000062221698,0.000272413],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974171,0.0010743212,0.00046184042,0.0003261748,0.0003505566,0.00037003262],"domain_scores_gemma":[0.9613314,0.013090223,0.016748838,0.0020928842,0.0020701403,0.004666482],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003628923,0.00040805596,0.0005724451,0.0022405302,0.0009093215,0.002038986,0.0016635671,0.0020415452,0.0032645387],"category_scores_gemma":[0.02358171,0.0006089708,0.0010151955,0.0040248786,0.0004870665,0.0027762186,0.0016845986,0.0025841368,0.0006799839],"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":[0.00003206758,0.00005881919,0.9991848,0.000009922076,0.00003709722,0.000028729515,0.00014317517,0.000013035149,0.000016515036,0.000010372357,0.000060091057,0.00040552783],"study_design_scores_gemma":[0.000005441768,0.00008533914,0.9976768,0.000041470765,0.000067733425,0.00015110755,0.0015092508,0.00019523874,0.000036885354,0.000025293144,0.00019808178,0.0000074412856],"about_ca_topic_score_codex":0.030808644,"about_ca_topic_score_gemma":0.027139675,"teacher_disagreement_score":0.9963711,"about_ca_system_score_codex":0.0005377918,"about_ca_system_score_gemma":0.0010197205,"threshold_uncertainty_score":0.061258674},"labels":[],"label_agreement":null},{"id":"W2054324875","doi":"10.1371/journal.pone.0117938","title":"Information Seeking Regarding Tobacco and Lung Cancer: Effects of Seasonality","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Institute of Automation, Chinese Academy of Sciences; National Institutes of Health; Chinese Academy of Sciences; National Institute on Drug Abuse; National Natural Science Foundation of China","keywords":"Lung cancer; Seasonality; China; The Internet; Environmental health; Demography; Medicine; Geography; Statistics; Oncology; Computer science; World Wide Web; Mathematics","score_opus":0.02765182495619536,"score_gpt":0.26759652335821577,"score_spread":0.2399446984020204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054324875","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":[0.9946613,0.0004527633,0.001766498,0.00025285632,0.000027937052,0.000048858954,0.0013364302,0.000022323418,0.0014310776],"genre_scores_gemma":[0.99858594,0.00007303413,0.00048171234,0.00003639536,0.0000112670605,0.000020357465,0.00058137026,0.000011061524,0.0001988853],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99661666,0.0016368858,0.00034412448,0.00069755386,0.00043353846,0.0002712321],"domain_scores_gemma":[0.97273636,0.016561719,0.0063475664,0.0021168604,0.0014171894,0.00082029274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052128956,0.00022089534,0.000373853,0.0014789207,0.00038840828,0.0010060995,0.00042307275,0.0002960926,0.001824025],"category_scores_gemma":[0.020060968,0.00023774723,0.0010016522,0.002946013,0.0005715594,0.0010204833,0.0011321655,0.0006507421,0.00020869615],"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":[0.000090283625,0.00002875896,0.99475807,0.000035291538,0.00024137476,0.00002883418,0.0004908126,0.00021042723,0.00021690821,0.00013208624,0.00017435117,0.0035928087],"study_design_scores_gemma":[0.0000019473928,0.000057331268,0.99740005,0.000009333936,0.000067011126,0.000037019097,0.0004025051,0.0014691214,0.00010999403,0.00007968559,0.0003617512,0.0000043898062],"about_ca_topic_score_codex":0.016696826,"about_ca_topic_score_gemma":0.024638513,"teacher_disagreement_score":0.016696826,"about_ca_system_score_codex":0.00046524344,"about_ca_system_score_gemma":0.0006679823,"threshold_uncertainty_score":0.03319925},"labels":[],"label_agreement":null},{"id":"W2054826800","doi":"10.4018/jhisi.2012100103","title":"CliFin","year":2012,"lang":"en","type":"article","venue":"International Journal of Healthcare Information Systems and Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Government of New Brunswick; National Research Council Canada; Université de Moncton","funders":"","keywords":"Computer science; Visualization; Geographic information system; Information system; Schedule; Frame (networking); Process (computing); Health care; World Wide Web; Data science; Data mining; Geography","score_opus":0.021226819959279757,"score_gpt":0.32652633246631707,"score_spread":0.3052995125070373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054826800","genre_codex":"other","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":[0.00949848,0.003392061,0.017516479,0.008444736,0.0019717237,0.00076756923,0.022305708,0.017403953,0.91869926],"genre_scores_gemma":[0.057062794,0.002786468,0.0358345,0.0048923814,0.00042035064,0.0008879923,0.05720495,0.0054072086,0.8355034],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997738,0.00020717877,0.00010728119,0.00046149714,0.0010437508,0.00044236088],"domain_scores_gemma":[0.9951095,0.0006130998,0.00018099652,0.0005890087,0.0023320885,0.0011752311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028349753,0.0009735748,0.0006315287,0.0030168004,0.0026147668,0.005078526,0.003055281,0.0018101033,0.25446847],"category_scores_gemma":[0.0081978915,0.00044884498,0.0005451685,0.0023560163,0.00091915077,0.0029058384,0.0035516247,0.0016938879,0.10129496],"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":[0.0006501343,0.00010990924,0.003524676,0.00037417267,0.000014558768,0.00081619644,0.00070933957,0.0004379984,0.0018749518,0.024041057,0.6695145,0.2979326],"study_design_scores_gemma":[0.000027808677,0.000023906918,0.00086863595,0.00008711708,0.0000033552,0.00023261792,0.00015185607,0.0003863595,0.0005341701,0.0013269183,0.99634224,0.00001493589],"about_ca_topic_score_codex":0.09481102,"about_ca_topic_score_gemma":0.10800731,"teacher_disagreement_score":0.25446847,"about_ca_system_score_codex":0.0055269054,"about_ca_system_score_gemma":0.011984885,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2055248655","doi":"10.1111/1753-6405.12222","title":"Geographic analysis of infant mortality in New Zealand, 1995–2008: an ethnicity perspective","year":2014,"lang":"en","type":"article","venue":"Australian and New Zealand Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Concordance; Demography; Ethnic group; Population; Geography; Cluster (spacecraft); Infant mortality; Medicine","score_opus":0.07629038583923962,"score_gpt":0.3723472511972444,"score_spread":0.29605686535800474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055248655","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":[0.98300904,0.0024906772,0.0005498791,0.0016758741,0.000029323706,0.00003119469,0.009034484,0.000017091224,0.0031624592],"genre_scores_gemma":[0.99422896,0.0014507964,0.00044960488,0.00010992312,0.000021760907,0.000025419688,0.0030987388,0.0000076691995,0.0006072196],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991549,0.00019221962,0.00014389283,0.00015888948,0.00021182669,0.00013834712],"domain_scores_gemma":[0.99850214,0.00013106473,0.0007169564,0.0001069459,0.0003762705,0.00016668963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011384446,0.0003952555,0.00050134194,0.0027045077,0.00039530755,0.00095361896,0.00075231976,0.00031531003,0.00086148985],"category_scores_gemma":[0.003220203,0.00026280098,0.0014302989,0.0056069866,0.00045159893,0.00070218043,0.0012213163,0.0006788607,0.00013513598],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008590068,0.000021827589,0.98779684,0.00012910478,0.00049324805,0.00018545402,0.00081131206,0.00066042884,0.00041319814,0.0003363924,0.0009421213,0.008124096],"study_design_scores_gemma":[0.0000022489394,0.000014038417,0.9981505,0.000018727647,0.000071387934,0.00005789591,0.00059509923,0.00019863468,0.000032179243,0.000030256888,0.0008244005,0.0000047100716],"about_ca_topic_score_codex":0.71234584,"about_ca_topic_score_gemma":0.7677826,"teacher_disagreement_score":0.71234584,"about_ca_system_score_codex":0.003509755,"about_ca_system_score_gemma":0.0028725567,"threshold_uncertainty_score":0.57869637},"labels":[],"label_agreement":null},{"id":"W2055886136","doi":"10.1016/j.ijid.2010.02.571","title":"Redevelopment and evaluation of EpiData: A practical software tool for use in the public health field","year":2010,"lang":"en","type":"article","venue":"International Journal of Infectious Diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Thunder Bay Regional Health Sciences Centre; Public Health Agency of Canada; Toronto Public Health","funders":"","keywords":"Public health; Agency (philosophy); Medicine; Public health informatics; Medical education; International health; Health policy; Nursing","score_opus":0.060306072758790644,"score_gpt":0.40144054967249193,"score_spread":0.3411344769137013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055886136","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08965561,0.00065663666,0.6894103,0.003440291,0.00057602406,0.011873481,0.011516856,0.1767828,0.016087888],"genre_scores_gemma":[0.10186508,0.0006785045,0.8560404,0.00065871,0.00014927515,0.005882172,0.01519418,0.01339909,0.0061324947],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9773473,0.010412972,0.0040408224,0.0018938348,0.0056296187,0.0006754099],"domain_scores_gemma":[0.84057254,0.10161048,0.007433861,0.018464679,0.02838129,0.0035372009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06424673,0.0021788948,0.0012735485,0.006755271,0.0010943479,0.004035219,0.00417088,0.0010735241,0.011100128],"category_scores_gemma":[0.14582697,0.0014941634,0.0014022125,0.002426031,0.0018507545,0.004927383,0.006168688,0.0024816392,0.0057411264],"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":[0.0020920692,0.0022792588,0.02737505,0.002594204,0.00027381303,0.00080924697,0.00864545,0.0070304438,0.018788628,0.0066692433,0.10831973,0.8151229],"study_design_scores_gemma":[0.0039742044,0.0055744145,0.0747049,0.006335526,0.00074504403,0.0025273773,0.004712026,0.12372824,0.13112864,0.021733792,0.6236542,0.0011816581],"about_ca_topic_score_codex":0.0042633186,"about_ca_topic_score_gemma":0.0028536848,"teacher_disagreement_score":0.06424673,"about_ca_system_score_codex":0.0023013789,"about_ca_system_score_gemma":0.007776749,"threshold_uncertainty_score":0.33977324},"labels":[],"label_agreement":null},{"id":"W2057327099","doi":"10.1002/cjs.5550350307","title":"Theoretical properties of tests for spatial clustering of count data","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cluster analysis; Null hypothesis; Null (SQL); Spatial analysis; Mathematics; Infinity; Statistical hypothesis testing; Data mining; Statistics; Computer science; Econometrics; Mathematical analysis","score_opus":0.06992896635715676,"score_gpt":0.29809116204275377,"score_spread":0.228162195685597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057327099","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":[0.07533406,0.0013628243,0.9077573,0.0048890524,0.00019059087,0.00025034044,0.00062096684,0.00034736778,0.009247597],"genre_scores_gemma":[0.83448833,0.0011699501,0.15597637,0.0016136675,0.00096528995,0.0017258782,0.0016932731,0.00024545036,0.0021218217],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9491409,0.031532556,0.002315638,0.0050780904,0.0104241185,0.0015087123],"domain_scores_gemma":[0.19826485,0.7678753,0.012123769,0.010118491,0.009707028,0.0019105352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.11261828,0.001224337,0.0033212863,0.011092719,0.0023651223,0.0059015024,0.0056971596,0.0046121217,0.0064800433],"category_scores_gemma":[0.51044965,0.0012944019,0.0027972988,0.008308071,0.016023876,0.012251583,0.0060359477,0.0055802427,0.0006991949],"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":[0.00020539266,0.00006517509,0.016450489,0.00033818473,0.00032536357,0.0003061743,0.0007883484,0.03536458,0.00038501652,0.9228474,0.0021968598,0.020727063],"study_design_scores_gemma":[0.00012429353,0.00017108805,0.0028211728,0.00018799577,0.00008039632,0.00033506358,0.00029726874,0.1901839,0.00066685956,0.80385756,0.0012168988,0.000057558394],"about_ca_topic_score_codex":0.0017957576,"about_ca_topic_score_gemma":0.00057078316,"teacher_disagreement_score":0.11261828,"about_ca_system_score_codex":0.0048851296,"about_ca_system_score_gemma":0.0032880472,"threshold_uncertainty_score":0.5955895},"labels":[],"label_agreement":null},{"id":"W2059815729","doi":"10.1016/s0277-9536(00)00275-6","title":"GIS approaches to the problem of disease clusters: a brief commentary","year":2001,"lang":"en","type":"letter","venue":"Social Science & Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Disease; Etiology; Epidemiology; Data science; Geographic information system; Health geography; Public health; Regional science; Geography; Medicine; Computer science; Cartography; Health policy; Pathology","score_opus":0.06755600165827395,"score_gpt":0.3026018099057711,"score_spread":0.23504580824749716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059815729","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000045517725,0.00090218405,0.00008417817,0.9946103,0.0039548804,0.0000036360198,0.00002383329,0.0000046460364,0.00037077855],"genre_scores_gemma":[0.0012504016,0.0011290391,0.00021942586,0.9811817,0.015284831,0.000032018062,0.000013796417,0.00001327596,0.00087546214],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98311377,0.0090824785,0.0015529547,0.0020088055,0.0033377793,0.00090425013],"domain_scores_gemma":[0.8029729,0.17620765,0.0037943206,0.0018708888,0.010960504,0.0041937106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023033798,0.0014101738,0.0029117907,0.0026531403,0.007088514,0.010744095,0.0073906905,0.102360606,0.009192269],"category_scores_gemma":[0.11335239,0.0015589962,0.0026864456,0.0034233856,0.015162466,0.013684251,0.005533564,0.08192017,0.0041066166],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028616985,0.000012685735,0.00018908332,0.00017607873,0.000029814562,0.00030151583,0.00057137763,0.000082128216,0.000040951923,0.012857336,0.97966707,0.0060432474],"study_design_scores_gemma":[0.00022230331,0.0000402053,0.00096055964,0.0024106302,0.00011028409,0.00068947545,0.0034046385,0.0005809039,0.00013484574,0.054804385,0.9365093,0.00013254223],"about_ca_topic_score_codex":0.02735247,"about_ca_topic_score_gemma":0.039604,"teacher_disagreement_score":0.102360606,"about_ca_system_score_codex":0.0101772165,"about_ca_system_score_gemma":0.010739452,"threshold_uncertainty_score":0.1218158},"labels":[],"label_agreement":null},{"id":"W2061175610","doi":"10.3934/publichealth.2015.1.27","title":"What Could Be Future Scenarios?—Lessons from the History of Public Health Surveillance for the Future","year":2015,"lang":"en","type":"article","venue":"AIMS Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Agency of Canada; Public Health Ontario; University of Toronto","funders":"","keywords":"Public health; Public health surveillance; Data collection; Public relations; Environmental planning; Political science; Medicine; Geography; Sociology","score_opus":0.14508737774715483,"score_gpt":0.36034480107789174,"score_spread":0.2152574233307369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061175610","genre_codex":"commentary","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":[0.0066758147,0.17256193,0.010786094,0.77828485,0.0037718911,0.000046456084,0.00023450913,0.00007980628,0.027558697],"genre_scores_gemma":[0.44917727,0.40022534,0.026748316,0.105812915,0.009921914,0.00032981642,0.00048747144,0.00014952895,0.0071474197],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9893863,0.0073172976,0.0005408811,0.0009162669,0.0011397374,0.0006995171],"domain_scores_gemma":[0.96647334,0.025226338,0.0013954034,0.0013686637,0.0032837186,0.0022524653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028745659,0.00063541345,0.000931469,0.0026587255,0.004247795,0.012890422,0.001953851,0.005236198,0.003958091],"category_scores_gemma":[0.035560336,0.0005843095,0.00079285295,0.0023207888,0.01643903,0.025477823,0.0047011515,0.011919951,0.0007733167],"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":[0.00014337218,0.00012360586,0.0056022634,0.0019911209,0.000071190014,0.0006816968,0.015400741,0.0024762677,0.00023574659,0.7125734,0.065960966,0.19473962],"study_design_scores_gemma":[0.00001900309,0.00007299399,0.0025134294,0.005139929,0.00002769023,0.0007350047,0.020979406,0.0010913706,0.0002583199,0.46262544,0.50643027,0.0001070625],"about_ca_topic_score_codex":0.010306033,"about_ca_topic_score_gemma":0.011931813,"teacher_disagreement_score":0.028745659,"about_ca_system_score_codex":0.012580706,"about_ca_system_score_gemma":0.009031321,"threshold_uncertainty_score":0.15202343},"labels":[],"label_agreement":null},{"id":"W2061244579","doi":"10.3138/carto.44.4.289","title":"Online Map Design for Public-Health Decision Makers","year":2009,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Public Health Ontario; University of Toronto; Toronto Metropolitan University; St. Michael's Hospital; Simon Fraser University","funders":"","keywords":"Variety (cybernetics); Public health; Set (abstract data type); Space (punctuation); Data science; Computer science; Geography; Medicine; Artificial intelligence; Nursing","score_opus":0.03842007328159899,"score_gpt":0.3500088197122713,"score_spread":0.31158874643067236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061244579","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":[0.019679418,0.00023360581,0.93688136,0.0015458791,0.00019446929,0.0010527638,0.0023430565,0.010960242,0.027109137],"genre_scores_gemma":[0.15053365,0.00031446244,0.8379834,0.00018142589,0.00006871114,0.0018305777,0.0021858963,0.0010346454,0.0058671716],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970751,0.0018931887,0.000224866,0.00021997333,0.0004726995,0.000114130235],"domain_scores_gemma":[0.9865601,0.009231231,0.00039530027,0.0015959295,0.0018041574,0.00041321383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057181837,0.0009145182,0.00092092325,0.0020277433,0.001002659,0.0057505327,0.0019458809,0.0012486987,0.03801568],"category_scores_gemma":[0.021740757,0.00071671413,0.0008313242,0.0019666615,0.00064460986,0.0048763133,0.0029300917,0.0010259163,0.0059659784],"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":[0.0011984507,0.0011449882,0.005127348,0.001834058,0.00017332297,0.0009479025,0.010366589,0.07043951,0.008533958,0.11693514,0.06938982,0.7139089],"study_design_scores_gemma":[0.0007129409,0.0004655123,0.0021127227,0.00057664036,0.0001857934,0.00040775322,0.0072279368,0.3656237,0.0099127,0.22216313,0.390441,0.00017015573],"about_ca_topic_score_codex":0.0016339949,"about_ca_topic_score_gemma":0.0025166161,"teacher_disagreement_score":0.03801568,"about_ca_system_score_codex":0.00093731907,"about_ca_system_score_gemma":0.0013577292,"threshold_uncertainty_score":0.12717515},"labels":[],"label_agreement":null},{"id":"W2061292249","doi":"10.1016/j.sste.2014.01.003","title":"Accuracy of prospective space–time surveillance in detecting tuberculosis transmission","year":2014,"lang":"en","type":"article","venue":"Spatial and Spatio-temporal Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sante Montreal; McGill University Health Centre; McGill University","funders":"","keywords":"Scan statistic; Transmission (telecommunications); Tuberculosis; Medicine; Prospective cohort study; Contact tracing; Constant false alarm rate; False alarm; Limiting; False positive paradox; Computer science; Statistics; Telecommunications; Artificial intelligence; Surgery; Pathology; Mathematics; Disease; Coronavirus disease 2019 (COVID-19)","score_opus":0.0171144298028059,"score_gpt":0.2930058786648998,"score_spread":0.2758914488620939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061292249","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":[0.97469985,0.0027100681,0.014192566,0.0006644998,0.00013355436,0.000039372375,0.004850607,0.00016241579,0.0025471472],"genre_scores_gemma":[0.9965814,0.00022618347,0.001572413,0.000043900305,0.000022130678,0.000005710233,0.0013854038,0.000006003147,0.00015691736],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9934344,0.0033408445,0.00093347253,0.001110885,0.00087791035,0.00030255632],"domain_scores_gemma":[0.9396442,0.044801146,0.0055595245,0.0052958815,0.0039217025,0.0007774749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014998325,0.00049966696,0.00067645404,0.0014944816,0.00034128138,0.0020643706,0.0010475607,0.0013576541,0.00085647014],"category_scores_gemma":[0.06270953,0.00058653165,0.0012402099,0.001183158,0.00049104035,0.0015162381,0.00090704486,0.00070133345,0.0004229828],"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":[0.0003341253,0.0000337953,0.9830498,0.000049657585,0.00021831447,0.000030191493,0.000047847454,0.0055452543,0.00021289794,0.00011988833,0.00023371096,0.010124556],"study_design_scores_gemma":[0.000038443057,0.0004590287,0.8175846,0.00010955961,0.0006725333,0.00078420877,0.00027788433,0.1749874,0.0026399815,0.001094329,0.0013111838,0.000040863764],"about_ca_topic_score_codex":0.008509581,"about_ca_topic_score_gemma":0.008281101,"teacher_disagreement_score":0.014998325,"about_ca_system_score_codex":0.00059699186,"about_ca_system_score_gemma":0.0008532611,"threshold_uncertainty_score":0.079319715},"labels":[],"label_agreement":null},{"id":"W2063404424","doi":"10.1016/j.econlet.2011.10.001","title":"The importance of industry classification in estimating concentration ratios","year":2011,"lang":"en","type":"article","venue":"Economics Letters","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Econometrics; Industrial organization; Statistics; Economics; Mathematics","score_opus":0.03977684044852747,"score_gpt":0.26055137938694717,"score_spread":0.2207745389384197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063404424","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":[0.462596,0.008633986,0.5071058,0.0066868155,0.00067405147,0.00042694886,0.0022296072,0.00050833606,0.011138546],"genre_scores_gemma":[0.9149257,0.0011158645,0.07970688,0.0006194378,0.0005517768,0.00010172428,0.0011669829,0.000060456277,0.0017512349],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9827725,0.013227574,0.00086590264,0.0011197403,0.0016393056,0.0003749042],"domain_scores_gemma":[0.8152478,0.16362663,0.006877387,0.007924676,0.005563439,0.0007600853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023314742,0.0008626238,0.0015652461,0.0038406316,0.0010658579,0.004657552,0.0016259752,0.0027072176,0.0012286999],"category_scores_gemma":[0.1393144,0.00071626884,0.0012344227,0.0036255263,0.0011252423,0.002562808,0.001163741,0.002069948,0.0009193723],"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":[0.0008837949,0.00044682313,0.5792823,0.00039928214,0.00089585973,0.0003010596,0.00020987004,0.09340732,0.0018253233,0.015399695,0.007515158,0.29943353],"study_design_scores_gemma":[0.00015812938,0.0001945581,0.07091376,0.000153609,0.00023999806,0.00042584082,0.00016081175,0.8880599,0.0028508806,0.032373607,0.0044052065,0.000063607586],"about_ca_topic_score_codex":0.009094233,"about_ca_topic_score_gemma":0.0078919055,"teacher_disagreement_score":0.023314742,"about_ca_system_score_codex":0.00074318465,"about_ca_system_score_gemma":0.001133236,"threshold_uncertainty_score":0.123301625},"labels":[],"label_agreement":null},{"id":"W2063584357","doi":"10.1186/1471-2458-14-147","title":"Measuring underreporting and under-ascertainment in infectious disease datasets: a comparison of methods","year":2014,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":418,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"International Livestock Research Institute; Universität Bielefeld; Universiteit Utrecht; European Centre for Disease Prevention and Control","keywords":"Medicine; Biostatistics; Public health; Epidemiology; Environmental health; Infectious disease (medical specialty); Disease; Pathology","score_opus":0.16040707867309054,"score_gpt":0.4423991248874735,"score_spread":0.281992046214383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063584357","genre_codex":"methods","genre_gemma":"methods","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.100921415,0.2602205,0.57398045,0.00867131,0.0021010502,0.018389245,0.02497356,0.00091495,0.009827473],"genre_scores_gemma":[0.36801758,0.06796734,0.5165351,0.0031604918,0.001032743,0.023695774,0.018094134,0.00057333143,0.0009234919],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.36238325,0.49678493,0.071830094,0.024039688,0.043525893,0.0014361757],"domain_scores_gemma":[0.1205628,0.78071,0.046791013,0.027252186,0.023805868,0.00087812776],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.49018452,0.002578557,0.004637378,0.018496145,0.0013277448,0.009493454,0.0059732986,0.0031074835,0.002884796],"category_scores_gemma":[0.6742056,0.0020488093,0.010096404,0.021402624,0.0037527208,0.008406091,0.007862131,0.0036200802,0.0005515369],"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":[0.0033288253,0.0003558764,0.34638068,0.10178976,0.064566605,0.00029172623,0.00817838,0.007492893,0.000889885,0.013828781,0.009876824,0.44301984],"study_design_scores_gemma":[0.0029857329,0.004186484,0.5029129,0.13923423,0.052405145,0.003802825,0.0106858015,0.04585116,0.006422815,0.08384129,0.14581786,0.0018537361],"about_ca_topic_score_codex":0.0036536516,"about_ca_topic_score_gemma":0.005728785,"teacher_disagreement_score":0.50981545,"about_ca_system_score_codex":0.004580258,"about_ca_system_score_gemma":0.008737096,"threshold_uncertainty_score":0.6286932},"labels":[],"label_agreement":null},{"id":"W2063829298","doi":"10.1016/j.healthplace.2009.07.001","title":"Using population attributable risk to understand geographic disease clusters","year":2009,"lang":"en","type":"article","venue":"Health & Place","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Attributable risk; Geography; Relative risk; Cluster (spacecraft); Population; Demography; Public health interventions; Environmental health; Medicine; Confidence interval; Computer science; Sociology","score_opus":0.046941753987233256,"score_gpt":0.3568919446294659,"score_spread":0.3099501906422326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063829298","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":[0.6949521,0.0011220139,0.27915797,0.0019417832,0.00011464203,0.0004094551,0.014846911,0.000693218,0.0067619816],"genre_scores_gemma":[0.94948125,0.0002251052,0.04583178,0.00011674496,0.000032920572,0.00013844146,0.0037996906,0.000023653347,0.0003504696],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985033,0.00070134015,0.00015297018,0.000367004,0.00019542043,0.000079953104],"domain_scores_gemma":[0.99177706,0.0054505216,0.00089791574,0.0011563558,0.0005868061,0.00013139723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030835336,0.0003944261,0.0007259294,0.003792596,0.00040648723,0.001215403,0.00061384094,0.0004474374,0.0011725164],"category_scores_gemma":[0.01818678,0.00024026092,0.0010968983,0.0033545883,0.00026381973,0.0012583314,0.0010896672,0.0006808119,0.00016284274],"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":[0.00014639659,0.00010822561,0.8410138,0.00017943386,0.0011983775,0.00016653516,0.001121189,0.04310682,0.00060969614,0.015342385,0.0041367277,0.09287049],"study_design_scores_gemma":[0.00006606027,0.00016835185,0.49882326,0.00011413483,0.00083969795,0.0003918481,0.002708822,0.38900572,0.0013429858,0.09521087,0.011259766,0.00006847342],"about_ca_topic_score_codex":0.04223455,"about_ca_topic_score_gemma":0.03009767,"teacher_disagreement_score":0.04223455,"about_ca_system_score_codex":0.0007370903,"about_ca_system_score_gemma":0.0011758007,"threshold_uncertainty_score":0.0839774},"labels":[],"label_agreement":null},{"id":"W2066870804","doi":"10.1097/00124784-200603000-00003","title":"Characteristics, Desired Functionalities, and Datasets of State Web-based Data Query Systems","year":2006,"lang":"en","type":"article","venue":"Journal of Public Health Management and Practice","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Process (computing); State (computer science); Key (lock); Core (optical fiber); Information retrieval; World Wide Web; Data mining; Data science","score_opus":0.11080071867705413,"score_gpt":0.3513195969587868,"score_spread":0.24051887828173266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066870804","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":[0.65151244,0.0027551497,0.28558517,0.002609465,0.000052234485,0.0015791543,0.018749068,0.010167971,0.02698927],"genre_scores_gemma":[0.8119108,0.001141523,0.15693566,0.0002394191,0.000039709765,0.0010555802,0.026156424,0.0006595838,0.0018612827],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9864027,0.0034729019,0.0032722487,0.0014545387,0.004742991,0.0006546074],"domain_scores_gemma":[0.9301174,0.04304329,0.004681381,0.008620792,0.0124433655,0.0010938065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01373075,0.00052042,0.00073461205,0.0067851003,0.0011250665,0.0063086855,0.0014756373,0.0008645398,0.0012057555],"category_scores_gemma":[0.039937206,0.00089949486,0.00078729447,0.0104536945,0.00096437987,0.0061299936,0.001794188,0.0010522683,0.0006407999],"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":[0.0016737636,0.0008668105,0.43446273,0.0031563973,0.00041084015,0.0010338178,0.010284044,0.021879025,0.028452951,0.07094256,0.015541119,0.411296],"study_design_scores_gemma":[0.00024734327,0.0008731103,0.33026633,0.0017023705,0.00071052683,0.0048018047,0.0113274995,0.22997774,0.07807168,0.041660283,0.2996274,0.00073394604],"about_ca_topic_score_codex":0.011323252,"about_ca_topic_score_gemma":0.008563066,"teacher_disagreement_score":0.01373075,"about_ca_system_score_codex":0.002512692,"about_ca_system_score_gemma":0.0033827752,"threshold_uncertainty_score":0.07261604},"labels":[],"label_agreement":null},{"id":"W2068372477","doi":"10.1371/journal.pmed.0050151","title":"Surveillance Sans Frontières: Internet-Based Emerging Infectious Disease Intelligence and the HealthMap Project","year":2008,"lang":"en","type":"article","venue":"PLoS Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":398,"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":"U.S. National Library of Medicine; Canadian Institutes of Health Research; National Institutes of Health","keywords":"The Internet; Infectious disease (medical specialty); Emerging infectious disease; Medicine; Data science; Disease surveillance; Public health; Disease; Computer science; World Wide Web; Pathology","score_opus":0.032763499176366506,"score_gpt":0.2926836683852794,"score_spread":0.2599201692089129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068372477","genre_codex":"commentary","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":[0.07686164,0.016800364,0.14585105,0.49749663,0.007813311,0.0013674399,0.035649493,0.0078010326,0.21035905],"genre_scores_gemma":[0.61896265,0.021477811,0.2707274,0.017869636,0.0060891532,0.0015667124,0.03186144,0.0013231598,0.030122096],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9954277,0.0032295021,0.0002694671,0.00023557691,0.00063753093,0.00020008601],"domain_scores_gemma":[0.95831484,0.034008045,0.0013672617,0.0021174294,0.0019262063,0.0022662496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015073372,0.0005453146,0.00044385288,0.0029298477,0.00087015633,0.004984726,0.0010240283,0.0016109501,0.010444495],"category_scores_gemma":[0.026903585,0.00029873982,0.00037697662,0.0032225116,0.00093645183,0.0065838504,0.003064343,0.0017484615,0.0012736049],"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":[0.00025145168,0.00028735315,0.035913784,0.00046712527,0.00007159221,0.0001864657,0.0011956719,0.003884769,0.0007225642,0.066115685,0.3340724,0.5568312],"study_design_scores_gemma":[0.0002472512,0.00031651338,0.04572964,0.0014881707,0.00012785492,0.00049220544,0.006138888,0.07842393,0.0034555383,0.11766107,0.7457706,0.0001483235],"about_ca_topic_score_codex":0.009053338,"about_ca_topic_score_gemma":0.00877881,"teacher_disagreement_score":0.015073372,"about_ca_system_score_codex":0.0010961551,"about_ca_system_score_gemma":0.0026620948,"threshold_uncertainty_score":0.07971656},"labels":[],"label_agreement":null},{"id":"W2068588614","doi":"10.6000/1929-6029.2012.01.01.05","title":"Cost Assessment of Epidemiologic Surveys in Dentistry","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Sample size determination; Sample (material); Coefficient of variation; Margin (machine learning); Mathematics; Sampling error; Sampling (signal processing); Econometrics; Variable (mathematics); Observational error; Computer science","score_opus":0.23238788375326563,"score_gpt":0.5859553681676988,"score_spread":0.35356748441443314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068588614","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4825384,0.036902986,0.4428331,0.011376604,0.00072491035,0.004694238,0.003226399,0.00021905798,0.017484292],"genre_scores_gemma":[0.9215608,0.004738902,0.07019819,0.00031821794,0.00021512738,0.0017432058,0.0006326124,0.000024026822,0.0005689549],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.80332494,0.17662422,0.005393191,0.0013516656,0.012669787,0.0006362569],"domain_scores_gemma":[0.62282544,0.33692122,0.022352565,0.0072332285,0.009895966,0.0007715127],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06378143,0.0008655442,0.0010817312,0.0029284146,0.0003501798,0.0016059146,0.0012519635,0.0013672567,0.0019567115],"category_scores_gemma":[0.3282911,0.00035428535,0.0014365729,0.003904675,0.0011653106,0.0018344457,0.002280093,0.00083370134,0.00013209778],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027861474,0.0004910342,0.3326437,0.0060201366,0.0031235826,0.0004063881,0.0008742018,0.18209632,0.0005881092,0.04885003,0.0041476535,0.41797262],"study_design_scores_gemma":[0.00059921783,0.007233947,0.43110558,0.005566168,0.003068863,0.0025531333,0.0026622775,0.43184856,0.0025631639,0.088885665,0.02361084,0.00030247972],"about_ca_topic_score_codex":0.0020828124,"about_ca_topic_score_gemma":0.0015284417,"teacher_disagreement_score":0.93621856,"about_ca_system_score_codex":0.00329799,"about_ca_system_score_gemma":0.0026116015,"threshold_uncertainty_score":0.33731246},"labels":[],"label_agreement":null},{"id":"W2069516633","doi":"10.1126/science.346.6213.1063","title":"Social media for large studies of behavior","year":2014,"lang":"en","type":"article","venue":"Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":646,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Social media; Data science; Computer science; World Wide Web","score_opus":0.06830385733705342,"score_gpt":0.40874860345973907,"score_spread":0.34044474612268566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069516633","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":[0.0294915,0.024656205,0.73780954,0.052502595,0.008482479,0.008973024,0.059522036,0.0058244844,0.07273815],"genre_scores_gemma":[0.21785362,0.0105791865,0.69524,0.008406244,0.0051752436,0.028060265,0.019400235,0.0015327885,0.013752374],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.93491334,0.04759921,0.0035847533,0.0038651144,0.009442275,0.00059527234],"domain_scores_gemma":[0.4787662,0.38982174,0.014413741,0.09426269,0.017880257,0.004855451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06615482,0.0011990909,0.0021585238,0.005570456,0.0019501194,0.0064128265,0.002542112,0.0030731498,0.034007255],"category_scores_gemma":[0.27876806,0.001473548,0.0016383772,0.0059227226,0.0029010288,0.008688573,0.0066864695,0.004102194,0.010025447],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018556636,0.0006417204,0.044014692,0.010485519,0.0019580983,0.0005836176,0.0021710964,0.0029406834,0.010756146,0.17685652,0.14435461,0.60338163],"study_design_scores_gemma":[0.00076301745,0.0007844489,0.083503485,0.0067305504,0.0010932692,0.00080285774,0.0024638001,0.012211261,0.00750419,0.30202323,0.58182913,0.00029078926],"about_ca_topic_score_codex":0.0023223844,"about_ca_topic_score_gemma":0.0034991198,"teacher_disagreement_score":0.06615482,"about_ca_system_score_codex":0.0013075218,"about_ca_system_score_gemma":0.0035331103,"threshold_uncertainty_score":0.3498643},"labels":[],"label_agreement":null},{"id":"W2069657069","doi":"10.1016/s0277-9536(03)00081-9","title":"Zones of prevention: the geography of fall injuries in the elderly","year":2003,"lang":"en","type":"article","venue":"Social Science & Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":61,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Health; University of Alberta","funders":"Administration for Community Living","keywords":"Geography; Statistic; Geographic information system; Incidence (geometry); Poison control; Cluster (spacecraft); Injury prevention; Occupational safety and health; Suicide prevention; Demography; Socioeconomics; Cartography; Environmental health; Medicine; Statistics; Sociology; Computer science","score_opus":0.01748747242572424,"score_gpt":0.3261326009680871,"score_spread":0.30864512854236287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069657069","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":[0.98507535,0.0033929846,0.0017100279,0.0034861935,0.000027635824,0.00004080164,0.0018160073,0.00002282526,0.0044281334],"genre_scores_gemma":[0.99879116,0.0004478888,0.0003813154,0.00004168427,0.000011570745,0.000010439679,0.0001791228,0.0000021389303,0.00013480971],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99931145,0.00031754712,0.00006289259,0.00009034605,0.00010694127,0.000110731984],"domain_scores_gemma":[0.99714833,0.000718135,0.001232388,0.00016049466,0.00041313932,0.00032735945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079213135,0.00013651856,0.00027716393,0.0030225553,0.0006828246,0.002193474,0.00047193177,0.0004339861,0.0023461797],"category_scores_gemma":[0.0058686757,0.00021155187,0.00032921953,0.0038674888,0.00095065706,0.0013544512,0.0021678342,0.0004634786,0.00011791605],"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":[0.00016361382,0.000028105349,0.9743702,0.0001097266,0.00010237168,0.000161025,0.002774736,0.00058059156,0.00034801307,0.007401752,0.0012361868,0.012723691],"study_design_scores_gemma":[0.000010733013,0.000049958224,0.9839316,0.00007525697,0.00005727891,0.0003631411,0.008269323,0.0008936394,0.00010669591,0.0034303644,0.0027961584,0.000015979489],"about_ca_topic_score_codex":0.06439648,"about_ca_topic_score_gemma":0.0914528,"teacher_disagreement_score":0.06439648,"about_ca_system_score_codex":0.0013902129,"about_ca_system_score_gemma":0.0015665853,"threshold_uncertainty_score":0.1280433},"labels":[],"label_agreement":null},{"id":"W2070686661","doi":"10.1080/10810730.2011.626503","title":"A Flu By Any Other Name: Why the World Health Organization Should Adopt the World Meteorological Association's Storm Naming System as a Model for Naming Emerging Infectious Diseases","year":2012,"lang":"en","type":"article","venue":"Journal of Health Communication","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Toronto","funders":"","keywords":"Credibility; Public health; Pandemic; Association (psychology); Context (archaeology); Infectious disease (medical specialty); Public relations; Coronavirus disease 2019 (COVID-19); Political science; Psychology; History; Medicine; Disease; Law","score_opus":0.033284980946862594,"score_gpt":0.34516946323816977,"score_spread":0.31188448229130716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070686661","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058437925,0.0026146944,0.09528258,0.81067204,0.005498096,0.00022289003,0.00034762145,0.00042345907,0.02650069],"genre_scores_gemma":[0.7951363,0.0028851938,0.093473874,0.09920731,0.0019405759,0.00029580554,0.00046298542,0.00042515484,0.006172783],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9668437,0.02104917,0.002496456,0.002319212,0.0060324743,0.0012589355],"domain_scores_gemma":[0.91137075,0.049115364,0.008376137,0.010341735,0.017899282,0.002896715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06056511,0.0005112016,0.00054843794,0.0012970151,0.0037802067,0.007003988,0.00239179,0.0053226063,0.0019745578],"category_scores_gemma":[0.09832044,0.0005983028,0.0008122091,0.0021935813,0.0086341975,0.017241929,0.003652183,0.008875767,0.0009777294],"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":[0.00046127598,0.00023596663,0.053956322,0.00056003046,0.00012030247,0.00069723336,0.025392113,0.006729514,0.0046059624,0.588673,0.15086316,0.167705],"study_design_scores_gemma":[0.00023160645,0.00033980334,0.020738991,0.0013825218,0.00012707905,0.001148258,0.03523423,0.033678487,0.0051493286,0.40639988,0.4949897,0.0005801437],"about_ca_topic_score_codex":0.02655657,"about_ca_topic_score_gemma":0.016844425,"teacher_disagreement_score":0.06056511,"about_ca_system_score_codex":0.0066352547,"about_ca_system_score_gemma":0.01026995,"threshold_uncertainty_score":0.32030278},"labels":[],"label_agreement":null},{"id":"W2071282762","doi":"10.3201/eid1807.120055","title":"Electronic Event–based Surveillance for Monitoring Dengue, Latin America","year":2012,"lang":"en","type":"article","venue":"Emerging infectious diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Sante Montreal","funders":"U.S. National Library of Medicine; Audrey and Theodor Geisel School of Medicine at Dartmouth; National Institutes of Health; Dartmouth College","keywords":"Dengue fever; Latin Americans; Electronic surveillance; Event (particle physics); Geography; Computer science; Computer security; Environmental health; Virology; Medicine; Political science","score_opus":0.01091218329583128,"score_gpt":0.2982654745810686,"score_spread":0.28735329128523734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071282762","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":[0.9024818,0.0035020774,0.055740353,0.0023152283,0.00015454808,0.0012948224,0.01055593,0.0007912215,0.02316394],"genre_scores_gemma":[0.96039987,0.0009721873,0.03429645,0.0002449017,0.0000467092,0.0004691731,0.0025777873,0.000019039077,0.000973866],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991505,0.0005299471,0.00007187583,0.000089691974,0.000119701755,0.000038279195],"domain_scores_gemma":[0.9981688,0.00072944403,0.00036550756,0.00019842776,0.000462588,0.0000750915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020094544,0.00028119478,0.00019748024,0.0011020761,0.00018241254,0.00057760463,0.00045828187,0.00020665977,0.0012007379],"category_scores_gemma":[0.00587722,0.000104163264,0.00019313066,0.0010541194,0.00013496935,0.00057909015,0.0005534672,0.00017003315,0.00013601143],"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":[0.00067121506,0.0005446228,0.70627224,0.00051028084,0.00022800502,0.00019517932,0.0007035196,0.020127099,0.010203549,0.0024410835,0.005247533,0.25285563],"study_design_scores_gemma":[0.00049334957,0.0014954156,0.6899609,0.00057440775,0.00044062771,0.0005665612,0.0030429566,0.23877762,0.016529925,0.0045102984,0.043500938,0.00010700099],"about_ca_topic_score_codex":0.01127953,"about_ca_topic_score_gemma":0.010489354,"teacher_disagreement_score":0.01127953,"about_ca_system_score_codex":0.00065524655,"about_ca_system_score_gemma":0.0005811811,"threshold_uncertainty_score":0.022427738},"labels":[],"label_agreement":null},{"id":"W2071898334","doi":"10.1371/journal.pone.0082183","title":"Syndromic Surveillance Using Veterinary Laboratory Data: Algorithm Combination and Customization of Alerts","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture, Food and Rural Affairs; University of Guelph; University of Prince Edward Island","funders":"Ontario Ministry of Agriculture, Food and Rural Affairs; University of Guelph","keywords":"Computer science; EWMA chart; Data mining; Algorithm; Exponential smoothing; Smoothing; Outbreak; Robustness (evolution); Control chart; Real-time computing; Process (computing); Medicine","score_opus":0.0705340216215932,"score_gpt":0.27259808404825514,"score_spread":0.20206406242666194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071898334","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":[0.13768956,0.0003536622,0.8469952,0.00033898998,0.00006244837,0.0006455798,0.00027345298,0.012547668,0.0010935323],"genre_scores_gemma":[0.45627126,0.00013720509,0.54193145,0.000136384,0.00004524734,0.00038740595,0.0004641956,0.00023168922,0.00039513316],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968162,0.0011969295,0.00045515317,0.0008303153,0.00055904983,0.00014233238],"domain_scores_gemma":[0.9931564,0.003921083,0.0006336236,0.0008115477,0.0012755911,0.00020176855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00613918,0.0012179977,0.0012451101,0.0032291294,0.0003477225,0.0018354048,0.0015743094,0.00095720135,0.0010074053],"category_scores_gemma":[0.01877896,0.000642233,0.00068046415,0.0016204208,0.00034667572,0.0014427381,0.0014261765,0.0009880587,0.0004515585],"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":[0.0010666256,0.0007887435,0.031448025,0.00022706391,0.00038446026,0.0002117603,0.00039186922,0.10829272,0.026351126,0.00071327545,0.0018452851,0.828279],"study_design_scores_gemma":[0.00013629599,0.00028535168,0.007149239,0.0000349432,0.000120327495,0.00026912766,0.000070034956,0.9538363,0.034841802,0.0013834264,0.0018080763,0.00006511523],"about_ca_topic_score_codex":0.0018129746,"about_ca_topic_score_gemma":0.0013848593,"teacher_disagreement_score":0.00613918,"about_ca_system_score_codex":0.0006895304,"about_ca_system_score_gemma":0.00097202335,"threshold_uncertainty_score":0.032467425},"labels":[],"label_agreement":null},{"id":"W2073382943","doi":"10.1016/s0277-9536(02)00380-5","title":"Geographic analysis of diabetes prevalence in an urban area","year":2003,"lang":"en","type":"article","venue":"Social Science & Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":144,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; Manitoba Health","funders":"","keywords":"Socioeconomic status; Regression analysis; Population; Geography; Demography; Statistic; Ecological study; Scan statistic; Logistic regression; Medicine; Linear regression; Environmental health; Statistics; Mathematics","score_opus":0.01801143006629395,"score_gpt":0.31031543861586364,"score_spread":0.29230400854956967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073382943","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":[0.99775773,0.00010917666,0.00028634458,0.00004551169,0.000002757896,0.0000072904654,0.0015271681,0.000014431777,0.00024965155],"genre_scores_gemma":[0.99841964,0.000063702675,0.0003465075,0.000008471306,0.000003959705,0.000009601289,0.0010426878,0.0000019517572,0.00010356021],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994677,0.00024679373,0.000056527082,0.00009694703,0.00008163205,0.00005048797],"domain_scores_gemma":[0.99809605,0.0005929733,0.0005485184,0.00019652276,0.00042708512,0.0001388722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005723848,0.00017948501,0.00025833183,0.0018984355,0.00025426885,0.00040900699,0.00035124802,0.00017905937,0.00093853404],"category_scores_gemma":[0.0021508725,0.0001676366,0.00054715946,0.0030529303,0.00017902182,0.00018086609,0.0004822311,0.0001895939,0.00013442001],"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":[0.00016037465,0.000030705574,0.9931109,0.000032435877,0.00017270575,0.00007876378,0.00014502149,0.0009062487,0.00038814076,0.000099374825,0.00032078096,0.0045545604],"study_design_scores_gemma":[0.000008807002,0.000054346387,0.9975738,0.0000040099017,0.000070620605,0.000115602204,0.00033844993,0.0014588716,0.00010007992,0.000030634277,0.00024088973,0.0000039630495],"about_ca_topic_score_codex":0.0782754,"about_ca_topic_score_gemma":0.08104276,"teacher_disagreement_score":0.0782754,"about_ca_system_score_codex":0.0004550894,"about_ca_system_score_gemma":0.0004652991,"threshold_uncertainty_score":0.15563953},"labels":[],"label_agreement":null},{"id":"W2074214170","doi":"10.1371/journal.pone.0057334","title":"Exploratory Analysis of Methods for Automated Classification of Laboratory Test Orders into Syndromic Groups in Veterinary Medicine","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture, Food and Rural Affairs; University of Guelph; University of Prince Edward Island","funders":"Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Interpretability; Naive Bayes classifier; Machine learning; Artificial intelligence; Classifier (UML); Decision tree; Rule induction; Computer science; Macro; Test set; Random forest; Bayes' theorem; Data mining; Support vector machine; Bayesian probability","score_opus":0.08106622573242704,"score_gpt":0.36196985667177206,"score_spread":0.280903630939345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074214170","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":[0.27893195,0.001904859,0.70700914,0.00053930405,0.0000730423,0.0019751394,0.0024106486,0.0032478075,0.003908107],"genre_scores_gemma":[0.5105606,0.0002839202,0.48470464,0.00008778146,0.000049847513,0.0014778622,0.0018904248,0.00020305467,0.0007419081],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98405474,0.00978472,0.0012370744,0.0012447275,0.0033759796,0.00030282149],"domain_scores_gemma":[0.8468612,0.13513349,0.0045069326,0.0029274998,0.0102028465,0.00036794474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023656584,0.0012685613,0.0007057569,0.0064840256,0.0005819583,0.0018359238,0.0011974483,0.00085837493,0.001734951],"category_scores_gemma":[0.06771694,0.0003007769,0.0013243657,0.0027057906,0.00065962464,0.0011004532,0.0013433659,0.0008315306,0.00066362275],"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":[0.0012770377,0.0010001202,0.09683662,0.0019740101,0.00086929207,0.0003435406,0.0028661357,0.026666127,0.012637376,0.0053838384,0.0041280286,0.84601796],"study_design_scores_gemma":[0.00026538863,0.0019575448,0.121725194,0.0006955978,0.00049701583,0.0012824798,0.0018101445,0.80156,0.035196178,0.021034889,0.013745616,0.0002299024],"about_ca_topic_score_codex":0.0022719523,"about_ca_topic_score_gemma":0.001960661,"teacher_disagreement_score":0.023656584,"about_ca_system_score_codex":0.0012932357,"about_ca_system_score_gemma":0.0014996163,"threshold_uncertainty_score":0.1251095},"labels":[],"label_agreement":null},{"id":"W2075577216","doi":"10.1503/cmaj.080878","title":"Suicide fad threatens neighbours, rescuers","year":2008,"lang":"en","type":"article","venue":"Canadian Medical Association Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Listing (finance); Suicide prevention; Poison control; Medical emergency; Injury prevention; Human factors and ergonomics; Computer security; World Wide Web; Computer science; Medicine; Internet privacy; Forensic engineering; Business; Engineering; Finance","score_opus":0.014765044969922135,"score_gpt":0.2554946807355257,"score_spread":0.24072963576560358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075577216","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":[0.77471715,0.0033796031,0.0024731779,0.067129046,0.001796756,0.00015763583,0.0018172876,0.00047900798,0.14805032],"genre_scores_gemma":[0.97121835,0.0024422326,0.0012138241,0.005769688,0.00026124195,0.00004095895,0.00089881435,0.00004802389,0.018106906],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99843186,0.00046480805,0.00009812749,0.00017095404,0.0005789785,0.00025530977],"domain_scores_gemma":[0.9940759,0.000596322,0.0012309761,0.00048783442,0.0019692923,0.0016396464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012840115,0.00029024718,0.00032114706,0.0011453436,0.0028005487,0.0021119858,0.00056074787,0.0011904638,0.009066023],"category_scores_gemma":[0.013062023,0.00023596476,0.00037426525,0.0006252847,0.001077011,0.0013241274,0.0028578152,0.0018690936,0.0034296904],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028519996,0.00020898772,0.44958505,0.000510377,0.00015059907,0.0064234585,0.027001977,0.000386235,0.0017721019,0.004033187,0.26514846,0.24449435],"study_design_scores_gemma":[0.000046628422,0.0006279362,0.4686632,0.001602814,0.00018083751,0.009673232,0.14881013,0.0017086772,0.002481287,0.0045347363,0.36149654,0.00017399201],"about_ca_topic_score_codex":0.040941946,"about_ca_topic_score_gemma":0.072225764,"teacher_disagreement_score":0.95905805,"about_ca_system_score_codex":0.00160555,"about_ca_system_score_gemma":0.002534735,"threshold_uncertainty_score":0.08140731},"labels":[],"label_agreement":null},{"id":"W2076404895","doi":"10.1016/j.sste.2011.10.003","title":"An examination of five spatial disease clustering methodologies for the identification of childhood cancer clusters in Alberta, Canada","year":2011,"lang":"en","type":"article","venue":"Spatial and Spatio-temporal Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta; University of Manitoba","funders":"","keywords":"Scan statistic; Spatial epidemiology; Cluster analysis; Statistic; Cluster (spacecraft); Medical diagnosis; Spatial analysis; Identification (biology); Geography; Bayesian probability; Cartography; Disease; Statistics; Data mining; Epidemiology; Computer science; Artificial intelligence; Medicine; Mathematics; Pathology; Biology; Ecology","score_opus":0.06788146936925112,"score_gpt":0.33908514919742555,"score_spread":0.27120367982817445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076404895","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":[0.96275395,0.0038141455,0.022210836,0.0019992534,0.000062985935,0.0005356787,0.0036376922,0.00015246384,0.0048329164],"genre_scores_gemma":[0.9563382,0.0010265599,0.038940594,0.00012363312,0.000011174535,0.000095818395,0.002259316,0.000022959994,0.0011816656],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9945364,0.0022406483,0.00039022163,0.00048543446,0.0020233854,0.0003239339],"domain_scores_gemma":[0.9770907,0.010133363,0.0012795656,0.0006804717,0.0104007535,0.00041524903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015926147,0.00051409734,0.00051147677,0.003620567,0.0018688287,0.0026567427,0.0019057682,0.00046633236,0.0005845463],"category_scores_gemma":[0.041239046,0.00027806807,0.0009848789,0.0062039783,0.00076605624,0.0005504075,0.0011931532,0.0004926137,0.000058111385],"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":[0.00048918725,0.00013806563,0.77510035,0.0005793436,0.0009956342,0.00015084475,0.0023942234,0.09100479,0.00081762153,0.007596146,0.0046134572,0.116120346],"study_design_scores_gemma":[0.000108910106,0.00014037582,0.7009752,0.0002473454,0.00057114416,0.00010453217,0.006614492,0.28058347,0.001132359,0.0021046165,0.0073303883,0.000087145956],"about_ca_topic_score_codex":0.9848418,"about_ca_topic_score_gemma":0.99099284,"teacher_disagreement_score":0.0297921,"about_ca_system_score_codex":0.0297921,"about_ca_system_score_gemma":0.03978575,"threshold_uncertainty_score":0.21615785},"labels":[],"label_agreement":null},{"id":"W2076687865","doi":"10.1186/1476-072x-8-18","title":"Easier surveillance of climate-related health vulnerabilities through a Web-based spatial OLAP application","year":2009,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada; Ouranos; Institut National de Santé Publique du Québec; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Health Canada; Institut National de Santé Publique du Québec","keywords":"Geospatial analysis; Online analytical processing; Geographic information system; Data science; Computer science; Health informatics; Online transaction processing; Decision support system; GIS and public health; Population; Geovisualization; Business intelligence; Data warehouse; Visualization; Database; Public health; Data mining; Geography; Database transaction; Cartography; Transaction processing; Information visualization; Environmental health","score_opus":0.012815617695049799,"score_gpt":0.33384527025738736,"score_spread":0.32102965256233756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076687865","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":[0.11285527,0.0006682174,0.62804943,0.0027801297,0.00025300722,0.0016138672,0.025834551,0.2024405,0.025505034],"genre_scores_gemma":[0.39527255,0.0007220058,0.5699982,0.0010306633,0.00012860868,0.0012387659,0.021185182,0.0032387665,0.0071853725],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990109,0.00023110434,0.00012553559,0.00022612594,0.00033787024,0.000068437876],"domain_scores_gemma":[0.9962011,0.0019626622,0.00030249433,0.00054639985,0.0006606145,0.0003266803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020077233,0.00088883634,0.00046912202,0.0028397092,0.00054469274,0.002549732,0.001236266,0.0009849726,0.0070665814],"category_scores_gemma":[0.004341479,0.0003668736,0.0006900629,0.002199116,0.0003248639,0.0023539392,0.002764338,0.0009561199,0.002275548],"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":[0.0021752652,0.0018213472,0.056993674,0.0032112913,0.000957129,0.005350726,0.006592036,0.038322058,0.05370224,0.015831044,0.13768025,0.67736286],"study_design_scores_gemma":[0.00039697462,0.00037971165,0.05064788,0.00079153694,0.00041020007,0.002624218,0.0036594972,0.604068,0.04491814,0.033564977,0.2580895,0.00044942537],"about_ca_topic_score_codex":0.0028506347,"about_ca_topic_score_gemma":0.002515905,"teacher_disagreement_score":0.0070665814,"about_ca_system_score_codex":0.00041823057,"about_ca_system_score_gemma":0.00083156076,"threshold_uncertainty_score":0.023640037},"labels":[],"label_agreement":null},{"id":"W2077638363","doi":"10.3109/15563650.2012.731512","title":"What the Internet can and cannot tell us about emerging toxicologic outbreaks","year":2012,"lang":"en","type":"letter","venue":"Clinical Toxicology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Health Science Centre; Institute for Clinical Evaluative Sciences; University of Toronto","funders":"","keywords":"Outbreak; The Internet; Internet privacy; Medicine; Medical emergency; Environmental health; Virology; World Wide Web; Computer science","score_opus":0.06688611363653016,"score_gpt":0.375533259953514,"score_spread":0.30864714631698387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077638363","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004625085,0.00089367444,0.00006031675,0.9938391,0.0028681948,0.000005467459,0.000019333054,0.000013213629,0.0018381726],"genre_scores_gemma":[0.005312113,0.0019076373,0.0002369839,0.97520864,0.014791049,0.0000124050775,0.000021572003,0.000013739657,0.002495936],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9965377,0.0016500112,0.00047424453,0.0002899344,0.00072762195,0.00032050023],"domain_scores_gemma":[0.9856595,0.008517757,0.0011176538,0.0004094101,0.0025419411,0.001753705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045736837,0.0006580449,0.000921611,0.00085956627,0.0037867618,0.004055079,0.0013582277,0.037906777,0.0046780086],"category_scores_gemma":[0.031181501,0.00062212854,0.0008847728,0.00081907737,0.0025575298,0.007226189,0.0014483092,0.038113967,0.0040762248],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006494259,0.00007262912,0.0026024473,0.00009287855,0.00002825001,0.005488695,0.0006110586,0.00007791602,0.0002944631,0.0025569363,0.96693355,0.0211763],"study_design_scores_gemma":[0.00018577512,0.00020370218,0.004892822,0.0010666805,0.000076286546,0.016077852,0.0038889037,0.0011839793,0.00044337576,0.019425867,0.95241505,0.00013973181],"about_ca_topic_score_codex":0.009703641,"about_ca_topic_score_gemma":0.012903462,"teacher_disagreement_score":0.037906777,"about_ca_system_score_codex":0.0040820152,"about_ca_system_score_gemma":0.0031793267,"threshold_uncertainty_score":0.02961719},"labels":[],"label_agreement":null},{"id":"W2078419353","doi":"10.1371/currents.rrn1251","title":"Applying a New Model for Sharing Population Health Data to National Syndromic Influenza Surveillance: DiSTRIBuTE Project Proof of Concept, 2006 to 2009","year":2011,"lang":"en","type":"article","venue":"PLoS Currents","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Institut National de Santé Publique du Québec; McGill University Health Centre","funders":"","keywords":"Disease surveillance; Health department; Data sharing; Flexibility (engineering); Medicine; Medical emergency; Computer science; Environmental health; Public health; Nursing","score_opus":0.26734702114687625,"score_gpt":0.4064758448041718,"score_spread":0.13912882365729556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078419353","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":[0.062457997,0.00017231684,0.9266218,0.0028778366,0.00025328185,0.000983633,0.00085149286,0.00061995065,0.0051617366],"genre_scores_gemma":[0.63285094,0.00027807913,0.35549918,0.0006539963,0.00018964885,0.002338752,0.00092759455,0.00013486853,0.0071268682],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99264777,0.004253122,0.00026402855,0.0013428926,0.0010481124,0.00044420687],"domain_scores_gemma":[0.9838763,0.011075766,0.00087057013,0.0012795058,0.0022358284,0.0006620223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020718308,0.0010888734,0.0013508487,0.00074168923,0.0008419268,0.0029984615,0.004550756,0.0022183624,0.0050956854],"category_scores_gemma":[0.03227602,0.0009257072,0.0016914787,0.00095585984,0.0017548123,0.005455092,0.0035829537,0.0031763222,0.0006142609],"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":[0.00087153167,0.0005280942,0.005881153,0.00019505805,0.00018426892,0.00024860405,0.00055288395,0.8542409,0.0009165955,0.08291321,0.0054760217,0.047991715],"study_design_scores_gemma":[0.00012594704,0.00013030342,0.0002181433,0.000010884937,0.000022778468,0.000030150877,0.000039816925,0.9867567,0.00021194977,0.01115511,0.0012840682,0.000014214571],"about_ca_topic_score_codex":0.015950685,"about_ca_topic_score_gemma":0.006560165,"teacher_disagreement_score":0.020718308,"about_ca_system_score_codex":0.0033944033,"about_ca_system_score_gemma":0.0049457345,"threshold_uncertainty_score":0.109570205},"labels":[],"label_agreement":null},{"id":"W2079245179","doi":"10.1002/asi.22737","title":"Modeling the relationship between an emerging infectious disease epidemic and the body of scientific literature associated with it: The case of <scp>HIV</scp>/<scp>AIDS</scp> in the <scp>U</scp>nited <scp>S</scp>tates","year":2012,"lang":"en","type":"article","venue":"Journal of the American Society for Information Science and Technology","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"U.S. National Library of Medicine; Centers for Disease Control and Prevention; Public Health Agency of Canada","keywords":"Scientific literature; Human immunodeficiency virus (HIV); Public health; Disease; Medicine; Demography; Gerontology; Family medicine; Internal medicine; Sociology; Pathology; Biology","score_opus":0.022365670023787018,"score_gpt":0.30461583486005356,"score_spread":0.2822501648362665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079245179","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9741256,0.0018660468,0.010855708,0.005346693,0.000046839003,0.00020423754,0.0010223318,0.00008223418,0.0064502656],"genre_scores_gemma":[0.98701584,0.0011808361,0.009577932,0.000111157235,0.000030276693,0.00016134078,0.0005808287,0.000019379668,0.0013223496],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99581224,0.0026999868,0.00031125374,0.0004958381,0.00039358283,0.0002871651],"domain_scores_gemma":[0.9015625,0.08429141,0.009350907,0.0012024959,0.0024128174,0.0011798237],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.012590455,0.00052342087,0.000601854,0.008498352,0.0013124164,0.005849184,0.0015110285,0.0018142487,0.0028712214],"category_scores_gemma":[0.053624913,0.0006598416,0.0012541285,0.008763401,0.0015897456,0.0040999544,0.002679516,0.001568849,0.000452431],"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":[0.00075047085,0.00097176,0.6895936,0.00093191146,0.0009906692,0.0018423239,0.009049489,0.20047079,0.000804872,0.053597484,0.0022618868,0.03873474],"study_design_scores_gemma":[0.00012732935,0.000558239,0.12697273,0.00044776374,0.00058598985,0.00054007606,0.013162781,0.8172381,0.0004836305,0.033054095,0.00671531,0.00011399935],"about_ca_topic_score_codex":0.038148098,"about_ca_topic_score_gemma":0.0302556,"teacher_disagreement_score":0.9915016,"about_ca_system_score_codex":0.004655141,"about_ca_system_score_gemma":0.0063113254,"threshold_uncertainty_score":0.075852096},"labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"agree"},{"id":"W2079254979","doi":"10.3390/ijgi4010367","title":"An Examination of Three Spatial Event Cluster Detection Methods","year":2015,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; Alberta Innovates","keywords":"Cluster (spacecraft); Poisson distribution; Cluster analysis; Scan statistic; Event (particle physics); Disease; Statistics; Disease surveillance; Type I and type II errors; Spatial epidemiology; Hypergeometric distribution; Geography; Statistical power; Computer science; Data mining; Cartography; Demography; Medicine; Epidemiology; Mathematics; Pathology","score_opus":0.020412987211899193,"score_gpt":0.3562068041865545,"score_spread":0.33579381697465527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079254979","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":[0.015743634,0.0010981099,0.97837,0.0013153952,0.00013070366,0.00056241755,0.00028733467,0.000323325,0.0021690696],"genre_scores_gemma":[0.16351284,0.0009586067,0.8322475,0.0004326263,0.00013186343,0.0008936578,0.0005002178,0.000106062216,0.0012165898],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9418209,0.036993083,0.003608824,0.004758254,0.012112927,0.0007059612],"domain_scores_gemma":[0.72106946,0.23757349,0.008507593,0.012194492,0.019618127,0.0010367116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07743044,0.0012324073,0.0015710951,0.005818688,0.0016531652,0.003865456,0.004889128,0.002639017,0.0031039242],"category_scores_gemma":[0.1906357,0.0008619143,0.0026928908,0.005910424,0.0026857161,0.0048451014,0.00434529,0.002638859,0.00051320775],"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":[0.0007768465,0.00046393133,0.080974996,0.0014846822,0.0013520867,0.0002632154,0.002213244,0.11970194,0.00094054616,0.2282507,0.006723335,0.5568544],"study_design_scores_gemma":[0.00022462901,0.00041659025,0.01421072,0.0003791014,0.000265978,0.0006842147,0.0010416906,0.8694266,0.0018837035,0.099557474,0.011698179,0.00021110558],"about_ca_topic_score_codex":0.011079681,"about_ca_topic_score_gemma":0.009748566,"teacher_disagreement_score":0.07743044,"about_ca_system_score_codex":0.0033690503,"about_ca_system_score_gemma":0.004686778,"threshold_uncertainty_score":0.4094962},"labels":[],"label_agreement":null},{"id":"W2079785438","doi":"10.1089/bsp.2007.0056","title":"Syndromic Surveillance for Influenzalike Illness","year":2008,"lang":"en","type":"article","venue":"Biosecurity and Bioterrorism Biodefense Strategy Practice and Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Public Health; Public Health Ontario; Institute of Population and Public Health; University of Ottawa; Public Health Agency of Canada","funders":"Public Health Agency of Canada","keywords":"Medicine","score_opus":0.030826840588608958,"score_gpt":0.31104822436910684,"score_spread":0.2802213837804979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079785438","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":[0.9479935,0.0034632578,0.0043700575,0.0010783485,0.00010946523,0.0004981572,0.03179273,0.00033238874,0.010362118],"genre_scores_gemma":[0.980649,0.0022538947,0.0059841992,0.0003087902,0.00004497142,0.00019060021,0.0094646765,0.0000075685384,0.0010962106],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999495,0.00011856137,0.000105880856,0.00006688797,0.00015712719,0.000056418437],"domain_scores_gemma":[0.9983784,0.00013831252,0.0005622643,0.00006704993,0.0006393902,0.00021462917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000676771,0.0001857992,0.00021000521,0.002069563,0.0003421781,0.0003604457,0.0003478273,0.00014548643,0.0007385457],"category_scores_gemma":[0.0016124194,0.00008176815,0.00017124091,0.0017627362,0.00016722424,0.00016897082,0.0004554991,0.00018413994,0.00013348059],"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":[0.00007304831,0.000029870233,0.96491677,0.00016005493,0.000042660624,0.00015882165,0.00044780277,0.00023871052,0.0012415838,0.00022964407,0.004274323,0.028186742],"study_design_scores_gemma":[0.000016631879,0.00015477702,0.9875293,0.00013143537,0.00005428704,0.00063450646,0.00074709463,0.0012953785,0.0015422971,0.00011903017,0.0077619418,0.0000132757805],"about_ca_topic_score_codex":0.19094248,"about_ca_topic_score_gemma":0.26341906,"teacher_disagreement_score":0.19094248,"about_ca_system_score_codex":0.0016419597,"about_ca_system_score_gemma":0.0032806762,"threshold_uncertainty_score":0.3796621},"labels":[],"label_agreement":null},{"id":"W2080381308","doi":"10.1016/j.prevetmed.2011.05.004","title":"Veterinary syndromic surveillance: Current initiatives and potential for development","year":2011,"lang":"en","type":"review","venue":"Preventive Veterinary Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":153,"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 Prince Edward Island","funders":"Ministry of Agriculture, Food and Rural Affairs; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Veterinary public health; Disease surveillance; Public health surveillance; Animal health; Public health; Population; One Health; Data science; Grey literature; Medicine; Risk analysis (engineering); Computer science; Environmental health; Veterinary medicine; MEDLINE; Pathology; Political science","score_opus":0.1437555848130295,"score_gpt":0.4120846947216823,"score_spread":0.2683291099086528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080381308","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":[0.00012352693,0.99737215,0.00039358932,0.0011093627,0.00027030497,0.000009937275,0.000034103483,0.000014676106,0.0006723304],"genre_scores_gemma":[0.0014006671,0.99639064,0.0010817538,0.0005917244,0.00024341766,0.000010389093,0.00005354341,0.000002156452,0.00022575664],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986708,0.00033238612,0.00026498362,0.00020693193,0.00043327291,0.000091690024],"domain_scores_gemma":[0.98776746,0.0075281262,0.0014962117,0.00015809304,0.0025263592,0.00052378786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060974034,0.001214823,0.0019933376,0.00392707,0.00030678546,0.0022134169,0.0015045854,0.0017865848,0.0034431217],"category_scores_gemma":[0.0081558395,0.000438305,0.0009940123,0.0036907468,0.0009726311,0.0025710466,0.0014118616,0.002363885,0.00089665817],"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":[0.00004787395,0.00005815093,0.00080032495,0.022860805,0.00009856615,0.000085425556,0.000071188515,0.00035921508,0.0003739236,0.0028174056,0.013290491,0.95913655],"study_design_scores_gemma":[0.000043837535,0.00023914644,0.0046927496,0.05688914,0.0006634084,0.0011210877,0.00049738435,0.0007231611,0.00066370296,0.0034024639,0.9310034,0.000060409202],"about_ca_topic_score_codex":0.004286738,"about_ca_topic_score_gemma":0.009447479,"teacher_disagreement_score":0.0060974034,"about_ca_system_score_codex":0.0021387902,"about_ca_system_score_gemma":0.008368585,"threshold_uncertainty_score":0.03224653},"labels":[],"label_agreement":null},{"id":"W2081937562","doi":"10.1186/1472-6963-11-s2-s14","title":"Population weighted raster maps can communicate findings of social audits: examples from three continents","year":2011,"lang":"en","type":"article","venue":"BMC Health Services Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Energy Training","funders":"Department for International Development; Organisation de Coopération et de Développement Économiques; European Commission; International Development Research Centre; World Bank Group","keywords":"Health informatics; Nursing research; Health administration; Audit; Medicine; Public health; Population health; Population; Nursing; Environmental health; Business; Accounting","score_opus":0.12756650317757293,"score_gpt":0.4010832585629959,"score_spread":0.27351675538542297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081937562","genre_codex":"other","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":[0.2291689,0.0060819485,0.13942698,0.024581969,0.0019955516,0.0022039588,0.039898198,0.0075356807,0.5491069],"genre_scores_gemma":[0.7522766,0.006568544,0.20069988,0.0014706885,0.00039655546,0.002098723,0.011239592,0.0015652307,0.02368416],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9961641,0.0024585852,0.00025552843,0.00011819004,0.0008292437,0.00017437096],"domain_scores_gemma":[0.9853695,0.009354462,0.000930154,0.0016034893,0.002477028,0.00026533214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048796325,0.00061347045,0.00021801454,0.006517378,0.0010805678,0.0019006875,0.0009499957,0.0008022395,0.015067873],"category_scores_gemma":[0.016006293,0.00028462295,0.00052911724,0.012466526,0.0012268353,0.002132151,0.0028126596,0.00078250945,0.0016005542],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040874325,0.00010683636,0.04351872,0.0033233727,0.00013606298,0.0034251309,0.06963306,0.0061518066,0.0011849748,0.06648597,0.2557773,0.549848],"study_design_scores_gemma":[0.000052088108,0.00010475499,0.054917708,0.001392118,0.000067291854,0.0013528032,0.044244576,0.0041921143,0.00091607595,0.019811062,0.87281525,0.00013412892],"about_ca_topic_score_codex":0.012069405,"about_ca_topic_score_gemma":0.014819586,"teacher_disagreement_score":0.015067873,"about_ca_system_score_codex":0.0012000073,"about_ca_system_score_gemma":0.0016459438,"threshold_uncertainty_score":0.050407052},"labels":[],"label_agreement":null},{"id":"W2083097771","doi":"10.1007/s12687-011-0046-0","title":"Newborn screening education on the internet: a content analysis of North American newborn screening program websites","year":2011,"lang":"en","type":"article","venue":"Journal of Community Genetics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Newborn screening; The Internet; Public health; Medicine; Medical education; Content analysis; Family medicine; World Wide Web; Pediatrics; Nursing; Computer science; Sociology","score_opus":0.1367791916871182,"score_gpt":0.3377305917988596,"score_spread":0.20095140011174142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083097771","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":[0.9956204,0.00023024665,0.00013507777,0.00018786092,0.0000057001266,0.00018966522,0.0011811038,0.00001243878,0.002437477],"genre_scores_gemma":[0.9947582,0.00048011393,0.0006526642,0.00017266933,0.000016878492,0.00018232162,0.00270873,0.000029779396,0.0009987495],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9960329,0.0014804155,0.00060088397,0.00025465508,0.0013149757,0.0003160595],"domain_scores_gemma":[0.91736424,0.054225463,0.010036903,0.0011251678,0.0146500375,0.002598105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004301828,0.00021120803,0.00046205227,0.008821614,0.0009178293,0.0019577064,0.0005398163,0.0006164314,0.0027216324],"category_scores_gemma":[0.0418792,0.00024163931,0.0006138579,0.009062322,0.00055887265,0.0019398107,0.0015589101,0.00056106976,0.00032228316],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004814002,0.000925379,0.94224167,0.000552569,0.00014942652,0.00024350757,0.013446848,0.00015150732,0.0007107706,0.00025149932,0.0016055307,0.03923989],"study_design_scores_gemma":[0.000014401794,0.00013810062,0.98515517,0.00019653377,0.00013401211,0.000114252405,0.011392182,0.0007338064,0.00027410864,0.0000739305,0.0017587671,0.000014611412],"about_ca_topic_score_codex":0.01201812,"about_ca_topic_score_gemma":0.017785845,"teacher_disagreement_score":0.01201812,"about_ca_system_score_codex":0.0019285062,"about_ca_system_score_gemma":0.0022651313,"threshold_uncertainty_score":0.023896337},"labels":[],"label_agreement":null},{"id":"W2084400560","doi":"10.1371/journal.pone.0039915","title":"A Protocol for the Secure Linking of Registries for HPV Surveillance","year":2012,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada; Institute for Clinical Evaluative Sciences; York University; Children's Hospital of Eastern Ontario; University of Ottawa","funders":"U.S. National Library of Medicine; Canadian Institutes of Health Research; Canada Research Chairs; Ontario Institute for Cancer Research; National Institutes of Health; National Science Foundation","keywords":"Computer science; Protocol (science); Context (archaeology); Confidence interval; Contingency table; Matching (statistics); Hash table; Medicine; Data mining; Statistics; Hash function; Computer security; Mathematics","score_opus":0.11693766928223534,"score_gpt":0.3369096023278986,"score_spread":0.2199719330456633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084400560","genre_codex":"methods","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"protocol","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063372245,0.00010191992,0.9705025,0.00091675116,0.00023172799,0.008771848,0.001899998,0.003836759,0.0074013034],"genre_scores_gemma":[0.12795739,0.00043482994,0.8255178,0.0010422382,0.0002962346,0.023964858,0.007549337,0.000770134,0.012467192],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9716404,0.014907267,0.004201182,0.0023102844,0.005737056,0.0012036805],"domain_scores_gemma":[0.95677805,0.01919734,0.002755867,0.013549545,0.006638027,0.0010812605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023742272,0.0008126589,0.0008492271,0.0015464011,0.002013016,0.0030222423,0.0019262286,0.0025827547,0.018448705],"category_scores_gemma":[0.039430454,0.0012442939,0.0010293307,0.0016586952,0.0021312018,0.0043820473,0.0072941477,0.0034323758,0.012120666],"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":[0.003930114,0.0010362761,0.0050369,0.0017149344,0.00024728474,0.0026092434,0.0054822345,0.023950135,0.07223611,0.3984184,0.09743444,0.38790396],"study_design_scores_gemma":[0.0016071723,0.002537658,0.0044195033,0.00075591914,0.00019564341,0.0031229118,0.0012540437,0.12545243,0.09697716,0.14380114,0.61932874,0.00054765673],"about_ca_topic_score_codex":0.001128704,"about_ca_topic_score_gemma":0.00064328575,"teacher_disagreement_score":0.023742272,"about_ca_system_score_codex":0.001916087,"about_ca_system_score_gemma":0.006645584,"threshold_uncertainty_score":0.12556267},"labels":[],"label_agreement":null},{"id":"W2084708041","doi":"10.1159/000365761","title":"Application of Three Focused Cluster Detection Methods to Study Geographic Variation in the Incidence of Multiple Sclerosis in Manitoba, Canada","year":2014,"lang":"en","type":"article","venue":"Neuroepidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba","funders":"Multiple Sclerosis Society; Multiple Sclerosis Society of Canada; Manitoba Health Research Council; Health Sciences Centre Foundation","keywords":"Scan statistic; Incidence (geometry); Cluster (spacecraft); Medicine; Confounding; Geographic variation; Demography; Population; Socioeconomic status; Statistic; Cartography; Geography; Environmental health; Statistics; Pathology","score_opus":0.05205174278524908,"score_gpt":0.3113736947226779,"score_spread":0.2593219519374288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084708041","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":[0.89865613,0.0010932742,0.08817332,0.0007289913,0.000047294732,0.0011377645,0.0071707466,0.0004364974,0.00255605],"genre_scores_gemma":[0.8848143,0.000288993,0.108938545,0.000100760255,0.000010247625,0.0005297339,0.0043727187,0.00003450269,0.0009102909],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99734277,0.0010931939,0.00021750625,0.0005597344,0.00054043083,0.00024633622],"domain_scores_gemma":[0.9923361,0.002664481,0.00067636976,0.0004565087,0.0034689296,0.00039758577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049986923,0.00074312586,0.0006065862,0.0040985416,0.0015142859,0.0011962621,0.0018524553,0.0004351452,0.00082184974],"category_scores_gemma":[0.020606697,0.0003158745,0.001093105,0.0049084392,0.00056820724,0.00021699586,0.0013814784,0.00048899685,0.00008540716],"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":[0.00028136422,0.00008951945,0.8662329,0.0002489285,0.001143359,0.00019458478,0.0016651554,0.031868145,0.001085136,0.002208075,0.0043471493,0.09063567],"study_design_scores_gemma":[0.00012311272,0.00011292395,0.7681983,0.00010882868,0.0004243852,0.00015877759,0.0026381176,0.22084494,0.0009257605,0.002184974,0.004183107,0.00009667635],"about_ca_topic_score_codex":0.9456008,"about_ca_topic_score_gemma":0.9441029,"teacher_disagreement_score":0.054399192,"about_ca_system_score_codex":0.011406412,"about_ca_system_score_gemma":0.026779285,"threshold_uncertainty_score":0.109439075},"labels":[],"label_agreement":null},{"id":"W2086022505","doi":"10.5210/ojphi.v5i1.4574","title":"Bayesian Contact Tracing for Communicable Respiratory Disease","year":2013,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Contact tracing; Computer science; Communicable disease; Bayesian probability; Population; Probabilistic logic; Dynamic Bayesian network; Pandemic; Tracing; Public health; Data science; Artificial intelligence; Data mining; Machine learning; Coronavirus disease 2019 (COVID-19); Medicine; Disease; Infectious disease (medical specialty); Environmental health","score_opus":0.09570321424380361,"score_gpt":0.3745769563889753,"score_spread":0.2788737421451717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086022505","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":[0.022449296,0.00045338267,0.9740795,0.00046616403,0.000022136772,0.00005831595,0.00037228165,0.0008379773,0.0012609436],"genre_scores_gemma":[0.74985826,0.000668065,0.245418,0.00019283926,0.000067989335,0.00017196307,0.0012037832,0.0001037023,0.002315378],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861825,0.0005353119,0.000078563164,0.00028210963,0.00041624287,0.00006948359],"domain_scores_gemma":[0.9943224,0.0044095544,0.00045224815,0.00030785473,0.00039899437,0.00010903115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019382224,0.00044452804,0.00069419364,0.0017346028,0.00061875914,0.0009443932,0.0010161279,0.0009937927,0.0015049041],"category_scores_gemma":[0.0175583,0.00049863855,0.0006051418,0.0010525801,0.00046531728,0.001389497,0.0011314395,0.0009275404,0.00036239155],"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":[0.00023187058,0.00020294712,0.032205448,0.00024625481,0.0001849383,0.00029173368,0.00040389405,0.6514416,0.0036204015,0.04581462,0.004864559,0.2604918],"study_design_scores_gemma":[0.000007197427,0.0000111687805,0.0011558569,0.000010918403,0.000009826483,0.000048128608,0.000012801271,0.981278,0.0005279954,0.015788304,0.0011420961,0.000007673379],"about_ca_topic_score_codex":0.014825917,"about_ca_topic_score_gemma":0.014912422,"teacher_disagreement_score":0.014825917,"about_ca_system_score_codex":0.0011204509,"about_ca_system_score_gemma":0.0011945713,"threshold_uncertainty_score":0.029479206},"labels":[],"label_agreement":null},{"id":"W2088488428","doi":"10.3414/me11-01-0001","title":"Multidimensional Point Transform for Public Health Practice","year":2011,"lang":"en","type":"article","venue":"Methods of Information in Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Public Health Agency; CHEO Research Institute","keywords":"Point (geometry); Public health; Medicine; Mathematics; Nursing","score_opus":0.12401220652287526,"score_gpt":0.4425657106612557,"score_spread":0.31855350413838046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088488428","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":[0.0022758443,0.0006019119,0.99086785,0.0015344509,0.00013611562,0.00011106382,0.00024803358,0.0003767603,0.003847935],"genre_scores_gemma":[0.11916909,0.0013260245,0.8738845,0.0005274924,0.00036792675,0.0007457064,0.0009245383,0.0002081772,0.002846486],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956697,0.0023414765,0.00027544066,0.0006068905,0.0009860246,0.00012046889],"domain_scores_gemma":[0.98937154,0.0068463976,0.00071559596,0.0013690569,0.0014910104,0.00020649079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00724592,0.0009916939,0.0009049286,0.0022223992,0.0007326692,0.0029915813,0.0015896746,0.0018239357,0.009949149],"category_scores_gemma":[0.033893965,0.00041997907,0.001491447,0.002733364,0.0023695761,0.0028699383,0.003798775,0.0035173933,0.0032047308],"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":[0.00018011488,0.00012453726,0.005776415,0.0006122046,0.00013516303,0.0003673503,0.00057071197,0.22934632,0.0018627406,0.3727026,0.019113304,0.36920857],"study_design_scores_gemma":[0.000037776146,0.00010036212,0.0009063219,0.00021009568,0.00002020795,0.00028306508,0.00026959032,0.62532675,0.0013515243,0.34216535,0.029294053,0.000034903922],"about_ca_topic_score_codex":0.0027238673,"about_ca_topic_score_gemma":0.001456468,"teacher_disagreement_score":0.009949149,"about_ca_system_score_codex":0.0018724238,"about_ca_system_score_gemma":0.0025627194,"threshold_uncertainty_score":0.03832048},"labels":[],"label_agreement":null},{"id":"W2088926111","doi":"10.1126/science.311.5765.1227d","title":"Disease Alert Network Proposed","year":2006,"lang":"en","type":"article","venue":"Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Disease; Computer science; Computational biology; Biology; Medicine; Pathology","score_opus":0.007612877762806912,"score_gpt":0.2594860301595336,"score_spread":0.2518731523967267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088926111","genre_codex":"other","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":[0.002649131,0.0031349966,0.024203168,0.17392643,0.04606348,0.0014931891,0.02563862,0.009422623,0.7134683],"genre_scores_gemma":[0.025605928,0.0050285067,0.020433983,0.042187713,0.008701951,0.0015361739,0.046624567,0.0008456429,0.84903544],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99827766,0.00029353012,0.00011434114,0.00038900293,0.00064770825,0.00027767426],"domain_scores_gemma":[0.9945808,0.0005967444,0.00014991946,0.00038852514,0.0023984716,0.0018855897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031231882,0.00084362365,0.00041457766,0.002112037,0.0018488264,0.0049990485,0.001579097,0.0038179834,0.21374],"category_scores_gemma":[0.008802734,0.00031764444,0.0005822201,0.0010265455,0.00045202364,0.003948181,0.0029493158,0.0028758885,0.12333002],"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":[0.000040711486,0.000027676271,0.00036701642,0.00005833253,0.0000043482187,0.000051965464,0.000024913834,0.00009199181,0.00017194597,0.009340457,0.94286406,0.046956558],"study_design_scores_gemma":[0.000026653206,0.0000258877,0.0003310922,0.00003876872,0.000006250996,0.000070321235,0.000043698397,0.0003310167,0.00011799242,0.002031429,0.9969675,0.000009358904],"about_ca_topic_score_codex":0.0046988027,"about_ca_topic_score_gemma":0.0066078706,"teacher_disagreement_score":0.21374,"about_ca_system_score_codex":0.001985888,"about_ca_system_score_gemma":0.004693857,"threshold_uncertainty_score":0.71503156},"labels":[],"label_agreement":null},{"id":"W2090118413","doi":"10.1186/1476-072x-9-21","title":"Feasibility and utility of mapping disease risk at the neighbourhood level within a Canadian public health unit: an ecological study","year":2010,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Cancer Care Ontario; University of Toronto","funders":"Natural Resources Canada; Centers for Disease Control and Prevention; Imperial College London; Cancer Care Ontario","keywords":"Health geography; Demography; Cancer registry; Geography; Public health; Neighbourhood (mathematics); Census; Population; Socioeconomic status; Geocoding; Medicine; Cartography; Environmental health; Health policy; Mathematics; Pathology","score_opus":0.14611220168819958,"score_gpt":0.38335875085905335,"score_spread":0.23724654917085378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090118413","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":[0.98898834,0.00047831342,0.0028447176,0.00029575473,0.000014900448,0.00037437258,0.002352722,0.000024483748,0.0046264194],"genre_scores_gemma":[0.9935382,0.00021957923,0.004826281,0.00007068195,0.000006244972,0.00018523677,0.0007277255,0.000005718643,0.00042038446],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.997707,0.0006327115,0.00010028447,0.00044949312,0.00068084005,0.00042974047],"domain_scores_gemma":[0.99551624,0.00065467,0.00051507173,0.00037793544,0.0023680232,0.0005681131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004159916,0.00044368263,0.00037837756,0.002490867,0.0036463751,0.0014382087,0.0020055205,0.0003960323,0.000945401],"category_scores_gemma":[0.01255603,0.00027497538,0.00059562357,0.004164359,0.00088709086,0.0005822695,0.0018441483,0.00029861112,0.00013576058],"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":[0.00011015752,0.00006170532,0.9778861,0.00009792307,0.00011007424,0.00010483403,0.002173173,0.0008748325,0.00038932927,0.00060657924,0.00070759293,0.016877783],"study_design_scores_gemma":[0.00001764999,0.00009253377,0.9917046,0.00004499859,0.0000717877,0.00006700097,0.00292911,0.0025746832,0.00013612093,0.00020675221,0.0021352305,0.000019511806],"about_ca_topic_score_codex":0.9834765,"about_ca_topic_score_gemma":0.9910347,"teacher_disagreement_score":0.019049866,"about_ca_system_score_codex":0.019049866,"about_ca_system_score_gemma":0.027416728,"threshold_uncertainty_score":0.13821709},"labels":[],"label_agreement":null},{"id":"W2090681413","doi":"10.3129/i10-022","title":"Frequency and seasonal variation of ophthalmology-related internet searches","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Ophthalmology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ophthalmology; Medicine; Glaucoma; Optometry; Sunlight; Population; Macular degeneration; Demography; Environmental health; Optics","score_opus":0.022271430286984285,"score_gpt":0.28171940089547487,"score_spread":0.25944797060849056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090681413","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":[0.9875271,0.00070888863,0.0002045971,0.0002474513,0.000018372692,0.000023680264,0.009156194,0.000040503677,0.00207321],"genre_scores_gemma":[0.99500364,0.0002356848,0.00012155106,0.000044426117,0.000018086364,0.0000119059905,0.003914481,0.0000148419385,0.0006354614],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99852645,0.00028006444,0.0003310592,0.00033014765,0.0003264912,0.0002057672],"domain_scores_gemma":[0.9805746,0.006314025,0.008462832,0.0009971553,0.0027506987,0.00090062374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011030313,0.00014531547,0.0003271589,0.0038623873,0.0002815623,0.0010915196,0.00059947325,0.0005800961,0.0028824732],"category_scores_gemma":[0.01133162,0.0002298196,0.00061295205,0.0047906726,0.0003121756,0.0008896056,0.0006600844,0.00067031157,0.00069905265],"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":[0.00013977432,0.000037490132,0.9932065,0.00005313873,0.0001077405,0.00005673298,0.00032705165,0.00018566176,0.00035723744,0.00010152073,0.0010140332,0.0044130613],"study_design_scores_gemma":[0.0000020313937,0.000018246836,0.9983851,0.000013195011,0.000017855282,0.000119817145,0.0003243942,0.00050773483,0.00006582818,0.00002515706,0.0005159775,0.0000047189938],"about_ca_topic_score_codex":0.029928513,"about_ca_topic_score_gemma":0.037589956,"teacher_disagreement_score":0.029928513,"about_ca_system_score_codex":0.0006193922,"about_ca_system_score_gemma":0.00045048344,"threshold_uncertainty_score":0.05950862},"labels":[],"label_agreement":null},{"id":"W2090855891","doi":"10.1016/j.sste.2013.08.001","title":"Exploring hotspots of pneumococcal pneumonia and potential impacts of ejecta dust exposure following the Christchurch earthquakes","year":2013,"lang":"en","type":"article","venue":"Spatial and Spatio-temporal Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Canterbury District Health Board; World Health Organization","keywords":"Ejecta; Incidence (geometry); Pneumococcal pneumonia; Cluster (spacecraft); Pneumonia; Geography; Geology; Streptococcus pneumoniae","score_opus":0.05348804063947827,"score_gpt":0.28558274139206413,"score_spread":0.23209470075258587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090855891","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":[0.9971909,0.00015396526,0.00023206192,0.0001793121,0.0000047101053,0.000011945393,0.0018609475,0.0000056139843,0.00036049698],"genre_scores_gemma":[0.9986827,0.00010856083,0.0002609273,0.000020255167,0.000004569309,0.000009916835,0.0007740158,0.0000010880678,0.00013811803],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995654,0.00010558842,0.000042117616,0.00009231058,0.00007018347,0.00012440205],"domain_scores_gemma":[0.9984518,0.00036697355,0.00057573535,0.00009434866,0.0002967133,0.00021433178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062145945,0.00020415019,0.00019375629,0.0012837553,0.00038261275,0.00066807936,0.00056575786,0.00041302093,0.0006541265],"category_scores_gemma":[0.002988926,0.00021959777,0.00029937422,0.0018508284,0.00029333564,0.00040707996,0.0009830152,0.00035396093,0.00007215175],"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":[0.000053148204,0.000017767461,0.9937797,0.000031012165,0.000096536154,0.00010194422,0.00029208386,0.0016532717,0.00034750448,0.00014154903,0.00036711927,0.0031182675],"study_design_scores_gemma":[0.0000019528977,0.000017190705,0.9967249,0.000011409031,0.000023000634,0.000036061436,0.00079230143,0.002009138,0.0000832477,0.000042123756,0.00025455997,0.0000041382173],"about_ca_topic_score_codex":0.27908397,"about_ca_topic_score_gemma":0.3764611,"teacher_disagreement_score":0.27908397,"about_ca_system_score_codex":0.0010442397,"about_ca_system_score_gemma":0.0014122718,"threshold_uncertainty_score":0.554919},"labels":[],"label_agreement":null},{"id":"W2095296110","doi":"10.1016/j.ijmedinf.2005.10.006","title":"The ALPHA Project: An architecture for leveraging public health applications","year":2005,"lang":"en","type":"article","venue":"International Journal of Medical Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Public Health Agency of Canada","funders":"China Scholarship Council; Public Health Agency; Public Health Agency of Canada","keywords":"Computer science; Flexibility (engineering); Architecture; Computer security; Reference architecture; Software architecture; Public health; Software; Process management; Software engineering; Risk analysis (engineering); Business; Operating system; Medicine","score_opus":0.05208690025431177,"score_gpt":0.39709860219983073,"score_spread":0.34501170194551894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095296110","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":[0.018189406,0.0005213851,0.7397644,0.0012474965,0.00026437535,0.0006687348,0.0014028256,0.2325626,0.005378831],"genre_scores_gemma":[0.21423902,0.0013858225,0.7416272,0.0017252614,0.00022824253,0.0010690236,0.012302439,0.017081784,0.010341189],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956195,0.0013361851,0.00047159233,0.0009314155,0.0013633559,0.0002780135],"domain_scores_gemma":[0.9889549,0.0040535093,0.0005477279,0.0041167242,0.0013567357,0.00097045995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009838042,0.0017201835,0.0013092073,0.0027638082,0.0009816104,0.00412014,0.004586737,0.0020042325,0.0046364204],"category_scores_gemma":[0.022397725,0.0021666216,0.0016499229,0.0027013922,0.0015416376,0.008043891,0.007281856,0.0031038327,0.00298307],"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":[0.001955031,0.0011832874,0.036284547,0.0014063483,0.0012992375,0.001535235,0.0042375675,0.05066156,0.028999113,0.050699867,0.19020511,0.63153315],"study_design_scores_gemma":[0.00036107437,0.00050296093,0.0074077863,0.00042713917,0.00067219994,0.0009547976,0.00074306363,0.660372,0.040044606,0.079635136,0.2085431,0.00033610605],"about_ca_topic_score_codex":0.007765906,"about_ca_topic_score_gemma":0.0043918486,"teacher_disagreement_score":0.009838042,"about_ca_system_score_codex":0.0011633183,"about_ca_system_score_gemma":0.0034002704,"threshold_uncertainty_score":0.052029192},"labels":[],"label_agreement":null},{"id":"W2095315710","doi":"10.1016/j.envres.2014.07.029","title":"Developing small-area predictions for smoking and obesity prevalence in the United States for use in Environmental Public Health Tracking","year":2014,"lang":"en","type":"article","venue":"Environmental Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Health Canada","funders":"Health Canada; Centers for Disease Control and Prevention","keywords":"Behavioral Risk Factor Surveillance System; Environmental health; Poisson regression; Public health; Statistics; Confounding; Random effects model; Poisson distribution; Lasso (programming language); Obesity; Public health surveillance; Geography; Medicine; Demography; Population; Mathematics; Computer science","score_opus":0.17762371793661155,"score_gpt":0.3694038718864355,"score_spread":0.19178015394982392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095315710","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":[0.8764043,0.00018545301,0.09716636,0.00046922054,0.00004425857,0.00028639933,0.02180408,0.0012549774,0.0023848119],"genre_scores_gemma":[0.9526342,0.00009205111,0.033816222,0.00006824953,0.000016922419,0.0001676405,0.012575502,0.000043589982,0.00058564026],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995523,0.00013907217,0.00003736621,0.00018394871,0.000053931253,0.00003325347],"domain_scores_gemma":[0.99660647,0.0020356213,0.00033103666,0.0003296002,0.0005571581,0.0001401219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024203819,0.0005104355,0.00045903298,0.0013132916,0.0003351996,0.0008904269,0.0009097739,0.00049033057,0.0016399527],"category_scores_gemma":[0.009265049,0.0004441504,0.00068376295,0.001071719,0.00013209059,0.000870134,0.0007171746,0.000557954,0.0006136479],"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":[0.00021088796,0.0003338182,0.8029108,0.00006952922,0.0003247849,0.00008860989,0.0000964734,0.13831809,0.0006832529,0.00090427214,0.005349181,0.050710298],"study_design_scores_gemma":[0.000056506477,0.00008931696,0.1562299,0.000032385196,0.000108795444,0.00004287854,0.0002067475,0.83773136,0.00092993444,0.0032824066,0.0012686864,0.000021090134],"about_ca_topic_score_codex":0.055863243,"about_ca_topic_score_gemma":0.057759907,"teacher_disagreement_score":0.055863243,"about_ca_system_score_codex":0.00050863624,"about_ca_system_score_gemma":0.0012410826,"threshold_uncertainty_score":0.11107612},"labels":[],"label_agreement":null},{"id":"W2095536874","doi":"10.4018/jehmc.2011010105","title":"Affordable System for Rapid Detection and Mitigation of Emerging Diseases","year":2011,"lang":"en","type":"article","venue":"International Journal of E-Health and Medical Communications","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Disease surveillance; Data science; Notifiable disease; Health informatics; Mobile phone; Communicable disease; Digitization; Computer science; Public health; Computer security; Medicine; Business; Telecommunications","score_opus":0.04378892246020336,"score_gpt":0.3527249999680665,"score_spread":0.3089360775078631,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095536874","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":[0.06764712,0.007084839,0.7140162,0.008844022,0.002473557,0.0031810983,0.020896468,0.08541436,0.09044231],"genre_scores_gemma":[0.65594345,0.0043243263,0.2596547,0.004497746,0.0011309084,0.0020432672,0.019390143,0.00078483345,0.0522305],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99830866,0.00033899833,0.00012518512,0.00034883487,0.00074288767,0.00013542232],"domain_scores_gemma":[0.9973748,0.0005804105,0.00031126922,0.00049544766,0.0010230086,0.00021508946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019138195,0.0007242513,0.0006922807,0.002120185,0.000452085,0.0016279744,0.001559838,0.001070925,0.012595506],"category_scores_gemma":[0.0035744742,0.00022772585,0.00032984317,0.0014331193,0.00027020182,0.0017177141,0.0022629146,0.00080275227,0.007852014],"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":[0.0008948932,0.00049475586,0.019065859,0.0009461822,0.00013006023,0.0005702569,0.00060348026,0.0029346042,0.037237946,0.017987281,0.18160452,0.7375302],"study_design_scores_gemma":[0.00030218522,0.0014931381,0.03468481,0.0006329349,0.00033198178,0.0015583559,0.00077344663,0.11722252,0.07070392,0.0156127345,0.7564509,0.00023302478],"about_ca_topic_score_codex":0.0017703477,"about_ca_topic_score_gemma":0.0010814788,"teacher_disagreement_score":0.012595506,"about_ca_system_score_codex":0.00062242063,"about_ca_system_score_gemma":0.00094511156,"threshold_uncertainty_score":0.042136133},"labels":[],"label_agreement":null},{"id":"W2095933166","doi":"10.1136/amiajnl-2012-000847","title":"Self-reported fever and measured temperature in emergency department records used for syndromic surveillance: Table 1","year":2012,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Emergency department; Medicine; Health records; Influenza-like illness; Medical emergency; Medical record; Public health surveillance; Public health; Population; Complaint; Emergency medicine; Environmental health; Health care; Internal medicine; Virology; Psychiatry","score_opus":0.01056919686486339,"score_gpt":0.28550577147552386,"score_spread":0.27493657461066046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095933166","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":[0.6282553,0.003645091,0.0143054,0.00083775463,0.00025416518,0.0019005882,0.33246335,0.0007783449,0.017559975],"genre_scores_gemma":[0.8799317,0.0019847166,0.028838683,0.00039671213,0.00010814748,0.0011444234,0.08574467,0.00006348604,0.0017874335],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99476445,0.0014708416,0.0018466194,0.0005551372,0.0012447753,0.00011814638],"domain_scores_gemma":[0.98329884,0.0066904845,0.005848676,0.0010167431,0.0028638747,0.0002813073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024351683,0.00037930752,0.00056542415,0.0033350235,0.00023525892,0.0011006288,0.00046044745,0.00022767766,0.00447831],"category_scores_gemma":[0.01517385,0.00015037184,0.0004938758,0.0042678756,0.00019576689,0.00055664784,0.000415699,0.00029580787,0.0008723634],"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":[0.00032331306,0.000075548945,0.94083023,0.0010394468,0.00030376448,0.00017749511,0.00027635976,0.00041185808,0.0013756243,0.00033288045,0.0055491882,0.049304258],"study_design_scores_gemma":[0.000042508,0.00030593565,0.9738628,0.00047754328,0.00023210164,0.0013533464,0.00050773827,0.0021472771,0.004070843,0.00024223344,0.01672039,0.00003714862],"about_ca_topic_score_codex":0.0031084158,"about_ca_topic_score_gemma":0.0039247395,"teacher_disagreement_score":0.00447831,"about_ca_system_score_codex":0.0004720496,"about_ca_system_score_gemma":0.0007045131,"threshold_uncertainty_score":0.014981449},"labels":[],"label_agreement":null},{"id":"W2096738650","doi":"10.1239/jap/1331216842","title":"Continuous, Discrete, and Conditional Scan Statistics","year":2012,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":29,"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; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scan statistic; Bernoulli trial; Mathematics; Sufficient statistic; Statistic; Statistics; Conditional probability distribution; Discrete-time stochastic process; Discrete phase-type distribution; Markov chain; Bernoulli's principle; Conditional expectation; Chain rule (probability); Applied mathematics; Probability distribution; Stochastic process; Probability mass function; Markov property; Regular conditional probability; Markov model; Continuous-time stochastic process","score_opus":0.012753173341647458,"score_gpt":0.2705407487586908,"score_spread":0.25778757541704334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096738650","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":[0.04647078,0.0009041207,0.9456495,0.0008599616,0.00007256397,0.00011159487,0.00066476426,0.0003409723,0.0049257553],"genre_scores_gemma":[0.80447227,0.0017087852,0.18468037,0.00033260757,0.00028889618,0.00070955936,0.0012403174,0.0002460234,0.0063212155],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99338704,0.0027751168,0.00030966476,0.0012687213,0.0017968392,0.0004627264],"domain_scores_gemma":[0.911084,0.073074244,0.004800905,0.0061362362,0.003604821,0.001299861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014335945,0.0007012469,0.001339688,0.0030802153,0.00050078076,0.003469352,0.00303611,0.0018244844,0.0065163653],"category_scores_gemma":[0.09640113,0.00069757295,0.001196893,0.0032086677,0.0058480143,0.0058500827,0.0025624144,0.003961143,0.000716919],"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":[0.0001012749,0.000035470977,0.0056834677,0.00017771029,0.000050035025,0.00032820003,0.00022486033,0.087968275,0.00093180063,0.87758386,0.0013448303,0.025570285],"study_design_scores_gemma":[0.00002548286,0.0000679364,0.00247989,0.00010121298,0.000029177485,0.0006095768,0.000101639744,0.46513954,0.000873146,0.5281759,0.0023456095,0.0000508909],"about_ca_topic_score_codex":0.0018311625,"about_ca_topic_score_gemma":0.0014360489,"teacher_disagreement_score":0.014335945,"about_ca_system_score_codex":0.0021037653,"about_ca_system_score_gemma":0.0017490117,"threshold_uncertainty_score":0.07581663},"labels":[],"label_agreement":null},{"id":"W2097129914","doi":"10.1007/s10109-005-0005-6","title":"Identifying insect infestation hot spots: an approach using conditional spatial randomization","year":2005,"lang":"en","type":"article","venue":"Journal of Geographical Systems","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Victoria","funders":"Natural Resources Canada; Government of Canada","keywords":"Infestation; Statistics; Kernel (algebra); Raster graphics; Spots; Spatial ecology; Ecology; Geography; Biology; Mathematics; Cartography; Computer science; Agronomy; Artificial intelligence; Botany","score_opus":0.041135442714317405,"score_gpt":0.30891134919091295,"score_spread":0.26777590647659555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097129914","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":[0.06172045,0.00012948239,0.93606454,0.0002955484,0.000037313086,0.0002672628,0.0003875674,0.00066200085,0.00043577203],"genre_scores_gemma":[0.6878193,0.000107034786,0.3093233,0.00017569019,0.00009358561,0.0005248508,0.00097433943,0.000065607004,0.00091629935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.993008,0.004391548,0.00027653325,0.0013955645,0.0005412523,0.00038714294],"domain_scores_gemma":[0.9609034,0.032612495,0.0017301157,0.0029187715,0.0013519572,0.0004832345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011990907,0.0008626057,0.0027164589,0.0029589296,0.0013121739,0.0014179818,0.0034317672,0.0014908591,0.0028665725],"category_scores_gemma":[0.030394634,0.0008273756,0.0028360889,0.002659764,0.0015512819,0.0017273288,0.0025557165,0.0014709459,0.00026048653],"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":[0.0021690642,0.00097449456,0.079252355,0.00041729223,0.0016853838,0.0006350335,0.0005221085,0.540267,0.004614207,0.101120986,0.0048679,0.26347414],"study_design_scores_gemma":[0.00008907751,0.00008500014,0.0022388364,0.000009981875,0.00009072376,0.00007462933,0.00003214761,0.9765877,0.0004991028,0.019921552,0.0003448067,0.000026409778],"about_ca_topic_score_codex":0.009602266,"about_ca_topic_score_gemma":0.008849387,"teacher_disagreement_score":0.011990907,"about_ca_system_score_codex":0.0011320971,"about_ca_system_score_gemma":0.0029902519,"threshold_uncertainty_score":0.06341475},"labels":[],"label_agreement":null},{"id":"W2097894326","doi":"10.1111/j.1467-8306.2003.09304014_9.x","title":"Statistical Methods in Spatial Epidemiology Spatial Cluster Modelling","year":2003,"lang":"en","type":"article","venue":"Annals of the Association of American Geographers","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Spatial epidemiology; Cluster (spacecraft); Geography; Epidemiology; Spatial analysis; Statistics; Cartography; Computer science; Data science; Mathematics; Medicine","score_opus":0.0519331461808144,"score_gpt":0.4008434855367434,"score_spread":0.348910339355929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097894326","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":[0.00026234495,0.0010807575,0.9958633,0.0008189579,0.00031374695,0.0000790722,0.00027569055,0.0008267566,0.00047935918],"genre_scores_gemma":[0.014064316,0.0028337282,0.9739314,0.0006082248,0.001091324,0.0021111434,0.001243605,0.0011455863,0.002970722],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9618979,0.02982102,0.0022032268,0.001960139,0.0037247683,0.0003929855],"domain_scores_gemma":[0.7676912,0.19983734,0.003899332,0.017867632,0.009627672,0.0010768608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.041217044,0.0026140346,0.0043042554,0.0050315713,0.001383659,0.0050838464,0.0045280643,0.003621856,0.016557463],"category_scores_gemma":[0.17279562,0.0027625456,0.003457566,0.009876791,0.0039979457,0.0043370766,0.004272886,0.01307212,0.009132552],"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":[0.00013257247,0.0002763676,0.004620527,0.0025005382,0.0011582006,0.00035476897,0.0006943003,0.06666373,0.00089133123,0.41171554,0.10896603,0.402026],"study_design_scores_gemma":[0.00018251159,0.00008727056,0.0023313456,0.000625361,0.00025393182,0.0004731914,0.00014784862,0.29280865,0.0010954697,0.6358989,0.065973185,0.00012232209],"about_ca_topic_score_codex":0.005516968,"about_ca_topic_score_gemma":0.0051412573,"teacher_disagreement_score":0.041217044,"about_ca_system_score_codex":0.0019010196,"about_ca_system_score_gemma":0.0064946385,"threshold_uncertainty_score":0.2179792},"labels":[],"label_agreement":null},{"id":"W2097936813","doi":"10.1093/cid/ciu647","title":"Using Clinicians' Search Query Data to Monitor Influenza Epidemics","year":2014,"lang":"en","type":"article","venue":"Clinical Infectious Diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Children's Hospital","funders":"Stanford Bio-X; U.S. National Library of Medicine; Baidu","keywords":"Medicine; Outbreak; Web search query; Data mining; Information retrieval; Database; Computer science; Virology; Search engine","score_opus":0.3011561238516651,"score_gpt":0.514192655922724,"score_spread":0.21303653207105888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097936813","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":[0.9103999,0.004284034,0.016132152,0.0031561279,0.0001843462,0.0006724801,0.053058058,0.0021915832,0.009921324],"genre_scores_gemma":[0.9661469,0.00058685074,0.014825903,0.00042668087,0.00006423758,0.00015198143,0.017250495,0.00003161913,0.000515276],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99533254,0.0020831905,0.00067734387,0.0008367526,0.00091666204,0.00015339778],"domain_scores_gemma":[0.9733027,0.017802611,0.0035551663,0.0015145681,0.0029954263,0.0008294971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046591302,0.00054165756,0.0010195135,0.0041093808,0.00028949935,0.0017181826,0.0008217499,0.0007127115,0.0011766555],"category_scores_gemma":[0.041355453,0.00017497092,0.0004048138,0.0041001122,0.00017887303,0.0012941018,0.00077853585,0.0004518007,0.0006460081],"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":[0.0019951959,0.00040230737,0.84301126,0.0010118647,0.00042975217,0.00024895332,0.00054192264,0.010037103,0.003413064,0.00089205586,0.014179937,0.123836495],"study_design_scores_gemma":[0.0007052464,0.0021669045,0.54913974,0.00043816754,0.0010004087,0.001723483,0.0018756732,0.38601178,0.020943206,0.004596682,0.03116234,0.00023633399],"about_ca_topic_score_codex":0.017529026,"about_ca_topic_score_gemma":0.021150988,"teacher_disagreement_score":0.017529026,"about_ca_system_score_codex":0.0008977353,"about_ca_system_score_gemma":0.0012774281,"threshold_uncertainty_score":0.034853935},"labels":[],"label_agreement":null},{"id":"W2098537850","doi":"10.1093/aje/152.9.889","title":"Use of World Wide Web-based Directories for Tracing Subjects in Epidemiologic Studies","year":2000,"lang":"en","type":"article","venue":"American Journal of Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Directory; The Internet; World Wide Web; Medicine; Tracing; Computer science","score_opus":0.11399952932931645,"score_gpt":0.39149712545437626,"score_spread":0.2774975961250598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098537850","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9356139,0.0035740614,0.022055786,0.0019809161,0.00018840942,0.012453269,0.007058195,0.0006081174,0.016467366],"genre_scores_gemma":[0.91751045,0.0030261907,0.057082523,0.00071169424,0.00020698509,0.015152297,0.0034462793,0.000073911986,0.0027896871],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.8870377,0.0981841,0.0053891875,0.0025161197,0.0057786093,0.0010942667],"domain_scores_gemma":[0.77369165,0.15358028,0.034738924,0.022217734,0.009189474,0.0065819747],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.056152184,0.00042483557,0.000993545,0.005346701,0.001525063,0.0020696947,0.0012654129,0.0010419189,0.00444012],"category_scores_gemma":[0.11501218,0.0007614074,0.00046869472,0.0072333384,0.0010860214,0.0024714565,0.0020872422,0.0007435998,0.0008298074],"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":[0.024743002,0.006599192,0.5174942,0.0016699269,0.0006903176,0.00017246431,0.0037216588,0.0023421426,0.0019518397,0.0022659241,0.010719627,0.4276297],"study_design_scores_gemma":[0.008506532,0.024350472,0.91567826,0.0010434524,0.00091425626,0.00045269847,0.0022779994,0.008792966,0.004312852,0.002077527,0.031215446,0.00037758233],"about_ca_topic_score_codex":0.021243604,"about_ca_topic_score_gemma":0.035599157,"teacher_disagreement_score":0.94384784,"about_ca_system_score_codex":0.0020616495,"about_ca_system_score_gemma":0.0055848993,"threshold_uncertainty_score":0.29696465},"labels":[],"label_agreement":null},{"id":"W2100071619","doi":"10.1002/sim.1021","title":"Spatio‐temporal modelling of rates for the construction of disease maps","year":2002,"lang":"en","type":"review","venue":"Statistics in Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University; University of British Columbia; Providence Health Care","funders":"","keywords":"Popularity; Generalized linear mixed model; Geography; Disease; Computer science; Econometrics; Statistics; Regional science; Medicine; Mathematics","score_opus":0.09536913431964626,"score_gpt":0.3928062166486516,"score_spread":0.29743708232900534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100071619","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":[0.0025545708,0.0619532,0.9222136,0.0019474874,0.00054465444,0.00022707648,0.0019375272,0.00072882953,0.00789305],"genre_scores_gemma":[0.10618394,0.17888695,0.70223314,0.0004508006,0.00054162147,0.0015458602,0.0032418622,0.00035432505,0.006561566],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99710375,0.0016445393,0.0002936878,0.00034087885,0.00055589847,0.000061138715],"domain_scores_gemma":[0.9917841,0.0053190202,0.0010713461,0.00078994466,0.000964157,0.000071377355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056823283,0.0012143314,0.0015746265,0.005310482,0.00042341527,0.0025912516,0.0031405122,0.0015547193,0.0032489887],"category_scores_gemma":[0.019706089,0.001073538,0.0025434012,0.0076885317,0.001012794,0.0022530432,0.0011860969,0.0018609468,0.0025171787],"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":[0.000043372136,0.000062988656,0.008248734,0.0037804896,0.00058712816,0.0002598394,0.000718593,0.17908502,0.0007907698,0.24504898,0.014709019,0.54666513],"study_design_scores_gemma":[0.00004045418,0.00010335341,0.009032641,0.0029645357,0.0005072168,0.001227179,0.00056050264,0.40340266,0.001924182,0.23376267,0.34622374,0.00025086696],"about_ca_topic_score_codex":0.019313283,"about_ca_topic_score_gemma":0.015570215,"teacher_disagreement_score":0.019313283,"about_ca_system_score_codex":0.0025118506,"about_ca_system_score_gemma":0.0022006743,"threshold_uncertainty_score":0.038401723},"labels":[],"label_agreement":null},{"id":"W2100589324","doi":"10.3414/me0546","title":"Combining Hashing and Enciphering Algorithms for Epidemiological Analysis of Gathered Data","year":2008,"lang":"en","type":"article","venue":"Methods of Information in Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Encryption; Computer science; Hash function; Field (mathematics); Data mining; Scheme (mathematics); Algorithm; Theoretical computer science; Information retrieval; Mathematics; Computer security","score_opus":0.2265448527286092,"score_gpt":0.4745068158063432,"score_spread":0.247961963077734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100589324","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":[0.020568559,0.0007270674,0.9769745,0.00018373819,0.00007858328,0.00016130164,0.00012578332,0.0006627566,0.0005176821],"genre_scores_gemma":[0.18270223,0.0006514173,0.8148851,0.00007950325,0.00011686632,0.00023220685,0.0003513131,0.000054550354,0.0009269059],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9925721,0.0034407277,0.00090166926,0.00076080096,0.002091974,0.00023276365],"domain_scores_gemma":[0.98073155,0.010714168,0.0019996858,0.0046017645,0.0016593256,0.00029350378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008177813,0.00091016054,0.0012672492,0.004121983,0.0006502663,0.0017762564,0.0013569775,0.0010290821,0.001391307],"category_scores_gemma":[0.023692956,0.00057651586,0.00082195,0.0035443404,0.0010692796,0.0040754722,0.0029181119,0.0012007607,0.0010530736],"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":[0.0007517243,0.00023028758,0.011018794,0.00054237025,0.00042326632,0.000293651,0.0006363037,0.022927413,0.026223065,0.02237429,0.0020133825,0.9125655],"study_design_scores_gemma":[0.00030210032,0.0010334763,0.012422336,0.0002829746,0.000432088,0.004251253,0.00085670094,0.76253825,0.103306636,0.0954146,0.018818103,0.00034148947],"about_ca_topic_score_codex":0.00042092538,"about_ca_topic_score_gemma":0.00043212227,"teacher_disagreement_score":0.008177813,"about_ca_system_score_codex":0.0006577258,"about_ca_system_score_gemma":0.0009993579,"threshold_uncertainty_score":0.04324895},"labels":[],"label_agreement":null},{"id":"W2101838573","doi":"10.1016/j.ijid.2010.02.582","title":"GLaDMap: Global Laboratory Directory Map","year":2010,"lang":"en","type":"article","venue":"International Journal of Infectious Diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Agency of Canada","funders":"","keywords":"Directory; Upload; Preparedness; Global network; International Health Regulations; Public health; Global health; Business; World Wide Web; Computer science; Public relations; Political science; Telecommunications; Medicine; Infectious disease (medical specialty); Nursing","score_opus":0.004334256363178904,"score_gpt":0.2913150241557276,"score_spread":0.28698076779254866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101838573","genre_codex":"dataset","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01126348,0.000627077,0.08944894,0.0029977744,0.00081306894,0.0018486382,0.5242007,0.18277961,0.18602082],"genre_scores_gemma":[0.0796665,0.0010559938,0.24921049,0.0006951425,0.00029716428,0.0031691268,0.5952249,0.022381363,0.04829925],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986669,0.00030204063,0.000090192596,0.00022649346,0.00053316675,0.00018119544],"domain_scores_gemma":[0.9960322,0.0010727458,0.00029736332,0.0008314777,0.0011455553,0.000620648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002166131,0.0010416568,0.0004754553,0.0061459714,0.0014454032,0.0035113632,0.0016228079,0.0009101224,0.11371621],"category_scores_gemma":[0.007461434,0.0005189078,0.0007001511,0.008097636,0.00053672644,0.0043089273,0.0045270477,0.0015140768,0.049359716],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030084592,0.00010407736,0.0033488765,0.0008054191,0.000025937434,0.00019902657,0.001271666,0.004089287,0.0010087403,0.013860837,0.8726379,0.102347404],"study_design_scores_gemma":[0.000062396626,0.000024152221,0.0024187202,0.00010949946,0.000009187366,0.000075728734,0.0006105055,0.00465867,0.0009690782,0.006818249,0.98419225,0.000051452113],"about_ca_topic_score_codex":0.01244858,"about_ca_topic_score_gemma":0.010991115,"teacher_disagreement_score":0.11371621,"about_ca_system_score_codex":0.0009814682,"about_ca_system_score_gemma":0.0030375277,"threshold_uncertainty_score":0.38041866},"labels":[],"label_agreement":null},{"id":"W2102528525","doi":"10.1016/j.sste.2013.05.003","title":"Spatio-temporal clustering of the incidence of schizophrenia in Quebec, Canada from 2004 to 2007","year":2013,"lang":"en","type":"article","venue":"Spatial and Spatio-temporal Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval; Douglas Mental Health University Institute; McGill University; Hôpital Louis-H Lafontaine; Université du Québec à Montréal; Université de Sherbrooke","funders":"AstraZeneca Canada","keywords":"Schizophrenia (object-oriented programming); Incidence (geometry); Cluster analysis; Geography; Demography; Psychiatry; Medicine; Computer science; Sociology; Artificial intelligence; Mathematics","score_opus":0.018305021635690893,"score_gpt":0.2683456307579675,"score_spread":0.25004060912227666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102528525","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":[0.8738363,0.0038581977,0.0009525428,0.0014173051,0.00006631711,0.00011362622,0.11588925,0.00012869216,0.0037376678],"genre_scores_gemma":[0.9759462,0.0009991184,0.0008353969,0.00015794946,0.000016747212,0.000048090435,0.019983375,0.000012320857,0.0020006902],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990577,0.000084602405,0.00011762898,0.0001960768,0.00028104446,0.0002628624],"domain_scores_gemma":[0.99528116,0.00021543569,0.0009233784,0.00016551901,0.0029320456,0.00048238813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080229,0.00035698546,0.00047832687,0.0026855154,0.0012376463,0.0010852726,0.0016195078,0.0005133017,0.0017256325],"category_scores_gemma":[0.0031960052,0.00029390503,0.0008302151,0.006812744,0.0004883985,0.00038309305,0.00083661906,0.00065157085,0.00022592436],"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":[0.0001229285,0.000022140206,0.9794166,0.00010152385,0.0002605642,0.00007964756,0.00039566887,0.002309941,0.00020508598,0.00033526545,0.0092071565,0.0075435704],"study_design_scores_gemma":[0.000006517013,0.000010364568,0.9951273,0.00004959256,0.000045872534,0.000038440096,0.00043221074,0.0020133818,0.000051025243,0.000025771355,0.002186415,0.0000131091665],"about_ca_topic_score_codex":0.99815613,"about_ca_topic_score_gemma":0.9987311,"teacher_disagreement_score":0.031106863,"about_ca_system_score_codex":0.031106863,"about_ca_system_score_gemma":0.037637148,"threshold_uncertainty_score":0.2256971},"labels":[],"label_agreement":null},{"id":"W2102616205","doi":"10.1038/527031a","title":"Informatics: Make sense of health data","year":2015,"lang":"en","type":"article","venue":"Nature","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Clinical Evaluative Sciences; Centre for Global Health Research; Institute of Population and Public Health; University of Toronto; Ottawa Hospital; University of Ottawa","funders":"","keywords":"Informatics; Join (topology); Data science; Computer science; Health informatics; Political science; Health care; Mathematics","score_opus":0.05146416648802207,"score_gpt":0.3624499634106339,"score_spread":0.31098579692261186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102616205","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008538002,0.039492957,0.23363376,0.671994,0.0182193,0.00025749803,0.0015109606,0.004327103,0.02971066],"genre_scores_gemma":[0.053988453,0.07346757,0.53343296,0.25355285,0.05529038,0.0017432348,0.003086759,0.0038971198,0.021540781],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.95931345,0.02732708,0.002743048,0.0025121514,0.0073512397,0.0007530871],"domain_scores_gemma":[0.7677365,0.18659036,0.004286118,0.025466414,0.0097028855,0.0062177805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.077790946,0.001863369,0.0017468068,0.008391945,0.0020295794,0.021068048,0.003239929,0.0060437885,0.014359365],"category_scores_gemma":[0.1731268,0.0015038406,0.002270781,0.006119262,0.014258611,0.037739277,0.013994346,0.017830467,0.0077168504],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008375195,0.000055106564,0.001626482,0.001444179,0.00031570747,0.00016500168,0.0030288459,0.00068081863,0.00042839034,0.2974885,0.46024653,0.23443666],"study_design_scores_gemma":[0.000035709232,0.000018480145,0.00035975734,0.0011580781,0.00005633211,0.00016442405,0.00096056226,0.0008565019,0.0003421988,0.52043027,0.47557864,0.000039147173],"about_ca_topic_score_codex":0.0015363692,"about_ca_topic_score_gemma":0.0018076495,"teacher_disagreement_score":0.077790946,"about_ca_system_score_codex":0.0025208918,"about_ca_system_score_gemma":0.010712044,"threshold_uncertainty_score":0.41140276},"labels":[],"label_agreement":null},{"id":"W2104931818","doi":"10.1177/1524839909334624","title":"Geographic Information Systems (GIS) for Health Promotion and Public Health: A Review","year":2009,"lang":"en","type":"review","venue":"Health Promotion Practice","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":197,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Health promotion; Public health; GIS and public health; Geographic information system; Profiling (computer programming); Health policy; Public participation GIS; Environmental health; Medicine; Public relations; Political science; Geography; Computer science; Nursing; Remote sensing","score_opus":0.1523135964450655,"score_gpt":0.4622337925787041,"score_spread":0.30992019613363864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104931818","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":[0.00016567188,0.9959269,0.0007250629,0.0011196976,0.00021953214,0.000031014322,0.000045368688,0.000015740336,0.0017510041],"genre_scores_gemma":[0.0012224426,0.99667704,0.0016702175,0.00013933268,0.00007897013,0.000028856291,0.000034895413,0.0000030763197,0.00014515939],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99666816,0.0015386584,0.00052854477,0.00020196495,0.0009614263,0.00010117126],"domain_scores_gemma":[0.98222417,0.014430978,0.0007885041,0.000210202,0.0021496927,0.00019648985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005316752,0.0011953475,0.002562406,0.00956527,0.0007140243,0.002974965,0.0018284286,0.002146996,0.004872062],"category_scores_gemma":[0.012956408,0.0006780241,0.0014147122,0.019469542,0.0013836325,0.0038884566,0.001463728,0.001681446,0.0012967286],"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":[0.000027507018,0.00005674989,0.000494037,0.05827705,0.00018628206,0.00014794561,0.00042075102,0.00072489,0.00024634955,0.008735812,0.021369154,0.9093135],"study_design_scores_gemma":[0.00002678562,0.00011503,0.0029637504,0.06857992,0.00046071672,0.00083895185,0.0011104454,0.0006560963,0.00047957743,0.00765116,0.9170416,0.00007591571],"about_ca_topic_score_codex":0.009335406,"about_ca_topic_score_gemma":0.013023537,"teacher_disagreement_score":0.00956527,"about_ca_system_score_codex":0.002553577,"about_ca_system_score_gemma":0.007977697,"threshold_uncertainty_score":0.028118014},"labels":[],"label_agreement":null},{"id":"W2107141213","doi":"10.1002/bimj.200310136","title":"Applications of Binary Segmentation to the Estimation of Quantal Response Curves and Spatial Intensity","year":2005,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University; Korea Science and Engineering Foundation","keywords":"Nonparametric statistics; Binary number; Inference; Bernoulli's principle; Sequence (biology); Mathematics; Bernoulli trial; Segmentation; Cluster (spacecraft); Point (geometry); Identification (biology); Monotone polygon; Pseudorandom binary sequence; Algorithm; Statistics; Computer science; Artificial intelligence; Geometry; Physics","score_opus":0.019802234324811013,"score_gpt":0.33066322558532524,"score_spread":0.31086099126051425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107141213","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":[0.010818387,0.00047064642,0.9864827,0.00048398695,0.000039356415,0.000049667877,0.000057844998,0.00015208274,0.0014453047],"genre_scores_gemma":[0.33301887,0.00095508556,0.66316706,0.00031016243,0.00015353033,0.0002392391,0.00020459732,0.00016731107,0.0017842251],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958335,0.0024312257,0.00019372546,0.000544548,0.00084429886,0.00015262856],"domain_scores_gemma":[0.9779609,0.016975159,0.0019539713,0.0016158058,0.0011629503,0.00033128943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006196492,0.00069883745,0.0011195126,0.0027348748,0.0008163101,0.0015282095,0.0014694617,0.0017568351,0.0028293289],"category_scores_gemma":[0.041371085,0.0007090873,0.0010208669,0.0023341952,0.0030191564,0.0014733523,0.0021326635,0.0017515854,0.0004977189],"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":[0.0005519901,0.00025694736,0.013680727,0.0007040774,0.00022915746,0.00036372908,0.0014160661,0.22835521,0.018364703,0.31671774,0.0021845421,0.41717502],"study_design_scores_gemma":[0.000068256726,0.00015708116,0.007941964,0.00012629952,0.000042485164,0.0004295226,0.00021887223,0.5700454,0.008792161,0.40627295,0.0058009694,0.000104018254],"about_ca_topic_score_codex":0.0029683628,"about_ca_topic_score_gemma":0.002032097,"teacher_disagreement_score":0.006196492,"about_ca_system_score_codex":0.0013720447,"about_ca_system_score_gemma":0.0010239913,"threshold_uncertainty_score":0.032770634},"labels":[],"label_agreement":null},{"id":"W2107192955","doi":"10.1073/pnas.1006219107","title":"Global capacity for emerging infectious disease detection","year":2010,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":250,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Agency of Canada; McGill University","funders":"U.S. National Library of Medicine; National Institutes of Health; World Health Organization","keywords":"Outbreak; Public health; Infectious disease (medical specialty); Medicine; Pandemic; Hazard ratio; Geography; Disease; Environmental health; Demography; Coronavirus disease 2019 (COVID-19); Confidence interval; Internal medicine; Virology; Pathology","score_opus":0.02992397184798421,"score_gpt":0.32462736355762895,"score_spread":0.2947033917096447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107192955","genre_codex":"other","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":[0.12965882,0.040649403,0.122953855,0.11809354,0.004589669,0.000843347,0.026026126,0.0045916256,0.55259365],"genre_scores_gemma":[0.908873,0.0174674,0.036571752,0.006654328,0.0024019452,0.00084109243,0.009914929,0.00045626462,0.016819376],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.98946536,0.0047767735,0.0005129522,0.0019804502,0.0024308134,0.00083364756],"domain_scores_gemma":[0.9558989,0.018799065,0.0076150233,0.007082997,0.008201766,0.0024021724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016523,0.0013000786,0.0010471885,0.007838885,0.0007873399,0.00596562,0.002020821,0.0013056584,0.024730181],"category_scores_gemma":[0.05432693,0.0004932015,0.001200121,0.00683278,0.0023274845,0.009081668,0.008872747,0.0020027242,0.0050094784],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001315322,0.00010836149,0.16277309,0.0015799414,0.00044778947,0.0003426556,0.0025831775,0.009243457,0.0011361347,0.109968625,0.11908427,0.59260106],"study_design_scores_gemma":[0.000052317635,0.00031853272,0.14916116,0.0018242382,0.00027001006,0.0012451054,0.0046666884,0.019277688,0.0017829328,0.15588862,0.6652753,0.00023748945],"about_ca_topic_score_codex":0.005618134,"about_ca_topic_score_gemma":0.0022094157,"teacher_disagreement_score":0.024730181,"about_ca_system_score_codex":0.002788182,"about_ca_system_score_gemma":0.003700642,"threshold_uncertainty_score":0.08738303},"labels":[],"label_agreement":null},{"id":"W2108516750","doi":"10.1186/1476-072x-7-61","title":"Spatial event cluster detection using an approximate normal distribution","year":2008,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Alberta Heritage Foundation for Medical Research; Fondation pour la Recherche Médicale","keywords":"Poisson distribution; Cluster (spacecraft); Computer science; Event (particle physics); Population; Statistics; Recursion (computer science); Data mining; Mathematics; Algorithm; Medicine","score_opus":0.032215049125142274,"score_gpt":0.3387293071314591,"score_spread":0.3065142580063168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108516750","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":[0.021133497,0.000110974746,0.9770354,0.00014392076,0.000026381598,0.00012313788,0.00012631915,0.0006579631,0.0006423829],"genre_scores_gemma":[0.55745083,0.00028640107,0.43875062,0.00015313127,0.00008167118,0.00039614787,0.0007763651,0.00015200251,0.0019527032],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994827,0.0024084433,0.00028915555,0.00100064,0.001175704,0.00029904448],"domain_scores_gemma":[0.9735005,0.019033374,0.0016574604,0.0017938133,0.0036887268,0.0003260956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0098056635,0.0007318063,0.001320591,0.0028417895,0.00082075724,0.0018008396,0.0029862996,0.0013396001,0.0025928507],"category_scores_gemma":[0.042503715,0.00052892463,0.0014381661,0.0025449998,0.0017851297,0.0026367253,0.0021132836,0.0015864666,0.0006504345],"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":[0.00050804985,0.000106515865,0.024763366,0.00016433733,0.00017181343,0.00036543934,0.00048021573,0.7798111,0.0014091791,0.050266948,0.0025215012,0.13943157],"study_design_scores_gemma":[0.000013457528,0.000022512695,0.0007728315,0.000008983758,0.000007974242,0.00008552923,0.00003387022,0.9878542,0.00036787873,0.010233863,0.0005865704,0.000012358548],"about_ca_topic_score_codex":0.01710466,"about_ca_topic_score_gemma":0.0081128245,"teacher_disagreement_score":0.01710466,"about_ca_system_score_codex":0.0021505982,"about_ca_system_score_gemma":0.0023206938,"threshold_uncertainty_score":0.05185795},"labels":[],"label_agreement":null},{"id":"W2109185206","doi":"10.1186/1472-6947-13-121","title":"Measuring the impact of a health information exchange intervention on provider-based notifiable disease reporting using mixed methods: a study protocol","year":2013,"lang":"en","type":"article","venue":"BMC Medical Informatics and Decision Making","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":25,"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":"Agency for Healthcare Research and Quality; McMaster University; University of Washington; U.S. Department of Veterans Affairs","keywords":"Health information exchange; Health informatics; Workflow; Medicine; Protocol (science); Health care; Public health; Intervention (counseling); Computer science; Nursing; Database","score_opus":0.14227956708978468,"score_gpt":0.4803131036966361,"score_spread":0.3380335366068514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109185206","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019033885,0.000034026903,0.002148195,0.00008604411,0.000041556938,0.99494755,0.00039506867,0.000026571364,0.00041766843],"genre_scores_gemma":[0.0006447357,0.000020419931,0.0026846179,0.000035635923,0.0000031504921,0.99652207,0.000030453883,0.0000010170189,0.00005783239],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.91739744,0.059559874,0.009259832,0.0037304848,0.0066029397,0.0034494486],"domain_scores_gemma":[0.91018873,0.043086693,0.010316553,0.010943064,0.020841861,0.004623053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12616025,0.0041429787,0.003139933,0.0045130244,0.006577459,0.0041283164,0.006040979,0.0061621373,0.029260417],"category_scores_gemma":[0.09832463,0.0035829796,0.004663741,0.0040397597,0.0039842525,0.003138383,0.0046966597,0.006546965,0.0047805393],"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":[0.15243524,0.13661842,0.01941679,0.059368476,0.0027432644,0.0018713104,0.019614045,0.014341042,0.0062344153,0.032706708,0.061700094,0.49295017],"study_design_scores_gemma":[0.42820024,0.18230723,0.0560703,0.032336775,0.0036100617,0.0005108949,0.016549684,0.016170328,0.016423767,0.023317514,0.22339173,0.0011115016],"about_ca_topic_score_codex":0.0062129432,"about_ca_topic_score_gemma":0.0089716865,"teacher_disagreement_score":0.12616025,"about_ca_system_score_codex":0.011862904,"about_ca_system_score_gemma":0.05929911,"threshold_uncertainty_score":0.6672071},"labels":[],"label_agreement":null},{"id":"W2110102136","doi":"10.1038/srep09112","title":"Monitoring Disease Trends using Hospital Traffic Data from High Resolution Satellite Imagery: A Feasibility Study","year":2015,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"U.S. National Library of Medicine; National Institute of Environmental Health Sciences; Intelligence Advanced Research Projects Activity; National Institutes of Health","keywords":"Satellite imagery; Remote sensing; Computer science; High resolution; Satellite; Geography; Engineering","score_opus":0.11678606024107799,"score_gpt":0.35944545458071003,"score_spread":0.24265939433963205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110102136","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":[0.98702025,0.00005295607,0.010577301,0.00031125764,0.000009039107,0.00043757,0.0009377633,0.0000472849,0.0006065682],"genre_scores_gemma":[0.9583373,0.00011298024,0.03904461,0.00004723842,0.000020525353,0.00045612964,0.0017908688,0.000009017691,0.0001812482],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9962748,0.0026866088,0.00016902878,0.00027004385,0.0004528534,0.00014667644],"domain_scores_gemma":[0.99098164,0.0049356534,0.0010097836,0.0010598717,0.0016710928,0.0003419122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007220158,0.00047364927,0.00031818362,0.0010907577,0.00034349185,0.0006078511,0.00094511284,0.00061667705,0.00057289295],"category_scores_gemma":[0.012675745,0.00029962006,0.00055569306,0.0014786477,0.00033335315,0.0015958361,0.0008160096,0.00045869054,0.00015389822],"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":[0.00064651767,0.002709171,0.885007,0.00026303838,0.0002886078,0.00051142805,0.00053116283,0.043591354,0.005735963,0.00082033675,0.00069716055,0.05919832],"study_design_scores_gemma":[0.000328491,0.00498793,0.55838287,0.00011140227,0.000248631,0.00039759697,0.002990457,0.42176652,0.0067413137,0.0009812136,0.0029564616,0.000107033615],"about_ca_topic_score_codex":0.020056283,"about_ca_topic_score_gemma":0.023918541,"teacher_disagreement_score":0.020056283,"about_ca_system_score_codex":0.000692002,"about_ca_system_score_gemma":0.0012874514,"threshold_uncertainty_score":0.039879084},"labels":[],"label_agreement":null},{"id":"W2110349231","doi":"10.1140/epjds/s13688-015-0054-0","title":"Enhancing disease surveillance with novel data streams: challenges and opportunities","year":2015,"lang":"en","type":"article","venue":"EPJ Data Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":239,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Institute of General Medical Sciences; Economic and Social Research Council; National Institutes of Health; Santa Fe Institute","keywords":"Computer science; Data science; Public health surveillance; Social media; Public health; Key (lock); Big data; Management science; Computer security; Data mining; World Wide Web; Engineering; Medicine","score_opus":0.2809649834510432,"score_gpt":0.349388351430047,"score_spread":0.06842336797900378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110349231","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":[0.0668778,0.013876489,0.7884562,0.10727621,0.0013705023,0.0019784675,0.007350435,0.0016927755,0.011121134],"genre_scores_gemma":[0.2930494,0.0075149876,0.6875908,0.004395901,0.0015117766,0.0010875073,0.003917546,0.00015134894,0.00078073144],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.93948376,0.045086794,0.003627999,0.0033726152,0.007933284,0.00049563334],"domain_scores_gemma":[0.6250343,0.30760282,0.013093391,0.029680183,0.021987233,0.0026020973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10886962,0.0013149631,0.0024273782,0.0056494037,0.0012174887,0.011348685,0.0034639323,0.0032461933,0.0009160823],"category_scores_gemma":[0.2281831,0.0012549123,0.0015058466,0.007335958,0.0037419011,0.020659273,0.0065264865,0.005443776,0.0005807038],"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":[0.00054431136,0.00084771554,0.16605537,0.0047468557,0.000984099,0.00037176555,0.004456537,0.0534224,0.0040220297,0.12672073,0.01988054,0.61794764],"study_design_scores_gemma":[0.00017094609,0.0007208571,0.026302923,0.0029116194,0.00036247907,0.00087963755,0.0073955776,0.3740222,0.005796499,0.484297,0.09683477,0.00030561548],"about_ca_topic_score_codex":0.003999163,"about_ca_topic_score_gemma":0.004765354,"teacher_disagreement_score":0.10886962,"about_ca_system_score_codex":0.0024541426,"about_ca_system_score_gemma":0.005320423,"threshold_uncertainty_score":0.5757644},"labels":[],"label_agreement":null},{"id":"W2110495607","doi":"10.1007/bf03404053","title":"“Google Flu Trends” and Emergency Department Triage Data Predicted the 2009 Pandemic H1N1 Waves in Manitoba","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":54,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Winnipeg Regional Health Authority; Manitoba Health; University of Manitoba","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Pandemic; Triage; Emergency department; Coronavirus disease 2019 (COVID-19); Lag time; Pandemic influenza; Emergency medicine; Demography; Internal medicine; Disease; Infectious disease (medical specialty)","score_opus":0.1643745384709177,"score_gpt":0.3274327258792087,"score_spread":0.16305818740829098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110495607","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":[0.9758498,0.00034163037,0.00030787327,0.0011519474,0.00003077231,0.00003912882,0.020624818,0.000058551395,0.0015953432],"genre_scores_gemma":[0.98887885,0.00032804254,0.00072401814,0.00020123279,0.00001380156,0.000021720876,0.009019429,0.000009898613,0.0008029669],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998273,0.000028953024,0.000015184477,0.00003974057,0.00003872625,0.00005013485],"domain_scores_gemma":[0.9989718,0.00015103842,0.00017037897,0.000040367184,0.0005449079,0.00012137887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048832403,0.0003546667,0.00022221668,0.001171092,0.0004116946,0.0008958793,0.0006205758,0.0003635438,0.00078087964],"category_scores_gemma":[0.002535222,0.00026384185,0.00043750976,0.0024062307,0.00022737656,0.0005049771,0.000542747,0.00051811605,0.00023853761],"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":[0.000049586222,0.000017289813,0.99209785,0.000030757325,0.00005791138,0.000046450412,0.00018900193,0.0013545904,0.00026597857,0.000105939005,0.0023444698,0.0034402828],"study_design_scores_gemma":[0.000016548312,0.000023327826,0.97904485,0.00006828468,0.0000911661,0.000050313727,0.0015854475,0.01575774,0.0003736375,0.00011898297,0.0028539456,0.000015836627],"about_ca_topic_score_codex":0.9435487,"about_ca_topic_score_gemma":0.9704856,"teacher_disagreement_score":0.05645132,"about_ca_system_score_codex":0.0037560489,"about_ca_system_score_gemma":0.006543067,"threshold_uncertainty_score":0.11356747},"labels":[],"label_agreement":null},{"id":"W2110576992","doi":"10.1186/1478-7954-9-58","title":"Using funnel plots in public health surveillance","year":2011,"lang":"en","type":"article","venue":"Population Health Metrics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Alberta Health; University of Alberta","funders":"University of Alberta","keywords":"Funnel plot; Funnel; Medicine; Public health; Covariate; Overdispersion; Environmental health; Raw data; Risk analysis (engineering); Statistics; Meta-analysis; Publication bias; Population; Engineering","score_opus":0.3908464497947009,"score_gpt":0.41416495179267804,"score_spread":0.023318501997977137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110576992","genre_codex":"methods","genre_gemma":"methods","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069979606,0.013520057,0.96062535,0.004669447,0.00093833735,0.0024184398,0.0017118423,0.0019724853,0.007146059],"genre_scores_gemma":[0.23246503,0.008210993,0.746342,0.0022444623,0.0008461489,0.0068129892,0.0009711087,0.000626817,0.0014804504],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.46054044,0.48864022,0.021561947,0.0101265395,0.018377887,0.0007528613],"domain_scores_gemma":[0.11322754,0.7732005,0.058551423,0.036086068,0.018127676,0.0008068024],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.42725688,0.0032228993,0.0040863072,0.017281832,0.002053988,0.012499031,0.005375354,0.007575957,0.008031482],"category_scores_gemma":[0.760104,0.002487895,0.0048609013,0.01660863,0.011850697,0.011562176,0.0066358717,0.00481461,0.0010095692],"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":[0.0016318236,0.00014392199,0.048899304,0.018035904,0.006939663,0.0013250847,0.008588731,0.04240098,0.00087985845,0.28587157,0.027814442,0.55746883],"study_design_scores_gemma":[0.0011072592,0.0007515341,0.0162015,0.014032132,0.0028316695,0.0029737684,0.0028264837,0.09277066,0.004240017,0.7613363,0.10013958,0.0007890071],"about_ca_topic_score_codex":0.004792981,"about_ca_topic_score_gemma":0.002864454,"teacher_disagreement_score":0.5727431,"about_ca_system_score_codex":0.004860671,"about_ca_system_score_gemma":0.006131126,"threshold_uncertainty_score":0.70629424},"labels":[],"label_agreement":null},{"id":"W2110634923","doi":"10.1186/1476-072x-10-54","title":"Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping","year":2011,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"National Institute of Environmental Health Sciences; National Institute of Allergy and Infectious Diseases; National Institute of Mental Health; North Carolina Department of Health and Human Services; U.S. Department of Health and Human Services","keywords":"Smoothing; Kriging; Estimator; Spatial analysis; Poisson distribution; Statistics; Autocorrelation; Geostatistics; Computer science; Bayesian probability; Sampling (signal processing); Spatial correlation; Mathematics; Data mining; Spatial variability","score_opus":0.5632188500573257,"score_gpt":0.41153548590096445,"score_spread":0.1516833641563613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110634923","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":[0.0103091495,0.0005533777,0.9878313,0.0005596455,0.000034112556,0.00006630238,0.00008830523,0.00018178699,0.00037607344],"genre_scores_gemma":[0.51000357,0.0018028178,0.48594052,0.00033561807,0.00028243163,0.0005072065,0.0003860644,0.00016905843,0.0005727381],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.988499,0.008988585,0.00037459563,0.00092391233,0.0010531285,0.00016078894],"domain_scores_gemma":[0.92958206,0.060556695,0.0031653866,0.00454565,0.0017778864,0.00037220828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019962648,0.00088969036,0.0016803474,0.0049369005,0.0008594221,0.0020159935,0.002572885,0.0012385648,0.0013162983],"category_scores_gemma":[0.07856416,0.0007703111,0.002013562,0.005309142,0.0027952231,0.0019669386,0.002594527,0.0017858741,0.00018503299],"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":[0.00007813916,0.00008493298,0.038844813,0.00048179226,0.0014757592,0.00025528966,0.0008340274,0.6855125,0.00062401075,0.068639256,0.0017294013,0.20144017],"study_design_scores_gemma":[0.00002064873,0.000048707498,0.007153829,0.00009527156,0.00010031571,0.00012834258,0.00022635379,0.82772756,0.0003126782,0.1622874,0.0018561286,0.000042805],"about_ca_topic_score_codex":0.011277409,"about_ca_topic_score_gemma":0.009534447,"teacher_disagreement_score":0.019962648,"about_ca_system_score_codex":0.0014657449,"about_ca_system_score_gemma":0.0020639217,"threshold_uncertainty_score":0.10557389},"labels":[],"label_agreement":null},{"id":"W2110799663","doi":"10.1093/aje/kwt242","title":"Re: \"The 'Case-Chaos Study' l Adjunct or Alternative to Conventional Case-Control Study Methodology\"","year":2013,"lang":"en","type":"letter","venue":"American Journal of Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Fogarty International Center; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Science and Technology Directorate; National Institutes of Health; U.S. Department of Homeland Security","keywords":"Adjunct; CHAOS (operating system); Medicine; Computer science; Philosophy","score_opus":0.15099304807355524,"score_gpt":0.4254732491526597,"score_spread":0.2744802010791045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110799663","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015858016,0.07623738,0.4962169,0.28999904,0.10928943,0.0029558407,0.0019511683,0.001194046,0.020570489],"genre_scores_gemma":[0.0455538,0.04685697,0.44415307,0.33713454,0.0928427,0.010919595,0.0014889624,0.0010424276,0.020007877],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.6881316,0.27232334,0.008974738,0.006194099,0.023805646,0.000570573],"domain_scores_gemma":[0.49519417,0.41297007,0.012680209,0.04865774,0.027285669,0.0032122624],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2438366,0.0015791835,0.0021480885,0.008477284,0.0014769566,0.005880475,0.0074575506,0.0054107374,0.010339447],"category_scores_gemma":[0.29931852,0.0009981975,0.0014974223,0.0052692806,0.011824459,0.006898848,0.0061156526,0.010726416,0.00533719],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031241993,0.00013690957,0.0065520233,0.00531556,0.0008187241,0.00042532003,0.0015109219,0.00035504904,0.00033645806,0.19905312,0.43382397,0.35135955],"study_design_scores_gemma":[0.00025156813,0.0002942698,0.004683995,0.0066875922,0.00047878872,0.0018659707,0.0009069379,0.0024165804,0.000329161,0.1815806,0.8003261,0.00017844093],"about_ca_topic_score_codex":0.0028262013,"about_ca_topic_score_gemma":0.0055214562,"teacher_disagreement_score":0.7561634,"about_ca_system_score_codex":0.002106151,"about_ca_system_score_gemma":0.0079112165,"threshold_uncertainty_score":0.9324841},"labels":[],"label_agreement":null},{"id":"W2110853801","doi":"10.1186/1751-0473-6-6","title":"Creating web applications for spatial epidemiological analysis and mapping in R using Rwui","year":2011,"lang":"en","type":"article","venue":"Source Code for Biology and Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"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":"University of British Columbia","keywords":"Scripting language; Computer science; World Wide Web; Web application; Web mapping; Field (mathematics); Web modeling; Web standards; Data science; Visualization; Geographic information system; Web analytics; Web development; Web page; Data mining; Web intelligence; Cartography; Geography","score_opus":0.08397323356560733,"score_gpt":0.3688851107915049,"score_spread":0.28491187722589756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110853801","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029417288,0.0002621754,0.642336,0.0005395515,0.00029060827,0.0006906229,0.011401008,0.3365923,0.004945948],"genre_scores_gemma":[0.027102083,0.0004858316,0.8549643,0.00078212604,0.00023518411,0.003972771,0.025818728,0.079184435,0.007454571],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964605,0.0014841184,0.0005577274,0.0005414079,0.00073104573,0.00022523009],"domain_scores_gemma":[0.9729019,0.01935086,0.0015966332,0.0030261625,0.0024312595,0.0006931878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007268194,0.0021688289,0.0016089824,0.0038333337,0.00064543146,0.0021260798,0.0044153756,0.0011286562,0.049275137],"category_scores_gemma":[0.03784638,0.0019531306,0.0028471684,0.0024282283,0.0006600988,0.0025481938,0.003930153,0.00247291,0.031896133],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012292635,0.00035626648,0.009549974,0.003466988,0.0011492545,0.0018956346,0.0015054948,0.01505676,0.012768788,0.01638396,0.501825,0.43481266],"study_design_scores_gemma":[0.0012606985,0.00029055093,0.013382179,0.0011171573,0.00070818054,0.0027292278,0.0004091716,0.21459506,0.05344885,0.050387923,0.66093856,0.00073245045],"about_ca_topic_score_codex":0.0018064015,"about_ca_topic_score_gemma":0.002015091,"teacher_disagreement_score":0.049275137,"about_ca_system_score_codex":0.0005277014,"about_ca_system_score_gemma":0.0014446271,"threshold_uncertainty_score":0.16484171},"labels":[],"label_agreement":null},{"id":"W2113834306","doi":"10.1186/1476-072x-9-16","title":"Review of software for space-time disease surveillance","year":2010,"lang":"en","type":"review","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":99,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Software; Disease surveillance; Usability; Context (archaeology); Data science; Data collection; Data mining; Geographic information system; Preprocessor; Geography; Artificial intelligence; Cartography; Disease; Medicine","score_opus":0.03848968808961087,"score_gpt":0.40918445276719606,"score_spread":0.3706947646775852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113834306","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":[0.00041032527,0.97786057,0.01316649,0.0014284907,0.0005325377,0.00016599309,0.0008649828,0.00076580734,0.0048047863],"genre_scores_gemma":[0.0015309245,0.97420925,0.0198424,0.00068019645,0.0002517064,0.00028468756,0.0015103206,0.00021437925,0.0014761363],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9955296,0.0012517909,0.0010515372,0.00038372917,0.0016736946,0.000109664164],"domain_scores_gemma":[0.97943854,0.013364731,0.0011760948,0.0006586474,0.00508932,0.00027262905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063620964,0.0015722383,0.0024942684,0.0108538065,0.0004283926,0.0015383975,0.0034038723,0.0013594197,0.0064917686],"category_scores_gemma":[0.027593829,0.00094752555,0.0019781422,0.012322217,0.000822968,0.0028198094,0.001273742,0.001612946,0.007427403],"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":[0.00003577471,0.000028081095,0.00027810654,0.022718655,0.00011147248,0.00007647295,0.000109442306,0.00046286927,0.00037131595,0.0021062442,0.039104678,0.934597],"study_design_scores_gemma":[0.00002797276,0.000067470464,0.0016178241,0.01599306,0.00026947833,0.00076010183,0.0000690772,0.0004774163,0.0009612712,0.0025949683,0.9771055,0.00005598157],"about_ca_topic_score_codex":0.005555889,"about_ca_topic_score_gemma":0.0049906233,"teacher_disagreement_score":0.0108538065,"about_ca_system_score_codex":0.0013133441,"about_ca_system_score_gemma":0.005235771,"threshold_uncertainty_score":0.033646405},"labels":[],"label_agreement":null},{"id":"W2114645350","doi":"10.1136/amiajnl-2011-000126","title":"Adjusting outbreak detection algorithms for surveillance during epidemic and non-epidemic periods","year":2011,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Outbreak; Disease surveillance; Incidence (geometry); Infectious disease (medical specialty); Foot-and-mouth disease; Algorithm; Disease; Geography; Computer science; Virology; Medicine; Mathematics; Pathology","score_opus":0.01752868203361374,"score_gpt":0.28729413663631437,"score_spread":0.2697654546027006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114645350","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":[0.29915303,0.00059396203,0.6971452,0.0005497603,0.00010750391,0.00024786315,0.00018113841,0.001551634,0.00046992916],"genre_scores_gemma":[0.7180706,0.0001429802,0.28097624,0.00012864385,0.00003450104,0.000113392314,0.00030260137,0.000057643476,0.00017345244],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966084,0.0013217018,0.0005489538,0.0007507517,0.0005865228,0.00018362064],"domain_scores_gemma":[0.97509557,0.015540309,0.0029346803,0.0019944052,0.004094831,0.00034016423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009780319,0.00082503073,0.0008366303,0.0019582766,0.00050102454,0.0010771798,0.0011723522,0.00084873964,0.0002558044],"category_scores_gemma":[0.051724628,0.00044363507,0.0005940707,0.0012637452,0.00038721118,0.0015007977,0.0008179016,0.0012022944,0.00011005987],"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":[0.0007612908,0.00048051,0.22111149,0.00021919393,0.00046594013,0.00011585559,0.0006006114,0.27514768,0.018898852,0.002421017,0.0015936763,0.4781839],"study_design_scores_gemma":[0.000085945,0.00027889156,0.025226751,0.000019971865,0.00008786992,0.0001452687,0.00011649736,0.9555008,0.014633841,0.0023395764,0.0015096158,0.000054947755],"about_ca_topic_score_codex":0.0052179475,"about_ca_topic_score_gemma":0.0048396075,"teacher_disagreement_score":0.009780319,"about_ca_system_score_codex":0.0009404724,"about_ca_system_score_gemma":0.002001435,"threshold_uncertainty_score":0.051723897},"labels":[],"label_agreement":null},{"id":"W2116019260","doi":"10.1177/1090198113493910","title":"Evaluating the Effectiveness of Contact Tracing on Tuberculosis Outcomes in Saskatchewan Using Individual-Based Modeling","year":2013,"lang":"en","type":"article","venue":"Health Education & Behavior","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"The Lung Association Saskatchewan; University of Saskatchewan","funders":"Office of Behavioral and Social Sciences Research; National Institute of Dental and Craniofacial Research; National Institute on Aging; Saskatchewan Health Research Foundation","keywords":"Contact tracing; Tuberculosis; Environmental health; Gerontology; Medicine; Psychology","score_opus":0.11291396434072357,"score_gpt":0.4515205770272883,"score_spread":0.3386066126865648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116019260","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":[0.9908811,0.00021897466,0.0023819238,0.00071916555,0.000027971502,0.00014032231,0.001895959,0.00005457061,0.0036799894],"genre_scores_gemma":[0.99346316,0.00024769644,0.00218154,0.00019390976,0.0000073438478,0.00012341123,0.0014168374,0.000012258127,0.002353922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819773,0.0009086391,0.00005613983,0.00024507756,0.0001228462,0.0004696323],"domain_scores_gemma":[0.9891601,0.0074797133,0.0006321526,0.00047193601,0.0016213182,0.0006347069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041113584,0.0012919977,0.0011740084,0.0010456379,0.001337154,0.0018478199,0.0027649468,0.0016182329,0.0031575477],"category_scores_gemma":[0.00851712,0.0007647984,0.0015786423,0.001425826,0.0009904782,0.00072159374,0.0016501412,0.0013671898,0.0003254585],"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":[0.00041674427,0.00044402303,0.07166117,0.00007322269,0.00051473716,0.00019628085,0.00011917371,0.91693515,0.00042680762,0.0021581466,0.0013938978,0.005660554],"study_design_scores_gemma":[0.00014925831,0.00024333832,0.024166765,0.000038092294,0.0001971623,0.000020275744,0.00045604704,0.97252434,0.00027206633,0.0011520564,0.00072379183,0.000056750094],"about_ca_topic_score_codex":0.92310137,"about_ca_topic_score_gemma":0.8980142,"teacher_disagreement_score":0.076898634,"about_ca_system_score_codex":0.01993388,"about_ca_system_score_gemma":0.020795638,"threshold_uncertainty_score":0.15470296},"labels":[],"label_agreement":null},{"id":"W2116148325","doi":"10.2196/jmir.2740","title":"Scoping Review on Search Queries and Social Media for Disease Surveillance: A Chronology of Innovation","year":2013,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":171,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Agency of Canada; University of Guelph","funders":"College of Engineering, Michigan State University; Centers for Disease Control and Prevention; Public Health Agency; Public Health Agency of Canada; University of Guelph; Michigan State University","keywords":"Social media; Disease surveillance; Pandemic; Outbreak; Internet privacy; Disease; Influenza A virus subtype H5N1; Warning system; Coronavirus disease 2019 (COVID-19); Computer science; Medicine; Medical emergency; Data science; World Wide Web; Infectious disease (medical specialty); Virology; Pathology","score_opus":0.11419644035205836,"score_gpt":0.46655618000076204,"score_spread":0.3523597396487037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116148325","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00062457565,0.98844016,0.0012674234,0.005642189,0.001118638,0.00060848123,0.00015902994,0.000021765722,0.0021177437],"genre_scores_gemma":[0.0044010472,0.98740155,0.003854088,0.0020524634,0.00046960684,0.0013458221,0.00015484632,0.00001859956,0.00030190573],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.941419,0.027261779,0.018413264,0.0022728452,0.00935686,0.0012763339],"domain_scores_gemma":[0.6853746,0.26159456,0.017563479,0.005355063,0.028374592,0.001737729],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06577842,0.0024262907,0.0065878276,0.048104618,0.0024842825,0.012874886,0.0029830474,0.005401154,0.0057876855],"category_scores_gemma":[0.1946622,0.0021584795,0.0052532796,0.05027965,0.006454383,0.016983097,0.007657203,0.004512568,0.0013260545],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021111043,0.000060856415,0.000861717,0.52902234,0.0011076252,0.00029976454,0.005303455,0.0004267554,0.00055189873,0.016206663,0.01485148,0.43109632],"study_design_scores_gemma":[0.000034983324,0.00011128647,0.0009936739,0.8838228,0.0012357082,0.0002621016,0.0021624365,0.00017035478,0.00019723126,0.004819265,0.10615197,0.00003814612],"about_ca_topic_score_codex":0.007259388,"about_ca_topic_score_gemma":0.014190197,"teacher_disagreement_score":0.93422157,"about_ca_system_score_codex":0.012125321,"about_ca_system_score_gemma":0.036080793,"threshold_uncertainty_score":0.3478737},"labels":[],"label_agreement":null},{"id":"W2116631908","doi":"10.1371/journal.pone.0059893","title":"Did Advances in Global Surveillance and Notification Systems Make a Difference in the 2009 H1N1 Pandemic?–A Retrospective Analysis","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Centers for Disease Control and Prevention; Georgetown University; U.S. Department of Health and Human Services","keywords":"Timeline; Pandemic; Outbreak; Disease surveillance; Contagious disease; Public health; Medicine; Identification (biology); Public health surveillance; Coronavirus disease 2019 (COVID-19); Medical emergency; Environmental health; Geography; Infectious disease (medical specialty); Disease; Virology; Biology; Pathology","score_opus":0.027574113467157636,"score_gpt":0.2577207700106415,"score_spread":0.23014665654348385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116631908","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":[0.9323694,0.044391125,0.003104445,0.0011300417,0.00010716593,0.00069659424,0.015841505,0.000035707006,0.0023240566],"genre_scores_gemma":[0.98524135,0.0078411475,0.0018027038,0.00034399988,0.000049156442,0.00038088375,0.0040188446,0.000023603428,0.0002983191],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9869567,0.0042978097,0.0046734214,0.0014367983,0.0020726616,0.0005624858],"domain_scores_gemma":[0.9227551,0.030796938,0.028552644,0.004831832,0.012397142,0.00066643895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02084669,0.0003757394,0.0008132096,0.006521013,0.00046627008,0.0014019121,0.0005133944,0.00059654086,0.0009732057],"category_scores_gemma":[0.047157913,0.00079227216,0.003021419,0.008469099,0.0005961535,0.0018215473,0.000984225,0.00073108106,0.00024106886],"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":[0.0005634423,0.00003836499,0.97861814,0.0024203975,0.0020723932,0.00017474692,0.0010429223,0.0001842946,0.00018532813,0.0002641815,0.0010236969,0.013412067],"study_design_scores_gemma":[0.00006286569,0.0005421851,0.97178054,0.003713212,0.004943584,0.0008407784,0.0045130765,0.0008061642,0.0007519021,0.0002656341,0.011693148,0.00008688988],"about_ca_topic_score_codex":0.013541478,"about_ca_topic_score_gemma":0.015420106,"teacher_disagreement_score":0.02084669,"about_ca_system_score_codex":0.0015830458,"about_ca_system_score_gemma":0.0036924179,"threshold_uncertainty_score":0.11024916},"labels":[],"label_agreement":null},{"id":"W2117318927","doi":"10.1186/1471-2458-11-454","title":"Physician privacy concerns when disclosing patient data for public health purposes during a pandemic influenza outbreak","year":2011,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":56,"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; College of Family Physicians of Canada; Bruyère; University of Ottawa; Agricultural Research Institute of Ontario","funders":"Natural Resources Canada; Canada Research Chairs","keywords":"Public health; Medicine; Context (archaeology); Pandemic; Public disclosure; Public health surveillance; Family medicine; Internet privacy; Public relations; Environmental health; Nursing; Disease; Coronavirus disease 2019 (COVID-19); Infectious disease (medical specialty); Political science","score_opus":0.29454348941475383,"score_gpt":0.3894454585748246,"score_spread":0.09490196916007076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117318927","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":[0.97163635,0.0017838125,0.0026577234,0.018411206,0.000113098424,0.000109915105,0.000072010975,0.000020352465,0.005195554],"genre_scores_gemma":[0.9959997,0.00062164397,0.0010386419,0.0019660585,0.00004598172,0.000028998342,0.000014082574,0.0000029474036,0.00028211603],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9641796,0.029696919,0.0016895678,0.00076396007,0.0023568661,0.0013130978],"domain_scores_gemma":[0.8556327,0.11764592,0.01895509,0.0021978307,0.0032276502,0.0023407626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02636993,0.00018800938,0.00033504813,0.0006134044,0.0045711775,0.0027363473,0.0007281996,0.0026342797,0.0024028313],"category_scores_gemma":[0.10072519,0.00040105815,0.00046970462,0.0006266255,0.0038438584,0.0027611784,0.002160139,0.0021918914,0.00017266868],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056458265,0.00016625712,0.11412198,0.0010372318,0.0001203253,0.005478261,0.818698,0.0003779652,0.0036213978,0.0031832596,0.004529939,0.04810074],"study_design_scores_gemma":[0.000089272566,0.0009208912,0.07579642,0.0015046784,0.00018969315,0.013134488,0.8605048,0.0015087075,0.0043014647,0.0034267819,0.038506206,0.000116646086],"about_ca_topic_score_codex":0.004572677,"about_ca_topic_score_gemma":0.004780589,"teacher_disagreement_score":0.02636993,"about_ca_system_score_codex":0.003413261,"about_ca_system_score_gemma":0.0040353457,"threshold_uncertainty_score":0.1394592},"labels":[],"label_agreement":null},{"id":"W2118667024","doi":"10.1186/1471-2458-12-166","title":"Patient, physician, encounter, and billing characteristics predict the accuracy of syndromic surveillance case definitions","year":2012,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal; Association des Médecins d'Urgence du Québec; McGill University","funders":"Canadian Institutes of Health Research; McGill University Health Centre; McGill University","keywords":"Medicine; Biostatistics; Public health; Epidemiology; Family medicine; Medical emergency; Pediatrics; Pathology","score_opus":0.07122645553676725,"score_gpt":0.31370292406927125,"score_spread":0.24247646853250399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118667024","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":[0.99836594,0.0002947407,0.00037598066,0.00018366154,0.0000076139236,0.000013049544,0.0002866197,0.000011011992,0.00046145654],"genre_scores_gemma":[0.9993173,0.000050172297,0.00029263,0.000018578345,0.0000095733785,0.0000037323007,0.0002751097,0.000002294676,0.000030592764],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9960593,0.0018338205,0.00051343115,0.00044048307,0.00083566434,0.00031726225],"domain_scores_gemma":[0.9292713,0.040512826,0.022285158,0.0023259744,0.0038326932,0.0017720488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006557098,0.0003627628,0.00026217784,0.0009755553,0.00035247742,0.0010882232,0.0005673903,0.00062624965,0.001221745],"category_scores_gemma":[0.055319674,0.00022917762,0.00049523823,0.00082383107,0.00048200315,0.00057758583,0.0005709343,0.00073135283,0.00018513852],"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":[0.00001766997,0.00000558662,0.9993819,0.000002975944,0.000012253902,0.000005911382,0.00001749972,0.000066040986,0.000013245383,0.0000041267663,0.00003822914,0.0004344993],"study_design_scores_gemma":[0.0000033249473,0.000032264703,0.9969215,0.000011344181,0.00002128499,0.00011696144,0.00007184809,0.0026575,0.00005427455,0.000028544628,0.00007821492,0.000002891653],"about_ca_topic_score_codex":0.019011192,"about_ca_topic_score_gemma":0.02380934,"teacher_disagreement_score":0.019011192,"about_ca_system_score_codex":0.00080660306,"about_ca_system_score_gemma":0.0009204035,"threshold_uncertainty_score":0.037801087},"labels":[],"label_agreement":null},{"id":"W2119608553","doi":"10.1186/1476-072x-6-28","title":"Adaptations for finding irregularly shaped disease clusters","year":2007,"lang":"en","type":"review","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; McMaster University","funders":"Fondation pour la Recherche Médicale","keywords":"Cluster (spacecraft); Adjacency list; Limit (mathematics); Adaptation (eye); Computer science; Pattern recognition (psychology); Artificial intelligence; Data mining; Algorithm; Mathematics; Biology","score_opus":0.15824333597975832,"score_gpt":0.47246256245871304,"score_spread":0.3142192264789547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119608553","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":[0.017905423,0.3838108,0.58232987,0.0033016107,0.00070025766,0.00046618536,0.00043674026,0.0013895939,0.009659544],"genre_scores_gemma":[0.12962352,0.2700355,0.5930374,0.0010687695,0.0005193303,0.0007201089,0.0009668889,0.00014880234,0.0038796447],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899334,0.00035389222,0.000084489,0.00021768887,0.00032103795,0.000029614223],"domain_scores_gemma":[0.99454004,0.0035226226,0.0004776947,0.0003439597,0.0010329991,0.00008269452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020354223,0.00067930145,0.00075171137,0.002800048,0.00019166581,0.0006051504,0.0018058667,0.0008093366,0.0017456533],"category_scores_gemma":[0.00940507,0.0003612071,0.0007993726,0.002594495,0.00067712006,0.0011586077,0.00077105436,0.0007113578,0.0007358849],"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":[0.00005098378,0.000043680906,0.00430917,0.003683799,0.00012692277,0.00015919104,0.00019373612,0.01475864,0.0019504466,0.008018886,0.0064036963,0.9603009],"study_design_scores_gemma":[0.00016449101,0.00067652756,0.037986767,0.0064741373,0.00081480184,0.012460359,0.0010232017,0.17056544,0.028116489,0.10180343,0.63962746,0.00028689657],"about_ca_topic_score_codex":0.0029889771,"about_ca_topic_score_gemma":0.0028745993,"teacher_disagreement_score":0.0029889771,"about_ca_system_score_codex":0.0005654915,"about_ca_system_score_gemma":0.0008834865,"threshold_uncertainty_score":0.01076448},"labels":[],"label_agreement":null},{"id":"W2119661986","doi":"10.1007/bf03404447","title":"A Spatial Analysis of Asthma Prevalence in Ontario","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"SickKids Foundation; Institute for Clinical Evaluative Sciences; Hospital for Sick Children; University of Ottawa; Health Canada","funders":"Government of Ontario; Institute for Clinical Evaluative Sciences","keywords":"Asthma; Cold spot; Geography; Demography; Public health; Spatial ecology; Environmental health; Population; Spatial analysis; Common spatial pattern; Cartography; Medicine; Ecology; Physical geography; Pathology","score_opus":0.05305089335612655,"score_gpt":0.30604230309765484,"score_spread":0.2529914097415283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119661986","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":[0.98691887,0.00045758422,0.0006768599,0.0004562854,0.000008076213,0.000037742382,0.009664631,0.000026212114,0.0017536865],"genre_scores_gemma":[0.99495703,0.00024303197,0.0008666221,0.000025287009,0.0000049983146,0.000021549611,0.0030685272,0.0000061638298,0.00080678635],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992551,0.00010505491,0.00006770717,0.00013208216,0.00026662837,0.00017341189],"domain_scores_gemma":[0.99766916,0.00033042685,0.00051094993,0.00014134613,0.0011339684,0.00021406045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006392569,0.00020536825,0.00031228873,0.0018627333,0.0012966563,0.00085699215,0.0007412003,0.00026458374,0.0011737177],"category_scores_gemma":[0.0033800513,0.00029201608,0.00076750334,0.0065258685,0.0004389267,0.00031628791,0.0009290613,0.00024214556,0.00015484943],"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":[0.00006196706,0.000019799063,0.98771465,0.00006384771,0.00012831831,0.0001299462,0.0008427451,0.0021532855,0.00036222397,0.00042124378,0.0017456606,0.0063564107],"study_design_scores_gemma":[0.0000057771495,0.0000094008865,0.99424464,0.000017251248,0.000039101204,0.0000368028,0.00093456043,0.003033366,0.000059958238,0.000051253413,0.001560448,0.0000073603496],"about_ca_topic_score_codex":0.992246,"about_ca_topic_score_gemma":0.99531543,"teacher_disagreement_score":0.014446169,"about_ca_system_score_codex":0.014446169,"about_ca_system_score_gemma":0.019242924,"threshold_uncertainty_score":0.10481477},"labels":[],"label_agreement":null},{"id":"W2120467164","doi":"10.1197/jamia.m2544","title":"HealthMap: Global Infectious Disease Monitoring through Automated Classification and Visualization of Internet Media Reports","year":2007,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":503,"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":"U.S. National Library of Medicine; Canadian Institutes of Health Research; National Institutes of Health","keywords":"The Internet; Visualization; Computer science; Infectious disease (medical specialty); Disease; World Wide Web; Data science; Medicine; Artificial intelligence; Pathology","score_opus":0.013291141399848121,"score_gpt":0.34652977210781216,"score_spread":0.33323863070796406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120467164","genre_codex":"software","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":[0.20948231,0.0019163026,0.22578393,0.00399991,0.00056092255,0.002146423,0.10013672,0.43814772,0.01782581],"genre_scores_gemma":[0.5735584,0.0008585879,0.34531403,0.0005150906,0.0006313219,0.0009422372,0.072335474,0.001942801,0.0039020693],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99750227,0.0008393591,0.00019015424,0.0005392033,0.00080654747,0.0001224573],"domain_scores_gemma":[0.99140817,0.0035780931,0.0017863896,0.0011914615,0.0015617018,0.0004742073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034301353,0.0016754448,0.00054297695,0.01049567,0.00046544525,0.0026602293,0.0012012802,0.00072954403,0.0036817573],"category_scores_gemma":[0.011914876,0.000397989,0.0006140873,0.0035545335,0.00042510967,0.0029636628,0.0020104828,0.00064223364,0.0025028882],"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":[0.0015181543,0.0006940847,0.14660487,0.0011776977,0.0005424603,0.0007319279,0.0013780575,0.021017535,0.009833995,0.0035465371,0.21963333,0.5933213],"study_design_scores_gemma":[0.00028418744,0.00070814765,0.1646074,0.00038089638,0.00027843638,0.0007459556,0.0013820205,0.7032286,0.030832749,0.011133123,0.08614567,0.0002728107],"about_ca_topic_score_codex":0.00572099,"about_ca_topic_score_gemma":0.004135867,"teacher_disagreement_score":0.01049567,"about_ca_system_score_codex":0.00064998434,"about_ca_system_score_gemma":0.0011263577,"threshold_uncertainty_score":0.018140495},"labels":[],"label_agreement":null},{"id":"W2121912070","doi":"10.1093/aje/kwt092","title":"Exploring the Implications of Small-Area Variation in the Incidence of Multiple Sclerosis","year":2013,"lang":"en","type":"article","venue":"American Journal of Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba; Agriculture Food and Rural Development; Ministry of Agriculture, Food and Rural Affairs","funders":"","keywords":"Demography; Poisson regression; Incidence (geometry); Socioeconomic status; Rate ratio; Population; Geography; Scan statistic; Epidemiology; Medicine; Statistics","score_opus":0.18707985714711278,"score_gpt":0.3314568178679614,"score_spread":0.1443769607208486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121912070","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":[0.9935475,0.0010661347,0.0026713717,0.00086970616,0.000026860323,0.000027836846,0.000576687,0.000011688725,0.0012022026],"genre_scores_gemma":[0.9989085,0.0001682044,0.00054081564,0.000086777305,0.000009330916,0.00000702122,0.00019098993,0.00000373568,0.00008454872],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.996512,0.0019867897,0.00013951368,0.00066054874,0.0003918475,0.00030936822],"domain_scores_gemma":[0.9899018,0.0057555917,0.0021511607,0.0009433684,0.0008523184,0.00039581055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046163993,0.00024456228,0.00028200125,0.0015195264,0.0006450822,0.0010134546,0.0009268673,0.0002921826,0.0005803904],"category_scores_gemma":[0.022724226,0.00019879267,0.00050892774,0.0034072564,0.0012609637,0.0006069029,0.0012618694,0.00046048584,0.000056286535],"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":[0.000016723661,0.0000073887577,0.99459296,0.0000130303515,0.00013088096,0.00007188016,0.00040112872,0.00057781435,0.00008033116,0.0003950578,0.00010438542,0.0036085201],"study_design_scores_gemma":[0.0000031671445,0.00002910081,0.99445325,0.000017736691,0.000064712825,0.00009314079,0.0011899123,0.0025779507,0.00007247347,0.00069165323,0.00080071815,0.0000063145694],"about_ca_topic_score_codex":0.45888326,"about_ca_topic_score_gemma":0.5214818,"teacher_disagreement_score":0.45888326,"about_ca_system_score_codex":0.0019304923,"about_ca_system_score_gemma":0.0028484452,"threshold_uncertainty_score":0.9124245},"labels":[],"label_agreement":null},{"id":"W2123089939","doi":"10.21083/surg.v3i2.1091","title":"Geographic distribution of West Nile virus reported in humans and corvids in southern Ontario, 2006","year":2010,"lang":"en","type":"article","venue":"SURG Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Geography; West Nile virus; Spatial distribution; Outbreak; Cluster (spacecraft); Cartography; Distribution (mathematics); Ecology; Biology; Virology; Computer science; Virus","score_opus":0.010894970012524758,"score_gpt":0.25382913523245193,"score_spread":0.24293416521992717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123089939","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":[0.9927781,0.00048760802,0.00011045988,0.00009127705,0.0000060684024,0.0000236546,0.0042013894,0.000013743001,0.002287679],"genre_scores_gemma":[0.9949538,0.0004704001,0.0002097985,0.00002693153,0.000004024224,0.000018590654,0.0025195791,0.0000036949914,0.0017931263],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997811,0.000011831327,0.000018240244,0.000038476304,0.00010731526,0.000043119704],"domain_scores_gemma":[0.9993507,0.000030234016,0.0002276092,0.000021124426,0.0002556135,0.00011461282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015814234,0.00018994548,0.00019661446,0.0010396112,0.00076070067,0.0004884162,0.0002823868,0.00012208644,0.0009001533],"category_scores_gemma":[0.0007941577,0.00016784723,0.0001621489,0.0016227454,0.000363765,0.00016229044,0.00040554014,0.00012326051,0.00013024104],"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":[0.000040837986,0.000004937814,0.9930512,0.000051033032,0.000027157539,0.00011374565,0.0010941072,0.00013904838,0.00071200443,0.000027507003,0.00095547375,0.0037830772],"study_design_scores_gemma":[0.0000025262696,0.000009509632,0.9981521,0.000011050194,0.0000069184243,0.00006184848,0.00056328234,0.000075473974,0.000053807496,0.0000048007246,0.0010555352,0.0000030994563],"about_ca_topic_score_codex":0.96494263,"about_ca_topic_score_gemma":0.98731345,"teacher_disagreement_score":0.035057366,"about_ca_system_score_codex":0.0065088808,"about_ca_system_score_gemma":0.005361862,"threshold_uncertainty_score":0.07052761},"labels":[],"label_agreement":null},{"id":"W2123248364","doi":"10.3201/eid1505.081174","title":"Real-Time Surveillance for Respiratory Disease Outbreaks, Ontario, Canada","year":2009,"lang":"en","type":"article","venue":"Emerging infectious diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"Ontario Ministry of Health and Long-Term Care","keywords":"Outbreak; Disease surveillance; Medicine; Public health surveillance; Coronavirus disease 2019 (COVID-19); Betacoronavirus; Environmental health; 2019-20 coronavirus outbreak; Geography; Virology; Disease; Intensive care medicine; Public health; Infectious disease (medical specialty); Pathology","score_opus":0.007862193783659054,"score_gpt":0.2529855301389011,"score_spread":0.24512333635524208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123248364","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":[0.88449967,0.008663122,0.010738633,0.0078111393,0.00026725265,0.0015839748,0.052167114,0.0008355793,0.033433527],"genre_scores_gemma":[0.9789393,0.0016784546,0.006199794,0.000446644,0.000029674624,0.00015765915,0.006381811,0.000035045236,0.0061316676],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989857,0.00015328452,0.00008322006,0.00013974615,0.00047998427,0.00015809902],"domain_scores_gemma":[0.9965185,0.00030553687,0.0004325752,0.00008332703,0.0022175305,0.0004425219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010613716,0.00028574205,0.0002140681,0.0008632845,0.0009950063,0.0009129955,0.0009338782,0.0002484868,0.001216789],"category_scores_gemma":[0.003949072,0.00029414918,0.00019209302,0.0013369318,0.0003415911,0.00035461312,0.0004458506,0.00038392938,0.00018712928],"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":[0.0007936149,0.00021854174,0.7930919,0.001040959,0.00025386465,0.00074305217,0.0033492497,0.010348958,0.007538033,0.0011490653,0.044233043,0.1372398],"study_design_scores_gemma":[0.00012596503,0.00012470248,0.96404546,0.0001801644,0.000064521286,0.000106028194,0.0014663056,0.012545117,0.001299483,0.00013294362,0.01986059,0.000048685855],"about_ca_topic_score_codex":0.99616987,"about_ca_topic_score_gemma":0.99728096,"teacher_disagreement_score":0.028470991,"about_ca_system_score_codex":0.028470991,"about_ca_system_score_gemma":0.039430454,"threshold_uncertainty_score":0.20657247},"labels":[],"label_agreement":null},{"id":"W2123737882","doi":"10.1186/1471-2458-14-1144","title":"A systematic review of barriers to data sharing in public health","year":2014,"lang":"en","type":"review","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":599,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University Health Network","funders":"National Institute of General Medical Sciences; Wellcome Trust; Bill and Melinda Gates Foundation","keywords":"Data sharing; Public health; Systematic review; Medicine; Grey literature; Context (archaeology); Global health; Biostatistics; Public relations; Health policy; Health informatics; Variety (cybernetics); Inclusion (mineral); Knowledge management; Political science; MEDLINE; Computer science; Sociology; Nursing; Alternative medicine; Social science","score_opus":0.23480805182443962,"score_gpt":0.45216720552337464,"score_spread":0.21735915369893502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123737882","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024911256,0.9871898,0.0016858741,0.002424734,0.00044330067,0.0030228174,0.0010600376,0.000036688463,0.0016455923],"genre_scores_gemma":[0.03191227,0.9521755,0.0057408577,0.0021730352,0.00015208957,0.006711998,0.00082274695,0.000035153364,0.00027642353],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.835289,0.08486423,0.04909956,0.0052642436,0.023140926,0.0023420346],"domain_scores_gemma":[0.5915476,0.33172193,0.037720658,0.006469106,0.030091725,0.0024489225],"candidate_categories":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.095629856,0.0024637487,0.009830826,0.02872491,0.0033897993,0.0071875686,0.0048410245,0.0044766925,0.0052082706],"category_scores_gemma":[0.35111547,0.0023382783,0.008686187,0.03027555,0.0042148456,0.010860105,0.0061915372,0.00346613,0.0006694507],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008798508,0.000018085686,0.00083442667,0.95306844,0.0026703307,0.00014640152,0.0020049238,0.00014435216,0.00008032546,0.0010691559,0.0022856924,0.037589855],"study_design_scores_gemma":[0.000040844345,0.000040665058,0.00071505556,0.9804735,0.005134996,0.000106081294,0.0010400544,0.00005007448,0.00006927367,0.00043070488,0.011878491,0.000020334495],"about_ca_topic_score_codex":0.01687506,"about_ca_topic_score_gemma":0.038900364,"teacher_disagreement_score":0.995159,"about_ca_system_score_codex":0.01808361,"about_ca_system_score_gemma":0.08504092,"threshold_uncertainty_score":0.50574505},"labels":[],"label_agreement":null},{"id":"W2124170079","doi":"10.1503/cmaj.090215","title":"Early detection of disease outbreaks using the Internet","year":2009,"lang":"en","type":"review","venue":"Canadian Medical Association Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":231,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Ottawa Hospital","funders":"U.S. National Library of Medicine; National Institutes of Health","keywords":"Outbreak; Public health; Government (linguistics); Disease surveillance; The Internet; Disease; Infectious disease (medical specialty); Internet privacy; Environmental health; Medicine; Computer science; Computer security; World Wide Web; Virology; Pathology","score_opus":0.0219338277227732,"score_gpt":0.30346913421467864,"score_spread":0.28153530649190545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124170079","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":[0.0003172353,0.9963527,0.00031153316,0.0005755954,0.00022170662,0.000030798397,0.00005845145,0.000022849525,0.002109224],"genre_scores_gemma":[0.0022700909,0.99625444,0.0005514425,0.00026238392,0.0002069607,0.000028779046,0.0000822194,0.0000021140058,0.00034161858],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992836,0.00020954541,0.00010385098,0.000082165265,0.00027369938,0.00004710998],"domain_scores_gemma":[0.99615675,0.0025425442,0.0004971178,0.00007892491,0.0006075097,0.00011722088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017779592,0.0011069356,0.0022249732,0.004098152,0.00024378636,0.001180671,0.0015319831,0.0014387018,0.0050039412],"category_scores_gemma":[0.0056954804,0.00042689382,0.001257016,0.0024791711,0.00037703267,0.00181806,0.0007723479,0.0017546328,0.0020762999],"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":[0.000054849705,0.000076926524,0.00050068105,0.015979074,0.00018410594,0.00006292106,0.00003288775,0.0002041602,0.00028574152,0.0006139121,0.012967154,0.96903765],"study_design_scores_gemma":[0.00024609378,0.0005458298,0.014503735,0.0856702,0.0015766261,0.0025848406,0.00033706523,0.00093193783,0.0022538416,0.004602869,0.88663846,0.00010844811],"about_ca_topic_score_codex":0.003621792,"about_ca_topic_score_gemma":0.004180995,"teacher_disagreement_score":0.0050039412,"about_ca_system_score_codex":0.0008108087,"about_ca_system_score_gemma":0.0016752421,"threshold_uncertainty_score":0.016739786},"labels":[],"label_agreement":null},{"id":"W2124623753","doi":"10.5210/ojphi.v2i3.3041","title":"DIAL: A Platform for real-time Laboratory Surveillance","year":2010,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Provincial Laboratory of Public Health; University of Alberta; Public Health Agency of Canada","funders":"","keywords":"Computer science; Data science; Domain (mathematical analysis); Data management; Data integration; Database; Data mining; World Wide Web","score_opus":0.04111636132465599,"score_gpt":0.3498060477025974,"score_spread":0.30868968637794136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124623753","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01919752,0.00065393606,0.782195,0.0010936098,0.00027237512,0.0012537964,0.0056973165,0.17779034,0.011845997],"genre_scores_gemma":[0.26107728,0.0009732122,0.68338376,0.0019710928,0.0003053755,0.0018031618,0.023888526,0.004741031,0.021856632],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970696,0.00049741106,0.00031629857,0.00046260108,0.0014002769,0.00025378246],"domain_scores_gemma":[0.9950073,0.0013616246,0.0004110055,0.0012208581,0.0010222183,0.0009769842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045781606,0.0007660172,0.0007181896,0.002740573,0.0005392886,0.0027505776,0.0032745758,0.00075770076,0.006066972],"category_scores_gemma":[0.008456691,0.0006501659,0.0006227725,0.0012011554,0.00081037683,0.0029992585,0.0066137603,0.0016304199,0.0044354685],"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":[0.0029882973,0.00078298245,0.023295455,0.0012435201,0.0002727832,0.0009998069,0.0020517788,0.009027173,0.056406964,0.04027935,0.16975504,0.69289696],"study_design_scores_gemma":[0.0007961715,0.00071136124,0.016390357,0.0004181985,0.00024996098,0.0018011787,0.0006611698,0.18860932,0.06937613,0.035356674,0.68503565,0.00059397134],"about_ca_topic_score_codex":0.00419683,"about_ca_topic_score_gemma":0.0030082834,"teacher_disagreement_score":0.006066972,"about_ca_system_score_codex":0.0013737581,"about_ca_system_score_gemma":0.0028920867,"threshold_uncertainty_score":0.024211884},"labels":[],"label_agreement":null},{"id":"W2125190682","doi":"10.1111/j.1541-0420.2006.00665.x","title":"Robustness of Prevalence Estimates Derived from Misclassified Data from Administrative Databases","year":2006,"lang":"en","type":"article","venue":"Biometrics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal General Hospital","funders":"","keywords":"Medical diagnosis; Database; Diagnosis code; Bayesian probability; Robustness (evolution); Computer science; Reimbursement; Data mining; Medicine; Artificial intelligence; Health care; Environmental health; Population","score_opus":0.1719976113805118,"score_gpt":0.3665172051345096,"score_spread":0.19451959375399777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125190682","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59034806,0.0082770055,0.37939346,0.004156866,0.00090718694,0.0013459348,0.006033526,0.0011667134,0.008371275],"genre_scores_gemma":[0.9699616,0.00053839356,0.025839133,0.00047807003,0.00014743277,0.00024979957,0.002327337,0.00012762858,0.00033055374],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.77301955,0.18105166,0.010652871,0.01830621,0.014481785,0.002487926],"domain_scores_gemma":[0.20279759,0.6973918,0.034359314,0.053954873,0.010551155,0.0009452857],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.25413132,0.0014432864,0.0024113778,0.00622725,0.001400211,0.006211539,0.0040511163,0.002668129,0.002084124],"category_scores_gemma":[0.64294213,0.0015015018,0.004614892,0.004559298,0.0044963197,0.004468872,0.0055476436,0.0035179532,0.00065379025],"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":[0.0041005085,0.00028744285,0.7245588,0.0015453675,0.020455675,0.000725424,0.0025277804,0.1316414,0.0010289499,0.019075321,0.0039535174,0.09009993],"study_design_scores_gemma":[0.0008685162,0.0012652803,0.4616958,0.0016760036,0.0067154295,0.0018848559,0.0021897075,0.42447954,0.004948399,0.08579624,0.007940248,0.000540072],"about_ca_topic_score_codex":0.012556161,"about_ca_topic_score_gemma":0.003599664,"teacher_disagreement_score":0.7458687,"about_ca_system_score_codex":0.0026317453,"about_ca_system_score_gemma":0.0013744994,"threshold_uncertainty_score":0.9197889},"labels":[],"label_agreement":null},{"id":"W2127196828","doi":"10.1186/1476-072x-9-25","title":"Injury surveillance in low-resource settings using Geospatial and Social Web technologies","year":2010,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Social Sciences and Humanities Research Council of Canada; Canadian Institutes of Health Research; Michael Smith Health Research BC","keywords":"Geospatial analysis; Health informatics; Health geography; Public health; Volunteered geographic information; Geolocation; Geomatics; Resource (disambiguation); Computer science; Environmental health; Environmental epidemiology; Data science; Geography; World Wide Web; Medicine; Remote sensing; Health policy; International health","score_opus":0.010577078323297982,"score_gpt":0.32074327018267607,"score_spread":0.3101661918593781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127196828","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":[0.9806802,0.0005343886,0.0027967058,0.0022010794,0.000029879686,0.0012341668,0.0016283818,0.00010694002,0.010788175],"genre_scores_gemma":[0.9883886,0.0009822121,0.007860342,0.0002444361,0.000029249128,0.00088301685,0.00055750663,0.000009788125,0.0010448651],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99809307,0.001167142,0.00009435003,0.00014464196,0.00029034927,0.00021042733],"domain_scores_gemma":[0.9961909,0.0018413885,0.0007237862,0.00021372554,0.00066541217,0.00036473543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002050728,0.00049896067,0.00029969777,0.0029474027,0.0011091745,0.0018706707,0.0008033583,0.00047183526,0.003101906],"category_scores_gemma":[0.007882276,0.0002464696,0.000364206,0.002090019,0.00057219365,0.0022867348,0.0021164657,0.00050405384,0.0005593616],"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":[0.00035958432,0.0021508457,0.6926621,0.0023966609,0.00022351067,0.0030708064,0.040436406,0.001725865,0.0035890033,0.0019604939,0.008215835,0.24320894],"study_design_scores_gemma":[0.00011904443,0.0028705746,0.77508193,0.0033441826,0.000229252,0.0018138447,0.16798288,0.009235411,0.002779778,0.0029135798,0.03347725,0.00015221469],"about_ca_topic_score_codex":0.022569608,"about_ca_topic_score_gemma":0.03998789,"teacher_disagreement_score":0.022569608,"about_ca_system_score_codex":0.0010498576,"about_ca_system_score_gemma":0.00189275,"threshold_uncertainty_score":0.044876456},"labels":[],"label_agreement":null},{"id":"W2127523571","doi":"10.1093/aje/kwq248","title":"Mapping Health Data: Improved Privacy Protection With Donut Method Geomasking","year":2010,"lang":"en","type":"article","venue":"American Journal of Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":135,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Institute of Environmental Health Sciences; National Institute of Allergy and Infectious Diseases; School of Medicine, University of North Carolina at Chapel Hill; National Institutes of Health; University of Toronto","keywords":"Geocoding; Computer science; Cluster (spacecraft); Masking (illustration); Data mining; Privacy protection; Statistics; Mathematics; Computer security; Geography; Remote sensing; Computer network","score_opus":0.06779756625519004,"score_gpt":0.39582309707788677,"score_spread":0.3280255308226967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127523571","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":[0.054957062,0.00031286484,0.940915,0.0004993997,0.00012577629,0.00012024478,0.00010031128,0.001104656,0.001864709],"genre_scores_gemma":[0.5598582,0.00016013587,0.43733156,0.00025806896,0.00007840923,0.00017935477,0.00026379098,0.00010261741,0.0017678761],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995344,0.0022868926,0.00021543141,0.00071747566,0.0012078194,0.00022838531],"domain_scores_gemma":[0.98836386,0.0054086815,0.000963504,0.0035676078,0.0015088564,0.00018741094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038922967,0.00060709036,0.0010483669,0.0011096738,0.0008836081,0.0013108305,0.0015718873,0.0011593826,0.00083828217],"category_scores_gemma":[0.019018365,0.00040659442,0.00059483334,0.0012939988,0.0010153189,0.0018250254,0.0024277638,0.0010550733,0.00027935684],"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":[0.0012883405,0.0002961104,0.015260163,0.000291217,0.00030890875,0.00036438738,0.0017195011,0.17011368,0.026561232,0.03827952,0.008562717,0.7369542],"study_design_scores_gemma":[0.0001095558,0.00026239548,0.0035088414,0.00003245912,0.00006257107,0.000605338,0.00035287996,0.93737024,0.028140929,0.016043486,0.013426951,0.00008426589],"about_ca_topic_score_codex":0.0033609457,"about_ca_topic_score_gemma":0.0027096688,"teacher_disagreement_score":0.0038922967,"about_ca_system_score_codex":0.00073619874,"about_ca_system_score_gemma":0.0013541888,"threshold_uncertainty_score":0.020584702},"labels":[],"label_agreement":null},{"id":"W2128379663","doi":"10.6000/1929-6029.2014.03.03.1","title":"Comparison of Methods for Clustered Data Analysis in a Non-Ideal Situation: Results from an Evaluation of Predictors of Yellow Fever Vaccine Refusal in the Global TravEpiNet (GTEN) Consortium","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Centers for Disease Control and Prevention; National Institutes of Health; Georgetown University; Johns Hopkins University; Kaiser Permanente; Northwestern University; Emory University; Tulane University; University of Southern California","keywords":"Cluster analysis; Statistics; Logistic regression; Random effects model; Generalized estimating equation; Standard error; Odds ratio; Econometrics; Cluster (spacecraft); Sample size determination; Mathematics; Population; Sample (material); Medicine; Computer science; Environmental health; Meta-analysis; Internal medicine","score_opus":0.20683399517557813,"score_gpt":0.5949867239918685,"score_spread":0.38815272881629037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128379663","genre_codex":"methods","genre_gemma":"empirical","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32192668,0.003138588,0.66165817,0.0021212043,0.000559187,0.0056359456,0.0016166717,0.0008119357,0.0025316544],"genre_scores_gemma":[0.50553274,0.00044204097,0.4868624,0.0003843675,0.00006760833,0.0050230706,0.0009390754,0.00042048306,0.00032819255],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.41784957,0.54583406,0.013309388,0.010268498,0.011729498,0.0010089477],"domain_scores_gemma":[0.25627977,0.6763219,0.01525553,0.032405272,0.01818295,0.0015545775],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.36655727,0.0015292467,0.0022606656,0.0035110281,0.0017423648,0.0028855542,0.0041003264,0.0019777128,0.0020866443],"category_scores_gemma":[0.60395944,0.0012343792,0.006922888,0.0043820515,0.0028829954,0.0033131533,0.005164719,0.00249645,0.00034682595],"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":[0.022888714,0.002542826,0.3672021,0.008429353,0.052912455,0.0008534756,0.019938001,0.13669366,0.0014335667,0.055815157,0.015209039,0.31608164],"study_design_scores_gemma":[0.00852746,0.0109479185,0.20716034,0.0039498243,0.0074681714,0.0011929445,0.008496871,0.6560908,0.0037272354,0.0770663,0.014445616,0.0009265593],"about_ca_topic_score_codex":0.0084108375,"about_ca_topic_score_gemma":0.0070737954,"teacher_disagreement_score":0.63344276,"about_ca_system_score_codex":0.003751509,"about_ca_system_score_gemma":0.006472179,"threshold_uncertainty_score":0.78114766},"labels":[],"label_agreement":null},{"id":"W2129813704","doi":"10.2196/jmir.2102","title":"FluBreaks: Early Epidemic Detection from Google Flu Trends","year":2012,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":103,"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":"York University; University of Michigan","keywords":"Disease control; Medicine; Disease surveillance; Baseline (sea); Service (business); Public health surveillance; Epidemic disease; Bird flu; Disease; Geography; Demography; Medical emergency; Environmental health; Public health; Virology; Business; Political science","score_opus":0.08038576323393631,"score_gpt":0.4395567260200853,"score_spread":0.35917096278614896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129813704","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":[0.52720183,0.0038317598,0.25857258,0.002114259,0.0009886357,0.0023790486,0.053241123,0.12736645,0.02430435],"genre_scores_gemma":[0.78399324,0.00070592295,0.17160466,0.00030509106,0.000279262,0.00048138158,0.038847126,0.0006264272,0.0031568466],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984428,0.00021228522,0.00015520118,0.00033888288,0.00065655797,0.00019425679],"domain_scores_gemma":[0.9967231,0.0011920029,0.0004596457,0.00043084574,0.0009992665,0.00019520508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021350079,0.0017433463,0.0009854974,0.0075228545,0.0006256794,0.0015301431,0.0014440598,0.0010869836,0.0013956638],"category_scores_gemma":[0.007944392,0.0005468435,0.00086700683,0.0024016122,0.00032495218,0.0025982773,0.0015845364,0.0008462484,0.0015068781],"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":[0.001644408,0.0006854076,0.21760151,0.0014651982,0.00068737654,0.0012791198,0.00078673614,0.050570883,0.017920947,0.0071483385,0.12393088,0.5762793],"study_design_scores_gemma":[0.00013547388,0.00054696656,0.051796276,0.00014426454,0.00014486759,0.0013034765,0.00039546326,0.88925296,0.018764922,0.007667805,0.029680708,0.00016672473],"about_ca_topic_score_codex":0.010235816,"about_ca_topic_score_gemma":0.0108410055,"teacher_disagreement_score":0.010235816,"about_ca_system_score_codex":0.00068321906,"about_ca_system_score_gemma":0.0010666131,"threshold_uncertainty_score":0.020352483},"labels":[],"label_agreement":null},{"id":"W2131314429","doi":"10.1371/journal.pone.0090536","title":"Factors Influencing Performance of Internet-Based Biosurveillance Systems Used in Epidemic Intelligence for Early Detection of Infectious Diseases Outbreaks","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Response Biomedical (Canada); Public Health Agency of Canada","funders":"","keywords":"The Internet; Infectious disease (medical specialty); Outbreak; Gold standard (test); Disease surveillance; Medicine; Epidemiology; Disease; Raw data; Computer science; Environmental health; World Wide Web; Pathology; Internal medicine","score_opus":0.04012075539345067,"score_gpt":0.2548559444686758,"score_spread":0.21473518907522515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131314429","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":[0.9978223,0.00018262945,0.0005194161,0.00021832214,0.000009897364,0.000047144746,0.00021787477,0.000029460143,0.00095303834],"genre_scores_gemma":[0.99929154,0.000048006368,0.00034921663,0.000024068064,0.000009505972,0.000012941828,0.00017559362,0.0000062654485,0.00008276235],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9899615,0.0058289226,0.0012257877,0.0009723092,0.0013417533,0.00066966534],"domain_scores_gemma":[0.8916905,0.07587489,0.016980642,0.0038782957,0.0084502725,0.003125361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014309173,0.0004408678,0.00031428214,0.0013636461,0.00034756295,0.0025305636,0.0005698924,0.0007192108,0.0012929117],"category_scores_gemma":[0.10101174,0.00022207203,0.00052298396,0.0011373877,0.0005340967,0.0021267333,0.0009656579,0.0007993829,0.00073761627],"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":[0.00041483893,0.00020384621,0.9835481,0.0000601033,0.00014136088,0.00006006949,0.00053627323,0.0019769543,0.00042428207,0.000065608765,0.00036704287,0.01220156],"study_design_scores_gemma":[0.000017156703,0.0006254347,0.98583823,0.000044977984,0.000074495336,0.00016208383,0.0008487719,0.010933545,0.0009043501,0.00010735065,0.00042431068,0.000019249077],"about_ca_topic_score_codex":0.0036090089,"about_ca_topic_score_gemma":0.0016855563,"teacher_disagreement_score":0.014309173,"about_ca_system_score_codex":0.00076803274,"about_ca_system_score_gemma":0.0005473037,"threshold_uncertainty_score":0.07567501},"labels":[],"label_agreement":null},{"id":"W2131476586","doi":"10.2196/mhealth.3114","title":"Real-Time Monitoring of School Absenteeism to Enhance Disease Surveillance: A Pilot Study of a Mobile Electronic Reporting System","year":2014,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Office of the Higher Education Commission; Mahidol University","keywords":"Disease surveillance; Electronic surveillance; Absenteeism; Medicine; Computer science; Medical emergency; Environmental health; Psychology; Public health; Internet privacy; Nursing","score_opus":0.03224327845778283,"score_gpt":0.3857247300951332,"score_spread":0.35348145163735034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131476586","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":[0.9966239,0.00003028154,0.0016015726,0.000078314864,0.000012873361,0.0009920441,0.00009932666,0.00006322215,0.0004985003],"genre_scores_gemma":[0.98650116,0.00012216726,0.011510482,0.0001351469,0.000026646387,0.0008946783,0.00030702303,0.000015237222,0.00048745982],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971814,0.0017087816,0.00021041084,0.00024911267,0.00033343225,0.00031691985],"domain_scores_gemma":[0.9905004,0.0047891885,0.0007077888,0.0005899621,0.002652332,0.00076037593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054764193,0.00060983456,0.0004931262,0.00070014474,0.0004840389,0.0007969915,0.000912725,0.00069394393,0.0014818255],"category_scores_gemma":[0.01217208,0.00026582828,0.00062319764,0.00042552038,0.0004010876,0.0014862749,0.0007862947,0.0005166078,0.00041492085],"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":[0.006305166,0.064040296,0.5352793,0.0027308508,0.00052047346,0.0037157955,0.01997976,0.008134388,0.04353306,0.00063564006,0.003916724,0.31120855],"study_design_scores_gemma":[0.0028302306,0.19594768,0.67426294,0.00037329644,0.00080454524,0.0016799191,0.023657681,0.07168656,0.020524684,0.00026427777,0.0077126627,0.00025552537],"about_ca_topic_score_codex":0.004074615,"about_ca_topic_score_gemma":0.003623694,"teacher_disagreement_score":0.0054764193,"about_ca_system_score_codex":0.000649436,"about_ca_system_score_gemma":0.0009745149,"threshold_uncertainty_score":0.028962374},"labels":[],"label_agreement":null},{"id":"W2131844896","doi":"10.1111/j.1865-1682.2011.01264.x","title":"A Minimum Data Set of Animal Health Laboratory Data to allow for Collation and Analysis across Jurisdictions for the Purpose of Surveillance","year":2011,"lang":"en","type":"article","venue":"Transboundary and Emerging Diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Yukon Department of Environment; Ministry of Agriculture; Ministry of Agriculture, Food and Rural Affairs; Government of British Columbia; Ministère de l'Agriculture, des Pêcheries et de l'Alimentation; Agriculture Food and Rural Development; Canadian Food Inspection Agency","funders":"","keywords":"Data set; Set (abstract data type); Computer science; Data mining; Identification (biology); Disease surveillance; Population; Data sharing; Identifier; Test (biology); Disease; Data science; Environmental health; Medicine; Pathology; Biology; Artificial intelligence","score_opus":0.11331886437677503,"score_gpt":0.38671035106749196,"score_spread":0.27339148669071694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131844896","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035017923,0.00024521342,0.0868234,0.0007881219,0.00023555601,0.010429112,0.84889996,0.0017586537,0.015802136],"genre_scores_gemma":[0.057502795,0.00015378838,0.11512457,0.00026689994,0.000046270954,0.010915932,0.81326807,0.0002159215,0.0025057632],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9755567,0.005008673,0.00720247,0.0020905077,0.008838342,0.0013032439],"domain_scores_gemma":[0.8791459,0.023351341,0.009413674,0.028364955,0.05687108,0.0028529968],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.019684272,0.0008256933,0.0023758058,0.009522953,0.0023405387,0.0035294539,0.0033032747,0.0015539274,0.008567334],"category_scores_gemma":[0.06280298,0.0010648521,0.0015363349,0.010725702,0.0008969756,0.0019721086,0.0027563048,0.0019228176,0.0049161823],"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":[0.0046727792,0.0026121705,0.1843147,0.008610685,0.00085527543,0.0013422625,0.0034282852,0.028178765,0.025411457,0.035374176,0.41227466,0.29292473],"study_design_scores_gemma":[0.0006133164,0.0010808342,0.20253098,0.0022567804,0.000300303,0.00096896157,0.0017611379,0.014453257,0.017298475,0.0060278284,0.7523729,0.000335274],"about_ca_topic_score_codex":0.040841654,"about_ca_topic_score_gemma":0.04086641,"teacher_disagreement_score":0.98031574,"about_ca_system_score_codex":0.0049037673,"about_ca_system_score_gemma":0.031205814,"threshold_uncertainty_score":0.1041016},"labels":[],"label_agreement":null},{"id":"W2132287323","doi":"10.1186/1476-072x-5-17","title":"A GIS-driven integrated real-time surveillance pilot system for national West Nile virus dead bird surveillance in Canada.","year":2006,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada","funders":"British Columbia Centre for Disease Control; York University; Public Health Agency; Public Health Agency of Canada","keywords":"Geographic information system; West Nile virus; Disease surveillance; Public health surveillance; Public health; Geography; Cartography; Medicine; Virus; Virology","score_opus":0.016394012921267315,"score_gpt":0.2890455310604535,"score_spread":0.2726515181391862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132287323","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":[0.6998232,0.0005792187,0.11253093,0.0015471189,0.00022260935,0.0074828337,0.030266969,0.09402589,0.053521227],"genre_scores_gemma":[0.8420127,0.0003554084,0.109185,0.00034338175,0.00003007022,0.0012679603,0.022360595,0.0007158223,0.02372898],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99951375,0.000043450997,0.000022442251,0.00010533334,0.00020964842,0.00010539744],"domain_scores_gemma":[0.99797124,0.00010872327,0.000054814205,0.00011180989,0.0013591971,0.00039420073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010230327,0.0006399793,0.000295506,0.0010113419,0.001162514,0.00083245063,0.0011946594,0.00032866382,0.007269674],"category_scores_gemma":[0.0015664443,0.00032774953,0.00025080628,0.0008889799,0.00047677715,0.00081365457,0.0010042039,0.00039368327,0.0016466454],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004659958,0.001606317,0.14623602,0.0008712638,0.00020138305,0.0036996289,0.006465554,0.018464562,0.097148694,0.0026834717,0.18792626,0.5300369],"study_design_scores_gemma":[0.0012093587,0.0019229813,0.34698263,0.00028304488,0.0003317592,0.0012927862,0.0062712077,0.30951628,0.05002156,0.0015180804,0.28018668,0.00046362745],"about_ca_topic_score_codex":0.8144006,"about_ca_topic_score_gemma":0.75515026,"teacher_disagreement_score":0.18559939,"about_ca_system_score_codex":0.006436129,"about_ca_system_score_gemma":0.0153493555,"threshold_uncertainty_score":0.37338477},"labels":[],"label_agreement":null},{"id":"W2133154607","doi":"10.1017/s1481803500009969","title":"Can Telehealth Ontario respiratory call volume be used as a proxy for emergency department respiratory visit surveillance by public health?","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Emergency Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"Queen's University; Ontario Ministry of Health and Long-Term Care","keywords":"Telehealth; Medicine; Emergency department; Proxy (statistics); Medical emergency; Emergency medicine; Medical diagnosis; Telemedicine; Diagnosis code; Public health; Health care; Environmental health; Statistics; Nursing","score_opus":0.11602237799208596,"score_gpt":0.3485213377527116,"score_spread":0.23249895976062562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133154607","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":[0.80436826,0.009114555,0.015061571,0.051740274,0.0023253995,0.00060694804,0.076187804,0.0004948722,0.04010035],"genre_scores_gemma":[0.98179966,0.0009651084,0.0058279554,0.002711184,0.0003007442,0.00024969288,0.006854474,0.000030000672,0.0012611791],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98394346,0.006729802,0.0021158117,0.001391069,0.0047438643,0.001076055],"domain_scores_gemma":[0.9352408,0.02605016,0.017709166,0.0031231092,0.015404615,0.0024721313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017465029,0.0007172303,0.0014568995,0.0038310804,0.0010712623,0.0037026675,0.0028836546,0.0017476785,0.0014561816],"category_scores_gemma":[0.1247667,0.0005984329,0.0014989304,0.0091741895,0.0011048454,0.0023992416,0.0012010668,0.0014181176,0.00044299563],"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":[0.00005817673,0.000037623744,0.9852813,0.00011229461,0.0002218126,0.000021833777,0.0002012317,0.0004273473,0.000036207322,0.0004298336,0.0056229,0.007549508],"study_design_scores_gemma":[0.000048597976,0.00011240287,0.9768956,0.00019669683,0.00034764098,0.00010878202,0.0010021016,0.010158657,0.00041275198,0.0007601719,0.009908934,0.000047657184],"about_ca_topic_score_codex":0.7345086,"about_ca_topic_score_gemma":0.79756594,"teacher_disagreement_score":0.26549143,"about_ca_system_score_codex":0.010943581,"about_ca_system_score_gemma":0.015604355,"threshold_uncertainty_score":0.5341099},"labels":[],"label_agreement":null},{"id":"W2133313519","doi":"10.1002/sim.5891","title":"A spatial scan statistic for compound Poisson data","year":2013,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; Alberta Innovates; University of Alberta; Government of Alberta","keywords":"Scan statistic; Poisson distribution; Cluster analysis; Statistic; Cluster (spacecraft); Spatial epidemiology; Computer science; Statistics; Spatial analysis; Data mining; Poisson regression; Count data; Epidemiology; Econometrics; Medicine; Artificial intelligence; Mathematics; Environmental health; Pathology","score_opus":0.0479821941350101,"score_gpt":0.37055638338498254,"score_spread":0.32257418924997244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133313519","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":[0.014904385,0.00040665746,0.9789761,0.0009799806,0.00016654513,0.00016411828,0.0012207365,0.00064449816,0.0025370289],"genre_scores_gemma":[0.36550698,0.00097590423,0.62109274,0.0008342604,0.0007586634,0.00174553,0.0046899035,0.00055245945,0.0038435224],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9916231,0.0055207447,0.00037247068,0.0007629592,0.0015010926,0.00021963807],"domain_scores_gemma":[0.93109965,0.059113294,0.0026940485,0.0031246948,0.0033305213,0.0006377424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012999299,0.0005467107,0.0011647413,0.0036782993,0.0005881932,0.0014327153,0.0020693117,0.0016374431,0.0067629414],"category_scores_gemma":[0.07536087,0.00046771285,0.0013596644,0.004176225,0.00206629,0.002416945,0.001859278,0.0024054,0.0011568443],"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":[0.00053572794,0.00015392032,0.044944838,0.0005532395,0.0004519291,0.0012155267,0.0006522685,0.108390346,0.003586868,0.592215,0.030272076,0.21702826],"study_design_scores_gemma":[0.00015596848,0.00033774858,0.0064650034,0.00014480906,0.00010624761,0.0014002454,0.0002481599,0.6695946,0.0017793713,0.29562828,0.024039708,0.00009984372],"about_ca_topic_score_codex":0.00205525,"about_ca_topic_score_gemma":0.001699016,"teacher_disagreement_score":0.012999299,"about_ca_system_score_codex":0.00082697754,"about_ca_system_score_gemma":0.002298793,"threshold_uncertainty_score":0.0687477},"labels":[],"label_agreement":null},{"id":"W2133351374","doi":"10.4081/gh.2013.79","title":"A scoping review of spatial cluster analysis techniques for point-event data","year":2013,"lang":"en","type":"review","venue":"Geospatial health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; Simon Fraser University","funders":"Canadian Institutes of Health Research; Michael Smith Health Research BC","keywords":"Spatial epidemiology; Spatial analysis; Cluster analysis; Data science; Computer science; Thematic map; Weighting; Data mining; Statistic; Cluster (spacecraft); Information retrieval; Exploratory data analysis; Geography; Cartography; Statistics; Machine learning; Mathematics; Epidemiology; Medicine","score_opus":0.10680875831489318,"score_gpt":0.4734307149912807,"score_spread":0.36662195667638753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133351374","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":[0.00014041834,0.9929455,0.003054575,0.0014092203,0.00056251045,0.00039564265,0.00024435596,0.000031566196,0.0012162735],"genre_scores_gemma":[0.0012657891,0.9919962,0.0050103893,0.0004315001,0.00013942849,0.0007069508,0.00020612562,0.000016327407,0.00022719926],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.97950804,0.0075838156,0.0066176048,0.0013092564,0.004679868,0.00030140684],"domain_scores_gemma":[0.85286564,0.11462488,0.009086909,0.0034034855,0.01940597,0.0006131064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.034297395,0.0020642537,0.0050598616,0.030847998,0.0016866577,0.00452386,0.0037348114,0.0036991586,0.006004372],"category_scores_gemma":[0.12769046,0.0016476081,0.006204993,0.037852377,0.0024627282,0.0053813127,0.0030163173,0.0032113988,0.0019550072],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054551583,0.00004024964,0.00040668374,0.41592583,0.0008004299,0.0001372459,0.0007779089,0.00069529196,0.00027965553,0.006917192,0.020191135,0.55377376],"study_design_scores_gemma":[0.000029890934,0.00008408823,0.0014705822,0.72622144,0.0021567529,0.0003926282,0.0006088422,0.00038244788,0.00033924126,0.005214042,0.26303488,0.00006513053],"about_ca_topic_score_codex":0.01087832,"about_ca_topic_score_gemma":0.019854788,"teacher_disagreement_score":0.034297395,"about_ca_system_score_codex":0.005803297,"about_ca_system_score_gemma":0.02685906,"threshold_uncertainty_score":0.18138409},"labels":[],"label_agreement":null},{"id":"W2134394016","doi":"10.4081/gh.2013.55","title":"A spatial analysis of private well water Escherichia coli contamination in southern Ontario","year":2013,"lang":"en","type":"article","venue":"Geospatial health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Kingston Health Sciences Centre; Kingston General Hospital; Public Health Ontario; Queen's University","funders":"Ministry of Natural Resources","keywords":"Contamination; Water quality; Geography; Groundwater contamination; Public health; Waterborne diseases; Environmental health; Transmission (telecommunications); Groundwater; Environmental science; Ecology; Biology; Medicine; Aquifer; Engineering","score_opus":0.009979293736886972,"score_gpt":0.2550655187595792,"score_spread":0.24508622502269226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134394016","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":[0.99649185,0.00009761857,0.00024770916,0.00005774593,0.0000012915339,0.000016086104,0.002338387,0.000007060872,0.0007421855],"genre_scores_gemma":[0.99658304,0.00016696959,0.000530119,0.000009390782,0.000001674622,0.000012575068,0.0018012763,0.0000032836042,0.0008917849],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99967694,0.000027700782,0.000022559741,0.000066909146,0.0001237287,0.00008224842],"domain_scores_gemma":[0.99907887,0.00009752437,0.00024644987,0.000054503744,0.00042939832,0.000093292554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022689188,0.00014740266,0.00019255916,0.0014798696,0.00087423855,0.000593075,0.00031010484,0.00014485106,0.00058878947],"category_scores_gemma":[0.0013450608,0.0001558545,0.00025408372,0.004394689,0.0004895497,0.00017583977,0.0005658231,0.00012686338,0.00008168694],"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":[0.00008383036,0.000014735048,0.9844924,0.000060534345,0.000046956007,0.00024284962,0.0021099085,0.0012862745,0.0021667848,0.00019780418,0.000646563,0.008651362],"study_design_scores_gemma":[0.0000016177503,0.000008661707,0.99667394,0.000007725631,0.000010735626,0.000034761113,0.0014130739,0.0008044855,0.00012236951,0.00002352095,0.0008948127,0.000004336928],"about_ca_topic_score_codex":0.9759862,"about_ca_topic_score_gemma":0.98890287,"teacher_disagreement_score":0.024013817,"about_ca_system_score_codex":0.0077616055,"about_ca_system_score_gemma":0.009116492,"threshold_uncertainty_score":0.056314707},"labels":[],"label_agreement":null},{"id":"W2135035282","doi":"10.4269/ajtmh.2012.11-0597","title":"Social and News Media Enable Estimation of Epidemiological Patterns Early in the 2010 Haitian Cholera Outbreak","year":2012,"lang":"en","type":"article","venue":"American Journal of Tropical Medicine and Hygiene","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":445,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"U.S. National Library of Medicine; National Institute of Allergy and Infectious Diseases","keywords":"Outbreak; Cholera; Epidemiology; Estimation; Social media; Environmental health; Government (linguistics); Geography; Basic reproduction number; Public health; Demography; Medicine; Socioeconomics; Political science; Virology; Sociology; Population; Pathology; Economics","score_opus":0.042926875546767274,"score_gpt":0.32927448536141357,"score_spread":0.2863476098146463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135035282","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":[0.9780619,0.00036935572,0.0038565018,0.00053368846,0.00003088599,0.00019908664,0.007655379,0.000115869785,0.009177286],"genre_scores_gemma":[0.9905612,0.00028037227,0.0053745583,0.000072045266,0.00005274876,0.0001422654,0.0027462603,0.000008000556,0.00076261366],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985695,0.00065424695,0.00017371698,0.00011750076,0.00032141813,0.0001636187],"domain_scores_gemma":[0.9899686,0.003999182,0.0032508776,0.0005368933,0.0018770988,0.00036729928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018871435,0.00038132042,0.00023027301,0.0029949085,0.00027668788,0.0012353649,0.00034734365,0.0003261577,0.0013760922],"category_scores_gemma":[0.011782023,0.00027074182,0.00019606372,0.002240742,0.00020173877,0.0011922222,0.00103592,0.0003507414,0.00038393782],"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":[0.00015468414,0.00010053526,0.96237767,0.00011953131,0.000069107286,0.000057671736,0.0006638581,0.00088396814,0.000993449,0.00027365846,0.0013480827,0.032957714],"study_design_scores_gemma":[0.000011661949,0.00019507857,0.98065865,0.00008995295,0.000059339316,0.00010615536,0.0014521811,0.009333494,0.003093717,0.00045859054,0.0045103943,0.00003085843],"about_ca_topic_score_codex":0.008757922,"about_ca_topic_score_gemma":0.019719122,"teacher_disagreement_score":0.008757922,"about_ca_system_score_codex":0.00042832195,"about_ca_system_score_gemma":0.00052300043,"threshold_uncertainty_score":0.017413914},"labels":[],"label_agreement":null},{"id":"W2135584462","doi":"10.5210/ojphi.v6i2.5470","title":"Google and Women’s Health-Related Issues: What Does the Search Engine Data Reveal?","year":2014,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Jewish General Hospital","funders":"","keywords":"Medicine; Urogynecology; Public health; Family medicine; Pregnancy; Gynecology; Breast cancer; Obstetrics and gynaecology; Alternative medicine; Obstetrics; Cancer; Internal medicine; Nursing; Surgery; Pathology","score_opus":0.07836314306491056,"score_gpt":0.3761862297215173,"score_spread":0.29782308665660673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135584462","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":[0.43598095,0.2204586,0.002413788,0.108324766,0.0009988485,0.0003598225,0.19313903,0.00038167206,0.037942506],"genre_scores_gemma":[0.81595737,0.09511656,0.0036973888,0.013679002,0.0017559924,0.00042922643,0.06632167,0.0002463686,0.0027963542],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99133754,0.0019775988,0.0023609987,0.0007324097,0.0029589392,0.00063246745],"domain_scores_gemma":[0.88536805,0.083059,0.01935444,0.0025235221,0.00800933,0.0016856444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00688293,0.00048192,0.0014837555,0.023930572,0.0005934264,0.005615218,0.00095475453,0.001632025,0.0049252585],"category_scores_gemma":[0.07569213,0.0004902852,0.0009726542,0.035987914,0.0015662512,0.008976317,0.0020908008,0.0011650404,0.0018695251],"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":[0.00061806414,0.00010943159,0.6406483,0.05090851,0.00080279534,0.0011236946,0.011148357,0.0005469002,0.0013524414,0.005568281,0.08076157,0.20641169],"study_design_scores_gemma":[0.000051981722,0.00018846302,0.7514782,0.02403427,0.0006550059,0.003130536,0.019379735,0.001160226,0.0007514139,0.0040344833,0.19498184,0.00015394921],"about_ca_topic_score_codex":0.024499828,"about_ca_topic_score_gemma":0.05110788,"teacher_disagreement_score":0.024499828,"about_ca_system_score_codex":0.0016929116,"about_ca_system_score_gemma":0.0035029575,"threshold_uncertainty_score":0.04871446},"labels":[],"label_agreement":null},{"id":"W2135611962","doi":"10.1136/jamia.2010.003210","title":"Developing syndrome definitions based on consensus and current use","year":2010,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"U.S. National Library of Medicine","keywords":"Computer science; Consensus conference; Ontology; MEDLINE; Data science; Medicine","score_opus":0.029499667205239537,"score_gpt":0.3065207395399176,"score_spread":0.27702107233467804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135611962","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040297564,0.0020817085,0.9232124,0.004049372,0.0006112559,0.0035964134,0.003276168,0.0012063382,0.021668795],"genre_scores_gemma":[0.10186252,0.0010645922,0.8827603,0.0005195424,0.00011843768,0.00369421,0.0085504055,0.00023692861,0.0011930829],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9166766,0.03085886,0.026710639,0.0072694966,0.01743472,0.0010497125],"domain_scores_gemma":[0.8404038,0.06625909,0.013968191,0.016108405,0.06173954,0.0015210772],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.07755487,0.0013153001,0.0014818967,0.017506555,0.002541626,0.0060161203,0.0051897597,0.0018267568,0.0031821162],"category_scores_gemma":[0.19921163,0.00077333703,0.0028993217,0.009644012,0.0027774242,0.012305346,0.00841901,0.0023432525,0.0010511515],"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":[0.00031642054,0.0002794146,0.066578284,0.0076252413,0.0005931794,0.0013982236,0.030352984,0.013812948,0.005617267,0.27370518,0.03735598,0.5623649],"study_design_scores_gemma":[0.00032284975,0.0007470328,0.03865074,0.014937248,0.0012511691,0.0040473216,0.03252418,0.080527134,0.017136434,0.29534155,0.51390046,0.0006138323],"about_ca_topic_score_codex":0.005328269,"about_ca_topic_score_gemma":0.005599096,"teacher_disagreement_score":0.9224451,"about_ca_system_score_codex":0.004691672,"about_ca_system_score_gemma":0.0123958625,"threshold_uncertainty_score":0.41015422},"labels":[],"label_agreement":null},{"id":"W2136405472","doi":"10.1186/1476-072x-4-23","title":"The Population Health Approach: health GIS as a bridge from theory to practice","year":2005,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Island Health","funders":"","keywords":"Population health; Health geography; HRHIS; Population; Health informatics; Public health; Health care; Health indicator; Public health informatics; Health promotion; Computer science; International health; Medicine; Environmental health; Nursing; Political science","score_opus":0.02547883929190581,"score_gpt":0.38791046422126085,"score_spread":0.36243162492935505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136405472","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":[0.008571873,0.007545515,0.7610675,0.09129422,0.0011890255,0.0009956567,0.00038271988,0.0005214112,0.12843204],"genre_scores_gemma":[0.22832952,0.006765024,0.7515809,0.0050132107,0.00064291776,0.0017925785,0.00026829916,0.00016810233,0.005439386],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9609827,0.033492737,0.0009913649,0.0012634352,0.0027104968,0.0005593381],"domain_scores_gemma":[0.9531122,0.039590314,0.0010166479,0.0030734823,0.002196211,0.0010111963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030589895,0.0012331336,0.0010981791,0.009758758,0.0037620845,0.015946262,0.006109578,0.006154736,0.008092865],"category_scores_gemma":[0.035627138,0.0008816889,0.0013072224,0.0072801365,0.032366127,0.012095764,0.012445591,0.0065151495,0.00097217987],"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":[0.000014582852,0.00006979724,0.0010969015,0.00064001104,0.000035877318,0.00019724343,0.01027213,0.0046580005,0.00014303353,0.92349666,0.005619253,0.053756487],"study_design_scores_gemma":[0.000020753943,0.00007212859,0.00052630133,0.0011611056,0.000026008747,0.00015400925,0.013099602,0.0068523437,0.0002930293,0.862205,0.11555782,0.000031831045],"about_ca_topic_score_codex":0.0071765827,"about_ca_topic_score_gemma":0.0050435546,"teacher_disagreement_score":0.030589895,"about_ca_system_score_codex":0.011166947,"about_ca_system_score_gemma":0.017893547,"threshold_uncertainty_score":0.16177678},"labels":[],"label_agreement":null},{"id":"W2137244100","doi":"10.2807/ese.15.30.19626-en","title":"Event-based biosurveillance of respiratory disease in Mexico, 2007–2009: connection to the 2009 influenza A(H1N1) pandemic?","year":2010,"lang":"en","type":"article","venue":"Eurosurveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Institutes of Health; U.S. National Library of Medicine; Georgetown University","keywords":"Pandemic; Event (particle physics); Coronavirus disease 2019 (COVID-19); Pandemic influenza; Influenza A virus subtype H5N1; H1N1 influenza; 2019-20 coronavirus outbreak; Connection (principal bundle); H1n1 pandemic; Medicine; Virology; Disease; Computer science; Internal medicine; Infectious disease (medical specialty); Virus; Mathematics; Outbreak","score_opus":0.021779086067297473,"score_gpt":0.3053437987717038,"score_spread":0.28356471270440636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137244100","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":[0.9960347,0.0002681664,0.0004966266,0.00022740607,0.00001001784,0.000018573985,0.0020989405,0.00001868884,0.0008268385],"genre_scores_gemma":[0.997247,0.00019481842,0.00058994855,0.00002079324,0.000020314354,0.000020102074,0.0017201884,0.0000024751016,0.00018443401],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99973065,0.000083900006,0.000034775043,0.000051855244,0.000056943372,0.00004176975],"domain_scores_gemma":[0.99843496,0.00042230592,0.00073241006,0.0000856272,0.00027591182,0.000048725946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006726713,0.00017444036,0.00015752336,0.0012459768,0.00019088287,0.00058618793,0.00025864752,0.00022688863,0.00049093505],"category_scores_gemma":[0.0027099214,0.000094176605,0.00016877244,0.0007806259,0.0001300467,0.00043549895,0.00040037683,0.00018807553,0.00007381955],"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":[0.00007625781,0.00002835396,0.9869174,0.000031767344,0.00006656039,0.000053889857,0.00013951876,0.0005531541,0.00039681833,0.000062215244,0.00040596005,0.011268026],"study_design_scores_gemma":[0.0000036358597,0.000031889118,0.9952743,0.000013588451,0.000039888888,0.00006575732,0.00042072203,0.002755673,0.0004137909,0.00002263918,0.00095371564,0.0000044735493],"about_ca_topic_score_codex":0.043764427,"about_ca_topic_score_gemma":0.045576986,"teacher_disagreement_score":0.043764427,"about_ca_system_score_codex":0.000616301,"about_ca_system_score_gemma":0.00027526836,"threshold_uncertainty_score":0.087019324},"labels":[],"label_agreement":null},{"id":"W2137414388","doi":"10.1186/1472-6947-10-37","title":"Detecting the start of an influenza outbreak using exponentially weighted moving average charts","year":2010,"lang":"en","type":"article","venue":"BMC Medical Informatics and Decision Making","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Outbreak; EWMA chart; Control chart; Chart; Seasonal influenza; Medicine; Statistics; Virology; Environmental health; Coronavirus disease 2019 (COVID-19); Computer science; Mathematics; Process (computing); Infectious disease (medical specialty); Disease; Pathology","score_opus":0.03394073176256906,"score_gpt":0.33557421871595683,"score_spread":0.30163348695338776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137414388","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":[0.2543457,0.00053680857,0.73759264,0.00031014945,0.000101437065,0.00029845076,0.0011241072,0.0039020989,0.0017886615],"genre_scores_gemma":[0.80868155,0.00021682028,0.18932521,0.000057508998,0.00006199387,0.00015168749,0.001127056,0.00007604586,0.00030212352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99799037,0.0007172863,0.00027886612,0.00041070944,0.00052473386,0.00007799576],"domain_scores_gemma":[0.9705033,0.021105282,0.003295666,0.0009984785,0.0036451458,0.0004522362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050589303,0.00097235496,0.0008751053,0.0030688592,0.000377188,0.0015279963,0.0009323233,0.00064042443,0.00072138815],"category_scores_gemma":[0.032607783,0.00024226426,0.0005372531,0.0015010395,0.0004965906,0.0011795795,0.00052490924,0.00081571104,0.00015689916],"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":[0.0017398848,0.0003919932,0.13567227,0.00047704225,0.00042357194,0.00082163815,0.0005573071,0.5126038,0.012840583,0.0075078458,0.0048389677,0.32212514],"study_design_scores_gemma":[0.000047230016,0.00021861025,0.013859128,0.000040726467,0.00006165121,0.0001481139,0.00004867454,0.9714883,0.009958038,0.002778324,0.0012787452,0.00007239032],"about_ca_topic_score_codex":0.008009952,"about_ca_topic_score_gemma":0.0034751066,"teacher_disagreement_score":0.008009952,"about_ca_system_score_codex":0.000587496,"about_ca_system_score_gemma":0.000915818,"threshold_uncertainty_score":0.026754498},"labels":[],"label_agreement":null},{"id":"W2138016069","doi":"10.1177/2053951714541861","title":"Surveillance, Snowden, and Big Data: Capacities, consequences, critique","year":2014,"lang":"en","type":"article","venue":"Big Data & Society","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":825,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Big data; Metadata; Sociology; Transparency (behavior); Panopticon; The Internet; Internet privacy; Data science; Political science; Computer security; Computer science; Law; Politics; World Wide Web","score_opus":0.11960032265849,"score_gpt":0.31827137554777013,"score_spread":0.19867105288928014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138016069","genre_codex":"commentary","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":[0.0046831123,0.06314507,0.007620291,0.87925476,0.006756978,0.00004137925,0.00009212005,0.00006878102,0.038337555],"genre_scores_gemma":[0.62621313,0.10522471,0.011926789,0.19428116,0.042403366,0.00042626404,0.0002086079,0.0005850626,0.018730987],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9533078,0.03188636,0.0015767658,0.003154195,0.00868987,0.0013850009],"domain_scores_gemma":[0.82302177,0.15661757,0.0023201222,0.0063621174,0.00979815,0.0018802931],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.048967097,0.0014322461,0.0015979573,0.011572267,0.011417144,0.026487628,0.0045740553,0.012938594,0.0029093192],"category_scores_gemma":[0.096880965,0.00075971795,0.0012129921,0.008904265,0.13430537,0.04215674,0.013632059,0.020739453,0.00076908444],"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":[0.000012906456,0.000011447536,0.00020108045,0.00015846494,0.000009129486,0.00009155064,0.017913619,0.00018729892,0.00003431784,0.9485841,0.024726816,0.008069336],"study_design_scores_gemma":[0.000012055325,0.000008742086,0.00020934734,0.00094035047,0.0000045563816,0.000083896775,0.015250744,0.00046192386,0.00008400838,0.8538311,0.1290855,0.000027816279],"about_ca_topic_score_codex":0.013506361,"about_ca_topic_score_gemma":0.009534524,"teacher_disagreement_score":0.98858285,"about_ca_system_score_codex":0.018122982,"about_ca_system_score_gemma":0.015175724,"threshold_uncertainty_score":0.25896585},"labels":[],"label_agreement":null},{"id":"W2138536241","doi":"10.1186/1471-2458-8-156","title":"The perceived impact of location privacy: A web-based survey of public health perspectives and requirements in the UK and Canada","year":2008,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Public health; Medicine; Biostatistics; Government (linguistics); Environmental health; Public participation GIS; Geographic information system; Information privacy; Public relations; Internet privacy; GIS and public health; Geography; Nursing; Political science","score_opus":0.10506143741540076,"score_gpt":0.36096288858132747,"score_spread":0.2559014511659267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138536241","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":[0.99417144,0.00028727928,0.000046820904,0.0013154487,0.000008045206,0.000043518114,0.0008465911,0.0000056785198,0.0032751588],"genre_scores_gemma":[0.9980781,0.00039780993,0.00008966392,0.00035378287,0.000004019113,0.00002022039,0.00029143796,0.000005398136,0.00075956923],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9955857,0.00062178896,0.00033701074,0.00032078792,0.0018616578,0.001273046],"domain_scores_gemma":[0.9867626,0.0016627259,0.0020573675,0.00023987968,0.005314006,0.0039634826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023577856,0.00020048505,0.00045395896,0.0017022274,0.0037947241,0.0028855132,0.0010250625,0.00063067366,0.0022364513],"category_scores_gemma":[0.009492059,0.00035808366,0.00045452797,0.0045298724,0.0019994103,0.00075782713,0.0022822763,0.0010365606,0.00021432503],"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":[0.00015935303,0.00008255632,0.9074843,0.00016748799,0.000036653295,0.0005101488,0.07550103,0.00010023746,0.0005075016,0.0002556321,0.0034590354,0.011735959],"study_design_scores_gemma":[0.000007611852,0.00005360111,0.8958001,0.00010408411,0.000011542246,0.00012538164,0.09967739,0.00015942458,0.000084734056,0.000021984744,0.0039177807,0.00003636568],"about_ca_topic_score_codex":0.9865291,"about_ca_topic_score_gemma":0.98706335,"teacher_disagreement_score":0.028193211,"about_ca_system_score_codex":0.028193211,"about_ca_system_score_gemma":0.03361953,"threshold_uncertainty_score":0.20455706},"labels":[],"label_agreement":null},{"id":"W2138627847","doi":"10.3390/ijerph110201479","title":"Cancer Cluster Investigations: Review of the Past and Proposals for the Future","year":2014,"lang":"en","type":"review","venue":"International Journal of Environmental Research and Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Health Canada","funders":"National Cancer Institute; National Heart, Lung, and Blood Institute; National Institute of Environmental Health Sciences; Centers for Disease Control and Prevention","keywords":"Public health; Cluster (spacecraft); Disease; Limiting; Exposome; Etiology; Population; Data science; Medicine; Environmental health; Computer science; Engineering; Psychiatry; Pathology","score_opus":0.10270907309747272,"score_gpt":0.46151584078743685,"score_spread":0.3588067676899641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138627847","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":[0.000025849227,0.99814,0.00013384498,0.0012333747,0.00024086653,0.0000069596076,0.000014907988,0.0000043086197,0.0001999756],"genre_scores_gemma":[0.000289252,0.9985441,0.00039680846,0.000449055,0.00020273702,0.000012588246,0.000020967138,0.0000019876622,0.000082482424],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978344,0.00078115653,0.00045247312,0.0003104874,0.00052345137,0.00009801725],"domain_scores_gemma":[0.9833122,0.011984614,0.0010604488,0.00035981624,0.0028683771,0.00041448735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00883044,0.0011560643,0.0033026624,0.008561548,0.000839434,0.0028795416,0.0030012394,0.0026906547,0.0032389269],"category_scores_gemma":[0.014941077,0.00080433534,0.0019252956,0.012906724,0.002230854,0.0052414862,0.0017957097,0.0030240028,0.0012347026],"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":[0.0001047229,0.0000702003,0.00060424895,0.068898454,0.00027840218,0.00020820978,0.00036858878,0.00052186154,0.00021021134,0.014546979,0.05816421,0.85602397],"study_design_scores_gemma":[0.00002305556,0.000083246676,0.0016970916,0.042264666,0.00035607387,0.0008880772,0.00045507247,0.00020165429,0.00014279617,0.009342389,0.94448787,0.00005790079],"about_ca_topic_score_codex":0.0063374406,"about_ca_topic_score_gemma":0.010112731,"teacher_disagreement_score":0.00883044,"about_ca_system_score_codex":0.003254596,"about_ca_system_score_gemma":0.0070764483,"threshold_uncertainty_score":0.046700418},"labels":[],"label_agreement":null},{"id":"W2139808481","doi":"10.1136/amiajnl-2011-000793","title":"Application of change point analysis to daily influenza-like illness emergency department visits","year":2012,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":86,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Centers for Disease Control and Prevention","keywords":"Emergency department; Pandemic; Influenza pandemic; Medicine; Influenza-like illness; Medical emergency; Emergency medicine; Coronavirus disease 2019 (COVID-19); Disease monitoring; Health care; Disease; Intensive care medicine; Infectious disease (medical specialty); Virology; Internal medicine; Nursing; Virus","score_opus":0.017168203468145692,"score_gpt":0.3239154762609535,"score_spread":0.30674727279280783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139808481","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":[0.7609845,0.0005336672,0.23424678,0.0003302115,0.00009503454,0.00050456275,0.0010021077,0.00073797343,0.0015651754],"genre_scores_gemma":[0.9373777,0.00009636566,0.061412275,0.000047533253,0.000041508378,0.00017609198,0.0006271617,0.00003277607,0.00018867066],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9933281,0.0042559616,0.00034016324,0.00088427187,0.0010320247,0.000159468],"domain_scores_gemma":[0.96226937,0.030006895,0.0024279275,0.0015058495,0.003295018,0.00049492123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075823814,0.0007208507,0.0008417704,0.004544213,0.0003752307,0.0009300772,0.0009514119,0.0007865484,0.0009203544],"category_scores_gemma":[0.044505306,0.00030492654,0.0010474304,0.0028581715,0.0004128601,0.0006739988,0.000859921,0.0012246199,0.00022687111],"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":[0.0013399852,0.0007697357,0.5379229,0.00029675872,0.0013861574,0.0005540134,0.00085709296,0.17224275,0.0029908665,0.001867808,0.0022421146,0.27752972],"study_design_scores_gemma":[0.00006292384,0.00062613026,0.10912314,0.000025545685,0.0001185613,0.00025858212,0.00025856486,0.8847036,0.0013710443,0.002449207,0.0009585651,0.000044199136],"about_ca_topic_score_codex":0.010887805,"about_ca_topic_score_gemma":0.005392815,"teacher_disagreement_score":0.010887805,"about_ca_system_score_codex":0.0008206016,"about_ca_system_score_gemma":0.0010920288,"threshold_uncertainty_score":0.04009992},"labels":[],"label_agreement":null},{"id":"W2140055604","doi":"10.1136/amiajnl-2011-000100","title":"A secure protocol for protecting the identity of providers when disclosing data for disease surveillance","year":2011,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Clinical Evaluative Sciences; Ontario Institute for Cancer Research; McGill University; Children's Hospital of Eastern Ontario; University of Ottawa","funders":"National Institute for Health and Care Research; Canadian Institutes of Health Research; Ontario Institute for Cancer Research; National Institutes of Health; U.S. National Library of Medicine; Natural Resources Canada; National Health Research Institutes","keywords":"Paillier cryptosystem; Protocol (science); Confidentiality; Computer science; Computer security; Masking (illustration); Public health; Patient privacy; Internet privacy; Cryptosystem; Medicine; Health care; Cryptography; Nursing","score_opus":0.05889504255153467,"score_gpt":0.3667707181943421,"score_spread":0.30787567564280743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140055604","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":[0.021189215,0.00005185398,0.97403866,0.0007521196,0.00006472247,0.0010753943,0.00013864701,0.0008512316,0.0018382123],"genre_scores_gemma":[0.33540246,0.00011621091,0.6597018,0.00027602393,0.00010527405,0.0018512311,0.00037319993,0.0000973636,0.0020764978],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9843982,0.0075193886,0.0020167015,0.0012371469,0.0042434554,0.0005852005],"domain_scores_gemma":[0.9555109,0.022125037,0.004716491,0.011969096,0.0046945447,0.0009839956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016767194,0.00053748,0.00057405554,0.0011380418,0.0015463679,0.0026592833,0.0015688324,0.0016964088,0.0024278874],"category_scores_gemma":[0.044062044,0.0006366922,0.00092641305,0.0008562012,0.0028527076,0.0041506602,0.0045077377,0.0023058974,0.0011543884],"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":[0.0027089864,0.0011320458,0.016163578,0.00086812687,0.00032961761,0.0018934546,0.005225534,0.06192411,0.077189416,0.45679575,0.015786368,0.3599831],"study_design_scores_gemma":[0.0019414899,0.002014577,0.0034441212,0.0003312746,0.00028437693,0.0028291794,0.0007675988,0.63848376,0.116515286,0.1738248,0.059288528,0.00027493844],"about_ca_topic_score_codex":0.0009236993,"about_ca_topic_score_gemma":0.00048349312,"teacher_disagreement_score":0.016767194,"about_ca_system_score_codex":0.0015010951,"about_ca_system_score_gemma":0.0057500694,"threshold_uncertainty_score":0.088674426},"labels":[],"label_agreement":null},{"id":"W2140552840","doi":"10.1080/09603123.2011.634386","title":"A geographical analysis of breast cancer clustering in southern Ontario: generating hypotheses on environmental influences","year":2011,"lang":"en","type":"article","venue":"International Journal of Environmental Health Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Sunnybrook Health Science Centre; University of Toronto; Health Sciences Centre; Windsor Regional Hospital; Cancer Care Ontario; University of Windsor; Western University","funders":"Canadian Institutes of Health Research","keywords":"Breast cancer; Relative risk; Statistic; Cancer; Medicine; Geography; Cluster analysis; Demography; Environmental health; Statistics; Internal medicine; Mathematics","score_opus":0.10416687795362442,"score_gpt":0.3928353883850321,"score_spread":0.28866851043140773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140552840","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":[0.99836165,0.00011178112,0.00057390454,0.00006921102,0.0000012547192,0.000016797183,0.00028931446,0.0000056970994,0.00057028997],"genre_scores_gemma":[0.99914694,0.000045107285,0.000451914,0.0000050689587,0.0000013383952,0.00000736928,0.00022433815,0.0000015659995,0.00011638353],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990651,0.00034176797,0.00005116368,0.00015403207,0.00025340193,0.0001345239],"domain_scores_gemma":[0.99762326,0.0010308073,0.0005503288,0.00015978471,0.0005156984,0.000120251876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088948314,0.0001687002,0.00023178452,0.0016421675,0.00076831307,0.00059140584,0.0002956348,0.00013042809,0.00075675873],"category_scores_gemma":[0.0069461563,0.00014711733,0.00052050437,0.002819041,0.0006553118,0.00017321449,0.0006194536,0.000118164135,0.000051972933],"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":[0.000047031313,0.000003859111,0.9939122,0.000024004914,0.00007268967,0.00011009695,0.0008040034,0.0011284028,0.0003884804,0.00020452648,0.0000938231,0.0032108559],"study_design_scores_gemma":[0.00000397315,0.000017737035,0.9962381,0.0000066160364,0.00003869162,0.00004594542,0.0012313204,0.0019188005,0.0000638245,0.00009792047,0.00033322643,0.0000038242183],"about_ca_topic_score_codex":0.79496145,"about_ca_topic_score_gemma":0.7982586,"teacher_disagreement_score":0.20503855,"about_ca_system_score_codex":0.0033983737,"about_ca_system_score_gemma":0.0038917104,"threshold_uncertainty_score":0.4124921},"labels":[],"label_agreement":null},{"id":"W2141021700","doi":"10.3201/eid1610.100249","title":"Mobile Phone–based Infectious Disease Surveillance System, Sri Lanka","year":2010,"lang":"en","type":"article","venue":"Emerging infectious diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":117,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Victoria","funders":"University of Peradeniya; University of Victoria; Infectious Diseases Society of America","keywords":"Sri lanka; Disease surveillance; Infectious disease (medical specialty); Mobile phone; Medicine; Internet privacy; Virology; Medical emergency; Computer security; Environmental health; Disease; World Wide Web; Geography; Computer science; Telecommunications; Pathology","score_opus":0.004918977340039656,"score_gpt":0.25353617125530875,"score_spread":0.2486171939152691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141021700","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":[0.8396385,0.0054672225,0.046624295,0.0048515797,0.00030668994,0.0048385747,0.017047733,0.010017613,0.071207725],"genre_scores_gemma":[0.90814066,0.0024431446,0.05864912,0.0012372455,0.00014799256,0.0014694867,0.014985762,0.00014633173,0.012780126],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9993079,0.00024228111,0.00009669028,0.00014377754,0.000115709765,0.000093662],"domain_scores_gemma":[0.9989592,0.00017261715,0.00009586943,0.00008978057,0.0005296026,0.00015299655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091778906,0.00039395713,0.00029070635,0.00081109424,0.00055186235,0.0010704324,0.0005394858,0.000383461,0.004553141],"category_scores_gemma":[0.0013268675,0.0002451319,0.00027526836,0.00048396876,0.0002330354,0.0007723085,0.00084152014,0.00038139007,0.0028321703],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027941184,0.0009378226,0.29570302,0.002277966,0.0004820357,0.003606339,0.005279943,0.0042771823,0.09120694,0.0016139024,0.05369361,0.5381271],"study_design_scores_gemma":[0.000992841,0.007752451,0.5919952,0.0010617723,0.0012114656,0.006558315,0.007358717,0.034164358,0.058776334,0.00087741576,0.2886865,0.00056469743],"about_ca_topic_score_codex":0.017610699,"about_ca_topic_score_gemma":0.013849151,"teacher_disagreement_score":0.017610699,"about_ca_system_score_codex":0.000708501,"about_ca_system_score_gemma":0.00089847425,"threshold_uncertainty_score":0.035016358},"labels":[],"label_agreement":null},{"id":"W2143008484","doi":"10.17269/cjph.104.3721","title":"Assessing the Relative Timeliness of Ontario’s Syndromic Surveillance Systems for Early Detection of the 2009 Influenza H1N1 Pandemic Waves","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Public Health Ontario; Kingston Health Sciences Centre; University of Ottawa; Public Health Agency of Canada; Toronto Public Health; University of Toronto","funders":"Institute of Population and Public Health; Canadian Institutes of Health Research","keywords":"Telehealth; Medicine; Medical emergency; Notice; Medical prescription; Pandemic; Pharmacy; H1n1 pandemic; Emergency department; Emergency medicine; Pediatrics; Family medicine; Coronavirus disease 2019 (COVID-19); Disease; Telemedicine; Health care; Internal medicine; Nursing","score_opus":0.06498916960647907,"score_gpt":0.3210576762496348,"score_spread":0.2560685066431557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143008484","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":[0.97643363,0.0010655003,0.00124236,0.002271033,0.0000735259,0.00019694591,0.012768274,0.000076232485,0.0058725267],"genre_scores_gemma":[0.9942638,0.00027463603,0.0013354326,0.000109466666,0.00002012094,0.000042397438,0.0034607712,0.0000057456236,0.0004874451],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99494654,0.0009173567,0.00054294517,0.00063896744,0.0022914798,0.00066263776],"domain_scores_gemma":[0.9705405,0.0063049947,0.006302491,0.0010706476,0.014026253,0.0017550625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068180915,0.00053364044,0.00040084106,0.001925983,0.0011318782,0.001941792,0.00134785,0.00073122914,0.0007127832],"category_scores_gemma":[0.026682917,0.0004676337,0.0006477247,0.003304839,0.0005518997,0.0009903952,0.0011044209,0.0005203169,0.000166285],"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":[0.0004198896,0.000051647574,0.9745068,0.00013647547,0.00035122124,0.000036842033,0.0007215697,0.0039711306,0.0006821301,0.00027039956,0.0025992235,0.016252756],"study_design_scores_gemma":[0.00004027264,0.00013187624,0.9842933,0.000056960587,0.00015684456,0.00002186366,0.0007554722,0.011355755,0.00041366115,0.00006134745,0.0026874158,0.0000254157],"about_ca_topic_score_codex":0.9629236,"about_ca_topic_score_gemma":0.97848916,"teacher_disagreement_score":0.037076414,"about_ca_system_score_codex":0.025311315,"about_ca_system_score_gemma":0.029673668,"threshold_uncertainty_score":0.18364727},"labels":[],"label_agreement":null},{"id":"W2143390259","doi":"10.5210/ojphi.v1i1.2778","title":"Public Health Informatics and the H1N1 Pandemic","year":2009,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public health; Pandemic; Medicine; Preparedness; Health informatics; Population; Influenza A virus subtype H5N1; Public health informatics; Public relations; Medical emergency; Health policy; Environmental health; International health; Disease; Political science; Infectious disease (medical specialty); Coronavirus disease 2019 (COVID-19); Nursing; Virology; Pathology","score_opus":0.11724523030329195,"score_gpt":0.3706207218342925,"score_spread":0.2533754915310006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143390259","genre_codex":"commentary","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":[0.0014180711,0.023746781,0.0009321927,0.9331308,0.006159899,0.0000115025605,0.00009192439,0.0000789182,0.03442995],"genre_scores_gemma":[0.22691981,0.168648,0.0057580294,0.48613322,0.074757315,0.00017090733,0.0005735944,0.00016022034,0.036878914],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9951768,0.0032741665,0.00022365437,0.00025285323,0.0008311995,0.0002413778],"domain_scores_gemma":[0.9684198,0.024459187,0.0013867929,0.0011137372,0.0021400503,0.0024804513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007250909,0.00025224756,0.00028779806,0.0019272089,0.0017492315,0.008396184,0.0005963857,0.003944663,0.008334257],"category_scores_gemma":[0.02677511,0.00030068686,0.00032601872,0.0020357613,0.0041235257,0.007426844,0.0030258663,0.0062606786,0.0011185574],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030331019,0.00005912395,0.003622087,0.0002888205,0.000015134568,0.00018131708,0.0015793543,0.00020712551,0.00006879028,0.12828137,0.7175852,0.14808132],"study_design_scores_gemma":[0.000019953031,0.000027161883,0.0038084863,0.0015594843,0.000016161634,0.00036084288,0.0022317395,0.00046291584,0.00012664663,0.06499059,0.9263785,0.000017506083],"about_ca_topic_score_codex":0.0040901536,"about_ca_topic_score_gemma":0.0053051123,"teacher_disagreement_score":0.008396184,"about_ca_system_score_codex":0.003458286,"about_ca_system_score_gemma":0.0055116625,"threshold_uncertainty_score":0.038346887},"labels":[],"label_agreement":null},{"id":"W2143932921","doi":"10.1001/dmp.2011.45","title":"Information Technology Systems for Critical Care Triage and Medical Response During an Influenza Pandemic: A Review of Current Systems","year":2011,"lang":"en","type":"review","venue":"Disaster Medicine and Public Health Preparedness","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; Toronto Metropolitan University","funders":"Canadian Institutes of Health Research; U.S. Department of Homeland Security","keywords":"Triage; Pandemic; Medical emergency; Preparedness; Medicine; Political science; Business; Coronavirus disease 2019 (COVID-19); Infectious disease (medical specialty); Disease","score_opus":0.19243417110895658,"score_gpt":0.48351822528996796,"score_spread":0.29108405418101135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143932921","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":[0.00021975563,0.9969171,0.00019322034,0.0011973857,0.00013731775,0.000021843123,0.000046933157,0.000010140332,0.0012562115],"genre_scores_gemma":[0.002513396,0.9961892,0.00062825467,0.0003880446,0.00009728524,0.000025823052,0.00006536495,0.0000028354516,0.000089900386],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9939739,0.0015810998,0.0017775036,0.00039471575,0.0019915833,0.0002812395],"domain_scores_gemma":[0.9610364,0.027207982,0.003447064,0.0005019858,0.0072458745,0.0005607067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009474971,0.0010065703,0.0013319674,0.021269314,0.0011134725,0.0036604148,0.0018031853,0.0020722137,0.0023370918],"category_scores_gemma":[0.023257768,0.00071412197,0.0015414424,0.024975823,0.0020110253,0.00507605,0.001230728,0.0017238035,0.0005885075],"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":[0.00006823383,0.00006025843,0.0021509968,0.14793524,0.00032689297,0.00014511876,0.0013732162,0.00062660914,0.0003991404,0.0059718806,0.027276928,0.8136655],"study_design_scores_gemma":[0.000034928074,0.00022153127,0.01773205,0.28397274,0.001526035,0.0006776582,0.0021480601,0.0005648116,0.00055487524,0.0029735363,0.6895115,0.00008227836],"about_ca_topic_score_codex":0.05485404,"about_ca_topic_score_gemma":0.0894782,"teacher_disagreement_score":0.05485404,"about_ca_system_score_codex":0.010236938,"about_ca_system_score_gemma":0.025172258,"threshold_uncertainty_score":0.109069526},"labels":[],"label_agreement":null},{"id":"W2145244857","doi":"10.15265/iy-2014-0008","title":"A Review and Framework for Categorizing Current Research and Development in Health Related Geographical Information Systems (GIS) Studies","year":2014,"lang":"en","type":"review","venue":"Yearbook of Medical Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":124,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Algoma University","funders":"","keywords":"Geographic information system; GIS and public health; Categorization; Data science; Conceptual framework; Information system; Geography; Spatial analysis; Knowledge management; Computer science; Cartography; Political science; Sociology; Social science; Remote sensing","score_opus":0.18306866739383867,"score_gpt":0.4896014633139451,"score_spread":0.3065327959201064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145244857","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":[0.00041081166,0.97687143,0.004868463,0.010312205,0.0021898602,0.0006070204,0.00030262247,0.000037227226,0.004400291],"genre_scores_gemma":[0.0073019257,0.9693327,0.015523555,0.004109645,0.0009376113,0.0017051729,0.0004012935,0.0000190045,0.0006690665],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9723389,0.012417666,0.008056445,0.0018245936,0.004499298,0.0008629751],"domain_scores_gemma":[0.91283655,0.06615016,0.006280043,0.0013334049,0.012311645,0.0010883121],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.028924491,0.0026045686,0.0042160866,0.056645136,0.0031252853,0.011983225,0.004219617,0.005556379,0.0045103366],"category_scores_gemma":[0.06623713,0.0010824868,0.004204096,0.051547002,0.0048449384,0.012962587,0.0053264927,0.004395831,0.0014272131],"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":[0.0001111186,0.00008909586,0.0021523768,0.32153758,0.00086382305,0.0009838579,0.009462417,0.0012936837,0.0008416699,0.15007548,0.06612675,0.44646206],"study_design_scores_gemma":[0.00003161376,0.000084990505,0.0022195736,0.45521525,0.0011232593,0.0006860744,0.0056537027,0.0004305257,0.0002301452,0.025351303,0.50889397,0.00007957551],"about_ca_topic_score_codex":0.009747771,"about_ca_topic_score_gemma":0.011681692,"teacher_disagreement_score":0.94335485,"about_ca_system_score_codex":0.017251424,"about_ca_system_score_gemma":0.036187593,"threshold_uncertainty_score":0.15296918},"labels":[],"label_agreement":null},{"id":"W2145361723","doi":"10.1197/jamia.m2902","title":"Evaluating Predictors of Geographic Area Population Size Cut-offs to Manage Re-identification Risk","year":2008,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada; Children's Hospital of Eastern Ontario; University of Ottawa","funders":"","keywords":"Geographic information system; Identification (biology); Location; Geography; Population; GIS and public health; Cartography; Environmental health; Medicine","score_opus":0.018686306430003848,"score_gpt":0.317223060104526,"score_spread":0.29853675367452215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145361723","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":[0.9535843,0.0007881225,0.04018953,0.0018511927,0.000075224365,0.0003855265,0.00096323784,0.00031730498,0.0018454375],"genre_scores_gemma":[0.9854047,0.00007913417,0.013568723,0.00007821775,0.000018003488,0.00012381896,0.00057095103,0.00001970493,0.00013688086],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9908512,0.0062672044,0.00058050203,0.0007636172,0.0010624417,0.00047486293],"domain_scores_gemma":[0.825757,0.14890063,0.011710322,0.0036025175,0.007297524,0.0027319933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031871226,0.0010383258,0.00090981845,0.0032811752,0.00085358514,0.0018392735,0.0013179114,0.0011851145,0.0017089987],"category_scores_gemma":[0.15138505,0.00039064055,0.0012540062,0.0025739365,0.00085447094,0.0021634847,0.0014498767,0.0018942497,0.0003061873],"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":[0.00034526057,0.00021809916,0.95546913,0.000058300684,0.00020595621,0.000058731384,0.00023971788,0.018033758,0.000083960025,0.00061758026,0.0015910243,0.023078378],"study_design_scores_gemma":[0.0001457518,0.0009874459,0.44757795,0.0002761728,0.0003711588,0.00022674767,0.0017099946,0.5395467,0.0014872794,0.006096871,0.0014908916,0.000083061765],"about_ca_topic_score_codex":0.012155445,"about_ca_topic_score_gemma":0.010917084,"teacher_disagreement_score":0.031871226,"about_ca_system_score_codex":0.0014635525,"about_ca_system_score_gemma":0.003484075,"threshold_uncertainty_score":0.16855317},"labels":[],"label_agreement":null},{"id":"W2145434273","doi":"10.1186/1476-072x-8-46","title":"Musings on privacy issues in health research involving disaggregate geographic data about individuals","year":2009,"lang":"en","type":"review","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Confidentiality; Internet privacy; Information privacy; Privacy by Design; Computer science; Mainstream; Health informatics; Public health; Computer security; Data science; Medicine; Political science; Law","score_opus":0.3244865994252352,"score_gpt":0.5455990841299037,"score_spread":0.22111248470466843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145434273","genre_codex":"commentary","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011313819,0.12403907,0.19958447,0.6063668,0.004994767,0.00023850241,0.0004568039,0.0001722433,0.05283354],"genre_scores_gemma":[0.37897092,0.26604044,0.1675063,0.15034446,0.021078678,0.0008204621,0.00079942826,0.00024263618,0.014196697],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.90273064,0.06663367,0.005718656,0.0056245276,0.017411508,0.0018809288],"domain_scores_gemma":[0.6669886,0.3050473,0.008087748,0.012321356,0.0059471256,0.001607933],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.093217835,0.00082469627,0.0015591951,0.0070494316,0.007430592,0.017253138,0.003103275,0.010293978,0.004756863],"category_scores_gemma":[0.104007535,0.001280504,0.0017988133,0.010099097,0.037711095,0.030388912,0.013195673,0.013987242,0.00090531714],"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":[0.000049858692,0.000028061542,0.0022056405,0.0016856084,0.000088494846,0.00091505377,0.027897935,0.0020101496,0.00039409703,0.8326113,0.020082371,0.11203145],"study_design_scores_gemma":[0.000012825986,0.00006591823,0.0015563827,0.0038734616,0.00005295477,0.0020070614,0.017303824,0.0019919048,0.000499478,0.6597737,0.31277558,0.00008695476],"about_ca_topic_score_codex":0.0062210597,"about_ca_topic_score_gemma":0.0073483028,"teacher_disagreement_score":0.90678215,"about_ca_system_score_codex":0.0071807965,"about_ca_system_score_gemma":0.0083524175,"threshold_uncertainty_score":0.49298894},"labels":[],"label_agreement":null},{"id":"W2147040401","doi":"10.1186/1476-072x-13-16","title":"A linear programming model for preserving privacy when disclosing patient spatial information for secondary purposes","year":2014,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Children's Hospital of Eastern Ontario; University of Ottawa","funders":"Korea University Business School; Korea University; CHEO Research Institute","keywords":"Health informatics; Health geography; Computer science; Public health; Geospatial analysis; Spatial epidemiology; Spatial analysis; Data science; Geography; Medicine; Health policy; Cartography; Epidemiology; Remote sensing; International health","score_opus":0.025423811167807134,"score_gpt":0.3304994067494215,"score_spread":0.3050755955816144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147040401","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":[0.012157928,0.0001920834,0.98281926,0.0010869513,0.000048424536,0.00014162017,0.00056848425,0.00015532115,0.0028299966],"genre_scores_gemma":[0.5684954,0.000733655,0.41131538,0.00086966256,0.00022294007,0.0021000395,0.0018657164,0.00015612338,0.014241012],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99418736,0.0036941723,0.00018005099,0.0009026785,0.00054053124,0.0004951922],"domain_scores_gemma":[0.98306847,0.014248713,0.0009596845,0.00038936097,0.0010649216,0.00026881447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008556615,0.0012612207,0.0015757302,0.0012806418,0.0007133516,0.0026888368,0.0030130823,0.0019026996,0.0046788817],"category_scores_gemma":[0.016837383,0.0010118497,0.001893261,0.001955667,0.0014849309,0.002469929,0.0022694827,0.0040145563,0.0006764122],"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":[0.00007011583,0.00006761698,0.0014742628,0.000078199635,0.000056796092,0.00010046348,0.0001139492,0.95132446,0.00011284301,0.033657443,0.0017439616,0.011199893],"study_design_scores_gemma":[0.000011086745,0.000027259153,0.00011087438,0.000009890894,0.000011047967,0.000015871718,0.000022520579,0.9866632,0.00006143765,0.012495473,0.00056430756,0.0000070009432],"about_ca_topic_score_codex":0.016019508,"about_ca_topic_score_gemma":0.011876998,"teacher_disagreement_score":0.016019508,"about_ca_system_score_codex":0.0032013599,"about_ca_system_score_gemma":0.0037222125,"threshold_uncertainty_score":0.045252264},"labels":[],"label_agreement":null},{"id":"W2147627663","doi":"10.3201/eid1505.081114","title":"Use of Unstructured Event-Based Reports for Global Infectious Disease Surveillance","year":2009,"lang":"en","type":"review","venue":"Emerging infectious diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":203,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Public Health Agency of Canada; University Health Network","funders":"U.S. National Library of Medicine","keywords":"Public health; Public health surveillance; Disease surveillance; Infectious disease (medical specialty); Context (archaeology); Outbreak; The Internet; Event monitoring; Computer science; Information Dissemination; International Health Regulations; Event (particle physics); Psychological intervention; Unstructured data; Data science; Medicine; Process (computing); Disease; Data mining; World Wide Web; Big data; Geography; Pathology","score_opus":0.027458261205098925,"score_gpt":0.34122541627120007,"score_spread":0.31376715506610114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147627663","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":[0.0009345752,0.9851621,0.0065490417,0.0007977533,0.0003275875,0.00018756233,0.0002915875,0.00010367031,0.005646047],"genre_scores_gemma":[0.011126304,0.97375214,0.013094302,0.00040454228,0.00018869025,0.00018530112,0.0004125446,0.000017482002,0.00081864407],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969447,0.00142002,0.0003535254,0.0002858132,0.000932835,0.00006310682],"domain_scores_gemma":[0.9857576,0.009773244,0.0015183253,0.0007169353,0.0020956555,0.00013818913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007156054,0.0010563964,0.0017164601,0.006062353,0.00023554219,0.0019994655,0.0017384281,0.0010161404,0.0026609974],"category_scores_gemma":[0.013763017,0.00046467525,0.0009948455,0.0050823125,0.0006606628,0.0022015779,0.0009076089,0.0010969186,0.0016400825],"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":[0.000035361994,0.00005240098,0.0009993399,0.013807058,0.0001691005,0.00006990699,0.00012450664,0.00061128085,0.0005574955,0.0038175152,0.0058749146,0.9738811],"study_design_scores_gemma":[0.00007078696,0.00037361175,0.0083012795,0.030624345,0.0007380196,0.0018887708,0.0005114794,0.0017765072,0.0038508014,0.0065685976,0.94515723,0.00013849004],"about_ca_topic_score_codex":0.002547016,"about_ca_topic_score_gemma":0.002840791,"teacher_disagreement_score":0.007156054,"about_ca_system_score_codex":0.0010030718,"about_ca_system_score_gemma":0.0020203767,"threshold_uncertainty_score":0.037845314},"labels":[],"label_agreement":null},{"id":"W2148821410","doi":"10.5210/ojphi.v3i1.3514","title":"Innovative technology for web-based data management during an outbreak","year":2011,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada","funders":"Alberta Health Services","keywords":"Computer science; Data management; Data sharing; Data extraction; Data collection; Web application; Record linkage; Data science; Data mining; World Wide Web; Database; Medicine; MEDLINE","score_opus":0.1762077208803555,"score_gpt":0.39201021372633477,"score_spread":0.21580249284597927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148821410","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":[0.038097184,0.0017744676,0.9029678,0.006124366,0.0007271686,0.002164947,0.0024791681,0.017727837,0.027937077],"genre_scores_gemma":[0.15865457,0.0017337967,0.8252305,0.0014254997,0.000506197,0.0016277512,0.0031092097,0.00054701377,0.007165474],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99258244,0.0029393018,0.0011341561,0.0008697899,0.002302438,0.00017178601],"domain_scores_gemma":[0.98145056,0.00887761,0.0013894436,0.004729474,0.0028698216,0.00068307563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008659077,0.0005934813,0.00048559654,0.0038533865,0.0009829617,0.0047902726,0.0017497452,0.0012882843,0.0030458374],"category_scores_gemma":[0.018388405,0.00061104755,0.0007203089,0.0036408994,0.0008547548,0.0061063212,0.0031522766,0.0015971183,0.0018216382],"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":[0.000553063,0.0006119123,0.013681733,0.000983768,0.00020838676,0.0009451714,0.0031316364,0.0036756922,0.028130587,0.031788338,0.031682447,0.8846073],"study_design_scores_gemma":[0.0005565915,0.0008532755,0.025940264,0.0019350086,0.0004237245,0.00507877,0.0023962376,0.088098414,0.07279645,0.069788754,0.73151594,0.0006165597],"about_ca_topic_score_codex":0.0014255281,"about_ca_topic_score_gemma":0.0010488059,"teacher_disagreement_score":0.008659077,"about_ca_system_score_codex":0.00087424647,"about_ca_system_score_gemma":0.0021735127,"threshold_uncertainty_score":0.04579413},"labels":[],"label_agreement":null},{"id":"W2149793156","doi":"10.1186/1476-072x-3-5","title":"Accuracy of city postal code coordinates as a proxy for location of residence","year":2004,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":99,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"Eli Lilly Canada; Government of Canada; Boston Scientific Corporation; Fondation pour la Recherche Médicale; Eli Lilly and Company","keywords":"Residence; Proxy (statistics); Geocoding; Geography; Computer science; Zip code; Statistic; Geographic information system; Code (set theory); Database; Statistics; Cartography; Demography; Mathematics","score_opus":0.029227903339320002,"score_gpt":0.38573846062974554,"score_spread":0.3565105572904255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149793156","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":[0.95744,0.0014864182,0.016502991,0.001183655,0.00010054804,0.0000903068,0.01129798,0.00013387948,0.011764224],"genre_scores_gemma":[0.99401444,0.00017677752,0.003504559,0.0000441766,0.000016432243,0.000021166084,0.0018107413,0.000008610606,0.00040306905],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99199957,0.0039662872,0.00074777176,0.00071008643,0.002173857,0.00040250682],"domain_scores_gemma":[0.9566673,0.016131964,0.012052428,0.004820781,0.009803201,0.00052430487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006687534,0.00018984679,0.00026696944,0.0023076972,0.00036990776,0.0013261038,0.0010011317,0.00032917497,0.0010731069],"category_scores_gemma":[0.051714525,0.00016796493,0.00020153286,0.0040898602,0.00060166017,0.0007345487,0.0009219483,0.0004129979,0.00037275013],"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":[0.000060498565,0.0000074011054,0.9798821,0.000058511643,0.00005621716,0.000021742128,0.00038906952,0.0010669247,0.00014899994,0.0003616476,0.0013845921,0.016562238],"study_design_scores_gemma":[0.0000054500465,0.000044516844,0.98808575,0.000091003094,0.0000613384,0.0001289546,0.0007615103,0.005818796,0.00078045233,0.00033769873,0.0038680688,0.00001644554],"about_ca_topic_score_codex":0.1424425,"about_ca_topic_score_gemma":0.17652182,"teacher_disagreement_score":0.1424425,"about_ca_system_score_codex":0.0012872803,"about_ca_system_score_gemma":0.0011074129,"threshold_uncertainty_score":0.2832268},"labels":[],"label_agreement":null},{"id":"W2151000035","doi":"10.1186/1476-072x-8-69","title":"An integrated framework for the geographic surveillance of chronic disease","year":2009,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta; Alberta Health; McMaster University","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Public health; Health geography; Geographic information system; GIS and public health; Spatial epidemiology; Geospatial analysis; Epidemiology; Environmental health; Population health; Population; Geography; Disease; Health informatics; Medicine; Data science; Health policy; Cartography; International health; Computer science; Pathology","score_opus":0.021428111865496726,"score_gpt":0.37133312695535076,"score_spread":0.34990501508985405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151000035","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":[0.0016734999,0.00056425255,0.98946065,0.003276371,0.00010341577,0.00044201998,0.0012188331,0.0002572086,0.0030036955],"genre_scores_gemma":[0.05027868,0.00059618766,0.94483936,0.00033081576,0.00012865812,0.001222626,0.0019329363,0.00003648941,0.000634253],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9640736,0.024750838,0.0028012772,0.0033025877,0.0044362536,0.0006353473],"domain_scores_gemma":[0.9688418,0.018311322,0.002377543,0.0038967,0.005542377,0.0010302515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.036808025,0.00107381,0.0015209096,0.007435284,0.0013358587,0.005601868,0.0038427042,0.0017334957,0.0027797536],"category_scores_gemma":[0.051731292,0.0008862599,0.0031181255,0.008124851,0.0035540373,0.004011962,0.006351666,0.0025320589,0.00056376506],"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":[0.000032871136,0.00006756475,0.010477496,0.00034500624,0.0003228875,0.00021771806,0.0015128563,0.06258395,0.00022906375,0.8537387,0.0076190154,0.06285284],"study_design_scores_gemma":[0.00009881144,0.00014034404,0.0065166284,0.00046216536,0.00019353151,0.00032134203,0.00092626014,0.25213367,0.00021099584,0.6501979,0.088696085,0.00010215944],"about_ca_topic_score_codex":0.062775955,"about_ca_topic_score_gemma":0.04852203,"teacher_disagreement_score":0.062775955,"about_ca_system_score_codex":0.006142987,"about_ca_system_score_gemma":0.014826988,"threshold_uncertainty_score":0.1946618},"labels":[],"label_agreement":null},{"id":"W2151374408","doi":"10.1186/2041-1480-1-2","title":"What's unusual in online disease outbreak news?","year":2010,"lang":"en","type":"article","venue":"Journal of Biomedical Semantics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":10,"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":"Institute of Genetics; Japan Science and Technology Agency; Universität Bielefeld; Kasetsart University; Murdoch University; University of Pittsburgh; Vanderbilt University Medical Center; National Electronics and Computer Technology Center; Fordham University; Precursory Research for Embryonic Science and Technology; Vanderbilt University","keywords":"Outbreak; Disease; Disease surveillance; Social media; Public health surveillance; MEDLINE","score_opus":0.01279795532113026,"score_gpt":0.3099166607974684,"score_spread":0.2971187054763381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151374408","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":[0.96592426,0.005140957,0.00991845,0.0043355906,0.00042255505,0.00027259553,0.0085585145,0.0004263172,0.005000679],"genre_scores_gemma":[0.98354256,0.002064643,0.00878535,0.00091540074,0.0005328345,0.00007113504,0.0035026486,0.00005353019,0.00053184555],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9953028,0.0019685691,0.0009517976,0.00061402173,0.0010029075,0.00015982283],"domain_scores_gemma":[0.8900007,0.072165884,0.027215503,0.0031724565,0.0063122227,0.0011332465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061243745,0.0003663651,0.0005083379,0.0037694601,0.00027129348,0.0020972397,0.00046034492,0.00079234916,0.0014714039],"category_scores_gemma":[0.054051735,0.00016655063,0.0004735267,0.0029710312,0.00054898084,0.001728645,0.00071001507,0.000460811,0.00037616692],"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":[0.00081839465,0.000107535074,0.8774131,0.0015659226,0.0003005052,0.0009958842,0.0016799336,0.0010395879,0.0019605176,0.00029853726,0.004708797,0.109111354],"study_design_scores_gemma":[0.00004957042,0.0007088324,0.95103854,0.0011849162,0.00062920403,0.003669273,0.0067321258,0.009044243,0.008088897,0.0020440703,0.016730592,0.000079677964],"about_ca_topic_score_codex":0.0013488637,"about_ca_topic_score_gemma":0.0018459406,"teacher_disagreement_score":0.0061243745,"about_ca_system_score_codex":0.0005065304,"about_ca_system_score_gemma":0.00040109313,"threshold_uncertainty_score":0.032389164},"labels":[],"label_agreement":null},{"id":"W2151485425","doi":"10.1371/journal.pone.0064323","title":"Monitoring Influenza Epidemics in China with Search Query from Baidu","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":254,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"University of Chinese Academy of Sciences; National Institutes of Health; Chinese Academy of Sciences; U.S. National Library of Medicine; National Natural Science Foundation of China","keywords":"Computer science; Web search query; China; Index (typography); Data mining; Information retrieval; Selection (genetic algorithm); Query expansion; Search engine; Geography; Artificial intelligence; World Wide Web","score_opus":0.05947158787726732,"score_gpt":0.2765700579275676,"score_spread":0.2170984700503003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151485425","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":[0.9966281,0.00014570495,0.00159365,0.000049999788,0.000004248085,0.00002604659,0.0011621237,0.000060754668,0.00032929445],"genre_scores_gemma":[0.99530053,0.00009671782,0.0022824602,0.000012997249,0.000007309049,0.000025928082,0.0021035923,0.0000031869388,0.00016719135],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995927,0.00010567239,0.000055364122,0.00010534487,0.00009145785,0.000049318332],"domain_scores_gemma":[0.99913627,0.000282463,0.00022515534,0.00007980315,0.00019280647,0.00008351566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082296215,0.0006806014,0.00064858585,0.0020542569,0.00027925326,0.0004538863,0.00053086184,0.00035920957,0.00029115457],"category_scores_gemma":[0.0018683998,0.00023708105,0.00047046435,0.0025764925,0.0002185219,0.0007361314,0.00052569207,0.00019566667,0.00008895776],"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":[0.00055596436,0.00020845661,0.86897993,0.00031552918,0.00022785852,0.00048084496,0.000527975,0.062445715,0.013209108,0.0004989869,0.0011816456,0.05136806],"study_design_scores_gemma":[0.00003826192,0.00031851884,0.48766813,0.000016972763,0.0001528359,0.00019240669,0.0003900991,0.5033826,0.006391051,0.00029395483,0.0011101498,0.000045005465],"about_ca_topic_score_codex":0.05711019,"about_ca_topic_score_gemma":0.056505702,"teacher_disagreement_score":0.05711019,"about_ca_system_score_codex":0.0010445969,"about_ca_system_score_gemma":0.00078700646,"threshold_uncertainty_score":0.11355555},"labels":[],"label_agreement":null},{"id":"W2151873615","doi":"10.2196/publichealth.4472","title":"Using Social Media to Perform Local Influenza Surveillance in an Inner-City Hospital: A Retrospective Observational Study","year":2015,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of General Medical Sciences","keywords":"Staffing; Social media; Public health; Observational study; Crowding; Locale (computer software); Environmental health; Public health surveillance; Medicine; Vaccination; Business; Medical emergency; Political science; Nursing; Psychology; Computer science; Virology; Pathology","score_opus":0.20098761510469124,"score_gpt":0.41021401084208303,"score_spread":0.2092263957373918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151873615","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":[0.9991197,0.00010148852,0.00012707584,0.000024634423,0.0000032633236,0.00007038001,0.00035919095,0.000003618179,0.00019063702],"genre_scores_gemma":[0.9987956,0.000120020384,0.0002894256,0.00007345395,0.000011080964,0.000112152346,0.00052341126,0.0000037041473,0.000071082875],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971898,0.0008691175,0.0004964393,0.0005655226,0.00052181556,0.00035727085],"domain_scores_gemma":[0.9920507,0.0014565992,0.003800134,0.0006918483,0.0010870048,0.00091366965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021782594,0.00054149755,0.0006823097,0.0019937332,0.0011410655,0.001338581,0.0008679392,0.00067482534,0.0010183462],"category_scores_gemma":[0.0062205144,0.00081540947,0.0008562102,0.0024019754,0.00086766423,0.0015529001,0.0016576342,0.0009307514,0.00038139898],"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":[0.000051737476,0.00012263669,0.9986014,0.000023255989,0.00005314457,0.00007467254,0.00037009976,0.000026519663,0.000080673606,0.000005607864,0.000075509386,0.0005147666],"study_design_scores_gemma":[0.000022038561,0.00082397147,0.99231875,0.000052059142,0.00012424287,0.00046239063,0.0050162044,0.000558089,0.00015431222,0.000015966241,0.00042502544,0.00002693914],"about_ca_topic_score_codex":0.017038912,"about_ca_topic_score_gemma":0.023132127,"teacher_disagreement_score":0.017038912,"about_ca_system_score_codex":0.0012918352,"about_ca_system_score_gemma":0.0015161841,"threshold_uncertainty_score":0.03387946},"labels":[],"label_agreement":null},{"id":"W2153140731","doi":"10.1111/j.1538-4632.2012.00858.x","title":"A Spatial Scan Approach to Detecting Focused‐Global Clustering in Case‐Control Data","year":2012,"lang":"en","type":"article","venue":"Geographical Analysis","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"","keywords":"Cluster analysis; Event (particle physics); Computer science; Geography; Cluster (spacecraft); Focus (optics); Data mining; Cartography; Artificial intelligence; Physics","score_opus":0.02631052244412712,"score_gpt":0.2913721833166618,"score_spread":0.2650616608725347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153140731","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":[0.0799404,0.00027835023,0.9143233,0.00019881167,0.000026269921,0.00044798525,0.00117678,0.001310924,0.0022971202],"genre_scores_gemma":[0.44738758,0.00019757512,0.5475125,0.00014408202,0.000055968383,0.0010866446,0.0021624311,0.00015725564,0.0012959964],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964354,0.0018000702,0.00019635659,0.0007077589,0.0007366399,0.0001237855],"domain_scores_gemma":[0.9860427,0.00875188,0.0015045401,0.0014347784,0.0020124274,0.00025369893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043133507,0.0007811718,0.0007498579,0.0073091686,0.00058532343,0.0011405809,0.0013051131,0.0009019661,0.002466855],"category_scores_gemma":[0.019453546,0.0005599329,0.0011635587,0.0055039753,0.0007426805,0.00091772876,0.0014712345,0.00065688504,0.00048125247],"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":[0.0010386633,0.0004916961,0.23306623,0.0007519418,0.0016228451,0.0014907515,0.002413423,0.14099494,0.016674062,0.019232206,0.0102534685,0.5719698],"study_design_scores_gemma":[0.00013415216,0.00050851674,0.09563159,0.00011097079,0.0002517986,0.0014446436,0.001601732,0.8600401,0.009473541,0.021844106,0.008824742,0.00013406342],"about_ca_topic_score_codex":0.008035806,"about_ca_topic_score_gemma":0.010829921,"teacher_disagreement_score":0.008035806,"about_ca_system_score_codex":0.0005952556,"about_ca_system_score_gemma":0.0011831518,"threshold_uncertainty_score":0.022811472},"labels":[],"label_agreement":null},{"id":"W2153675329","doi":"10.1186/1746-6148-8-3","title":"Suitability and limitations of portion-specific abattoir data as part of an early warning system for emerging diseases of swine in Ontario","year":2012,"lang":"en","type":"article","venue":"BMC Veterinary Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"Ministry of Agriculture, Food and Rural Affairs; Ontario Ministry of Research and Innovation; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Outbreak; Disease surveillance; Veterinary medicine; Disease; Agriculture; Geography; Medicine; Environmental health; Biology; Pathology; Ecology","score_opus":0.4458557396613974,"score_gpt":0.43967656134185823,"score_spread":0.00617917831953918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153675329","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":[0.9740224,0.00051826244,0.0041825073,0.0008237761,0.000019123416,0.00023444592,0.014706763,0.00010024446,0.0053924234],"genre_scores_gemma":[0.99102765,0.0001867701,0.004186799,0.000047119363,0.000006384458,0.0000766302,0.0037710436,0.000010921359,0.0006866807],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9964953,0.0011021296,0.00036388173,0.0004889079,0.0011117974,0.0004380023],"domain_scores_gemma":[0.9805228,0.006353199,0.0035300988,0.0015443781,0.0073422347,0.0007071821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055560027,0.0003400093,0.00037246634,0.0014692737,0.0010437781,0.0016879941,0.0014857478,0.00031709738,0.0008939796],"category_scores_gemma":[0.02343894,0.00037097125,0.0004391274,0.0040574237,0.00058596284,0.0009387894,0.0009080133,0.00025517787,0.00017028676],"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":[0.00014113796,0.000015600252,0.97946244,0.00016365,0.0000691614,0.000043571945,0.00079943397,0.0040668766,0.0006591809,0.00019038976,0.00085117493,0.013537298],"study_design_scores_gemma":[0.0000147453,0.000051311043,0.9793041,0.0000976331,0.00005587488,0.000044746947,0.0013233777,0.015703022,0.0004417844,0.000119380515,0.002824558,0.000019445039],"about_ca_topic_score_codex":0.95906985,"about_ca_topic_score_gemma":0.96781635,"teacher_disagreement_score":0.040930152,"about_ca_system_score_codex":0.015103154,"about_ca_system_score_gemma":0.018983923,"threshold_uncertainty_score":0.10958159},"labels":[],"label_agreement":null},{"id":"W2153765714","doi":"10.1111/j.0008-3658.2005.00096.x","title":"Diagnostic uncertainty and medical geography: what are we mapping?","year":2005,"lang":"en","type":"article","venue":"Canadian Geographies / Géographies canadiennes","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Alberta Health Services; Alberta Health; University of Alberta","funders":"","keywords":"Health geography; Medical diagnosis; Confounding; Scale (ratio); Geography; Representation (politics); Space (punctuation); Disease; Data science; Health care; Regional science; Cartography; Medicine; Public health; Health policy; Computer science; International health; Pathology; Political science","score_opus":0.009071120907510338,"score_gpt":0.21704722413903477,"score_spread":0.20797610323152443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153765714","genre_codex":"commentary","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":[0.026728911,0.13397758,0.11380608,0.7087393,0.0022038622,0.00017955528,0.0012752713,0.00029598325,0.012793496],"genre_scores_gemma":[0.7662289,0.095553346,0.10342719,0.02636394,0.005622796,0.00045376885,0.00088702753,0.0001713722,0.0012916985],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9635548,0.025894934,0.0018632602,0.0022811254,0.005228731,0.0011771838],"domain_scores_gemma":[0.71520466,0.23231217,0.018357817,0.012241754,0.018160976,0.0037226798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05866894,0.00140101,0.0032897478,0.0119962515,0.0044867424,0.01528918,0.0054849186,0.005041043,0.0029546448],"category_scores_gemma":[0.35492116,0.0013072909,0.0016931172,0.018678302,0.024810588,0.02718409,0.00843492,0.0063212616,0.0003708742],"study_design_candidate":"theoretical_or_conceptual","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":[0.00014239138,0.0000700511,0.11055063,0.002460374,0.0012384598,0.00041771264,0.013394239,0.012007607,0.00007550984,0.3213675,0.039442386,0.49883318],"study_design_scores_gemma":[0.000035853674,0.000028937498,0.014578032,0.0029375239,0.00020473638,0.00031498697,0.0068849334,0.006549856,0.00007053698,0.9437651,0.024509031,0.0001205298],"about_ca_topic_score_codex":0.16142367,"about_ca_topic_score_gemma":0.08042422,"teacher_disagreement_score":0.8385763,"about_ca_system_score_codex":0.012427431,"about_ca_system_score_gemma":0.015937861,"threshold_uncertainty_score":0.3209682},"labels":[],"label_agreement":null},{"id":"W2155134093","doi":"10.1186/1476-072x-5-56","title":"An unsupervised classification method for inferring original case locations from low-resolution disease maps.","year":2006,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":44,"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":"U.S. National Library of Medicine; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Geocoding; Health geography; Presentation (obstetrics); Identification (biology); Geographic information system; Health informatics; Computer science; Quality (philosophy); Metadata; Cartography; Geography; Information retrieval; Data science; Public health; Medicine; World Wide Web; Health policy","score_opus":0.0349796327408408,"score_gpt":0.3930163964248658,"score_spread":0.358036763684025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155134093","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":[0.036840416,0.00029791912,0.9517439,0.00031539056,0.0001303,0.0005894019,0.0019261403,0.0059910547,0.0021655413],"genre_scores_gemma":[0.15381703,0.00013891677,0.8390913,0.000104359475,0.00010067057,0.00052586856,0.0039334917,0.00028810836,0.0020002453],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99638796,0.0007669211,0.0004852555,0.0010017888,0.0011502093,0.00020783702],"domain_scores_gemma":[0.9859774,0.006766797,0.0019990026,0.0017521514,0.0033003637,0.00020432085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004454404,0.0008804107,0.0008121522,0.0076178103,0.00079081155,0.0021636854,0.00213271,0.0014151057,0.0033061253],"category_scores_gemma":[0.021081036,0.00045758937,0.0010563652,0.003664121,0.0007686686,0.0016346268,0.0013058287,0.0012430613,0.0026842311],"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":[0.00037376833,0.00024901202,0.026759485,0.00037379953,0.00018099345,0.00039377372,0.00065610115,0.022990756,0.009523669,0.0038890585,0.013838554,0.9207711],"study_design_scores_gemma":[0.00008196551,0.000091512156,0.021950176,0.00012373402,0.000139605,0.0012560808,0.0005332372,0.93510675,0.012827146,0.011973463,0.015816815,0.00009951424],"about_ca_topic_score_codex":0.006427956,"about_ca_topic_score_gemma":0.008135995,"teacher_disagreement_score":0.0076178103,"about_ca_system_score_codex":0.0011750971,"about_ca_system_score_gemma":0.0018481207,"threshold_uncertainty_score":0.023557425},"labels":[],"label_agreement":null},{"id":"W2155927957","doi":"10.4258/hir.2012.18.2.88","title":"Geographical Information Systems and Health: Current State and Future Directions","year":2012,"lang":"en","type":"article","venue":"Healthcare Informatics Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Algoma University","funders":"","keywords":"Geographic information system; Health informatics; GIS and public health; GIS applications; Informatics; Current (fluid); Information system; Action (physics); State (computer science); Data science; Health care; Computer science; Environmental planning; Geography; Political science; Engineering; Remote sensing","score_opus":0.10521163225157448,"score_gpt":0.4462978365809015,"score_spread":0.341086204329327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155927957","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":[0.0013114491,0.8203304,0.0033607741,0.16488023,0.0014943328,0.000022209779,0.00009438962,0.00007821236,0.008428061],"genre_scores_gemma":[0.02926099,0.9470145,0.0069909818,0.010434737,0.0051044347,0.00006360973,0.00011890687,0.000027025448,0.0009848983],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99290645,0.004175815,0.0005556137,0.0005725091,0.0013076331,0.0004819463],"domain_scores_gemma":[0.9290357,0.059447642,0.0021956512,0.0013908156,0.0053409333,0.0025893247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024601014,0.0008577703,0.0019482309,0.0055462606,0.0021944668,0.012302756,0.0023062965,0.0080133965,0.012223679],"category_scores_gemma":[0.018044446,0.0006519451,0.001152042,0.011118258,0.012857942,0.027696421,0.0049632974,0.007725227,0.0017796169],"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":[0.00018644225,0.00024098743,0.0050007002,0.008013717,0.00010005264,0.00016269165,0.0019395173,0.0013384264,0.00023295946,0.27553314,0.05109693,0.65615445],"study_design_scores_gemma":[0.000050096634,0.0002371138,0.0047122175,0.014725816,0.00009345476,0.0005654876,0.010944976,0.0019335581,0.00026520458,0.28932053,0.67699677,0.00015475841],"about_ca_topic_score_codex":0.008955636,"about_ca_topic_score_gemma":0.009241422,"teacher_disagreement_score":0.024601014,"about_ca_system_score_codex":0.0059621767,"about_ca_system_score_gemma":0.012229289,"threshold_uncertainty_score":0.13010412},"labels":[],"label_agreement":null},{"id":"W2157562685","doi":"10.1186/1471-2458-14-285","title":"Bowel disorders and its spatial trend in Manitoba, Canada","year":2014,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba","funders":"Manitoba Health Research Council","keywords":"Medicine; Epidemiology; Scan statistic; Biostatistics; Environmental health; Disease; Statistic; Incidence (geometry); Demography; Statistics; Pathology","score_opus":0.026490753446808027,"score_gpt":0.271604296379286,"score_spread":0.245113542932478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157562685","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":[0.84727806,0.011978061,0.0013258347,0.0073103583,0.00016249415,0.00033734448,0.12086267,0.00017316443,0.010572078],"genre_scores_gemma":[0.9600769,0.0065920423,0.0026675668,0.0006559307,0.00003696106,0.000110600515,0.025747355,0.00003602788,0.0040766546],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99909496,0.000071619565,0.00007348254,0.00016102959,0.00030804824,0.00029095716],"domain_scores_gemma":[0.9956157,0.00025886242,0.00056231546,0.00010776839,0.0028196923,0.0006357156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083297904,0.00045880355,0.0005051348,0.0041473424,0.002596912,0.0017289986,0.0016708608,0.00042298218,0.002758291],"category_scores_gemma":[0.0032729232,0.00031884987,0.0007884477,0.013852928,0.0008380489,0.00042753344,0.001253919,0.0008188991,0.0003271876],"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":[0.00007916652,0.000027385846,0.97311527,0.000313473,0.00016385532,0.00026872463,0.00093248056,0.001013142,0.00024947978,0.0005616133,0.009926934,0.01334847],"study_design_scores_gemma":[0.000012605391,0.000015921642,0.99047405,0.00021122392,0.0000625543,0.00014405194,0.0028126792,0.0013139134,0.00008340236,0.00011221398,0.0047338656,0.00002361474],"about_ca_topic_score_codex":0.9987607,"about_ca_topic_score_gemma":0.9990984,"teacher_disagreement_score":0.04515153,"about_ca_system_score_codex":0.04515153,"about_ca_system_score_gemma":0.086687796,"threshold_uncertainty_score":0.3275988},"labels":[],"label_agreement":null},{"id":"W2157631873","doi":"10.1186/1476-072x-5-46","title":"Identifying geographic areas with high disease rates: when do confidence intervals for rates and a disease cluster detection method agree?","year":2006,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Fondation pour la Recherche Médicale","keywords":"Confidence interval; Cluster (spacecraft); Spatial epidemiology; Statistics; Disease surveillance; Population; Computer science; Disease; Epidemiology; Medicine; Environmental health; Mathematics; Pathology","score_opus":0.022596051008557477,"score_gpt":0.3560781762782666,"score_spread":0.3334821252697091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157631873","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":[0.250812,0.019570464,0.6668018,0.029135982,0.0027958925,0.0030182884,0.0058732517,0.0015913411,0.020400902],"genre_scores_gemma":[0.8002101,0.0014368568,0.1867115,0.004611986,0.00084636867,0.0019624685,0.0029215482,0.0007189188,0.0005802432],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.5991622,0.2725461,0.037245102,0.035545018,0.04968521,0.005816318],"domain_scores_gemma":[0.08195728,0.8374731,0.0310278,0.024822267,0.022906229,0.0018133279],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.37315845,0.0011942209,0.0035182792,0.005693601,0.002432405,0.008057223,0.0075429315,0.0076855635,0.002910805],"category_scores_gemma":[0.8148112,0.0014031188,0.005104541,0.0087473355,0.0069086943,0.010367472,0.0058148275,0.0065537905,0.0009869772],"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":[0.0076525942,0.0004981369,0.4981767,0.004576562,0.008449963,0.0012940186,0.010368604,0.038760815,0.0018972089,0.07302513,0.039835766,0.3154645],"study_design_scores_gemma":[0.0025827389,0.0030373372,0.33396295,0.009223924,0.0044957735,0.0034020552,0.011081817,0.18044078,0.012807134,0.37567723,0.062074233,0.0012139707],"about_ca_topic_score_codex":0.005155046,"about_ca_topic_score_gemma":0.002705671,"teacher_disagreement_score":0.37315845,"about_ca_system_score_codex":0.0028390992,"about_ca_system_score_gemma":0.003005423,"threshold_uncertainty_score":0.7730072},"labels":[],"label_agreement":null},{"id":"W2159030990","doi":"10.5210/ojphi.v2i1.2837","title":"A Confidence-based Aberration Interpretation Framework For Outbreak Conciliation","year":2010,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Agency of Canada; University of Manitoba","funders":"University of Manitoba","keywords":"Outbreak; Computer science; Data mining; Infectious disease (medical specialty); Data science; Risk analysis (engineering); Medicine; Computer security; Medical emergency; Disease; Pathology","score_opus":0.062033957238748334,"score_gpt":0.38781710149183035,"score_spread":0.325783144253082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159030990","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":[0.0022897741,0.00008449822,0.9966833,0.0002279525,0.000018840548,0.00003789001,0.0000501695,0.00024484578,0.0003628438],"genre_scores_gemma":[0.25498098,0.00013688173,0.7431416,0.00024604335,0.00020859453,0.00023866087,0.00036938,0.00014051815,0.00053733535],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98396814,0.0069778105,0.0016113083,0.0021336516,0.0046598436,0.0006492693],"domain_scores_gemma":[0.93304557,0.04478275,0.0066458406,0.0043514087,0.010294319,0.00088004844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022550805,0.0015570926,0.0017316872,0.006456772,0.0013532354,0.005172168,0.00428426,0.0026852654,0.0017201557],"category_scores_gemma":[0.10292416,0.0008245874,0.0018779739,0.0030900398,0.00331666,0.004920694,0.003746045,0.003469139,0.00042321914],"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":[0.000452557,0.00020321888,0.013822436,0.00040079016,0.00040144834,0.0006080836,0.0013346333,0.42107618,0.0043429607,0.30141222,0.0045423773,0.25140303],"study_design_scores_gemma":[0.000029745748,0.0000710444,0.0008092067,0.000042496446,0.000050734325,0.00016675411,0.000065206565,0.91512126,0.0017311958,0.07992052,0.0019384146,0.00005354608],"about_ca_topic_score_codex":0.006576936,"about_ca_topic_score_gemma":0.003264669,"teacher_disagreement_score":0.022550805,"about_ca_system_score_codex":0.002840313,"about_ca_system_score_gemma":0.0032506618,"threshold_uncertainty_score":0.1192615},"labels":[],"label_agreement":null},{"id":"W2159949884","doi":"10.1186/1472-6947-13-70","title":"Development of ClickClinica: a novel smartphone application to generate real-time global disease surveillance and clinical practice data","year":2013,"lang":"en","type":"article","venue":"BMC Medical Informatics and Decision Making","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":24,"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 Institute for Health and Care Research; McGill University","keywords":"Medicine; Psychological intervention; Audit; Disease; Public health; Pandemic; Health care; Disease surveillance; Identification (biology); Medical emergency; Health informatics; Guideline; Infectious disease (medical specialty); Intensive care medicine; Coronavirus disease 2019 (COVID-19); Nursing; Business; Pathology","score_opus":0.06304016097966382,"score_gpt":0.41616878496986326,"score_spread":0.35312862399019945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159949884","genre_codex":"software","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":[0.088068016,0.0025803845,0.27011797,0.0056889965,0.002646091,0.012139902,0.1181687,0.47242698,0.028163007],"genre_scores_gemma":[0.40426502,0.0024694551,0.4318607,0.006490512,0.0014118121,0.017543713,0.093762,0.0115737505,0.03062312],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881184,0.00035117823,0.00018405293,0.00025163867,0.00029692132,0.00010437253],"domain_scores_gemma":[0.9945564,0.0031558124,0.00033928224,0.00046048794,0.0007107268,0.0007772442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018830524,0.0016366763,0.0009186123,0.0018994631,0.00030895867,0.0014320912,0.001591232,0.0012605145,0.03203208],"category_scores_gemma":[0.008519927,0.0005028402,0.0008858598,0.0007363296,0.00023664362,0.0015984039,0.0019551876,0.00081988174,0.010603314],"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":[0.0031222545,0.0009718187,0.021640394,0.004084221,0.0005485573,0.0020846566,0.0018742543,0.003295056,0.014378292,0.0021412976,0.45155546,0.49430373],"study_design_scores_gemma":[0.006187257,0.0037002482,0.09731208,0.0023672767,0.0008958645,0.0055343015,0.001996929,0.19346191,0.040100098,0.01501805,0.63201123,0.0014148011],"about_ca_topic_score_codex":0.0012833287,"about_ca_topic_score_gemma":0.0020207642,"teacher_disagreement_score":0.03203208,"about_ca_system_score_codex":0.00039420679,"about_ca_system_score_gemma":0.0008463378,"threshold_uncertainty_score":0.107157946},"labels":[],"label_agreement":null},{"id":"W2160183787","doi":"10.5210/ojphi.v7i1.5711","title":"Towards a Framework for Data Quality Properties of Indicators used in Surveillance","year":2015,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Bill and Melinda Gates Foundation","keywords":"Computer science; Data quality; Metadata; Data science; Disease surveillance; Quality (philosophy); Data mining; Data integration; Scalability; Population; Database; World Wide Web; Disease; Engineering; Medicine","score_opus":0.3886151866498914,"score_gpt":0.46501834316011187,"score_spread":0.07640315651022045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160183787","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":[0.0017068593,0.00023440628,0.9937768,0.0014848717,0.00006373893,0.0005095118,0.0004324971,0.0008679425,0.00092336285],"genre_scores_gemma":[0.031574775,0.00023415816,0.9653446,0.00027912314,0.00009674863,0.0006962418,0.0012965839,0.00023333693,0.00024446342],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8811328,0.055217426,0.025151778,0.010765482,0.024907338,0.002825211],"domain_scores_gemma":[0.7991624,0.09463108,0.017994756,0.03267324,0.05091789,0.004620684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.14858581,0.0022432832,0.0027545083,0.01584661,0.0038807935,0.021918237,0.007142468,0.00463176,0.0014531838],"category_scores_gemma":[0.18715791,0.002897555,0.0076038684,0.011455906,0.009553835,0.021194365,0.012571831,0.010848976,0.0007969927],"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":[0.000106084786,0.0002342494,0.007160604,0.0007668723,0.0002908946,0.00051109854,0.003970514,0.038924467,0.0018713214,0.878768,0.006258254,0.061137613],"study_design_scores_gemma":[0.00011143713,0.00016026545,0.0019383617,0.0014597417,0.00033449844,0.00034120385,0.0015883647,0.2610824,0.0050459714,0.6652859,0.062438797,0.0002130911],"about_ca_topic_score_codex":0.02743757,"about_ca_topic_score_gemma":0.018118137,"teacher_disagreement_score":0.14858581,"about_ca_system_score_codex":0.008986533,"about_ca_system_score_gemma":0.019035926,"threshold_uncertainty_score":0.7858063},"labels":[],"label_agreement":null},{"id":"W2160996683","doi":"10.1093/jamia/ocu002","title":"Using age, triage score, and disposition data from emergency department electronic records to improve Influenza-like illness surveillance","year":2015,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal; McGill University","funders":"","keywords":"Triage; Medicine; Emergency department; Confidence interval; Emergency medicine; Influenza-like illness; Early warning score; Pediatrics; Medical emergency; Internal medicine","score_opus":0.04153490928077576,"score_gpt":0.3493337837975727,"score_spread":0.30779887451679694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160996683","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":[0.9831024,0.0013705507,0.010611643,0.0006120008,0.000068973706,0.000109380926,0.0027765501,0.00016802455,0.001180412],"genre_scores_gemma":[0.9924016,0.00027066306,0.005236333,0.00007551967,0.000044809425,0.000030151583,0.0016958107,0.0000065586937,0.00023848671],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976138,0.001398182,0.00016483197,0.00045384705,0.00024567105,0.0001236804],"domain_scores_gemma":[0.9905524,0.0043302593,0.003296951,0.0005709342,0.0008977739,0.00035160687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060065472,0.00095989363,0.0007347899,0.0017151019,0.00024174163,0.0008680757,0.00067473046,0.0005867841,0.0009215868],"category_scores_gemma":[0.021080034,0.00033712713,0.0009907212,0.0013275911,0.00018956787,0.00096855714,0.0007304459,0.00072973,0.0002449672],"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":[0.00013837623,0.00009868436,0.98371106,0.000037899903,0.0003118092,0.000026133144,0.000042599495,0.004333642,0.00011893767,0.00004926617,0.00040920972,0.010722343],"study_design_scores_gemma":[0.000066208544,0.0006644781,0.87104905,0.00010217475,0.00035745848,0.0001148049,0.00009357351,0.12598315,0.00038092467,0.00029420535,0.0008631003,0.000030839263],"about_ca_topic_score_codex":0.029964177,"about_ca_topic_score_gemma":0.0415957,"teacher_disagreement_score":0.029964177,"about_ca_system_score_codex":0.000792245,"about_ca_system_score_gemma":0.0014510384,"threshold_uncertainty_score":0.05957955},"labels":[],"label_agreement":null},{"id":"W2161164234","doi":"10.1016/j.chom.2015.02.004","title":"Computational Approaches to Influenza Surveillance: Beyond Timeliness","year":2015,"lang":"en","type":"article","venue":"Cell Host & Microbe","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"U.S. National Library of Medicine; National Institute of Environmental Health Sciences; National Institutes of Health; Public Health Agency of Canada","keywords":"Biology; Computational biology; Virology; Data science; Computer science","score_opus":0.08883298094933957,"score_gpt":0.2862862964898796,"score_spread":0.19745331554054002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161164234","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":[0.09057187,0.0019655505,0.8857966,0.012682756,0.00045880882,0.000118666685,0.0014643106,0.00051814626,0.0064233313],"genre_scores_gemma":[0.8495063,0.0011320328,0.14561206,0.0007925287,0.000354772,0.00014614624,0.0006849152,0.0001419209,0.0016293431],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985007,0.0009081351,0.000087620436,0.00019952848,0.00023692244,0.00006716016],"domain_scores_gemma":[0.98317724,0.014360911,0.0006742346,0.00081800146,0.00060406426,0.00036550488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036794804,0.00055956905,0.0009552498,0.0008938689,0.0004375012,0.0021507097,0.002138537,0.0012406335,0.0024080765],"category_scores_gemma":[0.02179913,0.0006170113,0.0008776422,0.000830838,0.0010683932,0.0033447915,0.0015682761,0.001815981,0.00021517498],"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":[0.0000746741,0.0000740189,0.0031508142,0.0001657411,0.000118573815,0.000044595046,0.000101146274,0.9331141,0.00025642864,0.041647233,0.0016569425,0.019595778],"study_design_scores_gemma":[0.0000091086295,0.000010857982,0.00012279692,0.000011653825,0.0000108483355,0.000007688904,0.00002682079,0.9734153,0.0000930348,0.025636367,0.0006512839,0.0000041588314],"about_ca_topic_score_codex":0.008245138,"about_ca_topic_score_gemma":0.009016636,"teacher_disagreement_score":0.008245138,"about_ca_system_score_codex":0.0012000016,"about_ca_system_score_gemma":0.0019484357,"threshold_uncertainty_score":0.019459188},"labels":[],"label_agreement":null},{"id":"W2161518653","doi":"10.1016/j.jbi.2014.10.009","title":"Quantifying the determinants of outbreak detection performance through simulation and machine learning","year":2014,"lang":"en","type":"article","venue":"Journal of Biomedical Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; McGill University Health Centre; Polytechnique Montréal","funders":"Canadian Institutes of Health Research; U.S. Public Health Service","keywords":"Computer science; Outbreak; Data mining; Machine learning; Probabilistic logic; Bayesian probability; Artificial intelligence; Range (aeronautics); Engineering","score_opus":0.04316760951373722,"score_gpt":0.3256251716509549,"score_spread":0.2824575621372177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161518653","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":[0.97172326,0.00013455728,0.02640925,0.0005102241,0.000026782978,0.00004338716,0.00021689507,0.00019718503,0.0007384513],"genre_scores_gemma":[0.9967223,0.000026757372,0.0029672873,0.000021122622,0.0000068309455,0.000012480591,0.00015181884,0.0000058823343,0.00008559599],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981487,0.0010145785,0.000107777676,0.0003007291,0.000209071,0.00021911123],"domain_scores_gemma":[0.9141979,0.07700198,0.002908687,0.0029790904,0.002016705,0.00089559396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008108199,0.0008015258,0.00069502543,0.0012545222,0.00049772917,0.0014045361,0.0010296754,0.0018102153,0.0007487747],"category_scores_gemma":[0.046144802,0.0005356016,0.0006939162,0.0006658695,0.00089186954,0.0018517183,0.0006757965,0.0016195292,0.0001538567],"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":[0.0002115247,0.0003133517,0.02728954,0.000023938997,0.000062218176,0.00002648997,0.000033432596,0.9653014,0.0004702513,0.001030381,0.00023623294,0.005001259],"study_design_scores_gemma":[0.000008294278,0.00004891448,0.0016784475,0.0000027637668,0.000006005539,0.000009314557,0.000008536097,0.9971097,0.00034968372,0.0007485446,0.000025088602,0.0000046940727],"about_ca_topic_score_codex":0.009623844,"about_ca_topic_score_gemma":0.005739447,"teacher_disagreement_score":0.009623844,"about_ca_system_score_codex":0.0014563022,"about_ca_system_score_gemma":0.0018121499,"threshold_uncertainty_score":0.042880774},"labels":[],"label_agreement":null},{"id":"W2163485305","doi":"","title":"Human surveillance for West Nile virus infection in Ontario in 2000.","year":2002,"lang":"en","type":"article","venue":"PubMed","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Medicine; Disease surveillance; Suspect; West Nile virus; Virology; Emergency medicine; Virus; Epidemiology; Internal medicine","score_opus":0.04012816867985264,"score_gpt":0.2519830388984337,"score_spread":0.21185487021858107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163485305","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":[0.9521718,0.002482465,0.00040682612,0.0019213229,0.00010071429,0.00052855944,0.018737637,0.00008494666,0.023565765],"genre_scores_gemma":[0.98397475,0.0014409678,0.00059461157,0.00062861893,0.000042890184,0.0001614356,0.0051042964,0.00000765001,0.0080448035],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997002,0.000019170731,0.000020107238,0.0000393954,0.00013557167,0.00008547503],"domain_scores_gemma":[0.99904805,0.000029037705,0.00019337056,0.00001767111,0.000496787,0.00021508223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027808,0.00022365933,0.0001704141,0.0004256303,0.001186762,0.00044516643,0.00038850884,0.00018020198,0.0017544391],"category_scores_gemma":[0.0008052668,0.00014477069,0.00012123596,0.000809327,0.00031446075,0.00018086989,0.0004931218,0.00020804608,0.0002868847],"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":[0.00021122278,0.000037431277,0.9629928,0.00020420761,0.000016852078,0.00051613286,0.0022207585,0.0001248245,0.0013691114,0.000101870326,0.011467241,0.020737536],"study_design_scores_gemma":[0.000020798612,0.00005998395,0.9888024,0.000037131464,0.000010827575,0.00018518907,0.0006749746,0.00016611848,0.00014497335,0.000016723028,0.009874914,0.0000059820136],"about_ca_topic_score_codex":0.9643101,"about_ca_topic_score_gemma":0.9774099,"teacher_disagreement_score":0.03568989,"about_ca_system_score_codex":0.01151517,"about_ca_system_score_gemma":0.014592195,"threshold_uncertainty_score":0.083548784},"labels":[],"label_agreement":null},{"id":"W2164660697","doi":"10.1111/j.0008-3658.2006.00127.x","title":"Spatial/temporal mismatch: a conflation protocol for Canada Census spatial files","year":2006,"lang":"en","type":"article","venue":"Canadian Geographies / Géographies canadiennes","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University","keywords":"Census; Conflation; Geography; Geographic information system; Health geography; Spatial ecology; Spatial analysis; Cartography; Regional science; Data science; Computer science; Data mining; Demography; Population; Public health; Remote sensing; Sociology; Ecology; Medicine","score_opus":0.010942833851065892,"score_gpt":0.2283337366398252,"score_spread":0.2173909027887593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164660697","genre_codex":"dataset","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":[0.02050942,0.00045303628,0.15205307,0.006962331,0.0012902776,0.18040997,0.49794927,0.015208174,0.12516442],"genre_scores_gemma":[0.067776114,0.0006530821,0.34541163,0.0034304745,0.00025550587,0.28054512,0.2453781,0.005090707,0.051459286],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9514165,0.013829132,0.01289762,0.0029179505,0.015198834,0.0037399044],"domain_scores_gemma":[0.8183347,0.029866438,0.009614497,0.037525177,0.10073005,0.003929199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.054450877,0.0013693456,0.0015121641,0.010087248,0.00797626,0.0047403085,0.0042803073,0.0022229934,0.058791142],"category_scores_gemma":[0.16147411,0.0023953938,0.0014181897,0.015095185,0.0022693898,0.0035686826,0.006083401,0.0035483576,0.016149614],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016023688,0.00025626947,0.010019266,0.0010086909,0.00006499472,0.0004927502,0.009471371,0.001866266,0.0017800931,0.02547135,0.8292303,0.1187363],"study_design_scores_gemma":[0.00041907426,0.00006007563,0.024403054,0.0007437466,0.000042217805,0.00014816078,0.003988159,0.0035155548,0.0026181228,0.004522855,0.9592428,0.0002961875],"about_ca_topic_score_codex":0.6638547,"about_ca_topic_score_gemma":0.69152313,"teacher_disagreement_score":0.33614528,"about_ca_system_score_codex":0.034224275,"about_ca_system_score_gemma":0.11030705,"threshold_uncertainty_score":0.6762498},"labels":[],"label_agreement":null},{"id":"W2164674019","doi":"10.2196/mhealth.3216","title":"Mobile Technologies and Geographic Information Systems to Improve Health Care Systems: A Literature Review","year":2014,"lang":"en","type":"review","venue":"JMIR mhealth and uhealth","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":76,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geographic information system; Health care; Scopus; Information system; mHealth; Mobile technology; Mobile phone; Clinical decision support system; Computer science; Medicine; Knowledge management; Decision support system; Mobile device; MEDLINE; World Wide Web; Nursing; Geography; Psychological intervention; Data mining; Telecommunications; Engineering; Political science","score_opus":0.0201536974396192,"score_gpt":0.3756825502228389,"score_spread":0.3555288527832197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164674019","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":[0.00023818298,0.9982685,0.00009890894,0.0005869714,0.00011028567,0.000028150442,0.000024688947,0.0000030641716,0.0006411693],"genre_scores_gemma":[0.0021485945,0.99708146,0.00036091715,0.0002127693,0.00008471946,0.00002945307,0.00001978553,0.0000010727,0.000061177],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99689025,0.0010880065,0.00076973217,0.00022643425,0.0008631977,0.00016239093],"domain_scores_gemma":[0.9802368,0.015947059,0.0014726291,0.00013652396,0.0020094255,0.00019760088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004333882,0.0010406743,0.002219149,0.017286237,0.0007649897,0.0033590933,0.0015098404,0.0025575175,0.004895444],"category_scores_gemma":[0.015187587,0.0006439961,0.001796133,0.027343607,0.0010244874,0.004425854,0.0012186134,0.0011555176,0.0006787944],"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":[0.00006985294,0.00008264146,0.0014884846,0.38979933,0.00082036643,0.0004307549,0.0011485976,0.0006385505,0.00028804436,0.005726166,0.016713822,0.5827934],"study_design_scores_gemma":[0.0000667693,0.00019328299,0.008390676,0.6701185,0.003506038,0.001761965,0.0026534146,0.0006109495,0.0003812843,0.0035675457,0.3086755,0.00007397366],"about_ca_topic_score_codex":0.0067887297,"about_ca_topic_score_gemma":0.010729316,"teacher_disagreement_score":0.017286237,"about_ca_system_score_codex":0.0034135948,"about_ca_system_score_gemma":0.008736745,"threshold_uncertainty_score":0.024767458},"labels":[],"label_agreement":null},{"id":"W2165163532","doi":"10.1080/14498596.2008.9635141","title":"Effects of alternative point pattern geocoding procedures on first and second order statistical measures","year":2008,"lang":"en","type":"article","venue":"Journal of Spatial Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"","keywords":"Geocoding; Bivariate analysis; Scan statistic; Statistic; Statistics; Geography; Point (geometry); Orthophoto; Mathematics; Cartography; Remote sensing","score_opus":0.013005368355611124,"score_gpt":0.2717079209469472,"score_spread":0.25870255259133607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165163532","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":[0.748822,0.0013920979,0.24136394,0.001227673,0.0005039802,0.0007221259,0.0013955347,0.00052963145,0.0040429286],"genre_scores_gemma":[0.8961586,0.0003942894,0.10088143,0.00030112738,0.00008035748,0.0004425307,0.0008061746,0.00024137323,0.0006942101],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.7443367,0.2047206,0.010800824,0.01437851,0.022953074,0.002810288],"domain_scores_gemma":[0.18056212,0.74356663,0.029758409,0.032391056,0.012938487,0.0007833513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.15288997,0.0012688937,0.0013294125,0.0037326494,0.0014361994,0.004571761,0.0018942758,0.0018812875,0.0013269306],"category_scores_gemma":[0.54135114,0.0007150535,0.004157142,0.006104386,0.006019538,0.004119662,0.0035388612,0.0029963213,0.00037796044],"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":[0.009236244,0.00055681996,0.6620829,0.0015850855,0.008540215,0.0006291967,0.01218693,0.037801627,0.016014988,0.039396837,0.00261427,0.20935479],"study_design_scores_gemma":[0.00046579578,0.0069599203,0.7597233,0.0006564853,0.0030102674,0.0012909056,0.0060561313,0.13404728,0.04450505,0.035556354,0.0070191114,0.0007093707],"about_ca_topic_score_codex":0.011517081,"about_ca_topic_score_gemma":0.010572878,"teacher_disagreement_score":0.15288997,"about_ca_system_score_codex":0.002734603,"about_ca_system_score_gemma":0.0026890414,"threshold_uncertainty_score":0.8085691},"labels":[],"label_agreement":null},{"id":"W2166499541","doi":"10.1186/1472-6963-6-10","title":"Using Ontario's \"Telehealth\" health telephone helpline as an early-warning system: a study protocol","year":2006,"lang":"en","type":"article","venue":"BMC Health Services Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; Queen's University; SGS (Canada)","funders":"Queen's University; London School of Hygiene and Tropical Medicine","keywords":"Medicine; Medical emergency; Hotline; Health informatics; Telehealth; Warning system; Health administration; Nursing research; Public health surveillance; Public health; Telephone line; Telephone call; Health care; Telemedicine; Nursing; Telecommunications; Telephone network","score_opus":0.09154824735361826,"score_gpt":0.48351685874505407,"score_spread":0.3919686113914358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166499541","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003111973,0.000055828474,0.0006845247,0.00012808206,0.00003372854,0.9941367,0.001099804,0.000016026625,0.00073335896],"genre_scores_gemma":[0.0030837483,0.000079943486,0.0015871483,0.00016203392,0.000015345458,0.9944358,0.0002673756,0.0000018898228,0.00036674537],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98200905,0.009478047,0.003057907,0.0014377573,0.0022508116,0.0017663715],"domain_scores_gemma":[0.97818804,0.00444911,0.0036671902,0.0021248888,0.008296206,0.0032745667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.042848762,0.002697432,0.0028402575,0.0026964007,0.0072756615,0.003922369,0.0051796734,0.004648052,0.03192951],"category_scores_gemma":[0.02441013,0.0030729307,0.002710147,0.0032151665,0.0034774526,0.0026430516,0.0021697485,0.0037911152,0.009186829],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.20382962,0.13135517,0.06570589,0.07253199,0.0014892515,0.0057805013,0.038944893,0.009356277,0.007844132,0.021487253,0.15040709,0.291268],"study_design_scores_gemma":[0.22873838,0.1192569,0.20885211,0.02801717,0.0012910039,0.0009495067,0.02485608,0.0059516532,0.0052437466,0.0072603053,0.36856076,0.0010223596],"about_ca_topic_score_codex":0.118413344,"about_ca_topic_score_gemma":0.20867927,"teacher_disagreement_score":0.9703273,"about_ca_system_score_codex":0.029672699,"about_ca_system_score_gemma":0.098834164,"threshold_uncertainty_score":0.23544818},"labels":[],"label_agreement":null},{"id":"W2167286483","doi":"10.5210/ojphi.v5i1.4470","title":"Influenza Forecasting with Google Flu Trends","year":2013,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Division of Civil, Mechanical and Manufacturing Innovation; Natural Sciences and Engineering Research Council of Canada; U.S. Department of Homeland Security; National Science Foundation","keywords":"Computer science; Warning system; Selection (genetic algorithm); Seasonal influenza; Model selection; Coronavirus disease 2019 (COVID-19); Data mining; Data science; Medicine; Machine learning; Infectious disease (medical specialty); Telecommunications; Disease","score_opus":0.1193108027593693,"score_gpt":0.35464403582824616,"score_spread":0.23533323306887688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167286483","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":[0.8583023,0.000514036,0.11616058,0.0012288732,0.00021069785,0.0002039888,0.010400502,0.003651968,0.009327126],"genre_scores_gemma":[0.96550983,0.00017528699,0.028721154,0.000037460995,0.000038374015,0.000054323024,0.004488305,0.00004258889,0.00093276886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969506,0.00010023708,0.000027590602,0.000060192997,0.00008154386,0.000035330337],"domain_scores_gemma":[0.99893683,0.00055986154,0.000109549044,0.00009951465,0.00025055086,0.000043647302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094215217,0.00063257833,0.00043156403,0.0012513042,0.00017250296,0.00061513466,0.0005495347,0.00046833785,0.0013196863],"category_scores_gemma":[0.0047064377,0.00028426063,0.0005770381,0.0011104726,0.00013209764,0.0009857683,0.0004072453,0.0004352238,0.00043494446],"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":[0.00028826098,0.000091684444,0.053009655,0.00010845453,0.00010768691,0.0001521119,0.000074752345,0.8917009,0.0009272659,0.0036320686,0.0035991839,0.046307925],"study_design_scores_gemma":[0.0000095328305,0.00004276056,0.003536606,0.0000069162465,0.000015840415,0.000028239321,0.000017744673,0.9940509,0.0004953171,0.00080395304,0.0009841478,0.000007904155],"about_ca_topic_score_codex":0.045471605,"about_ca_topic_score_gemma":0.030089501,"teacher_disagreement_score":0.045471605,"about_ca_system_score_codex":0.000512007,"about_ca_system_score_gemma":0.00062779983,"threshold_uncertainty_score":0.09041387},"labels":[],"label_agreement":null},{"id":"W2167664108","doi":"10.2460/javma.229.7.1090","title":"Evaluation of ProMED-mail as an electronic early warning system for emerging animal diseases: 1996 to 2004","year":2006,"lang":"en","type":"article","venue":"Journal of the American Veterinary Medical Association","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bovine spongiform encephalopathy; Wildlife; Disease; Medicine; Warning system; Environmental health; Disease surveillance; Rabies; Emerging infectious disease; Ebola virus; Veterinary medicine; Family medicine; Biology; Virology; Pathology; Telecommunications","score_opus":0.014574874424087341,"score_gpt":0.33403743655624896,"score_spread":0.3194625621321616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167664108","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":[0.99412173,0.0005832305,0.00056756486,0.000485986,0.000043923792,0.00052039314,0.0020092535,0.000068233676,0.0015996122],"genre_scores_gemma":[0.99415874,0.00039357023,0.0014090824,0.0002876048,0.000057786037,0.0003526797,0.0026397253,0.000013873107,0.00068686093],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98770857,0.0067221806,0.0014956326,0.0006740437,0.0027537462,0.0006457821],"domain_scores_gemma":[0.95043784,0.017885704,0.014879901,0.0019767014,0.012786129,0.0020337345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025946237,0.0005335891,0.00042246943,0.0019147723,0.0003576485,0.0013310018,0.001269789,0.00068196876,0.0012592418],"category_scores_gemma":[0.06778817,0.00036346342,0.00038056853,0.002370574,0.0004459337,0.0015768042,0.00085238105,0.0007329027,0.00040255603],"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":[0.0027155452,0.0008462384,0.9405359,0.00035611336,0.00010302807,0.00011511141,0.0007083991,0.00064073637,0.00042685136,0.000074096664,0.0015736122,0.051904317],"study_design_scores_gemma":[0.00023086106,0.004928372,0.9878945,0.0001585505,0.00009606097,0.00019861518,0.0011387126,0.0015591476,0.0011088006,0.000022598972,0.0026506216,0.000013344906],"about_ca_topic_score_codex":0.008251735,"about_ca_topic_score_gemma":0.0064404546,"teacher_disagreement_score":0.025946237,"about_ca_system_score_codex":0.00265012,"about_ca_system_score_gemma":0.0019303757,"threshold_uncertainty_score":0.13721848},"labels":[],"label_agreement":null},{"id":"W2167752508","doi":"10.5210/ojphi.v5i1.4401","title":"Determinants of Outbreak Detection Performance","year":2013,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Polytechnique Montréal","funders":"","keywords":"Computer science; Outbreak; Data mining; Bayesian probability; Selection (genetic algorithm); Set (abstract data type); Data set; Machine learning; Artificial intelligence; Algorithm; Medicine","score_opus":0.04793860230450372,"score_gpt":0.33694941166596093,"score_spread":0.28901080936145723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167752508","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":[0.9412304,0.00069712446,0.053083356,0.000784225,0.000030503466,0.00014877044,0.0005289109,0.0003462485,0.0031504766],"genre_scores_gemma":[0.9931218,0.0000806188,0.0064028027,0.000025181076,0.000009239901,0.000020339856,0.0002020706,0.000025859115,0.00011210481],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9906623,0.004978289,0.00087698817,0.0014278701,0.0012298158,0.00082478055],"domain_scores_gemma":[0.7131905,0.25587487,0.010153387,0.0089228675,0.009895588,0.001962794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022596957,0.00061396527,0.00070649415,0.0022332934,0.00041027126,0.00304878,0.0007193654,0.0014074758,0.0011349488],"category_scores_gemma":[0.18558331,0.0004098053,0.0006335223,0.0017555475,0.0009166009,0.0026079414,0.00087525,0.001268403,0.00034106002],"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":[0.0009950864,0.00068045827,0.5791889,0.0003151036,0.00069442095,0.00021507264,0.00031589283,0.31114647,0.005950521,0.006852774,0.0015047827,0.09214057],"study_design_scores_gemma":[0.00005908099,0.0006091786,0.22865091,0.000076311226,0.00015654718,0.00041689453,0.00023158094,0.74709034,0.011280355,0.010418251,0.0009142203,0.00009641639],"about_ca_topic_score_codex":0.002276515,"about_ca_topic_score_gemma":0.0012938374,"teacher_disagreement_score":0.022596957,"about_ca_system_score_codex":0.0009874804,"about_ca_system_score_gemma":0.0011363747,"threshold_uncertainty_score":0.119505525},"labels":[],"label_agreement":null},{"id":"W2168861738","doi":"10.3201/eid1807.120249","title":"Timeliness of Nongovernmental versus Governmental Global Outbreak Communications","year":2012,"lang":"en","type":"article","venue":"Emerging infectious diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"U.S. National Library of Medicine","keywords":"Outbreak; Infectious disease (medical specialty); Public health; Environmental health; Medicine; Disease; Virology; Pathology","score_opus":0.01813229160326166,"score_gpt":0.3207432524714487,"score_spread":0.302610960868187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168861738","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":[0.9611835,0.003286391,0.005561969,0.0032193584,0.00023991012,0.00021763406,0.010942921,0.00026947234,0.015078918],"genre_scores_gemma":[0.99463284,0.00070259534,0.0014940142,0.0002011606,0.00012070274,0.00009187836,0.0021203475,0.000022401175,0.0006140896],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98873585,0.005992292,0.0017935502,0.0010619254,0.0018667196,0.0005495423],"domain_scores_gemma":[0.7958344,0.08303715,0.094197184,0.009155888,0.015464225,0.0023111822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011921013,0.00024353477,0.00040185935,0.0041130045,0.00039632688,0.001425501,0.0006667703,0.00049068546,0.0020642714],"category_scores_gemma":[0.098031126,0.00018109492,0.00033633853,0.004429381,0.00030091027,0.0015745807,0.0011797171,0.0007088735,0.0003532998],"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":[0.00043044728,0.00005700817,0.9217336,0.00033095255,0.00020052241,0.00012913122,0.0030156805,0.001567526,0.0006660035,0.000846768,0.0036663744,0.06735599],"study_design_scores_gemma":[0.000019016381,0.00020534637,0.98115873,0.00024383856,0.00010590698,0.00027160632,0.0034543204,0.0032752305,0.0018387795,0.00046942075,0.008922217,0.000035707373],"about_ca_topic_score_codex":0.009722754,"about_ca_topic_score_gemma":0.01130903,"teacher_disagreement_score":0.011921013,"about_ca_system_score_codex":0.0012835145,"about_ca_system_score_gemma":0.0014777452,"threshold_uncertainty_score":0.063045144},"labels":[],"label_agreement":null},{"id":"W2170365795","doi":"10.1093/pubmed/fdr088","title":"Perceived usefulness of syndromic surveillance in Ontario during the H1N1 pandemic","year":2011,"lang":"en","type":"article","venue":"Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa; National Research Council Canada; Ministry of Health and Long Term Care; McMaster University; Brampton Civic Hospital; University of Toronto; Kingston Health Sciences Centre; Public Health Ontario","funders":"Institute of Population and Public Health; Canadian Institutes of Health Research","keywords":"Public health surveillance; Public health; Pandemic; Medicine; Context (archaeology); Medical emergency; Disease surveillance; Environmental health; Family medicine; Nursing; Coronavirus disease 2019 (COVID-19); Geography; Disease","score_opus":0.12319330490891267,"score_gpt":0.30045610196593137,"score_spread":0.1772627970570187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170365795","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":[0.9956564,0.00019852,0.00010117329,0.00087414763,0.000007129256,0.000027066946,0.00022445849,0.0000071573636,0.0029038899],"genre_scores_gemma":[0.9992306,0.00018181388,0.00014949385,0.00007183013,0.0000033468252,0.000007785455,0.00007394232,0.0000019728802,0.00027916476],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99784124,0.0005520962,0.00016244418,0.00010658615,0.0010286499,0.00030882194],"domain_scores_gemma":[0.9899822,0.0024825383,0.0025446746,0.0002684027,0.0029999325,0.0017222597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024439946,0.00016475191,0.00022599932,0.0006249556,0.0018029321,0.0017434326,0.00045200918,0.00028544798,0.0014385037],"category_scores_gemma":[0.010553323,0.00021920471,0.0002724519,0.0010166154,0.0013780626,0.0006190591,0.0009965858,0.00040438952,0.000082101775],"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":[0.00018479188,0.00007717088,0.9364192,0.00016746619,0.000050122704,0.00032094182,0.04080682,0.00026341053,0.0010871195,0.00015595074,0.0015203952,0.018946625],"study_design_scores_gemma":[0.000012977632,0.00013729595,0.96196014,0.00009964887,0.00002109298,0.00008622542,0.033084285,0.0005576313,0.00018257299,0.00004231423,0.0037898722,0.000025877742],"about_ca_topic_score_codex":0.843805,"about_ca_topic_score_gemma":0.88036305,"teacher_disagreement_score":0.15619498,"about_ca_system_score_codex":0.013340792,"about_ca_system_score_gemma":0.013092498,"threshold_uncertainty_score":0.31422967},"labels":[],"label_agreement":null},{"id":"W2170773183","doi":"10.4081/gh.2008.243","title":"Spatio-temporal analysis of pneumonia and influenza hospitalizations in Ontario, Canada","year":2008,"lang":"en","type":"article","venue":"Geospatial health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Sunnybrook Health Science Centre; Health Sciences Centre; McMaster University; Women's College Hospital; Public Health Ontario; University of Toronto; University of Ottawa","funders":"","keywords":"Pneumonia; Geography; Demography; Public health; Medicine; Spatial ecology; Spatial epidemiology; Spatial variability; Seasonality; Epidemiology; Environmental health; Statistics; Ecology; Biology; Pathology","score_opus":0.018078323130066954,"score_gpt":0.2729612719171661,"score_spread":0.25488294878709916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170773183","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":[0.9711898,0.0018225712,0.00070846867,0.0006908279,0.000028130711,0.00009584159,0.020529421,0.000041938667,0.004893159],"genre_scores_gemma":[0.9908523,0.0006921212,0.0008345204,0.000062048464,0.000008093452,0.00003636863,0.0054826904,0.000009883364,0.0020220063],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993191,0.0000602141,0.00006261562,0.0001020141,0.00027788046,0.0001780816],"domain_scores_gemma":[0.997279,0.0002613335,0.0004300566,0.00007186337,0.0016561251,0.00030180678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005185726,0.0002243629,0.00030698295,0.0024790624,0.0016624461,0.0009946997,0.0007800972,0.00023230376,0.0015975799],"category_scores_gemma":[0.00292926,0.00019105675,0.0005906443,0.006411939,0.0004978366,0.00025093067,0.0006781918,0.00025919382,0.00017824053],"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":[0.00010160534,0.000023600294,0.9804654,0.00015044348,0.00014483828,0.0002398346,0.0014252827,0.0015975606,0.000717177,0.00034900886,0.003919678,0.010865613],"study_design_scores_gemma":[0.0000025123188,0.0000074060977,0.99615735,0.000017004995,0.000017995919,0.00003181002,0.001136508,0.0011151681,0.00004084277,0.000024850215,0.0014411323,0.0000073757187],"about_ca_topic_score_codex":0.997554,"about_ca_topic_score_gemma":0.9983935,"teacher_disagreement_score":0.025538903,"about_ca_system_score_codex":0.025538903,"about_ca_system_score_gemma":0.033125326,"threshold_uncertainty_score":0.18529862},"labels":[],"label_agreement":null},{"id":"W2171018733","doi":"10.1002/cjs.5550350105","title":"Space‐time cluster identification in point processes","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universidade Federal de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; National Cancer Institute; University of Texas at Austin","keywords":"Point process; Scan statistic; Poisson point process; Cluster (spacecraft); Test statistic; Statistic; Point (geometry); Space (punctuation); Statistical hypothesis testing; Population; Computer science; Identification (biology); Null hypothesis; Separable space; Monte Carlo method; Data mining; Statistics; Mathematics; Algorithm","score_opus":0.011116429399807894,"score_gpt":0.25594299841722856,"score_spread":0.24482656901742067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171018733","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":[0.34597015,0.00032223095,0.6493169,0.00069118454,0.00012550906,0.00017994878,0.0004157869,0.00028554496,0.0026927348],"genre_scores_gemma":[0.9099353,0.00007874975,0.08833532,0.00007996951,0.00007332076,0.00016642902,0.0003983426,0.00003802047,0.00089460233],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9904422,0.005288066,0.0003892853,0.0011614339,0.0022580374,0.00046083733],"domain_scores_gemma":[0.9157853,0.06880913,0.004329113,0.004027655,0.005825355,0.0012234222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013147441,0.0005036192,0.0013279022,0.006251489,0.0008239967,0.0022091002,0.0018697787,0.0016048593,0.0025448992],"category_scores_gemma":[0.070110396,0.00030704032,0.0015148,0.0033237878,0.003972617,0.0021012563,0.0028226452,0.0014003889,0.00029837262],"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":[0.0014304228,0.0003101498,0.31008258,0.00036436075,0.0012507277,0.00089035387,0.0016705936,0.21152675,0.0058681285,0.26157174,0.003950422,0.20108393],"study_design_scores_gemma":[0.00011169804,0.00031972816,0.029356971,0.000058608224,0.00010177033,0.00030713534,0.0005948903,0.84419686,0.0027256384,0.12004427,0.0021058826,0.00007654647],"about_ca_topic_score_codex":0.003848018,"about_ca_topic_score_gemma":0.0025285375,"teacher_disagreement_score":0.013147441,"about_ca_system_score_codex":0.0010336092,"about_ca_system_score_gemma":0.001555481,"threshold_uncertainty_score":0.06953114},"labels":[],"label_agreement":null},{"id":"W2171629721","doi":"10.1111/j.1525-1446.2009.00814.x","title":"Population Health Surveillance Practice of Public Health Nurses","year":2009,"lang":"en","type":"article","venue":"Public Health Nursing","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Capital District Health Authority; Nova Scotia Health Authority; University of Ottawa; Dalhousie University; University of King's College","funders":"Canadian Institutes of Health Research; Health Canada","keywords":"Public health; Public health nursing; Documentation; Public health surveillance; Population; Population health; Nursing; Medicine; Qualitative research; Health promotion; Health care; Community health; Environmental health; Political science; Sociology","score_opus":0.07092154336556962,"score_gpt":0.41402526863547484,"score_spread":0.34310372526990524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171629721","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":[0.9635651,0.0023138518,0.009461073,0.007653821,0.00018607026,0.00027821807,0.00009869468,0.00007839706,0.016364863],"genre_scores_gemma":[0.99102247,0.0009892609,0.0060444707,0.00064633647,0.000029621508,0.00014096488,0.000060057766,0.000010783981,0.0010560771],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9771088,0.016699716,0.0013864428,0.0009037911,0.0029895145,0.00091175607],"domain_scores_gemma":[0.94876593,0.02643101,0.007986148,0.003523427,0.010358625,0.0029348847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022044078,0.00017467319,0.00020596379,0.0010092672,0.0022538335,0.0018591912,0.0011305,0.0005542117,0.0009901663],"category_scores_gemma":[0.056368776,0.00031767652,0.00016699971,0.0007309811,0.0021975636,0.0015877587,0.0029490357,0.0007508712,0.00014944944],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014539607,0.00015615302,0.28889713,0.00067281903,0.000036600482,0.0008125768,0.43604234,0.00030800345,0.0024129862,0.0016943262,0.004278405,0.26454324],"study_design_scores_gemma":[0.000048820246,0.0006005872,0.21082981,0.0026402452,0.00005210357,0.0023510912,0.6604883,0.001705463,0.0041037365,0.0021986824,0.11487078,0.000110399546],"about_ca_topic_score_codex":0.025576422,"about_ca_topic_score_gemma":0.030825885,"teacher_disagreement_score":0.025576422,"about_ca_system_score_codex":0.0048825736,"about_ca_system_score_gemma":0.014034427,"threshold_uncertainty_score":0.11658162},"labels":[],"label_agreement":null},{"id":"W2172271292","doi":"10.1017/s0950268814003574","title":"Spatial-temporal clustering of companion animal enteric syndrome: detection and investigation through the use of electronic medical records from participating private practices","year":2014,"lang":"en","type":"article","venue":"Epidemiology and Infection","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Wilfrid Laurier University; Vancouver Coastal Health; University of Calgary","funders":"","keywords":"Electronic medical record; Companion animal; Medicine; Medical record; Geography; Cartography; Computer science; Veterinary medicine; Medical emergency; Internal medicine","score_opus":0.08738769938075246,"score_gpt":0.3449549755520656,"score_spread":0.25756727617131314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172271292","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":[0.9980381,0.000089520545,0.0007260565,0.00004957647,0.000001686538,0.000053806227,0.0006699216,0.000009926457,0.00036144367],"genre_scores_gemma":[0.9971112,0.00010029161,0.0018472698,0.000013611324,0.0000032381658,0.000033683402,0.00077782385,0.0000021788107,0.00011062019],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976736,0.0007661324,0.00038298365,0.0003589811,0.0006480501,0.00017029101],"domain_scores_gemma":[0.99102247,0.002387544,0.0038222037,0.0010002033,0.0014349238,0.00033271185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025679034,0.00013345972,0.00020354318,0.003285618,0.00033690396,0.00079542934,0.00057468895,0.0002498721,0.0004262422],"category_scores_gemma":[0.0112748835,0.00022931684,0.00018741642,0.0046239467,0.00035969104,0.00054906873,0.0009791767,0.00019901323,0.000085590684],"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":[0.000042272837,0.000016126494,0.9935632,0.000022754652,0.000021627846,0.000055473094,0.0007622875,0.00014893456,0.00032377374,0.000037128433,0.00006795117,0.0049385023],"study_design_scores_gemma":[0.000003335129,0.000055317418,0.9961241,0.000016203625,0.00001391609,0.00014032616,0.0016379659,0.0013543674,0.00025887013,0.000041683128,0.00034843042,0.000005536061],"about_ca_topic_score_codex":0.091003254,"about_ca_topic_score_gemma":0.11977831,"teacher_disagreement_score":0.091003254,"about_ca_system_score_codex":0.0011024761,"about_ca_system_score_gemma":0.0016008621,"threshold_uncertainty_score":0.18094707},"labels":[],"label_agreement":null},{"id":"W2183730683","doi":"10.82308/39437","title":"Evaluating the use of physician billing data for age and setting specific influenza surveillance","year":2009,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Medicine; Medical emergency; Family medicine","score_opus":0.1480414361966044,"score_gpt":0.36625438022108164,"score_spread":0.21821294402447725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2183730683","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":[0.9215739,0.0051946905,0.037729777,0.0023519332,0.00033513832,0.0019047518,0.024752196,0.0007505117,0.005407066],"genre_scores_gemma":[0.9440228,0.0007693899,0.04377316,0.0003043848,0.00006886964,0.0007127314,0.009617815,0.00007579772,0.0006552394],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9314431,0.052845202,0.004413205,0.0036020118,0.0068223625,0.00087428774],"domain_scores_gemma":[0.75629354,0.19757949,0.017907407,0.009723788,0.016987862,0.0015080101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05860918,0.0012435244,0.0011858327,0.0030255471,0.00046011157,0.0031022795,0.0017725789,0.0014227708,0.0014242159],"category_scores_gemma":[0.22202705,0.0006698559,0.002977226,0.004499125,0.00040147646,0.0022022736,0.0012388935,0.00091785163,0.000383484],"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":[0.0024542606,0.00041321001,0.90671843,0.0009949624,0.0030401149,0.00006897198,0.00045951668,0.021961797,0.00065912557,0.0007613404,0.0023017747,0.060166523],"study_design_scores_gemma":[0.0003793336,0.0033325406,0.7009089,0.0007049911,0.0023330818,0.00023585264,0.0009355179,0.2772176,0.0032162508,0.0015064836,0.009106264,0.00012329132],"about_ca_topic_score_codex":0.06705555,"about_ca_topic_score_gemma":0.049968507,"teacher_disagreement_score":0.06705555,"about_ca_system_score_codex":0.002758433,"about_ca_system_score_gemma":0.0059305998,"threshold_uncertainty_score":0.30995864},"labels":[],"label_agreement":null},{"id":"W2184686263","doi":"","title":"If all ambulances could fly: putting provincial standards of emergency care access to the test in Northern British Columbia.","year":2005,"lang":"en","type":"article","venue":"PubMed","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Christian ministry; Geography; Geographic information system; Medical emergency; Population; Service (business); Emergency medical services; Test (biology); Business; Environmental health; Medicine; Cartography; Political science","score_opus":0.018269092401971655,"score_gpt":0.28718923958803244,"score_spread":0.2689201471860608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2184686263","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":[0.9739701,0.0014001186,0.0005946419,0.0027423345,0.000033876877,0.00014653419,0.009881711,0.00004634157,0.011184307],"genre_scores_gemma":[0.99195063,0.0005207903,0.0012562703,0.00027109974,0.0000055609075,0.00007958682,0.0028978912,0.0000067371097,0.003011507],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99947757,0.0001099959,0.000040681913,0.00006912147,0.00014217757,0.0001605157],"domain_scores_gemma":[0.9970974,0.00043247454,0.00040187693,0.00010213347,0.0015551313,0.0004110399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079140445,0.00019171626,0.00021596879,0.0014846801,0.0009853495,0.0010831702,0.00091174146,0.00032325953,0.0018358879],"category_scores_gemma":[0.0057734684,0.00015189566,0.00015311819,0.0033664205,0.00050694705,0.00041737585,0.0009525034,0.00054528285,0.00019909408],"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":[0.00014056264,0.000032486758,0.9390679,0.00022936802,0.000036031168,0.00022870424,0.0020850387,0.0010745876,0.00023525314,0.0003403581,0.011433811,0.045095988],"study_design_scores_gemma":[0.0000148098015,0.00002720702,0.98464286,0.00014766204,0.0000359512,0.000057613797,0.008000166,0.002002217,0.00024191539,0.00012219544,0.004691949,0.00001547453],"about_ca_topic_score_codex":0.9933588,"about_ca_topic_score_gemma":0.99662185,"teacher_disagreement_score":0.016759375,"about_ca_system_score_codex":0.016759375,"about_ca_system_score_gemma":0.024685424,"threshold_uncertainty_score":0.1215983},"labels":[],"label_agreement":null},{"id":"W2186426280","doi":"10.1155/2013/386018","title":"Emergency Department Surveillance as a Proxy For The Prediction of Circulating Respiratory Viral Disease in Eastern Ontario","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Infectious Diseases and Medical Microbiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Kingston Health Sciences Centre; Queen's University","funders":"","keywords":"Emergency department; Proxy (statistics); Medicine; Disease; Respiratory system; Emergency medicine; Internal medicine; Intensive care medicine; Computer science; Machine learning","score_opus":0.009920789771133393,"score_gpt":0.24271901936614693,"score_spread":0.23279822959501353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2186426280","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":[0.99557745,0.0004377608,0.00016612343,0.00020070325,0.00000732713,0.000028392598,0.0011110482,0.000011143786,0.0024600048],"genre_scores_gemma":[0.99909663,0.00012428526,0.00011704707,0.000022233979,0.0000036715169,0.0000064804303,0.00035063425,0.0000013776752,0.00027758686],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999509,0.00007240428,0.000046183355,0.00007671178,0.00016041302,0.00013527274],"domain_scores_gemma":[0.9977755,0.00022203826,0.0008599634,0.00005812118,0.0006845498,0.0003998446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004553213,0.0002292093,0.00018009718,0.0006463203,0.0006721456,0.000685952,0.000631007,0.00018528916,0.0009771665],"category_scores_gemma":[0.0030232954,0.00016359631,0.00019206425,0.0009856978,0.00030661208,0.00026696306,0.00058074086,0.00024484485,0.00009405913],"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":[0.000046775272,0.000009092107,0.99675035,0.000021813783,0.00001898659,0.00010365748,0.00025936807,0.00019394894,0.00017676777,0.000026574211,0.00036814873,0.0020246173],"study_design_scores_gemma":[0.0000027216315,0.000010924394,0.99894065,0.000010582551,0.0000070933875,0.00003703652,0.00024062539,0.00040074662,0.00004024864,0.000012236618,0.00029457983,0.0000025655143],"about_ca_topic_score_codex":0.90924716,"about_ca_topic_score_gemma":0.9332037,"teacher_disagreement_score":0.09075284,"about_ca_system_score_codex":0.008687033,"about_ca_system_score_gemma":0.0052058776,"threshold_uncertainty_score":0.18257457},"labels":[],"label_agreement":null},{"id":"W2187568752","doi":"","title":"A NEW APPROACH FOR GEOCODING POSTAL CODE-BASED DATA IN HEALTH RELATED STUDIES","year":2014,"lang":"en","type":"article","venue":"QSpace (Queen's University Library)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geocoding; Computer science; Code (set theory); Health data; Data science; Geography; Set (abstract data type); Programming language; Political science; Cartography; Health care","score_opus":0.03296539391093914,"score_gpt":0.26440706555422033,"score_spread":0.2314416716432812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2187568752","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":[0.0025209992,0.00053001713,0.9850116,0.002845715,0.0014688773,0.001412332,0.0021685124,0.0005663426,0.003475571],"genre_scores_gemma":[0.01941754,0.0005456053,0.97318166,0.0007803611,0.0003897956,0.0029816222,0.001200785,0.00020988069,0.0012927391],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.84751135,0.0867024,0.018433679,0.016668929,0.029288445,0.0013952318],"domain_scores_gemma":[0.71096843,0.13039324,0.03104902,0.061178,0.063957624,0.0024536795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.090156496,0.0014929319,0.002061364,0.016077608,0.0036644677,0.009363822,0.004362061,0.0026504556,0.0052329754],"category_scores_gemma":[0.29831126,0.0016835243,0.003204417,0.031888608,0.005344647,0.009032513,0.012458984,0.007546537,0.0026899395],"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":[0.00030466326,0.00019182343,0.03594205,0.002595224,0.0011241698,0.00036998835,0.019630194,0.005879459,0.003141908,0.25331143,0.040100325,0.63740885],"study_design_scores_gemma":[0.00036620683,0.00061532826,0.03955432,0.0040782695,0.00116281,0.001631965,0.00990942,0.042660262,0.0063146316,0.33217475,0.5607183,0.0008136665],"about_ca_topic_score_codex":0.027813507,"about_ca_topic_score_gemma":0.036952544,"teacher_disagreement_score":0.090156496,"about_ca_system_score_codex":0.005852657,"about_ca_system_score_gemma":0.014534001,"threshold_uncertainty_score":0.47679883},"labels":[],"label_agreement":null},{"id":"W2207769643","doi":"10.5539/gjhs.v8n9p44","title":"Survey of Communicable Diseases Surveillance System in Hospitals of Iran: A Qualitative Approach","year":2015,"lang":"en","type":"article","venue":"Global Journal of Health Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Tehran University of Medical Sciences and Health Services","keywords":"Communicable disease; Non-communicable disease; Medicine; Qualitative research; Disease surveillance; Environmental health; Public health surveillance; Medical emergency; Public health; Nursing","score_opus":0.0884480003695363,"score_gpt":0.41512739205334465,"score_spread":0.32667939168380833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2207769643","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":[0.9894398,0.00055659085,0.002895953,0.0022007246,0.000038123348,0.0010976864,0.00047969507,0.000016235908,0.0032752322],"genre_scores_gemma":[0.99262416,0.00092537777,0.0034166682,0.00062036637,0.000013665466,0.0010396986,0.00017455018,0.0000075387447,0.0011780408],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9921176,0.0054379,0.00032388375,0.00029103333,0.0005937702,0.0012358497],"domain_scores_gemma":[0.99053663,0.0059271976,0.00097105396,0.00021445718,0.0014743482,0.00087621564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012568363,0.0003888006,0.00052921457,0.002006621,0.0034144141,0.001456683,0.0011568861,0.0008791307,0.002026252],"category_scores_gemma":[0.009546974,0.00041921268,0.000351206,0.0024049825,0.002851227,0.0019341359,0.0022427198,0.0009399788,0.00016882717],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008060311,0.00017903214,0.021365963,0.00090526347,0.000011884459,0.00086128805,0.9572123,0.0002067872,0.0019225558,0.0016866273,0.0012841662,0.014283552],"study_design_scores_gemma":[0.000007771084,0.00012839523,0.00753693,0.00024049373,0.0000051290303,0.00016389771,0.9856281,0.00018777064,0.00051887333,0.0002680155,0.005301171,0.000013473574],"about_ca_topic_score_codex":0.006848826,"about_ca_topic_score_gemma":0.009036704,"teacher_disagreement_score":0.012568363,"about_ca_system_score_codex":0.0063444385,"about_ca_system_score_gemma":0.006749,"threshold_uncertainty_score":0.066468656},"labels":[],"label_agreement":null},{"id":"W2217896664","doi":"10.7939/r3319s46r","title":"Finding More Canadian Indigenous Health Articles Using Search Filters","year":2015,"lang":"en","type":"article","venue":"University of Alberta Library","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Indigenous; Political science; Biology","score_opus":0.04404021187983052,"score_gpt":0.25767287922346005,"score_spread":0.21363266734362954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2217896664","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11637993,0.27994093,0.008017661,0.02091197,0.002594318,0.013522007,0.46508867,0.0013052137,0.092239246],"genre_scores_gemma":[0.51965874,0.14182562,0.05173794,0.028062234,0.0019763652,0.0148968715,0.20145981,0.00086411275,0.039518338],"study_design_codex":"systematic_review","study_design_gemma":"observational","domain_scores_codex":[0.97350895,0.0032078046,0.00974171,0.0027012513,0.008411425,0.0024288911],"domain_scores_gemma":[0.8703341,0.07626185,0.011842005,0.0033485985,0.03445761,0.003755836],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.023818668,0.0021117516,0.0037545823,0.087599926,0.005232335,0.010697345,0.0034431296,0.0031610301,0.024404317],"category_scores_gemma":[0.15308854,0.00090468937,0.0048092776,0.08342557,0.0014174121,0.0031058518,0.0039659264,0.0014802719,0.0023516852],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003561617,0.00032483443,0.14303124,0.2896718,0.010284381,0.0025461137,0.013403154,0.0009860812,0.0070793806,0.011073472,0.28498408,0.23305374],"study_design_scores_gemma":[0.0018926623,0.0003647052,0.23431103,0.07818383,0.03635336,0.0012916531,0.00859404,0.0012812036,0.0044465587,0.0048120255,0.6279618,0.0005071535],"about_ca_topic_score_codex":0.83320457,"about_ca_topic_score_gemma":0.9304472,"teacher_disagreement_score":0.9761813,"about_ca_system_score_codex":0.025586106,"about_ca_system_score_gemma":0.112196796,"threshold_uncertainty_score":0.33555543},"labels":[],"label_agreement":null},{"id":"W2242827712","doi":"10.24095/hpcdp.30.1.05","title":"Using administrative data to understand the geography of case ascertainment","year":2009,"lang":"en","type":"article","venue":"Chronic diseases and injuries in Canada","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Alberta Health; University of Alberta; McMaster University","funders":"","keywords":"Scan statistic; Statistic; Asthma; Geography; Cluster analysis; Geographic information system; Geographic variation; Spatial analysis; Data source; Location; Demography; Medicine; Data mining; Cartography; Statistics; Computer science; Environmental health; Population; Mathematics","score_opus":0.06909731051367694,"score_gpt":0.3525761444324012,"score_spread":0.28347883391872425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2242827712","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":[0.8721676,0.0019618687,0.10421347,0.0021729125,0.00006297907,0.00092147134,0.010689892,0.00015611539,0.007653658],"genre_scores_gemma":[0.9638214,0.000454723,0.031271707,0.00018420612,0.000023257482,0.00021052154,0.0037781633,0.000022031378,0.00023405618],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9838181,0.009594944,0.0013162779,0.0014071476,0.0032631208,0.0006004786],"domain_scores_gemma":[0.9505364,0.025707433,0.012031854,0.004761752,0.006623553,0.00033892493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015135817,0.00036795015,0.0004890241,0.004799896,0.000753248,0.0019378222,0.0011007058,0.0003270647,0.0004810957],"category_scores_gemma":[0.09770151,0.00032484657,0.00046330082,0.009485001,0.0009831501,0.00093225174,0.0015668746,0.0005092196,0.00007281919],"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":[0.000043401014,0.000013511771,0.96764845,0.00008918696,0.00023430995,0.00012745676,0.001587855,0.006960682,0.00025951382,0.003380658,0.000652684,0.019002348],"study_design_scores_gemma":[0.000017507202,0.000044775414,0.97348875,0.0000986752,0.00010312836,0.00015115796,0.0012091495,0.016663458,0.00042258922,0.0025297652,0.0052423626,0.000028670749],"about_ca_topic_score_codex":0.68443716,"about_ca_topic_score_gemma":0.6670716,"teacher_disagreement_score":0.31556284,"about_ca_system_score_codex":0.006342079,"about_ca_system_score_gemma":0.007609103,"threshold_uncertainty_score":0.6348425},"labels":[],"label_agreement":null},{"id":"W2243719208","doi":"","title":"B-Life project : new services for biological emergencies","year":2012,"lang":"en","type":"article","venue":"Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B))","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"GNSS applications; Service (business); Biological warfare; Computer science; Geography; Computer security; Environmental resource management; Business; Telecommunications; Global Positioning System; Environmental science; Political science","score_opus":0.020783448773590292,"score_gpt":0.23949850073860587,"score_spread":0.2187150519650156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2243719208","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05067078,0.012416304,0.24342409,0.03354687,0.00887383,0.0062219687,0.07017853,0.13913071,0.43553695],"genre_scores_gemma":[0.13000004,0.008469947,0.21773745,0.013033092,0.002199956,0.0051221657,0.1994502,0.011726947,0.4122602],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99742705,0.00058891275,0.000087611574,0.00031804809,0.0010552478,0.0005230516],"domain_scores_gemma":[0.99352634,0.000543819,0.0003003338,0.000407865,0.0018392501,0.0033823985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052566943,0.0013651698,0.00071585673,0.0020452791,0.001669266,0.0033617476,0.0028694756,0.0027455583,0.06575742],"category_scores_gemma":[0.004070103,0.00044536617,0.0006683029,0.0014569438,0.001166434,0.0035522443,0.0052522128,0.002129134,0.042914562],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016562637,0.000883765,0.0041422057,0.00096554117,0.000040628784,0.00035530067,0.0008119506,0.0023587053,0.020928543,0.020472249,0.6518694,0.29551542],"study_design_scores_gemma":[0.00039561527,0.0005845296,0.002539696,0.00015949637,0.000017657072,0.00027308753,0.00034246905,0.006358923,0.004691757,0.002747867,0.9818364,0.000052471874],"about_ca_topic_score_codex":0.012270438,"about_ca_topic_score_gemma":0.007887551,"teacher_disagreement_score":0.06575742,"about_ca_system_score_codex":0.0020802943,"about_ca_system_score_gemma":0.007923528,"threshold_uncertainty_score":0.21998048},"labels":[],"label_agreement":null},{"id":"W2250098747","doi":"","title":"Technical overview of the Alberta Veterinary Surveillance Network: Surveillance Information Management Systems Project.","year":2011,"lang":"en","type":"article","venue":"International Conference on Animal Health Surveillance (ICAHS), Lyon, France, 17-20 May, 2011.","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Analytics; Software; Collation; Computer science; Data science; Data visualization; Data management; Visualization; Computer security; World Wide Web; Business; Database; Data mining","score_opus":0.08612190338291477,"score_gpt":0.3426771971402011,"score_spread":0.25655529375728636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2250098747","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":[0.02180081,0.01431351,0.4535026,0.013906177,0.004228354,0.014217311,0.20623961,0.0449829,0.22680877],"genre_scores_gemma":[0.043049764,0.0058511985,0.4181559,0.001440647,0.0006488611,0.00328369,0.43310997,0.0028571829,0.091602735],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.994214,0.0009032409,0.00032861147,0.00040353654,0.0036891715,0.0004614143],"domain_scores_gemma":[0.99100596,0.0006648548,0.00026480504,0.0006418781,0.0066442247,0.0007781405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009159612,0.0010905926,0.00048745336,0.004022547,0.0009762392,0.0028760512,0.0017356862,0.00062834786,0.022031143],"category_scores_gemma":[0.008129248,0.0005172042,0.00034632505,0.004532251,0.00039991358,0.0013432417,0.002069933,0.000843424,0.016681293],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017271934,0.00010719653,0.0060077272,0.0006186367,0.000026586576,0.00019544769,0.00030253263,0.0014036708,0.008245226,0.0058212867,0.5863924,0.3907067],"study_design_scores_gemma":[0.000044580764,0.00006109715,0.011956036,0.00036404555,0.00004417574,0.0002606497,0.00035920533,0.0064481464,0.0066033076,0.0016232544,0.97219515,0.00004034029],"about_ca_topic_score_codex":0.25308424,"about_ca_topic_score_gemma":0.24044675,"teacher_disagreement_score":0.74691576,"about_ca_system_score_codex":0.0032917415,"about_ca_system_score_gemma":0.015955947,"threshold_uncertainty_score":0.5032222},"labels":[],"label_agreement":null},{"id":"W2254775140","doi":"10.1007/978-3-642-29262-0_1","title":"Social Media: A Systematic Review to Understand the Evidence and Application in Infodemiology","year":2012,"lang":"en","type":"review","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lawson Health Research Institute","funders":"","keywords":"Social media; Data science; Data source; Frontier; Open source; Computer science; World Wide Web; Medicine; Political science; Information retrieval","score_opus":0.08639992024649272,"score_gpt":0.3519154367629675,"score_spread":0.26551551651647476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2254775140","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":[0.0010131955,0.99707186,0.000383366,0.00040819045,0.00015556144,0.00046103576,0.00033664267,0.000008256855,0.00016178083],"genre_scores_gemma":[0.024165684,0.97106946,0.0024819055,0.00081651367,0.00014416617,0.00095550605,0.00024831647,0.000010199604,0.00010817925],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9765658,0.01041741,0.008114716,0.0012337278,0.0032545498,0.00041382364],"domain_scores_gemma":[0.92184633,0.062269796,0.009888089,0.0011060645,0.0042401184,0.0006495949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02189745,0.0022582994,0.01365143,0.02056967,0.0008931392,0.0039547216,0.0024562692,0.0028174731,0.0038628003],"category_scores_gemma":[0.086331904,0.001447741,0.010382649,0.01640725,0.0018958251,0.004670845,0.003816533,0.0019139407,0.0002741134],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002785835,0.00002445901,0.0011409475,0.9268402,0.028255409,0.00009032129,0.00020490258,0.00011948534,0.00012956832,0.0003366663,0.0011555183,0.04142405],"study_design_scores_gemma":[0.00056386075,0.00026266236,0.0038865344,0.8008345,0.17680256,0.00026706394,0.00048782077,0.00019166163,0.00021888029,0.001106334,0.0153128635,0.00006526988],"about_ca_topic_score_codex":0.008496958,"about_ca_topic_score_gemma":0.03039027,"teacher_disagreement_score":0.02189745,"about_ca_system_score_codex":0.0042584976,"about_ca_system_score_gemma":0.013251267,"threshold_uncertainty_score":0.11580616},"labels":[],"label_agreement":null},{"id":"W2259332251","doi":"10.2196/publichealth.5317","title":"Improving HIV Surveillance Data for Public Health Action in Washington, DC: A Novel Multiorganizational Data-Sharing Method","year":2016,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Institute of Child Health and Human Development; National Institute of Allergy and Infectious Diseases; District of Columbia Department of Health; National Institute of General Medical Sciences; Fogarty International Center; National Institute of Mental Health; National Heart, Lung, and Blood Institute; National Institute on Drug Abuse; Centers for Disease Control and Prevention; National Cancer Institute; National Institutes of Health; Georgetown University; Eunice Kennedy Shriver National Institute of Child Health and Human Development; George Washington University; National Institute of Diabetes and Digestive and Kidney Diseases; Organization for Autism Research","keywords":"Human immunodeficiency virus (HIV); Public health; Public health surveillance; Internet privacy; Data sharing; Environmental health; Medicine; Health care; Business; Computer science; Political science; Family medicine; Nursing; Alternative medicine","score_opus":0.16857530903895507,"score_gpt":0.40384081357388674,"score_spread":0.23526550453493167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2259332251","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":[0.13377337,0.000054154,0.86135393,0.00038180355,0.000029507219,0.00087478757,0.00055385503,0.0013586407,0.0016198658],"genre_scores_gemma":[0.37111416,0.000027945014,0.62707907,0.00005782765,0.000017183356,0.00045191275,0.0006312313,0.000053226402,0.0005674546],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99158806,0.003811681,0.00079241925,0.0021728808,0.0013328712,0.0003019957],"domain_scores_gemma":[0.98886067,0.00428908,0.001329392,0.0033960522,0.0018052455,0.00031960892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009309388,0.00044274534,0.0005425453,0.002013081,0.0013572036,0.0013874797,0.001471212,0.00052262243,0.0019024335],"category_scores_gemma":[0.022034006,0.00047011246,0.00090682966,0.0020947673,0.00044946454,0.0016981506,0.0027518587,0.00065590197,0.00042330957],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005927467,0.0006536221,0.12465197,0.00013015505,0.00036544751,0.000211829,0.0019120472,0.08258353,0.009697772,0.0106796315,0.003996175,0.7645251],"study_design_scores_gemma":[0.00016270696,0.00040310703,0.03105046,0.00003766769,0.00012630512,0.0003300469,0.0011538592,0.9338933,0.011439593,0.010693469,0.010629035,0.00008037659],"about_ca_topic_score_codex":0.009968951,"about_ca_topic_score_gemma":0.009461949,"teacher_disagreement_score":0.009968951,"about_ca_system_score_codex":0.0013542837,"about_ca_system_score_gemma":0.0039986693,"threshold_uncertainty_score":0.049233317},"labels":[],"label_agreement":null},{"id":"W2278391330","doi":"10.1186/s12889-016-2767-9","title":"Small-area spatio-temporal analyses of bladder and kidney cancer risk in Nova Scotia, Canada","year":2016,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia; Cancer Care Ontario; University of Toronto; Cancer Care Nova Scotia; Dalhousie University","funders":"Canadian Institutes of Health Research; Nova Scotia Health Research Foundation","keywords":"Bladder cancer; Medicine; Kidney cancer; Incidence (geometry); Cancer; Demography; Cancer registry; Population; Internal medicine; Environmental health","score_opus":0.08882192399906069,"score_gpt":0.34813763826994465,"score_spread":0.25931571427088396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2278391330","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":[0.99108607,0.0005964315,0.0006303358,0.00022958349,0.000011810537,0.0000718348,0.0057277465,0.00001900576,0.0016272069],"genre_scores_gemma":[0.9966731,0.00024539614,0.00057866203,0.00003204266,0.0000030011101,0.000028687375,0.0015153892,0.000004932437,0.0009187567],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995245,0.000090094596,0.000035110035,0.00010644102,0.00009076312,0.0001530167],"domain_scores_gemma":[0.99791366,0.0003222189,0.00033792542,0.00010465206,0.0009921142,0.00032941307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007543597,0.00029608863,0.00029663226,0.001393276,0.0012623841,0.0008589043,0.00072602177,0.00023824259,0.0011742299],"category_scores_gemma":[0.0024470296,0.00020539686,0.00065404584,0.0027451003,0.0005234358,0.00014106334,0.00085916213,0.00025801282,0.00012282182],"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":[0.0000868869,0.000011858879,0.99145776,0.00005651251,0.00010597137,0.00018206364,0.0004488495,0.0028839458,0.0003345102,0.00019638687,0.00069791585,0.0035372686],"study_design_scores_gemma":[0.0000065847935,0.000010909565,0.99445975,0.00003263798,0.000038874983,0.000053918913,0.0009734149,0.0035305996,0.00006427269,0.00004733389,0.00077367085,0.000007982941],"about_ca_topic_score_codex":0.996581,"about_ca_topic_score_gemma":0.99751675,"teacher_disagreement_score":0.017339505,"about_ca_system_score_codex":0.017339505,"about_ca_system_score_gemma":0.026334012,"threshold_uncertainty_score":0.12580752},"labels":[],"label_agreement":null},{"id":"W2279733272","doi":"10.1109/dsdis.2015.27","title":"Identification and Validation of Real-Time Health Events through Social Media","year":2015,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":17,"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":"Public Health Agency of Canada","keywords":"Social media; Disease surveillance; Context (archaeology); Computer science; Data science; Microblogging; Big data; Scale (ratio); Population; Cloud computing; Identification (biology); Disease; Internet privacy; Misinformation; Business; Computer security; World Wide Web; Geography; Medicine; Environmental health; Data mining; Biology","score_opus":0.05028381517622313,"score_gpt":0.34881257164246027,"score_spread":0.2985287564662371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2279733272","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":[0.9459049,0.0003596663,0.024125963,0.001677603,0.0003248865,0.000793124,0.016034074,0.0009100704,0.009869709],"genre_scores_gemma":[0.9664328,0.00018869898,0.020495947,0.00018489255,0.00013333501,0.00039332107,0.010395686,0.000050593782,0.0017247348],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947642,0.0019240605,0.00061279174,0.0008075513,0.0014624831,0.0004287836],"domain_scores_gemma":[0.98009264,0.008124237,0.0036043213,0.0030594845,0.004420052,0.0006993649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004974562,0.0007327997,0.00046149277,0.004229115,0.0008545072,0.0020220773,0.0010361277,0.0011789915,0.0012315683],"category_scores_gemma":[0.018903743,0.0003083566,0.00044020178,0.002999254,0.0007318712,0.0027534738,0.0016724581,0.0011365868,0.001426675],"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":[0.00044790286,0.0010799778,0.8358311,0.001173019,0.00043141146,0.001390356,0.006122951,0.008671579,0.027295157,0.0040779132,0.008167509,0.10531119],"study_design_scores_gemma":[0.00004318416,0.0004989475,0.8330359,0.00045211468,0.00013633235,0.00083728414,0.009311021,0.09757345,0.028965477,0.003180143,0.025848312,0.00011783648],"about_ca_topic_score_codex":0.008574696,"about_ca_topic_score_gemma":0.010477902,"teacher_disagreement_score":0.008574696,"about_ca_system_score_codex":0.0006212824,"about_ca_system_score_gemma":0.001018045,"threshold_uncertainty_score":0.026308298},"labels":[],"label_agreement":null},{"id":"W2280504174","doi":"10.1017/s0950268815001478","title":"Google Flu Trends in Canada: a comparison of digital disease surveillance data with physician consultations and respiratory virus surveillance data, 2010–2014","year":2015,"lang":"en","type":"article","venue":"Epidemiology and Infection","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Alberta Hospital Edmonton; University of Alberta","funders":"Alberta Innovates; Public Health Agency; Public Health Agency of Canada","keywords":"Human metapneumovirus; Medicine; Virology; Metapneumovirus; Influenza-like illness; Public health surveillance; Public health; Virus; Influenza A virus; Epidemiology; Respiratory system; Respiratory tract infections; Internal medicine","score_opus":0.10452596911922875,"score_gpt":0.360715250614557,"score_spread":0.25618928149532827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2280504174","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":[0.85989624,0.005321765,0.0009176398,0.0028589317,0.00011909818,0.00014083808,0.11717181,0.0002482773,0.01332535],"genre_scores_gemma":[0.9680864,0.0014346648,0.0010043187,0.00025430406,0.00002541645,0.000037073165,0.02815797,0.000033385884,0.00096654147],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99717295,0.00021832078,0.000256915,0.00037151165,0.0014440952,0.0005362679],"domain_scores_gemma":[0.9903638,0.000827552,0.0014712975,0.00026485688,0.00624807,0.00082444196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018517422,0.00050784706,0.0005794333,0.0055248197,0.001498406,0.0026039642,0.0011060559,0.0003788206,0.0013737391],"category_scores_gemma":[0.01605427,0.00035581933,0.00096253946,0.014704268,0.00070443243,0.0009617919,0.0014520972,0.0006874059,0.00023112468],"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":[0.00017557472,0.000020773727,0.9732048,0.00019464953,0.00030981158,0.00005990144,0.00046818046,0.0015106177,0.00007491602,0.0005889152,0.00802627,0.015365424],"study_design_scores_gemma":[0.000015342353,0.000017739521,0.98938227,0.00010777315,0.000098384386,0.00006373363,0.0008566272,0.003649161,0.00013709225,0.00007636812,0.005569878,0.00002562123],"about_ca_topic_score_codex":0.9973339,"about_ca_topic_score_gemma":0.9970849,"teacher_disagreement_score":0.043769628,"about_ca_system_score_codex":0.043769628,"about_ca_system_score_gemma":0.050940312,"threshold_uncertainty_score":0.31757236},"labels":[],"label_agreement":null},{"id":"W2288915740","doi":"10.4236/etsn.2016.51001","title":"Mobile EMR Use for Epidemiological Surveillance on a Medical Service Trip in Honduras: A Pilot Study","year":2016,"lang":"en","type":"article","venue":"E-Health Telecommunication Systems and Networks","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Grand River Hospital","funders":"","keywords":"Epidemiology; Medicine; Medical diagnosis; Observational study; Medical record; Family medicine; Health care; Documentation; Pediatrics; Surgery; Pathology","score_opus":0.0919961369887413,"score_gpt":0.36944284018400286,"score_spread":0.27744670319526155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2288915740","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":[0.99629587,0.000053579486,0.00035456865,0.00008894239,0.000008178038,0.0019725168,0.00066899735,0.000018157705,0.0005391476],"genre_scores_gemma":[0.9911232,0.00020021597,0.0033562898,0.00030830008,0.00003785711,0.003708674,0.00094599236,0.000008449609,0.00031106407],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99749327,0.0012250779,0.0002853689,0.0003158493,0.00030694055,0.00037351254],"domain_scores_gemma":[0.9919613,0.0020022453,0.0025945671,0.0008226537,0.0015949004,0.0010244206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038819883,0.0006091575,0.00039668143,0.0014122912,0.0010770566,0.0010352993,0.0011416532,0.00081508636,0.0014816265],"category_scores_gemma":[0.00842284,0.00048850465,0.00076364336,0.001089003,0.0007188325,0.0012409076,0.0015018979,0.0007889122,0.0005217651],"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":[0.0003669884,0.003948369,0.9643842,0.00032540155,0.000061889026,0.00080726366,0.0071523967,0.00022930704,0.0012610368,0.000034607645,0.0006363174,0.020792266],"study_design_scores_gemma":[0.00013713125,0.0058044493,0.9849006,0.00009846299,0.000057138106,0.00033793325,0.006600486,0.0007887439,0.00028764197,0.000023924384,0.0009360351,0.00002739705],"about_ca_topic_score_codex":0.042708542,"about_ca_topic_score_gemma":0.05717184,"teacher_disagreement_score":0.042708542,"about_ca_system_score_codex":0.0022286186,"about_ca_system_score_gemma":0.0033330668,"threshold_uncertainty_score":0.08491993},"labels":[],"label_agreement":null},{"id":"W2290091881","doi":"","title":"Physician surveillance of influenza: collaboration between primary care and public health.","year":2014,"lang":"en","type":"article","venue":"PubMed","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Hamilton Health Sciences","funders":"","keywords":"Workflow; Medicine; Primary care; Public health; Electronic medical record; Medical record; Family medicine; Health care; Health information exchange; Medical emergency; Nursing; Computer science; Health information; Database","score_opus":0.025372854954143535,"score_gpt":0.26453411630096446,"score_spread":0.23916126134682092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2290091881","genre_codex":"commentary","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":[0.29229382,0.044832557,0.07364139,0.44955653,0.0034087722,0.0039612767,0.0071828575,0.0039079078,0.1212149],"genre_scores_gemma":[0.8654628,0.011892352,0.07994321,0.03028025,0.0016501993,0.0009179418,0.00227559,0.00014620631,0.0074313767],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.98405635,0.010512252,0.0008892772,0.0014236511,0.0025477866,0.00057060906],"domain_scores_gemma":[0.960886,0.01471833,0.0044061965,0.0027206044,0.0084066875,0.008862214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01687157,0.0003031849,0.00041958128,0.0013627165,0.0012014827,0.0023994197,0.0010241512,0.0010096999,0.0039373236],"category_scores_gemma":[0.031718932,0.0004954438,0.0003269902,0.0014896046,0.00060401094,0.0015876421,0.0040754005,0.0013948733,0.0010064965],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015116342,0.00035742237,0.23714444,0.0013246111,0.00020549384,0.0003638634,0.0030061004,0.0005810524,0.0015329086,0.0010312373,0.077608146,0.6766937],"study_design_scores_gemma":[0.0003378245,0.0012801826,0.6893195,0.0026104045,0.00031550508,0.001258614,0.009711955,0.0060085934,0.0021577973,0.003567367,0.28335002,0.000082211925],"about_ca_topic_score_codex":0.029800382,"about_ca_topic_score_gemma":0.050926458,"teacher_disagreement_score":0.029800382,"about_ca_system_score_codex":0.0034595106,"about_ca_system_score_gemma":0.017545022,"threshold_uncertainty_score":0.089226484},"labels":[],"label_agreement":null},{"id":"W2293886080","doi":"10.1186/s12911-016-0272-9","title":"Utility of linking primary care electronic medical records with Canadian census data to study the determinants of chronic disease: an example based on socioeconomic status and obesity","year":2016,"lang":"en","type":"article","venue":"BMC Medical Informatics and Decision Making","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University; College of Family Physicians of Canada; University of Calgary; Kingston Health Sciences Centre","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Medical record; Medicine; Census; Socioeconomic status; Health informatics; Geocoding; Health care; Diagnosis code; Family medicine; Public health; Obesity; Environmental health; Gerontology; Geography; Population; Nursing; Cartography","score_opus":0.05212521319348684,"score_gpt":0.3389389990023709,"score_spread":0.28681378580888406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293886080","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":[0.9158002,0.0017262896,0.030916324,0.004465075,0.00008628723,0.002548718,0.014381365,0.00020984514,0.029865937],"genre_scores_gemma":[0.921036,0.0010695177,0.072296344,0.00027824787,0.00002681231,0.00042581462,0.004076455,0.000022781976,0.00076805527],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9913886,0.004124703,0.0005674771,0.0004613736,0.0028992794,0.00055849616],"domain_scores_gemma":[0.96826375,0.017643962,0.0020596096,0.0027631337,0.008863079,0.00040639844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012713997,0.0005527779,0.00035860462,0.003980167,0.0019530116,0.0020248052,0.00094685686,0.0006161988,0.0009555411],"category_scores_gemma":[0.05004607,0.00024720153,0.0008844913,0.012889851,0.00074356643,0.0008148188,0.0019960182,0.0005640723,0.00014305579],"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":[0.00055762497,0.00017405799,0.85283196,0.00087199814,0.0004501411,0.0012100769,0.0067491997,0.0063277856,0.0013787654,0.005238329,0.0055043586,0.11870571],"study_design_scores_gemma":[0.00013694444,0.00023825432,0.93293655,0.0006642114,0.0005842614,0.0007868848,0.008925128,0.030030115,0.0022561855,0.0034782484,0.019850887,0.00011230932],"about_ca_topic_score_codex":0.79090613,"about_ca_topic_score_gemma":0.81677,"teacher_disagreement_score":0.20909387,"about_ca_system_score_codex":0.006494998,"about_ca_system_score_gemma":0.01455997,"threshold_uncertainty_score":0.42065048},"labels":[],"label_agreement":null},{"id":"W2300443037","doi":"10.2196/publichealth.4319","title":"Use of Electronic Health Records and Geographic Information Systems in Public Health Surveillance of Type 2 Diabetes: A Feasibility Study","year":2016,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Public health surveillance; Health records; Public health; Environmental health; Geographic information system; Type 2 diabetes; Medicine; Geography; Business; Diabetes mellitus; Cartography; Health care; Political science; Nursing","score_opus":0.04819709387313481,"score_gpt":0.3186054064452497,"score_spread":0.2704083125721149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2300443037","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":[0.996915,0.00016111338,0.00055780757,0.00018263608,0.000008106788,0.00083851826,0.00033828625,0.000004436438,0.0009940851],"genre_scores_gemma":[0.9955177,0.00019609717,0.0028146915,0.0001052431,0.00002293438,0.000907171,0.00033703595,0.0000023903438,0.00009673404],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.97932273,0.015226118,0.0014042906,0.00087180705,0.0021215884,0.0010534569],"domain_scores_gemma":[0.96589404,0.019680342,0.0070761736,0.0021112128,0.003470539,0.0017676773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023532469,0.0004044202,0.00039505152,0.002515417,0.00065523846,0.0015215614,0.0006963763,0.0009937173,0.0013009111],"category_scores_gemma":[0.038494173,0.0006157318,0.0009887923,0.0025149612,0.00071752875,0.002688387,0.0023051384,0.000623956,0.00027779778],"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":[0.00044021916,0.0015724696,0.9854343,0.00014601475,0.000052602012,0.00014100998,0.0013624415,0.00009318894,0.00015466845,0.00007194191,0.00010346108,0.010427583],"study_design_scores_gemma":[0.00020020762,0.004719109,0.98485106,0.00016888701,0.00009980987,0.00045491554,0.006876866,0.0015939528,0.00015342196,0.000081401944,0.0007755633,0.000024710305],"about_ca_topic_score_codex":0.0076100747,"about_ca_topic_score_gemma":0.0077336826,"teacher_disagreement_score":0.023532469,"about_ca_system_score_codex":0.001008849,"about_ca_system_score_gemma":0.0029044556,"threshold_uncertainty_score":0.12445307},"labels":[],"label_agreement":null},{"id":"W2300521610","doi":"10.2196/publichealth.5255","title":"Development of the Health Atlas of Jalisco: A New Web-Based Service for the Ministry of Health and the Community in Mexico","year":2016,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Christian ministry; Atlas (anatomy); Public health; Environmental health; Business; Health services; Community health; World Wide Web; Geography; Medicine; Political science; Computer science; Population; Nursing","score_opus":0.05479539280419626,"score_gpt":0.3339483593259581,"score_spread":0.27915296652176186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2300521610","genre_codex":"dataset","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":[0.29716974,0.0017609313,0.10855637,0.011371466,0.0011507078,0.004916126,0.31149933,0.10378924,0.15978605],"genre_scores_gemma":[0.41813463,0.0021069043,0.3430116,0.0008270404,0.0005523297,0.009937952,0.1611343,0.0063833594,0.05791193],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994438,0.00021532137,0.000058292637,0.00011038193,0.00011143264,0.000060778508],"domain_scores_gemma":[0.99668807,0.0012140209,0.00043723933,0.0004469231,0.00064287364,0.0005708231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002151029,0.00040084313,0.0002726886,0.0035040728,0.0012015742,0.0023760092,0.00081358594,0.00046807036,0.024599513],"category_scores_gemma":[0.0048839133,0.0004022986,0.00032504296,0.003979538,0.00036016677,0.0022848286,0.0019927002,0.000616603,0.002879929],"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":[0.00052365224,0.00027791233,0.10214425,0.0013318589,0.00007509499,0.0012892924,0.012842049,0.0028750352,0.0050599966,0.007439596,0.43083903,0.43530226],"study_design_scores_gemma":[0.0001226093,0.00011498684,0.14807913,0.0002494243,0.00005542109,0.0003754919,0.0038739084,0.004268073,0.0020245656,0.0015372215,0.8392025,0.000096695745],"about_ca_topic_score_codex":0.022331541,"about_ca_topic_score_gemma":0.025536684,"teacher_disagreement_score":0.024599513,"about_ca_system_score_codex":0.0021529724,"about_ca_system_score_gemma":0.0035682907,"threshold_uncertainty_score":0.08229363},"labels":[],"label_agreement":null},{"id":"W2300547058","doi":"10.1136/sextrans-2015-052270.587","title":"P17.09 Cost-effectiveness of hiv self-testing promotion through grindr™, a smartphone social networking application","year":2015,"lang":"en","type":"article","venue":"Sexually Transmitted Infections","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Health and Medical Research Council; Australian Government","keywords":"Medicine; Test (biology); Promotion (chess); Human immunodeficiency virus (HIV); Men who have sex with men; mHealth; Family medicine; Health promotion; Cost effectiveness; Gerontology; Public health; Nursing; Psychological intervention","score_opus":0.05745534764302943,"score_gpt":0.32096040105988344,"score_spread":0.263505053416854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2300547058","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":[0.900656,0.005748995,0.001690063,0.013615856,0.0009983794,0.004150355,0.007580309,0.00047607758,0.065083995],"genre_scores_gemma":[0.9856043,0.0014423318,0.0039538797,0.0013092352,0.00028885808,0.0016357966,0.0011223528,0.000029129955,0.004614223],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99820614,0.0010286286,0.00010467475,0.00009643053,0.00038936367,0.00017473208],"domain_scores_gemma":[0.9940665,0.0034565225,0.0008945863,0.00015388912,0.00061661354,0.0008119103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023602804,0.00039506247,0.00030123777,0.0006136309,0.0004145103,0.0011832747,0.0006787119,0.0008668511,0.032917798],"category_scores_gemma":[0.014223528,0.00016736268,0.0017616568,0.00040174153,0.00029324618,0.0011067295,0.00074483134,0.0007588602,0.0018634311],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.03102372,0.014971908,0.08104214,0.0029179428,0.0008505009,0.00025732303,0.0002718485,0.004287186,0.0013665927,0.001723127,0.048172202,0.8131156],"study_design_scores_gemma":[0.03362355,0.2055599,0.57090497,0.0057381694,0.0067395563,0.0013592601,0.0031458803,0.06437832,0.011231949,0.0053634695,0.091579154,0.0003757917],"about_ca_topic_score_codex":0.0071254415,"about_ca_topic_score_gemma":0.0085590305,"teacher_disagreement_score":0.032917798,"about_ca_system_score_codex":0.0014643471,"about_ca_system_score_gemma":0.0022488912,"threshold_uncertainty_score":0.11012101},"labels":[],"label_agreement":null},{"id":"W2316795763","doi":"10.1017/s0317167100018692","title":"“Magic Google in my Hand, Who is the Sickest in my Land?”","year":2014,"lang":"en","type":"letter","venue":"Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Centre for Movement Disorders","funders":"","keywords":"MAGIC (telescope); Magic bullet; Internet privacy; Computer science; Biology; Bioinformatics","score_opus":0.033591234856361125,"score_gpt":0.27231207106572947,"score_spread":0.23872083620936835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2316795763","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015572722,0.0011319962,0.00025575518,0.9814516,0.005075987,0.000025506764,0.00020008245,0.000056383127,0.010245468],"genre_scores_gemma":[0.020700738,0.0027281821,0.0006369273,0.9447685,0.012435124,0.000061464816,0.000099089266,0.00004976761,0.018520243],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992237,0.00030258743,0.000097391465,0.0001147634,0.00013428542,0.00012724829],"domain_scores_gemma":[0.9981681,0.0007061902,0.00021178323,0.00005478697,0.000419017,0.0004400966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008430708,0.0005471642,0.00062861777,0.00053932436,0.0027560664,0.001965972,0.0009391875,0.011527112,0.010754463],"category_scores_gemma":[0.012189676,0.00036286376,0.0005346786,0.00054100074,0.0012458172,0.0040449295,0.00084302586,0.017976148,0.005597049],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046427747,0.000017361343,0.002379626,0.000059890095,0.000012762821,0.008093456,0.00063420786,0.00005313027,0.000158392,0.0021878919,0.977111,0.009245913],"study_design_scores_gemma":[0.00013680111,0.000087884735,0.007090296,0.0014334482,0.00007222022,0.04826098,0.0054830643,0.001202312,0.00056390924,0.013914758,0.92163414,0.00012027963],"about_ca_topic_score_codex":0.026618047,"about_ca_topic_score_gemma":0.03036663,"teacher_disagreement_score":0.026618047,"about_ca_system_score_codex":0.0034111592,"about_ca_system_score_gemma":0.0023885418,"threshold_uncertainty_score":0.052926183},"labels":[],"label_agreement":null},{"id":"W2321022077","doi":"10.1097/00132981-200606000-00017","title":"Googling Medical Topics Returns Decent Results","year":2006,"lang":"en","type":"article","venue":"Emergency Medicine News","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Business; Computer science","score_opus":0.027789648828841856,"score_gpt":0.33219548865427956,"score_spread":0.3044058398254377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2321022077","genre_codex":"other","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":[0.0144118415,0.027330024,0.03527118,0.07592879,0.030096658,0.0007447019,0.0883108,0.09512144,0.63278455],"genre_scores_gemma":[0.095315225,0.019017229,0.08626867,0.04443334,0.015574419,0.0005927952,0.1481283,0.05782027,0.53284967],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9928831,0.0011977213,0.00042297624,0.00061649707,0.0043561147,0.0005235442],"domain_scores_gemma":[0.98464096,0.006076207,0.0006219404,0.0029106315,0.00439425,0.0013559298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045827827,0.0021176846,0.0011636408,0.010328708,0.0021540287,0.012906427,0.0010810848,0.00207094,0.20166808],"category_scores_gemma":[0.041308917,0.0006600837,0.0017753172,0.0069361487,0.0014041542,0.010238576,0.005913261,0.0022755468,0.19313188],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018706122,0.0000395706,0.0006466149,0.0004777942,0.00003273327,0.00013195485,0.00023049927,0.00010313216,0.000712443,0.0055925017,0.8737869,0.11805873],"study_design_scores_gemma":[0.00005010345,0.00005327943,0.0012494783,0.00037089002,0.000041506486,0.00032552992,0.0004451769,0.00061115116,0.0013140338,0.007131578,0.9883609,0.000046527817],"about_ca_topic_score_codex":0.00540014,"about_ca_topic_score_gemma":0.010405898,"teacher_disagreement_score":0.20166808,"about_ca_system_score_codex":0.002137905,"about_ca_system_score_gemma":0.0023479415,"threshold_uncertainty_score":0.674647},"labels":[],"label_agreement":null},{"id":"W2323581590","doi":"","title":"population that is susceptible) can diff er according to population demographic structure.","year":2014,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Population; Demography; Pandemic; Cluster (spacecraft); Population structure; Indigenous; Age structure; Biology; Geography; Infectious disease (medical specialty); Coronavirus disease 2019 (COVID-19); Medicine; Disease; Ecology","score_opus":0.013928105590981652,"score_gpt":0.2697332254737658,"score_spread":0.25580511988278415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2323581590","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8144785,0.014767481,0.027567977,0.009172726,0.0011358922,0.0009879707,0.027671527,0.0006514314,0.103566475],"genre_scores_gemma":[0.97497606,0.0042141215,0.004288865,0.0030316166,0.00025203958,0.0002943765,0.0040358906,0.000041421183,0.008865657],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99898237,0.00024643922,0.00006836938,0.00024197987,0.00026110094,0.00019967044],"domain_scores_gemma":[0.9982693,0.0006227199,0.00033486699,0.0003169541,0.00027698404,0.00017909291],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015197296,0.0002812578,0.00052352116,0.0014455216,0.00046780895,0.0011719129,0.00066328194,0.00077332446,0.008189219],"category_scores_gemma":[0.0074347593,0.00023745092,0.00045013768,0.0009772084,0.00043709605,0.00090787525,0.0012474919,0.0007264931,0.0030845513],"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":[0.00028583626,0.00018090001,0.5427266,0.0003786676,0.00047813682,0.0006056318,0.001230384,0.0015927578,0.0022904126,0.012510784,0.04158609,0.39613372],"study_design_scores_gemma":[0.000071598406,0.0003194094,0.86993575,0.00048633222,0.0003095852,0.0031515786,0.001778764,0.0051637474,0.0012940974,0.017921893,0.099459834,0.00010748224],"about_ca_topic_score_codex":0.014556153,"about_ca_topic_score_gemma":0.013285496,"teacher_disagreement_score":0.9918108,"about_ca_system_score_codex":0.0006398308,"about_ca_system_score_gemma":0.00048912433,"threshold_uncertainty_score":0.028942823},"labels":[],"label_agreement":null},{"id":"W2324052368","doi":"10.1136/sextrans-2013-051184.0197","title":"O20.1 Two-Mode Analysis of HIV, HCV and Risk Behaviours: A Place-Based Analysis of Most At-Risk Populations in Winnipeg, Canada","year":2013,"lang":"en","type":"article","venue":"Sexually Transmitted Infections","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Betweenness centrality; Medicine; Respondent; Closeness; Public health; Demography; Environmental health; Sample (material); Centrality; Statistics; Pathology","score_opus":0.01172371481708356,"score_gpt":0.27397025607405284,"score_spread":0.2622465412569693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2324052368","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":[0.91754735,0.0008381835,0.0035424002,0.0005821868,0.0000532556,0.0009034601,0.06573064,0.00019660659,0.010605843],"genre_scores_gemma":[0.97471833,0.00038029478,0.0055349846,0.00008936161,0.000008903473,0.00034479826,0.012443276,0.000074598436,0.0064054844],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99901915,0.00012192992,0.0000463113,0.0001515581,0.00021769594,0.00044336886],"domain_scores_gemma":[0.9987306,0.0001353769,0.00016566773,0.00007544063,0.0006262715,0.00026676737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008134856,0.0007256008,0.0005822398,0.0037933742,0.0026524263,0.0021642393,0.0014802952,0.00034680136,0.007976101],"category_scores_gemma":[0.003466957,0.0004800553,0.0012260111,0.0063713756,0.000675629,0.00054398016,0.0022166723,0.0005008395,0.00073610997],"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":[0.00013319551,0.000046852743,0.9579762,0.0003126304,0.00017161656,0.0003135064,0.00342357,0.0014694501,0.00076528493,0.0013759274,0.010681524,0.023330158],"study_design_scores_gemma":[0.00001888969,0.000042878844,0.97455794,0.00017815907,0.00008085603,0.00019290503,0.009743627,0.0035406167,0.00028257293,0.00020218143,0.011127964,0.000031361138],"about_ca_topic_score_codex":0.996128,"about_ca_topic_score_gemma":0.9962993,"teacher_disagreement_score":0.017805874,"about_ca_system_score_codex":0.017805874,"about_ca_system_score_gemma":0.034521885,"threshold_uncertainty_score":0.12919128},"labels":[],"label_agreement":null},{"id":"W2325076983","doi":"10.4310/sii.2010.v3.n4.a4","title":"Empirical likelihood confidence intervals for ratio of hazard rates under right censorship","year":2010,"lang":"en","type":"article","venue":"Statistics and Its Interface","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Confidence interval; Statistics; Hazard ratio; Censorship; Empirical likelihood; Mathematics; Econometrics; Likelihood-ratio test; Political science; Law","score_opus":0.033310590004059254,"score_gpt":0.3690655084089494,"score_spread":0.33575491840489013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2325076983","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":[0.012167418,0.0023156742,0.9805833,0.0007945565,0.00011390389,0.00009322408,0.00041510942,0.00073288253,0.0027839257],"genre_scores_gemma":[0.6155828,0.003955556,0.37095335,0.0009665777,0.0010270417,0.0011132214,0.0025194434,0.0008219087,0.0030600003],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9640713,0.02458903,0.0019788917,0.003116962,0.0054523707,0.0007914321],"domain_scores_gemma":[0.37514696,0.5783431,0.014416052,0.019707931,0.010876291,0.0015096837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07136309,0.0017325422,0.0032344915,0.00590042,0.0006606555,0.005754348,0.0059832023,0.0041798796,0.008491897],"category_scores_gemma":[0.48821762,0.0011259918,0.0023229735,0.0044477526,0.004855977,0.0085102245,0.0040643374,0.0060216323,0.0014714601],"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":[0.00071438716,0.00015178663,0.010640629,0.0010173009,0.0006230306,0.00053546624,0.0009257381,0.21023606,0.0009388919,0.6651361,0.005943044,0.10313758],"study_design_scores_gemma":[0.00014702244,0.00019068587,0.003991837,0.0006116125,0.00017693982,0.0006683348,0.00022309569,0.6043338,0.0014268098,0.38399318,0.0040883976,0.00014818927],"about_ca_topic_score_codex":0.0017715514,"about_ca_topic_score_gemma":0.00067435857,"teacher_disagreement_score":0.07136309,"about_ca_system_score_codex":0.001887784,"about_ca_system_score_gemma":0.0015057713,"threshold_uncertainty_score":0.37740862},"labels":[],"label_agreement":null},{"id":"W2325098469","doi":"10.1097/00124784-200307000-00007","title":"Administrative Data as a Tool for Arthritis Surveillance","year":2003,"lang":"en","type":"article","venue":"Journal of Public Health Management and Practice","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Care Foundation","funders":"","keywords":"Demographics; Public health; Public health surveillance; Medicine; Arthritis; Environmental health; Health services; Population; Demography; Nursing","score_opus":0.18366093179751355,"score_gpt":0.4413124429906697,"score_spread":0.2576515111931561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2325098469","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":[0.18768597,0.007196316,0.38311923,0.013191423,0.0025503843,0.007822123,0.34340873,0.0064137382,0.048612118],"genre_scores_gemma":[0.4268249,0.0027646197,0.42010775,0.0019935134,0.0010343913,0.008070593,0.13607374,0.00035722132,0.0027733897],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.92285854,0.047864,0.013955839,0.0026599278,0.011901222,0.0007604446],"domain_scores_gemma":[0.853165,0.07270421,0.023950579,0.02260441,0.025834989,0.001740935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03213787,0.00061791315,0.0013612204,0.014077724,0.00065850595,0.00282175,0.0020656716,0.0009702958,0.0029651427],"category_scores_gemma":[0.15489615,0.0010053,0.0009956368,0.016551515,0.00031267217,0.0017710405,0.002460918,0.0016636971,0.0013627029],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006680226,0.00067438267,0.36976036,0.0026472346,0.001401107,0.0002984145,0.0014716407,0.009618317,0.0013626737,0.019163042,0.12861516,0.46431962],"study_design_scores_gemma":[0.0009018401,0.0012088297,0.51526076,0.0029168006,0.0011888804,0.0012008188,0.0018773266,0.102581464,0.008584295,0.023103671,0.34072903,0.00044628448],"about_ca_topic_score_codex":0.013701716,"about_ca_topic_score_gemma":0.008538997,"teacher_disagreement_score":0.03213787,"about_ca_system_score_codex":0.0013659741,"about_ca_system_score_gemma":0.0035920439,"threshold_uncertainty_score":0.16996336},"labels":[],"label_agreement":null},{"id":"W232539653","doi":"","title":"인터넷 건강정보 게이트웨이시스템 개발","year":2003,"lang":"ko","type":"book","venue":"한국보건사회연구원 eBooks","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Political science","score_opus":0.01757535488220919,"score_gpt":0.2605796757411179,"score_spread":0.24300432085890872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W232539653","genre_codex":"other","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01026495,0.008985195,0.010115858,0.071820244,0.004100687,0.00018194094,0.00062275236,0.00028270623,0.8936257],"genre_scores_gemma":[0.41999894,0.024642907,0.020952264,0.025719194,0.003478823,0.00057635293,0.0013463567,0.0005444078,0.50274074],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9940807,0.0021432603,0.00031458176,0.0009472813,0.0018414124,0.0006726834],"domain_scores_gemma":[0.9929605,0.0018419211,0.00057739316,0.0006937665,0.0029773188,0.0009490018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048341765,0.0008205918,0.0005314998,0.0021368677,0.0057406803,0.01666651,0.0014851629,0.003650086,0.054308083],"category_scores_gemma":[0.011549612,0.00050497835,0.00097282004,0.0024410891,0.011336649,0.012709114,0.0045778174,0.0057114563,0.02616029],"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":[0.000069327005,0.0000847746,0.0034057628,0.0005132514,0.00005153577,0.00030607288,0.011196621,0.0005084092,0.00060531474,0.69923556,0.16614294,0.11788045],"study_design_scores_gemma":[0.000015054544,0.000034096,0.0020230438,0.00072241557,0.000027640575,0.00022968263,0.00803661,0.00029289044,0.00044680308,0.10948935,0.8786413,0.00004102503],"about_ca_topic_score_codex":0.013328597,"about_ca_topic_score_gemma":0.011761509,"teacher_disagreement_score":0.054308083,"about_ca_system_score_codex":0.008001106,"about_ca_system_score_gemma":0.01185063,"threshold_uncertainty_score":0.18167865},"labels":[],"label_agreement":null},{"id":"W2328026057","doi":"10.5367/te.2014.0402","title":"<i>Research Note:</i> Nowcasting Tourist Arrivals in Barbados – Just Google it!","year":2014,"lang":"en","type":"article","venue":"Tourism Economics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nowcasting; Tourism; The Internet; Autoregressive model; Empirical evidence; Econometrics; Advertising; Regional science; Empirical research; Economics; Business; Computer science; Geography; World Wide Web; Statistics; Mathematics","score_opus":0.052062552191512436,"score_gpt":0.3518166956320248,"score_spread":0.2997541434405123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2328026057","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":[0.43735364,0.011268036,0.007293694,0.2789062,0.010571403,0.00029724147,0.10876268,0.00095603167,0.14459112],"genre_scores_gemma":[0.8821516,0.008316672,0.009827839,0.015562379,0.0043426272,0.00013930953,0.030035947,0.00041213704,0.04921148],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991768,0.00020178476,0.000074363765,0.00015416835,0.0002799653,0.00011293695],"domain_scores_gemma":[0.9901007,0.0041620415,0.0015898787,0.0005292507,0.0031194855,0.0004986868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014344021,0.0002426961,0.00039711013,0.0021593662,0.0014406218,0.0028517477,0.00078853226,0.001202658,0.016180813],"category_scores_gemma":[0.012773201,0.00014503059,0.0005561111,0.005498732,0.00063093216,0.0015219476,0.0010531738,0.0013685778,0.0047215745],"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":[0.00026348425,0.0001528516,0.33410627,0.0009487329,0.0002816672,0.0005214735,0.005077787,0.002329371,0.0011387228,0.0061468887,0.56006837,0.08896433],"study_design_scores_gemma":[0.000039046135,0.00018189641,0.633692,0.001816803,0.00033662101,0.00045026792,0.027852425,0.0054448787,0.002456029,0.002997148,0.32455873,0.00017419057],"about_ca_topic_score_codex":0.42949748,"about_ca_topic_score_gemma":0.5401702,"teacher_disagreement_score":0.42949748,"about_ca_system_score_codex":0.0014841803,"about_ca_system_score_gemma":0.0026674555,"threshold_uncertainty_score":0.8539951},"labels":[],"label_agreement":null},{"id":"W2330164020","doi":"10.15265/iy-2014-0016","title":"Big Data Usage Patterns in the Health Care Domain: A Use Case Driven Approach Applied to the Assessment of Vaccination Benefits and Risks","year":2014,"lang":"en","type":"review","venue":"Yearbook of Medical Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Big data; Data science; Computer science; Cloud computing; Risk analysis (engineering); Analytics; Social media; Medicine; Data mining; World Wide Web","score_opus":0.18231044588388728,"score_gpt":0.42579182927213,"score_spread":0.2434813833882427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2330164020","genre_codex":"empirical","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":[0.64834416,0.002496792,0.31092533,0.004591607,0.000120240395,0.010130755,0.0061386554,0.0004884476,0.01676402],"genre_scores_gemma":[0.6459797,0.00096948055,0.34537572,0.00039019258,0.00004867087,0.003577359,0.0023248147,0.00009666131,0.0012373207],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9673935,0.023089256,0.0023069982,0.0017403676,0.0048113544,0.00065847137],"domain_scores_gemma":[0.8746631,0.10585876,0.0063275388,0.004184971,0.007897009,0.001068584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028904254,0.0010319916,0.00084121997,0.014925813,0.0018336673,0.0066641313,0.0026787287,0.0024819532,0.0015981528],"category_scores_gemma":[0.06657774,0.00086959114,0.002311091,0.010523658,0.002420002,0.0043017603,0.0032843205,0.0017626742,0.00028021444],"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":[0.0010725053,0.0028766168,0.39106923,0.0076324125,0.0018844716,0.013607785,0.06871177,0.09355931,0.008247617,0.102121696,0.011422584,0.297794],"study_design_scores_gemma":[0.00032694917,0.0012210204,0.1165241,0.0039583286,0.00093818636,0.0048191077,0.0771557,0.6607709,0.009617542,0.07216487,0.0520251,0.00047821953],"about_ca_topic_score_codex":0.015538234,"about_ca_topic_score_gemma":0.018354034,"teacher_disagreement_score":0.028904254,"about_ca_system_score_codex":0.005471062,"about_ca_system_score_gemma":0.003938646,"threshold_uncertainty_score":0.15286213},"labels":[],"label_agreement":null},{"id":"W2332067108","doi":"10.1177/1461355715621070","title":"Geographic profiling survey","year":2015,"lang":"en","type":"article","venue":"International Journal of Police Science & Management","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; Carleton University; Ontario Tech University","funders":"","keywords":"Profiling (computer programming); Psychology; Geographic information system; Geography; Data science; Computer science; Cartography","score_opus":0.04448658259307133,"score_gpt":0.3664456661762133,"score_spread":0.321959083583142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2332067108","genre_codex":"dataset","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":[0.32148665,0.0008171502,0.011126133,0.005001229,0.00032826973,0.0245906,0.44467476,0.00084955426,0.19112565],"genre_scores_gemma":[0.49324194,0.0042648152,0.030549191,0.0052191727,0.00031309226,0.038833197,0.3182204,0.00029958645,0.10905862],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99843603,0.0004787499,0.00032129302,0.00019299537,0.00036893488,0.00020195443],"domain_scores_gemma":[0.9940574,0.00074953056,0.0009145278,0.0004587361,0.0033299956,0.0004898379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015822259,0.00040777284,0.0004999778,0.002818158,0.00086405646,0.0006699609,0.0008754981,0.0007026607,0.03459718],"category_scores_gemma":[0.007336979,0.00027124045,0.0003042302,0.0028037184,0.00022048817,0.0011643935,0.0013091317,0.0011693197,0.014896753],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002933423,0.0012072182,0.28748387,0.0013993043,0.000086758795,0.0008631122,0.006851853,0.0015697491,0.0015050037,0.006082145,0.51117164,0.18148604],"study_design_scores_gemma":[0.00008946764,0.00052040914,0.40217024,0.000528849,0.000038117672,0.0007857955,0.008774312,0.0016492939,0.00064721605,0.0013333749,0.5833793,0.00008354649],"about_ca_topic_score_codex":0.016086163,"about_ca_topic_score_gemma":0.015808225,"teacher_disagreement_score":0.03459718,"about_ca_system_score_codex":0.0012405762,"about_ca_system_score_gemma":0.0026210023,"threshold_uncertainty_score":0.11573911},"labels":[],"label_agreement":null},{"id":"W2336229224","doi":"10.1111/cag.12262","title":"An improved approach for geocoding Canadian postal code–based data in health‐related studies","year":2016,"lang":"en","type":"article","venue":"Canadian Geographies / Géographies canadiennes","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"","keywords":"Geocoding; Code (set theory); Public health; Geography; Health data; Zip code; Population; Computer science; Key (lock); Environmental health; Cartography; Medicine; Health care; Political science; Computer security","score_opus":0.030507498283792945,"score_gpt":0.2789734659293801,"score_spread":0.24846596764558715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2336229224","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":[0.047663983,0.003402765,0.7578739,0.046893053,0.0043866257,0.011650619,0.08007616,0.0043127486,0.043740105],"genre_scores_gemma":[0.12862861,0.0015816911,0.84271276,0.002733832,0.0005477431,0.0042691897,0.012754335,0.00041546335,0.0063564894],"study_design_codex":"design_other","study_design_gemma":"design_other","domain_scores_codex":[0.95243746,0.020182168,0.0053004823,0.00377579,0.016362743,0.001941407],"domain_scores_gemma":[0.87478316,0.020668916,0.007296076,0.015912754,0.07887924,0.0024598711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03833252,0.00084579247,0.0010804964,0.014140044,0.0036728892,0.0056957067,0.0039046246,0.0012274267,0.007328954],"category_scores_gemma":[0.17507994,0.00081291364,0.0014201845,0.025076123,0.0012998377,0.0022844533,0.006322262,0.0022596675,0.0022782735],"study_design_candidate":"design_other","study_design_consensus":"design_other","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003942295,0.00014407276,0.13152826,0.0031817474,0.0006191717,0.00043203638,0.012168336,0.009949382,0.005604556,0.031413548,0.2322451,0.57231957],"study_design_scores_gemma":[0.00038419288,0.00021504909,0.3567728,0.004863343,0.0008395618,0.00078747934,0.0088686105,0.050537605,0.009446715,0.03143707,0.53504086,0.00080680446],"about_ca_topic_score_codex":0.8837665,"about_ca_topic_score_gemma":0.9313261,"teacher_disagreement_score":0.11623353,"about_ca_system_score_codex":0.020245936,"about_ca_system_score_gemma":0.07183626,"threshold_uncertainty_score":0.23383605},"labels":[],"label_agreement":null},{"id":"W2336425538","doi":"10.1145/2896338.2896369","title":"Proof of Concept Paper","year":2016,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Provincial Health Services Authority; Public Health Agency of Canada; Ministry of Health; Government of New Brunswick","funders":"Public Health Agency of Canada","keywords":"Computer science; Identification (biology); Proof of concept; Public health; Data science; Track (disk drive); Health data; Risk analysis (engineering); Computer security; Health care; Medicine; Political science","score_opus":0.013441500226750155,"score_gpt":0.26625297622410726,"score_spread":0.2528114759973571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2336425538","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010125808,0.017088834,0.5076171,0.022421733,0.083576426,0.011604514,0.01064585,0.0072322837,0.32968742],"genre_scores_gemma":[0.19356094,0.014093589,0.35652307,0.017181592,0.008064075,0.011155361,0.0119330995,0.0018370658,0.3856512],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99311006,0.0010214038,0.00028203893,0.0010824843,0.003722252,0.0007817957],"domain_scores_gemma":[0.99248344,0.000990551,0.0004683386,0.0011020021,0.0041924133,0.0007632578],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0067904177,0.0017307246,0.0013813818,0.0011907887,0.0016313454,0.005383046,0.0037991775,0.0045451755,0.22406854],"category_scores_gemma":[0.013876419,0.00076899753,0.0014038801,0.00075788924,0.0015507893,0.0043836683,0.0035654367,0.0028284006,0.09530777],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010908844,0.0005499994,0.00095592067,0.0038674106,0.00015933478,0.0017854183,0.00028143363,0.0025724014,0.050123002,0.16116904,0.46659204,0.3108531],"study_design_scores_gemma":[0.0002793891,0.00080087205,0.00025872013,0.0005249498,0.000051837324,0.0013286532,0.00013260778,0.0024670018,0.03440183,0.012086155,0.94759315,0.000074916024],"about_ca_topic_score_codex":0.0008428949,"about_ca_topic_score_gemma":0.000665161,"teacher_disagreement_score":0.7759315,"about_ca_system_score_codex":0.0018787815,"about_ca_system_score_gemma":0.0047937883,"threshold_uncertainty_score":0.74958396},"labels":[],"label_agreement":null},{"id":"W2339186904","doi":"10.7448/ias.19.1.20707","title":"A picture is worth a thousand words: maps of HIV indicators to inform research, programs, and policy from NA‐ACCORD and CCASAnet clinical cohorts","year":2016,"lang":"en","type":"article","venue":"Journal of the International AIDS Society","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre; University of Calgary; AIDS Vancouver","funders":"National Institute of Allergy and Infectious Diseases; National Eye Institute; National Institute on Drug Abuse; U.S. Public Health Service; National Institute on Minority Health and Health Disparities; National Center for Research Resources; Centers for Disease Control and Prevention; National Cancer Institute; National Institute on Alcohol Abuse and Alcoholism; Kaiser Permanente; Case Western Reserve University; Universidad de Chile; Eunice Kennedy Shriver National Institute of Child Health and Human Development; University of North Carolina at Chapel Hill; Agency for Healthcare Research and Quality; Government of Alberta; Johns Hopkins University; Canadian Institutes of Health Research; University of Washington; National Institutes of Health; Vanderbilt University; National Center for Advancing Translational Sciences; Health Resources and Services Administration","keywords":"Medicine; Human immunodeficiency virus (HIV); Antiretroviral therapy; Demography; Cohort; Epidemiology; Health care; Viral load; Geography; Gerontology; Family medicine; Economic growth; Pathology","score_opus":0.037483146320496694,"score_gpt":0.38262710578483466,"score_spread":0.34514395946433796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2339186904","genre_codex":"commentary","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":[0.038195454,0.03866402,0.10430584,0.6593412,0.021149278,0.0008844189,0.049348105,0.005201144,0.08291052],"genre_scores_gemma":[0.38388988,0.0743937,0.39117795,0.060152978,0.013049792,0.0030813934,0.03744829,0.004821315,0.031984612],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99411017,0.0036932337,0.00037698873,0.00038925247,0.0012209635,0.0002094116],"domain_scores_gemma":[0.96600294,0.01689092,0.0026285136,0.004788582,0.007590704,0.0020983194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012169345,0.0010913886,0.0006397094,0.0067891954,0.0020567323,0.009694646,0.002380418,0.0021190525,0.021066519],"category_scores_gemma":[0.07555018,0.0007756959,0.0010965472,0.011642905,0.003190235,0.013895014,0.0052788146,0.004288228,0.004859499],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021668988,0.0000691131,0.02577431,0.0012938126,0.00019497154,0.00022093621,0.0059816223,0.0023395,0.00017278777,0.056124475,0.62236243,0.28524935],"study_design_scores_gemma":[0.000110657005,0.0000759474,0.02237486,0.0034067158,0.00011226588,0.00024813152,0.016535558,0.0019603611,0.00020016763,0.14772263,0.80705863,0.00019412389],"about_ca_topic_score_codex":0.022687757,"about_ca_topic_score_gemma":0.026339488,"teacher_disagreement_score":0.022687757,"about_ca_system_score_codex":0.0038068532,"about_ca_system_score_gemma":0.0074404343,"threshold_uncertainty_score":0.070474505},"labels":[],"label_agreement":null},{"id":"W2340973513","doi":"10.5210/ojphi.v8i1.6440","title":"Real-Time Surveillance of Environmental and Demographic Data in Ontario with PHIMS","year":2016,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Kingston Health Sciences Centre","funders":"","keywords":"Computer science; Environmental data; Data science; Interface (matter); Track (disk drive); Data mining; World Wide Web","score_opus":0.04074268528334491,"score_gpt":0.29378816965701293,"score_spread":0.25304548437366803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2340973513","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":[0.8273584,0.0007376682,0.010235416,0.0030859504,0.00006692379,0.0010094728,0.10193238,0.0017131289,0.053860594],"genre_scores_gemma":[0.94544095,0.0009002234,0.016074661,0.00023382036,0.000028696026,0.00055178034,0.021813784,0.000071110306,0.014884889],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99954164,0.0000652682,0.000030793253,0.000058718553,0.00021668726,0.00008688532],"domain_scores_gemma":[0.9987857,0.0001558966,0.00015050276,0.0000733345,0.00058268005,0.00025185462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005213686,0.00022327305,0.00016486694,0.0012303565,0.0010060761,0.000738589,0.00041763476,0.00017264397,0.0041362927],"category_scores_gemma":[0.0025775824,0.00020991472,0.0001902371,0.0025753067,0.00026103298,0.00039736743,0.00097556884,0.00019461263,0.00050730543],"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":[0.00051059155,0.00012766437,0.74023736,0.0006178761,0.00010101673,0.0011704359,0.0093360515,0.0051195044,0.006540313,0.0017226442,0.087035514,0.14748114],"study_design_scores_gemma":[0.000085440835,0.00010671268,0.8813505,0.0002044945,0.000049134032,0.00024919314,0.0060008117,0.011451559,0.0020456368,0.0005412636,0.09784658,0.00006864631],"about_ca_topic_score_codex":0.9638801,"about_ca_topic_score_gemma":0.9759571,"teacher_disagreement_score":0.03611988,"about_ca_system_score_codex":0.009271009,"about_ca_system_score_gemma":0.01893698,"threshold_uncertainty_score":0.072665155},"labels":[],"label_agreement":null},{"id":"W2341999960","doi":"","title":"Forecasting Unemployment with Google Searches","year":2016,"lang":"en","type":"preprint","venue":"Econstor (Econstor)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"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":"University of Toronto; Aalto-Yliopisto; Helsingin Yliopisto","keywords":"Granger causality; Predictive power; Econometrics; Unemployment; Unemployment rate; Vector autoregression; Big data; Computer science; Causality (physics); Economics; Data mining; Macroeconomics","score_opus":0.04305200824986093,"score_gpt":0.27884262679488436,"score_spread":0.23579061854502342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2341999960","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":[0.92680895,0.0030824747,0.009536443,0.003176331,0.00016369735,0.00013988216,0.043424748,0.0006079682,0.013059452],"genre_scores_gemma":[0.9784061,0.00070827315,0.0054012765,0.00012248037,0.0000945102,0.000051450057,0.0138164135,0.000022468274,0.0013770615],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929714,0.00031243713,0.00006792345,0.00012665469,0.000121930236,0.0000738178],"domain_scores_gemma":[0.9960329,0.0024413175,0.0006760438,0.0002551602,0.0004017696,0.00019275866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011628158,0.00043339824,0.0005016539,0.002694915,0.00021887787,0.0010683999,0.00038469105,0.0006729496,0.0018052737],"category_scores_gemma":[0.009300627,0.0002432832,0.0005038259,0.0030361894,0.00019930337,0.0012058784,0.00074229634,0.0005203806,0.0009671329],"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":[0.0012162994,0.0002883626,0.704413,0.0007163264,0.00042725715,0.00039901043,0.00064359047,0.106375456,0.00078752486,0.0075434367,0.042194217,0.13499554],"study_design_scores_gemma":[0.00008292788,0.00026572566,0.3290795,0.00034991224,0.00023478971,0.00023409928,0.0012994185,0.635715,0.0014819637,0.011132618,0.02004333,0.00008069765],"about_ca_topic_score_codex":0.027874721,"about_ca_topic_score_gemma":0.027350606,"teacher_disagreement_score":0.027874721,"about_ca_system_score_codex":0.00048842526,"about_ca_system_score_gemma":0.00043240262,"threshold_uncertainty_score":0.05542493},"labels":[],"label_agreement":null},{"id":"W2342806467","doi":"10.1371/journal.pone.0152738","title":"Combining Healthcare-Based and Participatory Approaches to Surveillance: Trends in Diarrheal and Respiratory Conditions Collected by a Mobile Phone System by Community Health Workers in Rural Nepal","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":26,"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":"Grand Challenges Canada; Thrasher Research Fund; Brigham and Women's Hospital","keywords":"Medicine; Environmental health; Public health; Community health; Health care; Respiratory infection; Health facility; Epidemiology; Disease surveillance; Incidence (geometry); Diarrhea; Pediatrics; Nursing; Population; Internal medicine; Respiratory system","score_opus":0.12934332330770748,"score_gpt":0.30448495291044997,"score_spread":0.1751416296027425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2342806467","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":[0.9980134,0.00010121743,0.00066015427,0.00016060646,0.000004647878,0.00011126954,0.0003555844,0.000008814674,0.0005843146],"genre_scores_gemma":[0.9966875,0.00011487785,0.00241237,0.00008439099,0.000013341901,0.00029632173,0.0002808441,0.0000019960632,0.00010848756],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9967878,0.0020931119,0.00019051411,0.00036964862,0.00032763113,0.00023140822],"domain_scores_gemma":[0.9957567,0.0017571857,0.0011143832,0.00031153634,0.00074088445,0.0003193908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038163152,0.00032921674,0.00028714028,0.0019320162,0.0006918573,0.0012219325,0.00081827276,0.0005854409,0.00039904626],"category_scores_gemma":[0.0074526295,0.00035337004,0.00032928167,0.0018298015,0.0004338951,0.001024503,0.002221992,0.00045139517,0.00011169431],"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":[0.000072798786,0.00015388329,0.96568465,0.00014965131,0.00014021658,0.00014513364,0.008432237,0.00023824091,0.0010842546,0.00006367955,0.0003166291,0.023518704],"study_design_scores_gemma":[0.000023881426,0.0005862423,0.9744279,0.00014724185,0.000101896956,0.00032164963,0.01910465,0.0030816202,0.0006593851,0.00020176213,0.0013036994,0.000040136692],"about_ca_topic_score_codex":0.015234709,"about_ca_topic_score_gemma":0.029684551,"teacher_disagreement_score":0.015234709,"about_ca_system_score_codex":0.00071811327,"about_ca_system_score_gemma":0.001127949,"threshold_uncertainty_score":0.030292094},"labels":[],"label_agreement":null},{"id":"W2345552462","doi":"10.4172/2155-6180.s7-010","title":"Spatial Disease Cluster Detection: An Application to Childhood Asthma in Manitoba, Canada","year":2012,"lang":"en","type":"article","venue":"Journal of Biometrics & Biostatistics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Cluster (spacecraft); Spatial epidemiology; Computer science; Asthma; Data science; Medicine; Epidemiology; Pathology","score_opus":0.010985922643857657,"score_gpt":0.26123041880439357,"score_spread":0.2502444961605359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2345552462","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":[0.9784868,0.0012505281,0.0052519315,0.001446166,0.000034003184,0.00044134076,0.009782342,0.0003414902,0.0029654487],"genre_scores_gemma":[0.97221136,0.00089822826,0.019094251,0.00013381374,0.0000122153915,0.00009848296,0.005721344,0.000030420972,0.0017999697],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994166,0.00013361953,0.000035607496,0.00010868403,0.00018038784,0.00012511996],"domain_scores_gemma":[0.9981996,0.00070151,0.00011880801,0.00008663711,0.00071499957,0.00017847498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011427957,0.00057188526,0.0005047671,0.002157959,0.0020207055,0.00088586775,0.0012824123,0.0005464559,0.0011931299],"category_scores_gemma":[0.0046571386,0.00023712445,0.00054611766,0.0064095096,0.00056469324,0.00022628436,0.0008818447,0.00041997447,0.0001269274],"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":[0.0004922771,0.000359633,0.7586054,0.00062736846,0.00040749583,0.0021181118,0.0023848342,0.10007627,0.0019212895,0.0028027284,0.017267736,0.11293691],"study_design_scores_gemma":[0.00020003905,0.00011169967,0.69359887,0.00012860037,0.0001530071,0.0003929894,0.007600262,0.2859169,0.0012351399,0.0013671401,0.009194914,0.00010043718],"about_ca_topic_score_codex":0.9948114,"about_ca_topic_score_gemma":0.9958891,"teacher_disagreement_score":0.021053556,"about_ca_system_score_codex":0.021053556,"about_ca_system_score_gemma":0.02547818,"threshold_uncertainty_score":0.1527549},"labels":[],"label_agreement":null},{"id":"W2346048685","doi":"10.2196/cancer.5212","title":"“Googling” for Cancer: An Infodemiological Assessment of Online Search Interests in Australia, Canada, New Zealand, the United Kingdom, and the United States","year":2016,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cancer; Geography; Breast cancer; Demography; Medicine; Cancer registry","score_opus":0.10160494661210312,"score_gpt":0.4403358437134793,"score_spread":0.33873089710137616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2346048685","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":[0.9875975,0.0009601382,0.0000935661,0.00035186223,0.0000072215407,0.000041553452,0.0067005735,0.000018191347,0.0042293393],"genre_scores_gemma":[0.99586356,0.0005789319,0.00025884734,0.00008036616,0.000007948219,0.000022082344,0.0026556079,0.00000867056,0.00052395026],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990884,0.00012301964,0.00014739102,0.00009103675,0.0003577613,0.000192304],"domain_scores_gemma":[0.99183875,0.0016722063,0.002933486,0.00016874928,0.0022569965,0.0011298853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012295224,0.0001853275,0.00030323752,0.008355755,0.00085578684,0.0016282572,0.0005211979,0.00028878424,0.001453065],"category_scores_gemma":[0.008233921,0.00013485781,0.00047102192,0.014296165,0.00072865875,0.0015996554,0.0015886268,0.0004054992,0.00019629567],"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":[0.000093751674,0.000016244821,0.985516,0.00023501494,0.00009149715,0.0001450341,0.004249705,0.000119403776,0.00015312241,0.00031119492,0.0019091856,0.0071598454],"study_design_scores_gemma":[0.0000016466203,0.000017020138,0.9925096,0.00004786444,0.00003090673,0.000100774116,0.005298894,0.00040591176,0.000061128994,0.00003516274,0.0014813747,0.000009679792],"about_ca_topic_score_codex":0.71397316,"about_ca_topic_score_gemma":0.7867168,"teacher_disagreement_score":0.28602684,"about_ca_system_score_codex":0.003913842,"about_ca_system_score_gemma":0.0046031596,"threshold_uncertainty_score":0.5754226},"labels":[],"label_agreement":null},{"id":"W2346350382","doi":"10.1016/j.healthplace.2016.04.004","title":"Re-creating daily mobility histories for health research from raw GPS tracks: Validation of a kernel-based algorithm using real-life data","year":2016,"lang":"en","type":"article","venue":"Health & Place","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Centre Hospitalier de l’Université de Montréal","funders":"","keywords":"Global Positioning System; Computer science; Kernel (algebra); TRIPS architecture; Raw data; Kernel density estimation; Algorithm; Mathematics; Statistics; Telecommunications","score_opus":0.23210906136032303,"score_gpt":0.4669632635970006,"score_spread":0.23485420223667755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2346350382","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":[0.5338343,0.0005941268,0.45635027,0.00046288167,0.00024397498,0.00022539705,0.0031850517,0.004407267,0.00069681625],"genre_scores_gemma":[0.757795,0.0001591661,0.23526148,0.00005376276,0.000033438137,0.00010889561,0.005765879,0.00020577047,0.0006166183],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974003,0.0009652251,0.00029974626,0.0008244033,0.0003661262,0.00014424771],"domain_scores_gemma":[0.9849979,0.007810342,0.00081009604,0.0035821702,0.002442234,0.0003572011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007396181,0.000672866,0.0009524435,0.0020885088,0.0004918953,0.0017380698,0.001971161,0.0012975644,0.0007844113],"category_scores_gemma":[0.036290355,0.00041793723,0.0009483267,0.0017775851,0.00041009038,0.0018556559,0.0016384474,0.0010675113,0.00095197],"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":[0.0014189573,0.0009767303,0.26740536,0.0005730465,0.0012977093,0.00026843415,0.0011334096,0.24003425,0.009184046,0.0030973624,0.00737943,0.46723133],"study_design_scores_gemma":[0.000049450333,0.00009933108,0.028430507,0.00003563653,0.000081239465,0.0001765374,0.00021982023,0.9638028,0.003167924,0.0017476345,0.0021562786,0.000032803495],"about_ca_topic_score_codex":0.020255744,"about_ca_topic_score_gemma":0.016373647,"teacher_disagreement_score":0.020255744,"about_ca_system_score_codex":0.0005871475,"about_ca_system_score_gemma":0.0017477799,"threshold_uncertainty_score":0.040275693},"labels":[],"label_agreement":null},{"id":"W2357519942","doi":"","title":"A Two-Layer Sample Rotation System for Quarterly Surveys","year":2009,"lang":"en","type":"article","venue":"Tongji yu xinxi luntan","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Rotation (mathematics); Sampling (signal processing); Sample (material); Quarter (Canadian coin); Statistics; Mathematics; Computer science; Geography; Telecommunications; Physics; Artificial intelligence","score_opus":0.020759027789968805,"score_gpt":0.3098954779862994,"score_spread":0.2891364501963306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2357519942","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":[0.024280438,0.0003281223,0.9002813,0.00043307498,0.0009207903,0.04292968,0.0155317625,0.005955997,0.009338834],"genre_scores_gemma":[0.043967895,0.00026880892,0.8770218,0.00026861826,0.0002278108,0.05791037,0.011502896,0.0003988407,0.008432948],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9444427,0.03958992,0.006160213,0.004015998,0.0045668017,0.0012243699],"domain_scores_gemma":[0.9436756,0.014300383,0.005959036,0.015909838,0.018657656,0.0014974556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.044176456,0.0014351406,0.0015262666,0.003537323,0.001573811,0.001633775,0.001835314,0.0009355413,0.026128076],"category_scores_gemma":[0.072805,0.0016345396,0.0013603411,0.0046136654,0.000936372,0.0015102179,0.0028538692,0.001977665,0.011527286],"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":[0.004976246,0.0010936378,0.044228334,0.0018706823,0.0004206908,0.00014789857,0.0021221659,0.01373216,0.00851238,0.022572631,0.123943426,0.77637976],"study_design_scores_gemma":[0.005256843,0.007807808,0.13972977,0.0008525674,0.0006684008,0.0006308777,0.0011955223,0.14281306,0.023880076,0.021394959,0.65495515,0.0008149566],"about_ca_topic_score_codex":0.006594406,"about_ca_topic_score_gemma":0.0065334593,"teacher_disagreement_score":0.044176456,"about_ca_system_score_codex":0.0021904337,"about_ca_system_score_gemma":0.007992969,"threshold_uncertainty_score":0.23363024},"labels":[],"label_agreement":null},{"id":"W2357803626","doi":"","title":"Consideration on Risk Communication Method in the Drug Risk Management","year":2010,"lang":"en","type":"article","venue":"Zhongguo yaofang","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Risk management; Risk communication; Risk analysis (engineering); China; Risk assessment; IT risk management; Business; Computer science; Computer security; Political science; Finance","score_opus":0.009234969170051385,"score_gpt":0.30856973099498336,"score_spread":0.29933476182493196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2357803626","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":[0.6283288,0.013585362,0.13817245,0.09182864,0.0013235911,0.0014090494,0.00020062596,0.00031364599,0.124837816],"genre_scores_gemma":[0.96173275,0.0025022165,0.02930386,0.0011947965,0.00023126409,0.00028753022,0.00006234095,0.000027732043,0.004657519],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9739543,0.018208956,0.0014198883,0.0008035935,0.0048163263,0.0007969939],"domain_scores_gemma":[0.9666668,0.019685099,0.003708099,0.0015268917,0.007130037,0.0012830206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017341103,0.0004530259,0.0002955753,0.0026208127,0.0035182943,0.005484305,0.0009244534,0.0012321614,0.0028266609],"category_scores_gemma":[0.03044672,0.00026658358,0.0003834195,0.0019429456,0.002844665,0.008105921,0.0020528184,0.0014436371,0.00025434233],"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":[0.0003808872,0.00041744843,0.11190114,0.0021620444,0.000099912446,0.0020454195,0.10722575,0.002600274,0.006274235,0.15583555,0.011052027,0.60000527],"study_design_scores_gemma":[0.0002106822,0.0012913109,0.18561547,0.005141091,0.0003368701,0.004041738,0.26059017,0.023348732,0.017660018,0.12836605,0.37290388,0.00049391104],"about_ca_topic_score_codex":0.0051700454,"about_ca_topic_score_gemma":0.0037346715,"teacher_disagreement_score":0.017341103,"about_ca_system_score_codex":0.005070988,"about_ca_system_score_gemma":0.008416578,"threshold_uncertainty_score":0.091709614},"labels":[],"label_agreement":null},{"id":"W2364137152","doi":"","title":"Use of syndromic surveillance to early detect bioterrorism-related diseases in foreign military facilities and some revelations","year":2012,"lang":"en","type":"article","venue":"Junshi yixue","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Warning system; Medical emergency; Environmental health; China; Health surveillance; Medicine; Computer security; Geography; Computer science; Telecommunications","score_opus":0.028739433002015542,"score_gpt":0.2605378581439037,"score_spread":0.23179842514188814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2364137152","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":[0.78722167,0.055449925,0.11214468,0.023303077,0.0008914482,0.00040176124,0.0023241502,0.000732103,0.01753118],"genre_scores_gemma":[0.9434263,0.016619042,0.03687893,0.0009809957,0.00029151473,0.000082086975,0.0005791804,0.000014503978,0.0011274385],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991103,0.00038778887,0.0001150629,0.00011954544,0.00021384489,0.000053493455],"domain_scores_gemma":[0.9986009,0.00026677342,0.0002356073,0.00009120326,0.0006953293,0.0001102453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018582366,0.0003690861,0.00022181025,0.0023137433,0.0002528252,0.000980061,0.00035633516,0.00028585977,0.00024552862],"category_scores_gemma":[0.0024453867,0.00014606898,0.00044127228,0.001401224,0.0003646097,0.00091309607,0.0004921422,0.0004063865,0.00004350243],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015660225,0.00013858704,0.41040435,0.0009546032,0.00018598908,0.0005239393,0.0013419264,0.004554083,0.01694453,0.009386713,0.0050340933,0.55037457],"study_design_scores_gemma":[0.00009810361,0.0013012596,0.6964953,0.0018787284,0.00080522586,0.0027406428,0.006088877,0.102023475,0.054285914,0.01100649,0.12297376,0.00030217489],"about_ca_topic_score_codex":0.0070121107,"about_ca_topic_score_gemma":0.009766158,"teacher_disagreement_score":0.0070121107,"about_ca_system_score_codex":0.0012164621,"about_ca_system_score_gemma":0.001491217,"threshold_uncertainty_score":0.01394254},"labels":[],"label_agreement":null},{"id":"W2368384259","doi":"","title":"Bibliometrical analysis on syndromic surveillance research","year":2013,"lang":"en","type":"article","venue":"Journal of Zhengzhou University","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"China; Standardization; Web of science; Citation index; Science Citation Index; Data science; Citation; Library science; Computer science; Medicine; Political science; Pathology; Meta-analysis","score_opus":0.0499838574173304,"score_gpt":0.33535027542586143,"score_spread":0.285366418008531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2368384259","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74181783,0.070551746,0.019724678,0.0058540413,0.0005855401,0.0015465786,0.08095336,0.0014574876,0.07750881],"genre_scores_gemma":[0.94850266,0.016215634,0.010776264,0.00014324649,0.00032846053,0.0006813634,0.021271257,0.00006920741,0.002011847],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9826058,0.003061911,0.003538514,0.0013392543,0.008808222,0.0006461725],"domain_scores_gemma":[0.94404924,0.030174838,0.009815369,0.0019558938,0.013044238,0.00096052565],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.007999085,0.0007852015,0.0015835599,0.19123106,0.0019374985,0.0051334337,0.00091831083,0.00060250337,0.0044465596],"category_scores_gemma":[0.045989633,0.0002381556,0.0016143988,0.24809729,0.00097536197,0.0046864646,0.0021811654,0.00048173856,0.00060619443],"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":[0.0002396594,0.00016419316,0.62354475,0.01648969,0.0014827576,0.0008257147,0.004862429,0.0038389212,0.0027423135,0.017915962,0.021989875,0.3059038],"study_design_scores_gemma":[0.000047588284,0.00017667675,0.88498396,0.0026522637,0.0012507975,0.001382809,0.010965194,0.013986969,0.0032163386,0.011737449,0.06943437,0.00016565745],"about_ca_topic_score_codex":0.008285758,"about_ca_topic_score_gemma":0.006835087,"teacher_disagreement_score":0.99200094,"about_ca_system_score_codex":0.003805776,"about_ca_system_score_gemma":0.0056502796,"threshold_uncertainty_score":0.04230368},"labels":[],"label_agreement":null},{"id":"W2370744073","doi":"","title":"Papers on H1N1 influenza covered in PubMed in 2005-2009","year":2011,"lang":"en","type":"article","venue":"Zhonghua yixue tushu qingbao zazhi","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"China; H1N1 influenza; Epidemiology; Medicine; Bibliometrics; Coronavirus disease 2019 (COVID-19); Environmental health; Library science; Family medicine; Geography; Computer science; Pathology; Disease; Infectious disease (medical specialty)","score_opus":0.043271175654222525,"score_gpt":0.26296009801138626,"score_spread":0.21968892235716372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2370744073","genre_codex":"review","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":[0.110154346,0.8123603,0.0007838343,0.0026791503,0.0019122799,0.00084084,0.06243,0.000149493,0.008689773],"genre_scores_gemma":[0.36355537,0.5797437,0.0077830073,0.001874209,0.0016627794,0.0015112492,0.040104035,0.000049963102,0.003715711],"study_design_codex":"systematic_review","study_design_gemma":"observational","domain_scores_codex":[0.99063534,0.0011269479,0.005425407,0.00058835116,0.0018565139,0.00036736563],"domain_scores_gemma":[0.9689071,0.013959707,0.010612134,0.0006063123,0.0050157383,0.00089897955],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0056453547,0.0010058082,0.0026286582,0.072373845,0.0008070262,0.002512669,0.00080830575,0.0007901536,0.0056965654],"category_scores_gemma":[0.03705615,0.0003801644,0.0022674224,0.08537784,0.00042139992,0.0019037821,0.0012725712,0.00029430992,0.00065602135],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020852431,0.00012754842,0.1341554,0.47000068,0.008261116,0.0022868174,0.0036891967,0.00041322265,0.007152424,0.0014556553,0.035545953,0.33482677],"study_design_scores_gemma":[0.00056862435,0.00062617107,0.6429916,0.13935013,0.0237269,0.0051510497,0.004532963,0.00040933883,0.00415238,0.0013879236,0.17696817,0.00013472533],"about_ca_topic_score_codex":0.006332665,"about_ca_topic_score_gemma":0.012630949,"teacher_disagreement_score":0.92762613,"about_ca_system_score_codex":0.0035005219,"about_ca_system_score_gemma":0.008158452,"threshold_uncertainty_score":0.029855847},"labels":[],"label_agreement":null},{"id":"W2390859812","doi":"","title":"ARIMA Model Prediction and Analysis of H1N1 Epidemic Situation in Canada in 2009","year":2011,"lang":"en","type":"article","venue":"Journal of Jishou University","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Autoregressive integrated moving average; Outbreak; Pandemic; Epidemic model; Statistics; Coronavirus disease 2019 (COVID-19); Mean absolute error; Demography; Geography; Econometrics; Time series; Operations research; Mathematics; Mean squared error; Virology; Medicine; Disease; Infectious disease (medical specialty); Sociology","score_opus":0.023050869261188874,"score_gpt":0.21057894093553303,"score_spread":0.18752807167434415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2390859812","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":[0.9840057,0.00042406778,0.007366442,0.0007286844,0.00005223881,0.000046299163,0.0041988697,0.00026049456,0.0029172155],"genre_scores_gemma":[0.9925672,0.00015163966,0.0034552526,0.000036715104,0.000008037665,0.000013844914,0.0019350823,0.000013253058,0.0018189255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997651,0.00003977761,0.000009945208,0.0000543961,0.00006241565,0.00006834111],"domain_scores_gemma":[0.9993451,0.0001793774,0.00004098449,0.000021873575,0.00035533024,0.00005720941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006679953,0.00045441248,0.0004297163,0.00054063066,0.000694236,0.00069149537,0.00092036714,0.00033192037,0.0009740276],"category_scores_gemma":[0.002262812,0.00017634146,0.00039287124,0.0006795222,0.00025822077,0.00023744366,0.00031181244,0.00064159464,0.00018908804],"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":[0.00038303385,0.00017159546,0.16263086,0.00008043368,0.00020771367,0.00020785259,0.0001639061,0.7957414,0.0014541898,0.0032658712,0.007157837,0.028535228],"study_design_scores_gemma":[0.000017275754,0.000016856615,0.024528842,0.000007237118,0.00002750902,0.000011024536,0.00006019328,0.9738874,0.00037460733,0.0002591834,0.00079559465,0.0000141890205],"about_ca_topic_score_codex":0.9768149,"about_ca_topic_score_gemma":0.96333694,"teacher_disagreement_score":0.023185074,"about_ca_system_score_codex":0.0081857145,"about_ca_system_score_gemma":0.012157898,"threshold_uncertainty_score":0.059391797},"labels":[],"label_agreement":null},{"id":"W2402566201","doi":"10.1017/s1481803500009878","title":"Revision of the Canadian Emergency Department Information System (CEDIS) Presenting Complaint List Version 1.1","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Emergency Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":104,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Jewish General Hospital; Children's Hospital of Western Ontario; University of Alberta Hospital; Canadian Association of Emergency Physicians; McGill University; University of British Columbia; St. Paul's Hospital","funders":"","keywords":"Complaint; Emergency department; Computer science; Medical emergency; Information retrieval; Medicine; Political science; Nursing","score_opus":0.05290382443326339,"score_gpt":0.2968109132575537,"score_spread":0.24390708882429032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2402566201","genre_codex":"dataset","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":[0.01263079,0.006469064,0.01826866,0.05308616,0.10100646,0.0049206438,0.68350863,0.007822225,0.112287335],"genre_scores_gemma":[0.060751136,0.009473498,0.07183595,0.022004493,0.006877648,0.0051710405,0.61735547,0.0028474333,0.20368336],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98316133,0.0012680914,0.0026240882,0.00057420257,0.011384331,0.0009880379],"domain_scores_gemma":[0.7882997,0.010083,0.0032284192,0.0052950783,0.19012421,0.0029695805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012260033,0.0011843503,0.0012516184,0.015732111,0.003142219,0.0058689956,0.004187683,0.0020495162,0.042777997],"category_scores_gemma":[0.093004815,0.00096852623,0.0018614313,0.012233885,0.0008092645,0.001742068,0.0017588097,0.0037081828,0.02543031],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057250007,0.00003413706,0.0021530427,0.0002473475,0.000016023327,0.00006759809,0.000111069174,0.00022181329,0.00017703447,0.0011485453,0.9613274,0.034438808],"study_design_scores_gemma":[0.00006479601,0.000042629756,0.026670102,0.00079069217,0.00006438822,0.00020131562,0.00025170358,0.0007025104,0.0008236903,0.000571305,0.9696842,0.00013283112],"about_ca_topic_score_codex":0.73967093,"about_ca_topic_score_gemma":0.72216874,"teacher_disagreement_score":0.9787551,"about_ca_system_score_codex":0.021244884,"about_ca_system_score_gemma":0.06421572,"threshold_uncertainty_score":0.5237244},"labels":[],"label_agreement":null},{"id":"W2404564893","doi":"","title":"Geozones: an area-based method for analysis of health outcomes.","year":2012,"lang":"en","type":"article","venue":"PubMed","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Census; Geography; Population; Rural area; Strengths and weaknesses; Socioeconomics; Regional science; Demography; Environmental health; Medicine; Psychology; Sociology","score_opus":0.10314558304521201,"score_gpt":0.3835431614707818,"score_spread":0.2803975784255698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2404564893","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":[0.0264095,0.0026463666,0.65234274,0.0013244364,0.00093791744,0.004085726,0.2771258,0.027021972,0.008105503],"genre_scores_gemma":[0.123688124,0.0008992747,0.7565399,0.00028225282,0.00033851413,0.014659235,0.09847909,0.0024286655,0.0026849522],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9909869,0.0049567604,0.0012745246,0.0011319101,0.0014957889,0.00015403515],"domain_scores_gemma":[0.9682901,0.023031056,0.0030327067,0.003528192,0.0016604856,0.00045749225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013098691,0.0010808927,0.001716211,0.011462609,0.00045176473,0.0019660438,0.0018080084,0.0007018379,0.017734123],"category_scores_gemma":[0.06156196,0.0007917876,0.0026568878,0.010548204,0.0006800866,0.001852659,0.0030277153,0.0015229187,0.0035324101],"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":[0.0017108538,0.00020808436,0.097850114,0.004822952,0.0043034125,0.00035234442,0.002176528,0.009014256,0.0012135543,0.03854151,0.2460214,0.5937849],"study_design_scores_gemma":[0.0012005179,0.000577553,0.14501594,0.0016547779,0.0015149774,0.0013105348,0.0022332422,0.11502975,0.0031859118,0.12598936,0.6019625,0.00032491668],"about_ca_topic_score_codex":0.005143665,"about_ca_topic_score_gemma":0.0055167796,"teacher_disagreement_score":0.017734123,"about_ca_system_score_codex":0.00076829334,"about_ca_system_score_gemma":0.0024973394,"threshold_uncertainty_score":0.06927329},"labels":[],"label_agreement":null},{"id":"W2405162660","doi":"10.1016/s0140-6736(16)30602-x","title":"Mental health surveillance after the terrorist attacks in Paris","year":2016,"lang":"en","type":"letter","venue":"The Lancet","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"Defence Science and Technology Group; Defence Science and Technology Laboratory; Engineering and Physical Sciences Research Council; Deutsche Forschungsgemeinschaft","keywords":"Scopus; Mental health; Social media; Public health; Terrorism; Population; Psychology; Medicine; Geography; MEDLINE; Psychiatry; Environmental health; Political science; Computer science; World Wide Web","score_opus":0.023940817753797236,"score_gpt":0.3071673477925493,"score_spread":0.28322653003875203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2405162660","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001538987,0.0013371467,0.00010912386,0.9852722,0.008951426,0.000020075899,0.00031544283,0.000031231193,0.0024243933],"genre_scores_gemma":[0.018069936,0.001747237,0.00034540275,0.9576934,0.015550462,0.00007527651,0.00020690256,0.000038089172,0.0062732557],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99081486,0.003614919,0.0011152956,0.00078256545,0.0019087192,0.0017635796],"domain_scores_gemma":[0.9780934,0.009576118,0.003256043,0.00074015546,0.003721434,0.004612958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009576746,0.00084129174,0.0009978128,0.0010053014,0.0032240883,0.0041245245,0.0021787528,0.03188544,0.002808637],"category_scores_gemma":[0.03948893,0.0007993733,0.0013129709,0.0009897179,0.0025299762,0.0029806192,0.0028777309,0.025809478,0.0017021487],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001054735,0.000039387953,0.0066556423,0.0002216569,0.00008146552,0.0012044332,0.0011609539,0.00027888094,0.000377494,0.0026857,0.9667705,0.020418199],"study_design_scores_gemma":[0.0003046681,0.00020336152,0.0313617,0.0027628022,0.000189546,0.00079171464,0.0049391575,0.0014439214,0.0010799898,0.007572504,0.9491752,0.00017553796],"about_ca_topic_score_codex":0.10884101,"about_ca_topic_score_gemma":0.13423324,"teacher_disagreement_score":0.10884101,"about_ca_system_score_codex":0.008926769,"about_ca_system_score_gemma":0.014539602,"threshold_uncertainty_score":0.21641493},"labels":[],"label_agreement":null},{"id":"W2405355880","doi":"10.1145/2897683.2897693","title":"Mining Twitter data for influenza detection and surveillance","year":2016,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Popularity; Social media; Computer science; Data science; Internet privacy; Sentiment analysis; Health care; Microblogging; World Wide Web; Artificial intelligence; Political science","score_opus":0.06218218636085783,"score_gpt":0.33162234644775423,"score_spread":0.2694401600868964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2405355880","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":[0.68062377,0.002621838,0.25601524,0.0051301015,0.00057970645,0.0014652865,0.04048682,0.0032333923,0.009843869],"genre_scores_gemma":[0.82025594,0.0011354084,0.14764446,0.00023853019,0.00033924423,0.0005099356,0.027854117,0.00006791999,0.0019544847],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990038,0.0002735596,0.00017137821,0.00020479992,0.0002471947,0.00009922186],"domain_scores_gemma":[0.99746513,0.0011052772,0.00047875004,0.00028884414,0.00053911546,0.00012287154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014176449,0.0007810623,0.0007568399,0.004755816,0.0006790788,0.0012164493,0.0008275656,0.0010275694,0.0007960879],"category_scores_gemma":[0.0060678744,0.00038485174,0.0010320497,0.0028648353,0.00027207652,0.0016379912,0.00096589344,0.0006983086,0.0010235758],"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":[0.0012391241,0.0011064723,0.3512759,0.0014217437,0.0006565411,0.0015372018,0.001109124,0.08450402,0.043037806,0.0055142,0.036472186,0.47212577],"study_design_scores_gemma":[0.000058447535,0.0003376763,0.06272725,0.00013817094,0.00017186011,0.00044837504,0.0019958408,0.88374954,0.023558566,0.007709199,0.019028852,0.00007631936],"about_ca_topic_score_codex":0.0043338467,"about_ca_topic_score_gemma":0.006039304,"teacher_disagreement_score":0.004755816,"about_ca_system_score_codex":0.0005790852,"about_ca_system_score_gemma":0.00073365954,"threshold_uncertainty_score":0.008617282},"labels":[],"label_agreement":null},{"id":"W2406390257","doi":"","title":"Is the year of first publication a good proxy of scholars’ academic age?","year":2015,"lang":"en","type":"article","venue":"ISSI","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Proxy (statistics); Bibliometrics; Demography; Library science; Sociology; Statistics; Computer science","score_opus":0.052212513415970496,"score_gpt":0.33115455055842813,"score_spread":0.27894203714245763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2406390257","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66293377,0.09823142,0.040731117,0.02140967,0.0071635423,0.000653282,0.052881815,0.0007544305,0.11524099],"genre_scores_gemma":[0.94592077,0.0146120675,0.015222124,0.0009618678,0.0026640643,0.0003296767,0.00863384,0.0001648051,0.011490804],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9894192,0.0038489916,0.0018955185,0.0012735409,0.002881739,0.0006809629],"domain_scores_gemma":[0.7851704,0.09573833,0.0695655,0.013885141,0.026234565,0.009406062],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.021928385,0.0003486081,0.0011691076,0.011415379,0.0011736203,0.006354062,0.0017627111,0.0010000898,0.011159168],"category_scores_gemma":[0.11663342,0.00027608208,0.00083366834,0.026145006,0.0012356295,0.00633008,0.0016385175,0.0010033117,0.00438479],"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":[0.000469562,0.00012540637,0.815317,0.002482137,0.00049382896,0.0001944148,0.0038832247,0.00065872876,0.00082897674,0.012105066,0.022695826,0.14074588],"study_design_scores_gemma":[0.000035820238,0.00030127788,0.88751215,0.0009874909,0.00023045766,0.0005231182,0.0028814466,0.0010742352,0.0010050093,0.0071436423,0.09819388,0.00011144874],"about_ca_topic_score_codex":0.011727887,"about_ca_topic_score_gemma":0.016254792,"teacher_disagreement_score":0.98858464,"about_ca_system_score_codex":0.002459669,"about_ca_system_score_gemma":0.004493119,"threshold_uncertainty_score":0.11596978},"labels":[],"label_agreement":null},{"id":"W2409922180","doi":"10.5539/ijsp.v5n4p31","title":"Collapsed Double Symmetry Model and Its Decomposition for Square Contingency Tables","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Contingency table; Symmetry (geometry); Square (algebra); Mathematics; Table (database); Decomposition; Statistics; Computer science; Geometry; Data mining","score_opus":0.024410188522696195,"score_gpt":0.32747129179864637,"score_spread":0.30306110327595015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2409922180","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":[0.009844569,0.00016290584,0.9879148,0.00021881858,0.000049099253,0.000088404544,0.0003968084,0.00017335381,0.00115122],"genre_scores_gemma":[0.47566724,0.00061680545,0.5129613,0.00034144754,0.0003012514,0.001114187,0.002951103,0.00027652108,0.005770217],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9899052,0.005387471,0.000495722,0.0015257245,0.002065544,0.00062025565],"domain_scores_gemma":[0.9783246,0.012496389,0.0020722812,0.00371759,0.002659066,0.00073002314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011413453,0.0014254106,0.0013615219,0.002735961,0.0007628596,0.0025213729,0.0027869071,0.0009904843,0.008989192],"category_scores_gemma":[0.035223123,0.00085618254,0.003388928,0.0030495075,0.0020357177,0.0043570027,0.0026261064,0.0036547417,0.0013809581],"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":[0.00035612372,0.00015213722,0.0137957465,0.00023979231,0.00040766655,0.0007621238,0.0007999574,0.09688283,0.0017829449,0.7976665,0.007989919,0.07916425],"study_design_scores_gemma":[0.000041746545,0.000081416336,0.0018870132,0.00004222068,0.000059411377,0.00028306304,0.00010929801,0.5026326,0.00046417577,0.49107388,0.0032722852,0.000052895724],"about_ca_topic_score_codex":0.0040082787,"about_ca_topic_score_gemma":0.0026079824,"teacher_disagreement_score":0.011413453,"about_ca_system_score_codex":0.0016319636,"about_ca_system_score_gemma":0.0020226787,"threshold_uncertainty_score":0.06036085},"labels":[],"label_agreement":null},{"id":"W2411983070","doi":"10.1186/s12889-016-3144-4","title":"Estimating micro area behavioural risk factor prevalence from large population-based surveys: a full Bayesian approach","year":2016,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Lambton College; Public Health Ontario; Cancer Care Ontario","funders":"Cancer Research Society","keywords":"Biostatistics; Medicine; Bayesian probability; Epidemiology; Public health; Environmental health; Behavioral Risk Factor Surveillance System; Population; Statistics; Demography; Internal medicine; Pathology","score_opus":0.06068150910610407,"score_gpt":0.31645414024983864,"score_spread":0.25577263114373455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2411983070","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":[0.028051928,0.00051190675,0.9694211,0.0002745379,0.000010787533,0.00020383955,0.00069412467,0.00022154697,0.00061038625],"genre_scores_gemma":[0.42978913,0.0013480191,0.5628598,0.00028723164,0.00012299344,0.0010993239,0.0028005918,0.00011012797,0.0015828226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9910842,0.00647789,0.0004061894,0.0010490214,0.0008063214,0.00017630079],"domain_scores_gemma":[0.97053134,0.02444729,0.001579545,0.0019515738,0.0012540523,0.00023621981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01996608,0.0009955311,0.0019497344,0.0035956674,0.00097612053,0.0020713403,0.003124792,0.0014665875,0.0017558092],"category_scores_gemma":[0.057264462,0.0018863892,0.0027670865,0.0025494208,0.0013250754,0.0019753492,0.0028676698,0.00166256,0.00050314225],"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":[0.0002503402,0.00014556068,0.06125369,0.00069333205,0.0017114063,0.0003275319,0.00078194356,0.7600744,0.00093295454,0.024390621,0.0019050975,0.14753315],"study_design_scores_gemma":[0.00004543048,0.00007202113,0.014713497,0.00013973808,0.00020572064,0.00015084786,0.00012377158,0.92375,0.00016548729,0.058997445,0.0015802246,0.00005587352],"about_ca_topic_score_codex":0.050560564,"about_ca_topic_score_gemma":0.0473418,"teacher_disagreement_score":0.050560564,"about_ca_system_score_codex":0.0016229685,"about_ca_system_score_gemma":0.002408239,"threshold_uncertainty_score":0.10559201},"labels":[],"label_agreement":null},{"id":"W2414792569","doi":"10.2196/publichealth.5814","title":"Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak","year":2016,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":135,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"U.S. National Library of Medicine; University of California, San Diego; National Institutes of Health; United States Agency for International Development","keywords":"Zika virus; Outbreak; Transmission (telecommunications); Disease surveillance; Disease; Exponential smoothing; Incidence (geometry); Estimation; Basic reproduction number; Medicine; Demography; Statistics; Virology; Population; Environmental health; Computer science; Virus; Mathematics; Telecommunications; Internal medicine","score_opus":0.02861590574009464,"score_gpt":0.3141996389414115,"score_spread":0.28558373320131686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2414792569","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":[0.92230636,0.0014017308,0.03562352,0.0006360181,0.000082700695,0.00026595534,0.0336454,0.00081927195,0.0052190875],"genre_scores_gemma":[0.9500387,0.00035458,0.028155962,0.000058463007,0.00003743802,0.00015481429,0.020740971,0.00003244468,0.00042653913],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99873835,0.00046328208,0.00013565453,0.00031112068,0.0002643168,0.000087304266],"domain_scores_gemma":[0.99490947,0.002034703,0.0014007855,0.00057440577,0.00093345717,0.00014724798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018872846,0.0006427385,0.00031301007,0.003300491,0.0002861125,0.0014936457,0.0006116579,0.00037636585,0.0010749846],"category_scores_gemma":[0.011979736,0.00028721915,0.00047021176,0.0027162316,0.00024050227,0.0010958074,0.0009341234,0.00049593573,0.0002856015],"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":[0.00018445987,0.00015264838,0.8550315,0.0006221313,0.00039818374,0.00022494014,0.00060776656,0.053846695,0.0013241994,0.0014404119,0.003932362,0.08223464],"study_design_scores_gemma":[0.000045155863,0.00010537028,0.648675,0.0002612069,0.0001557906,0.00017571941,0.0011972891,0.3357707,0.0017773964,0.0015263427,0.0102006765,0.00010943827],"about_ca_topic_score_codex":0.100344166,"about_ca_topic_score_gemma":0.12606087,"teacher_disagreement_score":0.100344166,"about_ca_system_score_codex":0.0010896454,"about_ca_system_score_gemma":0.0009048911,"threshold_uncertainty_score":0.19952023},"labels":[],"label_agreement":null},{"id":"W2416117239","doi":"10.1371/journal.pone.0155971","title":"Assessing Commitment and Reporting Fidelity to a Text Message-Based Participatory Surveillance in Rural Western Uganda","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fogarty International Center; Economic and Social Research Council; Centers for Disease Control and Prevention; U.S. Department of Health and Human Services; National Institutes of Health; National Science Foundation","keywords":"Citizen journalism; Outbreak; Disease surveillance; Medicine; Data collection; Participatory sensing; Disease; Environmental health; Computer science; Data science; World Wide Web; Pathology","score_opus":0.1388278209388615,"score_gpt":0.3516968733448679,"score_spread":0.21286905240600637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2416117239","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9978877,0.000051881667,0.0011197878,0.00016716172,0.0000066675625,0.00017625296,0.000045839453,0.0000060174857,0.0005386206],"genre_scores_gemma":[0.9969398,0.00006646086,0.0021499903,0.00008353918,0.000005208184,0.00050385186,0.00004568851,0.000003980045,0.00020151028],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9726404,0.02189478,0.0015942575,0.0011809,0.0015655905,0.0011240514],"domain_scores_gemma":[0.9258487,0.044668544,0.016082078,0.0058091655,0.005721072,0.0018703074],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.035256438,0.0003648277,0.00038194275,0.0009174065,0.0016109747,0.0019857425,0.001193023,0.00086841866,0.001187592],"category_scores_gemma":[0.13469514,0.00064609986,0.00039527947,0.0007857947,0.0016788418,0.0015987449,0.0038946911,0.0009144659,0.00024067338],"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":[0.0010557423,0.0009375162,0.64942586,0.00048213263,0.0001656688,0.0006104492,0.26634595,0.0010128221,0.0016496808,0.0006541508,0.0006833568,0.0769766],"study_design_scores_gemma":[0.0001481901,0.0070789503,0.7051989,0.00088940444,0.00016028612,0.0010189879,0.2641635,0.007027959,0.0028662833,0.001984874,0.009295211,0.00016743463],"about_ca_topic_score_codex":0.0053297374,"about_ca_topic_score_gemma":0.0052772877,"teacher_disagreement_score":0.96474355,"about_ca_system_score_codex":0.00093925005,"about_ca_system_score_gemma":0.001422147,"threshold_uncertainty_score":0.18645608},"labels":[],"label_agreement":null},{"id":"W2417168758","doi":"10.1038/srep40841","title":"Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks","year":2017,"lang":"en","type":"preprint","venue":"Scientific Reports","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Institute of Environmental Health Sciences; Intelligence Advanced Research Projects Activity; National Institutes of Health","keywords":"Outbreak; Infectious disease (medical specialty); Disease; Dengue fever; Disease surveillance; China; Geography; Incidence (geometry); Demography; Medicine; Environmental health; Pathology; Sociology","score_opus":0.0714903096583521,"score_gpt":0.3560032230490909,"score_spread":0.2845129133907388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2417168758","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":[0.5329754,0.003559706,0.4467725,0.001393616,0.0002828481,0.0003246552,0.008916273,0.0016696111,0.0041053887],"genre_scores_gemma":[0.9242812,0.00092603103,0.064001344,0.00011292832,0.00037968444,0.0003041589,0.008719033,0.000110340836,0.0011653432],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807644,0.00089508836,0.00021749487,0.000516417,0.00019740041,0.00009715235],"domain_scores_gemma":[0.9745222,0.020814668,0.0025467302,0.0008293301,0.0010111276,0.0002759325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007392324,0.0010300099,0.00079122314,0.0056763366,0.00040742333,0.0015288229,0.00078554056,0.0009744592,0.0013681744],"category_scores_gemma":[0.026257874,0.0004086496,0.001434775,0.004248283,0.00034868985,0.0020250566,0.0007764968,0.0011693165,0.00054050825],"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":[0.0011080923,0.0006624165,0.5051947,0.00078945066,0.0025275599,0.0004219099,0.0015297376,0.18971473,0.0056173117,0.008783887,0.0101005575,0.27354962],"study_design_scores_gemma":[0.000037980037,0.00018139354,0.075162716,0.00007196054,0.00031786985,0.00018169756,0.00036594554,0.91024977,0.0016571931,0.008360618,0.0033692492,0.00004357694],"about_ca_topic_score_codex":0.0058695856,"about_ca_topic_score_gemma":0.0048374445,"teacher_disagreement_score":0.007392324,"about_ca_system_score_codex":0.0006125701,"about_ca_system_score_gemma":0.00068782823,"threshold_uncertainty_score":0.039094806},"labels":[],"label_agreement":null},{"id":"W2418408586","doi":"","title":"Media scanning and verification system as a supplemental tool to disease outbreak detection & reporting at National Centre for Disease Control, Delhi.","year":2012,"lang":"en","type":"article","venue":"PubMed","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Disease surveillance; Outbreak; Communicable disease; Public health; The Internet; Public health surveillance; Environmental health; Social media; Medicine; Medical emergency; Business; Computer science; Pathology; World Wide Web","score_opus":0.024126668422826136,"score_gpt":0.26845559666069885,"score_spread":0.24432892823787272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2418408586","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":[0.29839864,0.0065743863,0.11245733,0.022042282,0.008246757,0.018431114,0.16307609,0.16240357,0.2083698],"genre_scores_gemma":[0.5588137,0.0033695102,0.14626287,0.0048719025,0.001983362,0.005181417,0.109145656,0.0028123928,0.16755924],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.997288,0.0009058176,0.0003577595,0.00044089457,0.0007456049,0.0002619257],"domain_scores_gemma":[0.9883008,0.0030824635,0.0013146563,0.0015547967,0.004499545,0.0012476449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005241143,0.0008419397,0.0006768287,0.005103633,0.0007357365,0.0016407673,0.0016094773,0.00079386117,0.031632137],"category_scores_gemma":[0.0064258887,0.0006533699,0.000450582,0.002156331,0.00051583303,0.0012920118,0.0017836153,0.0011442963,0.013386701],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018826749,0.00062296237,0.10202243,0.0012141183,0.00013883649,0.0014605876,0.0031924064,0.0011496714,0.02863184,0.001826445,0.4392116,0.41864645],"study_design_scores_gemma":[0.00060226925,0.0018896711,0.21079506,0.00074794516,0.0004047329,0.0016087139,0.0034735696,0.019245954,0.041675333,0.0015557017,0.71765476,0.00034636076],"about_ca_topic_score_codex":0.01398051,"about_ca_topic_score_gemma":0.007634334,"teacher_disagreement_score":0.031632137,"about_ca_system_score_codex":0.0011740666,"about_ca_system_score_gemma":0.0028274474,"threshold_uncertainty_score":0.10582},"labels":[],"label_agreement":null},{"id":"W2440482312","doi":"10.1186/s13104-016-2098-z","title":"SCM: a practical tool to implement hospital-based syndromic surveillance","year":2016,"lang":"en","type":"article","venue":"BMC Research Notes","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Medicine; Outbreak; Medical emergency; Pediatrics; Emergency medicine; Warning system; Disease surveillance; Disease; Computer science; Internal medicine; Pathology; Telecommunications","score_opus":0.15507113615848442,"score_gpt":0.46948148125176986,"score_spread":0.31441034509328547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2440482312","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":[0.05617082,0.00020367626,0.7050246,0.0016067714,0.00032314978,0.004549478,0.0055246945,0.21867,0.00792677],"genre_scores_gemma":[0.11188925,0.00010664986,0.8794928,0.00026634446,0.00009385697,0.0017847336,0.002796487,0.002054835,0.0015150408],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955461,0.0018118432,0.0006314809,0.00063280115,0.0012043125,0.00017341149],"domain_scores_gemma":[0.9719105,0.01662099,0.0024004446,0.0038934161,0.00386531,0.0013093858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012819862,0.0015737975,0.0007641187,0.0052638273,0.00064206927,0.001719279,0.0018388402,0.00096369523,0.011198859],"category_scores_gemma":[0.037947986,0.0008490074,0.0007615322,0.0021061322,0.0005197766,0.0026856025,0.003112279,0.0010103986,0.0036542937],"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":[0.0014115422,0.0010044642,0.046591494,0.0012152459,0.00022767148,0.0009431044,0.003561867,0.010132097,0.019498277,0.004347935,0.07596056,0.8351058],"study_design_scores_gemma":[0.0013323013,0.0033125572,0.06469604,0.0016222418,0.00034905726,0.0019897288,0.0028023757,0.67921424,0.048332453,0.013260678,0.1824653,0.00062306825],"about_ca_topic_score_codex":0.0015784018,"about_ca_topic_score_gemma":0.0013421298,"teacher_disagreement_score":0.012819862,"about_ca_system_score_codex":0.0006971895,"about_ca_system_score_gemma":0.0017455234,"threshold_uncertainty_score":0.067798674},"labels":[],"label_agreement":null},{"id":"W2460090577","doi":"10.17269/cjph.107.5228","title":"Data-driven approach of CUSUM algorithm in temporal aberrant event detection using interactive web applications","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto; Public Health Ontario","funders":"","keywords":"CUSUM; Computer science; Event (particle physics); Data mining; Temporal database; Artificial intelligence; Algorithm; Statistics; Mathematics","score_opus":0.08768734676207837,"score_gpt":0.34762348844759383,"score_spread":0.2599361416855155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2460090577","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":[0.03776393,0.00032119895,0.9444743,0.00025602538,0.000102214755,0.00018388043,0.0008719514,0.014688021,0.0013385346],"genre_scores_gemma":[0.39838177,0.00021051879,0.59706414,0.00014057316,0.00008794152,0.0003016128,0.0015898651,0.0005900335,0.001633606],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930716,0.00018431463,0.00007040262,0.00015930943,0.00022930212,0.00004949514],"domain_scores_gemma":[0.99724823,0.0015544324,0.00012143586,0.00019039556,0.0007610334,0.0001244193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013658954,0.00087998173,0.00074397755,0.0021056442,0.00047874154,0.0012909097,0.0011810957,0.00061074307,0.0032528515],"category_scores_gemma":[0.006104715,0.00036967525,0.0006184339,0.0014525987,0.00020182747,0.00083967985,0.00090353284,0.00077192043,0.00082186697],"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":[0.0015296728,0.00065863016,0.029545821,0.0005138612,0.00035478862,0.0013202956,0.00066136214,0.12342213,0.026808681,0.008257079,0.024222735,0.782705],"study_design_scores_gemma":[0.000024258503,0.000046050136,0.0020176852,0.000014781414,0.000033137843,0.0001502389,0.00007094024,0.9833686,0.007631898,0.0025864227,0.004033281,0.000022748107],"about_ca_topic_score_codex":0.00799551,"about_ca_topic_score_gemma":0.008607451,"teacher_disagreement_score":0.00799551,"about_ca_system_score_codex":0.00035196787,"about_ca_system_score_gemma":0.0009218121,"threshold_uncertainty_score":0.01589793},"labels":[],"label_agreement":null},{"id":"W2465623454","doi":"10.1371/journal.pcbi.1004928","title":"Disease Surveillance on Complex Social Networks","year":2016,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Institutes of Health","keywords":"Situation awareness; Disease surveillance; Computer science; Data science; Social network (sociolinguistics); Public health; Situational ethics; Infectious disease (medical specialty); Public health surveillance; Disease; Medicine; Social media; Psychology; World Wide Web; Engineering","score_opus":0.04108917173986143,"score_gpt":0.30488634317154273,"score_spread":0.2637971714316813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2465623454","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":[0.6379603,0.0015258584,0.34637693,0.002789842,0.00010028056,0.00035540832,0.0013086491,0.00040844813,0.00917415],"genre_scores_gemma":[0.9815198,0.00046108686,0.016928315,0.00009779868,0.00003559459,0.00008249426,0.00030344314,0.000012031926,0.00055948587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99703896,0.0017564828,0.00012532434,0.00055709935,0.00033277186,0.00018938235],"domain_scores_gemma":[0.98298633,0.012877444,0.0021849065,0.0009547543,0.00066642486,0.00033007827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035017328,0.00083876424,0.0007520141,0.0017610488,0.00071382936,0.00199389,0.0010134339,0.0010531477,0.0013239818],"category_scores_gemma":[0.021070639,0.0004243881,0.0004711724,0.0012622746,0.0016288287,0.0037774853,0.0018099016,0.0007129853,0.00021517354],"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":[0.00011538869,0.00007271015,0.02174941,0.00016010633,0.00013226036,0.00015209244,0.00030985562,0.9191996,0.0012913053,0.025895001,0.0011687073,0.02975354],"study_design_scores_gemma":[0.000014490191,0.00004896763,0.0037440648,0.000018297169,0.000014846807,0.000047299833,0.00015695216,0.97349983,0.00023936319,0.021530252,0.000674466,0.000011137713],"about_ca_topic_score_codex":0.010889768,"about_ca_topic_score_gemma":0.005150978,"teacher_disagreement_score":0.010889768,"about_ca_system_score_codex":0.0017925757,"about_ca_system_score_gemma":0.00068388745,"threshold_uncertainty_score":0.021652758},"labels":[],"label_agreement":null},{"id":"W2470075794","doi":"","title":"Name game: Canada's blueprint for a nationwide master person index.","year":2004,"lang":"en","type":"article","venue":"PubMed","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Blueprint; Index (typography); Psychology; Computer science; Engineering; World Wide Web","score_opus":0.027548091489072175,"score_gpt":0.22709447680511935,"score_spread":0.19954638531604718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2470075794","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031175758,0.0027991591,0.006547295,0.09287684,0.021638531,0.001420054,0.25849628,0.023102531,0.5900017],"genre_scores_gemma":[0.008793039,0.0017301183,0.011625694,0.017184809,0.0022744515,0.00046154243,0.06751809,0.0029743926,0.8874378],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984634,0.0000868416,0.00010692503,0.00011576092,0.0008501745,0.00037689603],"domain_scores_gemma":[0.9867186,0.0012683225,0.0003777534,0.00053566427,0.0064247632,0.0046749352],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019386506,0.00091886526,0.0008885699,0.00589089,0.0034606957,0.0051982007,0.002340778,0.002987075,0.31755733],"category_scores_gemma":[0.012148886,0.00070197,0.0006644438,0.0056327055,0.0011091216,0.003712593,0.00272822,0.0022246249,0.15819097],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000878368,0.0000049793625,0.00016279847,0.000023697714,0.000001061434,0.0000075459457,0.000011734095,0.000007208052,0.000037312184,0.0004655973,0.99216145,0.007107881],"study_design_scores_gemma":[0.000019742078,0.000007932825,0.002311625,0.00006036964,0.0000037290163,0.000021102072,0.00007724038,0.00008429567,0.000075362375,0.00033193995,0.99698883,0.000017780016],"about_ca_topic_score_codex":0.7136401,"about_ca_topic_score_gemma":0.8411163,"teacher_disagreement_score":0.9925327,"about_ca_system_score_codex":0.007467331,"about_ca_system_score_gemma":0.039589748,"threshold_uncertainty_score":0.97342163},"labels":[],"label_agreement":null},{"id":"W2473954818","doi":"10.1016/j.socscimed.2016.06.047","title":"Identification of spatial and cohort clustering of tuberculosis using surveillance data from British Columbia, Canada, 1990–2013","year":2016,"lang":"en","type":"article","venue":"Social Science & Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":18,"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 British Columbia; BC Centre for Disease Control","funders":"British Columbia Centre for Disease Control; Public Health Agency of Canada","keywords":"Incidence (geometry); Demography; Cohort; Geography; Population; Cluster analysis; Census; Cohort effect; Cohort study; Cartography; Medicine; Statistics; Pathology; Mathematics","score_opus":0.02116744135129956,"score_gpt":0.287063965682525,"score_spread":0.26589652433122546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2473954818","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":[0.9548084,0.0021029755,0.0007325612,0.0007852394,0.000035399302,0.000067154804,0.039276883,0.000055490626,0.0021358584],"genre_scores_gemma":[0.98546326,0.00075084454,0.0007440368,0.000094627096,0.0000076102074,0.00003377188,0.011752826,0.000011512833,0.0011415236],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989309,0.000098472374,0.00012342537,0.00024410855,0.00028287477,0.0003202959],"domain_scores_gemma":[0.9946567,0.00031234577,0.0008163532,0.00031281798,0.0032439248,0.0006579481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014084384,0.00040751172,0.000463565,0.0031564897,0.0014452348,0.0014036795,0.0016635038,0.00050026266,0.0010500987],"category_scores_gemma":[0.005075279,0.000446546,0.0007274571,0.008314776,0.0005570868,0.00046975902,0.0012907443,0.00083601143,0.00026510717],"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":[0.00006752646,0.000015143438,0.98869294,0.00005899904,0.00015423536,0.00004643279,0.00048402793,0.00077125547,0.00016115364,0.00018171771,0.0037555045,0.0056109657],"study_design_scores_gemma":[0.0000052795885,0.0000059653294,0.9956963,0.000059227772,0.00005508077,0.000030967516,0.00089924224,0.0014358625,0.000084460604,0.00004400637,0.0016722013,0.0000114362465],"about_ca_topic_score_codex":0.99781597,"about_ca_topic_score_gemma":0.99871373,"teacher_disagreement_score":0.023702864,"about_ca_system_score_codex":0.023702864,"about_ca_system_score_gemma":0.041387744,"threshold_uncertainty_score":0.1719771},"labels":[],"label_agreement":null},{"id":"W2478105887","doi":"10.1186/s12942-016-0053-9","title":"Utilization of Google enterprise tools to georeference survey data among hard-to-reach groups: strategic application in international settings","year":2016,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Drug Abuse; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Geospatial analysis; Geography; Georeference; Data collection; Geolocation; Geocoding; Data science; Computer science; Cartography; World Wide Web; Sociology; Social science","score_opus":0.12466508985617315,"score_gpt":0.3927379329132896,"score_spread":0.2680728430571165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2478105887","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":[0.8668297,0.0012318734,0.06457245,0.0049051195,0.0002769844,0.0042722304,0.0108408015,0.006075912,0.04099507],"genre_scores_gemma":[0.78447783,0.0011636331,0.20714979,0.00024452969,0.0000534142,0.001805631,0.0029962678,0.0002642164,0.0018446809],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99470043,0.0036576807,0.00039506736,0.00038443017,0.0006006533,0.0002617354],"domain_scores_gemma":[0.9726006,0.018611649,0.001592871,0.0032417788,0.0026988506,0.0012543342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011979291,0.00078607927,0.0005260733,0.007393122,0.0013016212,0.0031342753,0.0013050074,0.0007378932,0.004424925],"category_scores_gemma":[0.03047168,0.0004397815,0.00063602143,0.0072516063,0.0008244507,0.0035620825,0.0042647338,0.00057017436,0.0009784381],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005387023,0.0007505335,0.20013078,0.0022660755,0.00017548163,0.0014466062,0.09298928,0.0026153557,0.0038206892,0.005831383,0.017725667,0.6717094],"study_design_scores_gemma":[0.00044913637,0.0019980508,0.33733484,0.0055053984,0.0005823178,0.0025820269,0.37892672,0.053103134,0.007469091,0.02910598,0.18220375,0.00073947245],"about_ca_topic_score_codex":0.015632624,"about_ca_topic_score_gemma":0.032189474,"teacher_disagreement_score":0.015632624,"about_ca_system_score_codex":0.0010226946,"about_ca_system_score_gemma":0.0030698762,"threshold_uncertainty_score":0.06335336},"labels":[],"label_agreement":null},{"id":"W2492481418","doi":"10.1002/9780470131886.ch5","title":"Putting It Together: The Biosurveillance Information System","year":2006,"lang":"en","type":"other","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Disease surveillance; World Wide Web; Visualization; Data science; Data visualization; Web application; Disease; Data mining; Medicine","score_opus":0.007825636726570336,"score_gpt":0.23488912771328202,"score_spread":0.2270634909867117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2492481418","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010730127,0.011551512,0.28873506,0.027651869,0.004595236,0.00059270783,0.0063902778,0.052685596,0.5970676],"genre_scores_gemma":[0.08385926,0.023143398,0.42105374,0.01606643,0.004720923,0.00042394424,0.023534305,0.010822972,0.4163751],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986376,0.00031018784,0.00012140181,0.00012955733,0.00069600635,0.000105245424],"domain_scores_gemma":[0.9977316,0.00072026375,0.000077562734,0.00052464905,0.0006443101,0.0003017083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025775614,0.0005765977,0.00058421347,0.00195695,0.00094083487,0.0062255124,0.0009640974,0.0010435848,0.04676129],"category_scores_gemma":[0.004138519,0.00051211147,0.00046199656,0.0022937253,0.00048714617,0.008254783,0.0024683576,0.001974013,0.02309346],"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":[0.000091526614,0.00012311086,0.0015749311,0.00019332183,0.000028265718,0.0001098374,0.0004227977,0.0009583505,0.0033695544,0.041941714,0.32282695,0.6283597],"study_design_scores_gemma":[0.000010836411,0.000025036847,0.0012483647,0.0001236845,0.00001745926,0.000119316144,0.00013011441,0.0023138784,0.0021937992,0.0063447296,0.98745036,0.000022450653],"about_ca_topic_score_codex":0.0036134834,"about_ca_topic_score_gemma":0.0033765868,"teacher_disagreement_score":0.04676129,"about_ca_system_score_codex":0.0008734983,"about_ca_system_score_gemma":0.0017124637,"threshold_uncertainty_score":0.15643215},"labels":[],"label_agreement":null},{"id":"W2504763731","doi":"10.1093/med/9780190209247.003.0002","title":"Did Advances in Global Surveillance and Notification Systems Make a Difference in the 2009 H1N1 Pandemic?","year":2015,"lang":"en","type":"book-chapter","venue":"Oxford University Press eBooks","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Timeline; Outbreak; Public health; H1n1 pandemic; Environmental health; Geography; Medicine; Coronavirus disease 2019 (COVID-19); Virology; Political science; Disease; Infectious disease (medical specialty)","score_opus":0.03369634234942303,"score_gpt":0.25419687337377816,"score_spread":0.22050053102435513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2504763731","genre_codex":"commentary","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":[0.17194451,0.13010767,0.029396916,0.49313864,0.013590182,0.00035474257,0.0011844042,0.00032771178,0.15995519],"genre_scores_gemma":[0.850784,0.08929862,0.019750087,0.024280475,0.0042304182,0.0001427651,0.00042995668,0.00013537938,0.010948353],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99665606,0.0016974428,0.00014329141,0.000269388,0.0007993232,0.00043444132],"domain_scores_gemma":[0.99113137,0.003981273,0.0008803739,0.00039584687,0.0031521388,0.0004590043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01061735,0.00052149885,0.00047156776,0.0011957581,0.00076557056,0.005524125,0.0008713706,0.0013947159,0.003488914],"category_scores_gemma":[0.013624353,0.0002793904,0.0007797439,0.0009354148,0.0017363391,0.008498998,0.001350477,0.0029220588,0.00051824615],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006086981,0.00024370503,0.07634077,0.002240779,0.00036531955,0.00031559195,0.004883795,0.008732679,0.0017845245,0.23437786,0.06713373,0.60297257],"study_design_scores_gemma":[0.00013765902,0.0011428578,0.10659451,0.009263843,0.0005207365,0.00067098386,0.027358633,0.021457477,0.005500743,0.17091984,0.6561233,0.00030951045],"about_ca_topic_score_codex":0.010732671,"about_ca_topic_score_gemma":0.009392173,"teacher_disagreement_score":0.010732671,"about_ca_system_score_codex":0.0055552623,"about_ca_system_score_gemma":0.0046547744,"threshold_uncertainty_score":0.056150556},"labels":[],"label_agreement":null},{"id":"W2512614704","doi":"","title":"#Outbreak: An Exploration of Twitter metadata as a means to supplement influenza surveillance in Canada during the 2013-2014 influenza season","year":2016,"lang":"en","type":"dissertation","venue":"The Atrium (University of Guelph)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Outbreak; Influenza season; Metadata; Geography; Virology; Environmental health; Medicine; Computer science; World Wide Web; Influenza vaccine; Virus","score_opus":0.025445571233029096,"score_gpt":0.2704020527522894,"score_spread":0.2449564815192603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2512614704","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":[0.8900562,0.0018650077,0.0032943164,0.0050064633,0.00010557074,0.0004598153,0.06950247,0.0008234731,0.028886633],"genre_scores_gemma":[0.9364369,0.0027310427,0.017648509,0.00053828914,0.00008944263,0.0002027777,0.03261083,0.00018216686,0.009560133],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993654,0.00008389592,0.000034552522,0.00007638657,0.00031667092,0.0001231211],"domain_scores_gemma":[0.9964575,0.0008985409,0.00039345943,0.00015841277,0.0017890807,0.00030307702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013494028,0.0003189268,0.00024785384,0.004781887,0.001991757,0.0026402548,0.00070129934,0.00029986666,0.0011784419],"category_scores_gemma":[0.0057842513,0.00017750582,0.00026638212,0.00967023,0.0006038864,0.0011547452,0.0013440562,0.00039332712,0.00027578665],"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":[0.00036254144,0.000101173755,0.6996449,0.0010071864,0.00010944791,0.00073888653,0.039029732,0.002446359,0.0050465856,0.0043316483,0.041373704,0.20580785],"study_design_scores_gemma":[0.000023805995,0.00007044771,0.8106334,0.00041282823,0.00014019183,0.00015302202,0.061279602,0.012018697,0.0030958266,0.0010151207,0.111052476,0.00010461082],"about_ca_topic_score_codex":0.9660562,"about_ca_topic_score_gemma":0.9865997,"teacher_disagreement_score":0.033943772,"about_ca_system_score_codex":0.014309302,"about_ca_system_score_gemma":0.025165267,"threshold_uncertainty_score":0.103821754},"labels":[],"label_agreement":null},{"id":"W2516585541","doi":"10.1002/sim.3925","title":"Comments on ‘Some methodological issues in biosurveillance’","year":2011,"lang":"en","type":"letter","venue":"Statistics in Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"McGill University; McGill University Health Centre","funders":"Canada Research Chairs","keywords":"Biostatistics; Library science; Citation; Computer science; Epidemiology; Medicine","score_opus":0.14166287861213966,"score_gpt":0.42258403642489334,"score_spread":0.2809211578127537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2516585541","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001079537,0.00025414152,0.00009112347,0.9910851,0.007913481,0.000008482038,0.000039307193,0.000013338075,0.0004869896],"genre_scores_gemma":[0.00048884336,0.00008718511,0.0001464456,0.9881207,0.0102709085,0.000020129635,0.000010212631,0.000012004192,0.000843556],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9434904,0.022115732,0.010277221,0.0062973113,0.011562067,0.0062572146],"domain_scores_gemma":[0.7864398,0.1550493,0.014670648,0.004346675,0.026442945,0.013050756],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05128352,0.0014979244,0.0030391845,0.002074778,0.008154189,0.013330203,0.0060085543,0.15547656,0.007919998],"category_scores_gemma":[0.2381365,0.0030041616,0.0034246761,0.0030934908,0.01006583,0.008776267,0.0055046286,0.10308329,0.007318242],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044118027,0.000013598351,0.00062457396,0.00007181634,0.000025790716,0.0005478294,0.00033945745,0.00007255245,0.000082218656,0.0021084559,0.99269557,0.0033738785],"study_design_scores_gemma":[0.00018704528,0.000081583916,0.0036392547,0.0011941798,0.00011223878,0.0013091356,0.0013809674,0.00086309353,0.00030564412,0.01096874,0.97969645,0.00026163555],"about_ca_topic_score_codex":0.026808193,"about_ca_topic_score_gemma":0.041274093,"teacher_disagreement_score":0.94871646,"about_ca_system_score_codex":0.011315749,"about_ca_system_score_gemma":0.013785385,"threshold_uncertainty_score":0.27121645},"labels":[],"label_agreement":null},{"id":"W2516913841","doi":"10.1111/cag.12295","title":"Guidelines for creating framework data for GIS analysis in low‐ and middle‐income countries","year":2016,"lang":"en","type":"article","venue":"Canadian Geographies / Géographies canadiennes","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"Grand Challenges Canada; Bill and Melinda Gates Foundation","keywords":"Mandate; Key (lock); Computer science; Data science; Open data; Scale (ratio); Low and middle income countries; World Wide Web; Geography; Developing country; Computer security; Cartography","score_opus":0.029060672300386673,"score_gpt":0.28247551056643116,"score_spread":0.2534148382660445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2516913841","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":[0.0046020444,0.0037221783,0.64570737,0.028127253,0.0033749128,0.049326677,0.097451344,0.01372068,0.15396756],"genre_scores_gemma":[0.0059926948,0.0019972182,0.9274258,0.0015886369,0.00015299824,0.028053785,0.025471138,0.0021265438,0.0071910582],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.93087375,0.03973455,0.016788723,0.0017112424,0.00913912,0.0017525661],"domain_scores_gemma":[0.7863628,0.08968443,0.009630735,0.0270227,0.08181684,0.005482479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08571279,0.0022475456,0.0019772989,0.015639368,0.0028445907,0.010056983,0.008974175,0.0041524405,0.04154813],"category_scores_gemma":[0.20792212,0.0030206307,0.0044255285,0.018658742,0.0033284856,0.008039121,0.0074477694,0.007244051,0.023218442],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018476386,0.00023705243,0.005433444,0.0049533844,0.00013624998,0.0008750817,0.007621535,0.0048841476,0.0011540272,0.07692062,0.71573985,0.18185979],"study_design_scores_gemma":[0.00012885932,0.000036231195,0.003262385,0.00836124,0.0000536889,0.00034689874,0.003702016,0.0024086388,0.0010597667,0.030731583,0.94977236,0.0001364467],"about_ca_topic_score_codex":0.038388964,"about_ca_topic_score_gemma":0.053805824,"teacher_disagreement_score":0.08571279,"about_ca_system_score_codex":0.0057864645,"about_ca_system_score_gemma":0.027257862,"threshold_uncertainty_score":0.45329797},"labels":[],"label_agreement":null},{"id":"W2524277497","doi":"10.1186/s12889-016-3674-9","title":"Evaluation of self-swabbing coupled with a telephone health helpline as an adjunct tool for surveillance of influenza viruses in Ontario","year":2016,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Ontario; Kingston Health Sciences Centre; Queen's University","funders":"Public Health Agency of Canada","keywords":"Medicine; Biostatistics; Helpline; Respiratory illness; Influenza-like illness; Medical emergency; Public health; Environmental health; Emergency medicine; Virus; Virology; Internal medicine; Pathology; Respiratory system","score_opus":0.09947558081594896,"score_gpt":0.3764538457887692,"score_spread":0.2769782649728203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2524277497","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":[0.99653673,0.00011250038,0.00025500558,0.00008841665,0.000007004048,0.00062680803,0.0002550973,0.000025925227,0.0020925088],"genre_scores_gemma":[0.9944665,0.00025668164,0.0024185602,0.0000716266,0.000011679671,0.0003353908,0.00043656607,0.0000072532202,0.0019956776],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975158,0.00066540466,0.00012932593,0.00019027219,0.0011469563,0.0003522941],"domain_scores_gemma":[0.9936173,0.00079846877,0.0009928987,0.00018426692,0.0034708255,0.00093624124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022650622,0.00047745183,0.00030198137,0.00063700357,0.001403147,0.0006669511,0.0006996602,0.0002415647,0.0011736042],"category_scores_gemma":[0.0060836603,0.00028718574,0.0003696379,0.00067077903,0.00048711986,0.00035419737,0.00067770557,0.0002144295,0.00017534253],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032473227,0.0021766634,0.85894156,0.0010447191,0.00013937952,0.0007888635,0.020021264,0.00093052076,0.021039426,0.000106715874,0.0017454929,0.08981806],"study_design_scores_gemma":[0.000117225194,0.005427082,0.98306745,0.000053903615,0.00010074891,0.0000971402,0.003813131,0.0012027861,0.0020033326,0.000011983145,0.0040775132,0.000027583306],"about_ca_topic_score_codex":0.86699665,"about_ca_topic_score_gemma":0.9336235,"teacher_disagreement_score":0.86699665,"about_ca_system_score_codex":0.018051235,"about_ca_system_score_gemma":0.021633258,"threshold_uncertainty_score":0.26757324},"labels":[],"label_agreement":null},{"id":"W2528106982","doi":"10.1101/076521","title":"Dynamic forecasting of Zika epidemics using Google Trends","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Autoregressive integrated moving average; Zika virus; Autoregressive model; Outbreak; Warning system; Computer science; Time series; Data mining; Statistics; Geography; Medicine; Machine learning; Virology; Mathematics; Virus; Telecommunications","score_opus":0.0328025770339365,"score_gpt":0.27228289598331384,"score_spread":0.23948031894937735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2528106982","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":[0.9495206,0.00047773856,0.04032658,0.0008186802,0.00007380542,0.000060905997,0.00460334,0.0006673703,0.0034509795],"genre_scores_gemma":[0.9906064,0.00016879715,0.0065134964,0.00002370181,0.000022157948,0.000018239309,0.0021054416,0.000017437993,0.0005243161],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972576,0.00007224677,0.000022546046,0.000077979304,0.000063288535,0.000038236347],"domain_scores_gemma":[0.99922204,0.00035403555,0.00015913324,0.000057179237,0.0001670701,0.000040484487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091335055,0.00044453502,0.0003701307,0.0014868123,0.00015218358,0.00084036356,0.0006036593,0.00043703747,0.0011001198],"category_scores_gemma":[0.0036212588,0.00021394508,0.0006490521,0.001278055,0.00014831344,0.0011097203,0.00033967936,0.0004927802,0.00039141628],"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":[0.00016075744,0.0000789483,0.1696733,0.00008532864,0.00014841525,0.00015628917,0.00010639908,0.79233056,0.0011809376,0.003994213,0.002229285,0.029855695],"study_design_scores_gemma":[0.0000035564588,0.0000133597305,0.008279688,0.000006960964,0.000010825841,0.000012879137,0.000020312667,0.99059945,0.00013813365,0.0004951105,0.00041364954,0.000005990537],"about_ca_topic_score_codex":0.06837182,"about_ca_topic_score_gemma":0.031224323,"teacher_disagreement_score":0.06837182,"about_ca_system_score_codex":0.0007695323,"about_ca_system_score_gemma":0.00056268496,"threshold_uncertainty_score":0.1359477},"labels":[],"label_agreement":null},{"id":"W2529900371","doi":"10.2196/publichealth.6504","title":"Disease Monitoring and Health Campaign Evaluation Using Google Search Activities for HIV and AIDS, Stroke, Colorectal Cancer, and Marijuana Use in Canada: A Retrospective Observational Study","year":2016,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Observational study; Colorectal cancer; Medicine; Retrospective cohort study; Disease; Stroke (engine); Family medicine; Human immunodeficiency virus (HIV); Cancer; Environmental health; Internal medicine","score_opus":0.17195627750299733,"score_gpt":0.39537570493348173,"score_spread":0.2234194274304844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2529900371","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":[0.99403834,0.0007240708,0.00010232729,0.00009672118,0.000006534893,0.00021063586,0.0038661123,0.000010916806,0.0009444631],"genre_scores_gemma":[0.99626476,0.0006783779,0.00026000902,0.0001057341,0.0000095187825,0.000103889164,0.00218922,0.0000060398984,0.00038239083],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99807334,0.00022208568,0.00031067934,0.0002806824,0.00072332524,0.00038992384],"domain_scores_gemma":[0.9911573,0.0008020827,0.0034682835,0.0003796078,0.0027593086,0.0014334942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015771143,0.0004705792,0.0007345204,0.0033594046,0.002809679,0.0017181255,0.0010444387,0.000687781,0.0014088737],"category_scores_gemma":[0.0061615063,0.0006352182,0.0010867236,0.0082091,0.001050058,0.0008796465,0.0013257951,0.0011823468,0.00027296875],"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":[0.000048278922,0.00006216726,0.9979387,0.00005439432,0.000036537254,0.00004590812,0.0004102038,0.000025353262,0.00003142861,0.000016001633,0.00021932057,0.001111726],"study_design_scores_gemma":[0.000008392855,0.000109271496,0.9978258,0.000048606445,0.000058275516,0.00008055769,0.0011607397,0.00022436479,0.000051168587,0.000010524388,0.00040688633,0.000015380749],"about_ca_topic_score_codex":0.93371785,"about_ca_topic_score_gemma":0.9325618,"teacher_disagreement_score":0.06628215,"about_ca_system_score_codex":0.013543944,"about_ca_system_score_gemma":0.0205275,"threshold_uncertainty_score":0.13334495},"labels":[],"label_agreement":null},{"id":"W2533147238","doi":"10.1016/j.dib.2016.09.032","title":"Infodemiological data of high-school drop-out related web searches in Canada correlating with real-world statistical data in the period 2004–2012","year":2016,"lang":"en","type":"article","venue":"Data in Brief","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Drop (telecommunication); Percentage point; Statistics; Lag; Demography; Drop out; Mathematics; Geography; Demographic economics; Computer science; Economics; Sociology","score_opus":0.07115387420654301,"score_gpt":0.32694255743818634,"score_spread":0.25578868323164333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2533147238","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":[0.83390576,0.0010336441,0.00055530993,0.00042764953,0.000019559104,0.000048005873,0.15706085,0.00015725891,0.00679192],"genre_scores_gemma":[0.9213555,0.0009144098,0.0006824034,0.0000645764,0.000011897864,0.00003928009,0.07290085,0.000025936617,0.004005228],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99896777,0.000046442663,0.000094256364,0.00013543901,0.00048817857,0.00026799794],"domain_scores_gemma":[0.99577314,0.00040514072,0.0010207787,0.00015407133,0.0022852663,0.00036158387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006098647,0.00038811477,0.00037846214,0.005721527,0.0008350926,0.0014184228,0.0007526829,0.00026297418,0.0021181128],"category_scores_gemma":[0.004097397,0.00020322825,0.00069263775,0.014632937,0.00043858044,0.00037156197,0.00071957277,0.0005144787,0.0005238176],"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":[0.00009747462,0.000031364274,0.9772056,0.0001959045,0.00010127767,0.00016445857,0.00066799927,0.0020849444,0.00026320937,0.00058916,0.0074257157,0.011173012],"study_design_scores_gemma":[0.000002062884,0.000011317668,0.99135196,0.000037829537,0.00003343211,0.000047702495,0.0010289585,0.0017760809,0.00024652813,0.000030559426,0.005416808,0.000016794536],"about_ca_topic_score_codex":0.99078953,"about_ca_topic_score_gemma":0.9923609,"teacher_disagreement_score":0.013127286,"about_ca_system_score_codex":0.013127286,"about_ca_system_score_gemma":0.023366142,"threshold_uncertainty_score":0.09524554},"labels":[],"label_agreement":null},{"id":"W2533740697","doi":"10.2196/publichealth.5901","title":"Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis","year":2016,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Social media; Public health; Disease control; Public health surveillance; Page view; Disease surveillance; Medicine; Health care; Internet privacy; Computer science; World Wide Web; The Internet; Environmental health; Political science; Nursing; Web development","score_opus":0.24096464902510156,"score_gpt":0.4564590455823578,"score_spread":0.21549439655725622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2533740697","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":[0.96647245,0.008914949,0.01085592,0.00056120806,0.00021714003,0.001369937,0.005363405,0.0003449086,0.005900061],"genre_scores_gemma":[0.9711547,0.0015039663,0.018644081,0.00014575906,0.00012467064,0.0009376355,0.0067717093,0.000076851895,0.00064057705],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9847135,0.010113651,0.0010688544,0.0012538953,0.002522925,0.00032727068],"domain_scores_gemma":[0.8931095,0.08568528,0.005159962,0.0029491012,0.0119962,0.0010999569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018874697,0.0010989849,0.0012213483,0.008200892,0.00068615377,0.0030371898,0.001201881,0.0013249719,0.0012783811],"category_scores_gemma":[0.08664452,0.00037329528,0.0031098805,0.0046482305,0.00062450627,0.0049339803,0.0017556596,0.0013544512,0.00054573966],"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":[0.012654469,0.0031584804,0.6986267,0.007176499,0.011437322,0.00044521538,0.0049340813,0.01599676,0.0018234847,0.0030244114,0.01046176,0.23026082],"study_design_scores_gemma":[0.00075669773,0.007431196,0.6845267,0.001645844,0.007564665,0.0006396387,0.008842838,0.27098736,0.0031590983,0.0029740988,0.011059679,0.00041214374],"about_ca_topic_score_codex":0.011318363,"about_ca_topic_score_gemma":0.011551789,"teacher_disagreement_score":0.018874697,"about_ca_system_score_codex":0.0016040289,"about_ca_system_score_gemma":0.000780218,"threshold_uncertainty_score":0.09982008},"labels":[],"label_agreement":null},{"id":"W2534755101","doi":"10.1117/12.2241133","title":"Biological material detection identification and monitoring: combining point and standoff sensors technologies","year":2016,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Context (archaeology); Identification (biology); Software deployment; Computer science; Event (particle physics); Situation awareness; Point (geometry); Systems engineering; Real-time computing; Engineering; Aerospace engineering; Physics","score_opus":0.0211706953424639,"score_gpt":0.2672759009778799,"score_spread":0.246105205635416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2534755101","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":[0.42911437,0.01577849,0.5150201,0.0039044733,0.0009966956,0.0007568423,0.00073188177,0.0016499943,0.03204718],"genre_scores_gemma":[0.8308667,0.0040308726,0.15720281,0.0011915662,0.0001849948,0.00020900916,0.0003364185,0.0000484102,0.0059291814],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984376,0.00026927693,0.000048136633,0.00031967307,0.0007688337,0.0001564743],"domain_scores_gemma":[0.9991015,0.00022699367,0.0001295687,0.00009092315,0.00034090932,0.00011012945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011823373,0.00067044096,0.0005734163,0.0013132466,0.0004249018,0.0014245489,0.001010347,0.0012504518,0.0011083679],"category_scores_gemma":[0.0011920837,0.00037876767,0.00042624952,0.0006495806,0.0005872913,0.0017857264,0.0024436505,0.0010327386,0.00050545944],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057493255,0.00034534762,0.024338584,0.0011670892,0.0001649705,0.0007478363,0.0007918887,0.008163094,0.53066653,0.0068228035,0.0037966385,0.4224202],"study_design_scores_gemma":[0.00010668963,0.0049010194,0.04220882,0.00075785455,0.00051211857,0.0037308566,0.0020422693,0.12139641,0.69660324,0.011242331,0.11615864,0.00033964476],"about_ca_topic_score_codex":0.0017870833,"about_ca_topic_score_gemma":0.0033631234,"teacher_disagreement_score":0.0017870833,"about_ca_system_score_codex":0.00055223936,"about_ca_system_score_gemma":0.0007646931,"threshold_uncertainty_score":0.0062528253},"labels":[],"label_agreement":null},{"id":"W2538084642","doi":"10.1016/j.ijid.2016.10.005","title":"Drivers of earlier infectious disease outbreak detection: a systematic literature review","year":2016,"lang":"en","type":"review","venue":"International Journal of Infectious Diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Outbreak; Systematic review; CLARITY; Infectious disease (medical specialty); Psychological intervention; Medicine; MEDLINE; Disease; Disease surveillance; Pathology; Psychiatry","score_opus":0.010665984032928707,"score_gpt":0.3171464724594757,"score_spread":0.30648048842654696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2538084642","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":[0.0024521735,0.99479735,0.0003817872,0.000567771,0.00013753807,0.0008040578,0.00055330194,0.000012826297,0.00029323073],"genre_scores_gemma":[0.032696966,0.96258086,0.0016865042,0.00070062466,0.00009317292,0.0016401602,0.00049934525,0.0000090993,0.000093255505],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9733085,0.009953967,0.0100415945,0.0017584144,0.0042010765,0.0007364303],"domain_scores_gemma":[0.8696707,0.0992258,0.019285167,0.0015313356,0.009437772,0.00084920385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029465258,0.0022604698,0.009302605,0.0179687,0.0010677471,0.0043321187,0.003081238,0.002616704,0.004018512],"category_scores_gemma":[0.13453345,0.0022052566,0.0140045555,0.017948132,0.0012368532,0.0050338013,0.0025158538,0.0020762063,0.00032800212],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014814301,0.000022033817,0.0028635925,0.96386194,0.012036019,0.00012591427,0.0003772714,0.00011762442,0.000054790497,0.00024168522,0.00094448955,0.01920639],"study_design_scores_gemma":[0.0001688476,0.00013783608,0.007191198,0.912555,0.07227478,0.00030893463,0.0006026217,0.00012724822,0.00012687186,0.0003080447,0.0061540906,0.00004450131],"about_ca_topic_score_codex":0.011700021,"about_ca_topic_score_gemma":0.02984638,"teacher_disagreement_score":0.029465258,"about_ca_system_score_codex":0.007924521,"about_ca_system_score_gemma":0.029392203,"threshold_uncertainty_score":0.15582901},"labels":[],"label_agreement":null},{"id":"W2551221471","doi":"10.1371/journal.pone.0165688","title":"Avian Influenza Risk Surveillance in North America with Online Media","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"Canadian Wildlife Health Cooperative","keywords":"Social media; Context (archaeology); Situation awareness; Outbreak; Public health surveillance; Computer science; Disease surveillance; The Internet; Warning system; Data science; Public health; Internet privacy; Environmental health; Geography; Medicine; World Wide Web; Telecommunications; Engineering","score_opus":0.029334145342419547,"score_gpt":0.24190273183469344,"score_spread":0.2125685864922739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2551221471","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":[0.99429107,0.00019067521,0.0013038963,0.00029572856,0.0000104783685,0.00012373654,0.0012048344,0.00007767212,0.0025018745],"genre_scores_gemma":[0.9865807,0.00028208093,0.01007782,0.0000964703,0.000037095553,0.0001256083,0.0016257752,0.000012579213,0.0011618526],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99924684,0.0003040547,0.000055759945,0.00016073613,0.00016912995,0.000063515196],"domain_scores_gemma":[0.99683005,0.0012558443,0.00083234336,0.00020205596,0.000735119,0.00014462021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010125784,0.00038318548,0.00027098635,0.0020370637,0.00048343668,0.0008720422,0.0004096303,0.00032933915,0.00082422764],"category_scores_gemma":[0.0033739852,0.00027368515,0.0003472013,0.0018420051,0.0002705604,0.0011301371,0.00082095043,0.00021399894,0.00022863245],"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":[0.00015759502,0.00020504538,0.94065183,0.00016715855,0.000096590906,0.00027981965,0.0032897552,0.0018362881,0.006533858,0.00010046797,0.001103094,0.045578472],"study_design_scores_gemma":[0.00000948874,0.00017130194,0.9672279,0.00006236285,0.00006736483,0.00014528034,0.0055948156,0.022218296,0.0021375448,0.000118963544,0.0022258884,0.000020737294],"about_ca_topic_score_codex":0.07354958,"about_ca_topic_score_gemma":0.20253949,"teacher_disagreement_score":0.07354958,"about_ca_system_score_codex":0.0008214117,"about_ca_system_score_gemma":0.0005897701,"threshold_uncertainty_score":0.14624292},"labels":[],"label_agreement":null},{"id":"W2552325884","doi":"10.1017/s0950268816002636","title":"A look back: investigating Google Flu Trends during the influenza A(H1N1)pdm09 pandemic in Canada, 2009–2010","year":2016,"lang":"en","type":"letter","venue":"Epidemiology and Infection","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"Alberta Innovates; Public Health Agency; Public Health Agency of Canada","keywords":"Pandemic; Content (measure theory); Coronavirus disease 2019 (COVID-19); Virology; Action (physics); Medicine; Mathematics; Internal medicine; Physics","score_opus":0.03970311858399097,"score_gpt":0.29863622569627907,"score_spread":0.2589331071122881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2552325884","genre_codex":"commentary","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":[0.023767756,0.0027907854,0.00014393448,0.95758957,0.0035373128,0.00005974603,0.0027674423,0.000070227165,0.009273237],"genre_scores_gemma":[0.17618346,0.0071856724,0.0010341271,0.78075993,0.008901854,0.00009535356,0.0017330763,0.000080675534,0.024025884],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99849427,0.00020196119,0.00025097016,0.00016485763,0.0005247159,0.00036333292],"domain_scores_gemma":[0.99338806,0.001241401,0.00035133283,0.00016555304,0.0033357127,0.00151798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016519654,0.00029775032,0.00038422982,0.0007864079,0.004082166,0.00201057,0.0012108352,0.007391617,0.0024429325],"category_scores_gemma":[0.010439046,0.0002953404,0.00039704767,0.0016225338,0.001184338,0.0010894539,0.00073846657,0.006106961,0.0010001499],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015331346,0.00005523786,0.06833367,0.00017311727,0.000051402913,0.004101871,0.0023126083,0.000128216,0.00053924555,0.00077243475,0.88632166,0.037057266],"study_design_scores_gemma":[0.00025212928,0.00023653367,0.2630773,0.0020483923,0.00028631717,0.005625746,0.027891831,0.00201542,0.0010603332,0.003495363,0.69377834,0.00023226462],"about_ca_topic_score_codex":0.90037477,"about_ca_topic_score_gemma":0.9494407,"teacher_disagreement_score":0.09962523,"about_ca_system_score_codex":0.021111883,"about_ca_system_score_gemma":0.027442312,"threshold_uncertainty_score":0.20042384},"labels":[],"label_agreement":null},{"id":"W2563503303","doi":"10.17269/cjph.107.5459","title":"Misclassification errors from postal code-based geocoding to assign census geography in Nova Scotia, Canada","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Geocoding; Nova scotia; Census; Geography; Nova (rocket); Cartography; Regional science; Library science; Computer science; Demography; Archaeology; Sociology; Engineering; Population","score_opus":0.06355595227202843,"score_gpt":0.29536538867534073,"score_spread":0.2318094364033123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2563503303","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":[0.9406698,0.0027846266,0.0035935382,0.0058106217,0.00064448523,0.00040215714,0.03528045,0.0001436384,0.010670758],"genre_scores_gemma":[0.98637944,0.0007432297,0.002221952,0.0007002776,0.000036829173,0.0001200479,0.006901836,0.00005460322,0.0028417152],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98980427,0.0021972721,0.0012714845,0.0013702422,0.003526453,0.0018302028],"domain_scores_gemma":[0.9643129,0.004674109,0.005198325,0.0019541995,0.021415172,0.0024453215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00735198,0.0006939172,0.0009341127,0.0032611024,0.0032003848,0.0024587591,0.0039722305,0.0008173703,0.0015091219],"category_scores_gemma":[0.041210935,0.00074182247,0.0010641529,0.008093688,0.0015194851,0.00072821765,0.0023360038,0.0015028614,0.00035014874],"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":[0.00017747977,0.000037994756,0.96154577,0.00013139099,0.00023710335,0.000118904885,0.0013639177,0.0028774051,0.00010149333,0.0011529565,0.019932663,0.012322969],"study_design_scores_gemma":[0.000043780994,0.000023245237,0.97942495,0.00038615943,0.0001750455,0.000096605705,0.003039051,0.0099793915,0.00034430803,0.00038330452,0.0060425294,0.00006170671],"about_ca_topic_score_codex":0.99911314,"about_ca_topic_score_gemma":0.99921465,"teacher_disagreement_score":0.068261,"about_ca_system_score_codex":0.068261,"about_ca_system_score_gemma":0.11418175,"threshold_uncertainty_score":0.4952705},"labels":[],"label_agreement":null},{"id":"W2564356125","doi":"10.1146/annurev-publhealth-031816-044348","title":"Public Health Surveillance Systems: Recent Advances in Their Use and Evaluation","year":2016,"lang":"en","type":"review","venue":"Annual Review of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":331,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Centers for Disease Control and Prevention","keywords":"Public health surveillance; Public health; Public health informatics; Health informatics; Context (archaeology); Health surveillance; Population health; Health care; Environmental health; Public relations; Data science; HRHIS; Business; Health policy; Risk analysis (engineering); Medicine; Computer science; Political science; Nursing; Geography","score_opus":0.21963873055402622,"score_gpt":0.44967677159693586,"score_spread":0.23003804104290965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564356125","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":[0.00022816696,0.9924132,0.002331557,0.0026432455,0.00033378098,0.00006202768,0.00013750068,0.000030143712,0.0018204115],"genre_scores_gemma":[0.0041116043,0.9887369,0.005375046,0.000798251,0.00054089015,0.00008797697,0.00016618783,0.000014174127,0.00016893294],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.97638696,0.012956481,0.0027252699,0.0018357841,0.005714577,0.0003809098],"domain_scores_gemma":[0.87542456,0.10399802,0.005164566,0.0027184703,0.011853249,0.00084108853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.045181494,0.001809559,0.0030864198,0.009666043,0.00062487216,0.004725152,0.0033704576,0.003135888,0.003344633],"category_scores_gemma":[0.070603855,0.0008803252,0.001977952,0.009449398,0.0026969304,0.005651512,0.003007278,0.0034842535,0.0011775723],"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":[0.000057976053,0.00005856018,0.0021833386,0.037040714,0.00035210967,0.00004695903,0.0004160691,0.00094696664,0.00022726395,0.014151564,0.013527085,0.93099135],"study_design_scores_gemma":[0.000037573078,0.00021006603,0.0059671765,0.074108176,0.0009170898,0.0006431559,0.0006273746,0.0014963265,0.00088400964,0.016997008,0.8979485,0.00016354329],"about_ca_topic_score_codex":0.008042373,"about_ca_topic_score_gemma":0.007195733,"teacher_disagreement_score":0.045181494,"about_ca_system_score_codex":0.0046880166,"about_ca_system_score_gemma":0.009077897,"threshold_uncertainty_score":0.23894548},"labels":[],"label_agreement":null},{"id":"W2579856973","doi":"10.1186/s12884-016-1199-y","title":"Geographic information system for improving maternal and newborn health: recommendations for policy and programs","year":2017,"lang":"en","type":"article","venue":"BMC Pregnancy and Childbirth","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Economic and Social Research Council; United States Agency for International Development","keywords":"Medicine; Reproductive medicine; Family medicine; Health policy; Maternal health; Environmental health; Public health; Pregnancy; Nursing; Population; Health services","score_opus":0.02648720884627835,"score_gpt":0.30004786452317184,"score_spread":0.27356065567689347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2579856973","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010638676,0.02758366,0.03018853,0.8658069,0.0063245893,0.0061215986,0.008417768,0.0021824173,0.05231067],"genre_scores_gemma":[0.027045079,0.1879107,0.5297476,0.13574475,0.0049165743,0.036534127,0.03505696,0.0008373566,0.04220677],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.97340876,0.016596545,0.0024511628,0.0007866321,0.0046109995,0.0021457295],"domain_scores_gemma":[0.9487319,0.018753937,0.0030658676,0.002668555,0.019316379,0.007463311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.049533185,0.0026348447,0.0019752195,0.009200912,0.0029015185,0.009577217,0.0083773,0.012926605,0.027637044],"category_scores_gemma":[0.06680565,0.0018858345,0.0030644147,0.0122551145,0.003065261,0.01461931,0.010087315,0.009141635,0.012938918],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047861446,0.0003677449,0.0020240261,0.003310593,0.000051561936,0.00014446912,0.0009373307,0.0024003398,0.00016859236,0.031792007,0.7498178,0.2089378],"study_design_scores_gemma":[0.00016124452,0.00014117533,0.0032715648,0.015307868,0.00009257303,0.00011147096,0.0035771031,0.0018897094,0.00023023185,0.02718663,0.94790775,0.00012265221],"about_ca_topic_score_codex":0.07220614,"about_ca_topic_score_gemma":0.059225354,"teacher_disagreement_score":0.07220614,"about_ca_system_score_codex":0.012920485,"about_ca_system_score_gemma":0.09927715,"threshold_uncertainty_score":0.26195967},"labels":[],"label_agreement":null},{"id":"W2580333805","doi":"10.20381/ruor-18183","title":"An evaluation of a perinatal surveillance system in eastern and southeastern Ontario","year":2003,"lang":"en","type":"dissertation","venue":"uO Research (University of Ottawa)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geography; Political science","score_opus":0.05481483392824007,"score_gpt":0.3406960804061081,"score_spread":0.28588124647786806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2580333805","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":[0.9762921,0.0004440548,0.001752421,0.0009960437,0.00003152072,0.0033519606,0.0033958547,0.00017347727,0.01356263],"genre_scores_gemma":[0.97577524,0.0009521937,0.012302576,0.00022994408,0.000022328759,0.0010464241,0.0052916245,0.000030269352,0.004349361],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9858612,0.0037017858,0.0012912495,0.0007059281,0.0073604584,0.0010792972],"domain_scores_gemma":[0.9538355,0.0059243008,0.0021749414,0.0017471795,0.034235362,0.0020826915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019030793,0.00039978704,0.0003689718,0.0016284961,0.0027092309,0.0017793087,0.0014809171,0.00028591184,0.0010055596],"category_scores_gemma":[0.03128463,0.0003863913,0.0003247543,0.0038248051,0.0008062138,0.0008577511,0.0013592268,0.00034797157,0.00018041946],"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":[0.0016119546,0.0007502243,0.7575358,0.0009909082,0.00017452864,0.0006361663,0.026338497,0.0037771058,0.0055149645,0.0017585831,0.0073769423,0.19353427],"study_design_scores_gemma":[0.00024355139,0.001293306,0.94511765,0.00031954012,0.00016643642,0.000117558295,0.019910993,0.007647013,0.0033495184,0.00008816506,0.02168086,0.00006543267],"about_ca_topic_score_codex":0.9686062,"about_ca_topic_score_gemma":0.9744553,"teacher_disagreement_score":0.047849536,"about_ca_system_score_codex":0.047849536,"about_ca_system_score_gemma":0.07187181,"threshold_uncertainty_score":0.3471743},"labels":[],"label_agreement":null},{"id":"W2581468326","doi":"10.5210/ojphi.v8i3.6937","title":"KIWI: A technology for public health event monitoring and early warning detection","year":2016,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Canadian Food Inspection Agency; Public Health Agency of Canada","funders":"Canadian Food Inspection Agency","keywords":"Warning system; Public health surveillance; Public health; Event monitoring; Event (particle physics); Disease surveillance; Computer science; Data collection; Data science; Medicine; Environmental health; Telecommunications; Wireless sensor network; Pathology","score_opus":0.07662715370780948,"score_gpt":0.37109115080232946,"score_spread":0.29446399709452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2581468326","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":[0.044415392,0.001988048,0.73020273,0.0034714423,0.00044292296,0.003949347,0.011887252,0.1587292,0.04491364],"genre_scores_gemma":[0.24650058,0.0019011653,0.71458066,0.0012718564,0.0002136157,0.0014160649,0.013888495,0.001712919,0.01851459],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792564,0.00028986737,0.0001346744,0.0003051509,0.0011619246,0.0001827414],"domain_scores_gemma":[0.9947694,0.001487273,0.0004848668,0.00079081336,0.0019250929,0.00054242753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031951214,0.0013887038,0.0006168625,0.005398071,0.0012565659,0.0028968265,0.0023449298,0.0010326442,0.0053547276],"category_scores_gemma":[0.00856154,0.00062062574,0.0005133406,0.0030950266,0.0008522609,0.003871656,0.002824318,0.0011472031,0.002408992],"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":[0.0010598763,0.00052386883,0.018651836,0.0013329017,0.0002566719,0.0009178187,0.003138675,0.004840637,0.035231955,0.011347652,0.096831955,0.8258661],"study_design_scores_gemma":[0.0005254365,0.0009636903,0.07676222,0.0013073194,0.0005605433,0.0022309294,0.0034000957,0.21345411,0.06693823,0.024474794,0.60838085,0.0010018317],"about_ca_topic_score_codex":0.118358895,"about_ca_topic_score_gemma":0.10035694,"teacher_disagreement_score":0.118358895,"about_ca_system_score_codex":0.0019836014,"about_ca_system_score_gemma":0.00507017,"threshold_uncertainty_score":0.23533994},"labels":[],"label_agreement":null},{"id":"W2581666783","doi":"10.20381/ruor-691","title":"Modelling Human Risk of West Nile Virus Using Surveillance and Environmental Data","year":2017,"lang":"en","type":"article","venue":"uO Research (University of Ottawa)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"West Nile virus; Geography; Environmental health; Risk analysis (engineering); Virology; Business; Virus; Biology; Medicine","score_opus":0.15335043453707503,"score_gpt":0.3595698745186261,"score_spread":0.20621943998155107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2581666783","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":[0.962198,0.00036001488,0.031261653,0.0009097339,0.000023018869,0.000081530095,0.00309903,0.00015090017,0.0019161053],"genre_scores_gemma":[0.9912838,0.00014930397,0.006499295,0.000021255604,0.000008022605,0.0000431652,0.0011144696,0.000008680478,0.0008720996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992563,0.00043015994,0.000038459704,0.0001355386,0.000056069548,0.0000833686],"domain_scores_gemma":[0.99748296,0.0018739055,0.00030556467,0.0000910283,0.00015191732,0.000094626026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020312269,0.0005464593,0.00036623556,0.00084799714,0.00025646423,0.0009852608,0.000808265,0.0004456755,0.000788178],"category_scores_gemma":[0.0063154176,0.00045397022,0.00079123775,0.0006452033,0.00043109243,0.0006182963,0.00061364705,0.0004264208,0.000115699586],"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":[0.00011418148,0.00004757818,0.11182337,0.000048604197,0.00016711495,0.00007242364,0.00019900028,0.87585664,0.00027467855,0.0021102857,0.00036624767,0.008919843],"study_design_scores_gemma":[0.000010515122,0.00003543865,0.013140764,0.000010791554,0.000030697454,0.000014057669,0.00009763952,0.9847631,0.000115625255,0.0013150937,0.00045569468,0.000010548447],"about_ca_topic_score_codex":0.4235136,"about_ca_topic_score_gemma":0.36067888,"teacher_disagreement_score":0.4235136,"about_ca_system_score_codex":0.003242864,"about_ca_system_score_gemma":0.002329813,"threshold_uncertainty_score":0.8420969},"labels":[],"label_agreement":null},{"id":"W2586682691","doi":"10.1093/aje/163.suppl_11.s28-c","title":"Bayesian Disease Mapping and Gis-Based Spatiotemporal Iatrogenic Injury Surveillance","year":2006,"lang":"en","type":"article","venue":"American Journal of Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Bayesian probability; Disease surveillance; Disease monitoring; Medicine; Geographic information system; Disease; Computer science; Geography; Remote sensing; Artificial intelligence; Internal medicine","score_opus":0.016437834102933188,"score_gpt":0.30262419678413327,"score_spread":0.28618636268120007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586682691","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":[0.062436923,0.0013872137,0.927777,0.0030077733,0.000068810674,0.00013975544,0.0019669135,0.0005869789,0.002628573],"genre_scores_gemma":[0.70004827,0.001004224,0.2954712,0.00024418204,0.000091440466,0.00023262747,0.0021505642,0.00004936448,0.0007079752],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961588,0.0026666229,0.00020933048,0.000358175,0.00052452506,0.000082580664],"domain_scores_gemma":[0.9826167,0.014613728,0.0010980049,0.00058635755,0.0009431764,0.00014191314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066525447,0.0005525324,0.0010378502,0.0031602415,0.0003226219,0.0015328162,0.0010300664,0.0008075024,0.00083177944],"category_scores_gemma":[0.041179087,0.0007210225,0.0007768113,0.0020388917,0.000679105,0.0017678484,0.0013825342,0.00077727303,0.0001551672],"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":[0.0002989352,0.00013730182,0.055698782,0.0002425328,0.0005543072,0.00013319329,0.00025898416,0.75637794,0.00069926534,0.047524065,0.004756351,0.13331835],"study_design_scores_gemma":[0.000025692956,0.000016454222,0.004245468,0.000021077769,0.000030311372,0.000044811404,0.000042635394,0.9644813,0.00019639963,0.02981375,0.0010679907,0.000014161109],"about_ca_topic_score_codex":0.02519297,"about_ca_topic_score_gemma":0.020923289,"teacher_disagreement_score":0.02519297,"about_ca_system_score_codex":0.0014373604,"about_ca_system_score_gemma":0.0015934267,"threshold_uncertainty_score":0.050092697},"labels":[],"label_agreement":null},{"id":"W2587297745","doi":"10.1093/aje/163.suppl_11.s224-d","title":"Be more Like CSI, Less Like CSI:Miami; A Call for Common-Sense Micro-Level Popperian Refutationism in Epidemiology","year":2006,"lang":"en","type":"article","venue":"American Journal of Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Miami; Epidemiology; Common sense; Medicine; Psychology; Epistemology; Pathology; Philosophy; Geology","score_opus":0.0710639470665816,"score_gpt":0.37618321835564605,"score_spread":0.30511927128906446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587297745","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015975375,0.001617178,0.090277985,0.8564668,0.004178064,0.000054736916,0.00024731449,0.00027206022,0.030910483],"genre_scores_gemma":[0.7447281,0.0010749882,0.06128019,0.18069433,0.0048361435,0.00037682513,0.00019525396,0.0003319263,0.006482334],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9555764,0.024700118,0.0034310576,0.008132785,0.0063626035,0.0017969924],"domain_scores_gemma":[0.79727864,0.13170865,0.008118351,0.038519233,0.018017326,0.006357789],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08027517,0.00055695005,0.0023994949,0.003530036,0.012845281,0.010138295,0.004911764,0.010949761,0.0059654047],"category_scores_gemma":[0.21774282,0.0009294509,0.0015090369,0.003445667,0.06978898,0.027057942,0.011080089,0.031058872,0.00074502756],"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":[0.000032057207,0.000016283277,0.0014402326,0.0000607116,0.00003601952,0.000116870535,0.0063907015,0.00017151354,0.00007626176,0.96526825,0.017480856,0.008910141],"study_design_scores_gemma":[0.000025956926,0.000008811309,0.00043709678,0.00009298832,0.000021674115,0.00010362946,0.0017434802,0.00065095903,0.00017239826,0.9651181,0.031588636,0.00003608756],"about_ca_topic_score_codex":0.0109917885,"about_ca_topic_score_gemma":0.011072097,"teacher_disagreement_score":0.9197248,"about_ca_system_score_codex":0.0052573723,"about_ca_system_score_gemma":0.007753772,"threshold_uncertainty_score":0.42454076},"labels":[],"label_agreement":null},{"id":"W2587516628","doi":"10.1097/phh.0000000000000517","title":"An Evaluation of Provincial Infectious Disease Surveillance Reports in Ontario","year":2017,"lang":"en","type":"article","venue":"Journal of Public Health Management and Practice","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Ontario","funders":"","keywords":"Infectious disease (medical specialty); Environmental health; Disease surveillance; Data science; Medicine; Disease; Computer science; Pathology","score_opus":0.10343441508520831,"score_gpt":0.4117081740086926,"score_spread":0.3082737589234843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587516628","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9503309,0.0021273147,0.0018143435,0.0017698229,0.00014006614,0.008344225,0.0112107815,0.00045820838,0.023804365],"genre_scores_gemma":[0.98334193,0.0013808543,0.005553979,0.00020162358,0.000053684846,0.0021270881,0.0049420046,0.000043453056,0.0023554263],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9481071,0.016324632,0.0063869734,0.0014590487,0.025538081,0.0021842443],"domain_scores_gemma":[0.70962787,0.0697826,0.038758293,0.008343751,0.16462289,0.008864614],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.059896845,0.0006447896,0.0007136477,0.0056703542,0.0024566115,0.002827524,0.0019511488,0.0004168798,0.0023764884],"category_scores_gemma":[0.14391944,0.0005574482,0.0011120457,0.010434348,0.001271891,0.0015032536,0.0021641017,0.00056503864,0.00032485832],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028734114,0.0014941291,0.7333289,0.0062104296,0.0005120944,0.0004105859,0.020621289,0.0022264498,0.0022874144,0.00053737586,0.013709948,0.21578793],"study_design_scores_gemma":[0.00029550586,0.00215261,0.9583339,0.0009693612,0.00029869904,0.000089864654,0.010010109,0.0029739907,0.001592268,0.00005801899,0.023138648,0.00008703031],"about_ca_topic_score_codex":0.8494295,"about_ca_topic_score_gemma":0.8601155,"teacher_disagreement_score":0.94701684,"about_ca_system_score_codex":0.052983135,"about_ca_system_score_gemma":0.08547827,"threshold_uncertainty_score":0.38442135},"labels":[],"label_agreement":null},{"id":"W2587534977","doi":"10.1093/aje/163.suppl_11.s28-b","title":"A Bayesian Disease Mapping and Ecological Study on Spatial Epidemiology of Youth Suicide Mortality and Hospitalization, British Columbia, Canada, 1991–2000","year":2006,"lang":"en","type":"article","venue":"American Journal of Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Epidemiology; Ecological study; Spatial epidemiology; Geography; Demography; Environmental epidemiology; Gerontology; Medicine; Environmental health; Ecology; Biology; Sociology; Population; Pathology","score_opus":0.031808942196273074,"score_gpt":0.30582206378274823,"score_spread":0.2740131215864752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587534977","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":[0.98651993,0.0005068146,0.0025665716,0.0007738329,0.00001502,0.00011986443,0.008640617,0.00004068901,0.0008166857],"genre_scores_gemma":[0.9869898,0.0004662887,0.003600502,0.0001268437,0.00000991146,0.00012306805,0.007283094,0.000017069557,0.001383376],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978771,0.0007575128,0.00015559104,0.00028234202,0.0005197876,0.00040762796],"domain_scores_gemma":[0.9941533,0.0009884248,0.0007525479,0.0006857563,0.0025948198,0.00082516886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047829603,0.000532141,0.0007035224,0.0021856867,0.0027480833,0.0009681249,0.0020036174,0.00068651553,0.00082511484],"category_scores_gemma":[0.013245002,0.0007095315,0.00065472745,0.0066570207,0.0010556189,0.00065757567,0.0018847873,0.0010869239,0.00017088842],"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":[0.00020783588,0.00010220771,0.98374647,0.000029254164,0.00023180572,0.00011112946,0.0008658305,0.0025613557,0.0001333958,0.00044658058,0.003241112,0.008323043],"study_design_scores_gemma":[0.000041531654,0.000029542818,0.9899094,0.000028535715,0.000103282066,0.000078763944,0.0011020411,0.0067827576,0.000052824817,0.00021396174,0.0016297731,0.000027549828],"about_ca_topic_score_codex":0.9955004,"about_ca_topic_score_gemma":0.9954417,"teacher_disagreement_score":0.01951534,"about_ca_system_score_codex":0.01951534,"about_ca_system_score_gemma":0.027178213,"threshold_uncertainty_score":0.14159435},"labels":[],"label_agreement":null},{"id":"W2587875170","doi":"10.1093/aje/163.suppl_11.s49-a","title":"Sources of Diagnosis in Administrative Data: Considerations in Epidemiologic Research and Surveillance","year":2006,"lang":"en","type":"article","venue":"American Journal of Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Health","funders":"","keywords":"Epidemiologic Surveillance; Medicine; Environmental health; Epidemiology; Pathology","score_opus":0.2216311536492803,"score_gpt":0.46437874016495134,"score_spread":0.24274758651567105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587875170","genre_codex":"commentary","genre_gemma":"methods","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016747091,0.096410416,0.29419017,0.56833565,0.008282264,0.001278316,0.0050386903,0.00044681656,0.009270602],"genre_scores_gemma":[0.4036727,0.035255868,0.4618788,0.07488498,0.014555805,0.003804046,0.0034531925,0.000496395,0.0019981696],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.45264748,0.46002978,0.04468362,0.0060813176,0.03530282,0.0012550667],"domain_scores_gemma":[0.10474205,0.79291606,0.02656471,0.034131393,0.038957365,0.0026884144],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.47302777,0.001420512,0.006006467,0.009903373,0.0033713179,0.0144224465,0.007870195,0.010264057,0.0013591225],"category_scores_gemma":[0.73327816,0.0035413005,0.0030350303,0.017032132,0.011666567,0.014447587,0.006726598,0.01101929,0.0006257291],"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":[0.0015089723,0.00031990444,0.17860562,0.007359409,0.0032056333,0.0015937194,0.012028694,0.0074728513,0.00067957037,0.23817526,0.1404862,0.40856418],"study_design_scores_gemma":[0.0010463194,0.00071471423,0.10305803,0.03744109,0.006645197,0.006363396,0.018134529,0.047436953,0.0033495922,0.5207317,0.25386173,0.0012167639],"about_ca_topic_score_codex":0.02349173,"about_ca_topic_score_gemma":0.027948763,"teacher_disagreement_score":0.52697223,"about_ca_system_score_codex":0.0072709303,"about_ca_system_score_gemma":0.016470384,"threshold_uncertainty_score":0.6498506},"labels":[],"label_agreement":null},{"id":"W2588047992","doi":"10.1007/s10916-017-0697-4","title":"Data Mining in HIV-AIDS Surveillance System","year":2017,"lang":"en","type":"article","venue":"Journal of Medical Systems","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Artificial intelligence; Public health; Health informatics; Machine learning; Population; Medicine; Support vector machine; Computer science; Health care; Human immunodeficiency virus (HIV); Data mining; Environmental health; Family medicine; Nursing; Political science","score_opus":0.06278689309198138,"score_gpt":0.3591777121686221,"score_spread":0.2963908190766407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588047992","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":[0.46722168,0.0061668167,0.41886392,0.00519942,0.00044486547,0.0027337745,0.06944517,0.016298223,0.013626228],"genre_scores_gemma":[0.8008072,0.0017125863,0.1634513,0.00041939045,0.00011791505,0.00071222294,0.030707754,0.0000739231,0.001997799],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976376,0.00056690385,0.0005808508,0.0005482272,0.000526779,0.00013969312],"domain_scores_gemma":[0.9962035,0.0017339343,0.00046734433,0.0004939012,0.0009339273,0.00016750493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031165471,0.0005371594,0.00082362856,0.0030390616,0.0006728304,0.001893569,0.0012896906,0.0008107376,0.0012464556],"category_scores_gemma":[0.0078052534,0.0003340814,0.00073847995,0.0026478122,0.00020636915,0.0014198471,0.00091887795,0.00063595903,0.0007347664],"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":[0.0012704905,0.0012326306,0.20388414,0.0026738078,0.00065240957,0.0030070057,0.0009989851,0.09931643,0.011919144,0.009451372,0.03356051,0.632033],"study_design_scores_gemma":[0.00009034953,0.000295433,0.04513031,0.00029760876,0.00017862734,0.0013562846,0.0006122696,0.8959875,0.016249832,0.009624071,0.030101614,0.00007604313],"about_ca_topic_score_codex":0.0059029236,"about_ca_topic_score_gemma":0.0034930992,"teacher_disagreement_score":0.0059029236,"about_ca_system_score_codex":0.00094136357,"about_ca_system_score_gemma":0.0014634667,"threshold_uncertainty_score":0.016482055},"labels":[],"label_agreement":null},{"id":"W2588357404","doi":"10.1186/s12889-017-4073-6","title":"Evaluation of the ability of standardized supports to improve public health response to syndromic surveillance for respiratory diseases in Canada","year":2017,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"University of Toronto; Hamilton Health Sciences; McMaster University; Kingston Health Sciences Centre; Public Health Ontario; Toronto Public Health","funders":"Institute of Population and Public Health; Canadian Institutes of Health Research","keywords":"Medicine; Public health; Public health surveillance; Context (archaeology); Communicable disease; Biostatistics; Environmental health; Health department; Intervention (counseling); Medical emergency; Family medicine; Nursing","score_opus":0.081273103008287,"score_gpt":0.38423241891887616,"score_spread":0.30295931591058917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588357404","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9918247,0.00037999984,0.00035782158,0.0004972236,0.000017873188,0.0034962767,0.00054680434,0.00003648426,0.0028428945],"genre_scores_gemma":[0.9942615,0.00040384228,0.0028416184,0.000099138095,0.000008700229,0.001463217,0.00034819968,0.0000032367698,0.00057062553],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99053884,0.00338188,0.000620957,0.00053068297,0.0033748564,0.001552676],"domain_scores_gemma":[0.98057485,0.0021537533,0.002605967,0.00063812593,0.0096562775,0.004371138],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.010845414,0.0008269331,0.00053266983,0.0008822709,0.0029645592,0.0014564735,0.0021963427,0.00053741876,0.0013109859],"category_scores_gemma":[0.017791428,0.00036862004,0.0006968344,0.0014113319,0.0013905077,0.0006868725,0.0018535155,0.00072804175,0.000108691704],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.013711848,0.020887138,0.6273761,0.0033351206,0.0007964777,0.00042226168,0.014814265,0.0051157493,0.004312924,0.0010941302,0.005638829,0.30249518],"study_design_scores_gemma":[0.0027215793,0.023975538,0.94576967,0.0007593114,0.00056939374,0.000057755147,0.009025555,0.005302019,0.0031219176,0.00018006252,0.008414714,0.000102496],"about_ca_topic_score_codex":0.9215244,"about_ca_topic_score_gemma":0.95827764,"teacher_disagreement_score":0.9891546,"about_ca_system_score_codex":0.068034254,"about_ca_system_score_gemma":0.1421981,"threshold_uncertainty_score":0.4936254},"labels":[],"label_agreement":null},{"id":"W2588401929","doi":"10.1128/jcm.01945-16","title":"The EpiQuant Framework for Computing Epidemiological Concordance of Microbial Subtyping Data","year":2017,"lang":"en","type":"article","venue":"Journal of Clinical Microbiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada; University of Lethbridge","funders":"Division of Electrical, Communications and Cyber Systems","keywords":"Subtyping; Concordance; Epidemiology; Exposome; Computational biology; Data science; Biology; Bioinformatics; Medicine; Computer science; Genetics; Pathology; Programming language","score_opus":0.2607124774694666,"score_gpt":0.5163996312184498,"score_spread":0.2556871537489832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588401929","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":[0.0069587925,0.00020577616,0.9843128,0.00019959865,0.000020124864,0.00015859985,0.0021592537,0.005657094,0.00032791274],"genre_scores_gemma":[0.095048256,0.00014409209,0.89911824,0.00012136457,0.00003653807,0.0004860609,0.0040905755,0.00072100933,0.00023389327],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9765891,0.011070984,0.0028638304,0.0026111058,0.006326902,0.0005382405],"domain_scores_gemma":[0.927442,0.04935598,0.0068302434,0.007714396,0.0074338913,0.0012234161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.045662444,0.0016527019,0.0026201035,0.009865397,0.000957537,0.005463862,0.004044972,0.0012809322,0.0028794925],"category_scores_gemma":[0.13589178,0.0016843188,0.0039816196,0.0062182187,0.0019778446,0.004592139,0.0067674145,0.0024857314,0.00084647984],"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":[0.0012592294,0.00034931005,0.05165964,0.002144382,0.0019822856,0.0008470331,0.0019669852,0.45491442,0.005230946,0.16041954,0.0140433125,0.30518293],"study_design_scores_gemma":[0.00005749476,0.000109342436,0.0033717493,0.00010527852,0.00006833045,0.00018829311,0.00016013195,0.9217574,0.0023790621,0.06437067,0.0073528998,0.00007927136],"about_ca_topic_score_codex":0.009582479,"about_ca_topic_score_gemma":0.009805918,"teacher_disagreement_score":0.045662444,"about_ca_system_score_codex":0.0029035597,"about_ca_system_score_gemma":0.0051505947,"threshold_uncertainty_score":0.241489},"labels":[],"label_agreement":null},{"id":"W2590490357","doi":"10.1111/zph.12350","title":"A geographic study of West Nile virus in humans, dead corvids and mosquitoes in Ontario using spatial scan statistics with a survival time application","year":2017,"lang":"en","type":"article","venue":"Zoonoses and Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"","keywords":"West Nile virus; Cluster (spacecraft); Geography; Scan statistic; Geographic information system; Demography; Cartography; Warning system; Early warning system; Statistics; Biology; Virus; Virology","score_opus":0.04344375461810437,"score_gpt":0.31957920260759326,"score_spread":0.2761354479894889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2590490357","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":[0.9987795,0.00004773458,0.00018319539,0.000026453583,7.175963e-7,0.00001587234,0.0006862052,0.0000035836595,0.00025665102],"genre_scores_gemma":[0.9989761,0.000050034014,0.00024664082,0.0000028894638,8.351772e-7,0.000009624604,0.00049694174,0.0000013160915,0.00021561587],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99970955,0.00005804388,0.000020051242,0.000055638706,0.00009010363,0.00006666159],"domain_scores_gemma":[0.99888283,0.00024356243,0.0003308998,0.000073153235,0.00031822993,0.00015135284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046917348,0.0001650686,0.00016854586,0.0007498479,0.0005989242,0.00046308848,0.0003704311,0.00012004177,0.00057670486],"category_scores_gemma":[0.0024248704,0.00017672492,0.00027525472,0.001904148,0.0004699823,0.00021307053,0.00045851347,0.00012201613,0.00005705016],"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":[0.000058517093,0.000010243643,0.9940586,0.000026782902,0.000032648033,0.000077961464,0.0011912972,0.00055830774,0.0004035054,0.000089458474,0.00020597859,0.003286814],"study_design_scores_gemma":[0.0000036926544,0.000024902381,0.9972799,0.0000051095226,0.000010334757,0.000041425457,0.0010016534,0.0012052323,0.000059855585,0.000017507167,0.00034659452,0.000003943178],"about_ca_topic_score_codex":0.95299196,"about_ca_topic_score_gemma":0.9635473,"teacher_disagreement_score":0.047008038,"about_ca_system_score_codex":0.0056894184,"about_ca_system_score_gemma":0.005859683,"threshold_uncertainty_score":0.09456968},"labels":[],"label_agreement":null},{"id":"W2603612200","doi":"10.2196/publichealth.6033","title":"Using Web-Based Search Data to Study the Public’s Reactions to Societal Events: The Case of the Sandy Hook Shooting","year":2017,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Hook; Computer science; Data science; World Wide Web; Internet privacy; Information retrieval; Engineering","score_opus":0.19896619837135837,"score_gpt":0.44058916052966995,"score_spread":0.24162296215831158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2603612200","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":[0.9969332,0.00021584393,0.00012178156,0.00030376294,0.0000051410952,0.00004190731,0.0012801301,0.000011674812,0.0010866012],"genre_scores_gemma":[0.9976179,0.00025221333,0.00040504787,0.00007674296,0.000016359383,0.00003978161,0.001362013,0.000007391875,0.00022243022],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99777395,0.000998332,0.0002645213,0.00022243318,0.00046055042,0.00028017277],"domain_scores_gemma":[0.9753352,0.013537809,0.0077677704,0.0010216185,0.0015744007,0.00076323614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025278004,0.00025077257,0.00035700673,0.005095987,0.000602322,0.001805266,0.00044567784,0.00076670875,0.001134684],"category_scores_gemma":[0.015512464,0.00020644412,0.00035666244,0.006720369,0.0005076525,0.0023805003,0.0010955804,0.00078503136,0.0003565076],"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":[0.00042713445,0.00031662424,0.9648606,0.000505049,0.000114603616,0.0011870171,0.01032178,0.00057058,0.0015350495,0.0003596356,0.0015604913,0.01824136],"study_design_scores_gemma":[0.00001347906,0.00027044717,0.970437,0.00011040771,0.00006867359,0.0006388897,0.02183152,0.002624491,0.0008387257,0.0001661003,0.0029649467,0.000035349993],"about_ca_topic_score_codex":0.02737291,"about_ca_topic_score_gemma":0.0320435,"teacher_disagreement_score":0.02737291,"about_ca_system_score_codex":0.0012810279,"about_ca_system_score_gemma":0.00084995193,"threshold_uncertainty_score":0.054427147},"labels":[],"label_agreement":null},{"id":"W2604251580","doi":"10.2196/publichealth.7304","title":"Determinants of Participants’ Follow-Up and Characterization of Representativeness in Flu Near You, A Participatory Disease Surveillance System","year":2017,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":77,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences","keywords":"Representativeness heuristic; Population; Demography; Medicine; Psychology; Social psychology; Sociology","score_opus":0.07757247962941927,"score_gpt":0.37012484860945394,"score_spread":0.29255236898003467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604251580","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":[0.9985663,0.00005464859,0.00062623504,0.000106009,0.000004216937,0.000052440886,0.0002790372,0.0000034070135,0.0003076924],"genre_scores_gemma":[0.9994666,0.000018027311,0.00023001619,0.000028031089,0.000002621419,0.000054529217,0.00013263943,9.723192e-7,0.00006662937],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99497813,0.002927764,0.0004124212,0.00066654757,0.00055519555,0.0004598994],"domain_scores_gemma":[0.98813695,0.0036706398,0.004909532,0.00092518283,0.0014831356,0.0008744381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007659272,0.00016667471,0.00026075862,0.0006826707,0.00085939514,0.00067037897,0.0005629816,0.00048394268,0.00071726996],"category_scores_gemma":[0.021905283,0.00022313622,0.000339759,0.00070014974,0.0005424212,0.00095769484,0.0013300552,0.00039269408,0.00011751856],"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":[0.00004536485,0.000025894768,0.99535453,0.000018360863,0.000033569013,0.000034205783,0.002148171,0.000060818045,0.0002622687,0.00004045656,0.00012803612,0.0018483247],"study_design_scores_gemma":[0.0000028884297,0.00009988869,0.99497104,0.00002364514,0.000012433536,0.000092711496,0.0036431365,0.00058444036,0.00016351113,0.000051084222,0.00034885432,0.0000063470743],"about_ca_topic_score_codex":0.01553957,"about_ca_topic_score_gemma":0.017399332,"teacher_disagreement_score":0.01553957,"about_ca_system_score_codex":0.00049978535,"about_ca_system_score_gemma":0.00066160475,"threshold_uncertainty_score":0.0405066},"labels":[],"label_agreement":null},{"id":"W2604899200","doi":"10.1016/j.clineuro.2017.04.008","title":"Internet search volumes in brain aneurysms and subarachnoid hemorrhage: Is there evidence of seasonality?","year":2017,"lang":"en","type":"article","venue":"Clinical Neurology and Neurosurgery","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Michael's Hospital; University of Toronto","funders":"Brain Aneurysm Foundation","keywords":"Medicine; Subarachnoid hemorrhage; Aneurysm; Incidence (geometry); Seasonality; Demography; Internal medicine; Surgery; Statistics","score_opus":0.0970718156096424,"score_gpt":0.3850848567171053,"score_spread":0.2880130411074629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604899200","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":[0.9804653,0.0032825295,0.00072455127,0.002949398,0.00014704578,0.000026015292,0.009111423,0.000045613517,0.0032481244],"genre_scores_gemma":[0.99556303,0.000591187,0.00023203912,0.0002348928,0.00008106073,0.000016421125,0.0029321208,0.000020414915,0.00032881298],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971118,0.00083210116,0.0006778981,0.0005831905,0.00043065776,0.00036436185],"domain_scores_gemma":[0.9728996,0.007191839,0.013233287,0.0018915582,0.0031604855,0.0016231685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041416646,0.00022169459,0.00040792217,0.0037565718,0.00038715248,0.0015095702,0.0010645863,0.00084269734,0.00265391],"category_scores_gemma":[0.021678677,0.0003877527,0.0005768308,0.007802341,0.00067250984,0.0027098544,0.0011707285,0.0010694982,0.00045008832],"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":[0.0000678147,0.00002582751,0.9937535,0.00005223848,0.00013119595,0.000033117856,0.00042646073,0.000114904215,0.00010620165,0.0001881964,0.0010226467,0.004077932],"study_design_scores_gemma":[0.000002208661,0.000019665169,0.99727124,0.000042751937,0.00002520621,0.00008767795,0.00072373135,0.0006473645,0.00004741681,0.00016399061,0.0009620108,0.000006788702],"about_ca_topic_score_codex":0.036232248,"about_ca_topic_score_gemma":0.044882562,"teacher_disagreement_score":0.036232248,"about_ca_system_score_codex":0.0008439794,"about_ca_system_score_gemma":0.00080798933,"threshold_uncertainty_score":0.0720427},"labels":[],"label_agreement":null},{"id":"W2604967843","doi":"10.2196/publichealth.7015","title":"Using Google Trends Data to Study Public Interest in Breast Cancer Screening in Brazil: Why Not a Pink February?","year":2017,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Institute of Cancer Research","funders":"","keywords":"Christian ministry; Breast cancer; Public health; Environmental health; Public interest; Identification (biology); Internet privacy; Medicine; Cancer; Computer science; Political science; Biology; Pathology; Internal medicine","score_opus":0.24663161610987516,"score_gpt":0.4380518735808973,"score_spread":0.19142025747102215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604967843","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":[0.9504484,0.0016635332,0.0013593518,0.0013815331,0.000051819865,0.00009730264,0.034195814,0.00018462156,0.010617704],"genre_scores_gemma":[0.9880426,0.0005767437,0.00091125444,0.00008021697,0.000036960184,0.00004967696,0.009696244,0.000029729797,0.00057654735],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991849,0.00014086129,0.00011575225,0.00013755295,0.00028264735,0.00013827477],"domain_scores_gemma":[0.9964798,0.0010211574,0.0012465318,0.00022924878,0.0008444484,0.0001788087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089640915,0.00026647002,0.00034303157,0.004802958,0.0003258006,0.0011571187,0.00032174005,0.00033351494,0.0008606136],"category_scores_gemma":[0.0077434443,0.00015506914,0.0005125844,0.0075296885,0.00032430596,0.0012849676,0.0006995641,0.00033902872,0.00032242545],"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":[0.00012930647,0.00006182404,0.9517911,0.0006314086,0.00011945877,0.00037326934,0.002491983,0.0015532094,0.0012428229,0.0022103784,0.0065722098,0.03282306],"study_design_scores_gemma":[0.000009794253,0.00009081207,0.9568453,0.0002363794,0.00010810757,0.00047940548,0.004831682,0.010222381,0.0009855268,0.0009456754,0.02520527,0.00003967645],"about_ca_topic_score_codex":0.106197976,"about_ca_topic_score_gemma":0.09665848,"teacher_disagreement_score":0.106197976,"about_ca_system_score_codex":0.0012735255,"about_ca_system_score_gemma":0.0012366199,"threshold_uncertainty_score":0.2111597},"labels":[],"label_agreement":null},{"id":"W2605035389","doi":"10.1097/der.0000000000000273","title":"Eczema, Atopic Dermatitis, or Atopic Eczema: Analysis of Global Search Engine Trends","year":2017,"lang":"en","type":"article","venue":"Dermatitis","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Atopic dermatitis; Dermatology","score_opus":0.022871224061699554,"score_gpt":0.3238079579002906,"score_spread":0.30093673383859104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605035389","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":[0.9405149,0.016268099,0.0008130931,0.0005870117,0.00004183583,0.000093413495,0.037025224,0.00010166732,0.0045548035],"genre_scores_gemma":[0.9786895,0.0045147743,0.0011669798,0.00015026653,0.000060054615,0.00009484262,0.014875998,0.00003929612,0.00040826545],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984097,0.00026255936,0.00051708036,0.000290247,0.00036789716,0.00015267137],"domain_scores_gemma":[0.98904675,0.0035708065,0.005204911,0.0004296034,0.0013186998,0.00042919145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019070557,0.0002820588,0.0005926142,0.010729705,0.00020220346,0.0011495327,0.00036496634,0.00024658666,0.0016184011],"category_scores_gemma":[0.008744946,0.00012195157,0.00095204345,0.014109401,0.00030894126,0.0017675371,0.00079208455,0.00031881704,0.00035153696],"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":[0.00018059993,0.000011768489,0.9825392,0.0007693998,0.00027649596,0.000089298046,0.00024394794,0.00014807329,0.00027042083,0.0002382794,0.001543656,0.013688979],"study_design_scores_gemma":[0.000008989001,0.00007722625,0.9901714,0.00030599,0.00025274372,0.0009991052,0.0008299446,0.0007358675,0.000378374,0.00020025217,0.006025542,0.00001456116],"about_ca_topic_score_codex":0.005581338,"about_ca_topic_score_gemma":0.009002102,"teacher_disagreement_score":0.010729705,"about_ca_system_score_codex":0.00049897877,"about_ca_system_score_gemma":0.0006545213,"threshold_uncertainty_score":0.011097729},"labels":[],"label_agreement":null},{"id":"W2605151798","doi":"10.1186/s12889-017-4200-4","title":"Ethical issues in public health surveillance: a systematic qualitative review","year":2017,"lang":"en","type":"review","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":87,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"World Health Organization","keywords":"Public health; Engineering ethics; Normative; Informed consent; Medicine; Qualitative research; Research ethics; Scope (computer science); Public relations; Political science; Sociology; Law; Alternative medicine; Nursing; Social science; Computer science; Pathology","score_opus":0.4265300923781523,"score_gpt":0.5641285614307677,"score_spread":0.1375984690526154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605151798","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":[0.047706854,0.8785588,0.019272158,0.018480299,0.00094621966,0.023506539,0.003180418,0.000100315025,0.008248417],"genre_scores_gemma":[0.32789424,0.58150643,0.028654624,0.00839504,0.0002444668,0.049978882,0.0012827187,0.00010100936,0.001942608],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.8994789,0.07562548,0.013897806,0.0022745063,0.007057121,0.0016662161],"domain_scores_gemma":[0.76876086,0.19865423,0.012867926,0.0027934588,0.015554673,0.0013688086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10067583,0.0011604147,0.0040708994,0.012608827,0.003585643,0.004403728,0.0027287172,0.0024529754,0.0040973504],"category_scores_gemma":[0.19666207,0.001456896,0.0029653555,0.014078194,0.004777904,0.009265947,0.006627908,0.002332357,0.00041889827],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024646072,0.00005757054,0.0031341722,0.6790738,0.0009483825,0.001187772,0.19029666,0.00041360938,0.0006926261,0.011723488,0.009027152,0.10319835],"study_design_scores_gemma":[0.00012036588,0.00013051284,0.0021214571,0.75964886,0.0017310372,0.00083053927,0.15663126,0.00029010655,0.00054690166,0.0053466316,0.072484516,0.0001177512],"about_ca_topic_score_codex":0.010190694,"about_ca_topic_score_gemma":0.022158979,"teacher_disagreement_score":0.10067583,"about_ca_system_score_codex":0.018092848,"about_ca_system_score_gemma":0.04312346,"threshold_uncertainty_score":0.532431},"labels":[],"label_agreement":null},{"id":"W2605256546","doi":"","title":"Géographie des seringues à la traîne : analyse du quartier montréalais Centre-Sud","year":2016,"lang":"fr","type":"article","venue":"EspaceINRS Institutional Digital Repository (Institut National de la Recherche Scientifique)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.05085484286828301,"score_gpt":0.30699156465422156,"score_spread":0.25613672178593855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605256546","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":[0.97789884,0.0012673446,0.0018480122,0.00057914073,0.000027345337,0.00011148033,0.007093759,0.000067349996,0.011106704],"genre_scores_gemma":[0.9851955,0.000510668,0.0011768339,0.000049582355,0.000008006988,0.000049713464,0.0021624889,0.000019704432,0.010827511],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99951553,0.000061959945,0.000013121445,0.00010673994,0.00012232347,0.00018029596],"domain_scores_gemma":[0.99906284,0.00013017366,0.00012432135,0.000043268014,0.00042624865,0.00021313844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000506861,0.0004187422,0.00042956683,0.0022631243,0.0015763028,0.0019098896,0.0009153024,0.00025776718,0.006455894],"category_scores_gemma":[0.001363941,0.0002691397,0.00062023546,0.00577164,0.0009330187,0.00049822184,0.0013914626,0.00052722957,0.00038759157],"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":[0.00013531877,0.000050904877,0.93435216,0.0002746048,0.00027315572,0.00060802134,0.011486245,0.0034577358,0.0026930415,0.0041021924,0.0050683604,0.037498295],"study_design_scores_gemma":[0.000003140584,0.000017399545,0.9838028,0.000041859203,0.000027095859,0.000033997978,0.004930894,0.0008415363,0.0001638437,0.000076192424,0.010044103,0.00001723568],"about_ca_topic_score_codex":0.98070014,"about_ca_topic_score_gemma":0.99068755,"teacher_disagreement_score":0.019299865,"about_ca_system_score_codex":0.013515952,"about_ca_system_score_gemma":0.013107922,"threshold_uncertainty_score":0.098065555},"labels":[],"label_agreement":null},{"id":"W2605545985","doi":"10.2196/publichealth.6925","title":"Zika in Twitter: Temporal Variations of Locations, Actors, and Concepts","year":2017,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":111,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Zika virus; Geography; Computer science; Data science; Biology; Virology","score_opus":0.04625401294774238,"score_gpt":0.37211920924186875,"score_spread":0.32586519629412636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605545985","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":[0.96883196,0.0010536762,0.003223785,0.0021240388,0.000100204634,0.00015851569,0.010483475,0.000103218386,0.013921187],"genre_scores_gemma":[0.991867,0.0004738814,0.0024092298,0.0001100493,0.00005212907,0.00018514048,0.003463438,0.000039555605,0.0013995636],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99880433,0.0003996489,0.000119622855,0.00026490155,0.00025857936,0.0001528827],"domain_scores_gemma":[0.9947246,0.002969748,0.0011519922,0.00024642824,0.00069963845,0.0002075807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010974355,0.00022491634,0.00024960635,0.0029052952,0.00123961,0.002738111,0.0003706052,0.00041687064,0.0028838904],"category_scores_gemma":[0.010171213,0.00025625894,0.00018471428,0.0051622405,0.0009465281,0.003893069,0.0025885087,0.0007163235,0.00080067007],"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":[0.00060597144,0.000072054005,0.6638035,0.0012406667,0.00015652677,0.001558802,0.20119245,0.0011618995,0.009916988,0.01665481,0.02031107,0.08332523],"study_design_scores_gemma":[0.000020786334,0.000055203436,0.76288575,0.00035943065,0.00008514127,0.0006827029,0.13268249,0.005996927,0.0015103803,0.0042795087,0.091333784,0.000107920336],"about_ca_topic_score_codex":0.014204646,"about_ca_topic_score_gemma":0.025003528,"teacher_disagreement_score":0.014204646,"about_ca_system_score_codex":0.0011349932,"about_ca_system_score_gemma":0.000426821,"threshold_uncertainty_score":0.028243959},"labels":[],"label_agreement":null},{"id":"W2605721770","doi":"10.2196/publichealth.6764","title":"Health Care Professionals’ Evidence-Based Medicine Internet Searches Closely Mimic the Known Seasonal Variation of Lyme Borreliosis: A Register-Based Study","year":2017,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"The Internet; Public health; Health care; Health informatics; Medicine; Lyme disease; Family medicine; Variation (astronomy); Epidemiology; MEDLINE; Medical record; World Wide Web; Internet privacy; Nursing; Computer science; Pathology","score_opus":0.09754976004921569,"score_gpt":0.39647631298817004,"score_spread":0.29892655293895437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605721770","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":[0.9943463,0.0023168216,0.00029608162,0.00010447784,0.000014256308,0.00023330897,0.0015914037,0.00001038058,0.0010869987],"genre_scores_gemma":[0.99752814,0.0005439012,0.00029303404,0.000074963755,0.000019368932,0.00024396298,0.0011196139,0.000006570644,0.0001703968],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9897301,0.0032875843,0.0031271717,0.0015853576,0.0015957794,0.00067406456],"domain_scores_gemma":[0.9663395,0.008475422,0.019677859,0.002569326,0.0022854586,0.0006523375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052184965,0.00034649664,0.00092722726,0.005135509,0.0008494083,0.0017393958,0.001047678,0.0011489985,0.0018884446],"category_scores_gemma":[0.03329523,0.00077084906,0.0011798262,0.0075129885,0.0005805129,0.001607707,0.0016547344,0.00059056235,0.0005706634],"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":[0.00024249019,0.00007571584,0.9957295,0.00025510511,0.00020593696,0.00009127101,0.00078368775,0.000022195763,0.00007513293,0.000036955542,0.00027648755,0.0022055402],"study_design_scores_gemma":[0.000067604495,0.0002501313,0.99636286,0.00017325362,0.00034561614,0.00041663827,0.0013298398,0.00022071862,0.000069747104,0.000030951593,0.0007184255,0.0000142757135],"about_ca_topic_score_codex":0.013350663,"about_ca_topic_score_gemma":0.01773046,"teacher_disagreement_score":0.013350663,"about_ca_system_score_codex":0.0010255665,"about_ca_system_score_gemma":0.0017147984,"threshold_uncertainty_score":0.027598321},"labels":[],"label_agreement":null},{"id":"W2605857286","doi":"10.5539/ijsp.v6n3p188","title":"Estimating Disease Risk of Diabetes Cases in the Presence of Underreporting","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Negative binomial distribution; Count data; Uncorrelated; Poisson distribution; Statistics; Binomial distribution; Mathematics; Variance (accounting); Econometrics; Distribution (mathematics); Variable (mathematics); Economics","score_opus":0.03307649307900908,"score_gpt":0.3479503584491537,"score_spread":0.3148738653701446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605857286","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":[0.91684014,0.0009789574,0.07695659,0.0015982891,0.00008958747,0.00009491679,0.0017187825,0.0001128395,0.0016097957],"genre_scores_gemma":[0.99266654,0.0002595361,0.0057350458,0.000055442644,0.000025694415,0.000036494235,0.00045530283,0.000007091908,0.0007588345],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972263,0.0016995847,0.00014216325,0.0005744833,0.00014337983,0.00021406094],"domain_scores_gemma":[0.98439956,0.011751802,0.0024671224,0.00066262874,0.0004743357,0.00024455768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007006817,0.0005025395,0.000963545,0.0011395899,0.00024280557,0.0016249068,0.0011129216,0.001191242,0.0012828081],"category_scores_gemma":[0.023997106,0.0005834022,0.0011341376,0.001219859,0.0007114622,0.0012130752,0.0012217415,0.001188335,0.00022717609],"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":[0.00040058847,0.000115678944,0.79713553,0.00014848997,0.0004631366,0.00082438777,0.000552751,0.17137408,0.0005247579,0.006470858,0.0007145101,0.021275135],"study_design_scores_gemma":[0.000064251755,0.00031211379,0.16228849,0.000104364815,0.000552619,0.00078829844,0.0007775564,0.82096934,0.000673073,0.011301647,0.002113694,0.0000546024],"about_ca_topic_score_codex":0.017510748,"about_ca_topic_score_gemma":0.010227887,"teacher_disagreement_score":0.017510748,"about_ca_system_score_codex":0.0009084044,"about_ca_system_score_gemma":0.0009990128,"threshold_uncertainty_score":0.03705609},"labels":[],"label_agreement":null},{"id":"W2608335508","doi":"10.1371/journal.pone.0176438","title":"Automated detection of hospital outbreaks: A systematic review of methods","year":2017,"lang":"en","type":"review","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Outbreak; Computer science; Data mining; MEDLINE; Epidemiology; Medicine; Statistics; Data science; Mathematics; Pathology; Biology","score_opus":0.13600638173589402,"score_gpt":0.42005474848146673,"score_spread":0.2840483667455727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2608335508","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00048855523,0.9968315,0.0007863311,0.00037990487,0.00013567199,0.00062942936,0.00046571007,0.000021468639,0.00026139966],"genre_scores_gemma":[0.008247345,0.9843322,0.0046992404,0.0005429327,0.00008898773,0.0015519292,0.00043303773,0.000020969572,0.000083294304],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.95027936,0.019375471,0.020081049,0.0023670015,0.0073913974,0.0005057299],"domain_scores_gemma":[0.7434134,0.21362358,0.023117669,0.003773332,0.015301757,0.00077022746],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04240834,0.0025377437,0.008963317,0.03024962,0.0010852267,0.0045866,0.0042957235,0.0022939763,0.0047431416],"category_scores_gemma":[0.17644474,0.0019344002,0.010857684,0.021510746,0.0018684185,0.0050677704,0.0030468863,0.0020632725,0.0005522565],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015062345,0.000018438506,0.0007905958,0.91074085,0.005112148,0.000061661296,0.00028507248,0.00015911875,0.000074014526,0.00030273484,0.0017813421,0.080523364],"study_design_scores_gemma":[0.000109068096,0.00009859055,0.0019217234,0.9604743,0.021354636,0.00018479685,0.000246339,0.00014584878,0.0001805026,0.0003719756,0.014872271,0.0000398248],"about_ca_topic_score_codex":0.0102761695,"about_ca_topic_score_gemma":0.020042267,"teacher_disagreement_score":0.95759165,"about_ca_system_score_codex":0.0073704775,"about_ca_system_score_gemma":0.021486124,"threshold_uncertainty_score":0.2242794},"labels":[],"label_agreement":null},{"id":"W2610490986","doi":"10.1371/journal.pone.0056176","title":"Influenza Forecasting with Google Flu Trends","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":360,"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":"Division of Civil, Mechanical and Manufacturing Innovation; Natural Sciences and Engineering Research Council of Canada; U.S. Department of Homeland Security; National Science Foundation","keywords":"Seasonal influenza; Statistics; Autoregressive integrated moving average; Autoregressive model; Sample (material); Computer science; Negative binomial distribution; Coronavirus disease 2019 (COVID-19); Econometrics; Time series; Mathematics; Medicine; Infectious disease (medical specialty)","score_opus":0.08243873784778845,"score_gpt":0.24993826469855013,"score_spread":0.1674995268507617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2610490986","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":[0.70447785,0.00062932394,0.27040726,0.00092457,0.00028000562,0.00021142466,0.0068909237,0.0063521233,0.009826498],"genre_scores_gemma":[0.94821215,0.00015978304,0.047318105,0.00005058056,0.00005374495,0.000070833805,0.0029049246,0.000059578007,0.0011703289],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99976176,0.00007098571,0.00001840658,0.00006420295,0.00005779679,0.000026839429],"domain_scores_gemma":[0.99909306,0.00048601546,0.00013095008,0.00007478594,0.00017873713,0.000036475918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008531766,0.0006128668,0.00044220223,0.0007588425,0.00015493626,0.0005327711,0.00069948944,0.00045656238,0.0020520976],"category_scores_gemma":[0.0033750003,0.00027520276,0.0007129017,0.0007503586,0.00018797908,0.0008975442,0.0003105421,0.00055779447,0.0005097963],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001891279,0.000042937725,0.018741857,0.00006688315,0.00007216932,0.00008819438,0.00004836881,0.94636726,0.0005799888,0.0021419558,0.002025706,0.02963543],"study_design_scores_gemma":[0.000007373051,0.000020846437,0.0015419379,0.000004449906,0.000008430951,0.000014799151,0.0000064050296,0.99686974,0.0002036086,0.0008061575,0.000511208,0.000005067018],"about_ca_topic_score_codex":0.033492096,"about_ca_topic_score_gemma":0.017811103,"teacher_disagreement_score":0.033492096,"about_ca_system_score_codex":0.0006041722,"about_ca_system_score_gemma":0.0006333801,"threshold_uncertainty_score":0.0665943},"labels":[],"label_agreement":null},{"id":"W2610787465","doi":"10.5210/ojphi.v9i1.7791","title":"2016 International Society for Disease Surveillance Conference New Frontiers in Surveillance: Data Science and Health Security","year":2017,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Disease surveillance; Public health; Public health surveillance; Globe; Global health; Political science; Data sharing; Communicable disease; Medicine; Public relations; Alternative medicine; Pathology","score_opus":0.14579728472914188,"score_gpt":0.42252552483246536,"score_spread":0.2767282401033235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2610787465","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023076022,0.103179745,0.022789678,0.40237764,0.290594,0.0020238953,0.014067138,0.0015357607,0.16112459],"genre_scores_gemma":[0.04532345,0.22705589,0.059772752,0.09173038,0.2536539,0.006227898,0.059172027,0.0019518781,0.25511187],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.94441354,0.017795885,0.009089154,0.004512269,0.021002008,0.0031871416],"domain_scores_gemma":[0.89149785,0.0195681,0.008062433,0.008438024,0.055683564,0.016750012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0773007,0.0037312098,0.003478348,0.009335529,0.0032614588,0.018780801,0.0036032402,0.00854083,0.07119613],"category_scores_gemma":[0.08488363,0.0014039221,0.0033470448,0.0059589525,0.0036969783,0.010202855,0.01377716,0.01359258,0.04327439],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009963392,0.00006135171,0.0023654008,0.000936713,0.0000764626,0.00012369452,0.00048991985,0.00020500972,0.00055265543,0.006449998,0.8703688,0.11827029],"study_design_scores_gemma":[0.0000148836,0.00002880384,0.0013724059,0.0014028763,0.000020656227,0.00012224547,0.00024532323,0.00023340559,0.00019726266,0.0037056375,0.99262214,0.000034398152],"about_ca_topic_score_codex":0.00725323,"about_ca_topic_score_gemma":0.006758283,"teacher_disagreement_score":0.0773007,"about_ca_system_score_codex":0.0083690565,"about_ca_system_score_gemma":0.026466427,"threshold_uncertainty_score":0.40881002},"labels":[],"label_agreement":null},{"id":"W2610800167","doi":"10.5210/ojphi.v9i1.7693","title":"Key elements of infectious disease syndromic surveillance systems: A scoping review","year":2017,"lang":"en","type":"review","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University; University of Guelph","funders":"Public Health England","keywords":"Public health; Disease surveillance; Medicine; CINAHL; Public health surveillance; Communicable disease; Government (linguistics); Scopus; Grey literature; Disease; MEDLINE; Family medicine; Psychological intervention; Political science; Pathology; Nursing","score_opus":0.19944158341158652,"score_gpt":0.47249908376147465,"score_spread":0.2730575003498881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2610800167","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":[0.00055934465,0.988907,0.0014509783,0.0036080028,0.00067168876,0.0014858854,0.0007929915,0.00003281052,0.0024914125],"genre_scores_gemma":[0.006852294,0.982615,0.006158727,0.0008395982,0.00020366641,0.0023429878,0.0006877584,0.000011287494,0.0002887212],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9409457,0.025599478,0.021877062,0.0021230667,0.00846131,0.0009934362],"domain_scores_gemma":[0.83052367,0.12909251,0.015269793,0.002903568,0.021182833,0.0010275486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05772421,0.0025431605,0.005578354,0.048917398,0.002280977,0.010007006,0.0033508854,0.003935893,0.006931381],"category_scores_gemma":[0.17086552,0.0023511907,0.0065029757,0.046664782,0.0027696984,0.011432482,0.0059178816,0.002975897,0.0010674126],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006586853,0.00003117653,0.0010981525,0.7505534,0.0012830575,0.00017158786,0.0022409998,0.0005173814,0.00017712162,0.00695151,0.010002779,0.22690685],"study_design_scores_gemma":[0.000012383716,0.000035484423,0.00089351856,0.94763863,0.0018992414,0.000111285626,0.000980485,0.0001141631,0.000081004495,0.001368885,0.04684198,0.000022776618],"about_ca_topic_score_codex":0.016992906,"about_ca_topic_score_gemma":0.031029869,"teacher_disagreement_score":0.05772421,"about_ca_system_score_codex":0.012587853,"about_ca_system_score_gemma":0.051578898,"threshold_uncertainty_score":0.30527842},"labels":[],"label_agreement":null},{"id":"W2611529541","doi":"10.5210/ojphi.v9i1.7641","title":"Integrated spatiotemporal surveillance system: Data, Analysis and Visualization","year":2017,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Ontario; University of Toronto; Toronto Public Health","funders":"","keywords":"Geography; Census; Population; Cartography; Geographic information system; Data science; Computer science; Data mining; Environmental health; Medicine","score_opus":0.10977466578626442,"score_gpt":0.40542182868516624,"score_spread":0.2956471628989018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611529541","genre_codex":"software","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":[0.009480959,0.00047201564,0.37268218,0.0015116791,0.0003628331,0.001912969,0.19939353,0.39948225,0.014701498],"genre_scores_gemma":[0.10955379,0.0010173304,0.50996125,0.0010312198,0.00036157455,0.0041147247,0.3328806,0.025184307,0.015895206],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968167,0.00052098563,0.00053760887,0.00071381906,0.0012079104,0.00020298727],"domain_scores_gemma":[0.9923273,0.0013569344,0.00063146895,0.0015839287,0.0035026034,0.0005978272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004643071,0.0019227727,0.0013567022,0.0036686205,0.0008573372,0.003570022,0.0022453237,0.00091622514,0.027954029],"category_scores_gemma":[0.0122404685,0.0008915124,0.001227131,0.002762572,0.00042899945,0.003017,0.004364806,0.0014388936,0.0133868335],"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":[0.0010147693,0.0002702307,0.017437046,0.0013164543,0.00034669816,0.00043202474,0.0011995344,0.015507711,0.013200955,0.008875323,0.7016228,0.23877653],"study_design_scores_gemma":[0.00040586133,0.00015659007,0.02502725,0.00047610962,0.00018095192,0.0004267453,0.00043943335,0.21449119,0.027629001,0.014773054,0.7155966,0.00039718088],"about_ca_topic_score_codex":0.028126067,"about_ca_topic_score_gemma":0.019953638,"teacher_disagreement_score":0.028126067,"about_ca_system_score_codex":0.0017365541,"about_ca_system_score_gemma":0.0039181774,"threshold_uncertainty_score":0.093515575},"labels":[],"label_agreement":null},{"id":"W2611761054","doi":"10.5210/ojphi.v9i1.7684","title":"Facilitating Public Health Action through Surveillance Dashboards","year":2017,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Health Services","funders":"","keywords":"Dashboard; Computer science; Stakeholder; Flexibility (engineering); Process (computing); Knowledge management; Public health surveillance; Public health; Process management; Data science; Medicine; Business; Public relations; Nursing","score_opus":0.2531390825386769,"score_gpt":0.452632418154966,"score_spread":0.19949333561628912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611761054","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":[0.06601848,0.00079954346,0.72559553,0.010238212,0.0011124755,0.0069515277,0.006154264,0.113331825,0.069798164],"genre_scores_gemma":[0.30867884,0.001525156,0.65426433,0.00146224,0.00044363664,0.005053458,0.011453615,0.0032543968,0.013864399],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9691608,0.01580751,0.002715996,0.0034403945,0.00696289,0.001912475],"domain_scores_gemma":[0.92613864,0.0324527,0.008654449,0.014908147,0.013369236,0.004476899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039939534,0.0035056735,0.00089121505,0.0045377356,0.0017843506,0.010690474,0.004616173,0.001334634,0.017101781],"category_scores_gemma":[0.06363732,0.0011574394,0.0012700072,0.003670081,0.0016458225,0.011122128,0.010002303,0.003631638,0.004201342],"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":[0.00067676534,0.002627197,0.028313441,0.0030486772,0.00036073299,0.0005117641,0.010722817,0.03946624,0.015549298,0.02884287,0.12444886,0.74543136],"study_design_scores_gemma":[0.00058850035,0.0017438227,0.026890602,0.0035418298,0.00035379088,0.00026229268,0.010149521,0.17368007,0.026370456,0.041176535,0.7145611,0.0006814338],"about_ca_topic_score_codex":0.0064427922,"about_ca_topic_score_gemma":0.006693637,"teacher_disagreement_score":0.039939534,"about_ca_system_score_codex":0.0023404018,"about_ca_system_score_gemma":0.0077149854,"threshold_uncertainty_score":0.211223},"labels":[],"label_agreement":null},{"id":"W2611898996","doi":"10.2196/publichealth.7313","title":"Saúde na Copa: The World’s First Application of Participatory Surveillance for a Mass Gathering at FIFA World Cup 2014, Brazil","year":2017,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Skoll Foundation","keywords":"Context (archaeology); Medicine; Disease surveillance; Public health; Public health surveillance; Social media; International Health Regulations; Environmental health; Internet privacy; Business; Disease; Computer science; Geography; World Wide Web; Coronavirus disease 2019 (COVID-19); Infectious disease (medical specialty)","score_opus":0.04689767392779711,"score_gpt":0.3581855211249667,"score_spread":0.3112878471971696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611898996","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":[0.8285638,0.0032443989,0.024745904,0.021108167,0.0012384836,0.00574712,0.0025244665,0.001523053,0.11130457],"genre_scores_gemma":[0.93482405,0.0022007187,0.034734793,0.0025314554,0.00023575974,0.0026052375,0.0008782054,0.00041298004,0.021576772],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9957919,0.0026266386,0.00010009837,0.00039197534,0.000503158,0.00058613706],"domain_scores_gemma":[0.9929543,0.0031405275,0.00045626168,0.0007580153,0.0012904127,0.0014003492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0070152557,0.0007640368,0.00038968294,0.0012263107,0.007082264,0.003126398,0.0013431725,0.0012003239,0.0054600127],"category_scores_gemma":[0.013442406,0.000539201,0.0007470652,0.0009917165,0.0031366588,0.0021618663,0.0049606184,0.0016278265,0.00101289],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040931854,0.0010745333,0.0398687,0.002201494,0.00009621473,0.0064361724,0.63547385,0.0004946666,0.0076609026,0.0065494496,0.049581308,0.2501534],"study_design_scores_gemma":[0.000130654,0.0013626533,0.09894174,0.0019980322,0.000102733764,0.0014010875,0.3939565,0.0024729979,0.0035518457,0.0034941975,0.49238276,0.00020475055],"about_ca_topic_score_codex":0.062807575,"about_ca_topic_score_gemma":0.1655766,"teacher_disagreement_score":0.062807575,"about_ca_system_score_codex":0.003338836,"about_ca_system_score_gemma":0.007903623,"threshold_uncertainty_score":0.12488395},"labels":[],"label_agreement":null},{"id":"W2613544722","doi":"10.1007/s10109-017-0252-3","title":"Geospatial cryptography: enabling researchers to access private, spatially referenced, human subjects data for cancer control and prevention","year":2017,"lang":"en","type":"article","venue":"Journal of Geographical Systems","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Western University","funders":"U.S. National Library of Medicine; National Cancer Institute","keywords":"Geospatial analysis; Data science; Geography; Cartography; Computer science","score_opus":0.13405421045156476,"score_gpt":0.43291753769151864,"score_spread":0.2988633272399539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613544722","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":[0.06495132,0.0047046565,0.87467104,0.020034477,0.00111993,0.00063485216,0.007858664,0.014136527,0.011888481],"genre_scores_gemma":[0.7268991,0.0037313001,0.25558323,0.0018671389,0.0007521248,0.00047473508,0.005389782,0.00046282238,0.004839749],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99297285,0.0034626469,0.00062212435,0.0007613455,0.0017104595,0.00047047238],"domain_scores_gemma":[0.96086544,0.015456464,0.003329547,0.016527321,0.0024170473,0.0014042141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015235052,0.00062109204,0.0009492013,0.0028180142,0.00092046795,0.0041689062,0.0022028668,0.0013885543,0.006087147],"category_scores_gemma":[0.04631562,0.0007086171,0.0007231743,0.0045255204,0.0017170266,0.010360659,0.009893761,0.0022903015,0.002333205],"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":[0.0031609396,0.0005453544,0.028404478,0.0008097991,0.0004400434,0.00060670765,0.0020091273,0.029586045,0.018842459,0.25458044,0.084230706,0.5767839],"study_design_scores_gemma":[0.0008850306,0.00046312736,0.007947756,0.00031800254,0.0002679997,0.0008313633,0.0013601922,0.23591112,0.032045167,0.59607124,0.12370137,0.00019762681],"about_ca_topic_score_codex":0.0014960847,"about_ca_topic_score_gemma":0.0013176008,"teacher_disagreement_score":0.015235052,"about_ca_system_score_codex":0.0011205882,"about_ca_system_score_gemma":0.0028401238,"threshold_uncertainty_score":0.08057159},"labels":[],"label_agreement":null},{"id":"W2614092723","doi":"10.1016/j.annepidem.2017.05.002","title":"Ethics, big data and computing in epidemiology and public health","year":2017,"lang":"en","type":"article","venue":"Annals of Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":88,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; McMaster University","funders":"","keywords":"Big data; Epidemiology; Medicine; Harm; Public health; Confidentiality; Research ethics; Engineering ethics; Information ethics; Public relations; Political science; Law; Computer science; Pathology; Data mining; Engineering","score_opus":0.6947970834722285,"score_gpt":0.5445765864036305,"score_spread":0.15022049706859797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2614092723","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077867345,0.041901395,0.066505596,0.8466814,0.005496018,0.00008822331,0.0002977355,0.000087952976,0.031154884],"genre_scores_gemma":[0.66248983,0.04520322,0.058727365,0.1841086,0.037612252,0.00094619795,0.00029570435,0.00036267823,0.010254014],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.928289,0.059624515,0.0025635557,0.0020837686,0.006426587,0.0010125072],"domain_scores_gemma":[0.58425736,0.37598455,0.009473769,0.014258012,0.010105592,0.0059207184],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0668697,0.00046305315,0.001282574,0.0022992874,0.0034747717,0.013508955,0.0013352069,0.008321679,0.0034789795],"category_scores_gemma":[0.18008389,0.0006598089,0.0005142032,0.003458406,0.03381631,0.013688757,0.0050872,0.013381381,0.0005827175],"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":[0.000047124067,0.00005625208,0.0031524752,0.00045007264,0.000049178052,0.00010662936,0.0016201033,0.0007548304,0.00010574953,0.92589015,0.035284664,0.03248274],"study_design_scores_gemma":[0.000023405453,0.000015134427,0.000917147,0.00046721217,0.000011649895,0.00011368793,0.00084860664,0.0014230692,0.00010985415,0.96163464,0.03441384,0.000021739063],"about_ca_topic_score_codex":0.004908435,"about_ca_topic_score_gemma":0.0044203126,"teacher_disagreement_score":0.9331303,"about_ca_system_score_codex":0.005349534,"about_ca_system_score_gemma":0.01309156,"threshold_uncertainty_score":0.35364497},"labels":[],"label_agreement":null},{"id":"W2615868739","doi":"10.2105/ajph.2017.303767","title":"Use of a Digital Health Application for Influenza Surveillance in China","year":2017,"lang":"en","type":"article","venue":"American Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":30,"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":"U.S. National Library of Medicine; National Institute on Drug Abuse; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research","keywords":"China; Medicine; Environmental health; Outbreak; Commission; Disease surveillance; People's Republic; Family medicine; Public health; Geography; Business; Virology; Nursing","score_opus":0.07744805167144073,"score_gpt":0.3904357768784743,"score_spread":0.31298772520703355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615868739","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":[0.99333715,0.00077922415,0.0009322082,0.000357171,0.000014381971,0.00016570551,0.0022415784,0.000083665305,0.0020889954],"genre_scores_gemma":[0.9956852,0.00041728807,0.0017021609,0.00011322968,0.000012543924,0.00006127227,0.0015045784,0.0000049608557,0.00049868523],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99911493,0.00021524684,0.00014856954,0.0001617448,0.00026642857,0.00009306425],"domain_scores_gemma":[0.997253,0.0009073265,0.0008128383,0.0002359445,0.0005400267,0.00025085523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017889802,0.00036573905,0.00027019385,0.0019500202,0.0004076619,0.00082753366,0.00064396905,0.00029168912,0.0014881436],"category_scores_gemma":[0.0053621796,0.0001778399,0.0004428899,0.0024215013,0.00029589335,0.000952384,0.0008670126,0.00018418788,0.00021111603],"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":[0.00015396862,0.00014136286,0.920661,0.00046426075,0.00008813051,0.00038941912,0.0008719289,0.000926186,0.0016938268,0.00027729152,0.0016731713,0.07265947],"study_design_scores_gemma":[0.00004004851,0.0002874246,0.9719997,0.00016855716,0.00024621683,0.00029151936,0.0012223918,0.015727274,0.0027344085,0.00018554005,0.0070549543,0.00004193719],"about_ca_topic_score_codex":0.039941393,"about_ca_topic_score_gemma":0.027984483,"teacher_disagreement_score":0.039941393,"about_ca_system_score_codex":0.0015360175,"about_ca_system_score_gemma":0.00208134,"threshold_uncertainty_score":0.079417825},"labels":[],"label_agreement":null},{"id":"W2620603451","doi":"10.1155/2017/9854103","title":"Evaluating the Timeliness of Enteric Disease Surveillance in British Columbia, Canada, 2012-13","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Infectious Diseases and Medical Microbiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Fraser Health; University of British Columbia; BC Centre for Disease Control; Island Health; Vancouver Coastal Health; Interior Health","funders":"British Columbia Centre for Disease Control","keywords":"Outbreak; Medicine; Disease surveillance; Salmonella; Disease control; Environmental health; Shigella; Subtyping; Listeria; Disease; Listeria monocytogenes; Internal medicine; Pathology; Computer science; Biology","score_opus":0.010714653610295185,"score_gpt":0.2728379816898564,"score_spread":0.26212332807956124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2620603451","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":[0.964275,0.004001058,0.0012191569,0.002312779,0.00007379629,0.00030820796,0.018220471,0.00013654072,0.009453009],"genre_scores_gemma":[0.9894825,0.0013049626,0.0018384125,0.00027583697,0.000010273529,0.000065336455,0.0050377017,0.000019553047,0.001965466],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.996806,0.00043413183,0.00034534294,0.00031865493,0.0013964864,0.0006994085],"domain_scores_gemma":[0.9865703,0.00096370344,0.0015927552,0.0002219859,0.009290685,0.001360494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027930336,0.00047713314,0.0003746056,0.0024226003,0.0018454706,0.0018550536,0.0013324058,0.00028576358,0.0010545207],"category_scores_gemma":[0.01221638,0.00036637907,0.00031106418,0.006703622,0.0005524249,0.00056551414,0.0009212041,0.000592331,0.0001626041],"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":[0.0002953224,0.000073691954,0.9448133,0.00031324828,0.00013783431,0.00013580134,0.0012333873,0.0020459604,0.00059123285,0.00020749001,0.0062211845,0.043931518],"study_design_scores_gemma":[0.0000149116095,0.000070595735,0.99165463,0.00010755,0.00004243509,0.00003468859,0.0018418194,0.002300651,0.00039646606,0.00002817364,0.003487426,0.000020620002],"about_ca_topic_score_codex":0.99732274,"about_ca_topic_score_gemma":0.99818903,"teacher_disagreement_score":0.05443368,"about_ca_system_score_codex":0.05443368,"about_ca_system_score_gemma":0.05843734,"threshold_uncertainty_score":0.39494586},"labels":[],"label_agreement":null},{"id":"W2621324357","doi":"10.1017/cjn.2017.134","title":"P.049 Seasonal variations in aneurysmal subarachnoid hemorrhage: revisiting the myth using google trends","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Systems, Applications & Products in Data Processing (Canada); Toronto Public Health","funders":"","keywords":"Subarachnoid hemorrhage; Aneurysm; Medicine; Incidence (geometry); Seasonality; Univariate; Demography; Internal medicine; Statistics; Multivariate statistics; Surgery","score_opus":0.04753442111474168,"score_gpt":0.30999707961607076,"score_spread":0.2624626585013291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621324357","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":[0.79068476,0.029344857,0.011657355,0.1194963,0.0022329697,0.00017739815,0.02963042,0.00052750425,0.016248453],"genre_scores_gemma":[0.9770802,0.007536822,0.005989706,0.002883548,0.0011378585,0.00006319257,0.0042517167,0.00013446284,0.00092234684],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99812835,0.0006747528,0.0003247825,0.0002685886,0.00046115098,0.0001422456],"domain_scores_gemma":[0.97621584,0.013706731,0.0056397556,0.0016083483,0.0021397034,0.0006897157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045966227,0.00025640338,0.00031717206,0.003927458,0.000407618,0.0027036038,0.00090062775,0.0005093186,0.003109663],"category_scores_gemma":[0.027789209,0.0002004719,0.0009396999,0.010160565,0.0009943387,0.0038072134,0.0012557612,0.0010612063,0.00054389465],"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":[0.00009231555,0.000020557545,0.9007051,0.00061546726,0.00034322066,0.00026287822,0.0023786526,0.0004457516,0.00016655591,0.0033917837,0.021425448,0.07015231],"study_design_scores_gemma":[0.0000144739,0.00010651413,0.9355715,0.001313087,0.0002832986,0.00072353525,0.0062604193,0.0070938966,0.0002691062,0.004497169,0.043820076,0.000046833],"about_ca_topic_score_codex":0.039352022,"about_ca_topic_score_gemma":0.0427837,"teacher_disagreement_score":0.039352022,"about_ca_system_score_codex":0.0008630147,"about_ca_system_score_gemma":0.0018424488,"threshold_uncertainty_score":0.07824594},"labels":[],"label_agreement":null},{"id":"W2625578542","doi":"","title":"MedISys: A Multilingual Media Monitoring Tool for Medical Intelligence and Early Warning","year":2008,"lang":"en","type":"article","venue":"Joint Research Centre (European Commission)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Data science; Business","score_opus":0.12708399322912683,"score_gpt":0.3782333616237653,"score_spread":0.2511493683946385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2625578542","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06725307,0.0032667299,0.11906614,0.0014103091,0.00080231373,0.0023974145,0.25592312,0.50548124,0.04439975],"genre_scores_gemma":[0.2745428,0.0024368837,0.2891958,0.0013540704,0.0013716312,0.0032116612,0.372536,0.010529884,0.04482125],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849546,0.0003665962,0.00021109982,0.00030891952,0.0005206516,0.00009727196],"domain_scores_gemma":[0.9942082,0.002512896,0.0010507982,0.0004429271,0.001219142,0.0005660617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021270046,0.002137189,0.0007288685,0.0120459795,0.00064640876,0.0025216292,0.0010717998,0.0009609762,0.022760853],"category_scores_gemma":[0.008803758,0.00054987817,0.000659007,0.0033980655,0.0003629655,0.004208248,0.0030323379,0.0007871681,0.012043533],"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":[0.004741693,0.00048070826,0.026063958,0.004437933,0.0005774901,0.0013788175,0.002944201,0.0024578802,0.029289443,0.0032826613,0.3985152,0.5258301],"study_design_scores_gemma":[0.0006550398,0.0010781392,0.065124184,0.0007533712,0.00051280344,0.0017685358,0.002508937,0.06950151,0.07442482,0.007197843,0.7759169,0.0005579266],"about_ca_topic_score_codex":0.0037458283,"about_ca_topic_score_gemma":0.004188391,"teacher_disagreement_score":0.022760853,"about_ca_system_score_codex":0.00063780125,"about_ca_system_score_gemma":0.0009773453,"threshold_uncertainty_score":0.07614261},"labels":[],"label_agreement":null},{"id":"W2636810058","doi":"10.1109/icassp.2017.7952893","title":"A data centric approach to utility change detection in online social media","year":2017,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Marketing buzz; Computer science; Social media; Ground truth; Online advertising; Function (biology); Point (geometry); Preference; Change detection; Maximization; Data mining; Artificial intelligence; Machine learning; Mathematical optimization; The Internet; Mathematics","score_opus":0.2059609445873789,"score_gpt":0.3687787098622943,"score_spread":0.16281776527491537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2636810058","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":[0.10627301,0.00061775267,0.8890238,0.0008104319,0.000040337833,0.00019627702,0.0012184125,0.0005297908,0.0012901307],"genre_scores_gemma":[0.8600846,0.00022948228,0.13729209,0.00015294459,0.00008959324,0.00015739512,0.0010780244,0.0000375531,0.00087839866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99664944,0.001473914,0.00021939933,0.00075695687,0.0007050354,0.0001951832],"domain_scores_gemma":[0.9828514,0.009983026,0.0030303022,0.0024206066,0.0012933783,0.0004212913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044199014,0.0009906763,0.0014157459,0.0032633443,0.00056400587,0.002023324,0.002147136,0.0018184262,0.0005892096],"category_scores_gemma":[0.021761248,0.0007132289,0.00096305955,0.0035616579,0.0011140242,0.0030318447,0.0016615465,0.0024149157,0.0002586404],"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":[0.0007098097,0.0011589983,0.081673145,0.0006107827,0.0005857975,0.00087980565,0.0007467412,0.5382871,0.013242302,0.06569882,0.0049524433,0.29145426],"study_design_scores_gemma":[0.000009386634,0.00009903777,0.0051079434,0.00001529729,0.000019487934,0.00015449761,0.00009777795,0.9625509,0.0025746312,0.028445547,0.0009014374,0.000024054083],"about_ca_topic_score_codex":0.0031402449,"about_ca_topic_score_gemma":0.0036856756,"teacher_disagreement_score":0.0044199014,"about_ca_system_score_codex":0.0013379393,"about_ca_system_score_gemma":0.00084773335,"threshold_uncertainty_score":0.023374975},"labels":[],"label_agreement":null},{"id":"W2676449823","doi":"10.1186/s12911-017-0491-8","title":"Completeness and timeliness of notifiable disease reporting: a comparison of laboratory and provider reports submitted to a large county health department","year":2017,"lang":"en","type":"article","venue":"BMC Medical Informatics and Decision Making","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Agency for Healthcare Research and Quality; McMaster University; University of Washington; Robert Wood Johnson Foundation; Health Services Research and Development; U.S. Department of Veterans Affairs","keywords":"Medicine; McNemar's test; Medical emergency; Family medicine; Public health; Health informatics; Health department; Completeness (order theory); Notifiable disease; Emergency medicine; Statistics; Pathology; Mathematics","score_opus":0.057827112403358354,"score_gpt":0.41304909481496543,"score_spread":0.3552219824116071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2676449823","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99394023,0.00046523474,0.0017479153,0.00013572263,0.00001590557,0.00009070854,0.002791724,0.000063829466,0.0007487824],"genre_scores_gemma":[0.99477816,0.00020516592,0.001460886,0.000026195627,0.000021028492,0.000066432025,0.0032511302,0.000019797666,0.0001711814],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.97478884,0.010676101,0.005109961,0.0027007922,0.0059324205,0.0007918728],"domain_scores_gemma":[0.7820335,0.08668404,0.0930963,0.011020285,0.02491186,0.0022540847],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.027223833,0.0003162047,0.0005563203,0.005070483,0.0003134341,0.0015301163,0.00089458714,0.00036994249,0.00068181404],"category_scores_gemma":[0.11166048,0.00044768525,0.00082782476,0.0052473173,0.0005708644,0.0013725602,0.0013143409,0.00034875318,0.00022154902],"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":[0.00019601478,0.000023145743,0.99248743,0.00010218665,0.00014485569,0.0000333238,0.00090716046,0.00037309268,0.00018836111,0.000034905857,0.00024874837,0.0052609183],"study_design_scores_gemma":[0.000008146114,0.00019262495,0.99663,0.00003875978,0.000044778295,0.00015401402,0.00086741435,0.0010838131,0.00032369708,0.000020984722,0.00062428706,0.000011361183],"about_ca_topic_score_codex":0.010136141,"about_ca_topic_score_gemma":0.006274985,"teacher_disagreement_score":0.9727762,"about_ca_system_score_codex":0.0010327268,"about_ca_system_score_gemma":0.0014807082,"threshold_uncertainty_score":0.14397514},"labels":[],"label_agreement":null},{"id":"W2704254100","doi":"10.14236/jhi.v24i2.940","title":"Understanding the use of geographical information systems (GIS) in health informatics research: A review","year":2017,"lang":"en","type":"review","venue":"Journal of Innovation in Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Algoma University; Sault Area Hospital","funders":"","keywords":"Health informatics; Public health informatics; GIS and public health; Geographic information system; Public health; Informatics; Health geography; Data science; Health Administration Informatics; Information system; Environmental health; Medicine; HRHIS; Computer science; Geography; Health policy; International health; Pathology; Engineering; Cartography","score_opus":0.7092200099249943,"score_gpt":0.5230569497591335,"score_spread":0.18616306016586082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2704254100","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":[0.000060923132,0.99899036,0.000106609405,0.00043813963,0.00010159933,0.000006560875,0.000010131616,0.0000027694468,0.00028289945],"genre_scores_gemma":[0.0007714294,0.998662,0.00024522498,0.00017380617,0.000085180174,0.000012921826,0.000010980729,0.0000011693178,0.000037228612],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99607277,0.0015663361,0.0008601735,0.00036413874,0.0009670323,0.00016953658],"domain_scores_gemma":[0.969676,0.02587516,0.0017282624,0.00026704426,0.002176521,0.00027704012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056899446,0.0014321485,0.0026540502,0.015467705,0.00071191526,0.0041923444,0.0018095623,0.0033923506,0.0034842454],"category_scores_gemma":[0.019083442,0.00079669955,0.0020346486,0.018622395,0.0017792628,0.005322995,0.0017438725,0.0020404954,0.00078544806],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059498056,0.000059383772,0.00069501356,0.33290067,0.00060385355,0.00025669538,0.00096926553,0.00056652696,0.0003632057,0.008834371,0.0167246,0.6379669],"study_design_scores_gemma":[0.00003240375,0.0001462127,0.0031254096,0.38861334,0.0018863127,0.0017054485,0.0013556044,0.00035294774,0.00041804498,0.006306575,0.5959719,0.00008573174],"about_ca_topic_score_codex":0.005770751,"about_ca_topic_score_gemma":0.0069718,"teacher_disagreement_score":0.015467705,"about_ca_system_score_codex":0.003084925,"about_ca_system_score_gemma":0.008651999,"threshold_uncertainty_score":0.030091703},"labels":[],"label_agreement":null},{"id":"W2726042507","doi":"10.1371/journal.pone.0180721","title":"Exploring spatial patterns of sudden cardiac arrests in the city of Toronto using Poisson kriging and Hot Spot analyses","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University","funders":"Scheme for Promotion of Academic and Research Collaboration","keywords":"Kriging; Poisson distribution; Population; Poisson regression; Geography; Spatial ecology; Demography; Cartography; Statistics; Mathematics; Sociology","score_opus":0.3357053897252245,"score_gpt":0.3674553144240848,"score_spread":0.03174992469886029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2726042507","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":[0.9933827,0.00013560794,0.0049411478,0.000059843103,0.0000028226434,0.000031199852,0.0010173294,0.000030467403,0.0003989133],"genre_scores_gemma":[0.9963567,0.000065116714,0.0027735257,0.000002844412,0.0000015253306,0.000011852055,0.0006178052,0.0000026543948,0.0001679154],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99956447,0.0001685685,0.00003191328,0.00008090064,0.00009047456,0.00006368516],"domain_scores_gemma":[0.99865365,0.0006398941,0.0002950079,0.00006729523,0.0002710666,0.00007305427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077674695,0.0003291659,0.00025434862,0.0019024732,0.0003968422,0.0005208945,0.00048979523,0.0001844195,0.00057618675],"category_scores_gemma":[0.0033896544,0.00018030268,0.0005194982,0.0022736478,0.00026531704,0.0002109484,0.0005212528,0.00016270057,0.000057238],"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":[0.00012948303,0.00002516346,0.9154514,0.00016166049,0.00027728753,0.000377885,0.002387075,0.060879584,0.0014514826,0.00066470954,0.0005579205,0.017636348],"study_design_scores_gemma":[0.0000064749456,0.000055761528,0.88764197,0.00003780469,0.000078299156,0.00008223057,0.00265522,0.10806065,0.00037227053,0.00025648612,0.00072538643,0.000027470967],"about_ca_topic_score_codex":0.6692693,"about_ca_topic_score_gemma":0.6971756,"teacher_disagreement_score":0.33073068,"about_ca_system_score_codex":0.0024211786,"about_ca_system_score_gemma":0.0023958702,"threshold_uncertainty_score":0.6653568},"labels":[],"label_agreement":null},{"id":"W2728587074","doi":"10.2196/medinform.7123","title":"Validation of an Improved Computer-Assisted Technique for Mining Free-Text Electronic Medical Records","year":2017,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"RCVS Knowledge; University of Glasgow","keywords":"Computer science; Medical record; Text messaging; Text mining; Data mining; Electronic medical record; Information retrieval; Data science; Medicine; World Wide Web; Radiology","score_opus":0.017684289571607982,"score_gpt":0.33264389104402425,"score_spread":0.31495960147241625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2728587074","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":[0.45545214,0.0011667142,0.487067,0.0011035773,0.00040187966,0.0038158565,0.021676626,0.025433077,0.0038830733],"genre_scores_gemma":[0.31824398,0.0002259467,0.65671813,0.00023868884,0.00013626798,0.001321719,0.021684201,0.0003346932,0.0010963854],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.98648226,0.005329712,0.0027881383,0.002664143,0.0024557472,0.0002799387],"domain_scores_gemma":[0.9303062,0.0463429,0.0026786348,0.0055849324,0.014617252,0.0004701924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013967704,0.0011277588,0.0007946948,0.0072013144,0.0006267554,0.0024364984,0.0015296304,0.0013013069,0.001936914],"category_scores_gemma":[0.058163673,0.00031295817,0.0011580278,0.004080579,0.0004951669,0.0016766127,0.0016462017,0.00085355464,0.0023041363],"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":[0.002000781,0.0010919952,0.09085306,0.0014368162,0.00089636765,0.0008039541,0.0022168583,0.013759793,0.04416798,0.0017954961,0.014412646,0.8265642],"study_design_scores_gemma":[0.00084462453,0.002440796,0.10391826,0.00042871328,0.00075993815,0.0028560024,0.0017022026,0.7518555,0.09055413,0.0037945793,0.040619593,0.00022568152],"about_ca_topic_score_codex":0.0038938979,"about_ca_topic_score_gemma":0.003388529,"teacher_disagreement_score":0.013967704,"about_ca_system_score_codex":0.0005583594,"about_ca_system_score_gemma":0.0023413973,"threshold_uncertainty_score":0.07386917},"labels":[],"label_agreement":null},{"id":"W2730334513","doi":"10.1038/s41598-017-05091-9","title":"Google Trends can improve surveillance of Type 2 diabetes","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; University of Warwick; York University; King's College London; Alan Turing Institute; Center for Urban Science and Progress","keywords":"Disease; Type 2 diabetes; Medicine; Obesity; Diabetes mellitus; Perception; Disease surveillance; Computer science; Internet privacy; Data science; Psychology; Pathology","score_opus":0.015542303333812383,"score_gpt":0.2841521667950272,"score_spread":0.2686098634612148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2730334513","genre_codex":"dataset","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":[0.37503874,0.008786776,0.015012574,0.007986718,0.000886888,0.00053592114,0.5518772,0.008884474,0.030990725],"genre_scores_gemma":[0.63077337,0.0031169814,0.043341517,0.00077483285,0.00043030365,0.00023782736,0.31832388,0.00023417614,0.0027671368],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99862325,0.00040529875,0.00018452836,0.00031412026,0.0003739931,0.00009880086],"domain_scores_gemma":[0.993898,0.0028437926,0.0010003793,0.00086560723,0.0011226747,0.00026969935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001563435,0.0008222516,0.0006247483,0.0070359004,0.00026204486,0.0016968466,0.00059571356,0.000587349,0.0019298502],"category_scores_gemma":[0.013593034,0.00019429724,0.0008984297,0.0073877703,0.00015059584,0.0021111018,0.0011475021,0.00045793186,0.0013981175],"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":[0.0008113767,0.00021858767,0.5842242,0.0027704067,0.000704033,0.00037241497,0.0012236396,0.009334335,0.002345165,0.0054271785,0.1134095,0.27915913],"study_design_scores_gemma":[0.00010754936,0.00040865203,0.624207,0.0009344902,0.0005785633,0.0007396156,0.003748817,0.120766014,0.0042946655,0.01567166,0.22834794,0.00019498942],"about_ca_topic_score_codex":0.050088264,"about_ca_topic_score_gemma":0.06653313,"teacher_disagreement_score":0.050088264,"about_ca_system_score_codex":0.00059405685,"about_ca_system_score_gemma":0.0007505615,"threshold_uncertainty_score":0.0995934},"labels":[],"label_agreement":null},{"id":"W2737202790","doi":"10.4018/978-1-5225-2515-8.ch017","title":"Notifiable Disease Databases for Client Management and Surveillance","year":2017,"lang":"en","type":"book-chapter","venue":"Advances in healthcare information systems and administration book series","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Notifiable disease; Infectious disease (medical specialty); Database; Public health; Data collection; Disease surveillance; Disease; Medicine; Environmental health; Computer science; Pathology","score_opus":0.026837504101322192,"score_gpt":0.32070613113800733,"score_spread":0.29386862703668515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737202790","genre_codex":"other","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":[0.0014800087,0.034240916,0.27664873,0.016598094,0.0056577707,0.0012815164,0.04321628,0.01107678,0.6097999],"genre_scores_gemma":[0.010924315,0.05399673,0.36252075,0.007706408,0.0029450567,0.0014798299,0.07946259,0.00557615,0.47538814],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99788505,0.00034109942,0.00022566978,0.00023760357,0.0012389204,0.00007160279],"domain_scores_gemma":[0.9958515,0.0015384625,0.00020741828,0.0007534593,0.0014460997,0.00020313989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027465157,0.00094457984,0.00077650865,0.0037874721,0.0010125147,0.0063428725,0.0025863894,0.0013314113,0.050533775],"category_scores_gemma":[0.009291173,0.00076872576,0.00061273185,0.010287267,0.00064711296,0.007859457,0.0031272096,0.0023601276,0.054329526],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011007203,0.000024873385,0.0002490428,0.00027220484,0.000007999148,0.00004430305,0.0003055235,0.00056194526,0.00030411506,0.08236903,0.6590538,0.25679615],"study_design_scores_gemma":[0.0000026041348,0.000002195607,0.00014847665,0.00017172929,0.0000016821496,0.00006453196,0.000062337676,0.0004082612,0.00012578178,0.012856639,0.9861492,0.000006703012],"about_ca_topic_score_codex":0.005101055,"about_ca_topic_score_gemma":0.006833498,"teacher_disagreement_score":0.050533775,"about_ca_system_score_codex":0.0020236946,"about_ca_system_score_gemma":0.0042075748,"threshold_uncertainty_score":0.1690523},"labels":[],"label_agreement":null},{"id":"W2739938577","doi":"10.1016/j.forsciint.2017.07.021","title":"Grave mapping in support of the search for missing persons in conflict contexts","year":2017,"lang":"en","type":"article","venue":"Forensic Science International","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Toronto","funders":"International Committee of the Red Cross","keywords":"Geographic information system; Data science; Work (physics); Action (physics); Geography; Computer science; Engineering; Cartography","score_opus":0.06717568729123365,"score_gpt":0.3771762254169456,"score_spread":0.310000538125712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2739938577","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":[0.5691636,0.0038066108,0.29001427,0.036628608,0.0010114439,0.0011325668,0.023828298,0.00239275,0.07202187],"genre_scores_gemma":[0.87469625,0.00060339354,0.11556476,0.00065582816,0.00009720045,0.00031641303,0.004884112,0.00008298327,0.0030990744],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9973864,0.0012823681,0.00018592693,0.00037697505,0.0005208074,0.00024749615],"domain_scores_gemma":[0.97866756,0.011892176,0.0017144325,0.0027406118,0.0038318862,0.0011533209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060759033,0.00048242602,0.00063594506,0.006427137,0.0020130917,0.0034935824,0.0030220053,0.0015137922,0.008438562],"category_scores_gemma":[0.037508488,0.00032954864,0.00042664466,0.0033946855,0.00090038107,0.0032996375,0.006105806,0.0015099037,0.0013360936],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069304503,0.00040211595,0.38650304,0.0015557847,0.00028586524,0.0023489455,0.019227957,0.020451479,0.005147482,0.051601086,0.045190778,0.46659243],"study_design_scores_gemma":[0.00016568942,0.0006256778,0.18026613,0.0026330028,0.00053534517,0.0037406369,0.07656861,0.2614422,0.014220002,0.26019174,0.19934455,0.00026636763],"about_ca_topic_score_codex":0.016233973,"about_ca_topic_score_gemma":0.030464375,"teacher_disagreement_score":0.016233973,"about_ca_system_score_codex":0.00096409646,"about_ca_system_score_gemma":0.004002049,"threshold_uncertainty_score":0.032278955},"labels":[],"label_agreement":null},{"id":"W2746261470","doi":"10.1111/jocn.14014","title":"Examining the social construction of surveillance: A critical issue for health visitors and public health nurses working with mothers and children","year":2017,"lang":"en","type":"review","venue":"Journal of Clinical Nursing","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"Menzies Centre for Australian Studies, King's College London, University of London; Amicus Therapeutics","keywords":"Meaning (existential); Privilege (computing); Context (archaeology); Public health; Health care; Nursing; Public relations; Sociology; Psychology; Medicine; Political science","score_opus":0.21824419262365016,"score_gpt":0.5305850628958415,"score_spread":0.3123408702721914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2746261470","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":[0.0014656272,0.9688703,0.0004930282,0.02657612,0.0012758636,0.00002685861,0.000007060685,0.00000400353,0.0012810722],"genre_scores_gemma":[0.038529173,0.9468781,0.0021863845,0.010292136,0.001210995,0.00022872647,0.000020622048,0.000013673136,0.0006401726],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.97064006,0.02375821,0.0015353111,0.00084677024,0.0024789528,0.00074070593],"domain_scores_gemma":[0.9055857,0.08246639,0.0036483912,0.0011447804,0.005649995,0.0015046349],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.03408557,0.0008849503,0.0018792026,0.0041167797,0.0042394185,0.006762954,0.0025533317,0.0064592594,0.001489555],"category_scores_gemma":[0.051968295,0.0006533384,0.0011088067,0.0047914484,0.012901993,0.011229429,0.006431877,0.006875499,0.00024574314],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086779066,0.00007239062,0.0031569796,0.07926088,0.0003513728,0.0017540012,0.22973837,0.00026438726,0.00040095634,0.070147276,0.046143815,0.5686229],"study_design_scores_gemma":[0.00002885661,0.00012734538,0.0051578106,0.20472641,0.00029929465,0.0027607821,0.17506261,0.00011846124,0.00027923705,0.024160758,0.58718574,0.00009267551],"about_ca_topic_score_codex":0.017659552,"about_ca_topic_score_gemma":0.030180095,"teacher_disagreement_score":0.99576056,"about_ca_system_score_codex":0.008207663,"about_ca_system_score_gemma":0.032595884,"threshold_uncertainty_score":0.18026388},"labels":[],"label_agreement":null},{"id":"W2748950811","doi":"10.2196/publichealth.7714","title":"Effectivity of Awareness Months in Increasing Internet Search Activity for Top Malignancies Among Women","year":2017,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"The Internet; Internet privacy; Environmental health; Medicine; Computer science; World Wide Web","score_opus":0.04057379474074904,"score_gpt":0.3556562055871893,"score_spread":0.3150824108464403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2748950811","genre_codex":"commentary","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":[0.01034509,0.061685704,0.00035659326,0.6716991,0.25069985,0.000079799625,0.0028303135,0.00011246524,0.0021911587],"genre_scores_gemma":[0.04254627,0.037012238,0.00079338386,0.2912553,0.6224775,0.00025274462,0.000694434,0.00015568949,0.004812427],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99716717,0.0011550336,0.00046212997,0.00046597174,0.0005474093,0.00020229147],"domain_scores_gemma":[0.95930886,0.027023744,0.0050479467,0.00053004414,0.0064181634,0.0016713792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064440803,0.00074586016,0.0014930064,0.0014482768,0.00074188365,0.0030182076,0.0018807192,0.005744206,0.005857034],"category_scores_gemma":[0.0545184,0.00046450965,0.0009162758,0.0013426936,0.0009635432,0.0026440183,0.00103859,0.0068206885,0.002287082],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053332327,0.00011876537,0.015290689,0.0031493783,0.00037526214,0.00034332156,0.0005286281,0.00005322783,0.00009816208,0.00037882844,0.9348604,0.044270076],"study_design_scores_gemma":[0.000525803,0.0009741662,0.09016403,0.010880593,0.0018051766,0.0021975117,0.00255151,0.0015864449,0.0008075521,0.0018424685,0.8863751,0.00028960026],"about_ca_topic_score_codex":0.0018039218,"about_ca_topic_score_gemma":0.0038416917,"teacher_disagreement_score":0.0064440803,"about_ca_system_score_codex":0.00069680647,"about_ca_system_score_gemma":0.0012222228,"threshold_uncertainty_score":0.03407997},"labels":[],"label_agreement":null},{"id":"W2749899146","doi":"10.2196/publichealth.7333","title":"Insights From Flutracking: Thirteen Tips to Growing a Web-Based Participatory Surveillance System","year":2017,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Social media; Usability; Focus group; Public health; Family medicine; Nursing; World Wide Web; Business; Computer science; Marketing","score_opus":0.060444208876749915,"score_gpt":0.33198768962202463,"score_spread":0.27154348074527473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2749899146","genre_codex":"commentary","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":[0.1783095,0.006499841,0.21422227,0.51516247,0.0041125612,0.0029233822,0.0011178125,0.0028322625,0.07481994],"genre_scores_gemma":[0.6137185,0.0065038716,0.30922544,0.05037629,0.0013935647,0.0034727298,0.00083013804,0.0011986688,0.013280752],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9056955,0.08493433,0.0019424177,0.0013671289,0.0040901056,0.0019704856],"domain_scores_gemma":[0.86963296,0.10419345,0.0025431828,0.0051049753,0.009067578,0.009457892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06287898,0.0011565314,0.00068071584,0.0018869289,0.007080541,0.011376887,0.002882147,0.0050017503,0.0040921527],"category_scores_gemma":[0.09968027,0.0010542269,0.0010046785,0.0012651972,0.008659077,0.01864616,0.013868017,0.0078025474,0.002166698],"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":[0.0002640441,0.0006331483,0.012843415,0.0020988416,0.00006226976,0.0021590188,0.6339024,0.00050015625,0.0035968276,0.019900776,0.071029514,0.25300965],"study_design_scores_gemma":[0.00014503872,0.0009896702,0.004649738,0.0028242203,0.0000856424,0.0022641204,0.39164615,0.0026304016,0.002110804,0.0507361,0.5417211,0.0001969996],"about_ca_topic_score_codex":0.002268756,"about_ca_topic_score_gemma":0.006299384,"teacher_disagreement_score":0.06287898,"about_ca_system_score_codex":0.0029809095,"about_ca_system_score_gemma":0.00665668,"threshold_uncertainty_score":0.3325398},"labels":[],"label_agreement":null},{"id":"W2751370245","doi":"10.5210/ojphi.v9i2.7985","title":"Applications of information and communications technologies to public health: A scoping review using the MeSH term “public health informatics”","year":2017,"lang":"en","type":"review","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Public health informatics; Health informatics; Public health; Interoperability; Informatics; Health Administration Informatics; Medicine; Health care; Data science; Computer science; Knowledge management; Health policy; World Wide Web; International health; Nursing; Political science","score_opus":0.35968180775898256,"score_gpt":0.5156273314611112,"score_spread":0.1559455237021286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751370245","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":[0.00066095707,0.9964478,0.00037480568,0.0006840603,0.00028765656,0.00042724397,0.00015566021,0.00000762032,0.0009541593],"genre_scores_gemma":[0.0043123974,0.99292666,0.0010618877,0.00045336896,0.00017857019,0.0007531355,0.00015475879,0.0000052450673,0.00015391686],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.980948,0.0066574235,0.007340989,0.0008255546,0.0036826015,0.0005454449],"domain_scores_gemma":[0.8832226,0.09766534,0.009206887,0.0010672315,0.008313431,0.00052443007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02170133,0.0017720837,0.0046843006,0.036652103,0.0017579099,0.006077822,0.002277866,0.004319749,0.006023148],"category_scores_gemma":[0.07000021,0.001314697,0.0049740523,0.037856102,0.0018627465,0.005789107,0.003136693,0.0021186601,0.0009165394],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011444703,0.000055819648,0.00076798414,0.8433648,0.001311607,0.0002358672,0.0009367426,0.00021956599,0.00033456803,0.0016181023,0.0036676547,0.14737287],"study_design_scores_gemma":[0.000029336165,0.00008989614,0.0018928347,0.9544123,0.0042449394,0.0003090582,0.00078319653,0.00012089778,0.00022944008,0.0006030051,0.037265137,0.000019926621],"about_ca_topic_score_codex":0.0060861865,"about_ca_topic_score_gemma":0.012213556,"teacher_disagreement_score":0.036652103,"about_ca_system_score_codex":0.0064692576,"about_ca_system_score_gemma":0.02302543,"threshold_uncertainty_score":0.11476898},"labels":[],"label_agreement":null},{"id":"W2751398136","doi":"10.1093/ije/dyx180","title":"Software Application Profile: Opal and Mica: open-source software solutions for epidemiological data management, harmonization and dissemination","year":2017,"lang":"en","type":"article","venue":"International Journal of Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":100,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Institute for Cancer Research; McGill University Health Centre","funders":"National Institute on Aging; Medical Research Council; Partenariat Canadien Contre Le Cancer; European Commission; Ontario Institute for Cancer Research","keywords":"Computer science; Metadata; World Wide Web; Dissemination; JavaScript; Interoperability; Software; Mica; License; Java; Web application; Database; Operating system; Telecommunications","score_opus":0.1253999023416296,"score_gpt":0.43718791768304227,"score_spread":0.31178801534141265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751398136","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029038189,0.00041261257,0.37319127,0.0034558335,0.00077425165,0.002702396,0.03543873,0.5442964,0.036824692],"genre_scores_gemma":[0.033768497,0.001144487,0.46510676,0.004298888,0.000953857,0.008834122,0.17326853,0.2632948,0.049330045],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9929744,0.0013089896,0.0011008668,0.0010934374,0.0028947315,0.00062748743],"domain_scores_gemma":[0.9545314,0.019306967,0.00328271,0.010430555,0.008418236,0.004030135],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.013347316,0.0024836515,0.0018511139,0.0067123114,0.0014730864,0.0070906347,0.0072326264,0.0033671982,0.11674333],"category_scores_gemma":[0.074283354,0.0029460562,0.0025853675,0.005860361,0.0018274185,0.00866739,0.012826012,0.0051886593,0.11953246],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00105616,0.00041538486,0.003796909,0.002480548,0.0003392504,0.00046180421,0.00086975,0.002487297,0.0051120096,0.019933641,0.72888184,0.23416536],"study_design_scores_gemma":[0.00068117573,0.00017429162,0.006144451,0.0009224107,0.00012274295,0.0006473614,0.00016866361,0.02474808,0.0071342373,0.035465296,0.92350626,0.000284997],"about_ca_topic_score_codex":0.003032978,"about_ca_topic_score_gemma":0.0018202428,"teacher_disagreement_score":0.9927674,"about_ca_system_score_codex":0.0017310872,"about_ca_system_score_gemma":0.006261598,"threshold_uncertainty_score":0.3905453},"labels":[],"label_agreement":null},{"id":"W2755423591","doi":"10.1007/978-3-319-68604-2_3","title":"Using Dynamic Bayesian Networks for Incorporating Nontraditional Data Sources in Public Health Surveillance","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in social networks","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"McGill University Health Centre; McGill University","funders":"","keywords":"Diagnosis code; Medicine; Public health; Health care; Population; Identification (biology); Data source; Estimation; Computer science; Medical emergency; Data mining; Environmental health; Pathology","score_opus":0.08703170186316006,"score_gpt":0.344344771609629,"score_spread":0.25731306974646895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2755423591","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":[0.0034145205,0.0006340514,0.99422026,0.00036394293,0.000056457488,0.00003281483,0.00026382288,0.00019728087,0.0008169041],"genre_scores_gemma":[0.2579758,0.0026168136,0.7318949,0.0003706221,0.00040961266,0.00034979254,0.0017558875,0.0002552293,0.0043713525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960989,0.0022529303,0.00024636052,0.00072558864,0.0005505701,0.00012563224],"domain_scores_gemma":[0.96721375,0.029580891,0.0009565101,0.00095620187,0.0010492443,0.00024334677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010429163,0.0015043168,0.001681181,0.002773432,0.00085251307,0.0032969182,0.0026152632,0.002194213,0.0027031451],"category_scores_gemma":[0.046633977,0.0020707569,0.0018172822,0.0030413985,0.0011422716,0.006607263,0.003256894,0.0034524438,0.0006895639],"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":[0.00009388877,0.00008840109,0.002605406,0.00016744954,0.0002509665,0.000113507944,0.0002090193,0.8290905,0.00038217416,0.051958267,0.002377217,0.112663284],"study_design_scores_gemma":[0.000004855002,0.000006200044,0.000113619695,0.000022416185,0.000020304089,0.000011990968,0.00001109615,0.9583135,0.000110726134,0.040638633,0.0007360896,0.000010468539],"about_ca_topic_score_codex":0.021167077,"about_ca_topic_score_gemma":0.029547825,"teacher_disagreement_score":0.021167077,"about_ca_system_score_codex":0.0021017331,"about_ca_system_score_gemma":0.0015363656,"threshold_uncertainty_score":0.055155337},"labels":[],"label_agreement":null},{"id":"W2755757810","doi":"10.1007/978-3-319-68604-2","title":"Public Health Intelligence and the Internet","year":2017,"lang":"en","type":"book","venue":"Lecture notes in social networks","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre","funders":"","keywords":"The Internet; Internet privacy; Field (mathematics); Public health; Computer science; Data science; World Wide Web; Medicine; Pathology","score_opus":0.0532569844746518,"score_gpt":0.3142863210398826,"score_spread":0.26102933656523075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2755757810","genre_codex":"other","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":[0.00088791293,0.13563204,0.010529882,0.029596303,0.008222812,0.000035315938,0.0004947278,0.00035210847,0.8142488],"genre_scores_gemma":[0.030117128,0.18772352,0.011879796,0.009422034,0.01651361,0.00011985005,0.0016546202,0.0004636531,0.7421058],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994842,0.00013148974,0.000020225718,0.000052436655,0.00027166874,0.00003993346],"domain_scores_gemma":[0.99866843,0.0009034197,0.000056984074,0.00013982013,0.00013296244,0.000098372555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007029444,0.0007433723,0.00049832376,0.0030559797,0.0008549201,0.0066003,0.00058863545,0.0016570647,0.046080768],"category_scores_gemma":[0.0023596068,0.00041085735,0.00038595704,0.0045346245,0.0013123333,0.009585267,0.0021382824,0.0031510768,0.014629734],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011611439,0.000038608465,0.00014335786,0.00023348279,0.000013607279,0.000059684873,0.00016404348,0.0006423879,0.00020367684,0.2882394,0.481543,0.22870715],"study_design_scores_gemma":[0.000003227238,0.0000045166357,0.00017610296,0.00030127968,0.000004514723,0.00009263554,0.00010030102,0.00049105624,0.00007739952,0.10987297,0.8888706,0.0000053042895],"about_ca_topic_score_codex":0.0014270935,"about_ca_topic_score_gemma":0.0024806112,"teacher_disagreement_score":0.046080768,"about_ca_system_score_codex":0.001393487,"about_ca_system_score_gemma":0.0008684489,"threshold_uncertainty_score":0.15415555},"labels":[],"label_agreement":null},{"id":"W2760560537","doi":"10.2196/iproc.8686","title":"Detecting Influenza Epidemics Using Self-reported Data Through Mobile App (FeverCoach)","year":2017,"lang":"en","type":"article","venue":"Iproceedings","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Public health surveillance; Public health; The Internet; Internet privacy; Health records; Mobile apps; Influenza-like illness; Lag time; Business; Environmental health; Medicine; Medical emergency; Computer science; Health care; Virology; World Wide Web; Biology; Political science; Nursing","score_opus":0.16112078126236473,"score_gpt":0.4094856190534962,"score_spread":0.24836483779113144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2760560537","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":[0.92096925,0.0019425637,0.027748073,0.00075998215,0.00036738283,0.0016340595,0.028247027,0.010439341,0.007892317],"genre_scores_gemma":[0.9352694,0.0006892692,0.04789499,0.00037515128,0.00013623513,0.0008662188,0.012176917,0.000065567554,0.0025263436],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999566,0.00010003268,0.00005917015,0.00014932598,0.00008478934,0.000040523766],"domain_scores_gemma":[0.9984634,0.00072948355,0.0002722522,0.000116411444,0.00031649857,0.000102011545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068967725,0.00094301027,0.0006319712,0.0016658541,0.00022528312,0.00061339943,0.0005033682,0.000547401,0.0015595393],"category_scores_gemma":[0.0031923933,0.00022833288,0.0005199914,0.00055512454,0.00010718591,0.0007198368,0.00057240116,0.0004015348,0.000978123],"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":[0.0016531128,0.0011401721,0.6054904,0.0020176284,0.00053177984,0.0010083261,0.0009874747,0.0058225216,0.011443745,0.0005610622,0.02572094,0.3436229],"study_design_scores_gemma":[0.00018199837,0.0027215972,0.6600994,0.00062680576,0.0006934106,0.0019995854,0.0011603807,0.2930052,0.014896603,0.0017015176,0.022621244,0.00029232746],"about_ca_topic_score_codex":0.0038897013,"about_ca_topic_score_gemma":0.0064355955,"teacher_disagreement_score":0.0038897013,"about_ca_system_score_codex":0.0001790855,"about_ca_system_score_gemma":0.00032677673,"threshold_uncertainty_score":0.00773412},"labels":[],"label_agreement":null},{"id":"W2761296717","doi":"10.2196/publichealth.7540","title":"Participatory Disease Surveillance: Engaging Communities Directly in Reporting, Monitoring, and Responding to Health Threats","year":2017,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":159,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Disease surveillance; Citizen journalism; Citizen science; Participatory sensing; Participatory GIS; Disease; Best practice; Public health surveillance; Business; Public health; Public relations; Medicine; Knowledge management; Data science; Computer science; Political science; World Wide Web; Biology","score_opus":0.13968493397885914,"score_gpt":0.42512060912893646,"score_spread":0.2854356751500773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2761296717","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":[0.079319805,0.011691702,0.6423949,0.09699008,0.003155593,0.014277125,0.0028086135,0.0018135759,0.1475486],"genre_scores_gemma":[0.56875855,0.0070372284,0.3904054,0.01045412,0.0009673214,0.013244558,0.0012196143,0.00023582634,0.007677449],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.79144573,0.18793057,0.0043175234,0.0068850177,0.007266042,0.0021550818],"domain_scores_gemma":[0.804052,0.1493832,0.013874889,0.017314393,0.008983767,0.0063916887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.124506995,0.0013094548,0.0009553286,0.0051650302,0.0065165195,0.0071213273,0.0041875676,0.0028499314,0.0072816145],"category_scores_gemma":[0.111142814,0.0008736008,0.001367512,0.003635831,0.011178837,0.0077703386,0.022630291,0.004049958,0.0012341358],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044272916,0.0009124387,0.029012034,0.009843757,0.0004734306,0.0008577341,0.16267997,0.0033997141,0.00583706,0.08229251,0.046551086,0.65769756],"study_design_scores_gemma":[0.0004016424,0.0016930074,0.015651908,0.014655917,0.00035750112,0.0011481668,0.089609906,0.007392659,0.008358677,0.21525687,0.64510864,0.00036509355],"about_ca_topic_score_codex":0.004554317,"about_ca_topic_score_gemma":0.0055891615,"teacher_disagreement_score":0.124506995,"about_ca_system_score_codex":0.0040241233,"about_ca_system_score_gemma":0.02258854,"threshold_uncertainty_score":0.65846384},"labels":[],"label_agreement":null},{"id":"W2761356019","doi":"10.1007/978-3-319-58097-5_11","title":"The Competitive Landscape of Mobile Communications Industry in Canada: Predictive Analytic Modeling with Google Trends and Twitter","year":2017,"lang":"en","type":"book-chapter","venue":"Annals of information systems","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Competitor analysis; Analytics; Social media; Predictive analytics; Sentiment analysis; Consumption (sociology); Tertiary sector of the economy; Big data; Advertising; Marketing; Business; Computer science; Data science; World Wide Web; Data mining; Artificial intelligence; Social science; Sociology","score_opus":0.04813911698487055,"score_gpt":0.2953765382401987,"score_spread":0.24723742125532816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2761356019","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":[0.7926776,0.02406554,0.068639554,0.0376108,0.00054041983,0.00015422139,0.016302392,0.0007703436,0.05923913],"genre_scores_gemma":[0.9533043,0.00896645,0.016279502,0.00053324085,0.00019366985,0.000060528873,0.0041575762,0.00013097712,0.01637377],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996728,0.00007752435,0.000009952687,0.000044133012,0.000107050255,0.00008853019],"domain_scores_gemma":[0.9983438,0.0009868429,0.00011721436,0.000053535114,0.00037736318,0.00012117746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013466746,0.00063328474,0.00052483566,0.0013488567,0.0014699446,0.0039795437,0.001497158,0.0008874523,0.002025021],"category_scores_gemma":[0.005113045,0.00036873974,0.0008128347,0.004435422,0.00083436526,0.0017801109,0.000851164,0.0015884452,0.00040099604],"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":[0.00012719636,0.00019488751,0.19874713,0.0003116479,0.00028852103,0.00048169363,0.0029938375,0.39678538,0.00042083042,0.136723,0.09456477,0.16836107],"study_design_scores_gemma":[0.0000073804513,0.000021080383,0.025271438,0.0001555983,0.000071634044,0.000059777696,0.0024063583,0.924485,0.0002237948,0.024256108,0.022977335,0.0000645168],"about_ca_topic_score_codex":0.95955604,"about_ca_topic_score_gemma":0.9667273,"teacher_disagreement_score":0.040443957,"about_ca_system_score_codex":0.013015736,"about_ca_system_score_gemma":0.018781215,"threshold_uncertainty_score":0.09443617},"labels":[],"label_agreement":null},{"id":"W2761866945","doi":"10.2196/publichealth.7376","title":"Lessons From the Implementation of Mo-Buzz, a Mobile Pandemic Surveillance System for Dengue","year":2017,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Colombo; National Research Foundation","keywords":"Dengue fever; Pandemic; Developing country; Outreach; Public health; Outbreak; Environmental health; Sri lanka; Medicine; Disease surveillance; Geography; Socioeconomics; Infectious disease (medical specialty); Economic growth; Coronavirus disease 2019 (COVID-19); Disease; Virology","score_opus":0.05384890735594853,"score_gpt":0.3969275475096389,"score_spread":0.34307864015369033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2761866945","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":[0.4340719,0.0060215387,0.035752416,0.4339152,0.0036568604,0.0035328008,0.0014474513,0.0030968566,0.078505024],"genre_scores_gemma":[0.8526827,0.005548342,0.06735716,0.052771464,0.00078951486,0.0014624285,0.0009519858,0.00040866082,0.018027704],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9953538,0.0027956013,0.00021528096,0.00031121698,0.0007191851,0.00060497154],"domain_scores_gemma":[0.98941153,0.00507237,0.00040397688,0.0005450182,0.0027716632,0.001795423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009017121,0.00064973166,0.00026004037,0.00052461436,0.0018308077,0.0026951123,0.0020737348,0.003321719,0.0033760862],"category_scores_gemma":[0.026568249,0.00033436497,0.00078664586,0.00030402027,0.0014918334,0.0048383563,0.002330904,0.0037077249,0.001259918],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006578446,0.0019049838,0.08515783,0.0025608777,0.00011254061,0.0044661434,0.04797311,0.0018907684,0.005913311,0.0071616606,0.17850555,0.6636954],"study_design_scores_gemma":[0.0005603794,0.009315256,0.09742156,0.005398218,0.0003522451,0.0060379673,0.121644646,0.009580362,0.009409642,0.007860108,0.73191994,0.0004996248],"about_ca_topic_score_codex":0.014858979,"about_ca_topic_score_gemma":0.021680508,"teacher_disagreement_score":0.014858979,"about_ca_system_score_codex":0.0014465511,"about_ca_system_score_gemma":0.0035211388,"threshold_uncertainty_score":0.04768771},"labels":[],"label_agreement":null},{"id":"W2765073795","doi":"10.3934/publichealth.2016.1.65","title":"Exploring the Specifications of Spatial Adjacencies and Weights in Bayesian Spatial Modeling with Intrinsic Conditional Autoregressive Priors in a Small-area Study of Fall Injuries","year":2016,"lang":"en","type":"article","venue":"AIMS Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bayesian probability; Prior probability; Spatial analysis; Autoregressive model; Statistics; Adjacency list; Small area estimation; Computer science; Mathematics; Bayesian inference; Geography; Algorithm","score_opus":0.14872683968641395,"score_gpt":0.2926366523454052,"score_spread":0.14390981265899125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765073795","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":[0.2995735,0.00020784995,0.6981185,0.0005275323,0.000016224827,0.00012861624,0.00016364116,0.000062076535,0.0012020529],"genre_scores_gemma":[0.89626074,0.00017766465,0.102444604,0.000085125015,0.000022010148,0.00020889501,0.00020756609,0.000022854088,0.0005704941],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9823794,0.014081175,0.0006204409,0.0016673169,0.0007733587,0.00047834415],"domain_scores_gemma":[0.8842292,0.10010475,0.007230576,0.004934214,0.0026980273,0.0008032212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02883632,0.00082869723,0.0012813482,0.0016664703,0.00091834273,0.0026025975,0.0029019227,0.0016591058,0.0014764364],"category_scores_gemma":[0.13983004,0.0013185098,0.0016414532,0.002244229,0.0028318227,0.004814738,0.0031045955,0.0029381295,0.00012121897],"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":[0.00019186667,0.00012849798,0.07144706,0.00015364753,0.0004677629,0.00030719853,0.0023767955,0.6738675,0.00075677317,0.20876685,0.00035966866,0.04117636],"study_design_scores_gemma":[0.000039375194,0.00010422444,0.00948588,0.00006503398,0.00011401971,0.00007496346,0.0005287808,0.86096495,0.0002052488,0.1277535,0.00060959096,0.00005439605],"about_ca_topic_score_codex":0.04118223,"about_ca_topic_score_gemma":0.041916877,"teacher_disagreement_score":0.04118223,"about_ca_system_score_codex":0.002243376,"about_ca_system_score_gemma":0.0020268643,"threshold_uncertainty_score":0.1525029},"labels":[],"label_agreement":null},{"id":"W2765379141","doi":"10.2196/publichealth.8391","title":"Adverse Reactions Associated With Cannabis Consumption as Evident From Search Engine Queries","year":2017,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cannabis; Medicine; Adverse effect; Drug reaction; Drug; Psychiatry; Pharmacology","score_opus":0.05153186089486681,"score_gpt":0.3514285328396484,"score_spread":0.2998966719447816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765379141","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":[0.96975493,0.0026418967,0.0008622139,0.00047572193,0.000027559363,0.00065603503,0.0206436,0.000097387994,0.0048407414],"genre_scores_gemma":[0.98963225,0.0009060984,0.0012124185,0.00021721002,0.00003832935,0.00022544949,0.007399229,0.000009606512,0.0003593283],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9925487,0.0033745135,0.0015651843,0.0006595984,0.0015395053,0.00031250232],"domain_scores_gemma":[0.9629341,0.017316883,0.015570584,0.0016737352,0.0019449156,0.0005598929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004310108,0.00033880965,0.0004968941,0.0022262428,0.0003280209,0.0011807493,0.00035213382,0.0004025,0.002007647],"category_scores_gemma":[0.024264703,0.00016315156,0.00064666563,0.0022234896,0.00044004028,0.00083177467,0.0008304406,0.00038638024,0.00038066666],"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":[0.001755354,0.00018159865,0.9713291,0.001412937,0.0003218571,0.0004829675,0.0007912634,0.00071346737,0.0014970793,0.0002821693,0.0027583009,0.018473953],"study_design_scores_gemma":[0.0000599096,0.00048562582,0.9866457,0.00027673875,0.0002663882,0.0018855222,0.00056760287,0.0033124327,0.0022783184,0.00035467502,0.0038189602,0.000048139034],"about_ca_topic_score_codex":0.0064891297,"about_ca_topic_score_gemma":0.00606152,"teacher_disagreement_score":0.0064891297,"about_ca_system_score_codex":0.0008188577,"about_ca_system_score_gemma":0.00083908206,"threshold_uncertainty_score":0.022794247},"labels":[],"label_agreement":null},{"id":"W2765572340","doi":"10.2196/publichealth.7344","title":"Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches","year":2017,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"H2020 Future and Emerging Technologies; National Institute of General Medical Sciences; Horizon 2020 Framework Programme; European Commission; Skoll Foundation; Defense Threat Reduction Agency; National Institutes of Health; National Science Foundation","keywords":"Outbreak; Psychological intervention; Environmental health; Disease surveillance; Citizen journalism; Medicine; Public health; Medical emergency; Computer science; Virology","score_opus":0.2659580252915658,"score_gpt":0.3601577780503601,"score_spread":0.09419975275879428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765572340","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":[0.028472865,0.0006670179,0.9614585,0.002558758,0.00012963204,0.00029388486,0.00022640484,0.00027344195,0.005919643],"genre_scores_gemma":[0.45379168,0.000769283,0.5422066,0.00037891572,0.00021090076,0.0008026892,0.0002578844,0.00007207758,0.0015098755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98208827,0.012861514,0.0006366738,0.0017044807,0.002227746,0.0004812705],"domain_scores_gemma":[0.9684672,0.022521088,0.0019077722,0.0050143558,0.0016371958,0.00045239422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01482006,0.0025165584,0.0015574945,0.0023605453,0.001041014,0.0046514194,0.0035885845,0.002793634,0.0022038186],"category_scores_gemma":[0.040729426,0.001242509,0.0029970037,0.0027827371,0.001933396,0.004657625,0.00773854,0.0021335403,0.00028228882],"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":[0.0006546026,0.00056982425,0.025955735,0.001015498,0.0015726959,0.0005088457,0.0036499714,0.59571487,0.0035605675,0.14990227,0.0015403165,0.21535479],"study_design_scores_gemma":[0.00009382218,0.00022676976,0.0018879081,0.0001666255,0.00018639863,0.000096705466,0.00046494053,0.86115056,0.0013633462,0.12949875,0.0047355145,0.000128641],"about_ca_topic_score_codex":0.010455391,"about_ca_topic_score_gemma":0.008379261,"teacher_disagreement_score":0.01482006,"about_ca_system_score_codex":0.002215657,"about_ca_system_score_gemma":0.0026655153,"threshold_uncertainty_score":0.07837689},"labels":[],"label_agreement":null},{"id":"W2765607627","doi":"10.7202/1041222ar","title":"Géographie des seringues souillées à la traîne à Montréal","year":2017,"lang":"fr","type":"article","venue":"Cahiers de géographie du Québec","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Humanities; Geography; Political science; Art","score_opus":0.009036921308710304,"score_gpt":0.23512732840453787,"score_spread":0.22609040709582756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765607627","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":[0.9177206,0.002598817,0.00543838,0.0014130952,0.00007614935,0.00011459424,0.017779568,0.00021283903,0.054645874],"genre_scores_gemma":[0.96457034,0.0009098546,0.0019483719,0.00005528848,0.00001100628,0.000038759772,0.0026901984,0.000029211687,0.029746903],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99969375,0.000035566598,0.000009040712,0.000078160185,0.000090624606,0.00009297096],"domain_scores_gemma":[0.9991862,0.00010298855,0.00011821126,0.00004033997,0.00041400932,0.00013823435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002872435,0.00032426888,0.00022083257,0.0018893569,0.0017400327,0.0016470044,0.0005716033,0.00023699968,0.009069858],"category_scores_gemma":[0.00103536,0.00018902926,0.00031677407,0.0037275283,0.00091411493,0.00043632917,0.0009334895,0.00041143136,0.0005663389],"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":[0.0003747049,0.000055219825,0.79071456,0.00064793206,0.00027454915,0.0010395286,0.022951506,0.0127123315,0.010105418,0.019026158,0.02424331,0.117854744],"study_design_scores_gemma":[0.000004842795,0.00003980531,0.9337089,0.00008931408,0.00003779204,0.00009979016,0.007137012,0.0019322784,0.00078796066,0.00025460255,0.05585692,0.000050737563],"about_ca_topic_score_codex":0.9734245,"about_ca_topic_score_gemma":0.9897544,"teacher_disagreement_score":0.026575506,"about_ca_system_score_codex":0.012711858,"about_ca_system_score_gemma":0.012778139,"threshold_uncertainty_score":0.09223139},"labels":[],"label_agreement":null},{"id":"W2766113359","doi":"10.1016/j.aogh.2017.09.006","title":"#Globalhealth Twitter Conversations on #Malaria, #HIV, #TB, #NCDS, and #NTDS: a Cross-Sectional Analysis","year":2017,"lang":"en","type":"article","venue":"Annals of Global Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of Hong Kong","keywords":"Annals; Global health; Public health; Medicine; Publication; Health policy; Political science; Public relations; Nursing; Geography; Law","score_opus":0.08411958139302593,"score_gpt":0.4588306309779753,"score_spread":0.37471104958494933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766113359","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":[0.98833674,0.00016082832,0.0002563276,0.00029686015,0.000022327982,0.00013605191,0.0091097,0.0000142619565,0.0016669513],"genre_scores_gemma":[0.98637944,0.00036187217,0.00087338116,0.0005195978,0.000059335085,0.00076741126,0.009492251,0.00003582377,0.0015108249],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990711,0.00035212786,0.00011678083,0.00017826745,0.0001315312,0.0001501761],"domain_scores_gemma":[0.9952283,0.0020925913,0.0012180731,0.00023082,0.0008711546,0.00035901714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001664841,0.00021444134,0.00027893367,0.0014964865,0.0008734501,0.0010467292,0.0002983971,0.00048010336,0.0033946824],"category_scores_gemma":[0.0071952115,0.00036655235,0.00029112902,0.0017653148,0.00042300622,0.001620339,0.0018874598,0.0008256009,0.0013535041],"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":[0.00025745368,0.00017178692,0.9508408,0.00042561084,0.00008659434,0.0002482334,0.025277855,0.00009291121,0.0011519648,0.0003231087,0.008276242,0.012847464],"study_design_scores_gemma":[0.000008211654,0.000076273354,0.9702506,0.000119980876,0.00004437227,0.00019454955,0.022389121,0.0004559677,0.00037248127,0.00007583382,0.0059922906,0.000020438309],"about_ca_topic_score_codex":0.012570322,"about_ca_topic_score_gemma":0.019127369,"teacher_disagreement_score":0.012570322,"about_ca_system_score_codex":0.00077697646,"about_ca_system_score_gemma":0.0005174147,"threshold_uncertainty_score":0.024994314},"labels":[],"label_agreement":null},{"id":"W2770067167","doi":"10.2196/publichealth.7817","title":"Check and Report Ebola (CARE) Hotline: The User Perspective of an Innovative Tool for Postarrival Monitoring of Ebola in the United States","year":2017,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Centers for Disease Control and Prevention; Skoll Foundation; U.S. Department of Health and Human Services","keywords":"Hotline; Perspective (graphical); Ebola virus; Medicine; Medical emergency; Virology; Computer science; Telecommunications; Outbreak","score_opus":0.041122608529950644,"score_gpt":0.3797810482314684,"score_spread":0.33865843970151777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770067167","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":[0.9973436,0.00014143888,0.00066678436,0.0005811267,0.000007185421,0.000030919367,0.000065717686,0.000044646982,0.0011187168],"genre_scores_gemma":[0.9961662,0.0002470409,0.0024061855,0.00060153205,0.00001562419,0.000031863194,0.0000747683,0.000015827489,0.00044090755],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9969524,0.0021717132,0.00013678358,0.00013608384,0.00038287014,0.00022009439],"domain_scores_gemma":[0.9923671,0.0041738846,0.0009790864,0.00031954848,0.0013656998,0.0007947287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040891455,0.0003038412,0.0002191395,0.0004965308,0.00099784,0.0017867849,0.0004841877,0.00077285303,0.0014477817],"category_scores_gemma":[0.01070668,0.00027903426,0.00033003945,0.00027848035,0.0005759551,0.0015493924,0.0011223188,0.0005050195,0.000316322],"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":[0.0006259954,0.0007864466,0.7359381,0.0003696932,0.00010167232,0.0014588188,0.14694123,0.00029794275,0.009495598,0.00020686672,0.0053564245,0.09842129],"study_design_scores_gemma":[0.000041593397,0.004426767,0.69144636,0.00035155343,0.00019783013,0.0047172303,0.272563,0.0043388773,0.004201263,0.00015244035,0.01739817,0.00016489116],"about_ca_topic_score_codex":0.0040656906,"about_ca_topic_score_gemma":0.008113849,"teacher_disagreement_score":0.0040891455,"about_ca_system_score_codex":0.0004768544,"about_ca_system_score_gemma":0.0005319247,"threshold_uncertainty_score":0.021625757},"labels":[],"label_agreement":null},{"id":"W2770888236","doi":"10.2196/publichealth.8610","title":"Will Participatory Syndromic Surveillance Work in Latin America? Piloting a Mobile Approach to Crowdsource Influenza-Like Illness Data in Guatemala","year":2017,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Latin Americans; Citizen journalism; Internet privacy; Disease surveillance; Public health surveillance; mHealth; Influenza-like illness; Work (physics); Computer science; Data science; Medicine; Public health; Environmental health; Computer security; World Wide Web; Virology; Political science; Nursing; Psychological intervention; Engineering","score_opus":0.09991540923849587,"score_gpt":0.37257399774146904,"score_spread":0.2726585885029732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770888236","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":[0.9713288,0.00044286074,0.0059964876,0.012530663,0.000102088154,0.0028366707,0.00057886215,0.00023890454,0.0059445994],"genre_scores_gemma":[0.9788047,0.00034388693,0.011880204,0.0027268487,0.000041754956,0.0050606206,0.00024787083,0.000030017713,0.0008640527],"study_design_codex":"observational","study_design_gemma":"nonrandomized_trial","domain_scores_codex":[0.9905032,0.0077277226,0.00019095869,0.00047103304,0.00027672996,0.0008303684],"domain_scores_gemma":[0.9904026,0.00516847,0.00085082033,0.00069856615,0.0017461388,0.0011334204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016703239,0.00060413155,0.00044136905,0.0007571656,0.0028361266,0.0017914377,0.0013410291,0.0010309883,0.0032721113],"category_scores_gemma":[0.021549374,0.00036758886,0.0005654465,0.00079512206,0.0019221526,0.0016271508,0.0041519315,0.00085006334,0.00035620423],"study_design_candidate":"nonrandomized_trial","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":[0.0032023194,0.0044281865,0.43860364,0.002548875,0.0002977278,0.0031834468,0.22945067,0.002720094,0.014738657,0.0041510826,0.016267225,0.28040808],"study_design_scores_gemma":[0.002398978,0.009109841,0.46,0.003481805,0.0004705007,0.0007781819,0.40017635,0.018101444,0.0058007147,0.007920414,0.09144963,0.0003122247],"about_ca_topic_score_codex":0.06599913,"about_ca_topic_score_gemma":0.08443658,"teacher_disagreement_score":0.06599913,"about_ca_system_score_codex":0.0035911258,"about_ca_system_score_gemma":0.009005909,"threshold_uncertainty_score":0.13122994},"labels":[],"label_agreement":null},{"id":"W2770981113","doi":"10.2196/publichealth.8015","title":"Syndromic Surveillance Models Using Web Data: The Case of Influenza in Greece and Italy Using Google Trends","year":2017,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Hellenic Center for Disease Control & Prevention","keywords":"The Internet; Outbreak; Geography; Population; Correlation; Demography; Computer science; Medicine; Environmental health; World Wide Web; Virology; Mathematics","score_opus":0.1610787041681122,"score_gpt":0.40325177605473866,"score_spread":0.24217307188662646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770981113","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":[0.9897764,0.00039274668,0.006497893,0.0006565656,0.00003723772,0.000069378344,0.0013367817,0.00013527217,0.0010976306],"genre_scores_gemma":[0.99460286,0.00017924236,0.0036827617,0.000033703578,0.000029296978,0.000040063434,0.0010543392,0.000007399444,0.00037036918],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999084,0.0005134673,0.000069938185,0.00018045923,0.00007317446,0.00007898294],"domain_scores_gemma":[0.99590874,0.002696336,0.00074110815,0.00022383957,0.00032410907,0.00010588195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003906751,0.0008230178,0.0005824471,0.002157923,0.00027810756,0.0011883972,0.00087386963,0.0008791341,0.00085314846],"category_scores_gemma":[0.008667075,0.00029817017,0.0012049095,0.0015853732,0.00041174213,0.0008461549,0.00078783033,0.00071824976,0.0002365696],"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":[0.00032563746,0.00032323925,0.48378208,0.00016060517,0.00054170657,0.0009917236,0.0003659626,0.480731,0.000433031,0.0021485013,0.0023837236,0.02781279],"study_design_scores_gemma":[0.000013599375,0.000078574,0.048017688,0.000027260387,0.000057589335,0.00011582399,0.00021232001,0.9502959,0.00010684707,0.00072287105,0.00033636746,0.000015150191],"about_ca_topic_score_codex":0.06872476,"about_ca_topic_score_gemma":0.031604856,"teacher_disagreement_score":0.06872476,"about_ca_system_score_codex":0.0010663581,"about_ca_system_score_gemma":0.00069335697,"threshold_uncertainty_score":0.13664943},"labels":[],"label_agreement":null},{"id":"W2772254256","doi":"10.2196/publichealth.7794","title":"The Seasonal Periodicity of Healthy Contemplations About Exercise and Weight Loss: Ecological Correlational Study","year":2017,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Ecology; Environmental health; Environmental science; Medicine; Biology","score_opus":0.039031166211986434,"score_gpt":0.3435085969234407,"score_spread":0.3044774307114543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772254256","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":[0.99876523,0.00009435875,0.0002130546,0.000042425534,0.000005742082,0.000022559338,0.0003730783,0.0000049626187,0.00047861945],"genre_scores_gemma":[0.9991567,0.00004959456,0.00026218066,0.000021006028,0.00000785516,0.00003012259,0.00033609133,0.0000038546177,0.00013251234],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990395,0.00040081414,0.00007712044,0.00022890273,0.00011698524,0.00013666817],"domain_scores_gemma":[0.99692816,0.0010863259,0.0009329094,0.0003333088,0.0003671957,0.00035212332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014159671,0.00018334699,0.0003319536,0.0009810639,0.00056790025,0.00058521435,0.00036867734,0.00034323783,0.0014428179],"category_scores_gemma":[0.00394859,0.00036161332,0.0005816735,0.0016021781,0.0006274592,0.0004990282,0.0007439226,0.00056506804,0.00023030219],"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":[0.00008723253,0.00012574103,0.9966365,0.000017848573,0.000097951975,0.00003917614,0.00071661867,0.00007658781,0.00023287266,0.000050145332,0.00022163447,0.0016976353],"study_design_scores_gemma":[0.0000024977583,0.00004466445,0.9990087,0.0000037025038,0.00001244816,0.000031821615,0.00046253283,0.0002652331,0.000012158871,0.000024155195,0.00012908295,0.0000028938023],"about_ca_topic_score_codex":0.025668627,"about_ca_topic_score_gemma":0.056376297,"teacher_disagreement_score":0.025668627,"about_ca_system_score_codex":0.0004193544,"about_ca_system_score_gemma":0.0006471898,"threshold_uncertainty_score":0.051038444},"labels":[],"label_agreement":null},{"id":"W2774339002","doi":"10.7202/1042310ar","title":"Technologies d’exploitation du big data dans les organisations et transformations organisationnelles : une étude de cas au sein du Service de santé des armées françaises","year":2017,"lang":"fr","type":"article","venue":"Documentation et bibliothèques","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.06600912359147261,"score_gpt":0.35933345467121114,"score_spread":0.29332433107973854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774339002","genre_codex":"other","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":[0.2511837,0.06933813,0.25019762,0.130455,0.00232824,0.0011451915,0.020145904,0.0024154184,0.27279088],"genre_scores_gemma":[0.7194839,0.053821113,0.17110462,0.0042282557,0.0008026464,0.00097422814,0.010844463,0.00065265514,0.03808816],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9841006,0.0067362944,0.0014554106,0.001296821,0.0056091337,0.000801824],"domain_scores_gemma":[0.9228347,0.045821175,0.006023433,0.009618986,0.0141102215,0.0015914865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019087993,0.00070606807,0.0006180563,0.009441729,0.0046565994,0.017694097,0.0014313379,0.0023156737,0.005803121],"category_scores_gemma":[0.04471565,0.0007169631,0.000977823,0.026762862,0.0051889177,0.011809553,0.0044993022,0.0031299505,0.0013259143],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018361758,0.00017287918,0.075887546,0.005455552,0.00026088711,0.0020918134,0.10766732,0.0045585344,0.003434281,0.22574387,0.04744378,0.5271],"study_design_scores_gemma":[0.000028772505,0.00007742033,0.04320437,0.0044524395,0.000091369715,0.0012398954,0.043860678,0.0038953046,0.0030103046,0.03272431,0.8672354,0.00017972634],"about_ca_topic_score_codex":0.14403218,"about_ca_topic_score_gemma":0.120357394,"teacher_disagreement_score":0.14403218,"about_ca_system_score_codex":0.012550367,"about_ca_system_score_gemma":0.024316892,"threshold_uncertainty_score":0.28638762},"labels":[],"label_agreement":null},{"id":"W2774911261","doi":"10.2196/publichealth.9136","title":"Discrepancies Between Classic and Digital Epidemiology in Searching for the Mayaro Virus: Preliminary Qualitative and Quantitative Analysis of Google Trends","year":2017,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Outbreak; Epidemiology; Geography; Amazon rainforest; Virology; Biology; Medicine; Ecology","score_opus":0.17375798694387598,"score_gpt":0.4757386442727154,"score_spread":0.3019806573288394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774911261","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":[0.99435425,0.00035337094,0.00039723632,0.00053282775,0.000009775381,0.00012500524,0.0012238985,0.000007244024,0.0029964403],"genre_scores_gemma":[0.99843997,0.00019352789,0.00041618804,0.00007411338,0.000009488474,0.00009419076,0.0004748628,0.0000049133346,0.00029281367],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9959556,0.0018225536,0.00045776015,0.00035145538,0.0010509032,0.00036173308],"domain_scores_gemma":[0.97540694,0.014386551,0.0052776802,0.0006355885,0.0035747543,0.0007184976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008012679,0.00017509625,0.00029696416,0.0059005287,0.00085101766,0.0026207005,0.0005512922,0.00051846827,0.0014233685],"category_scores_gemma":[0.028402934,0.00020223393,0.00040643473,0.005922371,0.0019514313,0.0039756955,0.0024576788,0.00052307994,0.0001322514],"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":[0.00021032672,0.00005815651,0.7943333,0.0011356111,0.00010053729,0.00023528872,0.17846677,0.00027425395,0.0007128962,0.0037508833,0.0012790115,0.019443007],"study_design_scores_gemma":[0.000007906235,0.00010487668,0.68037534,0.0004748342,0.0000477967,0.00027978668,0.30931443,0.0016618314,0.00025258498,0.0008594808,0.006577973,0.00004317273],"about_ca_topic_score_codex":0.014074522,"about_ca_topic_score_gemma":0.014716316,"teacher_disagreement_score":0.014074522,"about_ca_system_score_codex":0.0025264001,"about_ca_system_score_gemma":0.0014045413,"threshold_uncertainty_score":0.042375624},"labels":[],"label_agreement":null},{"id":"W2777924017","doi":"10.2196/publichealth.7373","title":"A Smartphone App (AfyaData) for Innovative One Health Disease Surveillance from Community to National Levels in Africa: Intervention in Disease Surveillance","year":2017,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":163,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Disease surveillance; Smartphone app; Public health surveillance; Environmental health; Disease; Intervention (counseling); Smartphone application; Medicine; Public health; Internet privacy; mHealth; Psychological intervention; Computer science; Nursing; Multimedia; Pathology","score_opus":0.12434399507467711,"score_gpt":0.3859843105109424,"score_spread":0.2616403154362653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2777924017","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":[0.78670347,0.006242613,0.0646149,0.025685122,0.0016707538,0.0119686145,0.008918199,0.013451812,0.08074456],"genre_scores_gemma":[0.8002614,0.00354105,0.16312684,0.0048204614,0.00036039017,0.0081226835,0.0026062946,0.00023217201,0.016928716],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893004,0.00064837694,0.00006919174,0.00011920013,0.00009085511,0.0001423152],"domain_scores_gemma":[0.9971625,0.0016551967,0.00023796424,0.0001478835,0.00026996864,0.0005264686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017400119,0.0008350469,0.00032163458,0.00081074645,0.00089126045,0.0009460385,0.0008164576,0.00075608765,0.012935684],"category_scores_gemma":[0.0051824553,0.0003007637,0.0005596528,0.00035441102,0.00037868734,0.0016093027,0.0023060697,0.00065759756,0.0025854008],"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":[0.0012552408,0.0029306004,0.05574685,0.0037760576,0.000082363804,0.0017047724,0.0240236,0.00042811787,0.01152506,0.0010771662,0.100756645,0.79669356],"study_design_scores_gemma":[0.0024315417,0.0123763485,0.2115744,0.0072487206,0.0006779202,0.0059367334,0.041158017,0.0082506575,0.0129101975,0.0061336434,0.6907965,0.0005052637],"about_ca_topic_score_codex":0.0013270456,"about_ca_topic_score_gemma":0.0027103839,"teacher_disagreement_score":0.012935684,"about_ca_system_score_codex":0.0003391902,"about_ca_system_score_gemma":0.00096578203,"threshold_uncertainty_score":0.043274224},"labels":[],"label_agreement":null},{"id":"W2780527627","doi":"10.1016/j.puhe.2017.10.016","title":"Google and suicides: what can we learn about the use of internet to prevent suicides?","year":2017,"lang":"en","type":"article","venue":"Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"The Internet; Suicide prevention; Medical emergency; Poison control; Injury prevention; Occupational safety and health; Human factors and ergonomics; Medicine; Internet privacy; Computer security; World Wide Web; Computer science","score_opus":0.1367773310598664,"score_gpt":0.36838184867477763,"score_spread":0.23160451761491124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2780527627","genre_codex":"commentary","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":[0.10373683,0.3292854,0.0034248289,0.5224844,0.004436733,0.00016117947,0.010795288,0.00017333015,0.02550191],"genre_scores_gemma":[0.57215434,0.35206607,0.004824873,0.05103383,0.012158423,0.00017394265,0.0052884314,0.00007816395,0.0022219426],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9953129,0.0020779155,0.00058950955,0.00048988743,0.0012198183,0.0003098981],"domain_scores_gemma":[0.91359365,0.05785967,0.012415952,0.0027881183,0.01024637,0.0030962625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008309179,0.00067649595,0.0014364156,0.0040550814,0.00058885315,0.0065351306,0.0015071478,0.0030055272,0.004718188],"category_scores_gemma":[0.06579178,0.00052527303,0.0015590785,0.0064467946,0.002157844,0.010188693,0.0017434808,0.0051626787,0.00094196235],"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":[0.00039171215,0.00037714085,0.5547656,0.006926829,0.0013609332,0.0003404441,0.0024212347,0.0008782622,0.0001628439,0.016788714,0.07868843,0.33689794],"study_design_scores_gemma":[0.00021819075,0.00060461677,0.6521815,0.037672024,0.0026286931,0.0015158358,0.014498142,0.008264982,0.00063197216,0.06630848,0.2150801,0.00039548182],"about_ca_topic_score_codex":0.03581679,"about_ca_topic_score_gemma":0.04760125,"teacher_disagreement_score":0.03581679,"about_ca_system_score_codex":0.0018354328,"about_ca_system_score_gemma":0.0049957596,"threshold_uncertainty_score":0.07121664},"labels":[],"label_agreement":null},{"id":"W2781966411","doi":"10.14745/ccdr.v44i01a01","title":"Federal, provincial and territorial public health response plan for biological events","year":2018,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Response Biomedical (Canada); Public Health Agency of Canada","funders":"","keywords":"Public health; Plan (archaeology); Public administration; Business; Public relations; Emergency response; Health plan; Corporate governance; Political science; Health care; Medicine; Nursing; Medical emergency; Law","score_opus":0.06822679003890682,"score_gpt":0.3230616595729257,"score_spread":0.2548348695340189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2781966411","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026563656,0.003683824,0.14692436,0.16114949,0.0053245444,0.09955373,0.12801243,0.006047763,0.42274025],"genre_scores_gemma":[0.119074054,0.0037466593,0.46604398,0.04296701,0.00064154016,0.044476945,0.098263085,0.0006871093,0.22409973],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.987626,0.0025751847,0.0007286183,0.0006098834,0.0055136434,0.0029466045],"domain_scores_gemma":[0.9702616,0.0028625948,0.0013102927,0.0009690763,0.018112822,0.0064837383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026228623,0.0011047896,0.00064627134,0.0036425397,0.0039405692,0.0039866148,0.0040994133,0.00332478,0.011640622],"category_scores_gemma":[0.026141861,0.0011633642,0.0012121738,0.0029002635,0.0015029924,0.0013363436,0.0028771372,0.004278881,0.003356856],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020084665,0.00039242502,0.00901277,0.0008252504,0.000086607775,0.00041321453,0.002476019,0.008688467,0.0013663553,0.090331204,0.77623767,0.10996918],"study_design_scores_gemma":[0.00018068879,0.0002302641,0.016530728,0.00072588434,0.000057699526,0.000088147484,0.0016007033,0.0038908483,0.000652722,0.004373443,0.9715404,0.00012850345],"about_ca_topic_score_codex":0.8813271,"about_ca_topic_score_gemma":0.9063713,"teacher_disagreement_score":0.93658787,"about_ca_system_score_codex":0.06341215,"about_ca_system_score_gemma":0.40095672,"threshold_uncertainty_score":0.4600895},"labels":[],"label_agreement":null},{"id":"W2782311721","doi":"10.14745/ccdr.v44i01a04","title":"Canada’s Pandemic Influenza Preparedness: Surveillance strategy","year":2018,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Preparedness; Pandemic; Disease surveillance; Medical emergency; Medicine; Environmental health; Coronavirus disease 2019 (COVID-19); Political science; Disease; Public health; Infectious disease (medical specialty); Nursing","score_opus":0.029214191107679626,"score_gpt":0.2979343382069017,"score_spread":0.26872014709922204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782311721","genre_codex":"other","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":[0.01579038,0.02225488,0.016252825,0.309874,0.004047989,0.009838417,0.2687679,0.0037865283,0.34938708],"genre_scores_gemma":[0.21245617,0.043521963,0.12128163,0.12235052,0.0015675088,0.0058113276,0.26439348,0.0007695036,0.22784786],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9915074,0.0007765544,0.00043251182,0.00045554296,0.005048965,0.0017789652],"domain_scores_gemma":[0.9755106,0.00066225446,0.00059743295,0.0003071807,0.017633898,0.0052886815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007504179,0.0016119367,0.00059137004,0.005874318,0.0047789584,0.0053434763,0.0035751113,0.0020491446,0.009738657],"category_scores_gemma":[0.010838583,0.0008494277,0.0011929384,0.0066612344,0.0010342514,0.0014151239,0.0026856943,0.0024946148,0.0028922793],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007207149,0.00009161539,0.0098139,0.0007353841,0.000061637664,0.0001344771,0.0004672467,0.001676558,0.00045413128,0.015102699,0.8756421,0.09574819],"study_design_scores_gemma":[0.00007255511,0.00005264867,0.037051227,0.0008229165,0.00005671826,0.00007812563,0.0007052666,0.0019068241,0.0003585455,0.0011753816,0.9576345,0.00008530431],"about_ca_topic_score_codex":0.99624336,"about_ca_topic_score_gemma":0.995261,"teacher_disagreement_score":0.17643334,"about_ca_system_score_codex":0.17643334,"about_ca_system_score_gemma":0.6026166,"threshold_uncertainty_score":0.9552206},"labels":[],"label_agreement":null},{"id":"W2783264855","doi":"10.2196/publichealth.8950","title":"Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis","year":2018,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":106,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Centers for Disease Control and Prevention","keywords":"Metropolitan area; The Internet; Public health; Computer science; Public health surveillance; Data mining; Data science; Geography; Operations research; Environmental health; Medicine; World Wide Web; Engineering","score_opus":0.2718385187047663,"score_gpt":0.4377420375882823,"score_spread":0.165903518883516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783264855","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":[0.99368674,0.00016763543,0.0037088091,0.00087410276,0.000028779816,0.0000950384,0.0005903098,0.00014450733,0.0007041426],"genre_scores_gemma":[0.99350667,0.00013352309,0.004896286,0.00007722633,0.00004571509,0.000039213963,0.00089789054,0.00001752991,0.00038588577],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994168,0.00021289455,0.00004869467,0.00012859653,0.000121352365,0.0000715534],"domain_scores_gemma":[0.99737084,0.0014423431,0.0003148512,0.00020611852,0.00043932442,0.00022651143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015653626,0.00084033347,0.00041942872,0.0011437812,0.00069323665,0.00079353846,0.0011857913,0.0017239224,0.00043520596],"category_scores_gemma":[0.0037906074,0.00023518581,0.0003760065,0.0013085247,0.00047015346,0.0007728364,0.00068633823,0.0008468925,0.0001693959],"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":[0.0007090219,0.0017876714,0.66534114,0.00023103443,0.00037218852,0.023098,0.0027562196,0.22474055,0.0071561267,0.001732502,0.00880779,0.06326786],"study_design_scores_gemma":[0.00009906541,0.0004034492,0.18766432,0.000041628915,0.000098764314,0.0010206111,0.0034198058,0.7992567,0.00310918,0.000986004,0.0038371598,0.00006333309],"about_ca_topic_score_codex":0.077636674,"about_ca_topic_score_gemma":0.075277165,"teacher_disagreement_score":0.077636674,"about_ca_system_score_codex":0.0016369761,"about_ca_system_score_gemma":0.0006593664,"threshold_uncertainty_score":0.15436953},"labels":[],"label_agreement":null},{"id":"W2789021035","doi":"10.5383/swes.7.02.002","title":"GIS Based Surveillance of Road Traffic Accidents (RTA) Risk for Rawalpindi City: A Geostatistical Approach","year":2015,"lang":"en","type":"article","venue":"International Journal of Sustainable Water and Environmental Systems","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Transport engineering; Geography; Road traffic; Geographic information system; Spatial analysis; Environmental planning; Cartography; Engineering; Remote sensing","score_opus":0.01316919600163665,"score_gpt":0.25140813190923533,"score_spread":0.23823893590759868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789021035","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":[0.9811669,0.0003551858,0.005018091,0.0005633289,0.000026426009,0.00030497665,0.008666044,0.00016072215,0.0037382813],"genre_scores_gemma":[0.9838563,0.0003341006,0.01107675,0.000034494133,0.000016171827,0.00022467533,0.003877296,0.000009563614,0.00057064113],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994048,0.00021727067,0.00008083813,0.00010145971,0.00012982303,0.00006577825],"domain_scores_gemma":[0.9988518,0.00036615878,0.00033413476,0.00008076813,0.00027174683,0.00009545171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007670318,0.00041216525,0.00035271895,0.0066401693,0.0005008426,0.0009968061,0.0005395588,0.00031032338,0.0012263475],"category_scores_gemma":[0.0015386987,0.0002876351,0.0006826727,0.005109752,0.00029038114,0.00044238186,0.0008428106,0.00046914147,0.0001910369],"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":[0.00009408331,0.00019649099,0.9455701,0.00033386747,0.00025890028,0.0008067882,0.0013649849,0.0058511607,0.0019121472,0.0006839868,0.0020173271,0.04091018],"study_design_scores_gemma":[0.0000095421055,0.0003120094,0.96834636,0.00009238481,0.00013820952,0.000870695,0.006487416,0.018477459,0.0011920901,0.0005877858,0.0034296738,0.000056341316],"about_ca_topic_score_codex":0.022926904,"about_ca_topic_score_gemma":0.03477491,"teacher_disagreement_score":0.022926904,"about_ca_system_score_codex":0.0008129838,"about_ca_system_score_gemma":0.0015998733,"threshold_uncertainty_score":0.045586884},"labels":[],"label_agreement":null},{"id":"W2792114987","doi":"","title":"Les activités de gestion d’alerte épidémiologique : les transformations induites par l’utilisation d’un système de surveillance en temps réel Alert Management Activity: Cognitive and team activity modifications due to the use of an early warning system Las actividades de gestión de alerta epidemiológica : las transformaciones inducidas por la utilización de un sistema de vigilancia en tiempo real","year":2012,"lang":"fr","type":"article","venue":"Perspectives interdisciplinaires sur le travail et la santé","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motor activity; Medicine","score_opus":0.03565430335532411,"score_gpt":0.3252214080491868,"score_spread":0.2895671046938627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792114987","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":[0.96303666,0.00046673729,0.020331321,0.003190313,0.00005945766,0.00011394036,0.0004946791,0.00014669826,0.012160201],"genre_scores_gemma":[0.9934511,0.00020605364,0.004594225,0.000110783505,0.00003196168,0.000048688773,0.00019346447,0.000019858315,0.0013438155],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9950747,0.002572598,0.00038494248,0.0004900161,0.0010907083,0.00038698385],"domain_scores_gemma":[0.9779781,0.010982738,0.004182728,0.0022814877,0.0033148965,0.0012600389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054731485,0.00043344282,0.00025237072,0.0010754619,0.0004927809,0.0028445148,0.0007007369,0.0008909832,0.002024413],"category_scores_gemma":[0.027457356,0.0003145723,0.00044850537,0.00086742325,0.00087220216,0.0017604597,0.0017006213,0.0010947394,0.00039780806],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007223554,0.000898807,0.7090165,0.00034625208,0.00022851341,0.00046561993,0.02272338,0.0050689364,0.009652393,0.008007241,0.0014909452,0.24137908],"study_design_scores_gemma":[0.000043288073,0.00077905634,0.9266629,0.00023359993,0.000114639486,0.0006245153,0.013891037,0.015420634,0.0072134333,0.0073656915,0.027580144,0.000071109826],"about_ca_topic_score_codex":0.008188142,"about_ca_topic_score_gemma":0.005703551,"teacher_disagreement_score":0.008188142,"about_ca_system_score_codex":0.0013420499,"about_ca_system_score_gemma":0.0016295019,"threshold_uncertainty_score":0.028945148},"labels":[],"label_agreement":null},{"id":"W2792198181","doi":"10.1016/j.chiabu.2018.01.014","title":"The potential use of social media and other internet-related data and communications for child maltreatment surveillance and epidemiological research: Scoping review and recommendations","year":2018,"lang":"en","type":"article","venue":"Child Abuse & Neglect","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Agency of Canada","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Social media; The Internet; Child abuse; Poison control; Relevance (law); Internet privacy; Psychology; Suicide prevention; Data science; Computer science; Medicine; Environmental health; World Wide Web; Political science","score_opus":0.16687079643617556,"score_gpt":0.4140339073811381,"score_spread":0.24716311094496254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792198181","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00061963353,0.9673244,0.003934919,0.020978857,0.0019991642,0.0018091255,0.00068925327,0.000056604178,0.0025880337],"genre_scores_gemma":[0.005863229,0.9694745,0.011430468,0.0060726968,0.0005664396,0.0054715965,0.00061246636,0.000037016194,0.00047152708],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.8554008,0.07286868,0.04381779,0.0047316267,0.021076556,0.0021045322],"domain_scores_gemma":[0.35461345,0.54383683,0.03655314,0.008061106,0.05465819,0.0022773494],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.18279976,0.0031114998,0.009225334,0.04635613,0.0032582534,0.014479493,0.0055812616,0.009379072,0.0074699176],"category_scores_gemma":[0.43787605,0.0030407668,0.0137003055,0.03322748,0.0050782515,0.019864388,0.010497998,0.007553748,0.0016031554],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001058279,0.0000613529,0.002532512,0.672115,0.003401537,0.00032123204,0.0031045293,0.00052720605,0.00023150213,0.0075226906,0.016481979,0.29359454],"study_design_scores_gemma":[0.00002162827,0.0000315339,0.00089818315,0.9460835,0.0027844245,0.0001199173,0.0010681886,0.0001755788,0.00009468785,0.0025576013,0.046125736,0.0000390479],"about_ca_topic_score_codex":0.03641979,"about_ca_topic_score_gemma":0.07592304,"teacher_disagreement_score":0.81720024,"about_ca_system_score_codex":0.013763561,"about_ca_system_score_gemma":0.063180916,"threshold_uncertainty_score":0.9667491},"labels":[],"label_agreement":null},{"id":"W2792275468","doi":"10.5152/eurjrheum.2018.17105","title":"Infodemiology of antiphospholipid syndrome: Merging informatics and epidemiology","year":2018,"lang":"en","type":"article","venue":"European Journal of Rheumatology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Medicine; Informatics; Epidemiology; Health informatics; Antiphospholipid syndrome; Globe; Clinical trial; MEDLINE; Public health; Family medicine; Data science; Pathology; Computer science; Internal medicine","score_opus":0.021783735048834615,"score_gpt":0.2893226858028037,"score_spread":0.26753895075396905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792275468","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":[0.8612352,0.09170432,0.0074898787,0.008178979,0.00035428017,0.00023386761,0.014969048,0.00017725525,0.015657123],"genre_scores_gemma":[0.9612092,0.027597105,0.0051200525,0.0004794902,0.00052151293,0.00008624788,0.0043692067,0.000013738141,0.0006035063],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99849486,0.0005818285,0.00027666535,0.00022481175,0.00033240425,0.000089470515],"domain_scores_gemma":[0.9925895,0.002596485,0.0033132946,0.00029944372,0.000835195,0.0003662366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018353419,0.00028605497,0.0004392126,0.011975042,0.0002884785,0.0019932105,0.0003410685,0.00047844934,0.0017390638],"category_scores_gemma":[0.007865699,0.00013289435,0.0006583651,0.0137191,0.0003917991,0.0023735305,0.0010208801,0.0004326552,0.0002790297],"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":[0.000108035565,0.00006572189,0.92290777,0.0011509209,0.0003275182,0.00021187225,0.0003168315,0.0002457156,0.00029684702,0.0015725425,0.0019224373,0.07087368],"study_design_scores_gemma":[0.000011706265,0.00012718055,0.9832404,0.0013015212,0.00030550608,0.0012504527,0.0011190593,0.0017345574,0.00031299292,0.001977502,0.00860144,0.000017698332],"about_ca_topic_score_codex":0.0049437284,"about_ca_topic_score_gemma":0.0054452345,"teacher_disagreement_score":0.011975042,"about_ca_system_score_codex":0.00074315706,"about_ca_system_score_gemma":0.001388171,"threshold_uncertainty_score":0.009829879},"labels":[],"label_agreement":null},{"id":"W2792512647","doi":"10.2196/publichealth.8726","title":"Integrating Smart Health in the US Health Care System: Infodemiology Study of Asthma Monitoring in the Google Era","year":2018,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Environmental health; Asthma; Health care; Public health; Medicine; Internet privacy; Medical emergency; Computer science; Nursing; Political science; Immunology","score_opus":0.035292880951085695,"score_gpt":0.35897097835813,"score_spread":0.32367809740704434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792512647","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":[0.9942021,0.0009696752,0.00009409046,0.0021692049,0.000017315499,0.000026611842,0.0006264504,0.000003845198,0.0018907641],"genre_scores_gemma":[0.9986998,0.0004320408,0.000069756315,0.00029919142,0.00003402801,0.000012526325,0.00028959414,0.0000021565,0.0001609573],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989795,0.00043680586,0.000076150696,0.00013028998,0.00018940055,0.00018788702],"domain_scores_gemma":[0.99541736,0.0015081554,0.0017214733,0.00027231977,0.0004892149,0.00059149385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013859827,0.00021386879,0.00028340874,0.0016085636,0.0007592718,0.0015334389,0.00054606353,0.0008367797,0.0014110759],"category_scores_gemma":[0.0070687034,0.00019538405,0.00069219957,0.0037658196,0.0007497372,0.0018562425,0.0012120388,0.0012678071,0.00019852957],"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":[0.00006244683,0.0002468666,0.9886129,0.000065326734,0.00013334521,0.00013549744,0.0022981747,0.00021217135,0.000048470516,0.00069919426,0.0014122892,0.0060731503],"study_design_scores_gemma":[0.000008995203,0.0001158793,0.98975813,0.00005996599,0.00007904075,0.00011308415,0.0064599873,0.0015315335,0.000041719806,0.00022417585,0.0015992441,0.000008289631],"about_ca_topic_score_codex":0.12138544,"about_ca_topic_score_gemma":0.08832436,"teacher_disagreement_score":0.12138544,"about_ca_system_score_codex":0.0018294208,"about_ca_system_score_gemma":0.0018348868,"threshold_uncertainty_score":0.2413578},"labels":[],"label_agreement":null},{"id":"W2792690834","doi":"10.17269/s41997-018-0026-9","title":"Characterizing the health and information-seeking behaviours of Ontarians in response to the Zika virus outbreak","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Response Biomedical (Canada); Public Health Ontario; Mount Sinai Hospital","funders":"","keywords":"Zika virus; Outbreak; Virology; Biology; Virus","score_opus":0.03673771604887929,"score_gpt":0.303828893392049,"score_spread":0.2670911773431697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792690834","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":[0.9983317,0.00006315088,0.000042965105,0.00025923806,0.000006854764,0.000020522572,0.00052458677,0.0000035003163,0.000747401],"genre_scores_gemma":[0.99835724,0.00011338,0.0002113274,0.00035858192,0.0000146532,0.000028200964,0.00039869058,0.0000026388075,0.0005152503],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99925226,0.00020149096,0.00007412325,0.00008017539,0.00012997056,0.00026202048],"domain_scores_gemma":[0.99713254,0.00072019926,0.0008534779,0.0000948392,0.00051825773,0.0006806635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006352748,0.0001271269,0.00023594878,0.0009125733,0.0010973774,0.0012085572,0.00039581285,0.0008274591,0.001110333],"category_scores_gemma":[0.005841888,0.00018090266,0.00028666327,0.0015712813,0.00028263163,0.0004168947,0.00078233646,0.0006964699,0.00027956627],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018071893,0.00017920058,0.98625934,0.000033021057,0.000045167886,0.00012507914,0.0032055934,0.000060441253,0.00043086984,0.00003085033,0.0010262859,0.008423502],"study_design_scores_gemma":[0.0000084113335,0.00015746328,0.99142706,0.000030002708,0.0000335079,0.0000941674,0.006333428,0.0004338133,0.0001891907,0.000035553232,0.0012458825,0.000011513323],"about_ca_topic_score_codex":0.1660484,"about_ca_topic_score_gemma":0.24371767,"teacher_disagreement_score":0.8339516,"about_ca_system_score_codex":0.0012403233,"about_ca_system_score_gemma":0.0015936906,"threshold_uncertainty_score":0.33016378},"labels":[],"label_agreement":null},{"id":"W2796206458","doi":"10.2196/publichealth.7314","title":"Economic Recession and Obesity-Related Internet Search Behavior in Taiwan: Analysis of Google Trends Data","year":2018,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Obesity; Environmental health; Recession; Public health; The Internet; Body mass index; Proxy (statistics); Medicine; Online search; Business cycle; Business; Advertising; Economics; Computer science; World Wide Web","score_opus":0.05182043577786607,"score_gpt":0.3739270384540628,"score_spread":0.32210660267619673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796206458","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":[0.991017,0.00025031043,0.000106493295,0.00009886229,0.000005693783,0.000020456599,0.0078122574,0.000018513334,0.0006704983],"genre_scores_gemma":[0.9864916,0.0002148351,0.00016121894,0.000022600692,0.00001207098,0.00003847735,0.012783167,0.00000679005,0.0002691472],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995152,0.000079689795,0.00011490925,0.00008209187,0.00012421772,0.000083947954],"domain_scores_gemma":[0.9967706,0.0007783394,0.0015987768,0.0001889789,0.00045820532,0.00020517594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083662866,0.00029395154,0.00038185046,0.004463381,0.00023329949,0.0009129737,0.00026126928,0.00030568184,0.0009570988],"category_scores_gemma":[0.0036438205,0.00015409866,0.0008268239,0.0074388543,0.00017359694,0.0007527855,0.00070569845,0.0003918979,0.0003174879],"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":[0.000047832436,0.000019466726,0.99563307,0.000051102714,0.000070995986,0.00012867611,0.00014222597,0.00046732358,0.000099889876,0.00004923915,0.0007305389,0.0025596367],"study_design_scores_gemma":[0.0000033346525,0.000024525272,0.9955023,0.00001681349,0.000041560328,0.00007790117,0.0004108783,0.0031933645,0.00007066987,0.000023314853,0.0006292544,0.0000061441015],"about_ca_topic_score_codex":0.048742317,"about_ca_topic_score_gemma":0.036219135,"teacher_disagreement_score":0.048742317,"about_ca_system_score_codex":0.00052676996,"about_ca_system_score_gemma":0.00060277135,"threshold_uncertainty_score":0.09691721},"labels":[],"label_agreement":null},{"id":"W2797100809","doi":"10.1186/s12889-018-5259-2","title":"Geographic differences in overweight and obesity prevalence in Peruvian children, 2010–2015","year":2018,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Overweight; Obesity; Medicine; Biostatistics; Public health; Demography; Population; Childhood obesity; Environmental health; Epidemiology; Body mass index; Pediatrics; Internal medicine; Pathology","score_opus":0.03231030233787195,"score_gpt":0.3036539622129934,"score_spread":0.27134365987512143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797100809","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":[0.9958402,0.0010040104,0.00010093473,0.00012061506,0.0000062455006,0.000015895694,0.0019747752,0.000014477998,0.0009229319],"genre_scores_gemma":[0.99659413,0.000958123,0.0001906934,0.000031489402,0.000010556403,0.00004157283,0.0019419199,0.0000035819414,0.00022783193],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99965465,0.000093160714,0.000047202415,0.00007904238,0.0000624262,0.000063456515],"domain_scores_gemma":[0.99922884,0.00008623522,0.00044402177,0.00002990014,0.00014011515,0.00007084672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006384532,0.00026485304,0.00021234667,0.0015198389,0.00034163395,0.00051464257,0.0003179544,0.00025383625,0.0014449988],"category_scores_gemma":[0.0016848801,0.00025078907,0.00047483866,0.0018506497,0.0003743655,0.00042985624,0.00078576995,0.00031489186,0.0002121344],"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":[0.000040654064,0.0000127154735,0.9963911,0.00006548159,0.00006792386,0.000083747036,0.0007807348,0.00003952457,0.00015552447,0.000034938108,0.00026538994,0.0020623168],"study_design_scores_gemma":[0.0000012514761,0.000019379904,0.99886936,0.000011684463,0.000017949738,0.00010203843,0.0006170006,0.00002738724,0.000015985182,0.000006309057,0.00030976164,0.0000018777496],"about_ca_topic_score_codex":0.029212607,"about_ca_topic_score_gemma":0.03976803,"teacher_disagreement_score":0.029212607,"about_ca_system_score_codex":0.00050919584,"about_ca_system_score_gemma":0.0003742713,"threshold_uncertainty_score":0.058085144},"labels":[],"label_agreement":null},{"id":"W2799452530","doi":"10.2196/publichealth.8163","title":"Improving Visualization of Female Breast Cancer Survival Estimates: Analysis Using Interactive Mapping Reports","year":2018,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Center for Chronic Disease Prevention and Health Promotion; Centers for Disease Control and Prevention; Missouri Department of Health and Senior Services; University of Missouri","keywords":"Cancer registry; Breast cancer; Medicine; Cancer; National Death Index; Population; Census; Demography; Environmental health; Confidence interval; Hazard ratio; Internal medicine","score_opus":0.04815310783944624,"score_gpt":0.3818643130615018,"score_spread":0.33371120522205555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799452530","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":[0.31848848,0.0014586283,0.3851167,0.004320123,0.0006147005,0.001017042,0.10346565,0.13947415,0.046044607],"genre_scores_gemma":[0.5543572,0.0011329966,0.40743184,0.00017455842,0.0002304569,0.0010800654,0.022229176,0.0074686627,0.0058949864],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980001,0.0009137638,0.00018927154,0.00027753616,0.0005197341,0.00009958835],"domain_scores_gemma":[0.9754414,0.01629118,0.0021965508,0.0021122815,0.0036277706,0.00033080723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058107106,0.0013164505,0.00061732566,0.007329419,0.0005657473,0.0038327419,0.0011275595,0.00040895847,0.018459119],"category_scores_gemma":[0.026883675,0.00059720216,0.0010885259,0.005767551,0.00028806378,0.0019563707,0.0021032358,0.0007488489,0.00247329],"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":[0.0013164557,0.0003247623,0.14420329,0.0020774927,0.00055609975,0.00087734254,0.011429039,0.0404524,0.0059899925,0.012068725,0.16444172,0.61626273],"study_design_scores_gemma":[0.00049138785,0.0005077831,0.18187515,0.0013538862,0.000963281,0.0010269153,0.009708954,0.40041822,0.026532046,0.022772448,0.3537022,0.0006477847],"about_ca_topic_score_codex":0.009082012,"about_ca_topic_score_gemma":0.009483796,"teacher_disagreement_score":0.018459119,"about_ca_system_score_codex":0.0006380508,"about_ca_system_score_gemma":0.0011779784,"threshold_uncertainty_score":0.061751902},"labels":[],"label_agreement":null},{"id":"W2800084344","doi":"10.1177/0309132518772661","title":"Resisting geosurveillance: A survey of tactics and strategies for spatial privacy","year":2018,"lang":"en","type":"article","venue":"Progress in Human Geography","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Obfuscation; Resistance (ecology); Internet privacy; Government (linguistics); Survey data collection; Business; Information privacy; Computer security; Public relations; Computer science; Political science","score_opus":0.03341599377914575,"score_gpt":0.3464096581385803,"score_spread":0.31299366435943454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800084344","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":[0.46847537,0.09057757,0.11367756,0.08397537,0.00061469345,0.0011148762,0.00033636967,0.000491115,0.24073708],"genre_scores_gemma":[0.8937135,0.05374473,0.040685948,0.0046237493,0.00016579345,0.00040269544,0.00018125097,0.0001501927,0.0063322475],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.97344106,0.017145514,0.001674488,0.0014071164,0.005151538,0.0011802935],"domain_scores_gemma":[0.90771043,0.06970922,0.0099926395,0.0043161516,0.0065373653,0.0017342137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026707772,0.00084726734,0.0005985344,0.005638897,0.0033949036,0.0066857194,0.0019379135,0.0027755084,0.0026611115],"category_scores_gemma":[0.04862431,0.0005389248,0.00081890414,0.0039603286,0.012982522,0.008579396,0.005314052,0.0035170573,0.0005936651],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012575873,0.00017245696,0.040405788,0.0032754045,0.00012274832,0.00043147578,0.10005397,0.0012624268,0.0025028244,0.16720429,0.008838322,0.67560446],"study_design_scores_gemma":[0.00003543558,0.0006600628,0.036548775,0.007997248,0.00014404036,0.0032745155,0.20731632,0.0033260232,0.004931155,0.055880588,0.67964953,0.00023632224],"about_ca_topic_score_codex":0.002553209,"about_ca_topic_score_gemma":0.0028819004,"teacher_disagreement_score":0.026707772,"about_ca_system_score_codex":0.0026315665,"about_ca_system_score_gemma":0.0036929725,"threshold_uncertainty_score":0.14124584},"labels":[],"label_agreement":null},{"id":"W2800551540","doi":"10.2196/mhealth.9834","title":"A New Influenza-Tracking Smartphone App (Flu-Report) Based on a Self-Administered Questionnaire: Cross-Sectional Study","year":2018,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Nihon University","keywords":"mHealth; Smartphone app; Cross-sectional study; Tracking (education); Smartphone application; Medicine; Psychology; Computer science; World Wide Web; Multimedia; Psychological intervention; Pathology; Nursing","score_opus":0.05824842117110238,"score_gpt":0.4253655442396235,"score_spread":0.3671171230685212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800551540","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":[0.9954529,0.00030378954,0.0005890953,0.00006435522,0.000035575766,0.0011807198,0.0014421582,0.000012151941,0.0009191637],"genre_scores_gemma":[0.9908847,0.00040760697,0.0019831795,0.00059012295,0.00005272932,0.0022937593,0.0022464204,0.000014108762,0.0015274886],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986008,0.00030695708,0.00018362241,0.00042820346,0.00032691838,0.00015356808],"domain_scores_gemma":[0.99752957,0.0003974044,0.0005754751,0.00014130009,0.0010493828,0.00030695606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024155453,0.0007704646,0.0007381887,0.00091336493,0.0010018074,0.0007994651,0.00039548017,0.0009850238,0.0025200741],"category_scores_gemma":[0.0030588938,0.0010771253,0.001444315,0.0006479856,0.00034092512,0.0015694399,0.0006761311,0.0014884633,0.0012572412],"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":[0.00064053375,0.0035762968,0.9856499,0.00029855955,0.0003207943,0.00017132836,0.0010268012,0.00005215457,0.00092246005,0.000043211807,0.000949808,0.0063481643],"study_design_scores_gemma":[0.00022304176,0.0051259943,0.9895382,0.000073334595,0.00037128013,0.0004365913,0.0015252193,0.0006218235,0.0003997012,0.000035319965,0.0016039476,0.000045495028],"about_ca_topic_score_codex":0.0044512376,"about_ca_topic_score_gemma":0.006290495,"teacher_disagreement_score":0.0044512376,"about_ca_system_score_codex":0.000534243,"about_ca_system_score_gemma":0.00068977854,"threshold_uncertainty_score":0.012774825},"labels":[],"label_agreement":null},{"id":"W2802776178","doi":"10.17269/s41997-018-0059-0","title":"Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: an ecological study","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McGill University; Toronto Public Health; Kingston Health Sciences Centre; Public Health Ontario; University Health Network; University of Toronto; Queen's University","funders":"","keywords":"Helpline; Social media; Public health surveillance; Medicine; Public health; Big data; Categorization; Computer science; Data mining; World Wide Web; Artificial intelligence","score_opus":0.37562919431157676,"score_gpt":0.43654067364776056,"score_spread":0.060911479336183794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802776178","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":[0.98952484,0.00036589324,0.0021809794,0.0013281673,0.0000707462,0.0002845137,0.0046861926,0.000028576796,0.0015300749],"genre_scores_gemma":[0.9921731,0.00015542848,0.0022185873,0.00040139913,0.000035492583,0.00031353612,0.004366296,0.000029650218,0.00030655152],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9682549,0.022412963,0.0016736892,0.0028555945,0.0033271636,0.001475766],"domain_scores_gemma":[0.9193692,0.048888702,0.012107973,0.005454658,0.01190919,0.0022702052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024908459,0.00055375445,0.00054906844,0.0027525418,0.0014079699,0.0032246674,0.001877999,0.001500622,0.0013088711],"category_scores_gemma":[0.112823345,0.0007120757,0.0012395172,0.0052626417,0.0014251026,0.00567186,0.004153474,0.0017828565,0.0003951344],"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":[0.00070101675,0.0006677052,0.9777189,0.00028734392,0.00066185824,0.000091270485,0.0047448524,0.0010051278,0.00018667235,0.0011593895,0.0032102033,0.009565589],"study_design_scores_gemma":[0.00011952583,0.0006807224,0.94988906,0.0004777018,0.00055220444,0.00022917431,0.0220113,0.016347125,0.0005881904,0.0014390526,0.0075590904,0.00010688732],"about_ca_topic_score_codex":0.14680173,"about_ca_topic_score_gemma":0.1673512,"teacher_disagreement_score":0.14680173,"about_ca_system_score_codex":0.004089963,"about_ca_system_score_gemma":0.0033683649,"threshold_uncertainty_score":0.29189444},"labels":[],"label_agreement":null},{"id":"W2804591035","doi":"10.5210/ojphi.v10i1.8912","title":"Revitalizing the Global Public Health Intelligence Network (GPHIN)","year":2018,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Relevance (law); Computer science; Situation awareness; Data science; Government (linguistics); Artificial intelligence; Knowledge management; Engineering; Political science","score_opus":0.13939767008466714,"score_gpt":0.40894113398827864,"score_spread":0.2695434639036115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804591035","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03814355,0.008209696,0.41786134,0.28053114,0.012412475,0.002177317,0.015989045,0.060959946,0.16371556],"genre_scores_gemma":[0.15949005,0.0068189483,0.6750393,0.026395064,0.004376307,0.0010615832,0.05135104,0.010674444,0.06479335],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9899004,0.0036911233,0.00048501135,0.0014315288,0.0036288071,0.000863168],"domain_scores_gemma":[0.9466227,0.013100613,0.0016666596,0.012793569,0.01936611,0.006450368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030012816,0.0010081481,0.00051126105,0.0034210228,0.0017701557,0.006912036,0.0036559885,0.0018925078,0.008699961],"category_scores_gemma":[0.039799478,0.00063813897,0.00070543215,0.003174585,0.003546633,0.013882737,0.010042546,0.0044201845,0.0046065603],"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":[0.00030708613,0.00014743811,0.008223752,0.0005878735,0.00007444303,0.0002636624,0.002881086,0.006265698,0.003098672,0.06495057,0.4218169,0.49138287],"study_design_scores_gemma":[0.000030479225,0.00006134911,0.0022679148,0.00038676147,0.000017569406,0.00006539927,0.00059426756,0.009322575,0.0016309251,0.015072886,0.97049385,0.000056070618],"about_ca_topic_score_codex":0.0529335,"about_ca_topic_score_gemma":0.044405993,"teacher_disagreement_score":0.0529335,"about_ca_system_score_codex":0.0077851093,"about_ca_system_score_gemma":0.02502388,"threshold_uncertainty_score":0.15872484},"labels":[],"label_agreement":null},{"id":"W2804647275","doi":"10.1080/15427560.2018.1461100","title":"Collective Perception and Exchange Rates","year":2018,"lang":"en","type":"article","venue":"Journal of Behavioral Finance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Currency; Pound (networking); Liberian dollar; Exchange rate; Context (archaeology); Perception; Interest rate; Autoregressive model; Economics; Us dollar; Financial economics; Econometrics; Computer science; Monetary economics; Psychology; Finance; Geography; World Wide Web","score_opus":0.033835790522279675,"score_gpt":0.3483342921990538,"score_spread":0.3144985016767741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804647275","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":[0.9774061,0.00085003773,0.0016362256,0.002268811,0.000098439,0.000016763115,0.00018422493,0.00001765702,0.017521827],"genre_scores_gemma":[0.9984712,0.00018483488,0.00030512185,0.00011446162,0.00005679099,0.000008140035,0.00007843212,0.0000033903605,0.0007777039],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99795496,0.0010686218,0.00011667718,0.00027965708,0.00043455098,0.00014540498],"domain_scores_gemma":[0.9743688,0.012276756,0.008756324,0.0014924625,0.0011862566,0.0019193869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003185229,0.00032550286,0.0004298673,0.0016123051,0.0006099444,0.0032428098,0.00026926908,0.0007780841,0.0053209476],"category_scores_gemma":[0.02231394,0.00017105127,0.00046175063,0.001015393,0.001013095,0.0014502559,0.0015866184,0.0011400839,0.00036574688],"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":[0.0005294289,0.00047931602,0.88021237,0.0001973765,0.00039930054,0.00021959508,0.024279637,0.0013495731,0.0010378375,0.0086876275,0.002659939,0.07994793],"study_design_scores_gemma":[0.000042243668,0.00045976468,0.9448811,0.00013929188,0.00016230573,0.00028699788,0.028755309,0.0038988779,0.00042888758,0.012477068,0.008392468,0.00007562587],"about_ca_topic_score_codex":0.0020044758,"about_ca_topic_score_gemma":0.0013238386,"teacher_disagreement_score":0.0053209476,"about_ca_system_score_codex":0.0005927012,"about_ca_system_score_gemma":0.00022050522,"threshold_uncertainty_score":0.017800331},"labels":[],"label_agreement":null},{"id":"W2804904320","doi":"10.2196/mental.9152","title":"Diurnal Variations of Depression-Related Health Information Seeking: Case Study in Finland Using Google Trends Data","year":2018,"lang":"en","type":"article","venue":"JMIR Mental Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Depression (economics); Diurnal temperature variation; Medicine; Geography; Meteorology","score_opus":0.057551408528573406,"score_gpt":0.42255118086342913,"score_spread":0.3649997723348557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804904320","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":[0.9978957,0.00017696361,0.00027644078,0.00008494355,0.0000046491878,0.000024454155,0.0010702518,0.00001373132,0.00045272347],"genre_scores_gemma":[0.9973532,0.0002795663,0.0007529845,0.00002253834,0.000016405373,0.000020459524,0.0013118988,0.000006769296,0.00023625058],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993123,0.00018555456,0.00010581437,0.00012124517,0.0001536898,0.00012145649],"domain_scores_gemma":[0.9982405,0.0009717485,0.00028071628,0.00013520262,0.00025574965,0.000116180185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082834467,0.0002677678,0.00037452095,0.0019855618,0.00068920944,0.0008968496,0.0004786491,0.000805096,0.0005614546],"category_scores_gemma":[0.002693631,0.00017041426,0.00059116917,0.0039599543,0.00037640153,0.00071543525,0.00063681207,0.00028510147,0.00017566583],"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":[0.0003982243,0.00036121442,0.9238871,0.00061607553,0.00023016881,0.022751842,0.0122338245,0.0031055193,0.003496876,0.00042242446,0.0026990846,0.029797787],"study_design_scores_gemma":[0.000021527863,0.00019792464,0.9611771,0.000074896016,0.0001355783,0.0047295988,0.019233387,0.00988768,0.0012738219,0.00022721448,0.0029719009,0.00006936183],"about_ca_topic_score_codex":0.04255972,"about_ca_topic_score_gemma":0.04491439,"teacher_disagreement_score":0.04255972,"about_ca_system_score_codex":0.0009382005,"about_ca_system_score_gemma":0.00045506615,"threshold_uncertainty_score":0.08462399},"labels":[],"label_agreement":null},{"id":"W2806188267","doi":"10.2196/mhealth.9974","title":"Challenges and Potential Opportunities of Mobile Phone Call Detail Records in Health Research: Review","year":2018,"lang":"en","type":"review","venue":"JMIR mhealth and uhealth","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Economic and Social Research Council; Medical Research Council; Natural Environment Research Council; Sight Research UK","keywords":"Mobile phone; Computer science; Big data; Data science; Phone; Internet privacy; Public health; Health records; World Wide Web; Health care; Telecommunications; Medicine; Data mining; Political science","score_opus":0.4972742468481813,"score_gpt":0.5085401788063916,"score_spread":0.011265931958210296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806188267","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":[0.00016562038,0.9958566,0.00026789578,0.0029003748,0.00036685364,0.000060673607,0.000036729263,0.0000055311657,0.0003396709],"genre_scores_gemma":[0.0035010076,0.9935011,0.001069782,0.0013319257,0.00030629485,0.0001954366,0.00003825753,0.0000059161057,0.00005026767],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.9098656,0.05475304,0.019135633,0.0031840533,0.011721138,0.001340615],"domain_scores_gemma":[0.4297242,0.5223697,0.021175418,0.004358478,0.020802816,0.0015694697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07471424,0.0011468491,0.0038734348,0.016869238,0.0019436548,0.009689922,0.0033498933,0.005759042,0.0025011555],"category_scores_gemma":[0.28463584,0.0015596049,0.0039971215,0.023283182,0.00474237,0.011455279,0.004994745,0.0035932185,0.00069112773],"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":[0.000079353806,0.000027465716,0.0011022676,0.63881034,0.0011050474,0.00022101063,0.0030921113,0.0003100564,0.00012324832,0.008318826,0.010967093,0.33584327],"study_design_scores_gemma":[0.000016525015,0.00005020707,0.000954593,0.8880354,0.0015259781,0.00033202113,0.0018616617,0.0001225117,0.00009362184,0.0023009458,0.104665816,0.00004072773],"about_ca_topic_score_codex":0.012481116,"about_ca_topic_score_gemma":0.02590076,"teacher_disagreement_score":0.07471424,"about_ca_system_score_codex":0.009489397,"about_ca_system_score_gemma":0.039901923,"threshold_uncertainty_score":0.3951314},"labels":[],"label_agreement":null},{"id":"W2807122929","doi":"10.2196/publichealth.8218","title":"Identifying Methods for Monitoring Foodborne Illness: Review of Existing Public Health Surveillance Techniques","year":2018,"lang":"en","type":"review","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Economic and Social Research Council; Food Standards Agency","keywords":"Metadata; Public health; Public health surveillance; Environmental health; Disease surveillance; Under-reporting; Social media; Population; Data science; Computer science; Population health; Internet privacy; Medicine; World Wide Web; Statistics; Pathology","score_opus":0.2765418963498207,"score_gpt":0.5235214948979326,"score_spread":0.24697959854811197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807122929","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":[0.00019449543,0.99800724,0.000558766,0.0005584498,0.000103110215,0.000081286715,0.00007188992,0.000008992124,0.00041581842],"genre_scores_gemma":[0.0021917657,0.9951373,0.0020007459,0.0002919564,0.00007864026,0.00015765706,0.00008284968,0.0000058780906,0.000053207972],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.974638,0.008892519,0.00891325,0.0019323704,0.0051716412,0.00045226223],"domain_scores_gemma":[0.7755918,0.18485281,0.017062854,0.0031154132,0.018569214,0.00080784597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.041598413,0.0018792357,0.004470805,0.028406117,0.00107697,0.0050636986,0.004078237,0.0030948676,0.0037496432],"category_scores_gemma":[0.11367525,0.0018211476,0.0056419647,0.023517018,0.0030432006,0.00704388,0.0030275946,0.0023985442,0.000988433],"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":[0.0001274624,0.0000617371,0.0010616281,0.46068722,0.0010866225,0.00007899767,0.0008751349,0.00027288575,0.0002642851,0.0015349986,0.003675734,0.5302733],"study_design_scores_gemma":[0.000048039885,0.00023793458,0.0047470815,0.8518455,0.005079659,0.0004552814,0.0011577436,0.00033953146,0.00073581433,0.0021084552,0.13316804,0.00007686979],"about_ca_topic_score_codex":0.0097182,"about_ca_topic_score_gemma":0.013566074,"teacher_disagreement_score":0.041598413,"about_ca_system_score_codex":0.005394301,"about_ca_system_score_gemma":0.016062696,"threshold_uncertainty_score":0.21999604},"labels":[],"label_agreement":null},{"id":"W2808081817","doi":"10.2196/medinform.8204","title":"Validation of a Natural Language Processing Algorithm for Detecting Infectious Disease Symptoms in Primary Care Electronic Medical Records in Singapore","year":2018,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Medical Research Council; Medical Research Council","keywords":"Infectious disease (medical specialty); Medicine; Medical record; Computer science; Electronic medical record; Primary care; Disease; Algorithm; Natural language processing; Artificial intelligence; Family medicine; Pathology; Internal medicine","score_opus":0.005343844386947216,"score_gpt":0.29645749600437293,"score_spread":0.2911136516174257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808081817","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":[0.9526778,0.00072110386,0.034897417,0.00055456033,0.000120835844,0.0018190948,0.0048588202,0.002175178,0.0021752755],"genre_scores_gemma":[0.8620687,0.0002480184,0.12195617,0.0002565819,0.00004246057,0.00077285094,0.013753329,0.00007089356,0.0008311129],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9828182,0.006328239,0.0050824657,0.002782461,0.0026773189,0.00031122],"domain_scores_gemma":[0.9276797,0.039642446,0.0051994207,0.004553659,0.022163216,0.0007616016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02962507,0.00060897757,0.0007335232,0.0032634963,0.00073032343,0.0021931191,0.0012811318,0.0010628155,0.0010231901],"category_scores_gemma":[0.06986297,0.0003034906,0.0009280266,0.0012519045,0.0008569971,0.0012675324,0.0015024693,0.0006008799,0.00076838955],"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":[0.0025860842,0.00095515227,0.5529477,0.003409582,0.0010158393,0.002170547,0.0058611254,0.018104224,0.038399663,0.0007840943,0.011051926,0.36271405],"study_design_scores_gemma":[0.0010279779,0.002843215,0.5414371,0.0007971479,0.0009428193,0.0027707247,0.004014358,0.35875002,0.067232974,0.0014087369,0.018586209,0.0001887479],"about_ca_topic_score_codex":0.008899624,"about_ca_topic_score_gemma":0.008939772,"teacher_disagreement_score":0.02962507,"about_ca_system_score_codex":0.0015353096,"about_ca_system_score_gemma":0.003810881,"threshold_uncertainty_score":0.1566742},"labels":[],"label_agreement":null},{"id":"W2808303723","doi":"10.1186/s12942-018-0137-9","title":"A log-Weibull spatial scan statistic for time to event data","year":2018,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta Hospital; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Alberta","keywords":"Scan statistic; Weibull distribution; Statistics; Statistic; Censoring (clinical trials); Computer science; Mathematics","score_opus":0.03927904074749015,"score_gpt":0.40556893111439335,"score_spread":0.3662898903669032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808303723","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":[0.3378832,0.0006946031,0.6492552,0.0014936491,0.00018914836,0.000660425,0.0029604915,0.002074853,0.0047883363],"genre_scores_gemma":[0.92491996,0.000161527,0.071497336,0.00017805526,0.00008843432,0.0005712837,0.0016113213,0.000094016454,0.0008779806],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9911464,0.004427207,0.0006734599,0.0012794395,0.0020162086,0.0004573792],"domain_scores_gemma":[0.86958855,0.108962454,0.0088956775,0.0059171384,0.00530443,0.0013317738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017834224,0.0006839784,0.0010876936,0.0057054586,0.0007397922,0.0019338646,0.0026375942,0.0018244253,0.004731606],"category_scores_gemma":[0.09113342,0.0003568893,0.0021542083,0.004783925,0.002974754,0.0029483908,0.001413795,0.0021464683,0.0006386568],"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":[0.0013174716,0.00026051045,0.54285395,0.00053999096,0.0008491156,0.0016534412,0.0010312028,0.21959955,0.0028284844,0.071336985,0.007053831,0.15067543],"study_design_scores_gemma":[0.00012766122,0.00085943623,0.07581504,0.00015401878,0.00016678894,0.0016617159,0.00088702043,0.86345816,0.0020193525,0.049856253,0.004855949,0.00013872898],"about_ca_topic_score_codex":0.00481028,"about_ca_topic_score_gemma":0.0034935724,"teacher_disagreement_score":0.017834224,"about_ca_system_score_codex":0.0017378425,"about_ca_system_score_gemma":0.0022676047,"threshold_uncertainty_score":0.094317555},"labels":[],"label_agreement":null},{"id":"W2808373915","doi":"10.2196/publichealth.9800","title":"Cross-Jurisdictional Data Exchange Impact on the Estimation of the HIV Population Living in the District of Columbia: Evaluation Study","year":2018,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Public health; Population; Medicine; Confidentiality; Jurisdiction; Environmental health; Data sharing; Public health surveillance; Business; Medical emergency; Computer security; Computer science; Political science; Nursing","score_opus":0.07619448731277823,"score_gpt":0.40848194501975654,"score_spread":0.3322874577069783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808373915","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99607146,0.00015536654,0.00025103372,0.00017106104,0.000007675575,0.0012324243,0.0007861847,0.000018190667,0.0013066777],"genre_scores_gemma":[0.99544585,0.00015739254,0.0010043994,0.00013094323,0.000008757561,0.0017670554,0.0010764073,0.000008875314,0.00040025203],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.97460866,0.01714311,0.0012153253,0.0016242289,0.0037720634,0.0016366066],"domain_scores_gemma":[0.9327195,0.02962421,0.01052946,0.004404997,0.017611478,0.0051103295],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.024422906,0.0009741706,0.0008828528,0.001968392,0.0025605347,0.0023340087,0.0027472512,0.001096212,0.0025994887],"category_scores_gemma":[0.067587115,0.00056480686,0.0016843629,0.0031006401,0.0011890857,0.001648768,0.003948127,0.0015691342,0.00033489676],"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":[0.0067012617,0.012780455,0.90365773,0.0007373205,0.0018519352,0.00031496846,0.0036661834,0.0036413008,0.00013703282,0.00038043736,0.0030442667,0.06308707],"study_design_scores_gemma":[0.0017345608,0.012202386,0.95010453,0.0005546893,0.0019920883,0.00026355442,0.011111723,0.018632263,0.00048258007,0.00029520132,0.0025103912,0.00011590176],"about_ca_topic_score_codex":0.31112698,"about_ca_topic_score_gemma":0.26517528,"teacher_disagreement_score":0.9755771,"about_ca_system_score_codex":0.01438095,"about_ca_system_score_gemma":0.015097184,"threshold_uncertainty_score":0.618632},"labels":[],"label_agreement":null},{"id":"W2809163490","doi":"10.2196/publichealth.9749","title":"Establishing a Demographic, Development and Environmental Geospatial Surveillance Platform in India: Planning and Implementation","year":2018,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Geospatial analysis; Geographic information system; Sanitation; Urbanization; Population; Geography; Environmental resource management; Environmental planning; Business; Environmental health; Engineering; Cartography; Economic growth; Environmental engineering","score_opus":0.02195771109877484,"score_gpt":0.30970789878723637,"score_spread":0.2877501876884615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809163490","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":[0.82207996,0.0009206325,0.0906897,0.00856771,0.00021111839,0.027392361,0.012630338,0.003078256,0.034429967],"genre_scores_gemma":[0.694674,0.0010897194,0.27580604,0.0005718732,0.000047757218,0.009874183,0.013081519,0.00015372262,0.004701092],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99626935,0.0014654567,0.00038350027,0.00036230453,0.0006348621,0.0008844559],"domain_scores_gemma":[0.9877302,0.0019323855,0.00087448006,0.0012056311,0.0045083202,0.0037489897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0107983155,0.0005020296,0.0002514638,0.0027636609,0.0016215985,0.0027900084,0.0028942341,0.00057826145,0.0021873887],"category_scores_gemma":[0.007802847,0.00066226965,0.0005708865,0.0026325441,0.0011605849,0.0018877318,0.004086086,0.0011310574,0.0007101961],"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":[0.00040276238,0.0022429796,0.44743872,0.002031446,0.00012054212,0.0024706044,0.015709473,0.039738286,0.009417211,0.019927984,0.027347235,0.43315268],"study_design_scores_gemma":[0.0002464461,0.0028195726,0.74413943,0.0013338592,0.0002288513,0.0007378932,0.054783095,0.06424487,0.009977533,0.004689369,0.11651836,0.0002806995],"about_ca_topic_score_codex":0.1327373,"about_ca_topic_score_gemma":0.121766515,"teacher_disagreement_score":0.1327373,"about_ca_system_score_codex":0.0057931845,"about_ca_system_score_gemma":0.042995423,"threshold_uncertainty_score":0.2639293},"labels":[],"label_agreement":null},{"id":"W2809472587","doi":"10.1093/annweh/wxy052","title":"Development of a Coding and Crosswalk Tool for Occupations and Industries","year":2018,"lang":"en","type":"article","venue":"Annals of Work Exposures and Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"Canadian Institutes of Health Research; Cancer Research Society","keywords":"Schema crosswalk; Coding (social sciences); Computer science; Population; Statistics; Medicine; Engineering; Mathematics; Transport engineering; Environmental health","score_opus":0.170146835240855,"score_gpt":0.4260737662538271,"score_spread":0.2559269310129721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809472587","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":[0.071707636,0.0005230236,0.80850786,0.0013605796,0.0009746603,0.044793952,0.037912358,0.018482996,0.01573687],"genre_scores_gemma":[0.036583673,0.00026770603,0.91306096,0.00019832997,0.000082817256,0.02595368,0.01662859,0.0013996534,0.0058245244],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9772345,0.008248706,0.005621678,0.0021882383,0.0060132244,0.0006935963],"domain_scores_gemma":[0.9044195,0.036437925,0.007865614,0.008101164,0.041258827,0.0019169522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027867213,0.0017241422,0.0012874268,0.013053033,0.0017846769,0.0019246094,0.002581373,0.0007310121,0.018851835],"category_scores_gemma":[0.09173719,0.0011797891,0.0018208555,0.009968608,0.001087187,0.0026224146,0.0040370575,0.0018069571,0.0067595374],"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":[0.00082422234,0.00056723796,0.03614896,0.0029310966,0.00014354664,0.000569684,0.01376504,0.004027926,0.008929661,0.008360711,0.13339914,0.7903328],"study_design_scores_gemma":[0.00081607816,0.0013198792,0.15324613,0.0053924867,0.00027615848,0.002130369,0.017942553,0.07588643,0.039781854,0.020493725,0.6814237,0.0012906999],"about_ca_topic_score_codex":0.030038465,"about_ca_topic_score_gemma":0.039353635,"teacher_disagreement_score":0.030038465,"about_ca_system_score_codex":0.004406035,"about_ca_system_score_gemma":0.015140218,"threshold_uncertainty_score":0.14737767},"labels":[],"label_agreement":null},{"id":"W2809733825","doi":"10.2196/publichealth.9616","title":"Research Ethics in the European Influenzanet Consortium: Scoping Review","year":2018,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Horizon 2020 Framework Programme; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Research ethics; Ethical issues; Political science; Public relations; Environmental health; Medicine; Engineering ethics; Engineering","score_opus":0.20136369311941532,"score_gpt":0.4830954383003623,"score_spread":0.28173174518094696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809733825","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007327026,0.9878214,0.0010917194,0.0049811257,0.00077930465,0.0015990835,0.00025487458,0.000019530089,0.0027202496],"genre_scores_gemma":[0.017377919,0.96525216,0.0041464404,0.00469004,0.0006522323,0.0066876677,0.00062005164,0.0000362441,0.0005372508],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.84113663,0.09769652,0.039705206,0.003890331,0.015560171,0.0020110798],"domain_scores_gemma":[0.6086629,0.30195096,0.041354507,0.008019116,0.03728123,0.0027313074],"candidate_categories":["metaresearch","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.13599277,0.0017384042,0.0052945847,0.024414677,0.0027347016,0.010180588,0.003338033,0.007990002,0.005335823],"category_scores_gemma":[0.30031234,0.0013318083,0.0039778636,0.02169242,0.005047497,0.010241277,0.0069374917,0.0032379108,0.0011505154],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018788771,0.000084891144,0.0013361998,0.7397352,0.0013331895,0.00040057008,0.0037385107,0.00034019974,0.00017704126,0.010307614,0.020130543,0.22222811],"study_design_scores_gemma":[0.000037339898,0.00003646452,0.0006212012,0.94097036,0.0006663079,0.00019148654,0.0010577574,0.000049624578,0.000082875915,0.001371622,0.05489764,0.000017341346],"about_ca_topic_score_codex":0.004150948,"about_ca_topic_score_gemma":0.0052960766,"teacher_disagreement_score":0.99201,"about_ca_system_score_codex":0.011893216,"about_ca_system_score_gemma":0.05614917,"threshold_uncertainty_score":0.71920705},"labels":[],"label_agreement":null},{"id":"W2838749849","doi":"10.2196/publichealth.9876","title":"Automated Real-Time Collection of Pathogen-Specific Diagnostic Data: Syndromic Infectious Disease Epidemiology","year":2018,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Allergy and Infectious Diseases; Centers for Disease Control and Prevention; National Institutes of Health; BioFire Diagnostics; Utah Department of Health; New York State Department of Health","keywords":"Epidemiology; Infectious disease (medical specialty); Medicine; Disease; Pathogen; Virology; Biology; Pathology; Immunology","score_opus":0.04934978708906645,"score_gpt":0.34742318170449066,"score_spread":0.2980733946154242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2838749849","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":[0.4985319,0.008243547,0.41063866,0.01041824,0.0005812851,0.0015339226,0.049597554,0.009377314,0.011077514],"genre_scores_gemma":[0.6889386,0.0021678081,0.28720066,0.001059099,0.000451162,0.00036574353,0.018893605,0.00010182026,0.0008214986],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99513054,0.002300244,0.0006743636,0.0008505565,0.0009301337,0.00011404331],"domain_scores_gemma":[0.9829012,0.0064581507,0.0052255774,0.0023550594,0.0025207119,0.000539256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046994826,0.00062766584,0.00043329597,0.003408126,0.0003693952,0.0017459639,0.001034116,0.00070629583,0.0014696983],"category_scores_gemma":[0.011900441,0.00029266352,0.0004017322,0.0034920804,0.0004545117,0.0013995316,0.0014795898,0.0005006116,0.0006229482],"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":[0.00039404814,0.00043414635,0.54838115,0.0016409579,0.00037461036,0.00051502837,0.0006785789,0.015844963,0.017084675,0.0038728437,0.032076824,0.3787022],"study_design_scores_gemma":[0.00018965152,0.0009218529,0.5558496,0.001078534,0.00044477396,0.004388876,0.0020961934,0.26569352,0.038385816,0.020139799,0.110618256,0.00019313145],"about_ca_topic_score_codex":0.0029404757,"about_ca_topic_score_gemma":0.0039062593,"teacher_disagreement_score":0.0046994826,"about_ca_system_score_codex":0.0007491169,"about_ca_system_score_gemma":0.0013578642,"threshold_uncertainty_score":0.024853528},"labels":[],"label_agreement":null},{"id":"W2883063036","doi":"10.1016/j.ijid.2018.04.3626","title":"The role of regional surveillance networks in enhancing global outbreak reporting","year":2018,"lang":"en","type":"article","venue":"International Journal of Infectious Diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario","funders":"","keywords":"Outbreak; Business; Medicine; Virology","score_opus":0.007997647609493211,"score_gpt":0.30004031977694023,"score_spread":0.292042672167447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883063036","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2995098,0.04156284,0.17791007,0.26862636,0.0028483858,0.004263667,0.0038940285,0.0026754867,0.19870937],"genre_scores_gemma":[0.8873211,0.0077090533,0.09297681,0.0068090414,0.0007609665,0.0011777367,0.00078119885,0.00017878706,0.0022852707],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.95225567,0.040595658,0.0016914034,0.00155988,0.0025562884,0.0013410109],"domain_scores_gemma":[0.79517645,0.12187862,0.023209985,0.015217699,0.03408645,0.010430797],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.09594858,0.0007081611,0.00044675765,0.0028569577,0.0012248982,0.004934421,0.0026864468,0.0011217523,0.0053005926],"category_scores_gemma":[0.17232478,0.0005776082,0.0005804443,0.001522178,0.001018563,0.0069281417,0.008990542,0.0019674515,0.0009284667],"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":[0.0004625097,0.00030684273,0.1807924,0.0023271746,0.0003299869,0.00025564936,0.0068197325,0.009778793,0.001571497,0.016252957,0.0407441,0.74035835],"study_design_scores_gemma":[0.0007049421,0.00320299,0.32894513,0.017690476,0.0014432505,0.0018359295,0.029157117,0.072678804,0.008541901,0.0427036,0.49274817,0.00034773216],"about_ca_topic_score_codex":0.01211041,"about_ca_topic_score_gemma":0.014021632,"teacher_disagreement_score":0.9040514,"about_ca_system_score_codex":0.003243823,"about_ca_system_score_gemma":0.009463821,"threshold_uncertainty_score":0.5074307},"labels":[],"label_agreement":null},{"id":"W2883079412","doi":"10.2196/publichealth.9022","title":"Issues in Building a Nursing Home Syndromic Surveillance System with Textmining: Longitudinal Observational Study","year":2018,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Cohort; Minimum Data Set; Public health surveillance; Public health; Medical emergency; Cohort study; Population; Long-term care; Database; Family medicine; Environmental health; Pediatrics; Nursing homes; Computer science; Nursing; Internal medicine","score_opus":0.08159101760074638,"score_gpt":0.37554314282513107,"score_spread":0.2939521252243847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883079412","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":[0.99597126,0.00020440092,0.0023083135,0.00028144207,0.00002277726,0.00024067094,0.0004971153,0.000013113541,0.0004608214],"genre_scores_gemma":[0.99504423,0.00011801529,0.0031657526,0.00021983974,0.000028320806,0.00048245292,0.000757519,0.000010621598,0.00017323335],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.97755855,0.015507676,0.0022557403,0.0012545796,0.002282328,0.0011410785],"domain_scores_gemma":[0.9780588,0.0076495884,0.00463422,0.0031068756,0.005184068,0.0013663878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03717672,0.00044462082,0.000499303,0.0011280666,0.0011365218,0.0016423098,0.0010220025,0.0008426961,0.0008331656],"category_scores_gemma":[0.044901267,0.0005325061,0.0010202938,0.0016136619,0.0006804227,0.0031705014,0.001638853,0.0010058719,0.0002998721],"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":[0.00010518912,0.00036280573,0.9946911,0.000037000747,0.00005547144,0.00006355599,0.0014673063,0.00005928303,0.00006335768,0.000058627465,0.0003068349,0.0027294604],"study_design_scores_gemma":[0.0000560027,0.0021002586,0.9807022,0.00020221272,0.00015991674,0.00029892285,0.009724801,0.003909149,0.00034126994,0.00023274739,0.0022349835,0.000037586207],"about_ca_topic_score_codex":0.021700704,"about_ca_topic_score_gemma":0.022309111,"teacher_disagreement_score":0.03717672,"about_ca_system_score_codex":0.0013351204,"about_ca_system_score_gemma":0.0031403094,"threshold_uncertainty_score":0.19661158},"labels":[],"label_agreement":null},{"id":"W2883375453","doi":"10.14745/ccdr.v41i09a02","title":"Big Data and the Global Public Health Intelligence Network (GPHIN)","year":2015,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":110,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Response Biomedical (Canada); Public Health Agency of Canada; Western University","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Public health; Outbreak; Big data; Emerging infectious disease; Global health; Infectious disease (medical specialty); International Health Regulations; Situation awareness; Environmental health; Globalization; Capacity building; Public health surveillance; Social media; Business; Disease; Medicine; Political science; Computer science; Coronavirus disease 2019 (COVID-19); Economic growth; Engineering; Virology; Data mining; Pathology; Economics","score_opus":0.16886426918767275,"score_gpt":0.33655491640261054,"score_spread":0.1676906472149378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883375453","genre_codex":"commentary","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":[0.01993564,0.07565538,0.060334004,0.62874866,0.0140048275,0.0004918989,0.008266644,0.0022600102,0.19030301],"genre_scores_gemma":[0.5607949,0.13407327,0.14245299,0.0956569,0.022587446,0.0013695394,0.015119535,0.0007642346,0.027181111],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9932167,0.00369043,0.00039354918,0.0006517093,0.0016479578,0.00039972877],"domain_scores_gemma":[0.959294,0.024893072,0.0035536077,0.0036911487,0.005095879,0.003472232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01403452,0.00073926535,0.0005167188,0.0040444783,0.00160882,0.008714308,0.0014050725,0.0030711843,0.0052021155],"category_scores_gemma":[0.025803026,0.00032014563,0.0005650532,0.006545932,0.0036118855,0.011979262,0.0070967413,0.0031593046,0.0011432377],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015172154,0.000060228078,0.014072126,0.0011401902,0.00014905584,0.0002530766,0.0010430077,0.0047135274,0.000434606,0.34740263,0.2852148,0.3453651],"study_design_scores_gemma":[0.000031305968,0.00007909748,0.007230389,0.0016034137,0.00004194218,0.000278408,0.0014790319,0.009332039,0.00062519754,0.2705336,0.7086806,0.00008500614],"about_ca_topic_score_codex":0.0031930066,"about_ca_topic_score_gemma":0.0017410327,"teacher_disagreement_score":0.99723876,"about_ca_system_score_codex":0.002761252,"about_ca_system_score_gemma":0.0050174925,"threshold_uncertainty_score":0.074222565},"labels":[],"label_agreement":null},{"id":"W2883825770","doi":"10.2196/10834","title":"Characterizing Tweet Volume and Content About Common Health Conditions Across Pennsylvania: Retrospective Analysis","year":2018,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Volume (thermodynamics); Medicine","score_opus":0.04111159246956773,"score_gpt":0.3564484600302383,"score_spread":0.31533686756067053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883825770","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":[0.9639416,0.00046615762,0.0025046698,0.0008974195,0.000049401588,0.00025745755,0.029652232,0.00008318257,0.0021479537],"genre_scores_gemma":[0.9768152,0.00059657916,0.002657242,0.00041518576,0.000089884634,0.0007719991,0.01734626,0.0000352356,0.0012723472],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986156,0.00039898796,0.00022722172,0.00033878742,0.00027380793,0.00014559008],"domain_scores_gemma":[0.98703593,0.0059629623,0.0038069861,0.00059998635,0.0021634824,0.00043059845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014750053,0.000238298,0.00026812908,0.0024057077,0.00065879634,0.0008650842,0.00038720583,0.0003649296,0.0018173938],"category_scores_gemma":[0.014221657,0.00029788094,0.00025790362,0.002747963,0.00027155483,0.0013625738,0.0012633499,0.0006397375,0.0006986594],"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":[0.00025650676,0.00007780463,0.9663277,0.00030240777,0.00013983747,0.0002730599,0.0041204207,0.00023901742,0.001484497,0.00029486773,0.009132156,0.017351732],"study_design_scores_gemma":[0.000016228703,0.0001784401,0.97092855,0.00017715043,0.00016512016,0.000531295,0.005963996,0.0027871244,0.0019614452,0.0003743128,0.016881097,0.00003517556],"about_ca_topic_score_codex":0.010696058,"about_ca_topic_score_gemma":0.011874699,"teacher_disagreement_score":0.010696058,"about_ca_system_score_codex":0.0006072674,"about_ca_system_score_gemma":0.0005641123,"threshold_uncertainty_score":0.021267593},"labels":[],"label_agreement":null},{"id":"W2884011204","doi":"10.14745/ccdr.v41i09a03","title":"Big Data is changing the battle against infectious diseases","year":2015,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; Agriculture and Agri-Food Canada","funders":"Canadian Institutes of Health Research; Agriculture and Agri-Food Canada; Government of Canada","keywords":"Big data; Bespoke; Battle; Multidisciplinary approach; Data science; Computer science; Infectious disease (medical specialty); Computer security; Business; Disease; Medicine; Political science; Geography; Data mining","score_opus":0.0800135105782911,"score_gpt":0.293943672934306,"score_spread":0.21393016235601492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884011204","genre_codex":"commentary","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":[0.0023398278,0.074807435,0.0145147825,0.8595856,0.024583725,0.00006383651,0.0012213544,0.0006199397,0.022263547],"genre_scores_gemma":[0.13185988,0.27622524,0.054425333,0.41975814,0.09427602,0.00025810133,0.0032529416,0.0009236501,0.01902073],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9869266,0.0047668815,0.00069861463,0.0009786304,0.005880317,0.0007489172],"domain_scores_gemma":[0.9549509,0.022271387,0.0023105752,0.003762586,0.009391531,0.0073129567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020816442,0.0010994031,0.0015155353,0.0035087783,0.0032701013,0.016367273,0.0025259387,0.0063572847,0.010296034],"category_scores_gemma":[0.060499597,0.00061867567,0.0010360986,0.0043903203,0.008582503,0.023067081,0.0076582935,0.011083554,0.00544072],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018169232,0.00008682514,0.0046170456,0.0011419159,0.00022356457,0.00016518333,0.00071674195,0.0012267911,0.00064800546,0.19243352,0.57832724,0.22023144],"study_design_scores_gemma":[0.000026090776,0.000056351884,0.0017023865,0.0012981568,0.000042048086,0.0001273741,0.0010441045,0.00094209705,0.0002762918,0.20959382,0.78483546,0.00005580037],"about_ca_topic_score_codex":0.006880046,"about_ca_topic_score_gemma":0.0065160906,"teacher_disagreement_score":0.99311996,"about_ca_system_score_codex":0.003604841,"about_ca_system_score_gemma":0.0116990125,"threshold_uncertainty_score":0.11008918},"labels":[],"label_agreement":null},{"id":"W2884469341","doi":"10.14745/ccdr.v41i09a01","title":"Evaluation of a national pharmacy‐based syndromic surveillance system","year":2015,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada","funders":"Strong; Public Health Agency; Public Health Agency of Canada","keywords":"Outbreak; Pharmacy; Nova scotia; Medicine; Medical prescription; Respiratory illness; Public health; Environmental health; Emergency medicine; Family medicine; Medical emergency; Geography; Internal medicine; Respiratory system; Virology","score_opus":0.10722680784676016,"score_gpt":0.35151828570047666,"score_spread":0.24429147785371652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884469341","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":[0.9736332,0.0002339829,0.0122159505,0.00094452564,0.000058523452,0.0032250292,0.0036990168,0.00095680525,0.0050328793],"genre_scores_gemma":[0.97465205,0.00011022145,0.021513823,0.0001430764,0.000016790593,0.00039395213,0.0027630506,0.000011947328,0.00039494905],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99161494,0.0032890737,0.00090864697,0.0009951398,0.002801145,0.00039109428],"domain_scores_gemma":[0.97007227,0.006416336,0.0028949708,0.0013720681,0.017784996,0.0014592974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01776773,0.00055229623,0.00041330466,0.0012254704,0.00058054307,0.0014452477,0.0015830654,0.00051993364,0.0011293654],"category_scores_gemma":[0.027434308,0.00024159715,0.00032446586,0.0010249414,0.00033303327,0.0009925299,0.0012521007,0.00037265546,0.00026840786],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003183118,0.0022399472,0.7957102,0.0006809643,0.00038391593,0.00028845065,0.0008389434,0.019380081,0.0084641315,0.0006331838,0.005191537,0.16300549],"study_design_scores_gemma":[0.00090024126,0.008710156,0.6009762,0.00028368022,0.00047386653,0.00044845836,0.0021116447,0.3671482,0.010569325,0.00039143633,0.007894632,0.00009208382],"about_ca_topic_score_codex":0.12092414,"about_ca_topic_score_gemma":0.08432059,"teacher_disagreement_score":0.99337864,"about_ca_system_score_codex":0.0066213463,"about_ca_system_score_gemma":0.008992221,"threshold_uncertainty_score":0.24044055},"labels":[],"label_agreement":null},{"id":"W2884721711","doi":"10.2196/publichealth.8627","title":"Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study","year":2018,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Japan Agency for Medical Research and Development","keywords":"Popularity; Computer science; Social media; Information sensitivity; Internet privacy; Computer security; World Wide Web","score_opus":0.026265346446914695,"score_gpt":0.30777836688399246,"score_spread":0.28151302043707777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884721711","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":[0.98449427,0.0003535437,0.010879206,0.00040069048,0.000059542443,0.00010795716,0.0021973664,0.00010498671,0.0014024372],"genre_scores_gemma":[0.98955756,0.00022145055,0.0059790886,0.000060070368,0.000087162305,0.00006344713,0.0029224674,0.0000123530635,0.0010964182],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995726,0.00009694916,0.000049366,0.00012391333,0.000086052816,0.00007119864],"domain_scores_gemma":[0.9983552,0.0009457357,0.00028651414,0.000098734134,0.00024120859,0.00007255468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009572369,0.0006359978,0.00048126307,0.0017899479,0.00042104116,0.00061600446,0.00074319344,0.0006588795,0.0009656155],"category_scores_gemma":[0.0027787518,0.00022846136,0.0010828314,0.0011764601,0.00023915908,0.0011984565,0.00039975558,0.00048445573,0.000572372],"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":[0.00085853774,0.0011510795,0.8705514,0.0004818471,0.00044401627,0.0013625077,0.00081950176,0.026649153,0.0068384204,0.0014980797,0.0050966633,0.08424878],"study_design_scores_gemma":[0.000028053886,0.00032455678,0.25818533,0.00006925073,0.00031046485,0.0008418799,0.0015502776,0.7288711,0.005349929,0.0010126125,0.0033894,0.00006718953],"about_ca_topic_score_codex":0.0065659597,"about_ca_topic_score_gemma":0.006849822,"teacher_disagreement_score":0.0065659597,"about_ca_system_score_codex":0.00056505774,"about_ca_system_score_gemma":0.0003326788,"threshold_uncertainty_score":0.013055503},"labels":[],"label_agreement":null},{"id":"W2885238424","doi":"10.1371/currents.dis.47581b109e865f7b64d831f86a7fd7f4","title":"An Evaluation of Global Hazard Communication with Ethical Considerations","year":2018,"lang":"en","type":"article","venue":"PLoS Currents","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hazard; Natural hazard; Service (business); Media coverage; Environmental health; Business; Medicine; Geography; Sociology","score_opus":0.09111477916361561,"score_gpt":0.40720960529296324,"score_spread":0.31609482612934764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885238424","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":[0.77520186,0.0034880086,0.0149959745,0.044245645,0.0012749671,0.016013829,0.0016822129,0.00009380209,0.1430037],"genre_scores_gemma":[0.9701793,0.0008235653,0.008709664,0.006172921,0.0004055097,0.011586978,0.00025579496,0.000044637705,0.0018216831],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.53448284,0.32633224,0.027836371,0.005582437,0.09970809,0.0060579795],"domain_scores_gemma":[0.19565143,0.600707,0.08647542,0.016161148,0.09263378,0.008371211],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.33749005,0.0007817921,0.00091851776,0.008526406,0.0048743836,0.011138913,0.0027397366,0.0035029259,0.008103644],"category_scores_gemma":[0.5818961,0.00046761366,0.0022270172,0.010190257,0.016845647,0.008953851,0.011415928,0.004448162,0.0006615335],"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":[0.0016963353,0.0020854378,0.45024034,0.009223585,0.0007485057,0.0019875644,0.20889801,0.0018560011,0.00064846186,0.075738035,0.02440039,0.22247732],"study_design_scores_gemma":[0.00053425913,0.0057611763,0.43360782,0.010480517,0.0010834959,0.0023352555,0.36800095,0.00471677,0.0025651655,0.032704886,0.13788818,0.0003214794],"about_ca_topic_score_codex":0.002106124,"about_ca_topic_score_gemma":0.002734205,"teacher_disagreement_score":0.33749005,"about_ca_system_score_codex":0.019587608,"about_ca_system_score_gemma":0.021134265,"threshold_uncertainty_score":0.81699276},"labels":[],"label_agreement":null},{"id":"W2888230474","doi":"10.1016/j.jclinepi.2018.08.007","title":"Subgroup analyses are seldom possible and subgroup effects are rare in Cochrane HIV systematic reviews","year":2018,"lang":"en","type":"letter","venue":"Journal of Clinical Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Hamilton Health Sciences; McMaster University; Ontario HIV Treatment Network; Population Health Research Institute; St. Joseph’s Healthcare Hamilton; Impact","funders":"","keywords":"Systematic review; Subgroup analysis; Meta-analysis; Study heterogeneity; Medicine; Generalizability theory; MEDLINE; Publication bias; Statistics; Internal medicine; Mathematics; Biology","score_opus":0.29050822245373664,"score_gpt":0.5208990976662901,"score_spread":0.23039087521255341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888230474","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003159135,0.21214183,0.012688339,0.7074485,0.041471243,0.002472349,0.006008381,0.00033435234,0.014275804],"genre_scores_gemma":[0.08287952,0.08644608,0.028952323,0.72600055,0.061599042,0.008855125,0.0017566246,0.00037048216,0.0031402875],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.614438,0.26975864,0.085148625,0.0078074303,0.020680746,0.0021665434],"domain_scores_gemma":[0.106988624,0.8281521,0.035917126,0.013310623,0.013904468,0.0017270717],"candidate_categories":["metaresearch","metaepi_broad"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.20882972,0.0013706413,0.0056118537,0.0068532443,0.0018413785,0.006726649,0.0028667701,0.013091709,0.014580743],"category_scores_gemma":[0.80573773,0.0020899533,0.0057306704,0.0068355636,0.0038006231,0.008526881,0.0047649043,0.010070768,0.0037053195],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025613636,0.00006141272,0.010258424,0.14228934,0.025094744,0.0016204102,0.0020800477,0.0005101799,0.00019490378,0.024144307,0.61215794,0.17902695],"study_design_scores_gemma":[0.011870123,0.00057551044,0.010946259,0.22869551,0.035834745,0.00342223,0.0021432268,0.0025491102,0.00080846116,0.19513515,0.5075026,0.0005170462],"about_ca_topic_score_codex":0.00344214,"about_ca_topic_score_gemma":0.0112970555,"teacher_disagreement_score":0.99438816,"about_ca_system_score_codex":0.0038399345,"about_ca_system_score_gemma":0.010055316,"threshold_uncertainty_score":0.9756538},"labels":[],"label_agreement":null},{"id":"W2889680530","doi":"10.23889/ijpds.v3i4.740","title":"Where it’s at - linking data geographically.","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geocoding; Geographic information system; Geospatial analysis; Data science; Presentation (obstetrics); Location; Scale (ratio); Geography; Computer science; Data mining; Cartography; Medicine","score_opus":0.10069259066167668,"score_gpt":0.43354955170196735,"score_spread":0.33285696104029067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889680530","genre_codex":"commentary","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":[0.0064988495,0.020274384,0.35029414,0.39490482,0.02408494,0.0017252556,0.03017385,0.009203412,0.16284035],"genre_scores_gemma":[0.084278926,0.03825335,0.6206536,0.09140664,0.01316316,0.0028997776,0.024214368,0.008797209,0.11633297],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9764474,0.013659318,0.0017666991,0.0022257518,0.005317496,0.00058326096],"domain_scores_gemma":[0.9523755,0.02344348,0.004343993,0.008081818,0.010226981,0.0015282785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021881778,0.00090283283,0.000777894,0.0035663673,0.0021949073,0.010806479,0.0026234488,0.00299003,0.0841807],"category_scores_gemma":[0.101777874,0.0008519186,0.0010101628,0.0066365656,0.0028458487,0.015569309,0.007171831,0.00523406,0.026908908],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009703852,0.000044502147,0.0034838219,0.002009077,0.00005908534,0.00019730757,0.0037531527,0.0006478914,0.0006174015,0.16515365,0.5092315,0.3147056],"study_design_scores_gemma":[0.000009441873,0.00002328397,0.00088597,0.0018310366,0.00001850915,0.00019677663,0.0013056142,0.00042450117,0.00047106357,0.038763944,0.9560305,0.00003937492],"about_ca_topic_score_codex":0.0054515237,"about_ca_topic_score_gemma":0.0043587284,"teacher_disagreement_score":0.0841807,"about_ca_system_score_codex":0.0029694524,"about_ca_system_score_gemma":0.0063553057,"threshold_uncertainty_score":0.28161252},"labels":[],"label_agreement":null},{"id":"W2890026418","doi":"10.2139/ssrn.3395619","title":"Introduction to Blockchain","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Blockchain; Computer science; Computer security","score_opus":0.003808835091968883,"score_gpt":0.23886009237534422,"score_spread":0.23505125728337534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890026418","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":[0.005552909,0.0037287313,0.77245533,0.014028315,0.002961636,0.00067075633,0.0021037848,0.0034260987,0.19507243],"genre_scores_gemma":[0.30040085,0.010234616,0.3898788,0.0052399067,0.003198387,0.002052562,0.0042610546,0.002801114,0.28193274],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.995093,0.0017139497,0.00029509756,0.00062854774,0.0018245388,0.0004448603],"domain_scores_gemma":[0.99275726,0.0030023132,0.00025436233,0.0021721497,0.001403495,0.00041046378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036251578,0.000802859,0.0009008943,0.0013607162,0.0018450972,0.0048490874,0.0017173396,0.0022623162,0.061407577],"category_scores_gemma":[0.014306255,0.0006855724,0.0007825818,0.0030565376,0.0030648136,0.008397291,0.0051946216,0.004187977,0.02056297],"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":[0.00009356439,0.000050667935,0.00023444618,0.00017654769,0.000012276466,0.00009432611,0.00015593017,0.0053650867,0.00040065797,0.8710307,0.03912861,0.08325725],"study_design_scores_gemma":[0.00003962238,0.000045106102,0.00007843556,0.00018201316,0.000008687727,0.00017006883,0.000070451,0.020263737,0.00086442026,0.6890532,0.28919864,0.000025636327],"about_ca_topic_score_codex":0.0028757765,"about_ca_topic_score_gemma":0.0019014946,"teacher_disagreement_score":0.061407577,"about_ca_system_score_codex":0.0018517581,"about_ca_system_score_gemma":0.0030316042,"threshold_uncertainty_score":0.20542884},"labels":[],"label_agreement":null},{"id":"W2890055747","doi":"10.1111/phpp.12425","title":"An international comparison of Google searches for <i>sunscreen, sunburn, skin cancer,</i> and <i>melanoma</i>: Current trends and public health implications","year":2018,"lang":"en","type":"article","venue":"Photodermatology Photoimmunology & Photomedicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Johns Hopkins University","keywords":"Sunburn; Skin cancer; Melanoma; Medicine; Incidence (geometry); Public health; Cancer; Dermatology; Environmental health; Cancer research; Pathology; Internal medicine","score_opus":0.06449164048764343,"score_gpt":0.4124543223435189,"score_spread":0.3479626818558755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890055747","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":[0.9306823,0.038915724,0.00034509195,0.0012146008,0.00009082658,0.00008749205,0.019829135,0.000031752923,0.008803173],"genre_scores_gemma":[0.985592,0.007418666,0.00034615857,0.00019030491,0.000033581782,0.000052608924,0.0058487346,0.000015243811,0.0005027551],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99824715,0.0003979572,0.00054100604,0.00029756682,0.00032946502,0.00018683996],"domain_scores_gemma":[0.9893128,0.0031259188,0.004786776,0.00031000978,0.0019051877,0.0005592437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022280833,0.0002316705,0.00047173092,0.005849467,0.00026026386,0.0011970294,0.00040541124,0.00029399723,0.0022821613],"category_scores_gemma":[0.012646651,0.00015854397,0.0010341125,0.0090764435,0.00036463977,0.0022472483,0.0010634566,0.00035893449,0.00030969564],"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":[0.0007700216,0.000044290766,0.95182383,0.004708491,0.0011942068,0.00014133415,0.0020418002,0.00015622869,0.00033925002,0.00058012665,0.0031145168,0.0350859],"study_design_scores_gemma":[0.000016007596,0.000085209904,0.9913885,0.0009068506,0.0004357807,0.00024793824,0.0027741294,0.00021439264,0.00012144243,0.00008680488,0.003708503,0.000014528095],"about_ca_topic_score_codex":0.029459476,"about_ca_topic_score_gemma":0.052996263,"teacher_disagreement_score":0.029459476,"about_ca_system_score_codex":0.00082016067,"about_ca_system_score_gemma":0.001214314,"threshold_uncertainty_score":0.058575988},"labels":[],"label_agreement":null},{"id":"W2890205989","doi":"10.23889/ijpds.v3i4.1028","title":"Using Linked Data and Advanced Analytics to Prioritize Health Concerns within Regions","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University Health Centre","funders":"","keywords":"Data science; Population; Computer science; Health indicator; Public health; Decision support system; Population health; Analytics; Disease surveillance; Data mining; Environmental health; Medicine","score_opus":0.2669284483601198,"score_gpt":0.5166225883596763,"score_spread":0.24969413999955653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890205989","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":[0.11632011,0.0010840663,0.82050437,0.009445666,0.00030957122,0.0007404905,0.013023059,0.016027305,0.02254538],"genre_scores_gemma":[0.3632716,0.0006811693,0.6217939,0.00063344085,0.00010561929,0.00034250165,0.010209635,0.0005346421,0.0024274928],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963405,0.001462554,0.00033087956,0.00060644915,0.0011054539,0.00015417382],"domain_scores_gemma":[0.9911205,0.0042427485,0.001034076,0.0014211374,0.0018075481,0.00037398285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00480876,0.0009211786,0.00044068167,0.0058195237,0.0008893967,0.0063304245,0.0013559232,0.000726446,0.0045817867],"category_scores_gemma":[0.017143447,0.0003567677,0.0011432013,0.0052987095,0.0007140788,0.0038537763,0.00374499,0.0010493296,0.0009432197],"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":[0.00035987116,0.00050784927,0.091710985,0.0011623243,0.00061531534,0.0006769073,0.0037812004,0.17315525,0.008346928,0.11235468,0.039496373,0.56783235],"study_design_scores_gemma":[0.00009430721,0.00012311689,0.016569557,0.0005227542,0.00019231053,0.00017941132,0.0030613623,0.7248529,0.014135527,0.12610619,0.113957204,0.00020529573],"about_ca_topic_score_codex":0.022543088,"about_ca_topic_score_gemma":0.028962735,"teacher_disagreement_score":0.022543088,"about_ca_system_score_codex":0.002072684,"about_ca_system_score_gemma":0.003168308,"threshold_uncertainty_score":0.044823706},"labels":[],"label_agreement":null},{"id":"W2890238901","doi":"10.5210/ojphi.v10i2.8547","title":"User rating activity within KIWI: A technology for public health event monitoring and early warning signal detection","year":2018,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada; Canadian Women's Health Network","funders":"Canadian Food Inspection Agency","keywords":"Reliability (semiconductor); Rating scale; Applied psychology; Confidence interval; Computer science; Medicine; Psychology; Statistics; Mathematics","score_opus":0.0746915077265956,"score_gpt":0.376613282896167,"score_spread":0.3019217751695714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890238901","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":[0.6005849,0.14053623,0.07131646,0.022368489,0.0019237655,0.008153477,0.041491505,0.003731067,0.10989409],"genre_scores_gemma":[0.86514884,0.04953353,0.059556328,0.0020352618,0.00069356593,0.0026623372,0.013560862,0.00023069969,0.0065785134],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.98334044,0.0050705373,0.0015980538,0.0007941388,0.008638005,0.00055877835],"domain_scores_gemma":[0.88272965,0.041762237,0.015141199,0.0037472146,0.053466015,0.0031537516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020857053,0.00048363974,0.00073688274,0.008277205,0.0008933333,0.0029862314,0.0014113636,0.00054002804,0.0022560435],"category_scores_gemma":[0.09072358,0.00024080604,0.00068612857,0.008799768,0.0006159087,0.0030385284,0.0016335563,0.0007905207,0.0009087498],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035745883,0.00007112447,0.20200658,0.008904306,0.00031001933,0.00013509054,0.0061085396,0.00044449788,0.0012964915,0.00065457105,0.035724998,0.74398625],"study_design_scores_gemma":[0.00006161291,0.0005805511,0.7922415,0.010346581,0.0007062622,0.00055895996,0.009295415,0.00436035,0.0022291045,0.0006546803,0.17870402,0.00026102745],"about_ca_topic_score_codex":0.12057738,"about_ca_topic_score_gemma":0.24593763,"teacher_disagreement_score":0.12057738,"about_ca_system_score_codex":0.0042367703,"about_ca_system_score_gemma":0.009994854,"threshold_uncertainty_score":0.2397511},"labels":[],"label_agreement":null},{"id":"W2890258652","doi":"10.2196/11361","title":"Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study","year":2018,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Machine learning; Medicine","score_opus":0.10781658345416842,"score_gpt":0.425540823936369,"score_spread":0.3177242404822006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890258652","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":[0.9776913,0.005268369,0.012403867,0.0003825868,0.00022973475,0.00020637526,0.0016974144,0.00019353465,0.0019268391],"genre_scores_gemma":[0.98777384,0.0011196907,0.007792727,0.00007312087,0.00011414835,0.000098031574,0.0027677699,0.000020981111,0.00023971457],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99674964,0.0016334954,0.0003165794,0.000494936,0.00064230297,0.00016309651],"domain_scores_gemma":[0.9897301,0.0064603155,0.00082262297,0.0007156143,0.0020080314,0.0002633286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005040371,0.00086076243,0.00074388756,0.0026646242,0.00028527033,0.0010998364,0.0007219586,0.0008380884,0.00072572083],"category_scores_gemma":[0.009303906,0.00021648913,0.0012022698,0.0022580363,0.00027143216,0.0015068981,0.00067294453,0.00050226634,0.0002212752],"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":[0.006137157,0.0028282972,0.60063046,0.0021286835,0.0041002776,0.0005312536,0.00037398806,0.08331003,0.00381122,0.0011838516,0.0057358863,0.28922898],"study_design_scores_gemma":[0.00027946848,0.004256995,0.426759,0.00025871763,0.0011817937,0.0006287693,0.00094306987,0.5561819,0.004608098,0.0011905421,0.0035874934,0.00012408526],"about_ca_topic_score_codex":0.0057554506,"about_ca_topic_score_gemma":0.002659531,"teacher_disagreement_score":0.0057554506,"about_ca_system_score_codex":0.00075250835,"about_ca_system_score_gemma":0.0004207721,"threshold_uncertainty_score":0.02665639},"labels":[],"label_agreement":null},{"id":"W2890621114","doi":"10.14745/ccdr.v44i05a03","title":"Canadian Pandemic Influenza Preparedness: Communications strategy","year":2018,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Preparedness; Public relations; Pandemic; Credibility; Stakeholder; Government (linguistics); Public health; Health communication; Confidentiality; Business; Action plan; Political science; Medicine; Nursing; Coronavirus disease 2019 (COVID-19)","score_opus":0.04773650268123069,"score_gpt":0.33208380416220556,"score_spread":0.28434730148097487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890621114","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038280466,0.012493718,0.005171694,0.40528235,0.0097766295,0.0029286677,0.021776931,0.0011858995,0.53755605],"genre_scores_gemma":[0.08388701,0.02200028,0.03651119,0.22690254,0.0024596357,0.0028536,0.02735703,0.00053834263,0.5974904],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99242413,0.0005990421,0.00019376054,0.0003159974,0.0045074336,0.0019595565],"domain_scores_gemma":[0.98706996,0.00061285635,0.0003576688,0.00018487308,0.008121897,0.0036526776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057417443,0.0014861984,0.00031847373,0.0025711714,0.007654304,0.0045686658,0.0029652242,0.00811816,0.016659798],"category_scores_gemma":[0.013405842,0.00062164513,0.00081706035,0.0023406213,0.0014483533,0.0019152891,0.0035238236,0.0050982726,0.0051770527],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001513406,0.000022654269,0.0005522645,0.00016195193,0.000007681559,0.000089832516,0.00032964157,0.00030645012,0.00019417041,0.023253128,0.9511073,0.023959765],"study_design_scores_gemma":[0.000011040798,0.0000112438365,0.0021464059,0.0001123741,0.000005990002,0.000023827186,0.0002198542,0.00018310705,0.00008350648,0.00072319503,0.9964592,0.000020373185],"about_ca_topic_score_codex":0.9831088,"about_ca_topic_score_gemma":0.9797606,"teacher_disagreement_score":0.910502,"about_ca_system_score_codex":0.08949799,"about_ca_system_score_gemma":0.4332887,"threshold_uncertainty_score":0.6493564},"labels":[],"label_agreement":null},{"id":"W2890645415","doi":"10.5210/ojphi.v10i2.9114","title":"An innovative mobile data collection technology for public health in a field setting","year":2018,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Health Canada; University of Toronto; First Nations Health and Social Secretariat of Manitoba; Public Health Agency of Canada; Canadian Women's Health Network; Sunnybrook Health Science Centre","funders":"","keywords":"Data collection; Mobile technology; Computer science; Public health surveillance; Mobile device; Health informatics; Data science; Public health; World Wide Web; Knowledge management; Medicine; Nursing","score_opus":0.11623945183485741,"score_gpt":0.4403923307946353,"score_spread":0.3241528789597779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890645415","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":[0.17178848,0.0032197519,0.6574894,0.015149705,0.0024021615,0.024632454,0.011803552,0.021664757,0.09184977],"genre_scores_gemma":[0.28553784,0.0016382124,0.6698989,0.003836709,0.0005091046,0.014744337,0.002951403,0.00047101002,0.020412425],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9959039,0.0018937496,0.00022569501,0.00050733355,0.0010711967,0.00039814992],"domain_scores_gemma":[0.9936812,0.0032816627,0.00023739117,0.00052905444,0.0017882728,0.0004824217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051323776,0.00099502,0.00044644088,0.002262752,0.0012690816,0.0023036618,0.0018883682,0.0014446512,0.009998923],"category_scores_gemma":[0.011105143,0.00042196104,0.0006265649,0.0013243356,0.0008458094,0.002265837,0.0038247453,0.0010497296,0.0025423407],"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":[0.001311145,0.00056523667,0.009921306,0.0034518319,0.000098515724,0.0018911043,0.013578417,0.0010170371,0.041967288,0.009840488,0.06316493,0.85319275],"study_design_scores_gemma":[0.0011893279,0.00395714,0.053544916,0.004055198,0.00051547796,0.0051620603,0.016731838,0.018863976,0.041709263,0.019041967,0.8344702,0.0007586568],"about_ca_topic_score_codex":0.011237003,"about_ca_topic_score_gemma":0.013073382,"teacher_disagreement_score":0.011237003,"about_ca_system_score_codex":0.0013411804,"about_ca_system_score_gemma":0.004051807,"threshold_uncertainty_score":0.03344971},"labels":[],"label_agreement":null},{"id":"W2890787836","doi":"10.23889/ijpds.v3i3.443","title":"Multi-province epidemiological research using linked administrative data: a case study from Canada.","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Institut universitaire en santé mentale de Montréal; Cégep Marie-Victorin; Centre for Addiction and Mental Health; Women's College Hospital; University of Toronto; Manitoba Health; South Health Campus; Simon Fraser University; University of Manitoba; Alberta Hospital Edmonton","funders":"","keywords":"Comparability; Context (archaeology); Scope (computer science); Government (linguistics); Business; Investment (military); Information system; Health care; Data collection; Minimum Data Set; Data science; Computer science; Political science; Geography; Medicine; Politics","score_opus":0.6286096928129259,"score_gpt":0.5941129252567702,"score_spread":0.034496767556155716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890787836","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9509385,0.0058388887,0.003358115,0.019500172,0.00009320121,0.0011955472,0.004358457,0.00006640215,0.014650721],"genre_scores_gemma":[0.9813308,0.0034653125,0.008193346,0.002737313,0.000024546043,0.0002922713,0.0012599964,0.000032843636,0.0026636033],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.988562,0.0044034906,0.0007207296,0.0007132784,0.002914627,0.0026857764],"domain_scores_gemma":[0.971572,0.010431733,0.0036540038,0.0015939947,0.009075706,0.0036725688],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.013514275,0.00048069205,0.0006756268,0.0044666496,0.016269384,0.0041443855,0.003073515,0.0018631421,0.00160943],"category_scores_gemma":[0.027151147,0.00063430076,0.00075845025,0.01786117,0.0027677459,0.0012815386,0.004223452,0.002533672,0.0001627105],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023357637,0.0005767793,0.63972783,0.0019052443,0.000397536,0.021404324,0.2089194,0.0022852556,0.00081073336,0.017281221,0.023225602,0.0832324],"study_design_scores_gemma":[0.000081831604,0.00020847715,0.5146232,0.0026010643,0.00038998557,0.0048868125,0.4119778,0.004920281,0.0005362946,0.0028353243,0.05674273,0.00019617607],"about_ca_topic_score_codex":0.995082,"about_ca_topic_score_gemma":0.9971756,"teacher_disagreement_score":0.9864857,"about_ca_system_score_codex":0.1097353,"about_ca_system_score_gemma":0.19397807,"threshold_uncertainty_score":0.79618907},"labels":[],"label_agreement":null},{"id":"W2891031432","doi":"10.2196/10827","title":"Dynamics of Health Agency Response and Public Engagement in Public Health Emergency: A Case Study of CDC Tweeting Patterns During the 2016 Zika Epidemic","year":2018,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Public health; Social media; Public health surveillance; Government (linguistics); Agency (philosophy); Public engagement; Disease control; Zika virus; Political science; Medicine; Environmental health; Public relations; Sociology; Virology","score_opus":0.06505762580047174,"score_gpt":0.3651102102012641,"score_spread":0.30005258440079235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891031432","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":[0.9958627,0.00015603058,0.0004018408,0.000934184,0.000012925076,0.00005655399,0.0007000026,0.000015521573,0.0018602003],"genre_scores_gemma":[0.9980186,0.00021316414,0.000599263,0.00012463695,0.00001947134,0.000057972233,0.0005491971,0.000010325274,0.0004073531],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981298,0.0008287472,0.00013627262,0.00024108078,0.00023942033,0.00042476694],"domain_scores_gemma":[0.9953259,0.0019444518,0.0014315185,0.0003451725,0.00050426414,0.00044858654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020460754,0.0003496372,0.00033426043,0.0018646448,0.002043314,0.0018767142,0.00074069016,0.0011380012,0.0017446248],"category_scores_gemma":[0.010417582,0.00040244265,0.0004504446,0.0024821882,0.0009881138,0.0022954731,0.0027122796,0.0014983924,0.0003790113],"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":[0.00024377844,0.00023012212,0.91704744,0.00016254392,0.00012818417,0.0027857558,0.053072475,0.0009534641,0.0010900929,0.0018499354,0.0050826576,0.01735349],"study_design_scores_gemma":[0.000017658775,0.0001299389,0.86645114,0.00014719107,0.00005396452,0.0010429086,0.11672724,0.0061103157,0.00046850217,0.00067010324,0.008109809,0.0000712681],"about_ca_topic_score_codex":0.050221846,"about_ca_topic_score_gemma":0.072603896,"teacher_disagreement_score":0.050221846,"about_ca_system_score_codex":0.0019381645,"about_ca_system_score_gemma":0.001291727,"threshold_uncertainty_score":0.09985906},"labels":[],"label_agreement":null},{"id":"W2891471727","doi":"10.1016/j.micinf.2018.08.003","title":"Improving access to essential tests for infectious diseases","year":2018,"lang":"en","type":"editorial","venue":"Microbes and Infection","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"McGill University Health Centre; McGill University","funders":"","keywords":"Syphilis; Malaria; Virology; Biology; Human immunodeficiency virus (HIV); Hepatitis B; Immunology; Hepatitis B virus; Human papillomavirus; Medicine; Virus","score_opus":0.007986990321407796,"score_gpt":0.30994027414860986,"score_spread":0.30195328382720205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891471727","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014539705,0.006459565,0.0001967998,0.084556535,0.9077418,0.000019201621,0.00008492853,0.0000607864,0.00086576963],"genre_scores_gemma":[0.00022414184,0.0042514713,0.00018238842,0.051417034,0.9404665,0.000026351376,0.000037420297,0.00003624099,0.0033583834],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.97334284,0.0066790218,0.004374751,0.0025849838,0.011556669,0.0014618495],"domain_scores_gemma":[0.85711575,0.07871016,0.008295879,0.0035190415,0.039765626,0.012593498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03401834,0.006117563,0.0073395744,0.0058561983,0.004354188,0.013830382,0.0066267084,0.047455758,0.017790727],"category_scores_gemma":[0.12340032,0.002891564,0.0053431317,0.0029429735,0.005930923,0.009139272,0.004873248,0.051418673,0.011558536],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025265237,0.000015237169,0.00003361418,0.00032176147,0.000044326138,0.00005697509,0.000016269349,0.000031180913,0.000021746186,0.0005135177,0.9931357,0.005784503],"study_design_scores_gemma":[0.00019752311,0.000043795484,0.0004373305,0.0018689373,0.00023248355,0.00015901926,0.00006639475,0.00036971743,0.00011242363,0.003446287,0.9930127,0.000053475138],"about_ca_topic_score_codex":0.005295418,"about_ca_topic_score_gemma":0.012963258,"teacher_disagreement_score":0.047455758,"about_ca_system_score_codex":0.007914395,"about_ca_system_score_gemma":0.009405923,"threshold_uncertainty_score":0.17990834},"labels":[],"label_agreement":null},{"id":"W2891994155","doi":"10.23889/ijpds.v3i4.987","title":"Empathic Cultural Mapping: Little data, big data, knowledge transfer and exchange","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Government (linguistics); Public relations; Empowerment; Psychosocial; Sociology; Big data; Data science; Political science; Psychology; Computer science","score_opus":0.2582180060156925,"score_gpt":0.4432382665093945,"score_spread":0.18502026049370202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891994155","genre_codex":"other","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":[0.30099282,0.011226643,0.13777429,0.16787554,0.0015831991,0.0014182397,0.0034026457,0.0009631615,0.37476346],"genre_scores_gemma":[0.9381006,0.0045947134,0.0401776,0.004083666,0.00026372352,0.0009867884,0.001141619,0.00024041378,0.010410925],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.96117204,0.03067077,0.00084849127,0.0023513413,0.0037000752,0.0012573623],"domain_scores_gemma":[0.91308117,0.06021219,0.0045865597,0.013165921,0.0054692277,0.003484904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033825394,0.0006141467,0.00059070083,0.004174855,0.009939862,0.021816729,0.0030953882,0.0021219067,0.012080418],"category_scores_gemma":[0.077331394,0.0005252373,0.0004910446,0.008386094,0.01849428,0.022316983,0.020547722,0.0036569785,0.0012323721],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012770614,0.00022086602,0.025313735,0.0016384203,0.000096257194,0.0009612574,0.43279698,0.0013504875,0.0003975177,0.19412772,0.038966198,0.30400282],"study_design_scores_gemma":[0.000019292229,0.00007257608,0.011362038,0.0025474187,0.000031471678,0.0005624552,0.49690905,0.002984136,0.00062652834,0.22552781,0.25928044,0.000076748016],"about_ca_topic_score_codex":0.009901756,"about_ca_topic_score_gemma":0.0089766225,"teacher_disagreement_score":0.033825394,"about_ca_system_score_codex":0.007308678,"about_ca_system_score_gemma":0.008518565,"threshold_uncertainty_score":0.17888796},"labels":[],"label_agreement":null},{"id":"W2893214481","doi":"10.3390/su10103414","title":"Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization","year":2018,"lang":"en","type":"article","venue":"Sustainability","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center; National Research Foundation of Korea; Ministry of Science, ICT and Future Planning; National Research Foundation","keywords":"Scopus; Bibliometrics; Data science; Web of science; Flourishing; Citation; Citation analysis; Publishing; Visualization; Field (mathematics); Analytics; Computer science; Library science; Data mining; MEDLINE; Political science; Psychology","score_opus":0.040818264641003216,"score_gpt":0.4362929666343069,"score_spread":0.3954747019933037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893214481","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":[0.71182126,0.026089365,0.00962258,0.007942927,0.00050404505,0.00034698786,0.16785064,0.00221869,0.07360347],"genre_scores_gemma":[0.90034676,0.013730611,0.013758032,0.00015969887,0.00040698674,0.00031164978,0.06670848,0.00016177162,0.004416089],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.996784,0.0005738075,0.00064269494,0.00030138696,0.0014384224,0.00025976842],"domain_scores_gemma":[0.98452824,0.006160382,0.0041692713,0.00080259296,0.0038368488,0.00050259056],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0033430068,0.0006220749,0.00065944804,0.09615699,0.0007364436,0.0047528963,0.00046204892,0.0005442999,0.0029721316],"category_scores_gemma":[0.016658189,0.00015340258,0.00094122847,0.13043468,0.00048772112,0.0029997746,0.0017023269,0.00044070435,0.00085760816],"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":[0.00021697639,0.00013152076,0.47094986,0.0057371156,0.0007060357,0.0008718935,0.009198506,0.006710074,0.0035145835,0.028228782,0.055279087,0.41845554],"study_design_scores_gemma":[0.000022605915,0.00012467116,0.773035,0.0017346463,0.00039019887,0.0010431975,0.012237051,0.011892201,0.00370111,0.011176314,0.18451497,0.00012793926],"about_ca_topic_score_codex":0.012791096,"about_ca_topic_score_gemma":0.014479126,"teacher_disagreement_score":0.903843,"about_ca_system_score_codex":0.0022378284,"about_ca_system_score_gemma":0.0030140723,"threshold_uncertainty_score":0.025433302},"labels":[],"label_agreement":null},{"id":"W2897077740","doi":"10.2196/11730","title":"Public Views on Using Mobile Phone Call Detail Records in Health Research: Qualitative Study","year":2018,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Medical Research Council; Natural Environment Research Council; Sight Research UK","keywords":"Mobile phone; Phone; Public health; Qualitative research; Computer science; Internet privacy; Data science; Medicine; Telecommunications; Sociology; Nursing","score_opus":0.524494258507066,"score_gpt":0.5672514195706141,"score_spread":0.0427571610635481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897077740","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":[0.9834064,0.000973097,0.003166596,0.008499872,0.00009922465,0.00030489138,0.000098344375,0.000023390334,0.0034282736],"genre_scores_gemma":[0.9951361,0.0009266867,0.0010842892,0.0016586817,0.000038348273,0.000378633,0.00003346927,0.000018034427,0.0007258409],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9054088,0.08056956,0.0024249258,0.0017284494,0.004558137,0.005310181],"domain_scores_gemma":[0.804664,0.16277616,0.011130581,0.003909706,0.009942888,0.007576721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10926094,0.0006580612,0.0011544961,0.0025311427,0.010391756,0.0066821226,0.0023926506,0.0035196536,0.0024454307],"category_scores_gemma":[0.11070256,0.0013521591,0.0007988491,0.0024392307,0.014843665,0.0097619705,0.011606735,0.005715657,0.00041484035],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038494385,0.000043040185,0.0033369984,0.00023597137,0.0000058807505,0.00033578332,0.99123526,0.000033792876,0.0005042421,0.00091365684,0.00025784614,0.003059],"study_design_scores_gemma":[0.0000057725592,0.00007535133,0.00089904,0.0003220254,0.000005581516,0.0001789291,0.9945163,0.00009160322,0.000204114,0.0002965084,0.003389326,0.00001541188],"about_ca_topic_score_codex":0.004203679,"about_ca_topic_score_gemma":0.003488826,"teacher_disagreement_score":0.10926094,"about_ca_system_score_codex":0.0075731655,"about_ca_system_score_gemma":0.009695841,"threshold_uncertainty_score":0.577834},"labels":[],"label_agreement":null},{"id":"W2898671516","doi":"10.2196/11357","title":"Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment","year":2018,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Series (stratigraphy); Time series; Computer science; Statistics; Geology; Mathematics","score_opus":0.04066609681595218,"score_gpt":0.3176984916129984,"score_spread":0.2770323947970462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898671516","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":[0.996842,0.00010204725,0.0013247452,0.00015807046,0.000029075669,0.000069605114,0.0009968894,0.000061583196,0.00041589103],"genre_scores_gemma":[0.99427176,0.000057974223,0.0025795114,0.00005410155,0.00001878364,0.00010446432,0.0026363004,0.0000067142937,0.00027053047],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848443,0.00076373987,0.00010443328,0.0003657633,0.00015359836,0.00012814776],"domain_scores_gemma":[0.98824733,0.008856529,0.00085091306,0.0008203415,0.00085261033,0.00037229003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0086733205,0.0004938043,0.00040014027,0.000570216,0.00035232338,0.0006701263,0.0007208868,0.00066660735,0.00068330305],"category_scores_gemma":[0.012526876,0.00016797966,0.0007054863,0.0005233707,0.00040430087,0.0008393543,0.0006334028,0.0013027032,0.00017509492],"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":[0.0053585954,0.0040530586,0.71453124,0.0002616065,0.00059284014,0.00022888166,0.0007731758,0.21388209,0.0021331145,0.0019593656,0.0052803904,0.05094565],"study_design_scores_gemma":[0.0003445732,0.0027356148,0.27003297,0.00007725124,0.00021906677,0.000067747584,0.001220229,0.7177915,0.0033831534,0.0017498864,0.0023003358,0.000077677505],"about_ca_topic_score_codex":0.032719787,"about_ca_topic_score_gemma":0.02539695,"teacher_disagreement_score":0.032719787,"about_ca_system_score_codex":0.0010508673,"about_ca_system_score_gemma":0.0016591579,"threshold_uncertainty_score":0.06505871},"labels":[],"label_agreement":null},{"id":"W2899925163","doi":"10.1186/s12889-018-6051-z","title":"Non-communicable diseases surveillance: overview of magnitude and determinants in Kenya from STEPwise approach survey of 2015","year":2018,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":53,"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":"African Population and Health Research Center; International Development Research Centre; Eli Lilly and Company; World Bank Group; Novo Nordisk; Sanofi","keywords":"Medicine; Biostatistics; Environmental health; Public health; Epidemiology; Non-communicable disease; Pathology","score_opus":0.10351204564401248,"score_gpt":0.38056382143298445,"score_spread":0.277051775788972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899925163","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":[0.67145705,0.04769701,0.009263057,0.004999066,0.00029948232,0.0034459902,0.24404459,0.00034768198,0.018446138],"genre_scores_gemma":[0.8527333,0.033602126,0.019600807,0.0010574121,0.00011184011,0.002531723,0.087398104,0.000061646926,0.0029029944],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987048,0.0003527988,0.00031449305,0.0001582612,0.00028607348,0.00018353877],"domain_scores_gemma":[0.99812454,0.00022437632,0.00060953543,0.00006258743,0.0007514035,0.00022751665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002603137,0.0005918369,0.00035110858,0.004077127,0.00056283356,0.0006916994,0.0008570497,0.000327362,0.0018688074],"category_scores_gemma":[0.0034818884,0.00049081503,0.00056124467,0.005021562,0.00028538267,0.00081192394,0.00118058,0.00061343214,0.00055915315],"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":[0.00006869042,0.000061106635,0.94226676,0.0016842134,0.00016308179,0.00014039235,0.0009211946,0.00027256185,0.0007630919,0.00062331476,0.011246459,0.04178904],"study_design_scores_gemma":[0.000005953145,0.000062034975,0.9846454,0.0012010046,0.00009521736,0.0002718402,0.0011474342,0.00042780585,0.00035644506,0.00012522042,0.011641669,0.000019976067],"about_ca_topic_score_codex":0.11614442,"about_ca_topic_score_gemma":0.18256554,"teacher_disagreement_score":0.11614442,"about_ca_system_score_codex":0.0020509153,"about_ca_system_score_gemma":0.0044819103,"threshold_uncertainty_score":0.23093677},"labels":[],"label_agreement":null},{"id":"W2900518013","doi":"10.1371/journal.pone.0207468","title":"Use of physician billing claims to identify infections in children","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Ontario; Hospital for Sick Children; Pediatric Oncology Group; University Health Network; University of Toronto; SickKids Foundation; Institute for Clinical Evaluative Sciences","funders":"Canadian Institutes of Health Research; Department of Family and Community Medicine, University of Toronto; University of Toronto; Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences","keywords":"Medicine; Medical record; Medical diagnosis; Diagnosis code; Respiratory tract infections; Population; Otitis; Electronic medical record; Internal medicine; Pediatrics; Predictive value; Emergency medicine; Surgery; Respiratory system; Pathology","score_opus":0.06785607498185837,"score_gpt":0.3071043052741106,"score_spread":0.23924823029225223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900518013","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":[0.98999923,0.0015263717,0.00047724642,0.00032280406,0.000021960013,0.00006919498,0.005253683,0.000024156245,0.002305402],"genre_scores_gemma":[0.9969502,0.00033727026,0.00060806685,0.00006787258,0.000019645942,0.000032532782,0.0018891166,0.0000061870014,0.00008915916],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99239856,0.0022254037,0.0010810465,0.0009565534,0.0026873334,0.00065105304],"domain_scores_gemma":[0.9752466,0.008083434,0.012871522,0.0010770494,0.0020473609,0.00067406264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050496864,0.00038857476,0.0006389892,0.003448158,0.00049545756,0.0013359472,0.00093053415,0.00052963494,0.0008747956],"category_scores_gemma":[0.030519634,0.0005146092,0.0011987528,0.0035694982,0.00061084906,0.0007917348,0.0010186038,0.0008460932,0.00016002267],"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":[0.000022809272,0.000011383909,0.9982988,0.000027225156,0.000060612154,0.000028870574,0.00010023301,0.00015053962,0.00002849373,0.000035222933,0.0001921918,0.0010436444],"study_design_scores_gemma":[0.0000040963364,0.00002193323,0.9981744,0.00007700188,0.000031042455,0.00012701338,0.00009847197,0.0010603513,0.00008125584,0.000031486412,0.00028604377,0.0000068780287],"about_ca_topic_score_codex":0.11574169,"about_ca_topic_score_gemma":0.09536508,"teacher_disagreement_score":0.11574169,"about_ca_system_score_codex":0.0028784082,"about_ca_system_score_gemma":0.0020836082,"threshold_uncertainty_score":0.23013604},"labels":[],"label_agreement":null},{"id":"W2901797151","doi":"10.4081/gh.2018.696","title":"Examining the impact of the number of regions used in cluster detection methods: An application to childhood asthma visits to a hospital in Manitoba, Canada","year":2018,"lang":"en","type":"article","venue":"Geospatial health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba; Manitoba Health","funders":"Natural Sciences and Engineering Research Council of Canada; Children's Hospital Research Institute of Manitoba; University of Manitoba","keywords":"Scan statistic; Cluster (spacecraft); Statistic; Spatial epidemiology; Spatial analysis; Common spatial pattern; Spatial ecology; Bayesian probability; Asthma; Geography; Cartography; Statistics; Computer science; Medicine; Epidemiology; Mathematics; Remote sensing; Ecology; Pathology","score_opus":0.018780543130830375,"score_gpt":0.3477722647066129,"score_spread":0.3289917215757825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901797151","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":[0.9910239,0.0007189963,0.004402992,0.0008533712,0.000029411392,0.00023505867,0.0012341843,0.00009701591,0.0014050935],"genre_scores_gemma":[0.9741333,0.00051729975,0.023204517,0.00018295365,0.0000127927215,0.00008875879,0.00118503,0.000030260866,0.0006450218],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99533325,0.0023565085,0.00018283143,0.0006259726,0.0008959062,0.00060543],"domain_scores_gemma":[0.9780027,0.013779479,0.001125374,0.0008539772,0.0054484676,0.0007899027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008110169,0.0008445993,0.0008579861,0.0021271524,0.0038051142,0.0021684305,0.0021006125,0.0008991311,0.0007605456],"category_scores_gemma":[0.026824905,0.00040907445,0.0013575184,0.0058973134,0.0016252069,0.0007811044,0.0017942088,0.0010742781,0.00009218076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010524767,0.000494445,0.7815703,0.0008322949,0.0010760364,0.0012519859,0.0048398413,0.12724666,0.002009232,0.002757092,0.005017419,0.071852304],"study_design_scores_gemma":[0.00018112865,0.00029268916,0.712903,0.00023059931,0.0005117478,0.00028117019,0.019537807,0.25828037,0.0017623487,0.0014862969,0.004376745,0.00015611466],"about_ca_topic_score_codex":0.98198926,"about_ca_topic_score_gemma":0.98497677,"teacher_disagreement_score":0.023631081,"about_ca_system_score_codex":0.023631081,"about_ca_system_score_gemma":0.026314674,"threshold_uncertainty_score":0.17145628},"labels":[],"label_agreement":null},{"id":"W2902168363","doi":"10.1186/s12913-018-3714-5","title":"Practical utility of general practice data capture and spatial analysis for understanding COPD and asthma","year":2018,"lang":"en","type":"article","venue":"BMC Health Services Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal","funders":"Australian Primary Health Care Research Institute, Australian National University; Australian National University","keywords":"Medicine; Asthma; COPD; Logistic regression; Descriptive statistics; Bayes' theorem; Environmental health; Statistics; Internal medicine; Bayesian probability","score_opus":0.24339985585068766,"score_gpt":0.5207706136462523,"score_spread":0.27737075779556464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902168363","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":[0.49517652,0.011832279,0.37980318,0.028236741,0.00041810906,0.005055095,0.03622624,0.0011448113,0.042107042],"genre_scores_gemma":[0.82140803,0.002523673,0.16744195,0.0009810767,0.0001482824,0.0016259061,0.005347267,0.00005937162,0.00046454713],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.95070076,0.04004875,0.003227906,0.00267382,0.0029417616,0.00040700473],"domain_scores_gemma":[0.84827477,0.10448371,0.01726568,0.015671434,0.013193154,0.0011112456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.035641626,0.0006582776,0.00079129543,0.00679134,0.0006307362,0.0037395712,0.0016672807,0.00085946714,0.0020823188],"category_scores_gemma":[0.2031129,0.00062730286,0.0010822476,0.011762635,0.00091026287,0.0028935103,0.0039097746,0.00087328156,0.00047890723],"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":[0.00019792907,0.00013138088,0.67942625,0.0022163508,0.00088074285,0.00024612545,0.007117538,0.01629961,0.00038917462,0.0090200575,0.011320665,0.27275428],"study_design_scores_gemma":[0.00019705354,0.00037134168,0.7566898,0.0038109683,0.00081938185,0.0010040827,0.016573038,0.121460736,0.00081757247,0.045538962,0.052543957,0.00017307686],"about_ca_topic_score_codex":0.06050565,"about_ca_topic_score_gemma":0.05050863,"teacher_disagreement_score":0.06050565,"about_ca_system_score_codex":0.0026956543,"about_ca_system_score_gemma":0.0052624797,"threshold_uncertainty_score":0.18849319},"labels":[],"label_agreement":null},{"id":"W2905596073","doi":"10.2196/10842","title":"Text-Based Illness Monitoring for Detection of Novel Influenza A Virus Infections During an Influenza A (H3N2)v Virus Outbreak in Michigan, 2016: Surveillance and Survey","year":2018,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Centers for Disease Control and Prevention; U.S. Department of Health and Human Services","keywords":"Outbreak; Influenza-like illness; Outreach; Pandemic; Disease surveillance; Medicine; Environmental health; Public health; Influenza A virus; Public health surveillance; Virology; Virus; Disease; Coronavirus disease 2019 (COVID-19); Infectious disease (medical specialty); Nursing; Internal medicine","score_opus":0.04998771358455936,"score_gpt":0.3559063707147772,"score_spread":0.3059186571302178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905596073","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":[0.9911209,0.00006902844,0.0011128277,0.0001714186,0.000011649462,0.0005433445,0.005589042,0.00011078704,0.0012711243],"genre_scores_gemma":[0.98500293,0.000150299,0.006756,0.00020652932,0.000058457295,0.0008217967,0.0058490173,0.00001233994,0.0011426624],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994917,0.00017644992,0.00006110876,0.000114723036,0.00010883201,0.00004719418],"domain_scores_gemma":[0.99855214,0.00020646068,0.000464537,0.00007030556,0.0004713608,0.00023521548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010636887,0.00042169605,0.00013399438,0.00084299565,0.0003443796,0.0003455715,0.0004884169,0.00035981106,0.0011873104],"category_scores_gemma":[0.0020571945,0.00013741163,0.00017697466,0.0004851047,0.000119313016,0.0005442527,0.00043971484,0.00025126679,0.00046951618],"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":[0.00010540309,0.00034268308,0.97609574,0.00006133057,0.000030305031,0.00009255828,0.00055381516,0.00021279058,0.0037910894,0.000018287701,0.0024137397,0.016282152],"study_design_scores_gemma":[0.000017061067,0.00047916165,0.9914307,0.000028411108,0.000025455,0.00013687077,0.00068917265,0.0041026967,0.0016324801,0.000019552795,0.0014275693,0.000010879158],"about_ca_topic_score_codex":0.016370138,"about_ca_topic_score_gemma":0.03453984,"teacher_disagreement_score":0.016370138,"about_ca_system_score_codex":0.0005800115,"about_ca_system_score_gemma":0.00043522049,"threshold_uncertainty_score":0.03254974},"labels":[],"label_agreement":null},{"id":"W2907019052","doi":"10.2196/publichealth.9176","title":"Forecasting the West Nile Virus in the United States: An Extensive Novel Data Streams–Based Time Series Analysis and Structural Equation Modeling of Related Digital Searching Behavior","year":2019,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"West Nile virus; Arbovirus; STREAMS; Time series; Series (stratigraphy); Geography; Virology; Virus; Computer science; Biology; Machine learning","score_opus":0.0752580762977325,"score_gpt":0.3342868859628986,"score_spread":0.25902880966516606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907019052","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":[0.98821765,0.0002432753,0.007870537,0.0005508587,0.00002698757,0.00006395111,0.002369479,0.00007896177,0.0005783039],"genre_scores_gemma":[0.98674357,0.00029396077,0.007749164,0.000053508094,0.00002884608,0.000078968464,0.004641077,0.000009929032,0.00040104735],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994185,0.00025712056,0.00005350041,0.0001414563,0.00008295644,0.00004653448],"domain_scores_gemma":[0.996885,0.002258961,0.000363863,0.0001588813,0.0002458306,0.000087389184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024116682,0.0005893826,0.00045184098,0.0019426265,0.00029324868,0.000892801,0.00063400366,0.00063937664,0.0008207678],"category_scores_gemma":[0.006280738,0.000254248,0.0009946754,0.0018489495,0.00021936571,0.0012084662,0.0006095073,0.000770317,0.00018332759],"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":[0.00027829365,0.000869992,0.7393512,0.0001537719,0.0006206098,0.00036763705,0.00048035145,0.20382273,0.000869266,0.0023396744,0.0030893572,0.047757074],"study_design_scores_gemma":[0.0000123472155,0.00010744152,0.10229718,0.00003135894,0.0000748664,0.00003864469,0.0003310115,0.89474267,0.0003019275,0.0012547405,0.00078769674,0.000020191841],"about_ca_topic_score_codex":0.031841405,"about_ca_topic_score_gemma":0.025656542,"teacher_disagreement_score":0.031841405,"about_ca_system_score_codex":0.00092547433,"about_ca_system_score_gemma":0.0006184051,"threshold_uncertainty_score":0.06331211},"labels":[],"label_agreement":null},{"id":"W2908424731","doi":"10.1017/gheg.2018.17","title":"Evaluation of data accuracies within a comprehensive geospatial-health data surveillance platform: SOMAARTH Demographic Development and Environmental Surveillance Site, Palwal, Haryana, India","year":2018,"lang":"en","type":"article","venue":"Global Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Australia-India Strategic Research Fund; Canadian Institutes of Health Research; Australian Academy of Science","keywords":"Geospatial analysis; Citizen journalism; Public health; Health surveillance; Geography; Environmental health; Environmental planning; Population; Environmental resource management; Data science; Cartography; Medicine; Computer science; Environmental science","score_opus":0.10551141695306301,"score_gpt":0.38413800772529383,"score_spread":0.27862659077223084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908424731","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":[0.99300647,0.000012285043,0.0032311312,0.00010918986,0.0000074652817,0.00044548168,0.0008364476,0.000054083324,0.0022974904],"genre_scores_gemma":[0.98598635,0.00001984643,0.011708809,0.000024036635,0.0000031920702,0.00044854902,0.001079428,0.0000150532505,0.00071475125],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98940724,0.0063002887,0.0005786881,0.0008283939,0.0023422325,0.0005432628],"domain_scores_gemma":[0.9742158,0.012692532,0.0020047433,0.003061868,0.0072395694,0.00078551925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01574404,0.00031381863,0.0002814002,0.0011708913,0.0011415944,0.0019363473,0.0012330642,0.0003675195,0.0013865173],"category_scores_gemma":[0.027162353,0.00030243432,0.00030184494,0.0019746113,0.0010554288,0.001081127,0.0023447974,0.0006005638,0.00039754607],"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":[0.0011899666,0.0015666285,0.7627377,0.00050201314,0.0001608184,0.00076417514,0.05819737,0.014165912,0.011419731,0.0025238318,0.0030085363,0.1437634],"study_design_scores_gemma":[0.0001194165,0.00285476,0.8524431,0.00024207073,0.00013241089,0.00032740392,0.08029591,0.036876723,0.014371984,0.0010813227,0.011132154,0.00012275283],"about_ca_topic_score_codex":0.032376353,"about_ca_topic_score_gemma":0.056314632,"teacher_disagreement_score":0.032376353,"about_ca_system_score_codex":0.0020648048,"about_ca_system_score_gemma":0.0029198993,"threshold_uncertainty_score":0.08326346},"labels":[],"label_agreement":null},{"id":"W2908514305","doi":"10.5539/mas.v13n2p81","title":"Real-time Inferential Analytics Based on Online Databases of Trends: A Breakthrough Within the Discipline of Digital Epidemiology of Dentistry and Oral-Maxillofacial Surgery","year":2019,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Analytics; Computer science; Data science; Statistical inference; Microsoft excel; Data analysis; Database; Data mining; Statistics","score_opus":0.05023123102411392,"score_gpt":0.3366394464459869,"score_spread":0.286408215421873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908514305","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":[0.0621732,0.083927035,0.74691176,0.017656349,0.0012799983,0.0017971769,0.055646453,0.015659472,0.014948586],"genre_scores_gemma":[0.28632784,0.04123072,0.6371176,0.0019569644,0.00170347,0.0019576317,0.027179377,0.0010035795,0.0015228258],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98113203,0.0098209325,0.0028157497,0.0019624843,0.0039934167,0.00027556965],"domain_scores_gemma":[0.83755714,0.13340946,0.010701805,0.008772175,0.008274251,0.0012851423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01958959,0.0012261705,0.0022665055,0.015686663,0.00048026972,0.007328861,0.0020498966,0.0011399551,0.003593425],"category_scores_gemma":[0.092613935,0.0007171418,0.0023693317,0.020856056,0.0011363697,0.007933302,0.0030926147,0.0023329307,0.0013616009],"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":[0.0008722963,0.0002808295,0.04337986,0.021706145,0.0023859038,0.00066217396,0.0028987406,0.011730056,0.003777108,0.03290826,0.029249625,0.850149],"study_design_scores_gemma":[0.0005145274,0.0015220449,0.0966028,0.022323422,0.0035863444,0.0026247564,0.0073825037,0.18309034,0.016868118,0.30759865,0.357082,0.00080441614],"about_ca_topic_score_codex":0.0023471585,"about_ca_topic_score_gemma":0.0038470714,"teacher_disagreement_score":0.01958959,"about_ca_system_score_codex":0.0012082631,"about_ca_system_score_gemma":0.0037316564,"threshold_uncertainty_score":0.10360086},"labels":[],"label_agreement":null},{"id":"W2909577051","doi":"10.2196/11342","title":"Developing a Data Dashboard Framework for Population Health Surveillance: Widening Access to Clinical Trial Findings","year":2019,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council","keywords":"Dashboard; Usability; Dissemination; Context (archaeology); Computer science; Population; Data science; Task (project management); Data collection; Knowledge management; Medicine; Engineering; Geography; Environmental health; Human–computer interaction","score_opus":0.35847105258865813,"score_gpt":0.599659220947585,"score_spread":0.2411881683589268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909577051","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":[0.014598574,0.0024581538,0.89096695,0.029525815,0.0009814098,0.0098519325,0.0045242193,0.039868094,0.007224951],"genre_scores_gemma":[0.03524881,0.00089752756,0.95218825,0.0016458713,0.00022412589,0.004990377,0.0030976576,0.0010595932,0.0006478106],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8378259,0.10611594,0.026604611,0.009395045,0.017250998,0.0028074381],"domain_scores_gemma":[0.55225813,0.30391353,0.026309215,0.057642527,0.042606995,0.01726952],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2709105,0.002791557,0.0027892643,0.014852028,0.0030640515,0.018986957,0.007969003,0.004707505,0.0062792236],"category_scores_gemma":[0.34938163,0.0025826467,0.0057555456,0.009089391,0.0038780442,0.017070118,0.018397082,0.007884028,0.002360167],"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":[0.0016159485,0.0012422379,0.019382574,0.012037175,0.0013061583,0.0013574444,0.02393085,0.02963409,0.0075269123,0.05879034,0.077050865,0.76612544],"study_design_scores_gemma":[0.0019610408,0.0023537483,0.014551661,0.022047492,0.00096416555,0.001484751,0.009439399,0.14445902,0.012490505,0.2119907,0.5769273,0.0013302687],"about_ca_topic_score_codex":0.0045776023,"about_ca_topic_score_gemma":0.0069311056,"teacher_disagreement_score":0.2709105,"about_ca_system_score_codex":0.005581992,"about_ca_system_score_gemma":0.020246392,"threshold_uncertainty_score":0.89909714},"labels":[],"label_agreement":null},{"id":"W2910820167","doi":"10.2196/13142","title":"Google Trends Predicts Present and Future Plague Cases During the Plague Outbreak in Madagascar: Infodemiological Study","year":2019,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Plague (disease); Yersinia pestis; Public health; Outbreak; Disease; Infectious disease (medical specialty); Geography; Environmental health; Virology; Medicine; Biology; Virulence","score_opus":0.026563161345086135,"score_gpt":0.3134711012194208,"score_spread":0.28690793987433466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910820167","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":[0.99240303,0.00019472116,0.00012765803,0.00020550046,0.000011337428,0.00001615776,0.0060551027,0.000056525565,0.00092996296],"genre_scores_gemma":[0.99404883,0.00013802566,0.00021976125,0.000013673495,0.00001567629,0.000010771783,0.0053109163,0.0000044446915,0.00023795963],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997627,0.00004366731,0.000025999008,0.00006320812,0.000051706586,0.000052697047],"domain_scores_gemma":[0.99901044,0.00036906794,0.00030606176,0.000069229114,0.00014603346,0.00009916339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038709104,0.00045828582,0.00024079916,0.0034048301,0.00031410268,0.0011116025,0.00031228605,0.0004636987,0.0016364462],"category_scores_gemma":[0.002208642,0.00011633348,0.0006477346,0.0027826473,0.00022222876,0.0010918733,0.00047268113,0.00034643107,0.00039839323],"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":[0.00009298113,0.00007124041,0.9868932,0.000062685045,0.00007200981,0.0002647296,0.00021124126,0.0032421898,0.00021576045,0.00014328772,0.0016529358,0.0070777046],"study_design_scores_gemma":[0.000008156555,0.00011904915,0.9218052,0.00004134067,0.00008377656,0.00044938945,0.0017362981,0.07278341,0.00029853868,0.00014934673,0.0025057294,0.000019708927],"about_ca_topic_score_codex":0.074629955,"about_ca_topic_score_gemma":0.054259073,"teacher_disagreement_score":0.074629955,"about_ca_system_score_codex":0.00074347825,"about_ca_system_score_gemma":0.00042812215,"threshold_uncertainty_score":0.14839113},"labels":[],"label_agreement":null},{"id":"W2911606816","doi":"10.2196/12032","title":"Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks","year":2019,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Los Alamos National Laboratory; Advanced Research Projects Agency; Science and Technology Directorate; Defense Threat Reduction Agency; U.S. Department of Homeland Security","keywords":"Outbreak; Infectious disease (medical specialty); Data science; Computer science; Disease; Disease surveillance; Population; Analytics; Data mining; Geography; Medicine; Environmental health; Pathology","score_opus":0.04567055696194894,"score_gpt":0.32930907473412424,"score_spread":0.2836385177721753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911606816","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":[0.0439277,0.0004936209,0.85206926,0.0014231738,0.00011318837,0.0011114713,0.009878111,0.07955938,0.011424089],"genre_scores_gemma":[0.17859647,0.00038410738,0.8091595,0.00031643725,0.00006827374,0.0006576603,0.0071208742,0.0017954861,0.0019010958],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986461,0.0004911337,0.00013855149,0.0002498338,0.00040284518,0.000071555645],"domain_scores_gemma":[0.9860881,0.00993074,0.000923099,0.001354884,0.0012242926,0.00047885682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038604157,0.0013833948,0.0006003753,0.005210495,0.0005333775,0.004131403,0.0014302704,0.0008538146,0.006835204],"category_scores_gemma":[0.015188835,0.0004771188,0.0011271031,0.00253164,0.00065416086,0.0035489171,0.0028633645,0.0013048042,0.0019889458],"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":[0.0016593909,0.0010837282,0.03596113,0.0031641244,0.00044102734,0.0019529146,0.011201546,0.05546645,0.03141304,0.034264687,0.07608353,0.74730843],"study_design_scores_gemma":[0.00035343808,0.0004796121,0.026234012,0.0013884602,0.00024617065,0.001341245,0.003376139,0.65309006,0.034983594,0.09632609,0.18183388,0.0003473205],"about_ca_topic_score_codex":0.002652459,"about_ca_topic_score_gemma":0.0029021753,"teacher_disagreement_score":0.006835204,"about_ca_system_score_codex":0.00070989365,"about_ca_system_score_gemma":0.0010800307,"threshold_uncertainty_score":0.02286601},"labels":[],"label_agreement":null},{"id":"W2911707388","doi":"10.3233/jem-180458","title":"The racial ‘digital divide’ in the predictive power of Google trends data for forecasting the unemployment rate","year":2018,"lang":"en","type":"article","venue":"Journal of Economic and Social Measurement","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Predictive power; Unemployment; Unemployment rate; Sample (material); Big data; Time series; Power (physics); Econometrics; Computer science; Demographic economics; Economics; Data mining; Machine learning; Economic growth","score_opus":0.12876113062831285,"score_gpt":0.32504260827912934,"score_spread":0.1962814776508165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911707388","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":[0.9833454,0.0013923638,0.0051578865,0.0023328136,0.00014701881,0.000034550412,0.0023426018,0.000085973705,0.005161434],"genre_scores_gemma":[0.9955146,0.00030503378,0.0016938065,0.00011383072,0.00010977897,0.00001192521,0.0018805899,0.000009725103,0.00036081678],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99863154,0.00068708503,0.00010525341,0.00023723612,0.0002193203,0.00011963969],"domain_scores_gemma":[0.99022853,0.0070027816,0.00089030503,0.0009381483,0.0006471816,0.00029300965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045952876,0.00047954055,0.0004272983,0.0022973937,0.00065193896,0.0022700918,0.00047534407,0.0007326583,0.0010869513],"category_scores_gemma":[0.021957299,0.00019124868,0.0007690811,0.0022080273,0.00050391164,0.001901682,0.0011905829,0.0010641504,0.0005666942],"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":[0.00034559646,0.0001356792,0.9335624,0.00007934859,0.00018894498,0.00017403954,0.000582062,0.014990809,0.00047474698,0.0025060414,0.0031516787,0.043808673],"study_design_scores_gemma":[0.00003400347,0.00020176395,0.66686976,0.00034116334,0.00020524816,0.00039815533,0.0035247775,0.30549097,0.0017436622,0.009577319,0.011554683,0.000058588008],"about_ca_topic_score_codex":0.015297243,"about_ca_topic_score_gemma":0.016096303,"teacher_disagreement_score":0.015297243,"about_ca_system_score_codex":0.00041532173,"about_ca_system_score_gemma":0.00046797417,"threshold_uncertainty_score":0.03041637},"labels":[],"label_agreement":null},{"id":"W2911771107","doi":"10.1108/jes-11-2017-0346","title":"Workopolis or The Pirate Bay: what does Google Trends say about the unemployment rate?","year":2019,"lang":"en","type":"article","venue":"Journal of Economic Studies","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Unemployment; Unemployment rate; Originality; Order (exchange); Predictive power; Value (mathematics); Econometrics; Economics; Computer science; Macroeconomics; Sociology; Social science; Machine learning; Finance","score_opus":0.04189099592237088,"score_gpt":0.33610854864935225,"score_spread":0.29421755272698136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911771107","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":[0.9113932,0.0058558146,0.0016830523,0.009325441,0.00015182346,0.00008269879,0.050386168,0.000080627986,0.021041224],"genre_scores_gemma":[0.9874829,0.002002751,0.00077944895,0.00025025223,0.00007998869,0.000018413406,0.00814217,0.00001640195,0.0012276892],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99936825,0.000081028986,0.000048152797,0.000114922164,0.0002493694,0.00013832071],"domain_scores_gemma":[0.9956825,0.0011137386,0.0012141434,0.00024076297,0.0013567836,0.00039212836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011140768,0.00025581734,0.0005257567,0.0037192164,0.0012010935,0.0028891223,0.0007693534,0.00054214644,0.0021374808],"category_scores_gemma":[0.0134938015,0.00013194079,0.0004957419,0.0090457145,0.000739083,0.0015168706,0.0006255669,0.0007155948,0.0005153136],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015526208,0.000023142311,0.9603958,0.0002498519,0.0001024654,0.000105634856,0.0014973031,0.00096709444,0.00013855187,0.001964204,0.0067328415,0.027667876],"study_design_scores_gemma":[0.0000048347574,0.000031679047,0.97288734,0.00037902512,0.000087870394,0.00006623463,0.0070422604,0.0052846153,0.00024748978,0.0010344174,0.012905598,0.000028661592],"about_ca_topic_score_codex":0.8753139,"about_ca_topic_score_gemma":0.89084876,"teacher_disagreement_score":0.8753139,"about_ca_system_score_codex":0.004393306,"about_ca_system_score_gemma":0.005305379,"threshold_uncertainty_score":0.25084078},"labels":[],"label_agreement":null},{"id":"W2911960009","doi":"10.1038/s41598-018-38292-x","title":"A statistical tool for comparing seasonal ILI surveillance data","year":2019,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Data science; Computer science; Geography","score_opus":0.04048885980333561,"score_gpt":0.3237498711070221,"score_spread":0.28326101130368647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911960009","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":[0.030326238,0.0005357958,0.9414435,0.00070135255,0.00041871643,0.0008389648,0.014452159,0.00894863,0.0023346078],"genre_scores_gemma":[0.31536984,0.00032248834,0.66301364,0.0004907026,0.00037761414,0.004893749,0.012848091,0.0018571201,0.0008268261],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.94433796,0.042503532,0.004012326,0.0034610087,0.0052102422,0.00047487704],"domain_scores_gemma":[0.714721,0.2541422,0.009831632,0.016365243,0.0041247797,0.00081514864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.043110315,0.0014710453,0.0018752303,0.0113016525,0.00065387785,0.0021353401,0.001961878,0.0016656099,0.011228678],"category_scores_gemma":[0.22556451,0.00060185,0.0022512486,0.009689373,0.0014615963,0.0027503425,0.0029442443,0.003066453,0.0016761387],"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":[0.0042424453,0.0011567752,0.13535999,0.0049690655,0.009378497,0.0019111716,0.0018244243,0.06686377,0.0119844405,0.1044706,0.06641119,0.5914276],"study_design_scores_gemma":[0.0015567195,0.006720851,0.11693259,0.0013133681,0.0023280508,0.0038440542,0.0017965795,0.51412654,0.019566797,0.19266602,0.13862917,0.0005192805],"about_ca_topic_score_codex":0.0007675487,"about_ca_topic_score_gemma":0.00041644566,"teacher_disagreement_score":0.043110315,"about_ca_system_score_codex":0.00068063126,"about_ca_system_score_gemma":0.0010844914,"threshold_uncertainty_score":0.22799182},"labels":[],"label_agreement":null},{"id":"W2912066166","doi":"10.2196/12974","title":"Seasonality Patterns of Internet Searches on Mental Health: Exploratory Infodemiology Study","year":2019,"lang":"en","type":"article","venue":"JMIR Mental Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"McMaster University; St. Joseph’s Healthcare Hamilton","funders":"","keywords":"Mental health; Public health; The Internet; Psychiatry; Population; Psychology; Anxiety; Medicine; Geography; Environmental health; Computer science; World Wide Web; Pathology","score_opus":0.05839496926781887,"score_gpt":0.39820212685774753,"score_spread":0.33980715758992863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912066166","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":[0.99763775,0.000053729327,0.00019320463,0.000029150988,0.0000011067204,0.00005529259,0.0014862616,0.000007688598,0.00053588045],"genre_scores_gemma":[0.99744487,0.00006316595,0.00047995933,0.000015167259,0.0000049170208,0.00007375683,0.0016407628,0.0000056285076,0.0002719265],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993856,0.00014954412,0.000068341295,0.00011368713,0.00014294253,0.00013983315],"domain_scores_gemma":[0.99708647,0.0010890902,0.0009578023,0.00019514482,0.00039919143,0.00027234538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078684004,0.00017783091,0.00027792176,0.0039723115,0.000700419,0.0007699508,0.00035008113,0.00028078328,0.0011198332],"category_scores_gemma":[0.003530888,0.00016971453,0.00042430463,0.006278226,0.00052838447,0.00049846363,0.00078548666,0.00024592996,0.00019429057],"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":[0.00006080176,0.00007384428,0.9911528,0.00004233578,0.00004406984,0.00020622417,0.0030346427,0.000111125846,0.00052142504,0.000089489564,0.00040190597,0.0042611654],"study_design_scores_gemma":[0.000002386077,0.00004128424,0.9954348,0.0000066148014,0.000011986404,0.00011038145,0.0031733897,0.0006031254,0.00008706846,0.000021396869,0.0005021547,0.000005348386],"about_ca_topic_score_codex":0.18585944,"about_ca_topic_score_gemma":0.24208985,"teacher_disagreement_score":0.18585944,"about_ca_system_score_codex":0.0015300215,"about_ca_system_score_gemma":0.0018207679,"threshold_uncertainty_score":0.36955523},"labels":[],"label_agreement":null},{"id":"W2914156234","doi":"10.9778/cmajo.20180099","title":"Social licence and the general public’s attitudes toward research based on linked administrative health data: a qualitative study","year":2019,"lang":"en","type":"article","venue":"CMAJ Open","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Institute for Work & Health; Sunnybrook Health Science Centre","funders":"","keywords":"Public relations; Public health; Qualitative research; Focus group; Social research; Social security; Political science; Qualitative property; Business; Sociology; Medicine; Marketing; Nursing; Social science; Law","score_opus":0.4748689830636061,"score_gpt":0.5833047343622582,"score_spread":0.10843575129865207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914156234","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9910807,0.000327361,0.00079477136,0.0032968854,0.000042280655,0.00025786101,0.000159157,0.00001034341,0.004030659],"genre_scores_gemma":[0.9953732,0.00035581927,0.0004170103,0.0014235777,0.000020677397,0.00030192028,0.00005080543,0.00001267505,0.0020443262],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9788752,0.014763146,0.00045382578,0.0007672145,0.0016676668,0.0034730588],"domain_scores_gemma":[0.94850814,0.03749182,0.0034912068,0.00103971,0.004033339,0.005435724],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.026210131,0.00050203444,0.0007343051,0.0023962236,0.014041363,0.0047772215,0.0017542586,0.0021452091,0.0037092576],"category_scores_gemma":[0.029427351,0.00069448183,0.0004576048,0.0021430666,0.016687755,0.0041600605,0.007733335,0.003413293,0.00027578743],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020065614,0.000041721974,0.0035256175,0.00006291652,0.0000025343757,0.00026120065,0.99367213,0.000009915387,0.0001948618,0.00051167095,0.00027347656,0.001423909],"study_design_scores_gemma":[0.0000037324469,0.000024100615,0.0014753621,0.000062413055,0.0000016479711,0.00004520689,0.9955278,0.000027145908,0.00004989446,0.000102032034,0.0026749712,0.00000559023],"about_ca_topic_score_codex":0.11641518,"about_ca_topic_score_gemma":0.13462731,"teacher_disagreement_score":0.9737899,"about_ca_system_score_codex":0.019394815,"about_ca_system_score_gemma":0.017151829,"threshold_uncertainty_score":0.23147511},"labels":[],"label_agreement":null},{"id":"W2915883351","doi":"10.2196/11615","title":"Social Media Surveillance for Outbreak Projection via Transmission Models: Longitudinal Observational Study","year":2019,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Data mining; Data quality; Machine learning; Metric (unit); Projection (relational algebra); Leverage (statistics); Artificial intelligence; Algorithm","score_opus":0.13963177100195182,"score_gpt":0.3708583158334395,"score_spread":0.23122654483148766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2915883351","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":[0.97897696,0.0004975164,0.015121015,0.00042933546,0.000039327202,0.00020364243,0.004052542,0.00007046344,0.00060924585],"genre_scores_gemma":[0.99146265,0.00019324654,0.004262497,0.000067608635,0.00004486345,0.00020185173,0.0035514305,0.000011501313,0.00020432421],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.994783,0.0036434887,0.0003155413,0.0006714892,0.000319435,0.0002669901],"domain_scores_gemma":[0.9798877,0.011108458,0.004098464,0.003122437,0.0011789704,0.00060390425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0119614415,0.0008168856,0.0005969799,0.0014474929,0.00071835454,0.0011186645,0.0011154286,0.00085845264,0.0023294485],"category_scores_gemma":[0.03571563,0.00058530644,0.0015214933,0.0015948063,0.00042969463,0.0013704344,0.0014950664,0.0018037158,0.00070187735],"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":[0.00038391683,0.0005461577,0.9819873,0.00009040633,0.00063941156,0.00016133986,0.00035954706,0.004979272,0.00012046965,0.00074216124,0.0018375528,0.00815252],"study_design_scores_gemma":[0.0001414383,0.0013365484,0.6177999,0.00027204482,0.0008059615,0.00072002976,0.0023005647,0.3690187,0.0006202769,0.0038009903,0.003088688,0.000094876705],"about_ca_topic_score_codex":0.014634745,"about_ca_topic_score_gemma":0.011610346,"teacher_disagreement_score":0.014634745,"about_ca_system_score_codex":0.0006672585,"about_ca_system_score_gemma":0.0012966304,"threshold_uncertainty_score":0.063258946},"labels":[],"label_agreement":null},{"id":"W2919576157","doi":"10.1007/978-3-540-69839-5_7","title":"Exploratory Spatial Analysis of Illegal Oil Discharges Detected off Canada’s Pacific Coast","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary; University of Victoria","funders":"","keywords":"Oil spill; Exploratory analysis; Computer science; Exploratory data analysis; Spatial analysis; Exploratory research; Environmental science; Cartography; Geography; Remote sensing; Data mining; Data science; Environmental protection","score_opus":0.012091845828248493,"score_gpt":0.22460869844737835,"score_spread":0.21251685261912986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2919576157","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":[0.96835,0.00042164812,0.010524242,0.0002668793,0.000012073734,0.000055147713,0.010555362,0.00040461196,0.009410059],"genre_scores_gemma":[0.96888494,0.00031312183,0.01835633,0.000026890797,0.000007349425,0.000022551823,0.0070952894,0.000059694103,0.005233814],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99989665,0.000007185049,0.000004093761,0.000014499312,0.000041748084,0.00003584397],"domain_scores_gemma":[0.9996228,0.00011901486,0.000035660796,0.000018526745,0.00016606167,0.000037813446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014561939,0.00028650477,0.00016882428,0.0024148284,0.0006684169,0.0007608642,0.00035822313,0.00019158087,0.0018375665],"category_scores_gemma":[0.00058565714,0.0001403813,0.0004209294,0.0030937176,0.00025371116,0.0001755031,0.0005020751,0.00020624386,0.00024344791],"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":[0.00031213174,0.00011656139,0.6679237,0.00026283602,0.00021762229,0.001810149,0.003406943,0.04366865,0.019888297,0.004885275,0.018725263,0.23878251],"study_design_scores_gemma":[0.00000828954,0.00003474468,0.89314795,0.00006337813,0.00007436527,0.00041493657,0.0063001015,0.08295211,0.004576147,0.001314232,0.011072137,0.000041610954],"about_ca_topic_score_codex":0.7696115,"about_ca_topic_score_gemma":0.8969343,"teacher_disagreement_score":0.23038852,"about_ca_system_score_codex":0.0015093496,"about_ca_system_score_gemma":0.0032290877,"threshold_uncertainty_score":0.4634906},"labels":[],"label_agreement":null},{"id":"W2920773083","doi":"10.3389/fpubh.2019.00138","title":"Valuing Health Surveillance as an Information System: Interdisciplinary Insights","year":2019,"lang":"en","type":"article","venue":"Frontiers in Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; McGill University","funders":"World Health Organization","keywords":"Risk analysis (engineering); Health informatics; Computer science; Value of information; Information system; Knowledge management; Health care; Management science; Data science; Medicine; Economics; Engineering","score_opus":0.016672122121434627,"score_gpt":0.30258424047893506,"score_spread":0.2859121183575004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2920773083","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":[0.0802057,0.07792761,0.35959005,0.23359786,0.0011705036,0.0002837112,0.0003211572,0.000076563134,0.24682686],"genre_scores_gemma":[0.93222696,0.021884413,0.038062666,0.003212936,0.0012626429,0.0002458633,0.000072931936,0.000047818896,0.0029837075],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97179955,0.021164436,0.0007975273,0.0011628403,0.0040673623,0.001008294],"domain_scores_gemma":[0.94122475,0.052501887,0.0015686785,0.0019316663,0.0020252785,0.0007478007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024442684,0.0009931003,0.0014593697,0.008439619,0.0028547512,0.021571642,0.0019897372,0.00612382,0.002344439],"category_scores_gemma":[0.02393329,0.0006608889,0.0010530845,0.0061101452,0.031184113,0.022348894,0.0065990277,0.0044686175,0.00023152874],"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":[0.000008744041,0.000024330526,0.00031822405,0.00014044861,0.000020860498,0.00006494541,0.001723099,0.0019590273,0.00005502822,0.98856515,0.0005282531,0.0065919636],"study_design_scores_gemma":[0.00000536672,0.000018417857,0.00027060765,0.00035149555,0.000010613528,0.000054411965,0.0022433104,0.0043615676,0.0000877836,0.9841186,0.0084617855,0.00001602795],"about_ca_topic_score_codex":0.0019515603,"about_ca_topic_score_gemma":0.0010518213,"teacher_disagreement_score":0.024442684,"about_ca_system_score_codex":0.01229266,"about_ca_system_score_gemma":0.0046255738,"threshold_uncertainty_score":0.12926686},"labels":[],"label_agreement":null},{"id":"W2921249278","doi":"10.1186/s12889-019-6490-1","title":"Five-year lung cancer mortality risk analysis and topography in Xuan Wei: a spatiotemporal correlation analysis","year":2019,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":26,"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":"York University","keywords":"Lung cancer; Medicine; Demography; Biostatistics; Mortality rate; Epidemiology; Bivariate analysis; Population; Environmental health; Internal medicine; Statistics","score_opus":0.020780750894217898,"score_gpt":0.3418235152827016,"score_spread":0.3210427643884837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921249278","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":[0.9982888,0.00010636572,0.00063460623,0.000038226786,0.0000036402005,0.000011925836,0.00068819174,0.0000103743,0.00021795687],"genre_scores_gemma":[0.99867934,0.000058009176,0.00050060457,0.000005581969,0.0000031206803,0.000017987824,0.00061092037,0.0000017905287,0.00012271154],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99964917,0.000093017006,0.000046915826,0.0000983843,0.000053147643,0.00005926792],"domain_scores_gemma":[0.99924314,0.00017382654,0.00023295483,0.00009130841,0.00018054806,0.00007820755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007744908,0.00022550511,0.00023025696,0.0014982419,0.00042594934,0.00050552207,0.00032505413,0.00018572145,0.0008633692],"category_scores_gemma":[0.0014515235,0.00018890333,0.00084825495,0.0024644146,0.00021772287,0.00031082053,0.0006381849,0.00025996202,0.00011111036],"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":[0.000023596356,0.000013725672,0.9959805,0.0000146338825,0.00008646223,0.00010237832,0.00012167028,0.001043152,0.00015855514,0.000071016504,0.0001337883,0.002250514],"study_design_scores_gemma":[0.0000017606715,0.000023992183,0.992159,0.0000084389085,0.000080721424,0.000083865314,0.00041956775,0.0067233127,0.00009857643,0.00006304164,0.00033119714,0.000006601174],"about_ca_topic_score_codex":0.047613453,"about_ca_topic_score_gemma":0.045459997,"teacher_disagreement_score":0.047613453,"about_ca_system_score_codex":0.0008420323,"about_ca_system_score_gemma":0.00084909913,"threshold_uncertainty_score":0.09467262},"labels":[],"label_agreement":null},{"id":"W2929883578","doi":"10.1177/2056305119834585","title":"The Relationship Between Social Media Data and Crime Rates in the United States","year":2019,"lang":"en","type":"article","venue":"Social Media + Society","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"National Institute of Allergy and Infectious Diseases; National Human Genome Research Institute; National Institute of Mental Health; National Institutes of Health","keywords":"Social media; Law enforcement; Psychological intervention; Public health; Internet privacy; Enforcement; Criminology; Political science; Data science; Public relations; Business; Computer science; Medicine; Psychology; Law","score_opus":0.09434797182276677,"score_gpt":0.35278961435088285,"score_spread":0.2584416425281161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2929883578","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":[0.9883206,0.00039644606,0.00032981453,0.0018407528,0.000051054012,0.00002673594,0.00562884,0.000023521503,0.0033823126],"genre_scores_gemma":[0.99699855,0.00033159956,0.00035834394,0.00013404261,0.000046904905,0.000028368908,0.001770269,0.00000583846,0.00032604413],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988954,0.00048186205,0.00017024935,0.00014136879,0.00022083419,0.00009019801],"domain_scores_gemma":[0.9880211,0.004344076,0.0049618925,0.00048624672,0.0015834754,0.00060329644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010535567,0.00015283056,0.00017948226,0.0025802942,0.00044816665,0.0012080208,0.00028412463,0.00025352667,0.0012138012],"category_scores_gemma":[0.010503092,0.00018629112,0.00019394465,0.0028644674,0.00028326397,0.00079283974,0.00080414605,0.0006441956,0.00023188046],"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":[0.000031504893,0.00003966759,0.9917774,0.000023715733,0.0000682281,0.000032221848,0.00026839986,0.00015081835,0.00007245604,0.00013985473,0.0015625651,0.0058332696],"study_design_scores_gemma":[0.000002520545,0.000026406613,0.995165,0.00004210776,0.000033036802,0.00010471704,0.0011248109,0.00148597,0.00016546385,0.0001325641,0.0017074605,0.0000100336265],"about_ca_topic_score_codex":0.028384522,"about_ca_topic_score_gemma":0.047467034,"teacher_disagreement_score":0.028384522,"about_ca_system_score_codex":0.00055154314,"about_ca_system_score_gemma":0.0003908197,"threshold_uncertainty_score":0.056438565},"labels":[],"label_agreement":null},{"id":"W2936364519","doi":"10.3390/risks7020042","title":"Defining Geographical Rating Territories in Auto Insurance Regulation by Spatially Constrained Clustering","year":2019,"lang":"en","type":"article","venue":"Risks","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Toronto Metropolitan University","funders":"","keywords":"Cluster analysis; Statistic; Computer science; Econometrics; Entropy (arrow of time); Homogeneity (statistics); Actuarial science; Data mining; Statistics; Business; Economics; Mathematics; Machine learning","score_opus":0.01185182908226527,"score_gpt":0.27967241732990006,"score_spread":0.26782058824763477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2936364519","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":[0.05588609,0.00017892025,0.94101745,0.00013421777,0.000011112383,0.00017074644,0.00021140445,0.0001342531,0.0022558034],"genre_scores_gemma":[0.6099997,0.00022211079,0.3873772,0.00004186948,0.000015885678,0.00038044818,0.00052717264,0.0000820751,0.0013535323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947771,0.0029430434,0.0003013225,0.0008075336,0.0009325233,0.0002383998],"domain_scores_gemma":[0.9920218,0.0043802806,0.001160537,0.000969127,0.001240758,0.00022741711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031915272,0.0006354231,0.0011189618,0.0022912724,0.00085357786,0.0020563009,0.0015402678,0.0010381725,0.0014748934],"category_scores_gemma":[0.013271116,0.00055985234,0.0010783076,0.0020763306,0.0013132497,0.0017438965,0.0023533863,0.00074016344,0.0003057046],"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":[0.000103333834,0.000063555,0.012789784,0.00019569523,0.000078877776,0.00018351636,0.0006063758,0.83714396,0.0034926578,0.06144634,0.0013562092,0.082539685],"study_design_scores_gemma":[0.000010840964,0.00006822781,0.004642231,0.000039548886,0.00002700675,0.000135869,0.00039372224,0.9688071,0.0022206062,0.019633405,0.0039748666,0.000046599842],"about_ca_topic_score_codex":0.009457926,"about_ca_topic_score_gemma":0.009985941,"teacher_disagreement_score":0.009457926,"about_ca_system_score_codex":0.0016153221,"about_ca_system_score_gemma":0.0018681715,"threshold_uncertainty_score":0.018805742},"labels":[],"label_agreement":null},{"id":"W2937142967","doi":"10.1016/j.jbi.2019.103181","title":"A systematic review of aberration detection algorithms used in public health surveillance","year":2019,"lang":"en","type":"review","venue":"Journal of Biomedical Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre; McGill University","funders":"","keywords":"Computer science; Health informatics; Machine learning; Data mining; Public health surveillance; Artificial intelligence; Public health; Algorithm; Data science; Medicine","score_opus":0.07473254245173196,"score_gpt":0.3770703482479815,"score_spread":0.3023378057962496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2937142967","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00044084038,0.9985367,0.0003140604,0.00012799936,0.00007660441,0.00012484004,0.00026064372,0.0000076201586,0.00011074041],"genre_scores_gemma":[0.009461064,0.98703545,0.0024588075,0.00044540598,0.000067845765,0.00020642014,0.00025106085,0.0000096223375,0.00006443847],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9838306,0.005142894,0.006872836,0.00132934,0.0025940002,0.00023026552],"domain_scores_gemma":[0.94003606,0.04845434,0.006562549,0.00079121784,0.0038307859,0.00032506342],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.018137086,0.0020472996,0.008827148,0.011712074,0.00061929424,0.0033969965,0.0029441703,0.0021084766,0.0034824447],"category_scores_gemma":[0.081353396,0.0013089865,0.012075086,0.010093616,0.0011288486,0.0030543196,0.0021429139,0.0019003863,0.0003076835],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033827836,0.00002807249,0.0015296461,0.8419457,0.020204173,0.000060611896,0.00016884024,0.0003201085,0.00016105732,0.0003924489,0.0017994224,0.13305171],"study_design_scores_gemma":[0.00053341076,0.00048229762,0.006353247,0.790276,0.16706873,0.0004116401,0.00026745116,0.0004605967,0.00045856432,0.0008522644,0.032726016,0.00010973421],"about_ca_topic_score_codex":0.012746598,"about_ca_topic_score_gemma":0.03228854,"teacher_disagreement_score":0.9818629,"about_ca_system_score_codex":0.003448186,"about_ca_system_score_gemma":0.012449365,"threshold_uncertainty_score":0.09591919},"labels":[],"label_agreement":null},{"id":"W2940156422","doi":"10.2196/11036","title":"Identifying Key Topics Bearing Negative Sentiment on Twitter: Insights Concerning the 2015-2016 Zika Epidemic","year":2019,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Social media; Classifier (UML); Computer science; Topic model; Artificial intelligence; Information retrieval; Data science; World Wide Web","score_opus":0.05467089521911458,"score_gpt":0.34144526270042846,"score_spread":0.2867743674813139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2940156422","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":[0.98136145,0.0008817599,0.0019256928,0.0037746427,0.0001681883,0.00009195625,0.0028325345,0.000065768334,0.008897942],"genre_scores_gemma":[0.990666,0.0014931819,0.0023532787,0.00048546054,0.00024924247,0.00006640197,0.002521771,0.00003836983,0.0021263387],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996325,0.00010203188,0.00004076337,0.000052504136,0.00011003445,0.000062192295],"domain_scores_gemma":[0.9979249,0.0010633441,0.0005301048,0.000047630823,0.0003480863,0.00008588688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008836986,0.00029364903,0.00015426039,0.001749459,0.0010752067,0.0014783484,0.00019244812,0.00045552605,0.0011527456],"category_scores_gemma":[0.00480415,0.00014471341,0.00021905763,0.0011437981,0.0004866855,0.0016927972,0.00079832505,0.0005947899,0.00042537742],"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":[0.0007056601,0.00018635948,0.6350076,0.0019521825,0.00010342272,0.0044281213,0.10936176,0.0018714928,0.030717561,0.005406912,0.041645575,0.16861331],"study_design_scores_gemma":[0.00001805226,0.00020974156,0.7822634,0.00071513414,0.00013011071,0.0013102774,0.11283678,0.01681108,0.00637881,0.0036827927,0.07554947,0.00009440243],"about_ca_topic_score_codex":0.0076164547,"about_ca_topic_score_gemma":0.022697235,"teacher_disagreement_score":0.0076164547,"about_ca_system_score_codex":0.0008584605,"about_ca_system_score_gemma":0.00054934487,"threshold_uncertainty_score":0.015144229},"labels":[],"label_agreement":null},{"id":"W2940674603","doi":"10.1007/978-3-031-24340-0_3","title":"Toponym Identification in Epidemiology Articles – A Deep Learning Approach","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Identification (biology); Task (project management); Domain (mathematical analysis); Focus (optics); Natural language processing; Embedding; Information retrieval; Artificial intelligence","score_opus":0.04477128131344797,"score_gpt":0.30445012479287054,"score_spread":0.25967884347942255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2940674603","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":[0.083822176,0.010280529,0.86041427,0.0030282605,0.0010835102,0.0003519733,0.011261358,0.0068747676,0.022883175],"genre_scores_gemma":[0.42500654,0.0052955253,0.52308273,0.0006216563,0.0009090039,0.0002654126,0.01650071,0.0006320943,0.027686354],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860173,0.0003145494,0.0001671925,0.00037260624,0.00036505214,0.00017888758],"domain_scores_gemma":[0.9960376,0.002151847,0.0004026207,0.0003810926,0.00083957595,0.0001872546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001479735,0.0010304286,0.00096336746,0.007665265,0.0007659547,0.002569307,0.0013193658,0.001496855,0.0048129307],"category_scores_gemma":[0.005431161,0.0005140121,0.0014678773,0.0054226806,0.00038587814,0.004292086,0.001833374,0.0017995412,0.0035171928],"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":[0.00021636202,0.0002983365,0.012973943,0.0006187527,0.00015460764,0.0003258481,0.00027192617,0.014754883,0.0101817185,0.008035195,0.04090549,0.9112628],"study_design_scores_gemma":[0.000032298663,0.00013622711,0.014744148,0.00037641719,0.0002229811,0.00082432665,0.0005997418,0.8752978,0.011636206,0.056578282,0.03946633,0.000085352716],"about_ca_topic_score_codex":0.0053794854,"about_ca_topic_score_gemma":0.011820314,"teacher_disagreement_score":0.007665265,"about_ca_system_score_codex":0.00094354065,"about_ca_system_score_gemma":0.0012319736,"threshold_uncertainty_score":0.016100824},"labels":[],"label_agreement":null},{"id":"W2942914173","doi":"10.1139/geomat-2018-0016","title":"Space, time, and disease on social media: a case study of dengue fever in China","year":2018,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Social media; Event (particle physics); Data science; Computer science; China; Space (punctuation); Process (computing); Diffusion; Geography; World Wide Web","score_opus":0.014801438421745552,"score_gpt":0.2850278032329554,"score_spread":0.2702263648112099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2942914173","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":[0.99888986,0.00007362879,0.00014593815,0.00020817098,0.0000032192563,0.00001579819,0.00028127444,0.000003662592,0.00037841784],"genre_scores_gemma":[0.99896955,0.00013997304,0.00037373014,0.00003858182,0.000010702378,0.00001181849,0.00023219646,0.0000022402614,0.00022121042],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99953806,0.00014377499,0.00004194722,0.00006563499,0.00008422187,0.00012633993],"domain_scores_gemma":[0.9987098,0.0004988155,0.00031770542,0.00009747877,0.00017210086,0.00020405292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000718912,0.00036408956,0.00029073158,0.0018839237,0.0013893499,0.0008237047,0.00068248954,0.00086406514,0.00084426417],"category_scores_gemma":[0.0017478075,0.00023289525,0.0003298142,0.0029686422,0.00073812256,0.0011349076,0.0012040943,0.0005042807,0.00009812915],"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":[0.00003759455,0.00011145071,0.9763998,0.000048213285,0.000052028474,0.0050154733,0.010824004,0.00039671708,0.0006256641,0.00030762082,0.00051155774,0.0056699617],"study_design_scores_gemma":[0.000008586395,0.00014472449,0.9120638,0.000074929565,0.000103968596,0.0040795943,0.07182476,0.0073530595,0.0006603414,0.00047770623,0.0031641526,0.000044451448],"about_ca_topic_score_codex":0.15388738,"about_ca_topic_score_gemma":0.19010246,"teacher_disagreement_score":0.15388738,"about_ca_system_score_codex":0.0016945114,"about_ca_system_score_gemma":0.001208545,"threshold_uncertainty_score":0.3059833},"labels":[],"label_agreement":null},{"id":"W2943804616","doi":"10.14745/ccdr.v45i05a02","title":"Risk assessment strategies for early detection and prediction of infectious disease outbreaks associated with climate change","year":2019,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph; Natural Resources Canada; Canadian Forest Service; Université du Québec à Montréal; Public Health Agency of Canada","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Infectious disease (medical specialty); Warning system; Outbreak; Public health; Disease; Climate change; The Internet; Computer science; Data science; Social media; Early warning system; Disease surveillance; Environmental health; Risk analysis (engineering); Business; Medicine; Biology; Ecology; World Wide Web; Telecommunications","score_opus":0.013923623938673028,"score_gpt":0.2545924932677637,"score_spread":0.24066886932909065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943804616","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":[0.029765412,0.0053872634,0.93888867,0.004362645,0.00021639964,0.0009616775,0.0018644091,0.0021285692,0.016424932],"genre_scores_gemma":[0.5439232,0.004606736,0.44146088,0.0005944584,0.0002532753,0.00080860674,0.0021627916,0.00012917008,0.006060822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9970757,0.0012603163,0.0002476898,0.00038338377,0.0008723315,0.00016057634],"domain_scores_gemma":[0.9875635,0.0073185633,0.001667087,0.0004055511,0.0027703447,0.00027493524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007273609,0.0022139077,0.0011084726,0.0058589526,0.0007599319,0.0032214534,0.0018899819,0.001461478,0.004824371],"category_scores_gemma":[0.020906508,0.0007029424,0.0013872723,0.0017028376,0.0006274135,0.002575511,0.001826361,0.0014450476,0.00094626076],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026937533,0.00035902963,0.053980917,0.000944891,0.0007539178,0.0003990405,0.00060900283,0.4954692,0.0025142843,0.04366664,0.018043261,0.38299045],"study_design_scores_gemma":[0.00004062926,0.00023113909,0.011001452,0.00031179027,0.00021308176,0.00020101138,0.00055850693,0.92117023,0.001362209,0.051040377,0.013740279,0.00012930953],"about_ca_topic_score_codex":0.017854858,"about_ca_topic_score_gemma":0.012719486,"teacher_disagreement_score":0.98214513,"about_ca_system_score_codex":0.0022141205,"about_ca_system_score_gemma":0.002795671,"threshold_uncertainty_score":0.03846699},"labels":[],"label_agreement":null},{"id":"W2944969505","doi":"10.1016/j.jhin.2019.05.004","title":"Evaluation of a web-based tool for labelling potential hospital outbreaks: a mixed methods study","year":2019,"lang":"en","type":"article","venue":"Journal of Hospital Infection","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Université de Nantes; McGill University","keywords":"Usability; Outbreak; USable; Computer science; Web application; Data science; World Wide Web; Medicine; Human–computer interaction; Pathology","score_opus":0.0187206720044747,"score_gpt":0.3648220591264133,"score_spread":0.3461013871219386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944969505","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":[0.9875945,0.00015472349,0.004178044,0.00012473567,0.00003627105,0.006586273,0.0004968634,0.000044480625,0.0007840859],"genre_scores_gemma":[0.9379073,0.00034492998,0.033595834,0.000581922,0.00007614565,0.025367402,0.0009895863,0.0000536431,0.0010831808],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.95332545,0.03491087,0.004206816,0.002393995,0.0041076643,0.0010551579],"domain_scores_gemma":[0.84893024,0.11322556,0.010490188,0.0072057107,0.016973406,0.0031748454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.061292462,0.0014906859,0.0017570549,0.0025235538,0.0018355089,0.003282214,0.0022851217,0.002208289,0.0030360608],"category_scores_gemma":[0.093865916,0.0013337617,0.00259024,0.0015749338,0.0012541781,0.0030856833,0.002603937,0.0019021527,0.00082897005],"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":[0.06271159,0.23381145,0.32535502,0.007278367,0.006988906,0.0006952566,0.048914526,0.0045038345,0.004641254,0.0010421406,0.0027886815,0.301269],"study_design_scores_gemma":[0.042443953,0.32305652,0.50372154,0.0037755969,0.010258577,0.0008439971,0.04613725,0.046739716,0.0099503845,0.0023854563,0.009471211,0.0012157541],"about_ca_topic_score_codex":0.010079233,"about_ca_topic_score_gemma":0.012145551,"teacher_disagreement_score":0.061292462,"about_ca_system_score_codex":0.0032323815,"about_ca_system_score_gemma":0.0043737376,"threshold_uncertainty_score":0.32414937},"labels":[],"label_agreement":null},{"id":"W2945392980","doi":"10.2196/12383","title":"Flu Outbreak Prediction Using Twitter Posts Classification and Linear Regression With Historical Centers for Disease Control and Prevention Reports: Prediction Framework Study","year":2019,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Outbreak; Computer science; Social media; Control (management); Resource (disambiguation); Disease control; Disease; Disease surveillance; Internet privacy; Machine learning; Data science; Data mining; Environmental health; Artificial intelligence; Medicine; World Wide Web; Virology; Pathology","score_opus":0.039052200928282606,"score_gpt":0.33298576805249924,"score_spread":0.29393356712421664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945392980","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":[0.5144951,0.001275212,0.4732131,0.0023211923,0.00014412713,0.00037188674,0.0019895758,0.0018876884,0.0043021175],"genre_scores_gemma":[0.9327861,0.00038777853,0.06272553,0.0000878683,0.0001562042,0.00018001365,0.0013766252,0.00003324232,0.002266642],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922276,0.0002494118,0.000042504515,0.0002718856,0.000113493996,0.00009992788],"domain_scores_gemma":[0.9980934,0.0011338254,0.0002509077,0.0000737568,0.0003609093,0.000087130655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002655497,0.0010627821,0.0007245761,0.0018405356,0.00040958406,0.0008764167,0.0010666624,0.00082315353,0.0016830484],"category_scores_gemma":[0.004734326,0.0003557174,0.0011333779,0.0011135096,0.00032380765,0.0011759811,0.0006612073,0.00112612,0.0005096569],"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":[0.00051557383,0.000985335,0.1227793,0.00018324328,0.00052452966,0.00036218337,0.00021609956,0.6960055,0.0017237902,0.0053962595,0.005421023,0.16588722],"study_design_scores_gemma":[0.0000037106704,0.00002486267,0.0020229428,0.0000042502734,0.000012485245,0.000012475641,0.000012874791,0.99726224,0.00014480618,0.00035058992,0.00014509537,0.0000037247567],"about_ca_topic_score_codex":0.028654387,"about_ca_topic_score_gemma":0.016518831,"teacher_disagreement_score":0.028654387,"about_ca_system_score_codex":0.0012825552,"about_ca_system_score_gemma":0.0009779391,"threshold_uncertainty_score":0.056975245},"labels":[],"label_agreement":null},{"id":"W2947051005","doi":"10.2196/13439","title":"Google Trends in Infodemiology and Infoveillance: Methodology Framework","year":2019,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":509,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Data science; The Internet; Health informatics; World Wide Web; Health care","score_opus":0.06683018230169083,"score_gpt":0.3883553759672504,"score_spread":0.32152519366555954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947051005","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010589959,0.0049154647,0.9330535,0.00583145,0.00056641764,0.015076184,0.010806672,0.0008910936,0.01826925],"genre_scores_gemma":[0.053070802,0.0032577098,0.9055774,0.0009659251,0.00027702763,0.029847866,0.004717509,0.0002415999,0.0020440447],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95006573,0.0389925,0.0032472226,0.001762063,0.0052602626,0.0006721821],"domain_scores_gemma":[0.9506921,0.037552983,0.002748688,0.0031314157,0.0053578466,0.0005168596],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.050872497,0.0014555937,0.0013024972,0.018485969,0.0017845291,0.007081473,0.0028930714,0.0024501034,0.0051778853],"category_scores_gemma":[0.07282675,0.0008910121,0.0024077867,0.016786158,0.0039600725,0.0052929623,0.00481384,0.002543014,0.002283684],"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":[0.00013439351,0.00025669872,0.01094757,0.007619112,0.00039813045,0.0005233788,0.007271331,0.009883234,0.00077933393,0.7802098,0.025793368,0.15618363],"study_design_scores_gemma":[0.00018899069,0.0005075224,0.012615935,0.0076435893,0.00037605577,0.00088153296,0.011569678,0.044142548,0.0032474508,0.4745174,0.44403034,0.000278893],"about_ca_topic_score_codex":0.011773227,"about_ca_topic_score_gemma":0.009170004,"teacher_disagreement_score":0.9491275,"about_ca_system_score_codex":0.0047233608,"about_ca_system_score_gemma":0.012027228,"threshold_uncertainty_score":0.26904267},"labels":[],"label_agreement":null},{"id":"W2947293212","doi":"10.14288/1.0374224","title":"Bayesian adjustments for disease misclassification in epidemiological studies of health administrative databases, with applications to multiple sclerosis research","year":2018,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Epidemiology; Multiple sclerosis; Disease; Bayesian probability; Database; Medicine; Data science; Computer science; Data mining; Artificial intelligence; Pathology","score_opus":0.27125091922968225,"score_gpt":0.38796448717059306,"score_spread":0.11671356794091081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947293212","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011229334,0.0033394867,0.98032963,0.0029589979,0.00038042615,0.0003180335,0.00024070802,0.0002859618,0.0009174362],"genre_scores_gemma":[0.3225114,0.0039381967,0.6641068,0.0024177874,0.0008584066,0.0018412475,0.0007931233,0.00025893148,0.0032741372],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.84362817,0.1318816,0.0052212686,0.01019099,0.007859962,0.0012180668],"domain_scores_gemma":[0.5472948,0.37866193,0.030106092,0.031335834,0.011296548,0.0013048145],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.20580591,0.0022288214,0.0031241726,0.0036644533,0.0021905734,0.00450537,0.007947673,0.005222479,0.0021699641],"category_scores_gemma":[0.52385676,0.00229111,0.0041105733,0.004961751,0.0046698856,0.0060934075,0.006137004,0.0068278858,0.0005399452],"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":[0.00090921303,0.00019763118,0.0675015,0.0020966067,0.0047487672,0.00064183417,0.0036113488,0.16358413,0.0010800437,0.38571477,0.009225931,0.36068833],"study_design_scores_gemma":[0.0002809113,0.0003552315,0.018576032,0.0011575808,0.0009829336,0.0005667947,0.00036631263,0.34247023,0.0011452566,0.61907095,0.014824365,0.00020345006],"about_ca_topic_score_codex":0.01269322,"about_ca_topic_score_gemma":0.010524314,"teacher_disagreement_score":0.7941941,"about_ca_system_score_codex":0.0031480256,"about_ca_system_score_gemma":0.0039566024,"threshold_uncertainty_score":0.9793827},"labels":[],"label_agreement":null},{"id":"W2947448391","doi":"10.1016/j.jsxm.2019.03.536","title":"098 Persistent Genital Arousal Disorder: A Public Health Concern with Low Awareness","year":2019,"lang":"en","type":"article","venue":"The Journal of Sexual Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Arousal; Sexual arousal; Medicine; Sex organ; Public health; Psychiatry; Psychology; Pathology; Biology; Social psychology","score_opus":0.03988559676580354,"score_gpt":0.3054185336528451,"score_spread":0.26553293688704155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947448391","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":[0.90158284,0.015078043,0.0005684934,0.014814967,0.00033660876,0.00021052257,0.010986108,0.00015625503,0.056266088],"genre_scores_gemma":[0.98029476,0.008465849,0.00097365375,0.0022650028,0.0002959988,0.0000810231,0.0042701657,0.000030338639,0.003323148],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989454,0.00022936847,0.00022584583,0.000080327765,0.00035869118,0.00016032656],"domain_scores_gemma":[0.99386793,0.001950416,0.0024297547,0.00017138399,0.00096978754,0.0006107741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010392789,0.00013264318,0.00028494225,0.002239473,0.0005682489,0.0015178785,0.00027966383,0.000554879,0.009740786],"category_scores_gemma":[0.008480149,0.00013055114,0.0004837036,0.0032076947,0.0003054124,0.0016475008,0.00092115137,0.00066066877,0.0015604962],"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":[0.00013551937,0.0001408136,0.85037494,0.0017908221,0.00012119712,0.0007285644,0.0017882076,0.00005785303,0.00060197234,0.0007204498,0.02754492,0.115994625],"study_design_scores_gemma":[0.000014197794,0.00013400328,0.95009893,0.0015468398,0.00016259428,0.0031724386,0.0073640025,0.00023186262,0.00031386092,0.0004008455,0.036535367,0.000025046273],"about_ca_topic_score_codex":0.008970575,"about_ca_topic_score_gemma":0.014077955,"teacher_disagreement_score":0.009740786,"about_ca_system_score_codex":0.0006859724,"about_ca_system_score_gemma":0.00112283,"threshold_uncertainty_score":0.032586217},"labels":[],"label_agreement":null},{"id":"W2948305681","doi":"","title":"'Googling' for cancer: An infodemiological assessment of online search interests in Australia, Canada, New Zealand, the United Kingdom, and the United States","year":2016,"lang":"en","type":"other","venue":"QUT ePrints (Queensland University of Technology)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cancer; Geography; Breast cancer; Demography; Cancer registry; Medicine","score_opus":0.04130577097415178,"score_gpt":0.3418387277014983,"score_spread":0.30053295672734653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948305681","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9883916,0.00083446805,0.000074169555,0.00035693395,0.0000070085803,0.000042965115,0.0063464064,0.000015297232,0.003931231],"genre_scores_gemma":[0.9957337,0.0005587818,0.00024944302,0.000092354225,0.000008111031,0.000023613175,0.0027663894,0.000008526277,0.00055914826],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99913484,0.00012489718,0.00013657281,0.000085701206,0.0003252449,0.00019275029],"domain_scores_gemma":[0.99178046,0.0017447461,0.0029184408,0.00017657412,0.002167263,0.0012124812],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0012363491,0.00018937384,0.00030992247,0.0076398444,0.0009230773,0.0017483109,0.0005496337,0.0003239642,0.0015478956],"category_scores_gemma":[0.008219542,0.00013899975,0.00049224123,0.012982034,0.0007506505,0.0017144481,0.0016695975,0.00044579047,0.0002049809],"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":[0.0001122307,0.000020837315,0.9834451,0.00025597372,0.000100279,0.00016661406,0.005547307,0.00011275154,0.00014538062,0.00031342017,0.0022502262,0.007529753],"study_design_scores_gemma":[0.0000020763232,0.000019096993,0.9912254,0.000051589326,0.00003268374,0.00010639975,0.0064608827,0.0003813866,0.00005428162,0.000034296292,0.0016210878,0.000010643258],"about_ca_topic_score_codex":0.7351976,"about_ca_topic_score_gemma":0.8055324,"teacher_disagreement_score":0.9987637,"about_ca_system_score_codex":0.0041484633,"about_ca_system_score_gemma":0.0046623824,"threshold_uncertainty_score":0.5327237},"labels":[],"label_agreement":null},{"id":"W2950408549","doi":"10.2196/12214","title":"Improved Real-Time Influenza Surveillance: Using Internet Search Data in Eight Latin American Countries","year":2019,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of General Medical Sciences; Centers for Disease Control and Prevention; National Institutes of Health","keywords":"Latin Americans; The Internet; Medicine; Environmental health; Medical emergency; Computer science; World Wide Web; Political science","score_opus":0.04743898338876367,"score_gpt":0.3574445035445823,"score_spread":0.3100055201558186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950408549","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":[0.9954112,0.00016651995,0.0016483039,0.00010029053,0.000006953155,0.00005294868,0.0017256665,0.00006021545,0.00082795817],"genre_scores_gemma":[0.99111605,0.0001495891,0.004579328,0.000030765063,0.000009474114,0.00007739398,0.0038151625,0.000010522775,0.00021175081],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99942315,0.00032138778,0.000059235088,0.00011911041,0.000036928253,0.00004029309],"domain_scores_gemma":[0.9985331,0.00064521766,0.0003359392,0.00015090761,0.00027120428,0.000063677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021470902,0.0004936203,0.00037073472,0.0017446554,0.00018546415,0.0006469329,0.00039389724,0.00029135784,0.00041058502],"category_scores_gemma":[0.0044326326,0.00021930442,0.0005022922,0.0020880469,0.00021575205,0.00067273004,0.0006624467,0.00026169882,0.0001246492],"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":[0.00025177174,0.00015567787,0.94371384,0.000108090244,0.00020715332,0.00022379462,0.0003802437,0.024063013,0.00086953444,0.00016424898,0.0007006256,0.029161904],"study_design_scores_gemma":[0.00010629745,0.0002856117,0.8110783,0.00007024051,0.0002315141,0.00015295131,0.0018747244,0.18077406,0.0018105457,0.00025527363,0.003310908,0.000049705952],"about_ca_topic_score_codex":0.070175536,"about_ca_topic_score_gemma":0.04552639,"teacher_disagreement_score":0.070175536,"about_ca_system_score_codex":0.0008196743,"about_ca_system_score_gemma":0.00070211285,"threshold_uncertainty_score":0.13953412},"labels":[],"label_agreement":null},{"id":"W2951648472","doi":"10.2196/10218","title":"A Surveillance Infrastructure for Malaria Analytics: Provisioning Data Access and Preservation of Interoperability","year":2018,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Bill and Melinda Gates Foundation","keywords":"Interoperability; Dashboard; Provisioning; Data access; Domain (mathematical analysis); Malaria; Authentication (law); Epidemiological surveillance","score_opus":0.08356064972761648,"score_gpt":0.3914165651174651,"score_spread":0.3078559153898486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951648472","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":[0.03562678,0.00021445665,0.9097545,0.0020376695,0.00010907707,0.00091654,0.0008787906,0.042476326,0.007985859],"genre_scores_gemma":[0.36452734,0.00031970916,0.6254188,0.00060157944,0.000094500894,0.0005856035,0.0047440133,0.0016883268,0.0020200885],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951421,0.0011115028,0.00064137013,0.001086785,0.0015047411,0.0005136469],"domain_scores_gemma":[0.9903667,0.0018349052,0.0007840696,0.0039837523,0.002099166,0.00093142944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009232699,0.0007288158,0.0006436031,0.0026771582,0.0014602551,0.00438514,0.0033764231,0.001207984,0.0016649932],"category_scores_gemma":[0.015247719,0.0007037467,0.0012504339,0.001858947,0.0017429598,0.0075655174,0.0080470005,0.0024166668,0.0009131217],"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":[0.0011748148,0.0016707488,0.05881406,0.0011206579,0.00044632333,0.002008326,0.0066041625,0.05405256,0.06812193,0.23064873,0.067264244,0.50807333],"study_design_scores_gemma":[0.00016960085,0.00036899597,0.012585515,0.00038511714,0.00022198145,0.0010478109,0.0013483932,0.63696545,0.07042223,0.09552057,0.18068981,0.00027444345],"about_ca_topic_score_codex":0.0067042373,"about_ca_topic_score_gemma":0.0039019422,"teacher_disagreement_score":0.009232699,"about_ca_system_score_codex":0.002303508,"about_ca_system_score_gemma":0.0051566255,"threshold_uncertainty_score":0.048827767},"labels":[],"label_agreement":null},{"id":"W2951946244","doi":"10.1371/journal.pone.0165085","title":"Dynamic Forecasting of Zika Epidemics Using Google Trends","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":187,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"State Key Laboratory of Pathogen and Biosecurity","keywords":"Zika virus; Autoregressive integrated moving average; Autoregressive model; Computer science; Outbreak; Warning system; Time series; Data mining; Statistics; Machine learning; Medicine; Virology; Mathematics; Virus","score_opus":0.18444077159127176,"score_gpt":0.3325520008115284,"score_spread":0.14811122922025666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951946244","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":[0.8525944,0.00086434564,0.13483755,0.0011890484,0.00012918629,0.00010811722,0.005128243,0.0008915835,0.0042575942],"genre_scores_gemma":[0.9807916,0.00032540815,0.015421063,0.00003399865,0.000034354463,0.0000415374,0.0025132108,0.000022643577,0.00081607426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997452,0.00005425213,0.000024709343,0.00008248664,0.000052743384,0.000040565174],"domain_scores_gemma":[0.99942774,0.0002673138,0.00010996272,0.00003779994,0.0001262117,0.000030945645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008417584,0.0005154688,0.0004723938,0.0012846518,0.00019333886,0.0007706599,0.0007525908,0.00049794506,0.0007655125],"category_scores_gemma":[0.003222907,0.00027979,0.00070717145,0.0012264719,0.00017895884,0.0010941369,0.00040820983,0.00060509884,0.00025448375],"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":[0.000109749424,0.000058469977,0.06537973,0.00007195003,0.00010017597,0.00012759793,0.0001110858,0.89613676,0.0009131331,0.005827803,0.001613429,0.029550133],"study_design_scores_gemma":[0.0000033817666,0.0000114764525,0.0032939985,0.000004185633,0.000010889722,0.000011246291,0.000013388763,0.9954418,0.000107691114,0.00068595825,0.00041035368,0.0000056953327],"about_ca_topic_score_codex":0.055771776,"about_ca_topic_score_gemma":0.03303195,"teacher_disagreement_score":0.055771776,"about_ca_system_score_codex":0.0007239415,"about_ca_system_score_gemma":0.0006361354,"threshold_uncertainty_score":0.11089426},"labels":[],"label_agreement":null},{"id":"W2953484990","doi":"10.1371/journal.pone.0218324","title":"Identifying the appropriate spatial resolution for the analysis of crime patterns","year":2019,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"Economic and Social Research Council","keywords":"Computer science; Scale (ratio); Crime analysis; Grid; Conflation; Cluster analysis; Point (geometry); Spatial analysis; Data mining; Spatial ecology; Point pattern analysis; Aggregate (composite); Grid cell; Similarity (geometry); Statistics; Geography; Cartography; Common spatial pattern; Artificial intelligence; Mathematics","score_opus":0.06358659764990077,"score_gpt":0.28702734685581377,"score_spread":0.223440749205913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953484990","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":[0.259911,0.0015025794,0.7244614,0.0031067985,0.00013032966,0.0005309996,0.003017948,0.00043313036,0.0069058337],"genre_scores_gemma":[0.6781025,0.00035338788,0.3199064,0.000112167334,0.000044160828,0.0002700973,0.0008555483,0.00006718353,0.0002885741],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99436104,0.00327688,0.0004509936,0.0007959637,0.00091427425,0.00020081775],"domain_scores_gemma":[0.9654313,0.0240775,0.0030926766,0.0038356262,0.0031784496,0.00038460028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008880567,0.00044813319,0.0007431485,0.0027743632,0.0010295371,0.0027238633,0.0012978684,0.0009882174,0.0014528034],"category_scores_gemma":[0.06165341,0.00043458265,0.0009066625,0.003653806,0.001279859,0.0017205077,0.0022487822,0.0014078538,0.0004161651],"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":[0.00035402103,0.00025029242,0.48971087,0.0017425908,0.000544862,0.0007819845,0.0041391877,0.15202743,0.008534788,0.06785299,0.0105381375,0.26352292],"study_design_scores_gemma":[0.00014380764,0.0002429877,0.23302291,0.0012222612,0.00029298355,0.0012663717,0.0074254395,0.52627224,0.01457037,0.18713915,0.028160717,0.00024076532],"about_ca_topic_score_codex":0.01166615,"about_ca_topic_score_gemma":0.013614553,"teacher_disagreement_score":0.01166615,"about_ca_system_score_codex":0.0013618435,"about_ca_system_score_gemma":0.0017177959,"threshold_uncertainty_score":0.04696548},"labels":[],"label_agreement":null},{"id":"W2953867114","doi":"10.1186/s12911-019-0845-5","title":"Combining population-based administrative health records and electronic medical records for disease surveillance","year":2019,"lang":"en","type":"article","venue":"BMC Medical Informatics and Decision Making","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba; George & Fay Yee Centre for Healthcare Innovation","funders":"Canadian Institutes of Health Research; Research Manitoba","keywords":"Medicine; Population; Medical record; Statistics; Standard error; Mean squared error; Mathematics; Internal medicine; Environmental health","score_opus":0.030673283210291644,"score_gpt":0.3701466327592468,"score_spread":0.33947334954895514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953867114","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":[0.45385763,0.009634564,0.50050616,0.0027099156,0.0007741001,0.003808452,0.015370957,0.0015591178,0.011779181],"genre_scores_gemma":[0.7678059,0.0022999907,0.22105275,0.0006498286,0.00030033902,0.0011667641,0.0061545344,0.000062071085,0.00050781027],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9100501,0.06544527,0.0050507924,0.004603821,0.014243118,0.00060689234],"domain_scores_gemma":[0.8843021,0.07753202,0.013440887,0.009825882,0.014242046,0.00065711665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.053537585,0.0012736499,0.0018660884,0.0052846665,0.0004438423,0.0026226193,0.0016376621,0.00072489143,0.0010351377],"category_scores_gemma":[0.14242768,0.0007370727,0.002800457,0.0084159095,0.0004333253,0.0017898626,0.0022472057,0.0010242405,0.00040070177],"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":[0.00048343715,0.00031900904,0.64779675,0.00155365,0.011945434,0.00017300756,0.00049297354,0.05405175,0.0007374919,0.002310017,0.0039540054,0.27618238],"study_design_scores_gemma":[0.0005256657,0.0023726192,0.67791957,0.0017956276,0.009493536,0.00078857824,0.00064309576,0.27346784,0.003487147,0.012250625,0.016847033,0.00040858128],"about_ca_topic_score_codex":0.016852537,"about_ca_topic_score_gemma":0.0153895505,"teacher_disagreement_score":0.053537585,"about_ca_system_score_codex":0.0014428006,"about_ca_system_score_gemma":0.002910812,"threshold_uncertainty_score":0.28313726},"labels":[],"label_agreement":null},{"id":"W2955552028","doi":"10.1515/scid-2018-0004","title":"Implementation of Power Law Network Models of Epidemic Surveillance Data for Better Evaluation of Outbreak Detection Alarms","year":2019,"lang":"en","type":"article","venue":"Statistical Communications in Infectious Diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Lunenfeld-Tanenbaum Research Institute","funders":"","keywords":"Outbreak; Disease surveillance; Warning system; Infectious disease (medical specialty); Population; ALARM; Incidence (geometry); Computer science; Disease; Statistics; Demography; Geography; Computer security; Virology; Medicine; Environmental health; Telecommunications; Mathematics; Engineering; Pathology","score_opus":0.06608385048215118,"score_gpt":0.408373371859622,"score_spread":0.3422895213774708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955552028","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":[0.39340463,0.00011906367,0.6011202,0.00061467546,0.00010582574,0.0002432617,0.0002519643,0.0027065938,0.0014338022],"genre_scores_gemma":[0.92853,0.000032604254,0.07080133,0.000056873454,0.000013953799,0.0000961323,0.00013693757,0.00005600336,0.00027621133],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99754,0.0015609512,0.00012987487,0.00026400998,0.00039542135,0.000109858585],"domain_scores_gemma":[0.9668864,0.026388692,0.001317416,0.0023910976,0.0026174297,0.00039886322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007111654,0.0008454585,0.0007133173,0.0009143161,0.00035941543,0.0009875407,0.0015352892,0.00096355885,0.0010800215],"category_scores_gemma":[0.046352502,0.00042803356,0.00049325154,0.00056635396,0.00058640755,0.0015849726,0.0010005125,0.0013992145,0.00013414741],"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":[0.00015462181,0.0001206198,0.0045228824,0.00003142561,0.000041846324,0.000058269216,0.000055409626,0.9778811,0.0013066295,0.0030153703,0.00033110636,0.012480799],"study_design_scores_gemma":[0.000004793794,0.0000110675655,0.00010183195,9.471011e-7,0.0000012439274,0.0000029605396,0.0000023987618,0.99926,0.00026006802,0.0003290293,0.000024041543,0.0000015977387],"about_ca_topic_score_codex":0.008642339,"about_ca_topic_score_gemma":0.004818365,"teacher_disagreement_score":0.008642339,"about_ca_system_score_codex":0.0014734862,"about_ca_system_score_gemma":0.0011348769,"threshold_uncertainty_score":0.03761047},"labels":[],"label_agreement":null},{"id":"W2956324065","doi":"10.2196/13941","title":"A Fully Integrated Real-Time Detection, Diagnosis, and Control of Community Diarrheal Disease Clusters and Outbreaks (the INTEGRATE Project): Protocol for an Enhanced Surveillance System","year":2019,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Liverpool; University of Oxford; National Institute for Health Research Health Protection Research Unit; National Institute for Health and Care Research; Royal College of General Practitioners; Wellcome Trust; University of Surrey; University of East Anglia; Department of Health and Social Care; Central Manchester University Hospitals NHS Foundation Trust","keywords":"Outbreak; Environmental health; Medicine; Disease; Disease surveillance; Identification (biology); Diarrhea; Intensive care medicine; Point-of-care testing; Pathology; Biology","score_opus":0.09529253783669506,"score_gpt":0.4719017537951867,"score_spread":0.3766092159584916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2956324065","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010112034,0.00017945492,0.060415115,0.0005201944,0.00020322461,0.9164972,0.006398507,0.0007237053,0.004950512],"genre_scores_gemma":[0.010085776,0.00019557083,0.13593425,0.00040606892,0.00007327796,0.8467787,0.0051828674,0.00007250946,0.0012709984],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9339639,0.041975554,0.009891783,0.003345894,0.008243757,0.002579102],"domain_scores_gemma":[0.95097643,0.008260941,0.005855731,0.008292925,0.022820372,0.0037934715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08610192,0.002263063,0.0018335902,0.003174834,0.0033906968,0.0027291584,0.004953076,0.0032074708,0.019972486],"category_scores_gemma":[0.05070383,0.0022181852,0.002356532,0.0027456982,0.0024981424,0.0016995966,0.005357108,0.0030955162,0.007413516],"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":[0.06306225,0.016862934,0.059407353,0.023054745,0.0016246504,0.0030876512,0.011401225,0.028925572,0.06272187,0.02182779,0.21082324,0.4972007],"study_design_scores_gemma":[0.037064772,0.05811273,0.18827738,0.012762423,0.0015132949,0.002148834,0.0049592517,0.0382814,0.052207556,0.0070024217,0.59658885,0.0010811138],"about_ca_topic_score_codex":0.0077660163,"about_ca_topic_score_gemma":0.007323548,"teacher_disagreement_score":0.08610192,"about_ca_system_score_codex":0.0054969485,"about_ca_system_score_gemma":0.030266885,"threshold_uncertainty_score":0.45535594},"labels":[],"label_agreement":null},{"id":"W2963044346","doi":"10.2196/14295","title":"An Electronic Disease Early Warning System in Sana’a Governorate, Yemen: Evaluation Study","year":2019,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Centers for Disease Control and Prevention","keywords":"Representativeness heuristic; Medicine; Flexibility (engineering); Epidemiology; Disease surveillance; Environmental health; Strengths and weaknesses; Public health; Disease control; Medical emergency; Statistics; Nursing; Psychology; Mathematics","score_opus":0.02101234731785368,"score_gpt":0.330168356197092,"score_spread":0.30915600887923833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963044346","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":[0.99649686,0.0003164674,0.00022525164,0.0002434826,0.000009373025,0.0009947619,0.00041123584,0.000014206611,0.001288458],"genre_scores_gemma":[0.9969933,0.00034242286,0.0006425725,0.00011358384,0.000012496266,0.0008845489,0.00061525183,0.00000270917,0.00039318664],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99467814,0.0034979957,0.00045894724,0.00020687419,0.0005513095,0.0006067482],"domain_scores_gemma":[0.9934936,0.0022039658,0.0012318213,0.00029067308,0.0021641355,0.0006157841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009654926,0.0007081203,0.00047902024,0.0009280625,0.00084473734,0.0010491362,0.00042373687,0.0007680623,0.0026042056],"category_scores_gemma":[0.011066653,0.00023808317,0.00079364935,0.0010146961,0.0007501438,0.0011147817,0.0010597227,0.00046767184,0.00024677246],"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":[0.012777761,0.019225765,0.7970307,0.005131644,0.0013008703,0.0010087577,0.006453809,0.005907323,0.0040529487,0.001041212,0.004377233,0.14169186],"study_design_scores_gemma":[0.003541247,0.07686216,0.87771314,0.0011463654,0.0015120081,0.00041842824,0.013001605,0.013166574,0.004817841,0.00016709889,0.007555662,0.00009790186],"about_ca_topic_score_codex":0.018409677,"about_ca_topic_score_gemma":0.019768288,"teacher_disagreement_score":0.018409677,"about_ca_system_score_codex":0.0040723593,"about_ca_system_score_gemma":0.0049660164,"threshold_uncertainty_score":0.051060736},"labels":[],"label_agreement":null},{"id":"W2963829887","doi":"10.2196/13403","title":"Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation","year":2019,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Institutes of Health","keywords":"Influenza-like illness; Medicine; Demography; Health care; Disease control; Influenza season; Geography; Environmental health; Influenza vaccine; Vaccination","score_opus":0.12821258758985588,"score_gpt":0.4032343610841672,"score_spread":0.27502177349431134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963829887","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98106706,0.006949817,0.0023875353,0.0002777515,0.0000950384,0.0009411256,0.0058838986,0.00006442479,0.0023333281],"genre_scores_gemma":[0.9933107,0.00084649,0.0016067938,0.000058748068,0.00003712385,0.00052378315,0.0035038267,0.000015653271,0.00009685181],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.92537594,0.053555444,0.006707675,0.0058918037,0.0071808835,0.0012882574],"domain_scores_gemma":[0.9065776,0.054471217,0.01790599,0.006296421,0.013355781,0.0013929877],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.090382315,0.0011082481,0.0013103499,0.00687243,0.00069916056,0.0021180916,0.0023436886,0.00078290177,0.0013638734],"category_scores_gemma":[0.12516879,0.00059062144,0.0059861694,0.007543571,0.0013205343,0.0032893894,0.0031096202,0.0008312553,0.00019230304],"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":[0.0031034974,0.0003372225,0.9625344,0.00095986994,0.0074422094,0.000038432416,0.0012144323,0.0021714754,0.000091798895,0.00026584408,0.0007786849,0.021062111],"study_design_scores_gemma":[0.00045744854,0.0039468072,0.98291147,0.00040937358,0.0037375782,0.000120141005,0.0028733863,0.0041061407,0.00030047697,0.00020079096,0.00087664847,0.00005964524],"about_ca_topic_score_codex":0.026721584,"about_ca_topic_score_gemma":0.022878822,"teacher_disagreement_score":0.90961766,"about_ca_system_score_codex":0.0036006148,"about_ca_system_score_gemma":0.0020325475,"threshold_uncertainty_score":0.47799307},"labels":[],"label_agreement":null},{"id":"W2964431901","doi":"10.1186/s12963-019-0193-9","title":"Regional variation of premature mortality in Ontario, Canada: a spatial analysis","year":2019,"lang":"en","type":"article","venue":"Population Health Metrics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; Public Health Ontario; University of Toronto","funders":"Canadian Institutes of Health Research; Canada Excellence Research Chairs, Government of Canada; Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences","keywords":"Demography; Medicine; Population; Public health; Population health; Mortality rate; Epidemiology; Environmental health; Surgery","score_opus":0.03981862156918798,"score_gpt":0.3227579253105676,"score_spread":0.2829393037413796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964431901","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":[0.96790344,0.0021623264,0.0024026178,0.0008883945,0.000029180006,0.000135631,0.022453096,0.00007665826,0.003948595],"genre_scores_gemma":[0.99222493,0.0005724084,0.0015294021,0.000052320185,0.0000092406035,0.00006106369,0.004543219,0.000014720956,0.0009926923],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987674,0.00017388817,0.00010381277,0.00024790832,0.0004135458,0.00029348122],"domain_scores_gemma":[0.99599695,0.00040717257,0.0007985463,0.00020936114,0.0022394161,0.00034851735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014280411,0.00035210548,0.0004942488,0.0024960462,0.0020933961,0.000868357,0.0012512173,0.00023778102,0.0013210581],"category_scores_gemma":[0.005025447,0.0002844995,0.0011120162,0.0068192934,0.0007416239,0.00029120455,0.0013101271,0.00029981096,0.0001502814],"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":[0.00005157251,0.00000790455,0.9878641,0.0001084166,0.00019299646,0.00012484912,0.0011690366,0.0013948774,0.00016119583,0.0003696216,0.0025374328,0.0060179657],"study_design_scores_gemma":[0.0000044244093,0.000008312565,0.99571604,0.00003643356,0.00005411703,0.000033636974,0.0008540958,0.0013822629,0.000028044931,0.00008531238,0.0017881372,0.000009239862],"about_ca_topic_score_codex":0.9950334,"about_ca_topic_score_gemma":0.9961474,"teacher_disagreement_score":0.02357353,"about_ca_system_score_codex":0.02357353,"about_ca_system_score_gemma":0.034885358,"threshold_uncertainty_score":0.17103875},"labels":[],"label_agreement":null},{"id":"W2964741033","doi":"10.29173/cais1049","title":"Performance of Crowdsourcing to Monitor and Filter Relevant S c ien tific L iterature","year":2018,"lang":"en","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Crowdsourcing; Filter (signal processing); Data science; Measure (data warehouse); Computer science; Psychology; Data mining; World Wide Web","score_opus":0.01551869438075382,"score_gpt":0.2514552896136279,"score_spread":0.23593659523287408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964741033","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92005795,0.0028972172,0.04209899,0.0035030094,0.000968584,0.0014907524,0.003923121,0.0048185447,0.020241832],"genre_scores_gemma":[0.9642691,0.0002938097,0.028427048,0.0005786908,0.00019222277,0.00037907407,0.0021711534,0.00013548895,0.0035534722],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98816895,0.004910552,0.0006377837,0.0024469146,0.0031493974,0.00068641326],"domain_scores_gemma":[0.9503742,0.03106602,0.0035778584,0.00491857,0.007230388,0.002832983],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.017272182,0.0016498953,0.0017188797,0.005162757,0.0019464438,0.003253773,0.0016150698,0.002470836,0.001667491],"category_scores_gemma":[0.04644974,0.00040425,0.0008784592,0.0025924041,0.000972493,0.0022258065,0.0030626056,0.001131931,0.0020330139],"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":[0.010394628,0.0036796052,0.24937718,0.0022073062,0.002038658,0.0008424407,0.0068286913,0.090929516,0.03478457,0.0036784266,0.035824064,0.559415],"study_design_scores_gemma":[0.0008318844,0.004018204,0.10170754,0.00032692467,0.00050912,0.0004613264,0.0053332783,0.81232226,0.02631332,0.0114351995,0.03628382,0.00045713453],"about_ca_topic_score_codex":0.023207834,"about_ca_topic_score_gemma":0.013179706,"teacher_disagreement_score":0.9827278,"about_ca_system_score_codex":0.0014280996,"about_ca_system_score_gemma":0.0032379373,"threshold_uncertainty_score":0.09134513},"labels":[],"label_agreement":null},{"id":"W2965289405","doi":"10.2196/13212","title":"The Association Between Internet Searches and Moisturizer Prescription in Japan: Retrospective Observational Study","year":2019,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Observational study; Medical prescription; Moisturizer; Medicine; The Internet; Computer science; World Wide Web; Biology; Internal medicine; Pharmacology","score_opus":0.06582173085955705,"score_gpt":0.3383842375490024,"score_spread":0.2725625066894453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965289405","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":[0.9977356,0.0007828743,0.00007416995,0.000047749945,0.0000067512565,0.00003469969,0.0008874923,0.0000038488856,0.00042681184],"genre_scores_gemma":[0.99772173,0.00059618225,0.00013615118,0.00009454114,0.00002378738,0.000052617157,0.001192998,0.00000392669,0.00017801004],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99911755,0.00012871984,0.00023378567,0.00022032032,0.0001641398,0.00013543891],"domain_scores_gemma":[0.9971342,0.00028905412,0.0016249646,0.00019283786,0.00037163595,0.00038733074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007029183,0.0003651527,0.0007956655,0.0015008085,0.0007312803,0.0009302873,0.00051970966,0.0005978289,0.0013293505],"category_scores_gemma":[0.0022937853,0.00070612255,0.0009501142,0.0030010273,0.00041196315,0.00093207543,0.00073688064,0.00083624735,0.00027840922],"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":[0.00004562781,0.000043183478,0.99898976,0.000040824554,0.000054823806,0.00008841345,0.00011845274,0.000010685032,0.000052549352,0.000006340618,0.0001078829,0.00044139216],"study_design_scores_gemma":[0.000006881254,0.000071966555,0.99885213,0.000019342402,0.0000907314,0.00023572343,0.00037115355,0.00010308046,0.000021101447,0.00000803744,0.00021329579,0.0000065331774],"about_ca_topic_score_codex":0.022666296,"about_ca_topic_score_gemma":0.026061732,"teacher_disagreement_score":0.022666296,"about_ca_system_score_codex":0.0007801455,"about_ca_system_score_gemma":0.0010178889,"threshold_uncertainty_score":0.04506874},"labels":[],"label_agreement":null},{"id":"W2965327127","doi":"10.1093/jamia/ocz112","title":"Development of a global infectious disease activity database using natural language processing, machine learning, and human expertise","year":2019,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; BlueDot (Canada); St. Michael's Hospital","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Infectious disease (medical specialty); Representativeness heuristic; Classifier (UML); Information extraction; Natural language processing; Disease; Medicine; Pathology","score_opus":0.00696299589376862,"score_gpt":0.3142278333592521,"score_spread":0.30726483746548344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965327127","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":[0.4653707,0.0013166476,0.43243146,0.0028728102,0.00029355174,0.005882087,0.050757382,0.03368025,0.0073951194],"genre_scores_gemma":[0.4163519,0.00032528516,0.5168261,0.00040511298,0.00012897621,0.0016049383,0.06274127,0.0002832806,0.0013330745],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951278,0.0013538543,0.0009115976,0.0013187833,0.0011165289,0.00017153939],"domain_scores_gemma":[0.98228884,0.0072976686,0.0017374278,0.0024146736,0.005507162,0.000754375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008561022,0.000902816,0.001023918,0.007976286,0.000605256,0.0023622888,0.0014150053,0.00079371815,0.0013200697],"category_scores_gemma":[0.020650573,0.0003798934,0.00080204575,0.0029562032,0.00041190404,0.0032973066,0.0015469651,0.00090589304,0.0011330448],"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":[0.0006837667,0.0019692536,0.17929254,0.0012511775,0.00047592173,0.0010719806,0.0013067867,0.021194516,0.037695322,0.0020705217,0.03441988,0.7185684],"study_design_scores_gemma":[0.0003471348,0.0017703595,0.15225403,0.00041519856,0.00045464528,0.0012602243,0.002173089,0.6739021,0.10105397,0.0053655133,0.060736194,0.0002675457],"about_ca_topic_score_codex":0.011836505,"about_ca_topic_score_gemma":0.0117241135,"teacher_disagreement_score":0.011836505,"about_ca_system_score_codex":0.0015115242,"about_ca_system_score_gemma":0.0030850484,"threshold_uncertainty_score":0.04527551},"labels":[],"label_agreement":null},{"id":"W2969106514","doi":"10.2196/14276","title":"The Fever Coach Mobile App for Participatory Influenza Surveillance in Children: Usability Study","year":2019,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Korea Centers for Disease Control and Prevention; National Research Foundation of Korea; National Research Foundation","keywords":"Usability; Mobile apps; mHealth; Smartphone app; Medicine; Internet privacy; Computer science; World Wide Web; Psychological intervention; Human–computer interaction; Nursing","score_opus":0.049348826188682866,"score_gpt":0.4039689749730115,"score_spread":0.3546201487843286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969106514","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":[0.9961282,0.00022490519,0.0008804235,0.00010103538,0.00002090277,0.0015025148,0.00021904129,0.00005104131,0.0008719024],"genre_scores_gemma":[0.9819272,0.00058298296,0.010619584,0.00022946327,0.00002684823,0.0054543153,0.00034634408,0.000036354835,0.00077689864],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9946444,0.003181212,0.00052413193,0.0005391342,0.0006488573,0.00046239397],"domain_scores_gemma":[0.9779308,0.01637816,0.0011498447,0.0008441846,0.002833972,0.0008630729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012117195,0.0009924448,0.0012025435,0.0010896267,0.00089577783,0.0015793797,0.0007320316,0.0008183078,0.0018950134],"category_scores_gemma":[0.0318845,0.00055391865,0.0013491967,0.0005003874,0.0006799122,0.0019457559,0.0016977595,0.00091200176,0.00040536138],"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":[0.005572675,0.027303701,0.5187686,0.013179628,0.0010217569,0.0037539438,0.1655571,0.0016586784,0.008822072,0.0008839652,0.01005679,0.24342105],"study_design_scores_gemma":[0.0025372554,0.08406586,0.7272421,0.004467761,0.0022540533,0.004640713,0.11651963,0.01578448,0.0077994005,0.0009608447,0.033016898,0.00071097695],"about_ca_topic_score_codex":0.0023108302,"about_ca_topic_score_gemma":0.0041498938,"teacher_disagreement_score":0.012117195,"about_ca_system_score_codex":0.00086822786,"about_ca_system_score_gemma":0.0012349002,"threshold_uncertainty_score":0.06408262},"labels":[],"label_agreement":null},{"id":"W2969165462","doi":"10.1183/13993003.01076-2019","title":"Using Google Trends to investigate global COPD awareness","year":2019,"lang":"en","type":"letter","venue":"European Respiratory Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"COPD; Quarter (Canadian coin); Pulmonary disease; Public interest; The Internet; Public health; Psychological intervention; Population; Medicine; Internet privacy; Family medicine; Environmental health; Computer science; World Wide Web; Geography; Political science; Pathology; Nursing; Internal medicine","score_opus":0.10680291841611785,"score_gpt":0.3511677016048612,"score_spread":0.24436478318874338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969165462","genre_codex":"commentary","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":[0.001641769,0.0023479708,0.00009991697,0.98451823,0.008525366,0.000021135493,0.00017761068,0.000035885132,0.0026321304],"genre_scores_gemma":[0.033459704,0.006791392,0.00067455845,0.9048306,0.048848897,0.00012269703,0.000226465,0.0000548877,0.004990838],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.996151,0.0014301741,0.00068943563,0.0003910628,0.0009655149,0.00037276384],"domain_scores_gemma":[0.9768743,0.0128967315,0.0026846752,0.00059045537,0.004417202,0.0025366873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003632278,0.00041349579,0.00060485525,0.0021992663,0.0020807162,0.0025343506,0.00086242735,0.013954025,0.0042191767],"category_scores_gemma":[0.048423138,0.00040869752,0.00051256706,0.00191563,0.0012593655,0.0043553356,0.0015911487,0.010243081,0.0034104672],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048372505,0.000044477587,0.012307364,0.0002170812,0.000029953933,0.0019768137,0.0005835066,0.000032050484,0.00018738392,0.0012966653,0.95529914,0.02797719],"study_design_scores_gemma":[0.00011937972,0.00029652053,0.020251906,0.0022384895,0.000084490304,0.0098279985,0.004220084,0.0012130623,0.0005038634,0.0070309853,0.9540266,0.00018654042],"about_ca_topic_score_codex":0.0070157545,"about_ca_topic_score_gemma":0.009007709,"teacher_disagreement_score":0.013954025,"about_ca_system_score_codex":0.002926135,"about_ca_system_score_gemma":0.001922416,"threshold_uncertainty_score":0.021230757},"labels":[],"label_agreement":null},{"id":"W2970340713","doi":"10.2196/13130","title":"Access to Primary Care and Internet Searches for Walk-In Clinics and Emergency Departments in Canada: Observational Study Using Google Trends and Population Health Survey Data","year":2019,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Commonwealth; Population; Medicine; Observational study; Demography; Geography; Family medicine; Environmental health; Sociology","score_opus":0.2783553871619667,"score_gpt":0.4527284669340454,"score_spread":0.17437307977207867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970340713","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":[0.89730024,0.0028534161,0.0003814276,0.0009156048,0.000040822222,0.0005294138,0.09312377,0.000083639476,0.004771576],"genre_scores_gemma":[0.971858,0.0016420025,0.0008594715,0.00046491597,0.000019707524,0.00023736461,0.023864912,0.000037283036,0.0010162982],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99551636,0.0002991701,0.00061961,0.00067590136,0.001863482,0.0010255596],"domain_scores_gemma":[0.98556143,0.0011032948,0.0034139939,0.0005828622,0.0067539797,0.0025844693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002183347,0.0007236528,0.0011616145,0.0067447345,0.004689987,0.0029219855,0.002774561,0.00091109204,0.002812717],"category_scores_gemma":[0.011394857,0.00097031525,0.0020526797,0.026584527,0.0015657381,0.001593875,0.002820697,0.00227299,0.00066448154],"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":[0.00011234983,0.000082313614,0.98960507,0.00033893788,0.00020606321,0.000094581796,0.0013381968,0.00015105741,0.000047179336,0.0001886936,0.004815731,0.0030196905],"study_design_scores_gemma":[0.00002420585,0.000039923467,0.99362373,0.0002020403,0.00011399775,0.00010866885,0.0025198008,0.00071618304,0.000055280118,0.000046856116,0.0025077737,0.000041574458],"about_ca_topic_score_codex":0.9962604,"about_ca_topic_score_gemma":0.9964976,"teacher_disagreement_score":0.05172045,"about_ca_system_score_codex":0.05172045,"about_ca_system_score_gemma":0.083515495,"threshold_uncertainty_score":0.37525994},"labels":[],"label_agreement":null},{"id":"W2971910704","doi":"10.1186/s12889-019-7521-7","title":"Methods for detecting seasonal influenza epidemics using a school absenteeism surveillance system","year":2019,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Guelph General Hospital; University of Calgary; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Medicine; Biostatistics; Absenteeism; Public health; Environmental health; Epidemiology; Seasonal influenza; Virology; Coronavirus disease 2019 (COVID-19); Infectious disease (medical specialty); Internal medicine; Pathology","score_opus":0.11249044132937923,"score_gpt":0.4349421377713546,"score_spread":0.32245169644197536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971910704","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":[0.39018515,0.00072227407,0.6009766,0.00029203363,0.00009068469,0.000818963,0.0027882208,0.0022080822,0.0019179569],"genre_scores_gemma":[0.77277994,0.00029615176,0.22236149,0.000048243055,0.000052382275,0.00041840033,0.0031505818,0.000047896763,0.00084493443],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979761,0.0007705486,0.00027526397,0.00048778308,0.00039950988,0.000090797614],"domain_scores_gemma":[0.9934455,0.0030867152,0.0012913134,0.00048721413,0.0015325516,0.00015664827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004936369,0.00077533454,0.0007486531,0.0028160736,0.00035352993,0.00095627026,0.0011927765,0.0004946801,0.0010768833],"category_scores_gemma":[0.012263203,0.00043331663,0.0010405668,0.0012946178,0.00020037385,0.0009928108,0.0010271256,0.0005985412,0.00038197916],"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":[0.0006425454,0.00056307146,0.46036705,0.00062087696,0.001231296,0.00018619027,0.0005577952,0.20650637,0.006799242,0.001931393,0.0030548016,0.31753942],"study_design_scores_gemma":[0.00004002287,0.0002707152,0.06742423,0.00004574488,0.00012168157,0.00016105855,0.00017342254,0.92675865,0.0022536356,0.0013388792,0.0013603325,0.000051547453],"about_ca_topic_score_codex":0.016019644,"about_ca_topic_score_gemma":0.011907586,"teacher_disagreement_score":0.016019644,"about_ca_system_score_codex":0.00082424015,"about_ca_system_score_gemma":0.0010502337,"threshold_uncertainty_score":0.03185278},"labels":[],"label_agreement":null},{"id":"W2973377013","doi":"10.1057/s41284-019-00206-5","title":"Review of Monahan, T., &amp; Wood, D. M. (eds.): Surveillance studies: a reader","year":2019,"lang":"en","type":"article","venue":"Security Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","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 Victoria","funders":"","keywords":"Airport security; Computer security; Computer science","score_opus":0.02774041486481688,"score_gpt":0.3403231600399491,"score_spread":0.31258274517513224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973377013","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":[0.000029247802,0.9908343,0.00015854396,0.0056818565,0.0025067793,0.0000036566917,0.000033921475,0.0000070821006,0.00074456783],"genre_scores_gemma":[0.0003269037,0.99312085,0.00021580861,0.002566757,0.0030796032,0.000006778565,0.00005073893,0.0000058065684,0.0006267196],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984269,0.00044938954,0.0003401526,0.00019629957,0.00051389093,0.0000732888],"domain_scores_gemma":[0.98455936,0.009691111,0.0012088758,0.00039194874,0.0033467957,0.00080188847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005749224,0.0010621556,0.0019463885,0.010374847,0.0005559924,0.0024709548,0.0018897348,0.0023973228,0.0070536053],"category_scores_gemma":[0.016543034,0.0006741898,0.0011493136,0.008835901,0.0012952522,0.0035608674,0.0015478154,0.0036804758,0.0044532246],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040774787,0.000025198022,0.0003353868,0.013478131,0.000109944725,0.00010418355,0.00014656967,0.00019983745,0.0001484103,0.0034289544,0.4960771,0.48590547],"study_design_scores_gemma":[0.000007882318,0.0000289063,0.0010104423,0.014789888,0.00010350618,0.0006471436,0.00009346859,0.000045114095,0.00008520092,0.0014247623,0.9817422,0.00002141821],"about_ca_topic_score_codex":0.005262574,"about_ca_topic_score_gemma":0.013001967,"teacher_disagreement_score":0.010374847,"about_ca_system_score_codex":0.0022936282,"about_ca_system_score_gemma":0.0044900747,"threshold_uncertainty_score":0.030405164},"labels":[],"label_agreement":null},{"id":"W2973626166","doi":"10.1017/s1466252319000033","title":"A review of knowledge discovery process in control and mitigation of avian influenza","year":2019,"lang":"en","type":"review","venue":"Animal Health Research Reviews","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Centers for Disease Control and Prevention","keywords":"Influenza A virus subtype H5N1; Process (computing); Control (management); Medicine; Computational biology; Computer science; Virology; Biology; Artificial intelligence; Virus","score_opus":0.3264712216121715,"score_gpt":0.5732054806532304,"score_spread":0.2467342590410589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973626166","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":[0.00009309097,0.9982368,0.0004030795,0.00032971054,0.00013196694,0.000009856902,0.000026010723,0.0000053326453,0.00076405343],"genre_scores_gemma":[0.0007549171,0.9983494,0.0005110587,0.00012824341,0.0000847562,0.00001055084,0.000024583314,0.000001214506,0.0001351876],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.999057,0.00028787588,0.00017497144,0.00015638588,0.00026846086,0.00005537093],"domain_scores_gemma":[0.9960508,0.003078537,0.00026447754,0.00006180657,0.0004744379,0.00007004322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020334881,0.0011168837,0.0017468284,0.0045203976,0.00042538793,0.0016262634,0.0011437747,0.0015400046,0.0037964606],"category_scores_gemma":[0.004088596,0.00049046637,0.0013795038,0.005683624,0.0006155757,0.002095481,0.0007281618,0.0013584166,0.0011008962],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000071229275,0.00008334443,0.0003662498,0.102421075,0.0003531626,0.00019650928,0.00019835448,0.0012170831,0.0006358558,0.01252564,0.017513389,0.8644181],"study_design_scores_gemma":[0.000020984216,0.0002044752,0.0015814542,0.048259858,0.00076968846,0.0008103113,0.000214156,0.00060119474,0.0005702468,0.008084823,0.9388229,0.000059871414],"about_ca_topic_score_codex":0.0037581602,"about_ca_topic_score_gemma":0.004230155,"teacher_disagreement_score":0.0045203976,"about_ca_system_score_codex":0.0012840972,"about_ca_system_score_gemma":0.003761454,"threshold_uncertainty_score":0.0127004385},"labels":[],"label_agreement":null},{"id":"W2973805261","doi":"10.5539/gjhs.v11n11p109","title":"Measles Data Reporting in the District Health Information System: A Case Study of Gombe State","year":2019,"lang":"en","type":"article","venue":"Global Journal of Health Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Roehampton","keywords":"Measles; Environmental health; Health facility; Medicine; Public health; Health informatics; Nursing; Health services; Vaccination; Population","score_opus":0.07258739416291532,"score_gpt":0.4068883149516947,"score_spread":0.3343009207887794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973805261","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":[0.9981602,0.00024244454,0.00008947755,0.0006029103,0.0000030134747,0.00005939897,0.00006672543,0.0000037838809,0.0007721301],"genre_scores_gemma":[0.9991054,0.00031569184,0.00017151957,0.00016340685,0.000004232053,0.00003370329,0.00005672732,0.000002079609,0.00014719195],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99728525,0.0012672311,0.00018813557,0.0002122813,0.00031376854,0.00073346926],"domain_scores_gemma":[0.99499124,0.0019993451,0.0015095304,0.00029771082,0.0005181553,0.00068406813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002142724,0.00032051545,0.0002925726,0.0026637514,0.0036269787,0.0016840292,0.0010336674,0.0011271863,0.0018600082],"category_scores_gemma":[0.008464447,0.00046867708,0.00027710633,0.0043587736,0.0019839478,0.0012841698,0.0030344576,0.0013401792,0.00014614937],"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":[0.000057235684,0.000268308,0.90753037,0.0003330233,0.000044236956,0.022379462,0.05713648,0.00015158666,0.00083712826,0.0008387381,0.00089978054,0.009523619],"study_design_scores_gemma":[0.00001634549,0.00024438035,0.7470317,0.00050598936,0.000050857303,0.014766772,0.23156653,0.000860568,0.00040655528,0.00024372866,0.0042689685,0.000037647023],"about_ca_topic_score_codex":0.13681076,"about_ca_topic_score_gemma":0.17231143,"teacher_disagreement_score":0.13681076,"about_ca_system_score_codex":0.004025404,"about_ca_system_score_gemma":0.003697549,"threshold_uncertainty_score":0.27202892},"labels":[],"label_agreement":null},{"id":"W2977668213","doi":"10.1016/j.healthplace.2019.102207","title":"The evolution of Health &amp; Place: Text mining papers published between 1995 and 2018","year":2019,"lang":"en","type":"article","venue":"Health & Place","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Liverpool","keywords":"Neighbourhood (mathematics); Context (archaeology); Health geography; Geography; Regional science; Data science; Health policy; Sociology; Public health; Medicine; International health; Computer science; Nursing","score_opus":0.018251081899546207,"score_gpt":0.3034365026790788,"score_spread":0.2851854207795326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977668213","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22329657,0.14417289,0.0023824063,0.01370126,0.0035012725,0.00017327671,0.59005743,0.00031584126,0.02239902],"genre_scores_gemma":[0.5293286,0.116374366,0.008250972,0.0034004764,0.0047185556,0.00046970785,0.31571588,0.00043929144,0.021302136],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99668026,0.00039320168,0.0008697962,0.0006880805,0.0010872661,0.00028137205],"domain_scores_gemma":[0.963677,0.020519512,0.008614658,0.001161261,0.0049132565,0.0011142201],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0028603764,0.00068942393,0.0006880685,0.03491638,0.0006471373,0.003485953,0.0011182884,0.0009466534,0.005221344],"category_scores_gemma":[0.023052186,0.000338132,0.0011327503,0.06360963,0.0006111868,0.0024453844,0.0015150525,0.0007914927,0.002411107],"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":[0.00066403206,0.00009754325,0.45843437,0.018612808,0.0013620596,0.001644173,0.0046305787,0.0013884868,0.0025480548,0.006308506,0.19533123,0.30897814],"study_design_scores_gemma":[0.000044263266,0.00006659155,0.5396844,0.0062497044,0.00086882943,0.0010228212,0.0039012795,0.00094821525,0.0021614195,0.002062671,0.44290984,0.000079921214],"about_ca_topic_score_codex":0.018175093,"about_ca_topic_score_gemma":0.028944977,"teacher_disagreement_score":0.99713963,"about_ca_system_score_codex":0.0015628745,"about_ca_system_score_gemma":0.003745061,"threshold_uncertainty_score":0.036138594},"labels":[],"label_agreement":null},{"id":"W2977852562","doi":"","title":"Automated Identification of National Health Survey Research Topics in the Academic Literature.","year":2017,"lang":"en","type":"article","venue":"PubMed","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Identification (biology); Public health; Health care; Population; Data science; Health Information National Trends Survey; Environmental health; Health information; Medicine; Computer science; Political science; Nursing","score_opus":0.1887667733192991,"score_gpt":0.4529664531246465,"score_spread":0.2641996798053474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977852562","genre_codex":"dataset","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":[0.081287555,0.2642099,0.057210226,0.024508385,0.005516101,0.0066878065,0.5267549,0.0023325302,0.03149257],"genre_scores_gemma":[0.17847991,0.10533373,0.24955773,0.004715034,0.0024003629,0.009069608,0.44497088,0.0003797247,0.0050929626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9585478,0.012151628,0.017430125,0.0033644945,0.008029328,0.0004765433],"domain_scores_gemma":[0.62908894,0.24639854,0.044127923,0.013153478,0.064659566,0.0025715802],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.05142012,0.0010550618,0.0024932448,0.11504125,0.0018810064,0.006039095,0.0021589026,0.0014796462,0.005962829],"category_scores_gemma":[0.20999075,0.00063986855,0.0021257156,0.08192601,0.0013563352,0.0049515483,0.004524023,0.0011026489,0.0030297514],"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":[0.00061371544,0.0002308423,0.06005772,0.18342952,0.0024680095,0.002061586,0.010233049,0.00093202083,0.010319735,0.01946794,0.16170521,0.54848063],"study_design_scores_gemma":[0.0002973449,0.0004344344,0.09503493,0.059950333,0.005290325,0.0017125097,0.011793328,0.004370266,0.009691388,0.025568534,0.7855943,0.0002623539],"about_ca_topic_score_codex":0.0037648343,"about_ca_topic_score_gemma":0.010253566,"teacher_disagreement_score":0.88495874,"about_ca_system_score_codex":0.0026956007,"about_ca_system_score_gemma":0.014315234,"threshold_uncertainty_score":0.27193886},"labels":[],"label_agreement":null},{"id":"W2978297484","doi":"10.2196/15235","title":"Interoperability of Surveillance Data Collection Tools (District Health Information System 2 and District Vaccine Data Management Tools) in Enugu State, Nigeria, From 2015-2018","year":2019,"lang":"en","type":"article","venue":"Iproceedings","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Data collection; Interoperability; Computer science; Statistics; World Wide Web","score_opus":0.0340455167404649,"score_gpt":0.2923857232792034,"score_spread":0.2583402065387385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978297484","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":[0.99755305,0.00017473604,0.0003576045,0.00017520726,0.0000074413565,0.000099472,0.00027265243,0.000011207378,0.0013486332],"genre_scores_gemma":[0.99802077,0.00012503199,0.0010671243,0.00005382454,0.000002651963,0.000085401945,0.00037770692,0.000005146807,0.00026246536],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9918053,0.0035016309,0.0012054162,0.00083842094,0.0017449413,0.00090431614],"domain_scores_gemma":[0.9796211,0.007399085,0.0047014477,0.001368485,0.005890455,0.0010193734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017134557,0.00022525628,0.0002855925,0.002161575,0.0014457832,0.0015753433,0.0005344488,0.0003278108,0.0005353053],"category_scores_gemma":[0.029579913,0.00039501465,0.0003410669,0.002252503,0.0011479077,0.0025904842,0.002923311,0.0007540054,0.000091245594],"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":[0.00019599313,0.00022012909,0.90384597,0.0002892549,0.00003882635,0.00041817347,0.041544117,0.0005361067,0.0010565543,0.0006146989,0.0006678293,0.050572302],"study_design_scores_gemma":[0.00000825421,0.00033331523,0.94367486,0.0003030907,0.000042903037,0.00033669246,0.046238102,0.0011076642,0.0014947922,0.00020900427,0.0062160706,0.000035315115],"about_ca_topic_score_codex":0.03476704,"about_ca_topic_score_gemma":0.04513853,"teacher_disagreement_score":0.03476704,"about_ca_system_score_codex":0.0034868342,"about_ca_system_score_gemma":0.0041975235,"threshold_uncertainty_score":0.0906173},"labels":[],"label_agreement":null},{"id":"W2978592243","doi":"10.2196/15237","title":"Outcomes of Mobile Reporting to Enhance Disease Surveillance in 632 Districts of 29 States in Nigeria","year":2019,"lang":"en","type":"article","venue":"Iproceedings","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Disease surveillance; Mobile phone; Health care; Population; Medicine; Phone; Medical emergency; Public health; Public health surveillance; Environmental health; Business; Computer science; Nursing; Telecommunications","score_opus":0.010145923278397206,"score_gpt":0.31151128469527967,"score_spread":0.30136536141688247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978592243","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":[0.9988857,0.00006785697,0.00010497862,0.00006283996,0.0000045198535,0.000076496035,0.00020645856,0.000006048037,0.0005851857],"genre_scores_gemma":[0.99900657,0.00011508937,0.0003481701,0.00002661131,0.0000026308992,0.000054824202,0.00017895122,0.0000016834125,0.0002655816],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99822885,0.0010191442,0.0001586384,0.00012552753,0.00019973818,0.00026817466],"domain_scores_gemma":[0.9960748,0.0013373917,0.0013466252,0.00017820566,0.00074945507,0.0003135258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028200278,0.00032261177,0.00021843171,0.00064739457,0.00066438905,0.0009917883,0.00029717488,0.00031011103,0.00076700246],"category_scores_gemma":[0.0066522514,0.00020445602,0.0002929089,0.0009854378,0.00036291895,0.000487236,0.001066193,0.0003274497,0.00018508523],"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":[0.00013406333,0.00028182426,0.9758657,0.00009412216,0.00003187507,0.00025987122,0.0046783756,0.00039080792,0.0003339824,0.000071318136,0.00032209442,0.017535929],"study_design_scores_gemma":[0.000013223567,0.0008936726,0.9777924,0.00008511441,0.00003881881,0.0001945839,0.01808745,0.0011941503,0.0006428116,0.00005851669,0.0009844339,0.000014775243],"about_ca_topic_score_codex":0.020393545,"about_ca_topic_score_gemma":0.02990165,"teacher_disagreement_score":0.020393545,"about_ca_system_score_codex":0.0012703508,"about_ca_system_score_gemma":0.0014109194,"threshold_uncertainty_score":0.040549695},"labels":[],"label_agreement":null},{"id":"W2980868433","doi":"10.1007/978-3-030-06176-0_14","title":"Research Focused on Diagnostic Tests","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"McGill University","funders":"","keywords":"Diagnostic test; Test (biology); Set (abstract data type); Meaning (existential); Pre- and post-test probability; Medicine; Psychology; Computer science; Pediatrics; Radiology","score_opus":0.07619865668760692,"score_gpt":0.3618578512906369,"score_spread":0.28565919460303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980868433","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005344455,0.22304824,0.012462308,0.028853523,0.012135651,0.000060145365,0.00037456196,0.00015867258,0.72237253],"genre_scores_gemma":[0.012536424,0.2716239,0.008812703,0.031395823,0.013277901,0.000117048505,0.0005841399,0.00037018696,0.66128194],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99848,0.0005210384,0.0000736733,0.00031589862,0.0005279269,0.000081498365],"domain_scores_gemma":[0.9947461,0.004012268,0.0001352422,0.00027558274,0.00065558753,0.00017528201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003237457,0.0011834393,0.0008584633,0.002989818,0.0007172177,0.0045940923,0.0012758942,0.0027117324,0.046536826],"category_scores_gemma":[0.0070377365,0.00056594337,0.00047726,0.003230675,0.0039224676,0.006843596,0.0021102831,0.004320768,0.024231015],"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":[0.00001795691,0.000033731754,0.00017645239,0.001031769,0.000012335286,0.000058177284,0.0003206092,0.0004352493,0.00038913512,0.39541492,0.3086623,0.29344735],"study_design_scores_gemma":[0.0000028134355,0.000012935744,0.0001903114,0.000891084,0.0000059027366,0.00015977162,0.000106432875,0.00015523612,0.0001931663,0.06722508,0.9310488,0.000008412249],"about_ca_topic_score_codex":0.0033914866,"about_ca_topic_score_gemma":0.004824332,"teacher_disagreement_score":0.046536826,"about_ca_system_score_codex":0.00405976,"about_ca_system_score_gemma":0.0036013129,"threshold_uncertainty_score":0.1556812},"labels":[],"label_agreement":null},{"id":"W2981181520","doi":"10.2196/14627","title":"Media Reports as a Source for Monitoring Impact of Influenza on Hospital Care","year":2019,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Influenza season; Medical emergency; Seasonal influenza; Pandemic; Health care; Environmental health; Emergency medicine; Coronavirus disease 2019 (COVID-19); Influenza vaccine; Vaccination; Internal medicine; Disease; Infectious disease (medical specialty); Virology","score_opus":0.02223915768219064,"score_gpt":0.3521178242340541,"score_spread":0.32987866655186343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981181520","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":[0.8931318,0.0049242293,0.004249514,0.00068071246,0.00031983998,0.0026094886,0.073691905,0.00036441468,0.020028112],"genre_scores_gemma":[0.9134867,0.0047833133,0.022517608,0.00025221065,0.0009779899,0.0041220468,0.05018533,0.000103692786,0.003570991],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9916922,0.0032150915,0.0019347747,0.00067190535,0.0021954728,0.0002905686],"domain_scores_gemma":[0.854739,0.07543089,0.043673374,0.0032086961,0.02090102,0.0020469057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009411989,0.00076721417,0.00056446943,0.028713945,0.00047364703,0.002799609,0.0007273586,0.00055330235,0.0032800932],"category_scores_gemma":[0.04827951,0.00032306885,0.0005782733,0.015016512,0.00038976842,0.0027036862,0.001245937,0.00063871854,0.0010916621],"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":[0.0011170139,0.00031317555,0.8356596,0.004347285,0.0004284599,0.0005072917,0.0049854154,0.00040111155,0.0025788024,0.00045777243,0.009029694,0.1401743],"study_design_scores_gemma":[0.000077271565,0.00059479155,0.96898097,0.0012761493,0.0005497519,0.0005260761,0.0049095512,0.0033962256,0.003349593,0.00023824959,0.016008308,0.0000930469],"about_ca_topic_score_codex":0.004069593,"about_ca_topic_score_gemma":0.0043355157,"teacher_disagreement_score":0.028713945,"about_ca_system_score_codex":0.0007761894,"about_ca_system_score_gemma":0.0009643597,"threshold_uncertainty_score":0.049775958},"labels":[],"label_agreement":null},{"id":"W2981564093","doi":"10.1109/access.2019.2949115","title":"Geosocial Media as a Proxy for Security: A Review","year":2019,"lang":"en","type":"review","venue":"IEEE Access","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Henan University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Social media; Proxy (statistics); Microblogging; Situation awareness; Computer security; Internet privacy; Information security; Data science; World Wide Web","score_opus":0.1271178691273721,"score_gpt":0.4741338258781363,"score_spread":0.3470159567507642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981564093","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":[0.00007343078,0.9985588,0.00013787128,0.00033058148,0.00013292782,0.000006076521,0.00001850427,0.000004163431,0.00073759357],"genre_scores_gemma":[0.0009284776,0.99844474,0.00019145168,0.00013881424,0.00012120416,0.000009325206,0.00001689651,0.0000014322558,0.00014775839],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994948,0.00014765855,0.00008185474,0.00009411374,0.00015388933,0.000027618478],"domain_scores_gemma":[0.9964799,0.0027061347,0.00026130775,0.00005709012,0.0004094655,0.00008610287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013297739,0.0010852339,0.0015148578,0.0051312894,0.00046080136,0.0019468423,0.0010110833,0.0016027709,0.003791364],"category_scores_gemma":[0.003646108,0.00041204525,0.00097650336,0.00510105,0.0011452297,0.0027531544,0.0008205926,0.0015250681,0.0010537249],"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":[0.000051365747,0.00007376548,0.00077850564,0.053767443,0.00023385658,0.0001426637,0.0002189968,0.0005530154,0.00036762853,0.013452412,0.027308399,0.903052],"study_design_scores_gemma":[0.0000132734685,0.00011943046,0.0029412634,0.037088264,0.00052926515,0.0010353401,0.00032697603,0.00036635366,0.00040834994,0.007389123,0.9497143,0.00006801036],"about_ca_topic_score_codex":0.0038476542,"about_ca_topic_score_gemma":0.0042098393,"teacher_disagreement_score":0.0051312894,"about_ca_system_score_codex":0.0013234055,"about_ca_system_score_gemma":0.0026847685,"threshold_uncertainty_score":0.012683392},"labels":[],"label_agreement":null},{"id":"W2985187396","doi":"10.1016/j.acalib.2019.102070","title":"Issues with criteria to create blacklists: An epidemiological approach","year":2019,"lang":"en","type":"article","venue":"The Journal of Academic Librarianship","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Publishing; Odds; Epidemiology; Statistics; Bayesian probability; Computer science; Actuarial science; Medicine; Mathematics; Logistic regression; Business; Political science; Pathology; Law","score_opus":0.0506870482131276,"score_gpt":0.3416379293872782,"score_spread":0.29095088117415063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2985187396","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":[0.2803799,0.009409936,0.39062878,0.21608272,0.0040549776,0.0056985123,0.0033669854,0.00053928513,0.089838915],"genre_scores_gemma":[0.7926635,0.0016505885,0.17382184,0.01884067,0.0011325822,0.0023496335,0.001055596,0.00025193577,0.008233604],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8039513,0.12530157,0.019115467,0.009300081,0.03842915,0.003902494],"domain_scores_gemma":[0.42523623,0.37901887,0.05376512,0.03165089,0.10305551,0.007273331],"candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.26293916,0.00084406673,0.001588005,0.018672962,0.007850517,0.01437224,0.0089505715,0.0049263122,0.0074506924],"category_scores_gemma":[0.5553905,0.0011540668,0.001283245,0.013513028,0.007649402,0.012192806,0.0065006595,0.0064559244,0.0011584222],"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":[0.0003951364,0.0009856132,0.54585207,0.0010930796,0.00035149153,0.000591389,0.024263425,0.000859504,0.00051906327,0.2061139,0.0397871,0.17918828],"study_design_scores_gemma":[0.00043309733,0.0010659159,0.25701237,0.0127024865,0.001046574,0.0056074844,0.11900008,0.049812593,0.0067365,0.3570775,0.1890801,0.00042527338],"about_ca_topic_score_codex":0.01800475,"about_ca_topic_score_gemma":0.022723142,"teacher_disagreement_score":0.9856278,"about_ca_system_score_codex":0.006182832,"about_ca_system_score_gemma":0.012374759,"threshold_uncertainty_score":0.90892726},"labels":[],"label_agreement":null},{"id":"W2986598081","doi":"10.1186/s12942-019-0186-8","title":"Using Google Earth™ and Geographical Information System data as method to delineate sample domains for an urban household surveys: the case of Maroua (Far North Region-Cameroon)","year":2019,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institut National de Santé Publique du Québec; Ohio State University","keywords":"Sample (material); Geography; Grid; Global Positioning System; Computer science; Statistics; Mathematics; Telecommunications","score_opus":0.10162431286187704,"score_gpt":0.38908883270585015,"score_spread":0.28746451984397314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2986598081","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":[0.93139964,0.0019158622,0.033118505,0.0015931494,0.00008710234,0.002773156,0.00749849,0.000082165854,0.021531997],"genre_scores_gemma":[0.9477233,0.001044073,0.04472849,0.00011844855,0.000017576014,0.0019350919,0.0016854533,0.000012826442,0.0027346313],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979158,0.0014141675,0.00010917355,0.00015844686,0.00024964084,0.00015263354],"domain_scores_gemma":[0.99789184,0.0010721016,0.00038763965,0.00018552368,0.0003887892,0.000074122225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017934667,0.0003966223,0.00021350654,0.0031098567,0.0008489063,0.0008629189,0.0006491415,0.0003976846,0.0041479375],"category_scores_gemma":[0.0049288585,0.00025362917,0.0002690058,0.0053360355,0.0005168279,0.0010184587,0.00089631864,0.00034167332,0.0005410069],"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":[0.0004352284,0.00031480446,0.54175186,0.003513127,0.0001478887,0.0071577486,0.05101443,0.0058805924,0.0039005065,0.014535655,0.014464271,0.35688397],"study_design_scores_gemma":[0.00006963167,0.00060587726,0.67389274,0.0019811443,0.000177789,0.003350361,0.18545301,0.022229176,0.004707163,0.00598934,0.10138338,0.00016035084],"about_ca_topic_score_codex":0.04358731,"about_ca_topic_score_gemma":0.065881945,"teacher_disagreement_score":0.04358731,"about_ca_system_score_codex":0.001392648,"about_ca_system_score_gemma":0.001470093,"threshold_uncertainty_score":0.08666718},"labels":[],"label_agreement":null},{"id":"W2988489606","doi":"10.2196/10919","title":"Google Searches and Suicide Rates in Spain, 2004-2013: Correlation Study","year":2019,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Demography; Medicine; Poison control; Population; Suicide prevention; Nowcasting; Epidemiology; Injury prevention; Statistics; Environmental health; Geography; Mathematics; Sociology; Internal medicine","score_opus":0.03880508552637216,"score_gpt":0.34096343385706634,"score_spread":0.3021583483306942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2988489606","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":[0.99182105,0.0014317862,0.00013187659,0.00012345471,0.000015975318,0.000021383597,0.0052020135,0.000019602958,0.0012328229],"genre_scores_gemma":[0.9944516,0.00067008205,0.0001107169,0.000033094093,0.000025062607,0.000021584558,0.0042977715,0.000011315448,0.00037891892],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986039,0.00039562682,0.0002694703,0.0002534229,0.0003178384,0.0001597037],"domain_scores_gemma":[0.98960996,0.0039189295,0.0042343275,0.0005657789,0.0012517417,0.0004192146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017894802,0.0002985132,0.00061487936,0.005038455,0.00015019186,0.0011208176,0.00050102285,0.0005187347,0.0013810917],"category_scores_gemma":[0.008996348,0.00024456697,0.0012022228,0.006051378,0.00031427253,0.0006151317,0.0008403159,0.00058110885,0.0005705813],"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":[0.00007191527,0.000017241895,0.9978242,0.000038538798,0.00011866472,0.000029722252,0.000060499155,0.00012329369,0.000011962056,0.000014196004,0.0002177767,0.0014718762],"study_design_scores_gemma":[0.0000058896576,0.00004195384,0.9983734,0.000037756534,0.00006796286,0.00011962325,0.00034891145,0.00055571547,0.000023970235,0.000024460283,0.0003952977,0.0000050601193],"about_ca_topic_score_codex":0.026819559,"about_ca_topic_score_gemma":0.018018356,"teacher_disagreement_score":0.026819559,"about_ca_system_score_codex":0.0008081333,"about_ca_system_score_gemma":0.00077314134,"threshold_uncertainty_score":0.053326905},"labels":[],"label_agreement":null},{"id":"W2990170152","doi":"10.2196/13347","title":"Lifestyle Disease Surveillance Using Population Search Behavior: Feasibility Study","year":2019,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Overfitting; Generalizability theory; Multivariate statistics; Covariate; Computer science; Context (archaeology); Proxy (statistics); Population; Feature selection; Medicine; Statistics; Machine learning; Geography; Environmental health; Mathematics","score_opus":0.12385807382243655,"score_gpt":0.4834293947039309,"score_spread":0.35957132088149435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990170152","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":[0.98796296,0.00008473953,0.004914013,0.00036293178,0.000019824523,0.0041677775,0.0010297694,0.00005431824,0.0014036554],"genre_scores_gemma":[0.9820133,0.00014283587,0.010982629,0.00020245873,0.00004591196,0.0047422317,0.0014736351,0.000011230315,0.00038576315],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9893495,0.008499778,0.0003895527,0.0006467582,0.00063535414,0.00047916576],"domain_scores_gemma":[0.9743634,0.015932368,0.0020633142,0.0027637223,0.0032290302,0.0016481549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020682229,0.0008451154,0.00067118957,0.0016419855,0.00079301954,0.0008930222,0.001426751,0.0013802942,0.0023785876],"category_scores_gemma":[0.039536815,0.0006346046,0.0012546147,0.001312072,0.00080959656,0.0026320082,0.0015224621,0.0012314322,0.0011506205],"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":[0.005585179,0.035682306,0.85384214,0.00087279454,0.00054483657,0.00088464894,0.003623171,0.008697339,0.0014157043,0.0013689074,0.0026723424,0.08481058],"study_design_scores_gemma":[0.0031210028,0.089294285,0.7756477,0.00030865858,0.00063122815,0.001282051,0.007981896,0.11095881,0.0023564768,0.0021309294,0.006037412,0.0002495611],"about_ca_topic_score_codex":0.016885806,"about_ca_topic_score_gemma":0.0112751555,"teacher_disagreement_score":0.020682229,"about_ca_system_score_codex":0.0013022746,"about_ca_system_score_gemma":0.0034997151,"threshold_uncertainty_score":0.10937941},"labels":[],"label_agreement":null},{"id":"W2992613871","doi":"10.1017/s0950268819001948","title":"Predicting influenza-like illness-related emergency department visits by modelling spatio-temporal syndromic surveillance data","year":2019,"lang":"en","type":"article","venue":"Epidemiology and Infection","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; Alberta Innovates","keywords":"Emergency department; Medicine; Influenza-like illness; Medical emergency; Emergency medicine; Pediatrics; Virology; Virus","score_opus":0.03885992850106258,"score_gpt":0.32049827970061945,"score_spread":0.2816383511995569,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2992613871","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":[0.9513442,0.00046650946,0.040865652,0.0009284686,0.00004940952,0.00008431206,0.0052701375,0.00032173027,0.0006694323],"genre_scores_gemma":[0.98167187,0.00013301247,0.014557091,0.00007544201,0.000018421822,0.00005654647,0.0029808355,0.000014304551,0.00049241306],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999387,0.00029831455,0.000041873584,0.0001472048,0.00005753688,0.00006812802],"domain_scores_gemma":[0.9971409,0.00189173,0.00045087052,0.000096218086,0.000305877,0.00011445037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024633456,0.00075339014,0.00041458517,0.0010790887,0.00018808989,0.0007452541,0.0009963551,0.0005729301,0.00064676395],"category_scores_gemma":[0.0066390336,0.00046647576,0.0009901291,0.0009632449,0.0003251406,0.0005043509,0.00063320476,0.0006587982,0.00017143239],"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":[0.00014864693,0.00011114895,0.19197954,0.00007409209,0.000229939,0.000060719416,0.00010104632,0.79141575,0.00060195464,0.0010794877,0.0010436515,0.013154019],"study_design_scores_gemma":[0.000009044565,0.00003160192,0.013462973,0.0000090928415,0.000023364166,0.000008471291,0.00003247077,0.9856516,0.000090655965,0.00048800636,0.00018574896,0.000006955665],"about_ca_topic_score_codex":0.25540414,"about_ca_topic_score_gemma":0.33152908,"teacher_disagreement_score":0.25540414,"about_ca_system_score_codex":0.0022408052,"about_ca_system_score_gemma":0.002286885,"threshold_uncertainty_score":0.50783503},"labels":[],"label_agreement":null},{"id":"W2994256042","doi":"10.1038/s41598-019-54388-4","title":"The Assessment of Twitter’s Potential for Outbreak Detection: Avian Influenza Case Study","year":2019,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Chicken Farmers of Saskatchewan; Canada First Research Excellence Fund; University of Guelph; Canadian Poultry Research Council","keywords":"Outbreak; Influenza A virus subtype H5N1; Virology; Coronavirus disease 2019 (COVID-19); Computer science; Medicine; Virus; Pathology; Disease; Infectious disease (medical specialty)","score_opus":0.02273917717947949,"score_gpt":0.3423529118850957,"score_spread":0.3196137347056162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994256042","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":[0.9949378,0.00028365848,0.0021066505,0.00050552085,0.00003625569,0.00014228492,0.00055706216,0.00006213324,0.0013685816],"genre_scores_gemma":[0.9943849,0.00020940935,0.0042976798,0.000058855363,0.000056101246,0.00006505283,0.00060241285,0.000006974409,0.00031864765],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998243,0.0007650626,0.00018600465,0.00022841485,0.00041493037,0.00016256183],"domain_scores_gemma":[0.9892713,0.006902031,0.0012655075,0.00061075145,0.0014594506,0.0004909164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034959686,0.0005825143,0.0004450328,0.0027319917,0.0008539078,0.0011132561,0.00074313476,0.0015184842,0.0005828722],"category_scores_gemma":[0.011291592,0.00026360853,0.00062441017,0.0011939342,0.0004888426,0.0015750028,0.0010548824,0.000668314,0.0002258916],"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":[0.0008076813,0.0009366573,0.90115535,0.00056533905,0.00026889672,0.011483194,0.0038171124,0.011382856,0.005877486,0.0013224356,0.005723963,0.05665919],"study_design_scores_gemma":[0.00011923567,0.0021035473,0.54732656,0.0002272932,0.00060159224,0.009179776,0.01652656,0.39958635,0.013164661,0.0018292117,0.0090991575,0.0002361362],"about_ca_topic_score_codex":0.008577582,"about_ca_topic_score_gemma":0.008563106,"teacher_disagreement_score":0.008577582,"about_ca_system_score_codex":0.00088537287,"about_ca_system_score_gemma":0.00041124082,"threshold_uncertainty_score":0.018488646},"labels":[],"label_agreement":null},{"id":"W2994870774","doi":"10.1111/tgis.12606","title":"MaskMy.XYZ: An easy‐to‐use tool for protecting geoprivacy using geographic masks","year":2019,"lang":"en","type":"article","venue":"Transactions in GIS","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Children's Hospital; University of British Columbia; Simon Fraser University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Masking (illustration); Computer science; Usability; Documentation; Point (geometry); World Wide Web; Geographic information system; Interface (matter); Human–computer interaction; Geography; Cartography","score_opus":0.040495089069731156,"score_gpt":0.3166923520571586,"score_spread":0.27619726298742747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994870774","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02094006,0.0003803013,0.36700785,0.0008247673,0.00020433939,0.00070886046,0.0116341645,0.5858606,0.012439044],"genre_scores_gemma":[0.2960995,0.0010604613,0.54172754,0.0012249668,0.00018053465,0.002397813,0.021824274,0.10337149,0.032113343],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99934834,0.00012699659,0.00006330665,0.00009527329,0.00029655968,0.00006961341],"domain_scores_gemma":[0.9965641,0.0020777178,0.0002446592,0.0005718244,0.00033087458,0.00021092348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015499613,0.0014092985,0.00068616064,0.0015951305,0.0005201046,0.0014843252,0.0013408823,0.0009692323,0.047060337],"category_scores_gemma":[0.008087534,0.00089326984,0.00068603805,0.0007349534,0.00047636166,0.0027448118,0.0031775967,0.0009845307,0.0118857315],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024008325,0.0004215009,0.011321371,0.0027220906,0.00022747865,0.001663895,0.0027257989,0.010961155,0.03898882,0.020715987,0.49401292,0.41383815],"study_design_scores_gemma":[0.0008251913,0.00045251695,0.018308695,0.0007881942,0.00012004453,0.0018879344,0.000751255,0.19133104,0.10850919,0.030394347,0.64616305,0.00046854827],"about_ca_topic_score_codex":0.0018204568,"about_ca_topic_score_gemma":0.0019528051,"teacher_disagreement_score":0.047060337,"about_ca_system_score_codex":0.00054106954,"about_ca_system_score_gemma":0.0007693685,"threshold_uncertainty_score":0.1574325},"labels":[],"label_agreement":null},{"id":"W2995351909","doi":"10.5763/kjsm.2019.37.4.155","title":"Achilles Tendon Injury and Seasonal Variation: An Analysis Using Google Trends","year":2019,"lang":"en","type":"article","venue":"The Korean Journal of Sports Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Achilles tendon; Variation (astronomy); Tendon; Physical medicine and rehabilitation; Medicine; Surgery","score_opus":0.012618396154415446,"score_gpt":0.2882808416932949,"score_spread":0.2756624455388794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995351909","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":[0.6314008,0.07841126,0.0019900484,0.0020755427,0.00022712721,0.0015866926,0.2784763,0.00032369792,0.0055084643],"genre_scores_gemma":[0.876167,0.029508801,0.005586541,0.0004964282,0.000122062644,0.0018767103,0.08524214,0.00007082509,0.0009293607],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99628997,0.0004516091,0.0018014148,0.00039971486,0.0008239475,0.00023322657],"domain_scores_gemma":[0.98514026,0.008015574,0.0042932904,0.00044468325,0.0017995791,0.00030659686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022478302,0.0005165467,0.0016360141,0.029828213,0.0005085691,0.0012822138,0.00070285395,0.000504866,0.0020742845],"category_scores_gemma":[0.014472064,0.00023837006,0.0032876218,0.04222396,0.00032555623,0.0014906653,0.001196945,0.00039832262,0.00030642678],"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":[0.0007746115,0.00009480557,0.82852095,0.066705585,0.0036288213,0.0017790793,0.002045783,0.00058805576,0.0012194942,0.0008108861,0.012892081,0.08093987],"study_design_scores_gemma":[0.00008518764,0.00025445846,0.9453717,0.008112178,0.0061940113,0.002289589,0.0045399605,0.0020843677,0.0008195319,0.0005217522,0.029620584,0.000106663036],"about_ca_topic_score_codex":0.02586271,"about_ca_topic_score_gemma":0.039904192,"teacher_disagreement_score":0.029828213,"about_ca_system_score_codex":0.001002424,"about_ca_system_score_gemma":0.0032517926,"threshold_uncertainty_score":0.051424384},"labels":[],"label_agreement":null},{"id":"W2996445954","doi":"10.7189/jogh.09.020319","title":"Leveraging data visualization to improve the use of data for global health decision-making","year":2019,"lang":"en","type":"review","venue":"Journal of Global Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Muhimbili University of Health and Allied Sciences; University of Health and Allied Sciences; Johns Hopkins University; Global Affairs Canada; Johns Hopkins Bloomberg School of Public Health; Tanzania Commission for Science and Technology","keywords":"Computer science; Data science; Visualization; Health data; Data visualization; MEDLINE; Data mining; Health care; Biology","score_opus":0.2684326334055038,"score_gpt":0.5468009694134949,"score_spread":0.2783683360079911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996445954","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":[0.00033429152,0.9865346,0.004031735,0.0063748807,0.00061029475,0.00009630042,0.0005589959,0.00012416231,0.0013347218],"genre_scores_gemma":[0.0087932525,0.97184,0.01552128,0.0020689543,0.00052959623,0.00020309175,0.00068878254,0.00004746561,0.00030759198],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98320353,0.011087278,0.001746568,0.00087488454,0.002784675,0.00030301668],"domain_scores_gemma":[0.88311666,0.103552006,0.004358851,0.0026211194,0.005756046,0.00059526175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03058317,0.0018341909,0.0030830032,0.009436388,0.00060674327,0.006036159,0.0027059086,0.002971941,0.007010218],"category_scores_gemma":[0.087487034,0.00086569425,0.0049685095,0.008674132,0.001448952,0.005556837,0.003555412,0.0043634903,0.0012787539],"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":[0.00015366112,0.000065299,0.0010992029,0.08973041,0.0030499408,0.000069189395,0.0004970172,0.0012965231,0.0004142979,0.018350724,0.03661552,0.8486582],"study_design_scores_gemma":[0.0002020144,0.00017227771,0.0035446857,0.21483938,0.0054256325,0.0005500472,0.00067888445,0.0025248046,0.0015200853,0.05673788,0.71361905,0.00018521343],"about_ca_topic_score_codex":0.0050564543,"about_ca_topic_score_gemma":0.009054126,"teacher_disagreement_score":0.03058317,"about_ca_system_score_codex":0.0023313288,"about_ca_system_score_gemma":0.00785101,"threshold_uncertainty_score":0.1617412},"labels":[],"label_agreement":null},{"id":"W2996655932","doi":"10.1101/2019.12.12.873984","title":"Seasonal Variation in the Internet Searches for Cancer Recurrence: An Infodemiological Study","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Anhui Medical University; National Natural Science Foundation of China","keywords":"Seasonality; Southern Hemisphere; Northern Hemisphere; Demography; Cancer registry; Geography; Cancer; Population; Climatology; Medicine; Statistics; Internal medicine; Mathematics; Geology","score_opus":0.054806518529248206,"score_gpt":0.3223591523298556,"score_spread":0.2675526338006074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996655932","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":[0.9952335,0.0008218066,0.0001619917,0.00008284694,0.00000941987,0.00001857769,0.0027304913,0.000008741001,0.00093276787],"genre_scores_gemma":[0.9978079,0.00021560336,0.00011733537,0.000029811528,0.000018998542,0.000017618198,0.0015602467,0.000005933026,0.00022662281],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991565,0.00021777695,0.00013837425,0.00022925508,0.00015653517,0.000101582504],"domain_scores_gemma":[0.9963529,0.00093966845,0.0018075536,0.0002343577,0.0004604534,0.00020511985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008652424,0.00014172075,0.00032333814,0.0023582065,0.00022126021,0.00075957284,0.00030290845,0.00030639788,0.001989384],"category_scores_gemma":[0.0038366767,0.00012499676,0.0005875797,0.0049652103,0.00024805218,0.00059568713,0.00042212722,0.00031663637,0.0003728968],"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":[0.00012346223,0.00003350041,0.9943251,0.000089506015,0.0001271841,0.000067926674,0.00032218551,0.000060870847,0.00017333531,0.000054938864,0.00041247832,0.0042094775],"study_design_scores_gemma":[0.0000020394198,0.000041351104,0.99864393,0.000012616943,0.00004383537,0.000102474376,0.00038833544,0.00023374283,0.000048128863,0.000018530593,0.00046134318,0.0000037047998],"about_ca_topic_score_codex":0.014862705,"about_ca_topic_score_gemma":0.017614197,"teacher_disagreement_score":0.014862705,"about_ca_system_score_codex":0.00046150267,"about_ca_system_score_gemma":0.00037454217,"threshold_uncertainty_score":0.0295524},"labels":[],"label_agreement":null},{"id":"W2997649496","doi":"10.1002/cncr.32673","title":"Mapping the occurrence of acute myeloid leukemia: Methodological limitations and future direction","year":2019,"lang":"en","type":"letter","venue":"Cancer","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Cancer Care Ontario; Public Health Ontario; University of Toronto","funders":"","keywords":"Incidence (geometry); Demography; Medicine; Population; Geography; Environmental health","score_opus":0.1320492239126916,"score_gpt":0.3477234615814397,"score_spread":0.21567423766874808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997649496","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10670399,0.16213843,0.31363884,0.31747553,0.041011795,0.011915367,0.020970045,0.0010290885,0.025117002],"genre_scores_gemma":[0.41929635,0.04465654,0.3575175,0.1163832,0.01929599,0.027906472,0.009171502,0.00077591796,0.004996534],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.75794554,0.17644078,0.029063858,0.0117339725,0.02301873,0.0017971098],"domain_scores_gemma":[0.43597114,0.37404957,0.027469639,0.063950166,0.09267266,0.005886884],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.3400313,0.0023177047,0.0038206505,0.0077158753,0.003720929,0.0067239604,0.00876323,0.0025865948,0.0040794048],"category_scores_gemma":[0.5259675,0.0011116052,0.003015947,0.014306167,0.0047337264,0.007206703,0.006092273,0.0029968994,0.0010495395],"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":[0.001802701,0.0005473169,0.4115075,0.03185856,0.008436614,0.0006454541,0.013035542,0.0042679776,0.0010046406,0.03215668,0.11609667,0.37864038],"study_design_scores_gemma":[0.0008634174,0.0019350764,0.29439923,0.052193932,0.008359647,0.001978982,0.0254214,0.022406826,0.0032587314,0.14021468,0.44800347,0.0009646269],"about_ca_topic_score_codex":0.054524202,"about_ca_topic_score_gemma":0.066086456,"teacher_disagreement_score":0.3400313,"about_ca_system_score_codex":0.004507723,"about_ca_system_score_gemma":0.013315152,"threshold_uncertainty_score":0.8138589},"labels":[],"label_agreement":null},{"id":"W2999928772","doi":"10.1136/bmjinnov-2018-000329","title":"Electronic notifiable disease reporting system from primary care health centres in Qatar: a comparison of paper-based versus electronic reporting","year":2020,"lang":"en","type":"article","venue":"BMJ Innovations","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Representativeness heuristic; Communicable disease; Public health; Quarter (Canadian coin); Medicine; Disease surveillance; Public health surveillance; Business; Notifiable disease; Medical emergency; Environmental health; Geography; Nursing; Psychology","score_opus":0.06550302749904821,"score_gpt":0.370293145216578,"score_spread":0.30479011771752984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999928772","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":[0.98215854,0.0030818395,0.0032061131,0.0017409481,0.0002497384,0.0013360685,0.00249,0.0000936637,0.005643168],"genre_scores_gemma":[0.9924269,0.0010592217,0.0031848096,0.000633934,0.000093747505,0.00049231,0.0015208105,0.000009073465,0.00057926617],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.97171086,0.017566226,0.0033354077,0.0015302376,0.004708319,0.0011489962],"domain_scores_gemma":[0.9502931,0.022290044,0.012404091,0.0020522657,0.011929107,0.0010313286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.036821663,0.0005018053,0.0008083185,0.0024122691,0.00064334954,0.0019849543,0.0009659325,0.0007831939,0.0028860222],"category_scores_gemma":[0.060299084,0.00033555017,0.0014169861,0.002809334,0.00055993424,0.0025619492,0.0012751392,0.0006766259,0.00047877347],"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":[0.008216513,0.0020361661,0.74619186,0.0037344864,0.0009895833,0.00020877727,0.004912995,0.0034787022,0.0012313813,0.0010797303,0.005262737,0.22265717],"study_design_scores_gemma":[0.00061014947,0.010219304,0.9672289,0.0011163071,0.00071402034,0.0002724846,0.0044660564,0.008131486,0.0011769331,0.00023821965,0.005731332,0.00009479457],"about_ca_topic_score_codex":0.020015046,"about_ca_topic_score_gemma":0.011435661,"teacher_disagreement_score":0.036821663,"about_ca_system_score_codex":0.0024674912,"about_ca_system_score_gemma":0.0033716562,"threshold_uncertainty_score":0.19473392},"labels":[],"label_agreement":null},{"id":"W3003735557","doi":"10.1016/j.psychres.2020.112790","title":"A geographical exploration of environmental and land use characteristics of suicide in the greater Toronto area","year":2020,"lang":"en","type":"article","venue":"Psychiatry Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":21,"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 Toronto; St. Michael's Hospital; Toronto Metropolitan University","funders":"Canadian Institutes of Health Research","keywords":"Geography; Metropolitan area; Harm; Distribution (mathematics); Land use; Population; Spatial distribution; Land cover; Poison control; Environmental planning; Cartography; Environmental health; Ecology; Psychology; Medicine","score_opus":0.10677425186843018,"score_gpt":0.3491755663322873,"score_spread":0.24240131446385713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003735557","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":[0.9949927,0.00043997483,0.00012861897,0.0002007044,0.0000029881915,0.000017143508,0.0032215721,0.0000050077592,0.000991387],"genre_scores_gemma":[0.9985561,0.00028922403,0.00016164308,0.000021166932,0.000002982647,0.000008437598,0.0006933239,0.0000015480174,0.00026569457],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99969065,0.0000628987,0.000041108597,0.00005571326,0.00007796103,0.00007173659],"domain_scores_gemma":[0.9984622,0.0002548573,0.00056159124,0.00007148757,0.0004127228,0.00023708462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033140276,0.00021781631,0.00015832794,0.002608373,0.0011639057,0.0010060497,0.00042263963,0.0002219152,0.0013403536],"category_scores_gemma":[0.001731556,0.0001886866,0.000422608,0.0065625645,0.0004784678,0.00032447238,0.0011364938,0.00023963979,0.00010766942],"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":[0.00003555041,0.000009961268,0.9917275,0.000067923895,0.000076297976,0.00019175146,0.0029603075,0.00034674615,0.0004634192,0.00021481371,0.00054031005,0.003365523],"study_design_scores_gemma":[7.748501e-7,0.0000064449127,0.9968888,0.000015448973,0.000012555182,0.000044739078,0.0024962549,0.00015987689,0.000021848171,0.000013512744,0.00033648507,0.0000033285312],"about_ca_topic_score_codex":0.94667536,"about_ca_topic_score_gemma":0.9763263,"teacher_disagreement_score":0.05332464,"about_ca_system_score_codex":0.006264162,"about_ca_system_score_gemma":0.0062510706,"threshold_uncertainty_score":0.107277334},"labels":[],"label_agreement":null},{"id":"W3004162060","doi":"10.2196/16119","title":"Participatory Surveillance Based on Crowdsourcing During the Rio 2016 Olympic Games Using the Guardians of Health Platform: Descriptive Study","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Crowdsourcing; Citizen science; Citizen journalism; Descriptive research; Public health surveillance; Participatory sensing; Computer science; Environmental health; Public health; Internet privacy; Data science; Medicine; World Wide Web; Statistics; Nursing; Biology; Mathematics","score_opus":0.09406217652766308,"score_gpt":0.3359927223464151,"score_spread":0.241930545818752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004162060","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":[0.9946414,0.0002633453,0.0006543908,0.00017347297,0.000016606595,0.0009829027,0.00069201307,0.0000076249744,0.002568244],"genre_scores_gemma":[0.99607384,0.00035621188,0.0009801867,0.00013710244,0.000020187541,0.0013782111,0.00035330094,0.0000068074664,0.000694205],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99636155,0.0019175801,0.00028853473,0.0004588079,0.000439183,0.0005343496],"domain_scores_gemma":[0.9935341,0.0030040848,0.001617451,0.00048516726,0.000883169,0.0004760574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004168908,0.00045749205,0.00064848695,0.0023362448,0.002405337,0.0013336577,0.000955467,0.00074223406,0.002065996],"category_scores_gemma":[0.010789248,0.00047898275,0.00081030704,0.0017440062,0.001213837,0.0010255186,0.002696238,0.00080771366,0.00034658023],"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":[0.00050160027,0.0016662396,0.60551023,0.0022156055,0.0001778833,0.0028462452,0.33048725,0.000263261,0.0012761267,0.0013244023,0.0028038411,0.050927263],"study_design_scores_gemma":[0.00006599254,0.001184065,0.47511974,0.0011178863,0.00013324211,0.001018909,0.5061673,0.001175107,0.0006447861,0.0006264178,0.012652126,0.00009448011],"about_ca_topic_score_codex":0.023990164,"about_ca_topic_score_gemma":0.028726129,"teacher_disagreement_score":0.023990164,"about_ca_system_score_codex":0.0015862845,"about_ca_system_score_gemma":0.0029118196,"threshold_uncertainty_score":0.04770106},"labels":[],"label_agreement":null},{"id":"W3005283278","doi":"10.1038/s41746-020-0222-x","title":"Lymelight: forecasting Lyme disease risk using web search data","year":2020,"lang":"en","type":"article","venue":"npj Digital Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":27,"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":"U.S. National Library of Medicine; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Lyme disease; Disease; Computer science; Machine learning; Psychological intervention; Web application; LYME; Medicine; Classifier (UML); Artificial intelligence; World Wide Web; Borrelia burgdorferi; Pathology; Psychiatry; Immunology","score_opus":0.1929112390497841,"score_gpt":0.33943124847063755,"score_spread":0.14652000942085344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005283278","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":[0.9560091,0.0017612394,0.00795401,0.001903031,0.00010523763,0.0002091272,0.028470507,0.0012751758,0.0023125536],"genre_scores_gemma":[0.9623478,0.0004555649,0.014096871,0.00014976031,0.00010422541,0.0000755119,0.022090666,0.00002674969,0.00065280934],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999126,0.0003963198,0.0000990997,0.00017076122,0.00014117722,0.0000666596],"domain_scores_gemma":[0.99364763,0.003967837,0.0009641445,0.0004232426,0.0006113108,0.00038579444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002092694,0.0005425957,0.000541156,0.0053726346,0.00020290549,0.0010831332,0.0005966844,0.00077812787,0.0013443142],"category_scores_gemma":[0.010992944,0.0001787934,0.0005757997,0.0030654927,0.00016172489,0.0011602471,0.00056783855,0.00054305285,0.000851611],"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":[0.0005359572,0.0005725576,0.8467082,0.00043518984,0.0003527496,0.00014606716,0.00014884754,0.0645771,0.0010589762,0.00089401926,0.01491739,0.06965297],"study_design_scores_gemma":[0.00005770325,0.0002762562,0.15816411,0.000099249286,0.00007752996,0.00017374528,0.00025168192,0.83422047,0.0011357623,0.0015806922,0.003929477,0.000033336255],"about_ca_topic_score_codex":0.021260148,"about_ca_topic_score_gemma":0.019380582,"teacher_disagreement_score":0.021260148,"about_ca_system_score_codex":0.00064126635,"about_ca_system_score_gemma":0.00052119844,"threshold_uncertainty_score":0.042272806},"labels":[],"label_agreement":null},{"id":"W3005429949","doi":"10.2196/11512","title":"Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development","year":2020,"lang":"en","type":"review","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Scopus; Disease surveillance; Cluster analysis; Systematic review; Data mining; Data science; MEDLINE; Medicine; Disease; Machine learning; Pathology","score_opus":0.0458739249753697,"score_gpt":0.34770400226945264,"score_spread":0.30183007729408295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005429949","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008625476,0.9903736,0.0036470322,0.001161267,0.00020360734,0.0022233264,0.00078230456,0.00004365931,0.0007026852],"genre_scores_gemma":[0.021546194,0.9473192,0.022810448,0.00084926805,0.0001542009,0.0063541094,0.00075133855,0.000020883495,0.00019438224],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9657326,0.015248698,0.011068208,0.0024266276,0.004848386,0.000675658],"domain_scores_gemma":[0.87108755,0.10100254,0.012783585,0.0020931605,0.012293648,0.00073963345],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.046816498,0.002742548,0.009073231,0.041753396,0.0017509133,0.006059164,0.0044562467,0.0025816541,0.0045789103],"category_scores_gemma":[0.15771157,0.0016753187,0.013492937,0.02770279,0.0022607557,0.006591199,0.0043503637,0.0021676105,0.00043514644],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011536161,0.000026393076,0.0012504352,0.8826309,0.005974696,0.000078213896,0.000809373,0.0008616462,0.0000901159,0.0051291706,0.0029293147,0.10010438],"study_design_scores_gemma":[0.00012617209,0.00017782547,0.0023566228,0.9286568,0.02968704,0.00020897863,0.000906099,0.0009866445,0.00024533688,0.0036528055,0.032905746,0.000090038106],"about_ca_topic_score_codex":0.020106336,"about_ca_topic_score_gemma":0.03624752,"teacher_disagreement_score":0.95318353,"about_ca_system_score_codex":0.015483012,"about_ca_system_score_gemma":0.04814647,"threshold_uncertainty_score":0.24759227},"labels":[],"label_agreement":null},{"id":"W3007352303","doi":"10.17269/s41997-020-00295-5","title":"All together now: aggregating multiple records to develop a person-based dataset to integrate and enhance infectious disease surveillance in Ontario, Canada","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Toronto; Public Health Ontario","funders":"","keywords":"Gonorrhea; Syphilis; Disease; Infectious disease (medical specialty); Men who have sex with men; Medicine; Medical record; Disease surveillance; Sexually transmitted disease; Epidemiology; Human immunodeficiency virus (HIV); Family medicine; Pathology; Internal medicine","score_opus":0.04744165634194881,"score_gpt":0.283704940849971,"score_spread":0.2362632845080222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007352303","genre_codex":"dataset","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":[0.08605968,0.0011587064,0.005741215,0.0023768968,0.00013203389,0.00072777655,0.8950184,0.00072399713,0.008061354],"genre_scores_gemma":[0.20562461,0.0011135724,0.02240789,0.00079903594,0.000103961655,0.00092914765,0.7622846,0.00015535067,0.0065817866],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9973604,0.0003343274,0.00027479598,0.00055468304,0.00095862383,0.00051720935],"domain_scores_gemma":[0.9907381,0.00063936564,0.0010144847,0.0010324154,0.0056518824,0.0009236662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021064137,0.00062037,0.00063030294,0.0038232775,0.0025201077,0.0019107269,0.0023747978,0.00059958413,0.0041545685],"category_scores_gemma":[0.010700338,0.00044352195,0.0011075262,0.009768453,0.0006235434,0.0007860085,0.002235882,0.00090093585,0.0013176159],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030624366,0.00013190182,0.6454404,0.0009418659,0.0005011831,0.00026091613,0.0021618253,0.0047136787,0.00068232993,0.0030235157,0.29716203,0.044674076],"study_design_scores_gemma":[0.00012585851,0.000044731343,0.7983022,0.0005587018,0.0001677423,0.00012211755,0.0021469027,0.012554922,0.00056367664,0.0010000425,0.18430082,0.00011233924],"about_ca_topic_score_codex":0.9939528,"about_ca_topic_score_gemma":0.99513704,"teacher_disagreement_score":0.96696734,"about_ca_system_score_codex":0.033032674,"about_ca_system_score_gemma":0.044184126,"threshold_uncertainty_score":0.23966992},"labels":[],"label_agreement":null},{"id":"W3008443627","doi":"10.1016/s1473-3099(20)30120-1","title":"An interactive web-based dashboard to track COVID-19 in real time","year":2020,"lang":"en","type":"letter","venue":"The Lancet Infectious Diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11164,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Johns Hopkins University","keywords":"Dashboard; Coronavirus disease 2019 (COVID-19); Outbreak; Mainland China; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); China; Public health; Track (disk drive); 2019-20 coronavirus outbreak; Coronavirus; Fast track; Mainland; Pneumonia; Geography; Medicine; Computer science; Virology; Disease; Infectious disease (medical specialty); Data science; Operating system; Nursing; Internal medicine","score_opus":0.02717864563311139,"score_gpt":0.33015734791803786,"score_spread":0.30297870228492646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008443627","genre_codex":"software","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":[0.051201995,0.0016779191,0.10995524,0.005020046,0.006296723,0.0031560122,0.16997015,0.57051814,0.082203686],"genre_scores_gemma":[0.2945498,0.0032215116,0.27624324,0.0064036893,0.0032975266,0.0068464493,0.28202724,0.028214714,0.09919577],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839836,0.00031236964,0.00013806765,0.00035110617,0.00057329494,0.00022676837],"domain_scores_gemma":[0.9931437,0.0026214963,0.00047734077,0.0007657534,0.0015973178,0.0013943623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022680785,0.0034010026,0.0012069587,0.0035864878,0.0009706504,0.0037420874,0.0018789064,0.0014478017,0.05742237],"category_scores_gemma":[0.0076049627,0.00061721774,0.0008210678,0.0020693995,0.00042194378,0.0034532773,0.0037705724,0.0026614505,0.02058522],"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":[0.0018703399,0.00076752336,0.007743584,0.0010067405,0.00014187834,0.00073787576,0.0013917292,0.0032510115,0.005167975,0.0016608026,0.8418081,0.1344524],"study_design_scores_gemma":[0.0009396127,0.0010282929,0.031970717,0.00090750074,0.0002009652,0.00041704762,0.0019710928,0.054088123,0.010797428,0.00844125,0.8887045,0.00053346925],"about_ca_topic_score_codex":0.0036977371,"about_ca_topic_score_gemma":0.0050070165,"teacher_disagreement_score":0.05742237,"about_ca_system_score_codex":0.0005363569,"about_ca_system_score_gemma":0.00077738747,"threshold_uncertainty_score":0.19209695},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"commentary","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"software","about_ca_system":false,"about_ca_topic":false,"confidence":"medium"}],"label_agreement":"agree"},{"id":"W3009576683","doi":"10.2196/17175","title":"Identifying Influential Factors in the Discussion Dynamics of Emerging Health Issues on Social Media: Computational Study","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of North Carolina at Charlotte; Elon University","keywords":"Zika virus; Social media; Public health; Influencer marketing; Public health surveillance; Internet privacy; Computer science; Data science; Geography; Environmental health; Medicine; World Wide Web; Business; Marketing","score_opus":0.07270604384112919,"score_gpt":0.38682427036263367,"score_spread":0.31411822652150445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009576683","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":[0.9851805,0.0003786846,0.009847525,0.0008214993,0.000049851995,0.00014802431,0.001224609,0.00011067092,0.0022387356],"genre_scores_gemma":[0.99148375,0.0002101386,0.006480678,0.00004235396,0.000063930675,0.00008303937,0.0008368424,0.000012322354,0.0007869227],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998626,0.0006466286,0.000081584156,0.0003282691,0.00013535937,0.00018228323],"domain_scores_gemma":[0.97868735,0.018748399,0.0011827066,0.0005065264,0.00040170585,0.0004733969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027691794,0.0006257372,0.00063644955,0.0061366176,0.0010740105,0.0023032858,0.000842489,0.0010118209,0.0035712165],"category_scores_gemma":[0.01672908,0.00046851137,0.0018836341,0.004027741,0.00081746257,0.0028704554,0.0012206847,0.001260487,0.0005911911],"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":[0.00049287366,0.0014278858,0.8611912,0.0004362456,0.0008220074,0.0010189528,0.003141307,0.048239123,0.0013903215,0.008407732,0.004593584,0.06883867],"study_design_scores_gemma":[0.000029434274,0.000110824534,0.1524973,0.000054903896,0.00016456557,0.00026606122,0.0022126208,0.83720374,0.00044795382,0.004895645,0.0020772293,0.000039718823],"about_ca_topic_score_codex":0.023524845,"about_ca_topic_score_gemma":0.024291966,"teacher_disagreement_score":0.023524845,"about_ca_system_score_codex":0.001216271,"about_ca_system_score_gemma":0.0010643353,"threshold_uncertainty_score":0.046775818},"labels":[],"label_agreement":null},{"id":"W3010589507","doi":"","title":"Positional accuracy of geocoding from residential postal codes versus full street addresses.","year":2018,"lang":"en","type":"article","venue":"PubMed","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa; Statistics Canada","funders":"","keywords":"Geocoding; Geographic coordinate system; Geography; Geographic information system; Sample (material); Latitude; Population; Census; Statistics; Computer science; Cartography; Mathematics; Medicine; Geodesy; Environmental health","score_opus":0.03863002439946388,"score_gpt":0.2897272809117978,"score_spread":0.2510972565123339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010589507","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":[0.8996668,0.0017143277,0.06438367,0.001001135,0.00024013266,0.00026809308,0.016393699,0.00033302626,0.015999291],"genre_scores_gemma":[0.98266107,0.00022553798,0.0125194425,0.00010424062,0.000023592655,0.00008908344,0.0034511029,0.00004202634,0.00088398077],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9812106,0.009359744,0.0015052559,0.0029619988,0.004383238,0.000579206],"domain_scores_gemma":[0.92970353,0.030391926,0.0149039375,0.008177903,0.016386181,0.00043644293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012951976,0.00034280535,0.00031794165,0.0022995207,0.00067237497,0.0016325535,0.0011217133,0.00045259253,0.001319303],"category_scores_gemma":[0.11860338,0.00022267501,0.0004129479,0.0056068744,0.0010018388,0.0012564085,0.0017465983,0.00052615703,0.0006359927],"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":[0.00027996092,0.000018762652,0.91697794,0.00035213958,0.00031875723,0.000044793866,0.0041971314,0.004119085,0.00046261153,0.0023715445,0.004719698,0.06613754],"study_design_scores_gemma":[0.000022455479,0.00011049855,0.9479897,0.0004347489,0.00029939984,0.00027859004,0.004567872,0.023565413,0.004116251,0.003688366,0.014840807,0.00008600023],"about_ca_topic_score_codex":0.18944041,"about_ca_topic_score_gemma":0.19196631,"teacher_disagreement_score":0.18944041,"about_ca_system_score_codex":0.0022450308,"about_ca_system_score_gemma":0.0022780274,"threshold_uncertainty_score":0.37667555},"labels":[],"label_agreement":null},{"id":"W3010725448","doi":"10.1186/s12911-020-1068-5","title":"Methods to improve the quality of smoking records in a primary care EMR database: exploring multiple imputation and pattern-matching algorithms","year":2020,"lang":"en","type":"article","venue":"BMC Medical Informatics and Decision Making","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Health Sciences Centre; University of Alberta; University of Calgary","funders":"Canadian Institutes of Health Research; Alberta Innovates; Public Health Agency; Public Health Agency of Canada","keywords":"Missing data; Imputation (statistics); Medicine; Health informatics; Medical record; Data quality; Health care; Data mining; Population; Matching (statistics); Database; Family medicine; Computer science; Public health; Environmental health; Nursing; Machine learning","score_opus":0.10338490244322023,"score_gpt":0.4127406139001553,"score_spread":0.3093557114569351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010725448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05948813,0.0008109631,0.9328626,0.0014241033,0.00008907855,0.0020016716,0.0013395811,0.0012252618,0.0007586581],"genre_scores_gemma":[0.1432938,0.00022527928,0.8530887,0.00027790276,0.00007976944,0.0014332985,0.0013522513,0.000090103924,0.00015883576],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8905805,0.08567708,0.008117933,0.007657061,0.0072319345,0.00073558517],"domain_scores_gemma":[0.6694469,0.26456705,0.02363804,0.02218722,0.019131623,0.0010291861],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.13843133,0.0015993203,0.0025034596,0.0051643816,0.0011104187,0.0035484217,0.005605633,0.0019650531,0.0016162021],"category_scores_gemma":[0.3351254,0.0011889555,0.0032247258,0.0091176,0.0010860463,0.0038931393,0.0036398964,0.0029469824,0.00039016287],"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":[0.0011784937,0.0013925883,0.32671827,0.001803589,0.005761605,0.00018473348,0.0019186,0.07977092,0.0011322504,0.012675833,0.007380307,0.56008285],"study_design_scores_gemma":[0.0009485699,0.0007178163,0.04826619,0.0005438175,0.0009170474,0.00029757497,0.00053924724,0.91253066,0.0020566143,0.02869405,0.0043559144,0.0001324148],"about_ca_topic_score_codex":0.008658106,"about_ca_topic_score_gemma":0.006568377,"teacher_disagreement_score":0.8615687,"about_ca_system_score_codex":0.0023986436,"about_ca_system_score_gemma":0.005610289,"threshold_uncertainty_score":0.7321036},"labels":[],"label_agreement":null},{"id":"W3012062299","doi":"10.1016/j.cmi.2020.02.034","title":"Re: 'Diagnostic test accuracy of the BioFire® FilmArray® Meningitis/Encephalitis panel' by Chapin et al.","year":2020,"lang":"en","type":"letter","venue":"Clinical Microbiology and Infection","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"McGill University Health Centre; CARE Canada; Montreal Children's Hospital","funders":"Janssen Japan; Sanofi Pasteur; Janssen Pharmaceuticals; MedImmune; BD Biosciences","keywords":"Meningitis; Medicine; Encephalitis; Food and drug administration; Cerebrospinal fluid; Virology; Pathology; Pediatrics; Virus; Pharmacology","score_opus":0.04344145947783407,"score_gpt":0.3339266599615306,"score_spread":0.2904852004836965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012062299","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002018822,0.00051047123,0.00007920431,0.98066175,0.01561826,0.000010624751,0.00017113074,0.000053555883,0.0026932482],"genre_scores_gemma":[0.0015367416,0.00028553966,0.00018907554,0.9804791,0.013299512,0.000025967,0.000085693435,0.00003200942,0.0040662712],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99009144,0.0022041795,0.0015980283,0.001493853,0.003565872,0.001046695],"domain_scores_gemma":[0.9508512,0.028684396,0.002215156,0.0011676037,0.012531184,0.004550358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0082830945,0.0010472913,0.0016225955,0.0012292314,0.005476464,0.0057288962,0.003229347,0.06959847,0.013910259],"category_scores_gemma":[0.06712446,0.0013938653,0.0013379782,0.0010479295,0.003464811,0.004148028,0.0020984914,0.050385796,0.017060878],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025650143,0.000012956804,0.00035510125,0.000018558172,0.0000064613446,0.00022620473,0.00005293431,0.000020799911,0.00007946244,0.0003710417,0.99561477,0.003216028],"study_design_scores_gemma":[0.00023090883,0.00009750394,0.003604422,0.00070495415,0.000056236735,0.0019464305,0.00094727206,0.0006156966,0.0005404852,0.007517327,0.98355603,0.00018269666],"about_ca_topic_score_codex":0.035190422,"about_ca_topic_score_gemma":0.054767568,"teacher_disagreement_score":0.06959847,"about_ca_system_score_codex":0.005006601,"about_ca_system_score_gemma":0.011747124,"threshold_uncertainty_score":0.069971204},"labels":[],"label_agreement":null},{"id":"W3012205155","doi":"10.1007/s00405-020-05915-x","title":"Winter peaks in web-based public inquiry into epistaxis","year":2020,"lang":"en","type":"article","venue":"European Archives of Oto-Rhino-Laryngology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Karl-Franzens-Universität Graz; Medizinische Universität Graz; Medizinische Universität Wien; Universität Wien","keywords":"Incidence (geometry); Geography; Demography; Seasonality; Causality (physics); Nose; Representation (politics); Medicine; Statistics; Surgery; Political science; Mathematics","score_opus":0.03458016307989321,"score_gpt":0.2725904544707086,"score_spread":0.23801029139081542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012205155","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":[0.97665673,0.0035552774,0.00036406293,0.0006936038,0.000044299355,0.000031873842,0.013741624,0.00006795338,0.00484465],"genre_scores_gemma":[0.99574125,0.00074972515,0.00018084374,0.00006124294,0.000024697518,0.000014018348,0.0029699225,0.00000852417,0.00024968933],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989195,0.00024905615,0.00020141675,0.00017011548,0.00031356417,0.00014640333],"domain_scores_gemma":[0.99390465,0.0019899835,0.002986218,0.0002086985,0.00065282994,0.0002576343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00112342,0.00017465636,0.00032827992,0.0038127783,0.0001764949,0.0014271832,0.00026578113,0.0002631643,0.002215526],"category_scores_gemma":[0.0067634475,0.000092973685,0.0004933348,0.0074037537,0.00029279524,0.0011756024,0.0007501417,0.00031000286,0.0003501836],"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":[0.00027097826,0.00002861158,0.96518534,0.0008277886,0.00017718931,0.00023558387,0.0017213827,0.00031761223,0.0007480644,0.00044185243,0.0030506973,0.026994914],"study_design_scores_gemma":[0.00000334738,0.000040665105,0.99367404,0.00012906248,0.00004893924,0.00022993749,0.0023046602,0.0004561957,0.00019002047,0.00011422313,0.002800699,0.000008168076],"about_ca_topic_score_codex":0.014408308,"about_ca_topic_score_gemma":0.020177228,"teacher_disagreement_score":0.014408308,"about_ca_system_score_codex":0.0006156784,"about_ca_system_score_gemma":0.00054659654,"threshold_uncertainty_score":0.028648913},"labels":[],"label_agreement":null},{"id":"W3013056994","doi":"10.2196/18828","title":"Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":415,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Mean squared error; Data mining; Metric (unit); Incidence (geometry); Outbreak; Statistics; Computer science; Health care; Medicine; Environmental health; Mathematics; Engineering","score_opus":0.1985095149059298,"score_gpt":0.4130092291816802,"score_spread":0.2144997142757504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013056994","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":[0.99338067,0.00015537506,0.0019334435,0.0002947539,0.000040163308,0.00008290021,0.0033774306,0.00021137275,0.00052385556],"genre_scores_gemma":[0.9839729,0.00020995119,0.006921545,0.000046063276,0.00003575819,0.00008442362,0.008320632,0.000012477804,0.00039619757],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995359,0.000117183205,0.00006326747,0.00011199928,0.00009058757,0.00008108517],"domain_scores_gemma":[0.9981148,0.0008076329,0.0002008304,0.0001735182,0.0005713405,0.00013186173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017951748,0.00086551247,0.00054920116,0.0021277578,0.00029177676,0.0005280069,0.0006927206,0.0005729822,0.0006635032],"category_scores_gemma":[0.0044914433,0.00019509142,0.0013119746,0.001572457,0.00022645207,0.0006465058,0.00048519165,0.00090365723,0.00035245204],"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":[0.00077603007,0.0022343379,0.7635488,0.00032221797,0.0003825094,0.0006119062,0.0004739899,0.08255261,0.0020090858,0.00046999945,0.008859478,0.13775913],"study_design_scores_gemma":[0.00008976865,0.00068851083,0.2809579,0.000060208586,0.00020821496,0.00020968297,0.001130518,0.7108502,0.0030832337,0.0005021487,0.002171521,0.000048070364],"about_ca_topic_score_codex":0.049415614,"about_ca_topic_score_gemma":0.03220654,"teacher_disagreement_score":0.049415614,"about_ca_system_score_codex":0.0009950949,"about_ca_system_score_gemma":0.0013659514,"threshold_uncertainty_score":0.09825599},"labels":[],"label_agreement":null},{"id":"W3013207807","doi":"10.2196/16543","title":"Using Application Programming Interfaces to Access Google Data for Health Research: Protocol for a Methodological Framework","year":2020,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Protocol (science); Application programming interface; World Wide Web; Python (programming language); Population; Information retrieval; Online search; Search engine; Data science; Medicine","score_opus":0.9622985014111949,"score_gpt":0.7936543135085232,"score_spread":0.16864418790267166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013207807","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00067783584,0.00011422227,0.018318115,0.0009477458,0.00059391116,0.97183573,0.004788618,0.0005073726,0.0022165151],"genre_scores_gemma":[0.00032265973,0.00003246842,0.008524597,0.00019652463,0.000026808846,0.99050635,0.00016235324,0.000025089386,0.00020315747],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.80964726,0.12065465,0.04384772,0.0067423126,0.014563409,0.00454464],"domain_scores_gemma":[0.64712584,0.1532789,0.025523363,0.08983609,0.07917226,0.005063531],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.19682007,0.0038692495,0.004833498,0.0087002115,0.006940143,0.0061116894,0.003772812,0.009353839,0.08560574],"category_scores_gemma":[0.32175142,0.004789,0.005952386,0.011433085,0.007985373,0.005374768,0.006078012,0.014210704,0.02832383],"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":[0.035582855,0.0065978076,0.0040400806,0.08326227,0.0012747728,0.0018432772,0.017816138,0.007173603,0.0069713728,0.12931426,0.46642733,0.23969613],"study_design_scores_gemma":[0.033406068,0.005833139,0.0081852395,0.03417844,0.0005537744,0.0005185297,0.005249267,0.008155415,0.006849407,0.07201413,0.82430166,0.0007549917],"about_ca_topic_score_codex":0.004836648,"about_ca_topic_score_gemma":0.0062642954,"teacher_disagreement_score":0.8031799,"about_ca_system_score_codex":0.0095310435,"about_ca_system_score_gemma":0.03720074,"threshold_uncertainty_score":0.99046385},"labels":[],"label_agreement":null},{"id":"W3013917522","doi":"10.2196/18717","title":"Assessment of Health Information About COVID-19 Prevention on the Internet: Infodemiological Study","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":240,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"The Internet; Public health; Medicine; Odds ratio; Coronavirus disease 2019 (COVID-19); Upload; Pandemic; Internet privacy; Environmental health; Family medicine; Disease; World Wide Web; Infectious disease (medical specialty); Nursing; Computer science; Pathology","score_opus":0.0996359349607142,"score_gpt":0.4116395862603601,"score_spread":0.3120036512996459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013917522","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":[0.98582494,0.0031335168,0.0002298034,0.000455583,0.000028099486,0.0002970481,0.0025542052,0.00001084897,0.0074659768],"genre_scores_gemma":[0.995978,0.0016092858,0.0003828722,0.00015532218,0.000060333226,0.00012312322,0.0009566654,0.0000054078455,0.0007289708],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9966216,0.0012290656,0.00076639315,0.00027384947,0.00087744225,0.00023161713],"domain_scores_gemma":[0.9605331,0.019267317,0.013719122,0.0008647777,0.0040453095,0.0015703645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043765153,0.0002520848,0.00050879846,0.0069814855,0.00054066593,0.002154739,0.00043753747,0.0009750546,0.006141267],"category_scores_gemma":[0.031592198,0.00023184266,0.0010313272,0.007013276,0.0004670704,0.0027094497,0.0010002047,0.000953118,0.0009949588],"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":[0.00032499814,0.0005034101,0.983657,0.0007694481,0.00015653433,0.00012057004,0.0018114955,0.000028184013,0.0000796743,0.00012025689,0.00046984715,0.011958683],"study_design_scores_gemma":[0.000030901294,0.0006098999,0.99071056,0.0004747109,0.00031320268,0.00042620042,0.004657665,0.0002766633,0.000104262734,0.000117147174,0.0022603841,0.000018414315],"about_ca_topic_score_codex":0.0031064586,"about_ca_topic_score_gemma":0.0023579157,"teacher_disagreement_score":0.0069814855,"about_ca_system_score_codex":0.0007850493,"about_ca_system_score_gemma":0.0009502066,"threshold_uncertainty_score":0.023145497},"labels":[],"label_agreement":null},{"id":"W3014051546","doi":"10.23889/ijpds.v5i1.1155","title":"Is there an agreement between self-reported medical diagnosis in the CARTaGENE cohort and the Québec administrative health databases?","year":2020,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal; McGill University Health Centre; McGill University; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Concordance; Medicine; Logistic regression; Cohen's kappa; Kappa; Cohort; Medical record; Population; Database; Demography; Family medicine; Gerontology; Statistics; Environmental health; Internal medicine","score_opus":0.19508709330881696,"score_gpt":0.47367252900248025,"score_spread":0.2785854356936633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014051546","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":[0.9716673,0.0020398158,0.0027275805,0.0015773721,0.00008622774,0.00020559898,0.013473624,0.000060672664,0.008161862],"genre_scores_gemma":[0.9958687,0.00020907242,0.00086154626,0.00016662429,0.000019221046,0.000056612065,0.0025215074,0.00000726022,0.0002894495],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9790748,0.009405926,0.0018901002,0.0024308052,0.005803564,0.0013949051],"domain_scores_gemma":[0.8819051,0.055337038,0.025768887,0.009198951,0.025723565,0.002066469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030618712,0.00022187181,0.0005464514,0.005187232,0.0009040625,0.0024630867,0.0017577276,0.000575569,0.002423309],"category_scores_gemma":[0.08189084,0.00029280366,0.0007827022,0.0054836283,0.001036144,0.0010042604,0.0014424977,0.0005085643,0.000316471],"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":[0.000060202754,0.000008281964,0.9961338,0.000039065257,0.00017964724,0.0000147095425,0.00034740372,0.000081157734,0.000046394238,0.000077237455,0.00044903308,0.0025630349],"study_design_scores_gemma":[0.000005572345,0.000013871781,0.9978212,0.000055336415,0.000042143838,0.00002728294,0.00046788767,0.0006696393,0.000062168416,0.000041873896,0.0007836653,0.000009437656],"about_ca_topic_score_codex":0.60906017,"about_ca_topic_score_gemma":0.63490677,"teacher_disagreement_score":0.39093983,"about_ca_system_score_codex":0.0062653464,"about_ca_system_score_gemma":0.004310108,"threshold_uncertainty_score":0.7864843},"labels":[],"label_agreement":null},{"id":"W3014724356","doi":"10.2196/12842","title":"An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Foundation","keywords":"Seasonal influenza; Computer science; Algorithm; Data mining; Coronavirus disease 2019 (COVID-19); Medicine; Pathology; Disease; Infectious disease (medical specialty)","score_opus":0.07068713116738694,"score_gpt":0.373530575953328,"score_spread":0.30284344478594105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014724356","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":[0.50423104,0.0009129803,0.4827696,0.0007092966,0.00006647032,0.0008990449,0.0017477934,0.005469775,0.0031940136],"genre_scores_gemma":[0.5543849,0.00014474857,0.44078332,0.00018771095,0.00002511839,0.0006121648,0.0029925131,0.00012525619,0.00074422476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99741733,0.0011661584,0.00029915254,0.00044482,0.00048353456,0.00018895547],"domain_scores_gemma":[0.98248255,0.013367406,0.00065463164,0.0010189778,0.0023306513,0.00014576853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054603335,0.0011090988,0.0008591187,0.00230518,0.0008109173,0.001155995,0.0017355129,0.00174688,0.0015133764],"category_scores_gemma":[0.021609465,0.00037806097,0.0009552653,0.0018647921,0.0005265975,0.001178662,0.0010592343,0.0010869555,0.0003181542],"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":[0.0003606492,0.0008893503,0.030275406,0.00028990034,0.0002820587,0.00025251554,0.0002510666,0.70896685,0.0027864187,0.002718259,0.0039847596,0.24894273],"study_design_scores_gemma":[0.000036847086,0.000029965726,0.00088444346,0.00000709382,0.000012609023,0.000028416252,0.000026230084,0.99764013,0.00061611814,0.00046833217,0.0002461257,0.0000037111208],"about_ca_topic_score_codex":0.023901746,"about_ca_topic_score_gemma":0.021390133,"teacher_disagreement_score":0.023901746,"about_ca_system_score_codex":0.002007582,"about_ca_system_score_gemma":0.0037254961,"threshold_uncertainty_score":0.047525287},"labels":[],"label_agreement":null},{"id":"W3014820621","doi":"10.2196/18941","title":"Tracking COVID-19 in Europe: Infodemiology Approach","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":168,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preparedness; Pandemic; Outbreak; Public health; Epidemiology; Middle East respiratory syndrome; Coronavirus disease 2019 (COVID-19); Disease surveillance; Geography; Measles; Environmental health; Medicine; Demography; Disease; Vaccination; Virology; Political science; Infectious disease (medical specialty)","score_opus":0.1076621781573023,"score_gpt":0.3650213314746952,"score_spread":0.2573591533173929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014820621","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":[0.5464323,0.0131949065,0.33240324,0.006100984,0.0004360557,0.00061804906,0.04609251,0.0019716974,0.052750368],"genre_scores_gemma":[0.9146369,0.0027579998,0.069117144,0.00035081359,0.0002582324,0.00017556144,0.010469205,0.00008373192,0.0021504043],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99847895,0.00049323624,0.0001679641,0.0004925688,0.00022846647,0.00013885142],"domain_scores_gemma":[0.9954026,0.0020530215,0.001032947,0.00036270465,0.00089975115,0.00024892407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033900507,0.0006306968,0.0007009536,0.017059384,0.00047404182,0.003907419,0.0007874693,0.00146244,0.001973235],"category_scores_gemma":[0.011887014,0.00027460596,0.0012116255,0.012144046,0.0005773478,0.0034223234,0.0016405297,0.0008155474,0.0006609147],"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":[0.00021683858,0.00022447685,0.62278473,0.00078563514,0.0010626704,0.0009285886,0.0010639399,0.08605142,0.0008382845,0.042882115,0.015370943,0.22779028],"study_design_scores_gemma":[0.000038821443,0.00021290734,0.34455833,0.00074285746,0.0004968628,0.0012609176,0.0031349375,0.5091424,0.0017902548,0.08032553,0.058150817,0.0001454062],"about_ca_topic_score_codex":0.017152857,"about_ca_topic_score_gemma":0.008233656,"teacher_disagreement_score":0.017152857,"about_ca_system_score_codex":0.0014560472,"about_ca_system_score_gemma":0.0010507506,"threshold_uncertainty_score":0.034106016},"labels":[],"label_agreement":null},{"id":"W3014936749","doi":"10.1503/cmaj.75262","title":"Open access epidemiologic data and an interactive dashboard to monitor the COVID-19 outbreak in Canada","year":2020,"lang":"en","type":"letter","venue":"Canadian Medical Association Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":151,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Outbreak; Coronavirus disease 2019 (COVID-19); Dashboard; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Public health; Coronavirus; Public health surveillance; Pandemic; Medicine; Environmental health; Virology; Disease; Data science; Infectious disease (medical specialty); Computer science; Nursing","score_opus":0.08771267780075678,"score_gpt":0.402889161340072,"score_spread":0.31517648353931527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014936749","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037977928,0.001129459,0.0013126042,0.9693392,0.0051584984,0.00008966134,0.00096518797,0.0004660039,0.017741622],"genre_scores_gemma":[0.15771137,0.010577563,0.013392945,0.7486985,0.027152551,0.0003610089,0.0024222652,0.0004994172,0.03918434],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9937651,0.0010286694,0.0006994824,0.00044058939,0.0029713432,0.001094785],"domain_scores_gemma":[0.9586022,0.011304943,0.0021248632,0.0012382426,0.014837918,0.011891851],"candidate_categories":["scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.005066241,0.0005114507,0.0005370378,0.0018440929,0.0058474042,0.0045166267,0.0022556237,0.006790562,0.011179834],"category_scores_gemma":[0.03428781,0.0004079564,0.00045707013,0.002084009,0.0026588598,0.0024187164,0.0032447171,0.012486471,0.0030806973],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043214473,0.00005176783,0.0075423145,0.00006336983,0.000011893255,0.0011520585,0.00053635356,0.00013966001,0.00019119124,0.00089685235,0.9531139,0.036257304],"study_design_scores_gemma":[0.00012149426,0.00011607346,0.023106977,0.0011455427,0.00004297644,0.0016858957,0.0037906507,0.0029206662,0.0004551087,0.0053458554,0.9611008,0.00016787648],"about_ca_topic_score_codex":0.6100952,"about_ca_topic_score_gemma":0.7790855,"teacher_disagreement_score":0.9977444,"about_ca_system_score_codex":0.021860171,"about_ca_system_score_gemma":0.041344594,"threshold_uncertainty_score":0.784402},"labels":[],"label_agreement":null},{"id":"W3015874364","doi":"10.48550/arxiv.2004.04596","title":"Global Public Health Surveillance using Media Reports: Redesigning GPHIN","year":2020,"lang":"en","type":"preprint","venue":"PubMed","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Underpinning; Mainstream; Trustworthiness; Public health; Politics; Public relations; Political science; Public health surveillance; Computer science; Internet privacy; Engineering; Medicine","score_opus":0.13764873224223426,"score_gpt":0.31816408991976275,"score_spread":0.1805153576775285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015874364","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":[0.041263387,0.002711144,0.85266876,0.016706547,0.0036747318,0.0053639533,0.002687548,0.04711518,0.02780882],"genre_scores_gemma":[0.15761606,0.0021300672,0.8144324,0.004044425,0.0013262645,0.0022612344,0.00565231,0.0038103221,0.008726898],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96566564,0.01940342,0.004230184,0.0039142347,0.0059354138,0.0008511697],"domain_scores_gemma":[0.8073643,0.08298569,0.008831441,0.06623969,0.030568607,0.0040102853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.059276532,0.001360845,0.0009435666,0.0061014355,0.0007474967,0.010671609,0.0045082346,0.0020746496,0.0048508574],"category_scores_gemma":[0.15241195,0.0015530762,0.0014549098,0.00361042,0.0026146015,0.017658709,0.008369769,0.0038086493,0.003491702],"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":[0.0012371726,0.00052526506,0.019308157,0.0022901834,0.0003250588,0.0006595294,0.0059147044,0.006612159,0.010163641,0.08921969,0.0391159,0.82462853],"study_design_scores_gemma":[0.00059857126,0.0014014093,0.012147854,0.0028983841,0.0007695647,0.0009744185,0.0028493484,0.06711325,0.027367037,0.086575456,0.7967575,0.0005472616],"about_ca_topic_score_codex":0.0024374619,"about_ca_topic_score_gemma":0.001654588,"teacher_disagreement_score":0.059276532,"about_ca_system_score_codex":0.0013892204,"about_ca_system_score_gemma":0.0045259325,"threshold_uncertainty_score":0.313488},"labels":[],"label_agreement":null},{"id":"W3015897941","doi":"10.2196/18795","title":"A Case for Participatory Disease Surveillance of the COVID-19 Pandemic in India","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":90,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pandemic; Disease surveillance; Citizen journalism; Software deployment; Disease; Medicine; Disease control; Business; Coronavirus disease 2019 (COVID-19); Environmental health; Computer security; Political science; Computer science; Infectious disease (medical specialty)","score_opus":0.12677272492984681,"score_gpt":0.3797743086288005,"score_spread":0.2530015836989537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015897941","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025924778,0.002354263,0.015031404,0.91666335,0.0019842372,0.00040111085,0.00021988999,0.00017893917,0.037242025],"genre_scores_gemma":[0.72233963,0.0038586324,0.04225139,0.22198276,0.001593649,0.001229297,0.00028678516,0.00020010238,0.006257805],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8767159,0.097308084,0.0039121564,0.0057880515,0.00925592,0.0070198425],"domain_scores_gemma":[0.81045586,0.1408586,0.007356422,0.018696215,0.009724036,0.012908887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09303788,0.0006478569,0.0007435615,0.001813856,0.01183577,0.016411012,0.0049095345,0.01005881,0.0047891065],"category_scores_gemma":[0.11122836,0.00083938794,0.0017239504,0.0020134759,0.0209336,0.015611359,0.02118944,0.020634113,0.0009389821],"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":[0.0003970757,0.00037681666,0.042292424,0.0014258611,0.0001932639,0.011542998,0.24204095,0.001404047,0.0025389357,0.4107403,0.10981124,0.17723618],"study_design_scores_gemma":[0.00018004628,0.00032585324,0.012935557,0.0043857377,0.00015469432,0.007481124,0.13697638,0.0032832688,0.0019306312,0.23363927,0.5983305,0.0003769326],"about_ca_topic_score_codex":0.017481493,"about_ca_topic_score_gemma":0.016416902,"teacher_disagreement_score":0.09303788,"about_ca_system_score_codex":0.007208658,"about_ca_system_score_gemma":0.025630122,"threshold_uncertainty_score":0.49203724},"labels":[],"label_agreement":null},{"id":"W3018427020","doi":"10.1136/jech-2018-211654","title":"Glossary for public health surveillance in the age of data science","year":2020,"lang":"en","type":"article","venue":"Journal of Epidemiology & Community Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Glossary; Public health surveillance; Public health; Data science; Medicine; Big data; Disease surveillance; Field (mathematics); Data collection; Data mining; Computer science; Pathology; Social science; Sociology","score_opus":0.5088267886031829,"score_gpt":0.5005011080743682,"score_spread":0.00832568052881466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018427020","genre_codex":"dataset","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":[0.0011236091,0.17372777,0.06780629,0.08840642,0.04208016,0.00412712,0.344678,0.0035734691,0.2744771],"genre_scores_gemma":[0.014302828,0.25357798,0.1403633,0.111185566,0.032661248,0.010655304,0.30894244,0.0050365715,0.12327475],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9923113,0.002254018,0.0028346858,0.00073975866,0.0016611532,0.00019903378],"domain_scores_gemma":[0.96517974,0.019612236,0.004076716,0.002460071,0.007866429,0.0008047287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006596046,0.0022663549,0.002078723,0.010643449,0.001976137,0.0060884394,0.0024947121,0.0037539671,0.1043309],"category_scores_gemma":[0.044077203,0.0009310933,0.0013327872,0.016623113,0.0018859502,0.007842191,0.0031801234,0.00567111,0.06208318],"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":[0.000029405808,0.000010617445,0.0001752974,0.0022512123,0.0000133958765,0.00010033398,0.00032403404,0.00020434875,0.00020565923,0.030217845,0.93351376,0.032954063],"study_design_scores_gemma":[0.0000044263425,0.000005949914,0.00028979764,0.0017585859,0.0000074106088,0.00011301343,0.000101360834,0.000058049303,0.000028129443,0.005841438,0.99177647,0.00001538328],"about_ca_topic_score_codex":0.019202476,"about_ca_topic_score_gemma":0.013705139,"teacher_disagreement_score":0.1043309,"about_ca_system_score_codex":0.004137619,"about_ca_system_score_gemma":0.0048553688,"threshold_uncertainty_score":0.3490216},"labels":[],"label_agreement":null},{"id":"W3018657490","doi":"10.2196/14986","title":"Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Los Alamos National Laboratory; U.S. Department of Energy; National Nuclear Security Administration; National Science Foundation","keywords":"Social media; The Internet; Observational study; Logistic regression; Medicine; Representativeness heuristic; Psychology; Conversation; Computer science; Social psychology; World Wide Web; Pathology; Internal medicine","score_opus":0.23183663393160497,"score_gpt":0.4165496574576692,"score_spread":0.18471302352606422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018657490","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":[0.99600226,0.00019741748,0.00049166946,0.00007915839,0.000020027761,0.00014342948,0.0027510584,0.000008759123,0.00030618283],"genre_scores_gemma":[0.99388844,0.0001211242,0.00077355397,0.000093647795,0.000037669277,0.0002740808,0.004710001,0.000009271653,0.00009233186],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9881396,0.006224105,0.0014459717,0.0020020439,0.0014414736,0.00074684806],"domain_scores_gemma":[0.9607978,0.016889337,0.012082069,0.0041756146,0.0039335354,0.0021217223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010658397,0.0004162956,0.0006667462,0.00257656,0.0007138637,0.00132628,0.0008263458,0.00092444324,0.001358112],"category_scores_gemma":[0.03671683,0.0005269809,0.0009939536,0.002958204,0.0008691819,0.0018159844,0.0020009389,0.0012115586,0.0005148731],"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":[0.00022301792,0.00022322113,0.9968437,0.00008042017,0.00029201142,0.00005501896,0.00042621052,0.00006784831,0.00008696962,0.00005542077,0.00046812746,0.0011780093],"study_design_scores_gemma":[0.00006270871,0.0007276648,0.9933147,0.00008424638,0.00018506605,0.00034810405,0.0028533998,0.0011796515,0.00012218303,0.00011784691,0.0009822365,0.000022297705],"about_ca_topic_score_codex":0.0068638395,"about_ca_topic_score_gemma":0.008941821,"teacher_disagreement_score":0.010658397,"about_ca_system_score_codex":0.00096177077,"about_ca_system_score_gemma":0.0013961603,"threshold_uncertainty_score":0.056367695},"labels":[],"label_agreement":null},{"id":"W3019137431","doi":"10.14288/acme.v17i4.1622","title":"Second Generation Biometrics and the Future of Geosurveillance: A Minority Report on FAST","year":2017,"lang":"en","type":"article","venue":"Open Collections","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Biometrics; Psychology; Political science; Computer science; Computer security","score_opus":0.02533796632206983,"score_gpt":0.30820752017382275,"score_spread":0.28286955385175294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3019137431","genre_codex":"commentary","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":[0.05716509,0.13591419,0.018375054,0.55714893,0.01536961,0.00018282166,0.0022003124,0.00024028154,0.21340373],"genre_scores_gemma":[0.5919244,0.21777727,0.013741289,0.08761871,0.018132618,0.00047195496,0.0026926172,0.00045457206,0.06718657],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9933955,0.0019419052,0.00037597152,0.0006868798,0.003007068,0.00059267256],"domain_scores_gemma":[0.96592003,0.015848067,0.0022818928,0.0024553537,0.011913484,0.0015811862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015384492,0.00039522577,0.00044318405,0.0018678886,0.001609442,0.006352418,0.0011732649,0.002366662,0.009307453],"category_scores_gemma":[0.030119952,0.000204817,0.0005005057,0.0017039955,0.002260529,0.010242915,0.0048754476,0.0043391543,0.00202819],"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":[0.00020348151,0.00009225273,0.020090837,0.001152808,0.000041936917,0.00027889226,0.0063971174,0.0004484364,0.0025731516,0.20026752,0.16109303,0.6073606],"study_design_scores_gemma":[0.000009153371,0.00012755605,0.009600288,0.0014674823,0.0000408836,0.00047233904,0.0048668343,0.0006668224,0.0025304174,0.011617502,0.9685593,0.00004151776],"about_ca_topic_score_codex":0.008859363,"about_ca_topic_score_gemma":0.005889946,"teacher_disagreement_score":0.015384492,"about_ca_system_score_codex":0.0029532278,"about_ca_system_score_gemma":0.003937228,"threshold_uncertainty_score":0.08136195},"labels":[],"label_agreement":null},{"id":"W3022928586","doi":"","title":"Geographic Information Systems (GIS), an Informative Start for Challenging Process of Etiologic Investigation of Diseases and Public Health Policy Making","year":2017,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Process (computing); Public health; GIS and public health; Policy making; Geographic information system; Public health policy; Environmental planning; Data science; Environmental health; Geography; Business; Computer science; Medicine; Political science; Health policy; Cartography; Public administration; Pathology","score_opus":0.2663589458857825,"score_gpt":0.5541815420217793,"score_spread":0.28782259613599676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022928586","genre_codex":"commentary","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":[0.0086561255,0.13311726,0.20887703,0.52532417,0.012265163,0.00032524398,0.002663452,0.0009852157,0.10778635],"genre_scores_gemma":[0.36921936,0.21566197,0.34409747,0.031286307,0.017216409,0.0006589083,0.0023193164,0.0006728124,0.018867394],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9859089,0.010330521,0.00072983134,0.00086262764,0.0018621086,0.00030598982],"domain_scores_gemma":[0.95784575,0.032725018,0.001580918,0.0038418667,0.002939143,0.0010673512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019437771,0.0011335775,0.0015856533,0.009168864,0.0035213735,0.014425083,0.002024738,0.005212631,0.008104747],"category_scores_gemma":[0.02849604,0.0007184552,0.000870944,0.009549747,0.025883365,0.023975115,0.008411116,0.008001022,0.0015069197],"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":[0.000020250607,0.000025892103,0.0010422518,0.0007702763,0.000056744262,0.00023240816,0.0036011483,0.0018291777,0.00027713913,0.8839698,0.045637134,0.06253781],"study_design_scores_gemma":[0.000006078575,0.000031036274,0.0005978062,0.0013128491,0.000018974428,0.00019410369,0.0055814763,0.0011759176,0.00018861318,0.64495444,0.34589496,0.000043748965],"about_ca_topic_score_codex":0.00472635,"about_ca_topic_score_gemma":0.0037241548,"teacher_disagreement_score":0.019437771,"about_ca_system_score_codex":0.005442134,"about_ca_system_score_gemma":0.009688961,"threshold_uncertainty_score":0.102797985},"labels":[],"label_agreement":null},{"id":"W3023651006","doi":"10.1186/s12998-020-00314-9","title":"The use of internet analytics by a Canadian provincial chiropractic regulator to monitor, evaluate and remediate misleading claims regarding specific health conditions, pregnancy, and COVID-19","year":2020,"lang":"en","type":"article","venue":"Chiropractic & Manual Therapies","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Medicine; Chiropractic; Coronavirus disease 2019 (COVID-19); The Internet; Analytics; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Pregnancy; Regulator; Internet privacy; Alternative medicine; Family medicine; Psychiatry; Data science; Medical emergency; World Wide Web; Pathology; Disease","score_opus":0.09260649142433247,"score_gpt":0.35061580342730786,"score_spread":0.2580093120029754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023651006","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":[0.91813946,0.001353823,0.00557679,0.007802944,0.00016247448,0.002598594,0.004396459,0.0012707078,0.058698814],"genre_scores_gemma":[0.9733436,0.0009293959,0.013918694,0.0013357047,0.00006234033,0.00052868185,0.0012168151,0.00013509257,0.008529772],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9819547,0.004895011,0.0010156616,0.0014253099,0.009002333,0.0017069532],"domain_scores_gemma":[0.88175935,0.032815006,0.019571202,0.006234573,0.054841924,0.004778003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012869119,0.00045147413,0.0004385421,0.0077444483,0.0062459013,0.0037458797,0.0014713613,0.0008570612,0.002776443],"category_scores_gemma":[0.063853584,0.0004546064,0.00033371977,0.006799666,0.0019322747,0.0013350282,0.0025678165,0.0011538446,0.0009900235],"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":[0.0007268798,0.0006033731,0.54710656,0.0012382909,0.0001308738,0.0020655214,0.055205155,0.0014846806,0.009229389,0.0025939904,0.03355732,0.34605783],"study_design_scores_gemma":[0.00010543431,0.00091680425,0.7911984,0.0009759323,0.000257688,0.00078263297,0.057711754,0.014443868,0.009690876,0.001267884,0.1222356,0.0004130511],"about_ca_topic_score_codex":0.82468194,"about_ca_topic_score_gemma":0.87703574,"teacher_disagreement_score":0.17531806,"about_ca_system_score_codex":0.026564585,"about_ca_system_score_gemma":0.06879397,"threshold_uncertainty_score":0.35270107},"labels":[],"label_agreement":null},{"id":"W3024129393","doi":"10.2196/19702","title":"Correlations of Online Search Engine Trends With Coronavirus Disease (COVID-19) Incidence: Infodemiology Study","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":142,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dysgeusia; Pandemic; Medicine; Public health; Epidemiology; Population; Outbreak; Anosmia; Public health surveillance; Disease surveillance; Disease; Incidence (geometry); Demography; Environmental health; Coronavirus disease 2019 (COVID-19); Virology; Infectious disease (medical specialty); Internal medicine; Pathology; Adverse effect","score_opus":0.14151296793892268,"score_gpt":0.4234611878803751,"score_spread":0.28194821994145247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024129393","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":[0.995943,0.00037988802,0.000087800494,0.00007466697,0.0000070225556,0.00001730689,0.0026321004,0.0000076482165,0.0008505117],"genre_scores_gemma":[0.99774534,0.00016704577,0.000073073556,0.000024958548,0.0000139754775,0.000014577407,0.0017381978,0.000004806912,0.00021810138],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992175,0.00017205918,0.00016321274,0.00016730135,0.00015960443,0.000120284254],"domain_scores_gemma":[0.9922718,0.0019997726,0.003921738,0.00042664027,0.0007623691,0.0006177157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012592576,0.00023088836,0.00036892467,0.0036163551,0.00023984826,0.0010391066,0.0004186548,0.00042949515,0.0028673646],"category_scores_gemma":[0.007771743,0.00018434356,0.00079346175,0.0055856938,0.00025361637,0.0014000383,0.00066650065,0.000605849,0.000593455],"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":[0.000052074658,0.000030916013,0.9986915,0.00002045822,0.000059685673,0.000025105552,0.00004736539,0.00003222172,0.000031965545,0.000021828188,0.000117423144,0.00086953107],"study_design_scores_gemma":[0.0000026686814,0.000071308015,0.99863833,0.000012157911,0.000038054193,0.0001481959,0.0002508575,0.000506175,0.00004094394,0.000017412944,0.0002713806,0.0000025278762],"about_ca_topic_score_codex":0.008773086,"about_ca_topic_score_gemma":0.0076755444,"teacher_disagreement_score":0.008773086,"about_ca_system_score_codex":0.00043827028,"about_ca_system_score_gemma":0.0005598182,"threshold_uncertainty_score":0.017444074},"labels":[],"label_agreement":null},{"id":"W3025144767","doi":"10.2196/19357","title":"A Global Digital Citizen Science Policy to Tackle Pandemics Like COVID-19","year":2020,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":82,"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 Regina","funders":"Canadian Institutes of Health Research; Saskatchewan Health Research Foundation","keywords":"Pandemic; Civil liberties; Big data; Coronavirus disease 2019 (COVID-19); Political science; Democracy; Variety (cybernetics); Civil society; Existentialism; Citizen science; Public relations; Internet privacy; Law; Computer science; Politics; Infectious disease (medical specialty); Disease","score_opus":0.10433734913007416,"score_gpt":0.4859771627557122,"score_spread":0.38163981362563804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025144767","genre_codex":"commentary","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":[0.0035228294,0.0013837308,0.019202244,0.91346633,0.0031931836,0.00053592696,0.00015562205,0.00015101125,0.058389094],"genre_scores_gemma":[0.19362083,0.0040934086,0.06375502,0.7123486,0.0031739422,0.0022678114,0.00034119433,0.00017242985,0.020226758],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9237075,0.053048376,0.002881022,0.004532866,0.010638546,0.0051917676],"domain_scores_gemma":[0.8553987,0.09735723,0.007568004,0.011440888,0.015085628,0.013149439],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.1145928,0.0014129659,0.0011153356,0.0046172007,0.007903417,0.024949126,0.004655678,0.05143403,0.013787116],"category_scores_gemma":[0.15319875,0.0011641418,0.0026960277,0.0035639636,0.026470616,0.039956257,0.030594876,0.029626371,0.0035735404],"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":[0.00007834631,0.00031246105,0.0014143637,0.0005747901,0.000052089403,0.00029349947,0.0023708453,0.002161389,0.00048713898,0.916733,0.049249824,0.02627213],"study_design_scores_gemma":[0.00018182765,0.00018535375,0.0009093056,0.002283498,0.000059458835,0.00013566036,0.004903444,0.0024491607,0.0009071692,0.5122629,0.4756311,0.00009115487],"about_ca_topic_score_codex":0.00365799,"about_ca_topic_score_gemma":0.003981128,"teacher_disagreement_score":0.99534434,"about_ca_system_score_codex":0.012091036,"about_ca_system_score_gemma":0.06619687,"threshold_uncertainty_score":0.6060319},"labels":[],"label_agreement":null},{"id":"W3025523449","doi":"10.5210/ojphi.v12i1.10321","title":"Understanding Discussions of Health Issues on Twitter: A Visual Analytic Study","year":2020,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of British Columbia","funders":"","keywords":"Social media; Computer science; Data science; Health promotion; Variety (cybernetics); Analytics; Social media analytics; World Wide Web; Process (computing); Internet privacy; Public health; Medicine; Artificial intelligence","score_opus":0.4244830553391144,"score_gpt":0.4648751686996756,"score_spread":0.0403921133605612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025523449","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":[0.95508033,0.0005735407,0.012860469,0.004657504,0.00009595756,0.0006268514,0.0010715575,0.00013367517,0.024900129],"genre_scores_gemma":[0.98429793,0.00057121145,0.010327224,0.00039289126,0.000063955595,0.00049803586,0.0005048605,0.000098152115,0.0032458184],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9962843,0.0024531807,0.0001609958,0.00024838728,0.00056235376,0.000290707],"domain_scores_gemma":[0.97894776,0.016893135,0.0016198822,0.0004604603,0.0015841371,0.00049465353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006276014,0.00056286866,0.00033658888,0.0057181586,0.003594299,0.0061468016,0.001017632,0.001339688,0.0036277596],"category_scores_gemma":[0.026354559,0.0002911815,0.00060841476,0.004036948,0.0034190316,0.0063700136,0.0040886328,0.001439762,0.00060950796],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023256887,0.00014653253,0.027996661,0.00051052246,0.000025472547,0.0010926746,0.9184994,0.000491636,0.0043606935,0.008341672,0.0040631173,0.034239102],"study_design_scores_gemma":[0.000024965559,0.00009983547,0.02042399,0.00039806947,0.00002841745,0.00043004088,0.92977965,0.004925405,0.0017360416,0.006660283,0.035427384,0.00006595979],"about_ca_topic_score_codex":0.008006569,"about_ca_topic_score_gemma":0.00868907,"teacher_disagreement_score":0.008006569,"about_ca_system_score_codex":0.0029271564,"about_ca_system_score_gemma":0.0015193976,"threshold_uncertainty_score":0.033191144},"labels":[],"label_agreement":null},{"id":"W3026243475","doi":"10.1016/j.jvoice.2020.04.018","title":"Seasonal Variations in Public Inquiries into Laryngitis: An Infodemiology Study","year":2020,"lang":"en","type":"article","venue":"Journal of Voice","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Medizinische Universität Graz; Karl-Franzens-Universität Graz","keywords":"Laryngitis; Limiting; Reliability (semiconductor); Public health; Misinformation; Medicine; Demography; Distribution (mathematics); Environmental health; Geography; Psychology; Pathology; Computer science; Sociology; Mathematics","score_opus":0.060192929779523174,"score_gpt":0.34523347452656894,"score_spread":0.28504054474704577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3026243475","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":[0.9974597,0.00022022423,0.0000676733,0.00008586904,0.0000073797155,0.000022906304,0.0016075681,0.000003569019,0.0005250899],"genre_scores_gemma":[0.998691,0.000104480896,0.000061954444,0.000030627114,0.0000095949,0.000022763828,0.00095609296,0.0000036030629,0.00011991686],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985366,0.00041483613,0.00029116834,0.00029049936,0.00027094438,0.00019598294],"domain_scores_gemma":[0.993004,0.0018411904,0.0032947522,0.00045210033,0.0009163675,0.0004916525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019100677,0.00016926092,0.00033868858,0.0017950687,0.0003549808,0.0008853252,0.0004006313,0.0003881231,0.0014204815],"category_scores_gemma":[0.0076937727,0.00020140954,0.0005818451,0.0035141355,0.00037725992,0.0009403778,0.0009019318,0.00045587248,0.0003415647],"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":[0.00007987453,0.000033888064,0.9960819,0.000063330146,0.00005112575,0.000058224734,0.0013613784,0.00002489981,0.000089866524,0.00003637868,0.00028265125,0.0018365161],"study_design_scores_gemma":[0.0000015603204,0.000027359481,0.9981054,0.000014681449,0.00001368374,0.00004139377,0.0014376958,0.0000823643,0.000018686178,0.000007619664,0.00024713637,0.0000024727453],"about_ca_topic_score_codex":0.019647613,"about_ca_topic_score_gemma":0.022536641,"teacher_disagreement_score":0.019647613,"about_ca_system_score_codex":0.0006469563,"about_ca_system_score_gemma":0.00060185,"threshold_uncertainty_score":0.039066494},"labels":[],"label_agreement":null},{"id":"W3027545596","doi":"10.1016/j.jaip.2020.05.012","title":"A Phased Approach to Resuming Suspended Allergy/Immunology Clinical Services","year":2020,"lang":"en","type":"article","venue":"The Journal of Allergy and Clinical Immunology In Practice","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; University of British Columbia; Western University; BC Children's Hospital; Queen's University; McMaster University; University of Alberta","funders":"ALK-Abelló; Propeller Health; Abbott Nutrition; Genentech; Agency for Healthcare Research and Quality; De Blindas Vänner; Aimmune Therapeutics; Sandoz; Asthma and Allergy Foundation of America; National Institutes of Health; Mylan; Regeneron Pharmaceuticals; Johnson and Johnson; Teva Pharmaceutical Industries; Genzyme; Takeda Pharmaceuticals U.S.A.; North Carolina GlaxoSmithKline Foundation; Stallergenes Greer France; European Academy of Allergy and Clinical Immunology; Takeda Pharmaceuticals North America; Boettcher Foundation; Novartis; GlaxoSmithKline; Merck; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Sanofi; American Lung Association; Horizon Pharmaceuticals; Australasian Society of Clinical Immunology and Allergy; Boehringer Ingelheim; Food Allergy and Anaphylaxis Network; National Institute of Allergy and Infectious Diseases; Jeffrey Modell Foundation; DBV Technologies; American Academy of Allergy Asthma and Immunology; CSL Behring; Institute for Health Metrics and Evaluation; Bayer Corporation; Sun Pharma; Shire; AstraZeneca; Thermo Scientific; Pfizer","keywords":"Clinical immunology; Immunology; Allergy; Medicine","score_opus":0.05915107133444537,"score_gpt":0.3943642277181381,"score_spread":0.33521315638369276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027545596","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11365505,0.003809403,0.55463535,0.16466732,0.008541992,0.0438732,0.0041975128,0.011849478,0.094770744],"genre_scores_gemma":[0.52425116,0.0013548234,0.40558594,0.038934506,0.002423725,0.011324438,0.0029873035,0.0006888446,0.012449339],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.91820765,0.049495265,0.008133379,0.007028073,0.013507633,0.0036280795],"domain_scores_gemma":[0.83305985,0.060310427,0.017452184,0.022279138,0.038016237,0.028882135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.089480236,0.0010308557,0.0010400297,0.0042289197,0.0029137903,0.011114589,0.0062669143,0.0029837862,0.012569399],"category_scores_gemma":[0.12385478,0.0013476991,0.0015314412,0.0026838165,0.0013959025,0.0050944407,0.0108840065,0.006338129,0.004254268],"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":[0.0016982652,0.0041085947,0.06278048,0.0013324199,0.00044773612,0.00068464107,0.002874043,0.008772742,0.005795415,0.023768965,0.099963665,0.787773],"study_design_scores_gemma":[0.0042078383,0.013718976,0.10521578,0.0043262555,0.0005574563,0.0021804117,0.012774596,0.11476499,0.016844835,0.10652113,0.61817145,0.0007161881],"about_ca_topic_score_codex":0.0061452724,"about_ca_topic_score_gemma":0.00844088,"teacher_disagreement_score":0.089480236,"about_ca_system_score_codex":0.0065553994,"about_ca_system_score_gemma":0.04763596,"threshold_uncertainty_score":0.47322237},"labels":[],"label_agreement":null},{"id":"W3029070434","doi":"10.1101/2020.05.28.20115782","title":"Monitoring sick leave data for early detection of influenza outbreaks","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Canadian Institutes of Health Research; Association Nationale de la Recherche et de la Technologie; Agence Nationale de la Recherche; Institut National de la Santé et de la Recherche Médicale; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Outbreak; Sick leave; Absenteeism; Medicine; Emergency medicine; Environmental health; Virology; Physical therapy","score_opus":0.1150376054125652,"score_gpt":0.35917805808716446,"score_spread":0.24414045267459927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3029070434","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":[0.9101053,0.004804184,0.051389307,0.0017960042,0.0001909851,0.00035190908,0.025387743,0.0022392822,0.0037353488],"genre_scores_gemma":[0.96335024,0.00059055246,0.023647478,0.00019919184,0.00012285671,0.000111290254,0.01132181,0.0000371486,0.0006194428],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99873596,0.00064513215,0.0000853943,0.0002180645,0.00023187554,0.00008359533],"domain_scores_gemma":[0.99646866,0.0015280173,0.00089291687,0.00027021492,0.0006082243,0.0002319339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003112788,0.0005379174,0.00044126785,0.0020775434,0.00020682813,0.0010325335,0.00046679378,0.00041576935,0.0012293614],"category_scores_gemma":[0.006477305,0.00016008326,0.0005551523,0.0007191004,0.00013241204,0.0003650424,0.00046185454,0.00028827533,0.00046776925],"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":[0.00068504125,0.00023089096,0.8556821,0.00061432266,0.00044770286,0.00010035314,0.00030824164,0.012834416,0.008282108,0.00051017175,0.009461754,0.11084283],"study_design_scores_gemma":[0.00015322307,0.000677652,0.7920883,0.00024983144,0.00034300165,0.00024364039,0.0003894887,0.1784321,0.013428528,0.001471669,0.012450636,0.00007192688],"about_ca_topic_score_codex":0.010656843,"about_ca_topic_score_gemma":0.014633116,"teacher_disagreement_score":0.010656843,"about_ca_system_score_codex":0.0006403792,"about_ca_system_score_gemma":0.0009399726,"threshold_uncertainty_score":0.02118963},"labels":[],"label_agreement":null},{"id":"W3029198179","doi":"10.2196/17242","title":"An Online Influenza Surveillance System for Primary Care Workers in Switzerland: Observational Prospective Pilot Study","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Observational study; Context (archaeology); Epidemiology; Transmission (telecommunications); Pandemic; Family medicine; Attack rate; Emergency medicine; Medical emergency; Pediatrics; Disease; Infectious disease (medical specialty); Internal medicine; Coronavirus disease 2019 (COVID-19)","score_opus":0.09988175371833502,"score_gpt":0.352084590507791,"score_spread":0.252202836789456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3029198179","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":[0.99883944,0.00004513509,0.0001823326,0.000031062486,0.0000064466262,0.0004430898,0.00024908013,0.000010796376,0.00019260346],"genre_scores_gemma":[0.99832433,0.00004665799,0.00048104586,0.00007785685,0.00002746562,0.0005932758,0.00034581602,0.0000026427476,0.00010085953],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9970585,0.0015107637,0.00019474073,0.00040625272,0.00036293818,0.00046680693],"domain_scores_gemma":[0.9944916,0.0013380699,0.0011990897,0.00057271525,0.00094473927,0.0014538656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033477417,0.0008981027,0.0005239687,0.00086308876,0.0012893012,0.0008204166,0.00053987646,0.0010553292,0.0020132735],"category_scores_gemma":[0.004547659,0.0005241225,0.000602967,0.00051731657,0.0008470474,0.000880673,0.0010504123,0.000708891,0.00073271635],"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":[0.0007549195,0.00465431,0.98261684,0.00014511487,0.00006403503,0.00049768796,0.0029284782,0.00015860375,0.0016895636,0.000016249482,0.00044158392,0.0060326788],"study_design_scores_gemma":[0.00022758055,0.009380043,0.98697156,0.000028290797,0.00005373373,0.00022895177,0.0020708523,0.0005165832,0.00020418465,0.000012478325,0.00028729584,0.000018494165],"about_ca_topic_score_codex":0.014333892,"about_ca_topic_score_gemma":0.013960272,"teacher_disagreement_score":0.014333892,"about_ca_system_score_codex":0.0011252573,"about_ca_system_score_gemma":0.0016120763,"threshold_uncertainty_score":0.028500915},"labels":[],"label_agreement":null},{"id":"W3030850022","doi":"10.1186/s12961-020-00576-y","title":"Amplifying the role of knowledge translation platforms in the COVID-19 pandemic response","year":2020,"lang":"en","type":"letter","venue":"Health Research Policy and Systems","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":92,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Knowledge translation; 2019-20 coronavirus outbreak; Public health; Health services research; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Medicine; Translation (biology); Virology; Health administration; Computer science; Knowledge management; Nursing; Biology; Disease; Infectious disease (medical specialty); Internal medicine; Genetics","score_opus":0.5759526483627013,"score_gpt":0.5398048241707647,"score_spread":0.03614782419193663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3030850022","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024649416,0.00067809405,0.00022072216,0.99370664,0.003483381,0.00000955459,0.0000092047585,0.000010775923,0.0016350889],"genre_scores_gemma":[0.008480852,0.001095878,0.0007614997,0.9808162,0.006614201,0.000050421582,0.000010879906,0.000027840768,0.0021422154],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9403453,0.039047487,0.0037431072,0.0035470626,0.008667369,0.004649642],"domain_scores_gemma":[0.7362466,0.2246127,0.006460512,0.0038880578,0.01757908,0.011213043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.051548354,0.0007448963,0.0010746564,0.0013454343,0.014455195,0.016559185,0.0045045335,0.095444426,0.0064199544],"category_scores_gemma":[0.16901986,0.0010577439,0.0016584599,0.0013939893,0.021944497,0.024289066,0.014029235,0.06944314,0.002932359],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007675866,0.000046622685,0.0007426885,0.00062772183,0.000025472948,0.003992586,0.015629174,0.00033766066,0.0005651579,0.07580525,0.87183607,0.030314904],"study_design_scores_gemma":[0.000046702535,0.000045196462,0.0004066483,0.001319533,0.00002159737,0.0012900432,0.008711036,0.0006493557,0.00046509216,0.0500883,0.93684727,0.000109282904],"about_ca_topic_score_codex":0.0154669,"about_ca_topic_score_gemma":0.0213329,"teacher_disagreement_score":0.095444426,"about_ca_system_score_codex":0.0129037285,"about_ca_system_score_gemma":0.03842864,"threshold_uncertainty_score":0.27261698},"labels":[],"label_agreement":null},{"id":"W3032907911","doi":"10.1109/tcss.2020.2995497","title":"A Scalable Platform to Collect, Store, Visualize, and Analyze Big Data in Real Time","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Social Systems","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Brandon University; Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Big data; Computer science; Scalability; Social media; Data science; Analytics; Social media analytics; Field (mathematics); Focus (optics); World Wide Web; Data analysis; Database; Data mining","score_opus":0.0698136231509117,"score_gpt":0.3253849389644841,"score_spread":0.2555713158135724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3032907911","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":[0.025190057,0.0016302008,0.4914349,0.0068266653,0.0021774848,0.0040920223,0.09711542,0.3310343,0.040498856],"genre_scores_gemma":[0.21690637,0.0024479933,0.53593916,0.0043248194,0.0021220918,0.005067167,0.18752456,0.016206725,0.029461138],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974692,0.00027863015,0.0002449524,0.00055097893,0.0012278449,0.00022846994],"domain_scores_gemma":[0.9919574,0.0017480606,0.0006890829,0.0020785676,0.0019770337,0.0015498498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003375764,0.0018214717,0.0014504187,0.0051838225,0.0018165212,0.004199974,0.0033473917,0.0013668832,0.01759514],"category_scores_gemma":[0.009929816,0.0013671773,0.0012804162,0.0043229125,0.00092442124,0.008800883,0.008251255,0.0023696225,0.017739598],"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":[0.0019984043,0.0006175189,0.012528304,0.0014990177,0.00062254147,0.0008942669,0.0014807802,0.004302869,0.038186748,0.01107929,0.6413578,0.28543258],"study_design_scores_gemma":[0.0005774139,0.00065923564,0.02460788,0.0005741736,0.0002898067,0.00076328625,0.001701681,0.16672282,0.03844096,0.05997933,0.70487404,0.00080938917],"about_ca_topic_score_codex":0.006729342,"about_ca_topic_score_gemma":0.010044139,"teacher_disagreement_score":0.01759514,"about_ca_system_score_codex":0.0011442624,"about_ca_system_score_gemma":0.0022051097,"threshold_uncertainty_score":0.058861613},"labels":[],"label_agreement":null},{"id":"W3033100354","doi":"10.2196/19457","title":"A New System for Surveillance and Digital Contact Tracing for COVID-19: Spatiotemporal Reporting Over Network and GPS","year":2020,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":119,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Central South University; U.S. Department of Transportation","keywords":"Contact tracing; Internet privacy; Global Positioning System; Pandemic; Computer science; Population; Coronavirus disease 2019 (COVID-19); Transmission (telecommunications); mHealth; Disease; Computer security; Medicine; Infectious disease (medical specialty); Medical emergency; Environmental health; Telecommunications; Psychological intervention","score_opus":0.07767299889355897,"score_gpt":0.3888836172684709,"score_spread":0.31121061837491193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033100354","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":[0.20831555,0.0030190928,0.32059333,0.005673819,0.0024135576,0.0087178955,0.09965805,0.2893335,0.06227521],"genre_scores_gemma":[0.5957259,0.0024984437,0.27836466,0.003838122,0.0012715152,0.0086830985,0.062580705,0.004810327,0.042227294],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847335,0.00038995905,0.00020714746,0.00043602946,0.00037304504,0.000120487784],"domain_scores_gemma":[0.9950009,0.0017855286,0.00048802068,0.00088351517,0.0012414419,0.00060060155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017294843,0.0009961169,0.0009435636,0.0021861722,0.000574589,0.0013673368,0.0011462201,0.0009154807,0.012998339],"category_scores_gemma":[0.0067514395,0.00058926386,0.00056998746,0.0013163585,0.00026755806,0.0018753507,0.0022842481,0.00089704955,0.0061829872],"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":[0.0033242034,0.00072030077,0.07920894,0.0016560016,0.00040600044,0.001073023,0.0037810465,0.0015230435,0.021917423,0.0032135465,0.25156248,0.631614],"study_design_scores_gemma":[0.0012715632,0.0028313985,0.19813672,0.0011363997,0.0008591138,0.004270736,0.002233034,0.07037797,0.037624564,0.0060732756,0.6741662,0.0010190065],"about_ca_topic_score_codex":0.0043354193,"about_ca_topic_score_gemma":0.0047906907,"teacher_disagreement_score":0.012998339,"about_ca_system_score_codex":0.0003840385,"about_ca_system_score_gemma":0.0008516666,"threshold_uncertainty_score":0.043483794},"labels":[],"label_agreement":null},{"id":"W3033676767","doi":"10.14745/ccdr.v46i06a03","title":"A call for an ethical framework when using social media data for artificial intelligence applications in public health research","year":2020,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto; Public Health Agency of Canada; Université Laval","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Social media; Public health; Psychology; Data science; Computer science; Artificial intelligence; Engineering ethics; Political science; Sociology; Engineering; Medicine; World Wide Web; Nursing","score_opus":0.6267573233698036,"score_gpt":0.5049783098163965,"score_spread":0.12177901355340714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033676767","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":"methods","domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013896797,0.0026897723,0.027778119,0.95870036,0.002718415,0.00016572802,0.000047710328,0.0000500183,0.006460164],"genre_scores_gemma":[0.14313889,0.0061823805,0.11213907,0.7209648,0.00992348,0.002030859,0.0001901187,0.00034246407,0.0050879153],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.31531098,0.58544374,0.020360172,0.01602255,0.056663938,0.0061986377],"domain_scores_gemma":[0.14183742,0.7372657,0.01692487,0.048044145,0.04123095,0.014696926],"candidate_categories":["metaresearch","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.59246093,0.0014483843,0.0037621649,0.0044944226,0.020870298,0.05498664,0.008630418,0.04278191,0.0037936051],"category_scores_gemma":[0.5404242,0.002116949,0.003238802,0.0056052534,0.14709145,0.07235309,0.02572924,0.086526915,0.002124297],"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":[0.000086992004,0.00011145223,0.0018016835,0.00053029804,0.0000730183,0.00056598574,0.050179724,0.00053140416,0.00055944215,0.81604934,0.09478448,0.034726143],"study_design_scores_gemma":[0.00007964742,0.000088087276,0.00066179596,0.0039902157,0.00004305914,0.00083980436,0.022629384,0.0013537833,0.00052894925,0.7265861,0.2429821,0.00021709067],"about_ca_topic_score_codex":0.00929938,"about_ca_topic_score_gemma":0.012448797,"teacher_disagreement_score":0.9572181,"about_ca_system_score_codex":0.019127723,"about_ca_system_score_gemma":0.07634956,"threshold_uncertainty_score":0.50256824},"labels":[],"label_agreement":null},{"id":"W3033934096","doi":"10.2196/19969","title":"Fluctuation of Public Interest in COVID-19 in the United States: Retrospective Analysis of Google Trends Search Data","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Public interest; Coronavirus disease 2019 (COVID-19); Public health; The Internet; Pandemic; Medicine; Demography; Environmental health; Geography; Political science; Disease; Pathology; Infectious disease (medical specialty); Computer science; World Wide Web; Law; Sociology","score_opus":0.20707825060671367,"score_gpt":0.4121515480073384,"score_spread":0.20507329740062472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033934096","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":[0.9258089,0.00305433,0.000295429,0.00061393005,0.00002886469,0.00013276999,0.06702438,0.00007558824,0.002965884],"genre_scores_gemma":[0.9669253,0.0014382945,0.000475149,0.00028562674,0.000030162802,0.0001744227,0.03030225,0.000034569093,0.00033412199],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9954399,0.00083500287,0.0013364946,0.00073064695,0.0011707064,0.00048718858],"domain_scores_gemma":[0.9783053,0.005165067,0.010803924,0.0010000842,0.0037449426,0.000980611],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003094375,0.00031617543,0.00072274235,0.008850207,0.00061487965,0.0019481092,0.0006778212,0.0005862564,0.00129249],"category_scores_gemma":[0.016798101,0.00039889204,0.0010359022,0.017194048,0.0006242584,0.0023067761,0.0015917312,0.00081429334,0.00060994114],"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":[0.00016460163,0.000032904034,0.99116194,0.00036101826,0.00016612075,0.00011189083,0.00059365865,0.00016836464,0.000098810575,0.00014848253,0.0031210636,0.0038712237],"study_design_scores_gemma":[0.000008974607,0.000069370595,0.99251854,0.00024446458,0.0001117373,0.00041704054,0.0018122733,0.00082561467,0.00017485883,0.00006277748,0.003733213,0.000021102169],"about_ca_topic_score_codex":0.06571458,"about_ca_topic_score_gemma":0.06449604,"teacher_disagreement_score":0.06571458,"about_ca_system_score_codex":0.0014555424,"about_ca_system_score_gemma":0.0019605285,"threshold_uncertainty_score":0.13066417},"labels":[],"label_agreement":null},{"id":"W3034073691","doi":"10.1145/3404111.3404112","title":"Introduction to this special issue: Modeling and understanding the spread of COVID-19","year":2020,"lang":"en","type":"article","venue":"SIGSPATIAL Special","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Globe; German; 2019-20 coronavirus outbreak; Data science; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Position (finance); Geography; Computer science; Regional science; Business; Infectious disease (medical specialty); Disease; Psychology; Medicine; Virology","score_opus":0.04824369830473133,"score_gpt":0.29820721068652745,"score_spread":0.24996351238179612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034073691","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042124726,0.08355067,0.28259677,0.11522581,0.42578647,0.0005924024,0.0126188025,0.007995694,0.067420915],"genre_scores_gemma":[0.029813027,0.119140655,0.10811215,0.028756775,0.48234096,0.00071035314,0.030377213,0.005467612,0.19528121],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99867934,0.00027557905,0.00012460237,0.0003298759,0.0004991651,0.00009139293],"domain_scores_gemma":[0.9928886,0.0037612587,0.0003086546,0.00062422676,0.0015335949,0.0008836088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021541202,0.0016882272,0.0014239894,0.0026212328,0.0009282217,0.0045616035,0.0020763157,0.0028761374,0.04879069],"category_scores_gemma":[0.011094132,0.00074953894,0.0019545374,0.002179907,0.00084466377,0.0036307252,0.0028897172,0.005331525,0.020813342],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038713417,0.00007751413,0.0008146383,0.00039109835,0.000053832646,0.00016558361,0.000077130826,0.004180647,0.00047226058,0.006046827,0.91569465,0.07198708],"study_design_scores_gemma":[0.000014966499,0.000065200766,0.00085015164,0.00031342002,0.00003354433,0.0003660537,0.00007427529,0.008456267,0.00023541151,0.013320326,0.9762383,0.000032114323],"about_ca_topic_score_codex":0.004634854,"about_ca_topic_score_gemma":0.0054100524,"teacher_disagreement_score":0.04879069,"about_ca_system_score_codex":0.0010938789,"about_ca_system_score_gemma":0.0015992527,"threshold_uncertainty_score":0.16322112},"labels":[],"label_agreement":null},{"id":"W3037260400","doi":"","title":"Spatial Distribution and Characteristics of Restaurant Inspection Results in Toronto, Ontario, 2017–2018","year":2020,"lang":"en","type":"article","venue":"Food Protection Trends","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Distribution (mathematics); Geography; Spatial distribution; Remote sensing; Mathematics","score_opus":0.028709276171975173,"score_gpt":0.25965176046840954,"score_spread":0.23094248429643438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037260400","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":[0.81541437,0.0024972658,0.00074316957,0.0010602574,0.000053706564,0.000085451575,0.1738716,0.00014859719,0.0061255093],"genre_scores_gemma":[0.96137065,0.0013568613,0.0006463498,0.00013845794,0.00002355733,0.00006213174,0.031490486,0.000022785302,0.0048885806],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99903893,0.00005793943,0.0001296905,0.00018813652,0.00034782063,0.0002374907],"domain_scores_gemma":[0.9955577,0.00026265945,0.0015831391,0.00013306935,0.0019409746,0.00052233576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045490853,0.00034030684,0.0003154017,0.0019555292,0.0008588319,0.0010263342,0.00090055563,0.00033660588,0.0025825875],"category_scores_gemma":[0.0030578326,0.00038712888,0.0005820284,0.0056648296,0.00052457413,0.00047561552,0.00086570217,0.0004376979,0.00041359712],"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":[0.0001162522,0.000010379762,0.9805593,0.0002358434,0.000132266,0.00012217578,0.0013287128,0.000547409,0.00049082225,0.00023312464,0.010905461,0.005318365],"study_design_scores_gemma":[0.000002824852,0.000008954889,0.995698,0.000039380702,0.000021867028,0.000050770173,0.00081985217,0.000315227,0.00007036422,0.000010755177,0.0029537824,0.000008277291],"about_ca_topic_score_codex":0.98436975,"about_ca_topic_score_gemma":0.99254864,"teacher_disagreement_score":0.015707077,"about_ca_system_score_codex":0.015707077,"about_ca_system_score_gemma":0.016258247,"threshold_uncertainty_score":0.113963366},"labels":[],"label_agreement":null},{"id":"W3037960575","doi":"10.2196/21820","title":"How to Fight an Infodemic: The Four Pillars of Infodemic Management","year":2020,"lang":"en","type":"editorial","venue":"Journal of Medical Internet Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":559,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"JMIR Publications","funders":"","keywords":"Computer science","score_opus":0.06793965571996848,"score_gpt":0.4266770820376226,"score_spread":0.35873742631765415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037960575","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00003899946,0.0049821693,0.00018653074,0.07401835,0.9191139,0.000017884875,0.00002141127,0.00006548245,0.0015553392],"genre_scores_gemma":[0.0002922623,0.005608228,0.00017214639,0.027062941,0.96162593,0.000021924667,0.000016580572,0.000038057362,0.0051619173],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99385095,0.0013366665,0.00067744066,0.000616146,0.0030712972,0.0004475138],"domain_scores_gemma":[0.9680908,0.016565118,0.001355103,0.0006509635,0.007938781,0.005399398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008057584,0.0031272082,0.0025614677,0.0038088192,0.0055444036,0.015235337,0.0036471235,0.017841978,0.012039988],"category_scores_gemma":[0.030406572,0.0011462577,0.00195816,0.0018093281,0.0048574335,0.009370048,0.0029492374,0.02496427,0.01024836],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014533557,0.000017289667,0.00003961636,0.00019103079,0.0000075570792,0.000064565465,0.00005271892,0.000020780868,0.000040493454,0.0008010872,0.989442,0.009308315],"study_design_scores_gemma":[0.000021800299,0.000028715534,0.0001642394,0.00047328183,0.000023439841,0.00016466585,0.00015433987,0.00013178217,0.00006076152,0.0019490843,0.9968041,0.000023737754],"about_ca_topic_score_codex":0.0017249444,"about_ca_topic_score_gemma":0.0060636285,"teacher_disagreement_score":0.017841978,"about_ca_system_score_codex":0.0031702726,"about_ca_system_score_gemma":0.0052707456,"threshold_uncertainty_score":0.04261309},"labels":[],"label_agreement":null},{"id":"W3037968044","doi":"10.2196/18939","title":"Using Open-Source Intelligence to Detect Early Signals of COVID-19 in China: Descriptive Study","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Health and Medical Research Council","keywords":"Outbreak; China; Medicine; Pneumonia; Social media; Coronavirus disease 2019 (COVID-19); Family medicine; Environmental health; Disease; Pediatrics; Demography; Geography; Infectious disease (medical specialty); Virology; Internal medicine; Political science","score_opus":0.1358785595902893,"score_gpt":0.3970299192291632,"score_spread":0.2611513596388739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037968044","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":[0.9924132,0.0012108192,0.00022355586,0.000119993725,0.000010016393,0.00016125299,0.004876159,0.000015592574,0.0009693139],"genre_scores_gemma":[0.99363375,0.0006551081,0.00031717255,0.00013270685,0.000016745034,0.00017589203,0.0046674404,0.000011669411,0.0003896422],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99812955,0.0002439839,0.00044777442,0.00038448576,0.00039994915,0.00039430897],"domain_scores_gemma":[0.99431145,0.0011523135,0.0024263791,0.00041716642,0.0010470672,0.00064567744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017484631,0.00068605796,0.0007506906,0.00480044,0.00100115,0.0011391444,0.0006781462,0.0005250573,0.0021372999],"category_scores_gemma":[0.005140445,0.00039872553,0.0008773985,0.006520355,0.000546107,0.0012356674,0.0014211837,0.00062060414,0.00036057353],"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":[0.00007004867,0.000045203622,0.99460185,0.00023189801,0.000064248954,0.0002962956,0.00066515146,0.00003877312,0.0001611758,0.000049148483,0.00047356664,0.0033027288],"study_design_scores_gemma":[0.000009935575,0.00008582878,0.99556136,0.00011213011,0.00011648561,0.00036305896,0.0017746189,0.00036122592,0.00017027455,0.000050923165,0.0013730079,0.000021133703],"about_ca_topic_score_codex":0.044136766,"about_ca_topic_score_gemma":0.03920655,"teacher_disagreement_score":0.044136766,"about_ca_system_score_codex":0.0012913413,"about_ca_system_score_gemma":0.0036120985,"threshold_uncertainty_score":0.08775973},"labels":[],"label_agreement":null},{"id":"W3038910303","doi":"10.1016/j.healthplace.2020.102389","title":"The need for GIScience in mapping COVID-19","year":2020,"lang":"en","type":"article","venue":"Health & Place","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":94,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Data science; Coronavirus disease 2019 (COVID-19); Big data; Computer science; Disease surveillance; Tracking (education); Health care; Disease; Medicine; Political science; Data mining; Psychology; Infectious disease (medical specialty)","score_opus":0.09696465147541643,"score_gpt":0.3852968207297224,"score_spread":0.28833216925430594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3038910303","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035332855,0.022723148,0.34525353,0.39984062,0.0052466053,0.00048284535,0.00856794,0.0026341216,0.17991832],"genre_scores_gemma":[0.41099554,0.016038988,0.52968216,0.02367864,0.0019689584,0.0007279053,0.0039294143,0.0007345072,0.01224384],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9923003,0.004757921,0.00043643263,0.00078965956,0.0014109308,0.0003048273],"domain_scores_gemma":[0.9139671,0.049521517,0.002617468,0.010415069,0.021035628,0.0024432223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019601757,0.0009245128,0.0010359796,0.006869302,0.0019188283,0.011099176,0.0030215224,0.002834083,0.016372317],"category_scores_gemma":[0.07307225,0.0009232748,0.001163332,0.006000412,0.005288641,0.013443424,0.0056647863,0.0043325014,0.0027112581],"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":[0.00015909728,0.00018436046,0.06601965,0.0029153125,0.00037983718,0.00042528874,0.00425085,0.012872095,0.00205775,0.33057144,0.13890605,0.4412583],"study_design_scores_gemma":[0.000042252424,0.0001000871,0.019717423,0.0021396382,0.00012471528,0.0006704699,0.013963885,0.023018103,0.0016672486,0.24099632,0.69738996,0.00016979744],"about_ca_topic_score_codex":0.046832364,"about_ca_topic_score_gemma":0.07390335,"teacher_disagreement_score":0.046832364,"about_ca_system_score_codex":0.0037562237,"about_ca_system_score_gemma":0.0093485825,"threshold_uncertainty_score":0.10366529},"labels":[],"label_agreement":null},{"id":"W3040564926","doi":"10.1186/s12942-020-00219-z","title":"Street masking: a network-based geographic mask for easily protecting geoprivacy","year":2020,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia; BC Children's Hospital; Vancouver Island University; Simon Fraser University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Masking (illustration); Health geography; Health informatics; Computer science; Geospatial analysis; Public health; Geography; Cartography; Medicine; Health policy","score_opus":0.054646701699379394,"score_gpt":0.34805217557607737,"score_spread":0.29340547387669796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040564926","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":[0.3534758,0.0012518273,0.6096852,0.0009011941,0.00027196915,0.00061702006,0.0048719416,0.01004781,0.018877188],"genre_scores_gemma":[0.75515693,0.0003911857,0.23722148,0.0001866071,0.00007150744,0.00024444785,0.003328443,0.00020536494,0.003194079],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885166,0.00031429852,0.000061507235,0.00018927232,0.00046475345,0.000118494216],"domain_scores_gemma":[0.99720997,0.0007174277,0.00046521958,0.0010873667,0.00038253178,0.0001375201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010040781,0.00059965043,0.00047039465,0.0010666216,0.0007354789,0.0008365633,0.00088365236,0.0004933747,0.0024354882],"category_scores_gemma":[0.004487464,0.00020061014,0.00041673717,0.0010005878,0.00068585435,0.0014223576,0.0024975545,0.00046211988,0.000898553],"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":[0.0020675622,0.00034294478,0.047141537,0.0011184493,0.00028822772,0.0006639079,0.0022866954,0.088301964,0.06496601,0.03779286,0.033668954,0.72136086],"study_design_scores_gemma":[0.00023547508,0.0011668202,0.052927684,0.00031473854,0.00032883466,0.002609124,0.0025451907,0.6051892,0.12990926,0.04248629,0.16198495,0.00030242722],"about_ca_topic_score_codex":0.004087934,"about_ca_topic_score_gemma":0.006098871,"teacher_disagreement_score":0.004087934,"about_ca_system_score_codex":0.0005286164,"about_ca_system_score_gemma":0.0012993662,"threshold_uncertainty_score":0.008147478},"labels":[],"label_agreement":null},{"id":"W3041150558","doi":"10.5281/zenodo.3936037","title":"A Bayesian Approach to Improving Spatial Estimates After Accounting for Misclassification Bias in Surveillance Data for COVID-19 in Philadelphia, PA","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Bayesian probability; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Econometrics; Statistics; Computer science; Geography; Medicine; Economics; Mathematics; Virology","score_opus":0.13170211719271113,"score_gpt":0.31873601340680907,"score_spread":0.18703389621409794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041150558","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":[0.012933173,0.0006560529,0.9838741,0.00087906455,0.00006742972,0.00018443554,0.00034918284,0.00025444536,0.00080207764],"genre_scores_gemma":[0.19680928,0.0005492614,0.7991918,0.00047787194,0.00019551784,0.0007520154,0.00091400463,0.0001250794,0.0009851515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9467128,0.043330275,0.0024271798,0.004451984,0.0025285268,0.0005491487],"domain_scores_gemma":[0.8820954,0.09625724,0.006644912,0.0059142285,0.008419268,0.0006689633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.066484705,0.0012820899,0.0024691196,0.0049927123,0.0016153944,0.0031127112,0.0036515275,0.0020536117,0.0022003255],"category_scores_gemma":[0.19533625,0.0019902722,0.00308767,0.0041522887,0.0016530873,0.0024533523,0.0045147985,0.003577898,0.0004020406],"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":[0.00041134277,0.00020235225,0.08618434,0.000728274,0.0032795342,0.00056919386,0.0022971802,0.46796963,0.0012148785,0.08698583,0.0074810134,0.34267643],"study_design_scores_gemma":[0.00015656774,0.00015951786,0.016490608,0.0004249453,0.000714677,0.0002940658,0.00034270211,0.85460764,0.0009278879,0.11565131,0.010092023,0.0001380321],"about_ca_topic_score_codex":0.047929045,"about_ca_topic_score_gemma":0.05498988,"teacher_disagreement_score":0.066484705,"about_ca_system_score_codex":0.0031067894,"about_ca_system_score_gemma":0.00471453,"threshold_uncertainty_score":0.35160893},"labels":[],"label_agreement":null},{"id":"W3041278901","doi":"10.2196/19354","title":"Association of Search Query Interest in Gastrointestinal Symptoms With COVID-19 Diagnosis in the United States: Infodemiology Study","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Cancer Institute","keywords":"Nausea; Vomiting; Medicine; Diarrhea; Constipation; Abdominal pain; Disease; Population; Pediatrics; Internal medicine; Environmental health","score_opus":0.08929550732704744,"score_gpt":0.366214687262026,"score_spread":0.27691917993497855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041278901","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":[0.98886627,0.0013980434,0.000054418324,0.00022766175,0.000014494823,0.00003788967,0.008045399,0.000013298319,0.0013425031],"genre_scores_gemma":[0.9957753,0.000471802,0.00008251715,0.00009762906,0.000021743288,0.000028024622,0.0033158143,0.000008771244,0.00019851304],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987464,0.00027778215,0.00033316438,0.00027475675,0.0002225504,0.000145349],"domain_scores_gemma":[0.98696125,0.004515133,0.0065746536,0.0004768412,0.00077578984,0.0006962613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001125132,0.0002490622,0.00058530166,0.0031710814,0.0004037528,0.0012660265,0.00059922825,0.0006431601,0.0030329204],"category_scores_gemma":[0.011357199,0.00024253003,0.0009985024,0.0062900414,0.00034439916,0.0017928478,0.000934154,0.0008523039,0.00045229387],"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":[0.00018296317,0.000041773637,0.9977769,0.00010767878,0.00015461033,0.00006642038,0.00011273559,0.000030639843,0.000068326095,0.00002564116,0.00045999786,0.00097221986],"study_design_scores_gemma":[0.000009520342,0.000084384126,0.99809605,0.000046240675,0.00010818668,0.00029573447,0.0005069813,0.00028372376,0.000046397618,0.00001858201,0.00049758184,0.00000665645],"about_ca_topic_score_codex":0.018924298,"about_ca_topic_score_gemma":0.02068779,"teacher_disagreement_score":0.018924298,"about_ca_system_score_codex":0.0005494567,"about_ca_system_score_gemma":0.00067678554,"threshold_uncertainty_score":0.037628293},"labels":[],"label_agreement":null},{"id":"W3041470121","doi":"10.25318/82-003-x202000300001-eng","title":"Accuracy of matching residential postal codes to census geography","year":2020,"lang":"en","type":"article","venue":"PubMed","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Census; Geocoding; Geography; Population; Metropolitan area; Matching (statistics); Demography; Statistics; Cartography; Mathematics; Sociology","score_opus":0.02479618951707647,"score_gpt":0.264457402071348,"score_spread":0.23966121255427153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041470121","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":[0.8635799,0.0019885004,0.024565095,0.001963477,0.0003944954,0.00039377864,0.076195866,0.000404092,0.030514745],"genre_scores_gemma":[0.9705803,0.00046844463,0.0060105096,0.00027092316,0.000036977108,0.00010316215,0.020733016,0.000077419776,0.00171917],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98583627,0.0047950423,0.0012464816,0.0025849878,0.0043544276,0.0011829351],"domain_scores_gemma":[0.96978384,0.0083303405,0.006606158,0.005364948,0.009312196,0.000602541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010136146,0.0002893909,0.00042616663,0.0027470957,0.00075929856,0.001669324,0.0012692901,0.00042269763,0.0025774033],"category_scores_gemma":[0.09695889,0.0002621803,0.00060586573,0.005901044,0.0007594523,0.001011522,0.0016304244,0.00040569442,0.0010887738],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014560453,0.000016711552,0.9500365,0.00019985046,0.00021437123,0.0000482451,0.0013111077,0.0020992712,0.00021391154,0.0015673918,0.013138877,0.03100815],"study_design_scores_gemma":[0.000012827487,0.000026313468,0.96548736,0.0003279974,0.00011114576,0.00014080817,0.0018162355,0.0064102435,0.00081802683,0.0013714354,0.023437941,0.000039637573],"about_ca_topic_score_codex":0.6086806,"about_ca_topic_score_gemma":0.57865804,"teacher_disagreement_score":0.6086806,"about_ca_system_score_codex":0.0032743986,"about_ca_system_score_gemma":0.0043828213,"threshold_uncertainty_score":0.7872479},"labels":[],"label_agreement":null},{"id":"W3041763560","doi":"10.2196/20923","title":"General Public’s Information-Seeking Patterns of Topics Related to Obesity: Google Trends Analysis","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Institutes of Health","keywords":"Obesity; Quartile; Medicine; Public health; Popularity; Abdominal obesity; Demography; Psychology; Internal medicine; Waist; Social psychology; Pathology","score_opus":0.02717018891912205,"score_gpt":0.3024551501304959,"score_spread":0.27528496121137386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041763560","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":[0.9696285,0.002315359,0.00027916202,0.0006113046,0.000019105008,0.00006026481,0.023761936,0.00007998614,0.0032444599],"genre_scores_gemma":[0.98661035,0.00090501946,0.00043255527,0.00007689269,0.0000293947,0.000047730606,0.01133083,0.000017961764,0.0005492745],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99890006,0.00018136864,0.00017077962,0.00016901032,0.0003830807,0.00019571876],"domain_scores_gemma":[0.99400985,0.001702421,0.0025435316,0.00023539059,0.0011729941,0.0003358922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013307413,0.0002761979,0.0004492195,0.012142124,0.00033142898,0.0013811475,0.0003853003,0.00035549505,0.0021025704],"category_scores_gemma":[0.0060598794,0.0001229138,0.0012313625,0.016071266,0.00032122055,0.0017900375,0.0008949162,0.00044876582,0.0005806269],"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":[0.00021492841,0.00003739304,0.9761545,0.0006358506,0.00026255343,0.00014061302,0.0013375726,0.00021783929,0.00032122363,0.00035339355,0.003512765,0.016811457],"study_design_scores_gemma":[0.000005628114,0.000043160624,0.9929819,0.00009491051,0.00011183234,0.00025094146,0.002008896,0.0011371576,0.00015254329,0.0001381796,0.0030610866,0.000013736937],"about_ca_topic_score_codex":0.036534823,"about_ca_topic_score_gemma":0.037424196,"teacher_disagreement_score":0.036534823,"about_ca_system_score_codex":0.00076399895,"about_ca_system_score_gemma":0.000976009,"threshold_uncertainty_score":0.07264429},"labels":[],"label_agreement":null},{"id":"W3043823018","doi":"10.22862/kjsr.2020.21.1.003","title":"Continuous epi-link, disconnected info-link: The Lesson from the SARS outbreak in Canada and the MERS outbreak in Korea","year":2020,"lang":"en","type":"article","venue":"한국사회","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Outbreak; Link (geometry); Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Virology; Geography; Medicine; Computer science; Infectious disease (medical specialty); Computer network","score_opus":0.01517089590753116,"score_gpt":0.231698519363475,"score_spread":0.21652762345594384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043823018","genre_codex":"commentary","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":[0.016791075,0.010022893,0.029588664,0.89762604,0.0052902307,0.000106818065,0.004161744,0.0019186478,0.034493953],"genre_scores_gemma":[0.66318613,0.034582406,0.0845798,0.17465188,0.009435563,0.00026142708,0.007268474,0.0022020657,0.023832295],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99529135,0.002144651,0.00018551515,0.0004908118,0.0013791306,0.0005084718],"domain_scores_gemma":[0.9500044,0.026377698,0.0016591888,0.005183094,0.011132592,0.005642977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015555969,0.00074616977,0.00067148573,0.0013475958,0.0019253917,0.008093114,0.0024629147,0.0040808734,0.0051698154],"category_scores_gemma":[0.080757625,0.00054722204,0.0006505604,0.0019469367,0.0038023603,0.010947866,0.0035072397,0.009983777,0.0018938557],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033622046,0.00016229991,0.038699865,0.00046284706,0.00019591498,0.0004417055,0.0030619835,0.0068705827,0.00039557612,0.04094011,0.5580498,0.3503831],"study_design_scores_gemma":[0.00025394332,0.00021936026,0.027486917,0.0028576255,0.00027046076,0.00067288696,0.010184928,0.028147768,0.0016530375,0.22019175,0.70774424,0.0003171393],"about_ca_topic_score_codex":0.3297976,"about_ca_topic_score_gemma":0.38898668,"teacher_disagreement_score":0.6702024,"about_ca_system_score_codex":0.005378281,"about_ca_system_score_gemma":0.019257931,"threshold_uncertainty_score":0.6557559},"labels":[],"label_agreement":null},{"id":"W3044663179","doi":"10.22541/au.159569198.82432694","title":"The value of Hospital data for covid-19 pandemic surveillance and planning","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Medical emergency; Health care; Public health; Business; Medicine; Computer science; Nursing; Virology; Economic growth; Economics","score_opus":0.106319832120972,"score_gpt":0.385305476515569,"score_spread":0.278985644394597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044663179","genre_codex":"commentary","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":[0.20032665,0.016474128,0.23089193,0.24314411,0.007634491,0.00089146924,0.16743122,0.0055860747,0.12762003],"genre_scores_gemma":[0.7518964,0.008569889,0.16138256,0.0030456737,0.0020715191,0.000207857,0.06738144,0.0011310041,0.0043136813],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9892803,0.007229822,0.0005628049,0.00065625,0.0019293752,0.0003414893],"domain_scores_gemma":[0.91762394,0.05558082,0.0030097354,0.011824759,0.008829147,0.003131642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013178125,0.00087772455,0.0008792631,0.0039758864,0.00073791965,0.0071637724,0.0012586274,0.0016242354,0.0057270713],"category_scores_gemma":[0.08977705,0.0007435115,0.00060160534,0.00517513,0.001054598,0.0070716827,0.002913429,0.002636754,0.0020521053],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074650836,0.0002424501,0.1136063,0.0008853887,0.0005335523,0.0005712848,0.00056528,0.1642096,0.0010191695,0.12438077,0.22404987,0.36918974],"study_design_scores_gemma":[0.00019136752,0.00017718058,0.0382638,0.0018403592,0.00017581851,0.00025283263,0.003002658,0.3821399,0.0034238838,0.369404,0.2008558,0.00027237926],"about_ca_topic_score_codex":0.022362947,"about_ca_topic_score_gemma":0.012933847,"teacher_disagreement_score":0.022362947,"about_ca_system_score_codex":0.0028189057,"about_ca_system_score_gemma":0.004509262,"threshold_uncertainty_score":0.069693446},"labels":[],"label_agreement":null},{"id":"W3045521640","doi":"10.5539/ijsp.v9n5p23","title":"A Real Time and Interactive Web-Based Platform for Visualizing and Analyzing COVID-19 in Canada","year":2020,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Waterloo; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; Visualization; Computer science; Government (linguistics); Data science; Web application; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Outbreak; World Wide Web; Data mining; Medicine; Virology; Pathology","score_opus":0.02698138869634621,"score_gpt":0.33009818341162706,"score_spread":0.30311679471528086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045521640","genre_codex":"empirical","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3222447,0.001756298,0.09111476,0.0064335726,0.00085502164,0.001868395,0.28935972,0.21585156,0.07051591],"genre_scores_gemma":[0.6408954,0.0017336145,0.2170122,0.0010348607,0.00030102752,0.0007169255,0.10456785,0.006097058,0.027640983],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996495,0.00004342695,0.00001885687,0.0000610902,0.000149985,0.0000771256],"domain_scores_gemma":[0.9974765,0.00074126956,0.00012403284,0.00018870088,0.0008616839,0.00060770474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063942175,0.0007983837,0.00040592364,0.0028227686,0.0008459029,0.0017046648,0.00085144065,0.0004761583,0.011587571],"category_scores_gemma":[0.0024321538,0.0002924011,0.0005740747,0.0020792363,0.00039331472,0.00075424684,0.0012815581,0.0006691633,0.002145599],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019822584,0.00057533116,0.07607013,0.0010394916,0.00030097525,0.0024898883,0.0041116965,0.026824681,0.017010929,0.003984897,0.6340149,0.23159473],"study_design_scores_gemma":[0.000703964,0.000351184,0.21704544,0.0006826477,0.00024997114,0.0008740891,0.004498921,0.36034006,0.015135314,0.008137472,0.39118034,0.0008006739],"about_ca_topic_score_codex":0.6149016,"about_ca_topic_score_gemma":0.7665788,"teacher_disagreement_score":0.3850984,"about_ca_system_score_codex":0.003188469,"about_ca_system_score_gemma":0.006072095,"threshold_uncertainty_score":0.77473265},"labels":[],"label_agreement":null},{"id":"W3046167810","doi":"10.2196/19623","title":"Fingerprint Biometric System Hygiene and the Risk of COVID-19 Transmission","year":2020,"lang":"en","type":"article","venue":"JMIR Biomedical Engineering","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Biometrics; Fingerprint (computing); Fingerprint recognition; Computer science; Hygiene; Transmission (telecommunications); Infectious disease (medical specialty); Computer security; Coronavirus disease 2019 (COVID-19); Artificial intelligence; Medicine; Telecommunications; Pathology; Disease","score_opus":0.01071971629337461,"score_gpt":0.2512982193956642,"score_spread":0.2405785031022896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046167810","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":[0.76604646,0.059143234,0.06595644,0.023431743,0.0007775831,0.0003756281,0.0008015114,0.00024636218,0.083221026],"genre_scores_gemma":[0.98817015,0.004817557,0.0037576447,0.00075951,0.00015225138,0.000029985134,0.000103756036,0.000011112492,0.0021980167],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9942425,0.0020866941,0.0004272453,0.00056612684,0.0021431388,0.0005342036],"domain_scores_gemma":[0.98739284,0.0048243953,0.004864153,0.000852951,0.0016757995,0.00038994805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032334817,0.00035325266,0.0003647001,0.0013946241,0.0007897249,0.0028308749,0.0006135438,0.0017752639,0.0031235549],"category_scores_gemma":[0.0134421475,0.00022226607,0.0003445855,0.0006427492,0.0011663318,0.0014505528,0.0018356038,0.0011881228,0.0005297717],"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":[0.00080733333,0.00053774787,0.6459986,0.0014343814,0.0003259992,0.0033879532,0.003013426,0.0040428713,0.01651897,0.03768865,0.00824204,0.27800205],"study_design_scores_gemma":[0.00006778112,0.0042558443,0.67404354,0.005688223,0.000937229,0.040469132,0.012811821,0.046557404,0.05807029,0.048752308,0.1079859,0.0003605062],"about_ca_topic_score_codex":0.0026567767,"about_ca_topic_score_gemma":0.0013775541,"teacher_disagreement_score":0.0032334817,"about_ca_system_score_codex":0.00084824045,"about_ca_system_score_gemma":0.0008746164,"threshold_uncertainty_score":0.017100453},"labels":[],"label_agreement":null},{"id":"W3047279631","doi":"10.1109/icalt49669.2020.00049","title":"Analytics-oriented Preventive Care for Communicable Disease","year":2020,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Athabasca University","funders":"","keywords":"Syphilis; Communicable disease; Analytics; Government (linguistics); Disease; Computer science; Predictive analytics; Medicine; Epidemiology; Epidemiological surveillance; Infectious disease (medical specialty); Outbreak; Environmental health; Data science; Internet privacy; Computer security; Public health; Immunology; Virology; Human immunodeficiency virus (HIV); Nursing","score_opus":0.027478211143198535,"score_gpt":0.30657299272952004,"score_spread":0.2790947815863215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047279631","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":[0.07048194,0.004688222,0.86966366,0.01933041,0.0004941694,0.00083647884,0.0039849677,0.007668462,0.022851659],"genre_scores_gemma":[0.8643736,0.001993658,0.12707137,0.00085232616,0.00022836885,0.00019384926,0.0028369636,0.0001058045,0.0023439426],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99818844,0.0007316206,0.00007181225,0.00026156983,0.00061544793,0.00013118709],"domain_scores_gemma":[0.99389005,0.002926547,0.0005426716,0.001102211,0.0011895832,0.00034901727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032357832,0.00074953394,0.00079509773,0.001489204,0.0005756519,0.0024314898,0.001403012,0.00049993565,0.0022598184],"category_scores_gemma":[0.010556897,0.00028185826,0.0005768918,0.0013270298,0.0006324994,0.0011803529,0.0017667928,0.0010708128,0.00055680564],"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":[0.00048711625,0.00063050573,0.07804309,0.0011939676,0.0004472047,0.00043922593,0.0011382586,0.2252224,0.007692554,0.08382401,0.038320605,0.5625611],"study_design_scores_gemma":[0.00008744754,0.00025366942,0.015457947,0.000318782,0.00013807336,0.0001931509,0.00089149794,0.8532592,0.00643562,0.09649569,0.026411133,0.000057903566],"about_ca_topic_score_codex":0.036127828,"about_ca_topic_score_gemma":0.035704006,"teacher_disagreement_score":0.036127828,"about_ca_system_score_codex":0.0017789097,"about_ca_system_score_gemma":0.0057316,"threshold_uncertainty_score":0.0718351},"labels":[],"label_agreement":null},{"id":"W3047788174","doi":"10.1016/s2589-7500(20)30197-7","title":"Communicating in a public health crisis","year":2020,"lang":"en","type":"article","venue":"The Lancet Digital Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Center for Advancing Translational Sciences","keywords":"Preparedness; Scopus; Battle; Public health; Context (archaeology); Pandemic; Public relations; Political science; Global health; Medicine; Business; Coronavirus disease 2019 (COVID-19); Geography; MEDLINE; Nursing; Law; Disease","score_opus":0.1629753575368257,"score_gpt":0.37332901211500436,"score_spread":0.21035365457817864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047788174","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027655282,0.010760908,0.00044252264,0.9481759,0.009686106,0.000034749322,0.00024094622,0.00010173527,0.027791679],"genre_scores_gemma":[0.13010994,0.037304632,0.0024177262,0.78335685,0.020957427,0.0002682952,0.00087800884,0.00033089682,0.024376277],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.97631675,0.015582565,0.00078458706,0.0009800933,0.0026492898,0.0036867175],"domain_scores_gemma":[0.95932496,0.016624833,0.0025495228,0.0017214844,0.005805128,0.013974057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020079691,0.0011337439,0.0010933126,0.0027434637,0.015955558,0.019499503,0.0019190155,0.015401591,0.03009397],"category_scores_gemma":[0.060696367,0.0006835692,0.0012234383,0.0024212997,0.013147693,0.022469085,0.019820692,0.022095798,0.007063816],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062036466,0.00005758002,0.0013388885,0.00044817844,0.000043842432,0.0010119462,0.024967158,0.00007537647,0.00015862398,0.017097924,0.9098437,0.04489478],"study_design_scores_gemma":[0.000033424996,0.000060803817,0.0015092005,0.0022901343,0.000031551193,0.0006193868,0.08258168,0.00009934355,0.00013320148,0.020531295,0.8920409,0.00006902879],"about_ca_topic_score_codex":0.018383259,"about_ca_topic_score_gemma":0.018486878,"teacher_disagreement_score":0.03009397,"about_ca_system_score_codex":0.009255089,"about_ca_system_score_gemma":0.025058473,"threshold_uncertainty_score":0.10619283},"labels":[],"label_agreement":null},{"id":"W3048896201","doi":"10.1007/s40471-020-00242-5","title":"Trajectory Modeling with Latent Groups: Potentials and Pitfalls","year":2020,"lang":"en","type":"article","venue":"Current Epidemiology Reports","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences Centre; Université Laval; Foothills Medical Centre; University of Calgary","funders":"Alberta Children's Hospital Research Institute; Alberta Innovates - Health Solutions","keywords":"Trajectory; Data science; Latent class model; Computer science; Field (mathematics); Psychology; Machine learning; Mathematics","score_opus":0.1049471048114064,"score_gpt":0.3349753570556964,"score_spread":0.23002825224429002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048896201","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008517493,0.0044664084,0.97131,0.0127554685,0.0002625998,0.00022418998,0.0005448214,0.00041107455,0.0015079945],"genre_scores_gemma":[0.34986866,0.005726312,0.6355526,0.0029849426,0.0015070968,0.0011872908,0.0010122063,0.00029243846,0.001868519],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94440347,0.04662986,0.0015214043,0.0039050635,0.0030865811,0.00045369286],"domain_scores_gemma":[0.55284166,0.42324665,0.004459338,0.013438709,0.004476121,0.001537521],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.100699455,0.0019844812,0.0046537425,0.002938029,0.0019922403,0.0077883513,0.008213593,0.0044086417,0.0030212179],"category_scores_gemma":[0.24002588,0.0022400452,0.0026732082,0.0053828442,0.007517298,0.013127139,0.007423129,0.011942904,0.0010598273],"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":[0.00057532755,0.00029195807,0.04275771,0.0012721133,0.0012932261,0.00048575594,0.002477465,0.19577156,0.00029154544,0.533478,0.011537367,0.20976798],"study_design_scores_gemma":[0.000056193818,0.000045508834,0.0011000176,0.00022805264,0.00006627087,0.00014659461,0.00032815518,0.36692598,0.00013125475,0.6273779,0.0035361894,0.00005791693],"about_ca_topic_score_codex":0.025155509,"about_ca_topic_score_gemma":0.022989957,"teacher_disagreement_score":0.8993006,"about_ca_system_score_codex":0.0027079298,"about_ca_system_score_gemma":0.004603723,"threshold_uncertainty_score":0.53255594},"labels":[],"label_agreement":null},{"id":"W3080215321","doi":"10.1055/s-0040-1701989","title":"Precision, Equity, and Public Health and Epidemiology Informatics – A Scoping Review","year":2020,"lang":"en","type":"review","venue":"Yearbook of Medical Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Data science; Health informatics; Health equity; Public health; Psychological intervention; Equity (law); Informatics; Population health; Precision medicine; Computer science; Context (archaeology); Medicine; Political science; Geography","score_opus":0.23194774292629355,"score_gpt":0.4936794942610802,"score_spread":0.26173175133478666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080215321","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":[0.00017229059,0.9918098,0.001227196,0.0048928256,0.0004936578,0.00008593569,0.00008863281,0.0000138485,0.0012158832],"genre_scores_gemma":[0.004317218,0.99139434,0.0016832979,0.0015465927,0.0006015677,0.00023641562,0.00010593822,0.000010476527,0.00010423783],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.96524054,0.01701754,0.008450911,0.001807548,0.006627784,0.0008557895],"domain_scores_gemma":[0.68387896,0.2883819,0.009555684,0.0045224205,0.012757399,0.0009035951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.051586892,0.0019755512,0.0048775002,0.022036014,0.0018095322,0.01005458,0.0030991016,0.005466363,0.0066078636],"category_scores_gemma":[0.21167283,0.0015639589,0.0050849435,0.020948054,0.0048135384,0.009345647,0.005908091,0.005365222,0.000885926],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011313819,0.0000686261,0.0014606896,0.35568404,0.0020776114,0.00015004577,0.0013524697,0.0014228204,0.00012977445,0.03812732,0.018615257,0.5807982],"study_design_scores_gemma":[0.000030196154,0.000055398836,0.0011475359,0.87055266,0.0020766896,0.0002629307,0.00070921663,0.00036387914,0.00012939269,0.018585945,0.106046714,0.00003942545],"about_ca_topic_score_codex":0.008000352,"about_ca_topic_score_gemma":0.007940469,"teacher_disagreement_score":0.051586892,"about_ca_system_score_codex":0.007925867,"about_ca_system_score_gemma":0.030740712,"threshold_uncertainty_score":0.27282083},"labels":[],"label_agreement":null},{"id":"W3080437446","doi":"10.3390/ijgi9090497","title":"The Spatial-Comprehensiveness (S-COM) Index: Identifying Optimal Spatial Extents in Volunteered Geographic Information Point Datasets","year":2020,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Waterloo","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Volunteered geographic information; Social media; Metric (unit); Index (typography); Data science; Computer science; Scale (ratio); Citizen science; Spatial analysis; Information retrieval; Data mining; Quality (philosophy); Data quality; Geography; Crowdsourcing; Cartography; World Wide Web; Remote sensing","score_opus":0.012039500549974772,"score_gpt":0.27699398279413634,"score_spread":0.26495448224416157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080437446","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":[0.473137,0.0027026115,0.46198386,0.0017385544,0.00010922672,0.0012050875,0.0459836,0.0021531251,0.010986902],"genre_scores_gemma":[0.74838006,0.0005096888,0.21985954,0.00016770053,0.0000623663,0.0009143125,0.02920602,0.00025170224,0.0006486009],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9893288,0.0037239792,0.0015952738,0.0023134337,0.0025487517,0.00048977166],"domain_scores_gemma":[0.9580863,0.024908165,0.005682482,0.00603257,0.004492164,0.000798286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014680212,0.0009003875,0.0013927924,0.01179641,0.0011661685,0.004399862,0.0015075483,0.0012686375,0.0010860354],"category_scores_gemma":[0.06934472,0.0004522255,0.0013221817,0.015771706,0.001560479,0.0037778371,0.005026453,0.0009725333,0.00034290898],"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":[0.0004233358,0.00019121898,0.6703578,0.0018722091,0.0010332692,0.00053045823,0.004382585,0.08557542,0.003934787,0.03192713,0.019190457,0.18058133],"study_design_scores_gemma":[0.00015135886,0.0003528168,0.3703206,0.0010165413,0.0004802753,0.0013102604,0.008547997,0.4174817,0.007111982,0.13386151,0.059060156,0.0003047574],"about_ca_topic_score_codex":0.012659899,"about_ca_topic_score_gemma":0.019702382,"teacher_disagreement_score":0.014680212,"about_ca_system_score_codex":0.0017710036,"about_ca_system_score_gemma":0.0025446513,"threshold_uncertainty_score":0.077637255},"labels":[],"label_agreement":null},{"id":"W3080866408","doi":"10.2196/20426","title":"Google Trends Insights Into Reduced Acute Coronary Syndrome Admissions During the COVID-19 Pandemic: Infodemiology Study","year":2020,"lang":"en","type":"article","venue":"JMIR Cardio","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Chest pain; Medicine; Myocardial infarction; Acute coronary syndrome; Pandemic; Emergency department; Coronavirus disease 2019 (COVID-19); Internal medicine; Cardiology; Emergency medicine; Disease; Infectious disease (medical specialty)","score_opus":0.05338364781794635,"score_gpt":0.3556356116362014,"score_spread":0.30225196381825503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080866408","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":[0.77139646,0.012853228,0.00016450397,0.0033856237,0.00014065417,0.00050034723,0.1993468,0.00021976397,0.011992644],"genre_scores_gemma":[0.9197901,0.006583813,0.00064833194,0.0014063856,0.00027304687,0.00049637427,0.0684939,0.000080947604,0.0022271636],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99882156,0.00027440817,0.00023875489,0.00023326505,0.00026872338,0.00016324705],"domain_scores_gemma":[0.99325633,0.0019962033,0.0029893091,0.00038017888,0.00081777194,0.00056018477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011992177,0.00037020605,0.0008605554,0.004832654,0.000461601,0.0018313366,0.00067995826,0.000687156,0.004378548],"category_scores_gemma":[0.015252696,0.00022639355,0.0024346912,0.009607274,0.00031238998,0.0030194218,0.0012155824,0.0007795777,0.0011776076],"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":[0.0033994538,0.0005474493,0.8634459,0.011641515,0.002776818,0.0004952726,0.0022734005,0.00039871028,0.00025847615,0.0012373043,0.04838853,0.06513714],"study_design_scores_gemma":[0.0002771684,0.0003578032,0.98133975,0.00081139844,0.0012621329,0.00043070203,0.0011113858,0.0006234916,0.00012986187,0.00019570504,0.013416098,0.000044548502],"about_ca_topic_score_codex":0.043105666,"about_ca_topic_score_gemma":0.041930795,"teacher_disagreement_score":0.043105666,"about_ca_system_score_codex":0.0011259195,"about_ca_system_score_gemma":0.0016481393,"threshold_uncertainty_score":0.08570951},"labels":[],"label_agreement":null},{"id":"W3082078317","doi":"10.2196/20588","title":"Nowcasting Sexually Transmitted Infections in Chicago: Predictive Modeling and Evaluation Study Using Google Trends","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Institute of Allergy and Infectious Diseases; Northwestern University","keywords":"Gonorrhea; Medicine; Chlamydia; Syphilis; Public health; Men who have sex with men; Psychological intervention; Public health surveillance; Sexually transmitted disease; Demography; Reproductive health; Family medicine; Environmental health; Human immunodeficiency virus (HIV); Population; Immunology","score_opus":0.08756500780622882,"score_gpt":0.3724390649755229,"score_spread":0.2848740571692941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082078317","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":[0.9893289,0.0006018822,0.0032312698,0.00066443643,0.000058105416,0.00019426318,0.00408313,0.0003962976,0.0014416911],"genre_scores_gemma":[0.9859601,0.00052285765,0.0054866867,0.00006590402,0.00004638732,0.00010377943,0.0073008444,0.00003900735,0.00047432078],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899775,0.00045835142,0.00006808791,0.00020989862,0.00015812235,0.0001077467],"domain_scores_gemma":[0.9923821,0.005534708,0.0005217783,0.00045887494,0.0008075448,0.0002949444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049127964,0.0012137867,0.0008115504,0.0043680607,0.0005154838,0.0016513459,0.001564502,0.00085186545,0.0013293561],"category_scores_gemma":[0.010298284,0.0004935892,0.0014370668,0.003864744,0.0006095649,0.0016918934,0.0011612904,0.0012532889,0.000348762],"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":[0.00091621606,0.0014625739,0.47188345,0.0004481372,0.00056116655,0.00039857978,0.0006179176,0.4619445,0.00030498387,0.0025048659,0.009169379,0.049788207],"study_design_scores_gemma":[0.000040746057,0.00021035179,0.026851147,0.000035330464,0.000073465824,0.000055235978,0.00028471128,0.9709466,0.00015150334,0.00060158357,0.00072757533,0.00002173469],"about_ca_topic_score_codex":0.1857022,"about_ca_topic_score_gemma":0.096555844,"teacher_disagreement_score":0.1857022,"about_ca_system_score_codex":0.0028303594,"about_ca_system_score_gemma":0.0019944059,"threshold_uncertainty_score":0.3692426},"labels":[],"label_agreement":null},{"id":"W3082160916","doi":"10.2196/18540","title":"Geographic Differences in Cannabis Conversations on Twitter: Infodemiology Study","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Victoria; University of British Columbia","funders":"","keywords":"Cannabis; Sentiment analysis; Recreation; Legislation; Social media; Internet privacy; Legislature; Advertising; Government (linguistics); Content analysis; Public health; Psychology; Political science; Medicine; Computer science; Business; Law; Sociology; Psychiatry; Social science; Artificial intelligence","score_opus":0.06726751348316123,"score_gpt":0.3325211838282359,"score_spread":0.2652536703450747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082160916","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":[0.99224746,0.00016813098,0.00010591831,0.0006114469,0.000025364405,0.00006597718,0.0025274064,0.000007663233,0.0042406293],"genre_scores_gemma":[0.9964444,0.00033305207,0.0001741031,0.00031037326,0.00005510733,0.00010106486,0.0014406955,0.000016577591,0.0011246984],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991748,0.00022712378,0.000087830726,0.00013263422,0.00019485244,0.00018275667],"domain_scores_gemma":[0.99590635,0.0011855887,0.0013440016,0.00023671804,0.0008362966,0.00049099966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070917245,0.00015712762,0.00027860678,0.0017271494,0.0015160014,0.001675306,0.0003223437,0.00047566247,0.0040206504],"category_scores_gemma":[0.0058765626,0.0001759676,0.00028291688,0.0030426914,0.0006860982,0.0020978975,0.001617613,0.000671376,0.0010161477],"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":[0.00027054088,0.00014596453,0.9148491,0.0002705561,0.000058209927,0.0005504592,0.061760638,0.000058144342,0.0008768544,0.00064922543,0.0049550827,0.015555242],"study_design_scores_gemma":[0.0000064951632,0.00005754267,0.9095657,0.000079857855,0.000031147985,0.00025477132,0.08241778,0.00024286486,0.00022932512,0.000094836265,0.0069926283,0.000026976855],"about_ca_topic_score_codex":0.063269906,"about_ca_topic_score_gemma":0.09548379,"teacher_disagreement_score":0.063269906,"about_ca_system_score_codex":0.0014567329,"about_ca_system_score_gemma":0.0011804586,"threshold_uncertainty_score":0.12580329},"labels":[],"label_agreement":null},{"id":"W3086411451","doi":"10.2196/19589","title":"Using WeChat, a Chinese Social Media App, for Early Detection of the COVID-19 Outbreak in December 2019: Retrospective Study","year":2020,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Outbreak; Social media; Coronavirus disease 2019 (COVID-19); Pandemic; Medicine; Index (typography); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); China; Demography; Virology; Geography; Internal medicine; Computer science; Disease; Sociology; World Wide Web","score_opus":0.07865378927646081,"score_gpt":0.41053715009388453,"score_spread":0.3318833608174237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3086411451","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":[0.99180263,0.0007499048,0.00046285693,0.0001356686,0.000027205388,0.00039411953,0.004706995,0.000038008704,0.0016827228],"genre_scores_gemma":[0.9925371,0.0006477059,0.0006097521,0.00028286583,0.000055111024,0.00048905815,0.0041334135,0.000023472025,0.0012214334],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99856925,0.000244939,0.00023043338,0.000347333,0.0003194327,0.00028860735],"domain_scores_gemma":[0.99605656,0.00079865695,0.0011063894,0.00027475468,0.00130899,0.00045459252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013044378,0.0007106724,0.00064573786,0.0019642399,0.0012457483,0.0011890404,0.00058780453,0.00075629266,0.0026067633],"category_scores_gemma":[0.006223952,0.0005892442,0.0008467146,0.0015662109,0.00049559807,0.0015926962,0.0010574975,0.0010458436,0.0011721184],"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":[0.00028596204,0.00025027187,0.98802584,0.00022145154,0.00008362896,0.000868756,0.0010764784,0.000044556953,0.0002669061,0.000044542892,0.0019332377,0.0068982844],"study_design_scores_gemma":[0.000035114626,0.0007756763,0.9861921,0.00015031121,0.00030478268,0.0020226769,0.0037022252,0.0010188103,0.00060119556,0.00007769168,0.005041585,0.000077939025],"about_ca_topic_score_codex":0.024631718,"about_ca_topic_score_gemma":0.021323897,"teacher_disagreement_score":0.024631718,"about_ca_system_score_codex":0.000868462,"about_ca_system_score_gemma":0.0015989017,"threshold_uncertainty_score":0.04897666},"labels":[],"label_agreement":null},{"id":"W3087311639","doi":"10.2196/18281","title":"Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal Clustering","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Health, New Zealand","keywords":"Outbreak; Campylobacteriosis; Campylobacter; Geography; Population; Public health surveillance; Disease surveillance; Statistics; Environmental health; Medicine; Public health; Biology","score_opus":0.07182373639701652,"score_gpt":0.3354103447164423,"score_spread":0.2635866083194258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087311639","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":[0.9190576,0.0007692642,0.074612066,0.001261447,0.00007397394,0.00024493804,0.0017318006,0.00018519246,0.0020637312],"genre_scores_gemma":[0.98433095,0.00019721349,0.013377269,0.000056618803,0.00002148245,0.00006222512,0.0014044542,0.00001382959,0.0005360341],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99734974,0.001318281,0.00022739601,0.00065892056,0.00026967426,0.00017590511],"domain_scores_gemma":[0.98665917,0.009165135,0.0019101374,0.0007735595,0.0012375665,0.00025439123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007643414,0.0007622169,0.0006027616,0.0020210326,0.00044347285,0.0016956248,0.0012252335,0.00075389934,0.00087576476],"category_scores_gemma":[0.02136448,0.000471959,0.0015938772,0.0019110342,0.00050924823,0.0016187956,0.0012759739,0.0010921255,0.00019534305],"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":[0.0004078114,0.00025754826,0.79812455,0.00026976122,0.00090455636,0.000513095,0.0013139654,0.15015809,0.0014042716,0.004360538,0.0022612768,0.040024605],"study_design_scores_gemma":[0.00002067804,0.0001297734,0.14238685,0.00012111043,0.00017706721,0.00013610827,0.0007206228,0.85086393,0.00065070257,0.0034031973,0.0013360617,0.00005383851],"about_ca_topic_score_codex":0.054337673,"about_ca_topic_score_gemma":0.04530703,"teacher_disagreement_score":0.054337673,"about_ca_system_score_codex":0.0013144391,"about_ca_system_score_gemma":0.0011477814,"threshold_uncertainty_score":0.10804278},"labels":[],"label_agreement":null},{"id":"W3087523322","doi":"10.1016/j.vaccine.2020.09.017","title":"Improving the quality and use of immunization and surveillance data: Summary report of the Working Group of the Strategic Advisory Group of Experts on Immunization","year":2020,"lang":"en","type":"article","venue":"Vaccine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":128,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Izaak Walton Killam Health Centre; Dalhousie University","funders":"Pan American Health Organization; Centers for Disease Control and Prevention; GAVI Alliance; Universiteit Stellenbosch; UNICEF; World Health Organization; Bill and Melinda Gates Foundation","keywords":"Context (archaeology); Data quality; Immunization; Disease surveillance; Medicine; Psychological intervention; Quality management; Working group; Quality (philosophy); Data management; Public health; Business; Process management; Environmental health; Political science; Computer science; Geography; Nursing; Marketing; Data mining; Immunology","score_opus":0.08034385236043289,"score_gpt":0.29782534522819293,"score_spread":0.21748149286776003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087523322","genre_codex":"commentary","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":[0.008784035,0.11225696,0.018930087,0.7641844,0.035464767,0.009165385,0.012578918,0.0006867552,0.037948675],"genre_scores_gemma":[0.09992537,0.42744893,0.09847041,0.22587965,0.023072496,0.023517778,0.06743387,0.0015218188,0.032729693],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.8541647,0.055455867,0.026950441,0.005436556,0.046859372,0.011133067],"domain_scores_gemma":[0.6727328,0.08020346,0.02980121,0.008007952,0.18266943,0.026585054],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.24434477,0.0031608862,0.0023579607,0.0109928,0.003951693,0.011715636,0.0060069114,0.0095000025,0.0045639058],"category_scores_gemma":[0.1925259,0.001973514,0.0035647627,0.009697177,0.002985496,0.008118793,0.012491699,0.012344701,0.0034520535],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020893941,0.00023198854,0.010159216,0.016772376,0.00045286596,0.00028387315,0.0075412146,0.0013402601,0.0020863723,0.0058204923,0.71302277,0.24207965],"study_design_scores_gemma":[0.00009310703,0.00015624125,0.012072346,0.02369033,0.00032178708,0.00013752574,0.0032655434,0.0005835111,0.0015134044,0.003257175,0.95473593,0.00017313374],"about_ca_topic_score_codex":0.038191013,"about_ca_topic_score_gemma":0.026948169,"teacher_disagreement_score":0.24434477,"about_ca_system_score_codex":0.01431831,"about_ca_system_score_gemma":0.11418416,"threshold_uncertainty_score":0.9318574},"labels":[],"label_agreement":null},{"id":"W3087673298","doi":"10.2196/23098","title":"Online Public Attention During the Early Days of the COVID-19 Pandemic: Infoveillance Study Based on Baidu Index","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Pandemic; Public health; Index (typography); Coronavirus disease 2019 (COVID-19); Public health surveillance; Medicine; China; Environmental health; The Internet; Geography; Demography; Computer science; Pathology","score_opus":0.06968221900117026,"score_gpt":0.3378844671540791,"score_spread":0.26820224815290883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087673298","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":[0.99821824,0.00012867576,0.00009075834,0.00006561587,0.000007638112,0.00003634856,0.00054512214,0.000004669422,0.0009029599],"genre_scores_gemma":[0.9989735,0.00007694154,0.00008118529,0.000030651383,0.000011709934,0.000032897373,0.0005158576,0.0000022506435,0.000274917],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991296,0.00022161836,0.0001169476,0.00014653514,0.00018289243,0.00020237437],"domain_scores_gemma":[0.9970879,0.00060132885,0.0011623229,0.00016141047,0.00049631327,0.00049065886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009793531,0.0003383563,0.00034972155,0.0026795096,0.00043287672,0.00096693385,0.00037462576,0.00053861336,0.0014741444],"category_scores_gemma":[0.00483048,0.00025595518,0.00067905645,0.0017716518,0.0002730695,0.0012938775,0.00083547516,0.0007129194,0.00047094154],"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":[0.00007305209,0.00012660066,0.9954016,0.000026113688,0.000029236908,0.000116295436,0.0009495941,0.000039769322,0.0001284149,0.000048182093,0.0002998671,0.0027611766],"study_design_scores_gemma":[0.000003904689,0.00010733443,0.996521,0.000011586646,0.00002998371,0.00013522895,0.0019108107,0.00079377956,0.00010111023,0.000027729788,0.0003483592,0.0000093434255],"about_ca_topic_score_codex":0.014502734,"about_ca_topic_score_gemma":0.01216221,"teacher_disagreement_score":0.014502734,"about_ca_system_score_codex":0.0007402488,"about_ca_system_score_gemma":0.0005950527,"threshold_uncertainty_score":0.028836608},"labels":[],"label_agreement":null},{"id":"W3088646735","doi":"10.1016/s2589-7500(20)30215-6","title":"Evaluating interest in off-label use of disinfectants for COVID-19","year":2020,"lang":"en","type":"article","venue":"The Lancet Digital Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SickKids Foundation; Hospital for Sick Children","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Child Health and Human Development; National Institutes of Health","keywords":"Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Virology; Medicine; Outbreak; Internal medicine; Infectious disease (medical specialty)","score_opus":0.5036273771924256,"score_gpt":0.48753781473585495,"score_spread":0.016089562456570616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088646735","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":[0.8553056,0.008135093,0.0024761418,0.030753741,0.00067626586,0.0033759677,0.030394232,0.00007907755,0.06880383],"genre_scores_gemma":[0.978026,0.0016488328,0.0021274136,0.0077331034,0.00045482122,0.0015129012,0.0051892577,0.00002544046,0.0032822131],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9635792,0.021599434,0.004321253,0.0012932509,0.0074067116,0.0018003029],"domain_scores_gemma":[0.8031748,0.08484389,0.07535835,0.0046610567,0.025109386,0.0068526226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.044562012,0.00033948186,0.00043806565,0.002645072,0.0007738881,0.0023346362,0.0015829222,0.0014862841,0.0055419803],"category_scores_gemma":[0.15006112,0.00027665598,0.00064088724,0.002204814,0.0010224988,0.0024506897,0.0019702935,0.0020062774,0.0014102061],"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":[0.002849735,0.0009526564,0.90885705,0.0010183359,0.00027796061,0.00014427079,0.0025754038,0.00019922912,0.00023215245,0.0026389216,0.023632774,0.05662143],"study_design_scores_gemma":[0.00036793956,0.0046387087,0.9191607,0.0016854814,0.0004016191,0.00038822266,0.005815507,0.0028530615,0.002222206,0.0017498101,0.060632735,0.00008401727],"about_ca_topic_score_codex":0.008849483,"about_ca_topic_score_gemma":0.010362796,"teacher_disagreement_score":0.044562012,"about_ca_system_score_codex":0.0024791805,"about_ca_system_score_gemma":0.0028506808,"threshold_uncertainty_score":0.23566926},"labels":[],"label_agreement":null},{"id":"W3088912464","doi":"10.1007/s00405-020-06362-4","title":"Peaks in online inquiries into pharyngitis-related symptoms correspond with annual incidence rates","year":2020,"lang":"en","type":"article","venue":"European Archives of Oto-Rhino-Laryngology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Medizinische Universität Wien","keywords":"Pharyngitis; Sore throat; Incidence (geometry); Medicine; Throat; Demography; Statistics; Internal medicine; Mathematics; Surgery","score_opus":0.010546524114929401,"score_gpt":0.26195622710015454,"score_spread":0.25140970298522514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088912464","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":[0.9768859,0.0012527412,0.0013122794,0.00026277042,0.00003223892,0.000066490495,0.016874598,0.00008388305,0.0032291587],"genre_scores_gemma":[0.993404,0.00036248213,0.0006811491,0.00003421603,0.000024632074,0.000051929826,0.0051407763,0.000014054569,0.00028677352],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99774736,0.0005253976,0.0005066326,0.0004870735,0.00047393123,0.00025949234],"domain_scores_gemma":[0.9844024,0.0052166656,0.007515151,0.00072862615,0.0017827568,0.0003542915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024059343,0.0002025587,0.00038280356,0.0032607117,0.00017255594,0.0011916278,0.00031444366,0.00029321987,0.0023391831],"category_scores_gemma":[0.016180305,0.00016624284,0.00061220094,0.005333197,0.00027451015,0.0010771937,0.000782649,0.0003751184,0.0006129742],"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":[0.00013735563,0.000018653083,0.98580104,0.00031251519,0.00011825538,0.00008465286,0.0007236773,0.00032028405,0.00042518438,0.00013278298,0.0012699491,0.010655624],"study_design_scores_gemma":[0.0000021538683,0.000026188989,0.99724716,0.000053171694,0.000030356994,0.000100871934,0.00079559267,0.00051463104,0.00017758785,0.000053725198,0.0009929802,0.000005552831],"about_ca_topic_score_codex":0.006426412,"about_ca_topic_score_gemma":0.007857915,"teacher_disagreement_score":0.006426412,"about_ca_system_score_codex":0.0004135096,"about_ca_system_score_gemma":0.00052273326,"threshold_uncertainty_score":0.012778044},"labels":[],"label_agreement":null},{"id":"W3088930246","doi":"10.12688/wellcomeopenres.16147.1","title":"Tracking online searches for emotional wellbeing concerns and coping strategies in the UK during the COVID-19 pandemic: a Google Trends analysis","year":2020,"lang":"en","type":"preprint","venue":"Wellcome Open Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Bristol; National Institute for Health and Care Research; Wellcome Trust","keywords":"Pandemic; Coping (psychology); Public health; Mental health; Coronavirus disease 2019 (COVID-19); Distress; Psychological resilience; Psychology; Population; Government (linguistics); Environmental health; Disease; Medicine; Social psychology; Nursing; Clinical psychology; Psychiatry; Infectious disease (medical specialty); Pathology","score_opus":0.42125666390923366,"score_gpt":0.5076221190395908,"score_spread":0.0863654551303571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088930246","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":[0.6647744,0.009772207,0.00022642086,0.0020166896,0.000066439716,0.00021142083,0.31743166,0.00017813625,0.0053225327],"genre_scores_gemma":[0.82931834,0.011834018,0.0018725467,0.00091682497,0.00010392699,0.0005207778,0.15135509,0.00015151198,0.0039270595],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984365,0.00026686228,0.00041697876,0.00023826282,0.0003800101,0.00026141037],"domain_scores_gemma":[0.9875426,0.004845934,0.0044767736,0.00033796858,0.0020935652,0.0007030753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011235201,0.00046564653,0.0009460385,0.0139917815,0.0005306797,0.0024313582,0.0006947503,0.0008871268,0.0041996967],"category_scores_gemma":[0.010092706,0.0003022628,0.0013357259,0.02644199,0.0004691377,0.0029954552,0.0019027268,0.00080444035,0.0021143197],"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":[0.0012016207,0.0000992105,0.8389206,0.014114743,0.00079793297,0.0016653078,0.012533713,0.0007838447,0.0008969314,0.0010055195,0.06620559,0.061774965],"study_design_scores_gemma":[0.000032427215,0.0001166588,0.9598189,0.0022552903,0.0002238748,0.00092188874,0.010384774,0.0009783444,0.00023038076,0.0001700608,0.024789996,0.00007730401],"about_ca_topic_score_codex":0.1379538,"about_ca_topic_score_gemma":0.17824514,"teacher_disagreement_score":0.1379538,"about_ca_system_score_codex":0.0014091234,"about_ca_system_score_gemma":0.0015719899,"threshold_uncertainty_score":0.27430165},"labels":[],"label_agreement":null},{"id":"W3089474914","doi":"10.18103/mra.v8i9.2232","title":"Reviewing the Quality of “Big Data” in automatic data systems: An Example","year":2020,"lang":"en","type":"article","venue":"Medical Research Archives","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Exposition (narrative); Parsing; Quality (philosophy); Data quality; Big data; Data science; Limit (mathematics); Data mining; Artificial intelligence; Operations management; Mathematics","score_opus":0.6602538169065313,"score_gpt":0.5233501464087328,"score_spread":0.1369036704977985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089474914","genre_codex":"review","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":[0.022444656,0.4089228,0.19188257,0.3419997,0.009146206,0.00050803315,0.0012382648,0.0008583093,0.02299941],"genre_scores_gemma":[0.34644318,0.2881897,0.2982551,0.037831746,0.019577967,0.00056730956,0.002418496,0.0015686813,0.005147888],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.913217,0.041833516,0.009116336,0.0040770937,0.030763812,0.0009921098],"domain_scores_gemma":[0.5043343,0.35102963,0.013508237,0.01567394,0.113436505,0.0020173895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08176272,0.0010062965,0.001622044,0.013827547,0.0026660287,0.011692365,0.0027930045,0.0037314189,0.0016140398],"category_scores_gemma":[0.28451028,0.001333316,0.0017808955,0.018076211,0.008760013,0.020499418,0.0033995225,0.0058745486,0.0007148675],"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":[0.00029448204,0.0001817845,0.020884043,0.00976008,0.00045022872,0.0005731054,0.006656114,0.010766762,0.0012909774,0.18954669,0.15632068,0.60327506],"study_design_scores_gemma":[0.00009137173,0.0003136412,0.025553837,0.02056445,0.00035483408,0.0010818501,0.007436227,0.026031911,0.0052424055,0.19691879,0.7160335,0.00037724918],"about_ca_topic_score_codex":0.015423959,"about_ca_topic_score_gemma":0.014783192,"teacher_disagreement_score":0.08176272,"about_ca_system_score_codex":0.011214562,"about_ca_system_score_gemma":0.009169428,"threshold_uncertainty_score":0.4324078},"labels":[],"label_agreement":null},{"id":"W3089519365","doi":"10.2147/ceg.s266546","title":"&lt;p&gt;Googling on Colonoscopy: A Retrospective Analysis of Search Engine Statistics&lt;/p&gt;","year":2020,"lang":"en","type":"article","venue":"Clinical and Experimental Gastroenterology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Colonoscopy; Medicine; Colorectal cancer; Coronavirus disease 2019 (COVID-19); Pandemic; Internal medicine; Cancer","score_opus":0.034159837219657374,"score_gpt":0.3567011931313915,"score_spread":0.3225413559117341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089519365","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":[0.9408776,0.011127524,0.00039063685,0.00040819313,0.000031128948,0.00017042729,0.04397776,0.000061984516,0.002954734],"genre_scores_gemma":[0.9791815,0.0038615218,0.00028866326,0.00022494521,0.00003651731,0.0001490808,0.015694896,0.000046366138,0.0005164647],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9957516,0.0006074181,0.0014884236,0.0006296578,0.0010211208,0.0005018519],"domain_scores_gemma":[0.9811978,0.0044092014,0.010084282,0.0008388331,0.002647005,0.0008229864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025291895,0.00033448418,0.00077581196,0.009854169,0.0004823127,0.0016732838,0.00061048556,0.00057878665,0.0024347224],"category_scores_gemma":[0.01282078,0.00047463368,0.0014673866,0.01724349,0.00054365513,0.0022729307,0.0012365798,0.0006121501,0.0014226157],"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":[0.00018496497,0.000019236955,0.989698,0.0010933712,0.00022366189,0.00025534464,0.00040600114,0.00007246572,0.00018106803,0.00009128642,0.0021029913,0.00567165],"study_design_scores_gemma":[0.000009317763,0.000119029544,0.99134946,0.0006478054,0.0002801339,0.0012734695,0.0014843729,0.00030117363,0.00024613563,0.00004111284,0.0042234045,0.000024619372],"about_ca_topic_score_codex":0.021742532,"about_ca_topic_score_gemma":0.020887405,"teacher_disagreement_score":0.021742532,"about_ca_system_score_codex":0.0010933757,"about_ca_system_score_gemma":0.0020502966,"threshold_uncertainty_score":0.043231964},"labels":[],"label_agreement":null},{"id":"W3089592695","doi":"10.2196/21434","title":"COVID-19 Surveillance in a Primary Care Sentinel Network: In-Pandemic Development of an Application Ontology","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Public Health England; National Institute for Health and Care Research; Royal College of General Practitioners; Wellcome Trust","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Primary care; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Ontology; Medical emergency; Medicine; Virology; Computer science; Outbreak; Family medicine; Infectious disease (medical specialty); Pathology","score_opus":0.042554251747240826,"score_gpt":0.33648687638125063,"score_spread":0.2939326246340098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089592695","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":[0.07761036,0.0002708095,0.86029905,0.00660961,0.0002249597,0.0034991303,0.0032404563,0.002615211,0.045630444],"genre_scores_gemma":[0.16078667,0.0003546255,0.82738096,0.0006694978,0.000030259927,0.0012932869,0.0048090015,0.00036232182,0.00431343],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9926951,0.0029448504,0.0011592765,0.0007692512,0.0018788063,0.0005526874],"domain_scores_gemma":[0.9904779,0.004180549,0.00062086934,0.0013434907,0.0028513777,0.0005258203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012964624,0.0005024926,0.00034304694,0.0029470874,0.0018721189,0.0036282819,0.0017040482,0.0015176595,0.0017142212],"category_scores_gemma":[0.012616428,0.0005838082,0.0014739381,0.0021964323,0.0020190226,0.0056476276,0.004522696,0.0019684893,0.00055062235],"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":[0.0002605286,0.0010450449,0.064615145,0.0031561311,0.00023010005,0.0041817864,0.044324968,0.041707106,0.03551519,0.49887535,0.038817763,0.26727086],"study_design_scores_gemma":[0.000080955906,0.00020642576,0.01893243,0.002405369,0.0002188611,0.0025646505,0.016132148,0.16694736,0.023460863,0.10457473,0.6642825,0.00019366051],"about_ca_topic_score_codex":0.016611535,"about_ca_topic_score_gemma":0.019114017,"teacher_disagreement_score":0.016611535,"about_ca_system_score_codex":0.004869381,"about_ca_system_score_gemma":0.01100896,"threshold_uncertainty_score":0.068564296},"labels":[],"label_agreement":null},{"id":"W3089809590","doi":"10.24095/hpcdp.30.1.05f","title":"Utilisation des données administratives pour comprendre la distribution géographique de la détermination des cas","year":2009,"lang":"fr","type":"article","venue":"Maladies chroniques et blessures au Canada","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Alberta Health; University of Alberta; McMaster University","funders":"","keywords":"Humanities; Political science; Geography; Art","score_opus":0.021332764561171995,"score_gpt":0.30746113152599236,"score_spread":0.28612836696482036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089809590","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":[0.5377305,0.006037108,0.26185125,0.0045704884,0.00038483055,0.0010635915,0.16192979,0.0037670336,0.022665434],"genre_scores_gemma":[0.7752176,0.0017912253,0.15113708,0.0002750852,0.00009071477,0.00073015795,0.06367276,0.00036517074,0.0067202086],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98758787,0.0040696077,0.0016642229,0.001820148,0.0041185706,0.00073955226],"domain_scores_gemma":[0.9229454,0.047573816,0.0050477246,0.0052504395,0.018578175,0.0006044654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015699293,0.00065923063,0.0010937075,0.012382863,0.0012237885,0.005229172,0.0021213284,0.000781515,0.0051482986],"category_scores_gemma":[0.079417855,0.00052799174,0.0016451707,0.015060125,0.0006507027,0.0014972221,0.0018593824,0.0009862455,0.0014376191],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042475908,0.000052933232,0.7253064,0.0014162862,0.0012198833,0.00034453408,0.005809773,0.029366657,0.0025107998,0.009251982,0.021224374,0.20307164],"study_design_scores_gemma":[0.00004677436,0.00009556375,0.77116793,0.000875225,0.00069295114,0.00052536034,0.007951532,0.09437678,0.0074322745,0.0063204737,0.11032348,0.00019178928],"about_ca_topic_score_codex":0.6075352,"about_ca_topic_score_gemma":0.51348615,"teacher_disagreement_score":0.6075352,"about_ca_system_score_codex":0.0060953205,"about_ca_system_score_gemma":0.010214245,"threshold_uncertainty_score":0.7895522},"labels":[],"label_agreement":null},{"id":"W3090228042","doi":"10.23889/ijpds.v5i4.1389","title":"Developing a Data Integrated COVID-19 Tracking System for Decision-Making and Public Use.","year":2020,"lang":"en","type":"article","venue":"PubMed","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Dashboard; Computer science; Government (linguistics); Benchmarking; Data science; World Wide Web; Business; Marketing","score_opus":0.2329492532801744,"score_gpt":0.35038190297675964,"score_spread":0.11743264969658523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090228042","genre_codex":"software","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":[0.020214926,0.0007798183,0.38877997,0.0041958364,0.0011818776,0.004531989,0.16302663,0.38987613,0.027412878],"genre_scores_gemma":[0.09076026,0.0007472079,0.62070113,0.0014233096,0.00027862441,0.0033278386,0.25186118,0.014129552,0.01677088],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9934048,0.0018242928,0.00083956803,0.0016556475,0.0019507385,0.0003248912],"domain_scores_gemma":[0.97892636,0.007648357,0.0012630553,0.004842772,0.005386007,0.0019334608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014983399,0.0014533361,0.0013287732,0.0034822095,0.0015873581,0.005903061,0.003049561,0.0019460494,0.02628958],"category_scores_gemma":[0.036708128,0.0013588578,0.0016148606,0.004036375,0.0005784108,0.0072585503,0.006276843,0.0024642737,0.01642911],"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":[0.0020557828,0.0006201308,0.026411498,0.0017919418,0.00044376584,0.00076735805,0.0031708844,0.014722608,0.008580458,0.013635347,0.56590325,0.36189708],"study_design_scores_gemma":[0.0006040933,0.0004981601,0.017170312,0.0011729035,0.00020777257,0.00044935817,0.0013317934,0.18080315,0.014198424,0.027080972,0.7559681,0.00051498605],"about_ca_topic_score_codex":0.018429315,"about_ca_topic_score_gemma":0.014571735,"teacher_disagreement_score":0.02628958,"about_ca_system_score_codex":0.0021792548,"about_ca_system_score_gemma":0.005168327,"threshold_uncertainty_score":0.08794743},"labels":[],"label_agreement":null},{"id":"W3090233416","doi":"10.24095/hpcdp.29.1.04f","title":"Sources de données des populations utilisées en surveillance des maladies chroniques","year":2008,"lang":"fr","type":"article","venue":"Maladies chroniques au Canada","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Manitoba; Chronic Disease Prevention Alliance of Canada; University of Saskatchewan","funders":"","keywords":"Concordance; Humanities; Political science; Medicine; Gynecology; Philosophy","score_opus":0.03259479783266683,"score_gpt":0.2629647245194625,"score_spread":0.23036992668679568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090233416","genre_codex":"empirical","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":[0.3981602,0.047956478,0.124107525,0.0052773636,0.0009599283,0.00676256,0.38850626,0.00094651984,0.027323045],"genre_scores_gemma":[0.6644956,0.021174833,0.12442413,0.0026256528,0.00039072445,0.020670893,0.15544409,0.00030447665,0.010469655],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.92082965,0.037238583,0.015899193,0.005741997,0.018709535,0.0015810187],"domain_scores_gemma":[0.8161225,0.10919518,0.024698077,0.01874805,0.030351186,0.00088500907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05125379,0.0009838504,0.0019088074,0.011095597,0.0016973078,0.0039023275,0.0024746493,0.001416572,0.0036256728],"category_scores_gemma":[0.19764593,0.0010104144,0.0020940905,0.025999527,0.0007311636,0.0013412755,0.002508399,0.0013832717,0.0010321282],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007808538,0.00017534361,0.7890276,0.009564817,0.004008334,0.00064035226,0.013130255,0.003737531,0.001613422,0.00694517,0.014129103,0.15624726],"study_design_scores_gemma":[0.00023562086,0.00037896857,0.8056152,0.0077046277,0.0025983893,0.00097237877,0.0061939126,0.0032976344,0.004653775,0.005606032,0.16248803,0.0002554208],"about_ca_topic_score_codex":0.09669865,"about_ca_topic_score_gemma":0.070695244,"teacher_disagreement_score":0.9964103,"about_ca_system_score_codex":0.003589664,"about_ca_system_score_gemma":0.009592127,"threshold_uncertainty_score":0.27105916},"labels":[],"label_agreement":null},{"id":"W3091190124","doi":"10.1371/journal.pone.0239886","title":"Web and phone-based COVID-19 syndromic surveillance in Canada: A cross-sectional study","year":2020,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Ontario; Toronto Public Health; Sinai Health System; McGill University Health Centre; Women's College Hospital; University of Toronto; University Health Network","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; University of Toronto","keywords":"Medicine; Respondent; Cross-sectional study; Public health; Context (archaeology); Phone; Public health surveillance; Family medicine; Demography; Environmental health; Geography","score_opus":0.0662526051638153,"score_gpt":0.28214901243257967,"score_spread":0.21589640726876436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091190124","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":[0.98958886,0.0005596647,0.00020388537,0.00028867376,0.000012367717,0.00018070359,0.007465634,0.00001654095,0.0016838119],"genre_scores_gemma":[0.99542457,0.0005080846,0.00042019808,0.0003294449,0.000007198613,0.000078734636,0.0024164568,0.000008621924,0.0008066991],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981635,0.00014625768,0.00012557463,0.00029056176,0.0007478814,0.00052627764],"domain_scores_gemma":[0.99532795,0.00026871182,0.00087901513,0.00013245488,0.002370814,0.0010211108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014322386,0.00043832857,0.00046088084,0.0017199273,0.0032732564,0.0016896261,0.0012022738,0.0005963305,0.0018826445],"category_scores_gemma":[0.0028185141,0.0005551417,0.000641299,0.0053444067,0.00090337574,0.0006238564,0.0011832543,0.00089345506,0.0002954885],"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":[0.00003254324,0.00004109863,0.99703014,0.000039614166,0.00003708012,0.000046095756,0.00061758485,0.000029001418,0.000107749955,0.000032028285,0.0005173989,0.0014696666],"study_design_scores_gemma":[0.0000056873832,0.000035808305,0.9978497,0.000026517551,0.00002286904,0.000044011504,0.0012604542,0.0001644843,0.000039602553,0.000008342805,0.000534851,0.000007714545],"about_ca_topic_score_codex":0.9938652,"about_ca_topic_score_gemma":0.9945117,"teacher_disagreement_score":0.028083669,"about_ca_system_score_codex":0.028083669,"about_ca_system_score_gemma":0.035009984,"threshold_uncertainty_score":0.20376223},"labels":[],"label_agreement":null},{"id":"W3094152559","doi":"10.2196/23173","title":"A Data Visualization and Dissemination Resource to Support HIV Prevention and Care at the Local Level: Analysis and Uses of the AIDSVu Public Data Resource","year":2020,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":195,"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 Institute of Allergy and Infectious Diseases; Center for AIDS Research, University of Washington; Petroleum Technology Alliance Canada; Emory University; Gilead Sciences","keywords":"Infographic; Population; Data visualization; Census; Public health; Data science; Computer science; Resource (disambiguation); American Community Survey; Social media; Geocoding; Geography; Medicine; Visualization; World Wide Web; Environmental health; Data mining; Cartography","score_opus":0.18371200007225,"score_gpt":0.4728071293570879,"score_spread":0.28909512928483794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094152559","genre_codex":"dataset","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":[0.04286408,0.003553657,0.04294784,0.01528153,0.001180637,0.0029563042,0.6785797,0.06480672,0.14782956],"genre_scores_gemma":[0.16650021,0.0051090843,0.27187464,0.0030634678,0.00077206106,0.0070894808,0.4921991,0.017937936,0.035454083],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99511164,0.002289981,0.0005150814,0.00042729676,0.0013767858,0.00027920518],"domain_scores_gemma":[0.9644734,0.020093178,0.0014249987,0.004646458,0.0071209893,0.0022409365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011551103,0.0010477658,0.0006827189,0.00962874,0.0017989299,0.0065147793,0.0018767911,0.00093874417,0.04632191],"category_scores_gemma":[0.043245576,0.0007221991,0.000973811,0.012393071,0.00048521734,0.0073691662,0.0067229928,0.0016376209,0.016859625],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025617113,0.00019263281,0.015519396,0.0015184662,0.000073200776,0.00017415875,0.004428238,0.0009458089,0.0005324553,0.005375865,0.7767581,0.19422548],"study_design_scores_gemma":[0.00014019263,0.000106812935,0.017554538,0.002006257,0.00010841193,0.00017269183,0.0060433745,0.0060169576,0.0013841675,0.007346192,0.95892996,0.00019049401],"about_ca_topic_score_codex":0.015937645,"about_ca_topic_score_gemma":0.023295145,"teacher_disagreement_score":0.04632191,"about_ca_system_score_codex":0.0017338588,"about_ca_system_score_gemma":0.004597751,"threshold_uncertainty_score":0.15496218},"labels":[],"label_agreement":null},{"id":"W3094556160","doi":"10.1332/175795920x16009650086121","title":"Panel recovery after 22 years: how we reactivated a 45 year cohort study in Canada","year":2020,"lang":"en","type":"article","venue":"Longitudinal and Life Course Studies","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University; York University; Dalhousie University","funders":"","keywords":"Cohort; Attrition; Government (linguistics); TRACE (psycholinguistics); Cohort study; Gerontology; Geography; Demographic economics; Medicine; Economics","score_opus":0.045787478201952794,"score_gpt":0.27682302206462023,"score_spread":0.23103554386266745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094556160","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4374917,0.016146146,0.18153384,0.19757217,0.015679007,0.05183328,0.038129836,0.002579522,0.059034552],"genre_scores_gemma":[0.7057687,0.0050043026,0.15508746,0.063532166,0.0018841525,0.02807181,0.017979788,0.001149416,0.021522181],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.8683619,0.07511785,0.007944262,0.00840335,0.027493572,0.012679069],"domain_scores_gemma":[0.8273497,0.049094576,0.009061,0.03653439,0.07128375,0.0066766026],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.23120165,0.0009939517,0.0015052474,0.0031312748,0.013119834,0.007257114,0.007035201,0.002346884,0.003014065],"category_scores_gemma":[0.26493248,0.001252174,0.0026012622,0.004986174,0.0031954022,0.0019409604,0.008248547,0.006188713,0.00096856593],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022914854,0.0003580137,0.45204276,0.002213017,0.002519407,0.0019152156,0.08052953,0.005830482,0.0024254832,0.022307752,0.17136963,0.25619736],"study_design_scores_gemma":[0.0005674742,0.00082078495,0.3260083,0.004879047,0.0022794735,0.0003816009,0.045522127,0.009797061,0.0055681816,0.013115224,0.590282,0.00077876355],"about_ca_topic_score_codex":0.93241256,"about_ca_topic_score_gemma":0.9320985,"teacher_disagreement_score":0.95876485,"about_ca_system_score_codex":0.041235134,"about_ca_system_score_gemma":0.13885774,"threshold_uncertainty_score":0.9480652},"labels":[],"label_agreement":null},{"id":"W3095472877","doi":"10.1038/s41598-020-75889-7","title":"A decision support framework for prediction of avian influenza","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":17,"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 Guelph","funders":"Canada First Research Excellence Fund; Ministry of Agriculture, Food and Rural Affairs; Chicken Farmers of Saskatchewan; University of Guelph; Ontario Ministry of Agriculture, Food and Rural Affairs; Canadian Poultry Research Council","keywords":"Influenza A virus subtype H5N1; Situation awareness; Decision support system; Avian influenza virus; Situational ethics; Pandemic; Bird flu; Computer science; Disease; Disease control; Knowledge base; Business; Risk analysis (engineering); Operations research; Environmental health; Medicine; Virus; Coronavirus disease 2019 (COVID-19); Virology; Infectious disease (medical specialty); Artificial intelligence; Psychology; Engineering","score_opus":0.037955087255778265,"score_gpt":0.32081376698581937,"score_spread":0.2828586797300411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095472877","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":[0.026006106,0.00083910616,0.9595615,0.0018134868,0.00018235897,0.00051087537,0.003225634,0.003026646,0.004834146],"genre_scores_gemma":[0.4307121,0.0006754237,0.56171286,0.0003857193,0.00011307729,0.000667344,0.0034001893,0.000059424274,0.0022738934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990031,0.00030073605,0.00013630056,0.0002112978,0.0002561702,0.0000923983],"domain_scores_gemma":[0.99814105,0.0012199586,0.0001082666,0.00005492862,0.00036186914,0.000113983435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019481769,0.0011060386,0.00084481505,0.0014567467,0.0007107967,0.0022766101,0.0014720837,0.0010228276,0.0033807985],"category_scores_gemma":[0.0043488396,0.00034567263,0.0011794324,0.00085047854,0.00041019285,0.0011167405,0.0011661218,0.0011751077,0.0005768146],"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":[0.00023880249,0.00026673978,0.0031064912,0.0003334546,0.00015176242,0.00053578615,0.0001885827,0.8652427,0.0016604451,0.028008588,0.0056404495,0.09462624],"study_design_scores_gemma":[0.000022777187,0.000026583506,0.00015144738,0.000024901405,0.00002297771,0.000025608715,0.000027881708,0.9890169,0.0003828089,0.007885392,0.0024044935,0.000008213569],"about_ca_topic_score_codex":0.026211858,"about_ca_topic_score_gemma":0.0253489,"teacher_disagreement_score":0.026211858,"about_ca_system_score_codex":0.0013712446,"about_ca_system_score_gemma":0.0026068564,"threshold_uncertainty_score":0.0521186},"labels":[],"label_agreement":null},{"id":"W3095502049","doi":"10.1093/jssam/smaa023","title":"Machine Learning for Occupation Coding—A Comparison Study","year":2020,"lang":"en","type":"article","venue":"Journal of Survey Statistics and Methodology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":24,"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":"Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology; Robert Koch Institut; Universität Mannheim; Social Sciences and Humanities Research Council of Canada; Deutsche Forschungsgemeinschaft; Institut für Arbeitsmarkt- und Berufsforschung","keywords":"Computer science; Coding (social sciences); Machine learning; Artificial intelligence; Multinomial distribution; Data mining; Statistics; Mathematics","score_opus":0.40040143944183104,"score_gpt":0.47051340662495067,"score_spread":0.07011196718311963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095502049","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.964805,0.002601005,0.022625115,0.001633907,0.00029784927,0.00031596379,0.0012869291,0.00035723374,0.0060769855],"genre_scores_gemma":[0.9811615,0.00039646716,0.0147958705,0.00015736796,0.000102390346,0.00018276376,0.0022790295,0.000063862666,0.0008607627],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9881981,0.008676227,0.00046077734,0.001105959,0.0011823723,0.00037667152],"domain_scores_gemma":[0.9058338,0.07850874,0.0028032558,0.0048511615,0.0070319194,0.00097111677],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.016942155,0.00085015694,0.0008303429,0.0029422406,0.0008997315,0.0019965598,0.0018563361,0.0014488361,0.0037453193],"category_scores_gemma":[0.06901777,0.00028472987,0.0012256486,0.0029851699,0.0010623398,0.002800574,0.0013081041,0.0022369768,0.001336861],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006119942,0.0044771996,0.33913112,0.0018139206,0.0015624271,0.0002549437,0.0022522556,0.08193183,0.001026108,0.012912861,0.02636205,0.5221554],"study_design_scores_gemma":[0.0006985183,0.0027241933,0.2539698,0.0007449757,0.0006943414,0.00033830397,0.0049619055,0.7021742,0.0027090204,0.014903416,0.015891852,0.00018952318],"about_ca_topic_score_codex":0.011154591,"about_ca_topic_score_gemma":0.0068100956,"teacher_disagreement_score":0.98305786,"about_ca_system_score_codex":0.0025131055,"about_ca_system_score_gemma":0.0016008797,"threshold_uncertainty_score":0.08959979},"labels":[],"label_agreement":null},{"id":"W3097192197","doi":"10.20344/amp.15056","title":"Canadian Public Health Agency Lessons on Using Choropleth Maps to Characterize Geographic Distribution of COVID-19 Data","year":2020,"lang":"en","type":"article","venue":"Acta Médica Portuguesa","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Ontario; Baycrest Hospital; Toronto Public Health","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Agency (philosophy); Public health; 2019-20 coronavirus outbreak; Geography; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Distribution (mathematics); Cartography; Regional science; Statistics; Medicine; Sociology; Virology; Mathematics; Nursing","score_opus":0.20077045762259738,"score_gpt":0.3652420057610703,"score_spread":0.16447154813847292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097192197","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020765698,0.0065048356,0.076426044,0.5624036,0.0046990346,0.0015593882,0.066279456,0.006253575,0.2551083],"genre_scores_gemma":[0.31288776,0.018270485,0.50105697,0.055757154,0.0014809858,0.0015271349,0.044341784,0.0035693578,0.06110841],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9794194,0.0056600035,0.0017800523,0.0014575799,0.010159733,0.001523213],"domain_scores_gemma":[0.8583858,0.031772383,0.0030539886,0.01516736,0.08604852,0.005571949],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.032066397,0.0010275891,0.000774019,0.008143378,0.003799192,0.010060595,0.004897059,0.002152069,0.0119602075],"category_scores_gemma":[0.14343037,0.00095161126,0.0012189785,0.012895969,0.0028072537,0.00584183,0.003882298,0.004107399,0.0046866043],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000071918315,0.00008863247,0.043676212,0.0004320646,0.00008886954,0.00020235054,0.0035254085,0.0034243923,0.0005566894,0.021367475,0.68636084,0.24020514],"study_design_scores_gemma":[0.000072358656,0.000038865634,0.06043124,0.0026054508,0.0000791397,0.0002101499,0.010344212,0.008055815,0.0012806446,0.021326445,0.8952905,0.0002651426],"about_ca_topic_score_codex":0.97238636,"about_ca_topic_score_gemma":0.9715118,"teacher_disagreement_score":0.976279,"about_ca_system_score_codex":0.023720955,"about_ca_system_score_gemma":0.09627396,"threshold_uncertainty_score":0.17210841},"labels":[],"label_agreement":null},{"id":"W3097893232","doi":"10.1007/978-3-030-63092-8_21","title":"Digitalization of the Oil and Gas Industry: Practical Lessons Learned from Digital Responses During the First Stage of the COVID-19 Outbreak","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Memorial University of Newfoundland","funders":"","keywords":"Software deployment; Business; Digital health; Government (linguistics); Health care; Coronavirus disease 2019 (COVID-19); Personal protective equipment; Standardization; Infectious disease (medical specialty); Computer security; Computer science; Medicine; Disease; Economic growth; Economics","score_opus":0.06176467656772496,"score_gpt":0.3347804712073875,"score_spread":0.2730157946396626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097893232","genre_codex":"other","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":[0.11744782,0.034478508,0.012586259,0.13649607,0.0024413124,0.00013291597,0.00057554536,0.00015486176,0.6956868],"genre_scores_gemma":[0.77233744,0.05672805,0.011051685,0.013526402,0.0009645735,0.00010139651,0.0006093238,0.0001513789,0.14452969],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9993636,0.00029255956,0.000019592306,0.000060962575,0.00014795939,0.00011534788],"domain_scores_gemma":[0.9977071,0.0017356838,0.000099390636,0.00012082667,0.0001662787,0.0001706404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021101385,0.00038334733,0.00017673975,0.0007752935,0.0016200151,0.006099409,0.0008159684,0.0019272573,0.009360457],"category_scores_gemma":[0.0042687957,0.00014118663,0.00021538188,0.0011871465,0.0035123262,0.0060893055,0.0022064385,0.0025503398,0.00082483306],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013110174,0.00023886708,0.0076949005,0.0006498798,0.000022660137,0.0012603453,0.03507613,0.002689765,0.0013054411,0.3664203,0.11142036,0.47309023],"study_design_scores_gemma":[0.000019446868,0.00011228637,0.0086990325,0.0010869461,0.000014119782,0.0006071681,0.063167386,0.0016992429,0.0025131127,0.09411115,0.8279243,0.00004578777],"about_ca_topic_score_codex":0.00801889,"about_ca_topic_score_gemma":0.016202658,"teacher_disagreement_score":0.009360457,"about_ca_system_score_codex":0.0033018605,"about_ca_system_score_gemma":0.003358479,"threshold_uncertainty_score":0.031313837},"labels":[],"label_agreement":null},{"id":"W3098000326","doi":"10.1016/j.ssci.2020.105093","title":"An overview of scientometric mapping for the safety science community: Methods, tools, and framework","year":2020,"lang":"en","type":"article","venue":"Safety Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":251,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Scientometrics; Data science; Computer science; Context (archaeology); Domain (mathematical analysis); Thematic map; Process (computing); Management science; Engineering; World Wide Web; Geography; Cartography","score_opus":0.2517380484306804,"score_gpt":0.466025153135134,"score_spread":0.2142871047044536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3098000326","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":[0.0033741193,0.04721672,0.9037263,0.011048486,0.00087344355,0.00094794936,0.007894251,0.0048444024,0.020074349],"genre_scores_gemma":[0.04674565,0.082154915,0.85097086,0.0012606666,0.0025101828,0.002566475,0.009364362,0.0011318646,0.003295028],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97211975,0.014595004,0.0030388676,0.0022213564,0.007453347,0.00057168485],"domain_scores_gemma":[0.91825956,0.054004975,0.0042275977,0.008930148,0.012936771,0.0016408481],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.036810677,0.0035493711,0.004938899,0.06899102,0.0030723303,0.014294997,0.004492213,0.0029488336,0.007929512],"category_scores_gemma":[0.100414015,0.0016896196,0.0038945542,0.094674386,0.0033964089,0.009980338,0.007599164,0.00340606,0.0042292997],"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":[0.000056899564,0.00016129825,0.0077581736,0.008713309,0.000742745,0.00013714921,0.0011617794,0.0117839845,0.00058293523,0.22142136,0.055405635,0.69207466],"study_design_scores_gemma":[0.00003276662,0.00008067097,0.009661251,0.004736382,0.00029408114,0.00046988076,0.0009799211,0.04032032,0.001457168,0.5919998,0.34970874,0.00025897563],"about_ca_topic_score_codex":0.0119834235,"about_ca_topic_score_gemma":0.01181036,"teacher_disagreement_score":0.931009,"about_ca_system_score_codex":0.005813143,"about_ca_system_score_gemma":0.013052474,"threshold_uncertainty_score":0.1946758},"labels":[],"label_agreement":null},{"id":"W3099862276","doi":"10.2196/23379","title":"Starter Kit for Geotagging and Geovisualization in Health Care: Resource Paper","year":2020,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Geotagging; Geospatial analysis; Resource (disambiguation); Computer science; Nanolaser; Health care; Health geography; Geovisualization; Geographic information system; Geography; Data science; World Wide Web; Visualization; Cartography; Data mining; Health policy; International health","score_opus":0.059593803074628755,"score_gpt":0.4365722843399681,"score_spread":0.37697848126533934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3099862276","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066307914,0.0049853185,0.35593113,0.009864654,0.004650889,0.013718022,0.10378934,0.083831646,0.4165982],"genre_scores_gemma":[0.014834593,0.006474027,0.6024274,0.004572971,0.00144954,0.011144982,0.05241311,0.02380022,0.28288314],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99663466,0.0013346323,0.00038212264,0.00026161686,0.0011634311,0.0002235407],"domain_scores_gemma":[0.9724787,0.01841951,0.0010556849,0.002586505,0.004293558,0.001166078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073283575,0.0013106051,0.00092138234,0.005047301,0.0011544097,0.0031644308,0.0019991344,0.0017930433,0.25542167],"category_scores_gemma":[0.025901914,0.0007996735,0.000897734,0.0051625487,0.0006813039,0.0052833734,0.0042941472,0.0017904268,0.16278212],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000120517754,0.00014571694,0.00036850586,0.0021047355,0.000011449159,0.00021343089,0.0012728957,0.00020833273,0.0024921934,0.0042204005,0.64925057,0.33959135],"study_design_scores_gemma":[0.00003399645,0.000090127556,0.0011130202,0.00079334,0.000009873313,0.00019567805,0.00055802177,0.0002837276,0.0016996815,0.0032066815,0.991967,0.00004893911],"about_ca_topic_score_codex":0.0026769813,"about_ca_topic_score_gemma":0.005880807,"teacher_disagreement_score":0.25542167,"about_ca_system_score_codex":0.0011316217,"about_ca_system_score_gemma":0.0030128374,"threshold_uncertainty_score":0.8544706},"labels":[],"label_agreement":null},{"id":"W3104118410","doi":"10.22541/au.160391056.64115187/v1","title":"FAIR Access to Personal Health Information in Private and Public COVID-19 Health Applications","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital","funders":"","keywords":"eHealth; Internet privacy; Coronavirus disease 2019 (COVID-19); Business; Pandemic; Corporate governance; Personally identifiable information; Public health; Tracking (education); Health care; Public relations; Computer security; Computer science; Medicine; Psychology; Political science; Nursing","score_opus":0.08467306100200275,"score_gpt":0.4024963679076321,"score_spread":0.3178233069056293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104118410","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":[0.10670274,0.0036222572,0.64297783,0.08428276,0.0016587533,0.0014249742,0.0022320347,0.0016764345,0.15542232],"genre_scores_gemma":[0.87232476,0.0012979897,0.09540404,0.005117296,0.00094196683,0.0008217842,0.0012638312,0.00035172456,0.022476662],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9396353,0.036641818,0.0033403544,0.005463741,0.010945959,0.0039729136],"domain_scores_gemma":[0.8449946,0.07840164,0.006356068,0.052739844,0.011998327,0.005509554],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.06796626,0.00057041977,0.0014788151,0.0024663976,0.0039612353,0.017394636,0.0028119232,0.005811085,0.01381097],"category_scores_gemma":[0.18615656,0.0012134798,0.0012606105,0.0033890652,0.008440896,0.026102595,0.015113563,0.0044198614,0.0032086903],"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":[0.00042887378,0.00024052315,0.011368642,0.00017870004,0.000057295892,0.0004971498,0.0031266985,0.0030342236,0.000898949,0.8818314,0.018580258,0.07975728],"study_design_scores_gemma":[0.00011230823,0.00009324667,0.002829884,0.00032092468,0.000040873703,0.0003827724,0.001365429,0.015121787,0.0018757078,0.8972716,0.08052253,0.000062925355],"about_ca_topic_score_codex":0.0041398006,"about_ca_topic_score_gemma":0.00293431,"teacher_disagreement_score":0.9971881,"about_ca_system_score_codex":0.0037861338,"about_ca_system_score_gemma":0.0070024156,"threshold_uncertainty_score":0.35944426},"labels":[],"label_agreement":null},{"id":"W3107224297","doi":"10.1177/0002764220975067","title":"Google Trends as a Predictor of Presidential Elections: The United States Versus Canada","year":2020,"lang":"en","type":"article","venue":"American Behavioral Scientist","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Polling; Presidential system; Presidency; Presidential election; Political science; Voting; Presidential campaign; General election; Public administration; Politics; Computer science; Law","score_opus":0.024439430993091073,"score_gpt":0.3160087407263804,"score_spread":0.2915693097332893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107224297","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":[0.98561966,0.0011130386,0.0003367793,0.0010954414,0.000046583395,0.000020719708,0.005678752,0.000030862822,0.0060582627],"genre_scores_gemma":[0.99579215,0.00034162827,0.00023495089,0.00005487388,0.000011817109,0.0000053141,0.002694054,0.000010060029,0.00085510087],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999326,0.00012334132,0.00003674418,0.000090028276,0.00020060885,0.00022337871],"domain_scores_gemma":[0.9960658,0.0012428749,0.000580998,0.0001539556,0.0013400338,0.0006164052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001347402,0.00024622722,0.000355682,0.0024847125,0.0012068931,0.0017467536,0.0004365439,0.00026672444,0.0013253733],"category_scores_gemma":[0.008904117,0.00011687977,0.00047855012,0.005416052,0.0005253126,0.00046018744,0.0006220286,0.0007383037,0.00017251809],"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":[0.00015166297,0.000023055603,0.9857734,0.000035696925,0.00011058387,0.00009794777,0.00029272216,0.0018206353,0.000080144164,0.0008126679,0.00314992,0.0076515866],"study_design_scores_gemma":[0.000015391764,0.000025791993,0.97822833,0.000055715387,0.0001164933,0.00007080205,0.001700916,0.013927246,0.00030567255,0.00021337694,0.005314769,0.000025468762],"about_ca_topic_score_codex":0.9771936,"about_ca_topic_score_gemma":0.9814124,"teacher_disagreement_score":0.022806406,"about_ca_system_score_codex":0.0074258107,"about_ca_system_score_gemma":0.011374957,"threshold_uncertainty_score":0.053878307},"labels":[],"label_agreement":null},{"id":"W3107599099","doi":"10.2139/ssrn.3695335","title":"COVID-19 Surveillance Data and Models: Review and Analysis, Part 1","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Pandemic; Data science; Computer science; Virology; Medicine; Outbreak; Infectious disease (medical specialty)","score_opus":0.05256281332756093,"score_gpt":0.3249502823053192,"score_spread":0.2723874689777583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107599099","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":[0.0019193238,0.9459451,0.019552976,0.008209887,0.001088739,0.0003384909,0.01993799,0.00037416982,0.0026332818],"genre_scores_gemma":[0.044397958,0.8901864,0.031333353,0.0041057607,0.001827325,0.0008488779,0.026613615,0.00017437762,0.00051224773],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9886874,0.006205486,0.0016463874,0.0010832633,0.002192625,0.00018479725],"domain_scores_gemma":[0.8804658,0.101651974,0.005491134,0.004575513,0.0073597375,0.00045579392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02916727,0.0018683884,0.0033576041,0.0063455086,0.00028883875,0.0031042947,0.0042660623,0.0017164497,0.005085776],"category_scores_gemma":[0.12714498,0.0012657281,0.0049675056,0.008130878,0.00096515566,0.0040573543,0.0016270686,0.0028598364,0.0013224795],"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":[0.0004927215,0.00014407426,0.01628285,0.09097969,0.006309786,0.000058906764,0.00016297068,0.01190458,0.00021838833,0.018371519,0.11210992,0.7429646],"study_design_scores_gemma":[0.00039240232,0.0007091536,0.026677283,0.18731989,0.012811272,0.0007874282,0.00031122146,0.033051565,0.0015051331,0.051142637,0.68492705,0.0003648906],"about_ca_topic_score_codex":0.014425885,"about_ca_topic_score_gemma":0.011848899,"teacher_disagreement_score":0.02916727,"about_ca_system_score_codex":0.0033540367,"about_ca_system_score_gemma":0.00903769,"threshold_uncertainty_score":0.15425307},"labels":[],"label_agreement":null},{"id":"W3110302913","doi":"10.1016/j.socscimed.2020.113549","title":"Open government data, uncertainty and coronavirus: An infodemiological case study","year":2020,"lang":"en","type":"article","venue":"Social Science & Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","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 Waterloo; Public Health Ontario; University of Toronto; McMaster University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Transparency (behavior); Pandemic; Accountability; Government (linguistics); Open data; Open government; Public health; Population; Public relations; Value (mathematics); Business; Coronavirus disease 2019 (COVID-19); Political science; Actuarial science; Public economics; Medicine; Environmental health; Economics; Computer science; Law; Infectious disease (medical specialty)","score_opus":0.20514450182834665,"score_gpt":0.45154859260202257,"score_spread":0.24640409077367592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110302913","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":[0.9543112,0.0011766207,0.0038540778,0.016931528,0.00006983566,0.00018738945,0.0010631026,0.000028315953,0.022377921],"genre_scores_gemma":[0.99539477,0.0006306796,0.0019159626,0.00065267336,0.0000513266,0.00004529682,0.00022746684,0.000012518063,0.0010691662],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99570197,0.002613096,0.00025213018,0.0001985079,0.0006684376,0.0005658581],"domain_scores_gemma":[0.95309126,0.039227534,0.0028332507,0.001500555,0.0017634163,0.0015839078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005951608,0.0002688166,0.00032387034,0.0021999404,0.0048350417,0.0033864488,0.0010004172,0.004155632,0.004003509],"category_scores_gemma":[0.030911643,0.00022892623,0.0005254248,0.0037215864,0.0030491245,0.0031350772,0.0023493404,0.0023056057,0.00026790265],"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":[0.0013081391,0.0034892291,0.5745431,0.0008613787,0.0002839504,0.10493375,0.06533987,0.010943349,0.00085826224,0.13272098,0.029366154,0.0753518],"study_design_scores_gemma":[0.00048776076,0.0016686053,0.16658191,0.001787386,0.00044008455,0.13893048,0.388821,0.049510274,0.0035027808,0.09381829,0.15416549,0.00028597438],"about_ca_topic_score_codex":0.03717044,"about_ca_topic_score_gemma":0.03321974,"teacher_disagreement_score":0.03717044,"about_ca_system_score_codex":0.0030691628,"about_ca_system_score_gemma":0.002591052,"threshold_uncertainty_score":0.07390815},"labels":[],"label_agreement":null},{"id":"W3111370847","doi":"10.2196/20913","title":"Trends in Web Searches About the Causes and Treatments of Autism Over the Past 15 Years: Exploratory Infodemiology Study","year":2020,"lang":"en","type":"article","venue":"JMIR Pediatrics and Parenting","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Surrey Place Centre; Hospital for Sick Children; University of Toronto; McMaster University","funders":"","keywords":"The Internet; Autism; Autism spectrum disorder; Informatics; MEDLINE; Medicine; Intervention (counseling); Variety (cybernetics); World Wide Web; Psychology; Data science; Internet privacy; Computer science; Psychiatry; Artificial intelligence; Biology; Engineering","score_opus":0.061215873322714366,"score_gpt":0.3344269751090068,"score_spread":0.27321110178629243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111370847","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":[0.9630267,0.001287131,0.00019444618,0.0003223353,0.000011065962,0.00012129446,0.032524716,0.000048963993,0.0024633277],"genre_scores_gemma":[0.9702762,0.0013734484,0.00089104933,0.00019102651,0.00003376191,0.00034680756,0.025918636,0.000041580486,0.0009275469],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99806994,0.0004399631,0.00049624435,0.00036054736,0.00042742272,0.00020591414],"domain_scores_gemma":[0.98400766,0.00515067,0.0074712844,0.0006380481,0.0018648204,0.00086760713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020821681,0.00028531306,0.0005587169,0.012465521,0.0006000535,0.0017017461,0.0006096024,0.00061764225,0.0025884002],"category_scores_gemma":[0.012473753,0.00028526408,0.0013793822,0.014204127,0.00042327543,0.0024183737,0.0016002891,0.00078690035,0.0009862384],"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":[0.00022793686,0.00014917375,0.97932774,0.00065053167,0.00026665043,0.0002389521,0.0026491112,0.00014526083,0.00016349554,0.00018979999,0.0030848652,0.012906473],"study_design_scores_gemma":[0.0000148073095,0.0001159546,0.98986876,0.00024177965,0.00013339892,0.0004908154,0.0042347685,0.0005371159,0.00015498087,0.00007216551,0.0041148975,0.00002063261],"about_ca_topic_score_codex":0.020254921,"about_ca_topic_score_gemma":0.025262056,"teacher_disagreement_score":0.020254921,"about_ca_system_score_codex":0.0009362516,"about_ca_system_score_gemma":0.0012475586,"threshold_uncertainty_score":0.040274024},"labels":[],"label_agreement":null},{"id":"W3111577913","doi":"10.7189/jogh.10.020391","title":"Imagining a lean and agile digital health ecosystem – a measure of pandemic responsiveness","year":2020,"lang":"en","type":"review","venue":"Journal of Global Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Global Health Research; Hospital for Sick Children","funders":"","keywords":"Pandemic; Agile software development; Measure (data warehouse); Coronavirus disease 2019 (COVID-19); MEDLINE; 2019-20 coronavirus outbreak; Business; Computer science; Medicine; Biology; Virology; Internal medicine","score_opus":0.04705846314386273,"score_gpt":0.4098250253287233,"score_spread":0.36276656218486053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111577913","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":[0.00028493875,0.99072826,0.0005134553,0.0049579483,0.00048340578,0.000009161771,0.000015711423,0.000008645121,0.0029983793],"genre_scores_gemma":[0.010210855,0.9834516,0.0013120773,0.0036874402,0.0005361178,0.000031840085,0.000032692467,0.0000057967627,0.00073158555],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983908,0.00069125806,0.00014454444,0.000115354786,0.00055836304,0.00009978661],"domain_scores_gemma":[0.99328893,0.005313354,0.00049086206,0.00010378847,0.00062864384,0.00017451623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038559034,0.0008070009,0.0010427381,0.0017867985,0.00030503905,0.003057093,0.0011144583,0.0025660878,0.004041558],"category_scores_gemma":[0.0072642355,0.00032901464,0.0006006529,0.0022834484,0.0018204629,0.0044820337,0.0015220686,0.0030748844,0.0009166792],"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":[0.00008487128,0.00004749909,0.0004016906,0.02794091,0.00025942735,0.00010479385,0.00036305556,0.0006680076,0.0005695475,0.07417044,0.018759767,0.87662995],"study_design_scores_gemma":[0.00006751376,0.0002257787,0.0014580763,0.045083597,0.00042639705,0.00065228075,0.0010879177,0.00052026927,0.00068067847,0.048494063,0.9012381,0.000065305096],"about_ca_topic_score_codex":0.0025261226,"about_ca_topic_score_gemma":0.0050861333,"teacher_disagreement_score":0.004041558,"about_ca_system_score_codex":0.0019463855,"about_ca_system_score_gemma":0.004529623,"threshold_uncertainty_score":0.02039218},"labels":[],"label_agreement":null},{"id":"W3111783525","doi":"10.2196/24132","title":"Improving Detection of Disease Re-emergence Using a Web-Based Tool (RED Alert): Design and Case Analysis Study","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Los Alamos National Laboratory; National Nuclear Security Administration; Defense Threat Reduction Agency; U.S. Department of Energy","keywords":"Computer science; Web application; World Wide Web","score_opus":0.055330662955737075,"score_gpt":0.32558464479944954,"score_spread":0.27025398184371247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111783525","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":[0.9703865,0.00016009374,0.02006066,0.00015156505,0.000016634318,0.0075366087,0.00036200983,0.0001552037,0.001170635],"genre_scores_gemma":[0.9230471,0.0001858483,0.06831812,0.00017168254,0.000021619959,0.0069482313,0.00042802584,0.000041366813,0.00083806465],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9879111,0.008521983,0.00082161126,0.00087600213,0.0012640853,0.00060511916],"domain_scores_gemma":[0.9519877,0.03486506,0.0028607126,0.0025138392,0.0068851016,0.00088760175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016900908,0.0008232704,0.0005466656,0.0026366175,0.00072054204,0.0010705644,0.0014510799,0.0013277467,0.0015407747],"category_scores_gemma":[0.02364913,0.0005305687,0.0013073699,0.0012288562,0.0010733722,0.0010657862,0.0012131187,0.0007761033,0.00039107315],"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":[0.008267516,0.06736679,0.49653405,0.0037157834,0.0010286132,0.0059232586,0.014106657,0.05072512,0.018143844,0.0032486848,0.0059751994,0.32496455],"study_design_scores_gemma":[0.006788446,0.07850675,0.3112608,0.00127854,0.0020975117,0.0042024488,0.023929276,0.47418985,0.074354485,0.004878372,0.017863145,0.0006504753],"about_ca_topic_score_codex":0.006302719,"about_ca_topic_score_gemma":0.0066284137,"teacher_disagreement_score":0.016900908,"about_ca_system_score_codex":0.002999126,"about_ca_system_score_gemma":0.0024814587,"threshold_uncertainty_score":0.089381635},"labels":[],"label_agreement":null},{"id":"W3111867755","doi":"10.1080/19466315.2020.1863257","title":"Model-Based Clustering and Prediction With Mixed Measurements Involving Surrogate Classifiers","year":2020,"lang":"en","type":"article","venue":"Statistics in Biopharmaceutical Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Computer science; Artificial intelligence; Machine learning; Latent class model; Mixture model; Data mining; Pattern recognition (psychology)","score_opus":0.3801276332879626,"score_gpt":0.45537853059021133,"score_spread":0.07525089730224871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111867755","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":[0.01187336,0.00010714775,0.9871405,0.00021828787,0.000021137472,0.000044387856,0.00010420871,0.0002038664,0.00028713903],"genre_scores_gemma":[0.53350097,0.0002857813,0.4611485,0.00033948594,0.00012792578,0.0005888336,0.0012836898,0.00013234733,0.002592514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99520075,0.002871306,0.00027063242,0.0009155172,0.0005051441,0.00023666157],"domain_scores_gemma":[0.9780411,0.01628753,0.0015709872,0.0022775673,0.0014543008,0.00036858095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013371539,0.0012892344,0.0025557047,0.0015974224,0.0008502756,0.0025468983,0.0028279286,0.0026882489,0.0014237766],"category_scores_gemma":[0.04044594,0.0010199915,0.002107242,0.0018065117,0.0015754452,0.0031602131,0.0030654229,0.003460732,0.00085502886],"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":[0.00023251485,0.00011896032,0.01043866,0.00010892286,0.00016328382,0.00013248828,0.00028418496,0.8580462,0.0011200095,0.060720976,0.0014270331,0.067206755],"study_design_scores_gemma":[0.0000073515903,0.000018924784,0.00034788955,0.00001151797,0.0000071512745,0.000016782073,0.000011103037,0.97887266,0.0002884891,0.02017496,0.0002322844,0.000010821679],"about_ca_topic_score_codex":0.0040985956,"about_ca_topic_score_gemma":0.0034202668,"teacher_disagreement_score":0.013371539,"about_ca_system_score_codex":0.0016312662,"about_ca_system_score_gemma":0.0016759296,"threshold_uncertainty_score":0.07071632},"labels":[],"label_agreement":null},{"id":"W3112953570","doi":"10.23889/ijpds.v5i5.1507","title":"Involving the Public in Data Linkage Research","year":2020,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; University Health Network; University of British Columbia","funders":"","keywords":"Public relations; Public engagement; Data sharing; Session (web analytics); Government (linguistics); Political science; Moderation; Sociology; Psychology; Social psychology; Medicine; Business; Advertising","score_opus":0.5224479911836669,"score_gpt":0.536670488500658,"score_spread":0.0142224973169911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112953570","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006561523,0.013270268,0.079835534,0.78734726,0.012558623,0.0025610782,0.0008093974,0.0010616488,0.095994726],"genre_scores_gemma":[0.28066832,0.022734601,0.13233493,0.44190335,0.016666992,0.015067037,0.0026564677,0.0022123007,0.085756004],"study_design_codex":"not_applicable","study_design_gemma":"qualitative","domain_scores_codex":[0.496617,0.44338238,0.008434839,0.014067552,0.029773155,0.0077250544],"domain_scores_gemma":[0.5844218,0.31384346,0.014457295,0.032139096,0.039023288,0.016115064],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.32450464,0.0010601868,0.0013227692,0.0029723975,0.012171305,0.0202286,0.004945721,0.014896613,0.04100803],"category_scores_gemma":[0.33181715,0.0016652469,0.001750626,0.00452392,0.019594073,0.024880163,0.031605687,0.017931832,0.012388993],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026197967,0.00032540964,0.003108836,0.0035901915,0.000089701,0.00033491064,0.10295543,0.00051366043,0.0013672039,0.17707755,0.44989416,0.26048082],"study_design_scores_gemma":[0.00005840492,0.00012785332,0.0009824121,0.0042543984,0.000024463863,0.00013823672,0.023380212,0.0002935304,0.00070565304,0.06615645,0.9038034,0.00007495101],"about_ca_topic_score_codex":0.0035756514,"about_ca_topic_score_gemma":0.0028850029,"teacher_disagreement_score":0.6754954,"about_ca_system_score_codex":0.011709542,"about_ca_system_score_gemma":0.03724596,"threshold_uncertainty_score":0.833006},"labels":[],"label_agreement":null},{"id":"W3113787487","doi":"10.2196/24204","title":"The Global Infectious Diseases Epidemic Information Monitoring System: Development and Usability Study of an Effective Tool for Travel Health Management in China","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Science and Technology Planning Project of Guangdong Province; National Key Research and Development Program of China; Sun Yat-sen University; National Natural Science Foundation of China","keywords":"Infectious disease (medical specialty); China; Environmental health; Safeguarding; Global health; Medicine; Disease surveillance; Quarantine; Travel medicine; Usability; International Health Regulations; Preparedness; Disease; Public health; Computer science; Geography; Coronavirus disease 2019 (COVID-19); Political science","score_opus":0.019756180967250757,"score_gpt":0.32517928781672045,"score_spread":0.3054231068494697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113787487","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":[0.9940612,0.00014923533,0.0028668996,0.00019118753,0.000013839754,0.0006431016,0.000274424,0.0004457282,0.0013543947],"genre_scores_gemma":[0.96922463,0.00036496576,0.027018426,0.00016211561,0.0000140333,0.0006396888,0.0010363486,0.000091978196,0.0014479064],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9971239,0.0013801212,0.00039659985,0.00037660458,0.00049745856,0.00022520043],"domain_scores_gemma":[0.9896248,0.005943419,0.0005208416,0.0006757148,0.0024759122,0.0007593593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010079495,0.00080605864,0.0006018529,0.0024117152,0.00092025445,0.0015178394,0.0009166158,0.0005710949,0.0015289751],"category_scores_gemma":[0.018020896,0.00035609424,0.00060103426,0.00165023,0.0005561726,0.0027240664,0.0014992359,0.00044505336,0.00028962744],"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":[0.0011193504,0.0027907477,0.38854077,0.0028343888,0.0003122702,0.0012807082,0.05369072,0.002395171,0.020763889,0.0008620589,0.010308186,0.51510173],"study_design_scores_gemma":[0.000496246,0.007346984,0.8494896,0.0008872505,0.00084546814,0.0007735884,0.040613987,0.05192386,0.014607169,0.0007109528,0.03189126,0.00041368426],"about_ca_topic_score_codex":0.014350824,"about_ca_topic_score_gemma":0.012857415,"teacher_disagreement_score":0.014350824,"about_ca_system_score_codex":0.0016884714,"about_ca_system_score_gemma":0.003219491,"threshold_uncertainty_score":0.053306043},"labels":[],"label_agreement":null},{"id":"W3113806128","doi":"10.2196/24330","title":"Web-Based Apps for Responding to Acute Infectious Disease Outbreaks in the Community: Systematic Review","year":2020,"lang":"en","type":"review","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Outbreak; Disease; Medicine; Infectious disease (medical specialty); World Wide Web; Internet privacy; Virology; Computer science; Pathology","score_opus":0.06444999809872053,"score_gpt":0.3938566707003234,"score_spread":0.32940667260160283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113806128","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":[0.0017224241,0.9954562,0.00020024218,0.00031281722,0.00015784061,0.0012338557,0.0004507772,0.000019112116,0.00044671763],"genre_scores_gemma":[0.013700903,0.9824132,0.00090611866,0.00050476403,0.000091894995,0.0019811203,0.0002299875,0.000008148432,0.00016388118],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9910591,0.0033711977,0.002923797,0.00048264785,0.0018546089,0.0003085834],"domain_scores_gemma":[0.9469819,0.03998764,0.0077666054,0.0005655722,0.0041255443,0.0005726604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010233034,0.0016178016,0.0068846447,0.012098886,0.0008497945,0.003259489,0.0025379271,0.0021096577,0.0073187896],"category_scores_gemma":[0.060065944,0.0010146265,0.0067280987,0.0117699085,0.0009175915,0.0036114755,0.0021629857,0.0015933925,0.0005241892],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014847958,0.000026929585,0.00037038798,0.9651383,0.0026816858,0.00006215636,0.00024557568,0.000046986635,0.00004954044,0.000097868615,0.0011261618,0.03000597],"study_design_scores_gemma":[0.000227293,0.00023504162,0.0020800154,0.9648363,0.02146302,0.000177019,0.00035863655,0.00006466882,0.000086050895,0.00014563854,0.010300329,0.000026017144],"about_ca_topic_score_codex":0.0066998038,"about_ca_topic_score_gemma":0.018508114,"teacher_disagreement_score":0.012098886,"about_ca_system_score_codex":0.0038121608,"about_ca_system_score_gemma":0.013912706,"threshold_uncertainty_score":0.054118097},"labels":[],"label_agreement":null},{"id":"W3113990471","doi":"10.3390/info12010002","title":"The Spatial Analysis of the Malicious Uniform Resource Locators (URLs): 2016 Dataset Case Study","year":2020,"lang":"en","type":"article","venue":"Information","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Malware; Computer science; The Internet; Cluster (spacecraft); Phishing; Resource (disambiguation); Computer security; Population; World Wide Web; Demography","score_opus":0.013247295391695368,"score_gpt":0.2677901795738267,"score_spread":0.25454288418213133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113990471","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":[0.7906071,0.0015122608,0.008264557,0.0023175152,0.00017009607,0.00041748906,0.18656962,0.0007228894,0.00941841],"genre_scores_gemma":[0.81081605,0.0007538347,0.01540645,0.00022050101,0.000103682585,0.000272268,0.16930579,0.00008041754,0.003041041],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986719,0.00033142677,0.00015548065,0.00025941047,0.00035748855,0.00022426205],"domain_scores_gemma":[0.9975351,0.00057057966,0.000501084,0.000419924,0.0007842389,0.00018907695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011860789,0.00046613594,0.00042740014,0.004492232,0.00068192196,0.000938415,0.0009028861,0.00080964825,0.0010164329],"category_scores_gemma":[0.004574327,0.00013911046,0.0005733157,0.0055546723,0.0005060307,0.00087942864,0.0012669022,0.00042415192,0.00074202823],"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":[0.00045230877,0.0003895291,0.8071944,0.0011262721,0.00024253443,0.0030994029,0.0022100804,0.02548981,0.0033782865,0.005199646,0.081244364,0.069973335],"study_design_scores_gemma":[0.00007420811,0.0003018832,0.73262274,0.00046638885,0.00020789525,0.005058525,0.012071484,0.07891582,0.007955517,0.0029880025,0.15918088,0.00015662555],"about_ca_topic_score_codex":0.07311129,"about_ca_topic_score_gemma":0.08885406,"teacher_disagreement_score":0.07311129,"about_ca_system_score_codex":0.0015319379,"about_ca_system_score_gemma":0.0013541913,"threshold_uncertainty_score":0.1453715},"labels":[],"label_agreement":null},{"id":"W3115084376","doi":"10.15173/mumj.v17i1.2355","title":"geospatial analysis of pertussis and its risk factors in southern Ontario from 2005–2016","year":2020,"lang":"en","type":"article","venue":"McMaster University Medical Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Poisson regression; Public health; Incidence (geometry); Demography; Whooping cough; Population; Geography; Geospatial analysis; Health geography; Context (archaeology); Rate ratio; Bordetella pertussis; Spatial epidemiology; Epidemiology; Environmental health; Medicine; Cartography; Vaccination; Health policy; Mathematics; Immunology; Biology; Pathology","score_opus":0.016767241911330284,"score_gpt":0.2162318061559553,"score_spread":0.19946456424462503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115084376","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":[0.9414543,0.0007830647,0.0007773814,0.00055489695,0.000021798125,0.00014272518,0.050315406,0.00007269931,0.005877785],"genre_scores_gemma":[0.9793468,0.00048408844,0.0010939284,0.000035292305,0.000009698769,0.00008768506,0.01620537,0.000015311189,0.0027218245],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994466,0.000041979318,0.00004669182,0.00009104533,0.00023962306,0.00013413464],"domain_scores_gemma":[0.9982102,0.00012015772,0.00037706015,0.00010039573,0.0009878775,0.00020428722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005563871,0.000277784,0.00029779403,0.0025751118,0.0012919528,0.0007687752,0.00060253416,0.00016367837,0.0022138509],"category_scores_gemma":[0.0026973446,0.00023000309,0.00071118126,0.007318454,0.0004942908,0.00025714835,0.0010768494,0.00018831508,0.00024719493],"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":[0.00011837882,0.000014245919,0.9791551,0.00017261211,0.0001074809,0.00023882273,0.0022855562,0.0016556769,0.00040512416,0.00041550177,0.0053240503,0.010107359],"study_design_scores_gemma":[0.0000036159129,0.00000834991,0.9936388,0.000034226567,0.000027812921,0.000037393518,0.0013074885,0.0007399347,0.00005042748,0.000035315457,0.004109186,0.0000075003454],"about_ca_topic_score_codex":0.99215525,"about_ca_topic_score_gemma":0.9955308,"teacher_disagreement_score":0.019604161,"about_ca_system_score_codex":0.019604161,"about_ca_system_score_gemma":0.02560135,"threshold_uncertainty_score":0.1422388},"labels":[],"label_agreement":null},{"id":"W3116603799","doi":"10.21203/rs.3.rs-18251/v1","title":"Creation of a Case Definition to Identify People Living with HIV using Canadian Primary Care Sentinel Surveillance Network data","year":2020,"lang":"en","type":"preprint","venue":"Research Square (Research Square)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba; University of Ottawa; Memorial University of Newfoundland; Canadians Living with HIV; Winnipeg Regional Health Authority","funders":"","keywords":"Primary care; Human immunodeficiency virus (HIV); Computer science; Medicine; Virology; Family medicine","score_opus":0.17289361021146812,"score_gpt":0.4395140492461773,"score_spread":0.2666204390347092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116603799","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":[0.7982477,0.0017951913,0.089030154,0.0028496997,0.0004249341,0.02026728,0.070921585,0.0007087962,0.01575459],"genre_scores_gemma":[0.7908377,0.00073540903,0.1634248,0.00048641724,0.0000723914,0.005113024,0.038388073,0.00006208598,0.0008800662],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98915035,0.0020504454,0.0020607845,0.0015251588,0.00421197,0.0010013165],"domain_scores_gemma":[0.97709525,0.0034381514,0.0041471766,0.0018735587,0.012504313,0.00094143016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016780548,0.0008924242,0.0007190264,0.013453333,0.0022833864,0.0025659415,0.003247331,0.0007140199,0.0016748888],"category_scores_gemma":[0.043161776,0.0005330774,0.001535918,0.0065999404,0.0009299753,0.0011007012,0.002930575,0.0010563699,0.00029018297],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006982684,0.00006119493,0.9630603,0.0002804677,0.00017285293,0.00021501374,0.001108291,0.0012761547,0.0004349212,0.00167087,0.009371928,0.022278136],"study_design_scores_gemma":[0.00007018939,0.00015476288,0.93623674,0.0008977497,0.000309546,0.0006194035,0.0041342666,0.033268347,0.0017852394,0.0015968718,0.020844975,0.00008196124],"about_ca_topic_score_codex":0.70482737,"about_ca_topic_score_gemma":0.71540064,"teacher_disagreement_score":0.9848793,"about_ca_system_score_codex":0.015120663,"about_ca_system_score_gemma":0.024775084,"threshold_uncertainty_score":0.5938219},"labels":[],"label_agreement":null},{"id":"W3118303387","doi":"10.2196/25538","title":"Nowcasting for Real-Time COVID-19 Tracking in New York City: An Evaluation Using Reportable Disease Data From Early in the Pandemic","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of General Medical Sciences; Centers for Disease Control and Prevention; U.S. Department of Health and Human Services; National Institutes of Health; New York City Department of Health and Mental Hygiene; Carnegie Mellon University","keywords":"Nowcasting; Medicine; Context (archaeology); Pandemic; Poisson distribution; Bayesian probability; Statistics; Count data; Coronavirus disease 2019 (COVID-19); Geography; Disease; Mathematics; Meteorology; Internal medicine","score_opus":0.3456946776681874,"score_gpt":0.4553483820500662,"score_spread":0.10965370438187882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118303387","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":[0.9721439,0.0004532985,0.021236654,0.0003995783,0.00021963345,0.00067511847,0.0018152953,0.0018960142,0.0011605487],"genre_scores_gemma":[0.9515919,0.0002968328,0.043168735,0.000097533586,0.000056848694,0.00016624742,0.004127007,0.00006511684,0.00042984166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9977469,0.0010851431,0.00019561498,0.00051102974,0.0003333319,0.00012786359],"domain_scores_gemma":[0.98743314,0.006769372,0.0009278454,0.0012403411,0.002833721,0.0007955685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011834358,0.0010215952,0.000876653,0.0009921456,0.0005540773,0.0010037028,0.0016834225,0.0011131633,0.0007175233],"category_scores_gemma":[0.027115323,0.00046934478,0.00090342225,0.00076663936,0.00044954076,0.0015856895,0.0009943685,0.0012188612,0.0001618281],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008328773,0.0023060227,0.2447289,0.0010768098,0.0010450246,0.00029207737,0.0017031622,0.426441,0.00787307,0.0011504377,0.006138542,0.2989162],"study_design_scores_gemma":[0.00057023676,0.0022657202,0.0828821,0.00011625904,0.00027005628,0.00007470176,0.00035868835,0.9085594,0.0024697587,0.00046990975,0.0018814864,0.00008172608],"about_ca_topic_score_codex":0.08503052,"about_ca_topic_score_gemma":0.054318957,"teacher_disagreement_score":0.08503052,"about_ca_system_score_codex":0.0015325003,"about_ca_system_score_gemma":0.0022295804,"threshold_uncertainty_score":0.1690712},"labels":[],"label_agreement":null},{"id":"W3119185308","doi":"10.1016/j.patter.2020.100190","title":"How Do Data Bolster Pandemic Preparedness and Response? How Do We Improve Data and Systems to Be Better Prepared?","year":2021,"lang":"en","type":"article","venue":"Patterns","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"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":"Institute of Infection and Immunity; National Health and Medical Research Council; Medical Research Council","keywords":"Bolster; Preparedness; Pandemic; Coronavirus disease 2019 (COVID-19); Disaster response; H1n1 pandemic; Emergency response; Psychology; Computer science; Political science; Medicine; Engineering; Emergency management; Medical emergency","score_opus":0.07347344817865277,"score_gpt":0.3256946891852063,"score_spread":0.2522212410065535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119185308","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023389766,0.02885545,0.030399054,0.9045911,0.009108797,0.00027532573,0.003363034,0.001022686,0.020045638],"genre_scores_gemma":[0.14314196,0.18324293,0.26031598,0.34992787,0.03240273,0.0019422191,0.012691706,0.002563871,0.013770714],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.926665,0.04534636,0.0051406464,0.005333903,0.012910483,0.0046034954],"domain_scores_gemma":[0.60593474,0.22258069,0.016816039,0.056907937,0.07154144,0.0262192],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.11693457,0.0024318579,0.0031740703,0.008974464,0.0074085696,0.033265553,0.0076173376,0.013726516,0.020712549],"category_scores_gemma":[0.2718993,0.002175401,0.0035057336,0.01055903,0.017268185,0.10745304,0.022783112,0.020931577,0.0153955985],"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":[0.00033763147,0.00025975154,0.012857586,0.0047847847,0.00057774415,0.00022963385,0.003931051,0.003265226,0.0010306546,0.2512243,0.41340092,0.3081008],"study_design_scores_gemma":[0.000057482313,0.0001177323,0.0021481528,0.00621503,0.00012024343,0.00014127768,0.0043285135,0.0014190917,0.0006770621,0.22833721,0.7562397,0.00019849837],"about_ca_topic_score_codex":0.01602026,"about_ca_topic_score_gemma":0.009990203,"teacher_disagreement_score":0.88306546,"about_ca_system_score_codex":0.0103190765,"about_ca_system_score_gemma":0.045558963,"threshold_uncertainty_score":0.6184165},"labels":[],"label_agreement":null},{"id":"W3119911347","doi":"10.1101/2021.01.15.21249879","title":"Anticipating the curve: can online symptom-based data reflect COVID-19 case activity in Ontario, Canada?","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada; Toronto Public Health; Impact; Women's College Hospital; McMaster University; Public Health Ontario; University Health Network; University of Toronto","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Coronavirus disease 2019 (COVID-19); Medicine; Pandemic; Outbreak; Proxy (statistics); Demography; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Population; Public health; Lag time; 2019-20 coronavirus outbreak; Environmental health; Internal medicine; Statistics; Disease; Infectious disease (medical specialty); Virology; Pathology","score_opus":0.12638266867500916,"score_gpt":0.37956269941537757,"score_spread":0.25318003074036843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119911347","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":[0.97304505,0.000752202,0.00088184286,0.0022862633,0.000036546367,0.00013993417,0.014507317,0.00004958862,0.0083012255],"genre_scores_gemma":[0.99542505,0.0003030958,0.0005960137,0.0002015917,0.00001029633,0.00003255589,0.0024662435,0.000007642482,0.0009575532],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986866,0.00014168417,0.00011424495,0.00018320822,0.00043982305,0.00043447083],"domain_scores_gemma":[0.9928697,0.00060430536,0.0017634853,0.00017263039,0.003706629,0.0008832394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013817488,0.00032489703,0.0002758245,0.0014872184,0.0014118542,0.0016885382,0.0014771284,0.00039410984,0.0021743774],"category_scores_gemma":[0.008818842,0.00026533956,0.00039992295,0.0032464,0.00068340235,0.0006918873,0.0008042357,0.00044845557,0.00027941546],"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":[0.000030436298,0.000009286735,0.99395317,0.00003957248,0.000020773654,0.00003554162,0.00046275105,0.00021788082,0.000070077134,0.000060651557,0.001645644,0.0034542023],"study_design_scores_gemma":[0.000004511921,0.000014120698,0.99499327,0.00005312873,0.000020437388,0.000024404384,0.0018348949,0.0012955782,0.00007865528,0.000043908876,0.0016267184,0.000010343947],"about_ca_topic_score_codex":0.9960353,"about_ca_topic_score_gemma":0.9975102,"teacher_disagreement_score":0.031048348,"about_ca_system_score_codex":0.031048348,"about_ca_system_score_gemma":0.03275197,"threshold_uncertainty_score":0.2252726},"labels":[],"label_agreement":null},{"id":"W3120593931","doi":"10.1101/2021.01.11.21249624","title":"Emergence and Evolution of Big Data Science in HIV Research: Bibliometric Analysis of Federally Sponsored Studies 2000-2019","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Institute of Allergy and Infectious Diseases; National Institutes of Health","keywords":"Big data; Bibliometrics; Human immunodeficiency virus (HIV); China; Library science; Metadata; Data science; Political science; Medicine; Computer science; Family medicine; Data mining; World Wide Web","score_opus":0.24897965493622373,"score_gpt":0.43769117898587756,"score_spread":0.18871152404965383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120593931","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9159384,0.03737231,0.0010684221,0.0041138367,0.00013420539,0.000120596145,0.02570109,0.00011417411,0.015437006],"genre_scores_gemma":[0.9806349,0.009855155,0.00085740787,0.00012661562,0.00019282763,0.00010121378,0.007602082,0.000024320838,0.0006054372],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98971343,0.0019818745,0.002437251,0.0011329553,0.003988143,0.00074625836],"domain_scores_gemma":[0.9108327,0.03935874,0.03034305,0.0031869996,0.014043195,0.0022353868],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.00941906,0.0003087375,0.0008256886,0.10139473,0.0012633048,0.005564046,0.00077576534,0.0006822577,0.0019270309],"category_scores_gemma":[0.056703668,0.00019562675,0.0010332124,0.17005405,0.0010683979,0.0040684757,0.00310642,0.0005063634,0.00029812212],"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":[0.0001553647,0.000051020128,0.8984929,0.0033564104,0.0006836331,0.0004136217,0.004880618,0.001014207,0.0007649966,0.006334595,0.006959043,0.07689365],"study_design_scores_gemma":[0.000007069666,0.000026444928,0.9748632,0.0008405187,0.00022460466,0.00029742063,0.0046410216,0.001219813,0.00034849282,0.0009608344,0.016546508,0.000024088211],"about_ca_topic_score_codex":0.016291378,"about_ca_topic_score_gemma":0.020633098,"teacher_disagreement_score":0.9905809,"about_ca_system_score_codex":0.004033995,"about_ca_system_score_gemma":0.004634126,"threshold_uncertainty_score":0.04981333},"labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W3120882627","doi":"10.1186/s12879-020-05754-5","title":"Monitoring sick leave data for early detection of influenza outbreaks","year":2021,"lang":"en","type":"article","venue":"BMC Infectious Diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Association Nationale de la Recherche et de la Technologie; Agence Nationale de la Recherche; Institut National de la Santé et de la Recherche Médicale; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Sick leave; Outbreak; Medicine; Absenteeism; Emergency medicine; Virology; Physical therapy","score_opus":0.05194151012643901,"score_gpt":0.3326694082983195,"score_spread":0.2807278981718805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120882627","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":[0.94256246,0.0024622795,0.037912987,0.0008626858,0.00010265105,0.00023861307,0.013143183,0.0008818733,0.001833202],"genre_scores_gemma":[0.9806584,0.00027951648,0.012556621,0.000103364415,0.00006154103,0.00007332676,0.0060016625,0.00001622562,0.00024934416],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984316,0.0006980976,0.00013501641,0.00034372165,0.00027960635,0.00011197204],"domain_scores_gemma":[0.9928893,0.0029073227,0.002084817,0.00046161414,0.0012979382,0.00035900183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036201975,0.00061558245,0.0004970097,0.0029358116,0.00026721822,0.0009508953,0.00065157615,0.0004418706,0.00064214424],"category_scores_gemma":[0.009360488,0.00019674782,0.00064485625,0.00093115185,0.00014118083,0.0005653598,0.0005950474,0.00037644777,0.00030553676],"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":[0.0002930754,0.00013298514,0.93127865,0.00031326988,0.00027297993,0.00006159626,0.0002449323,0.010251594,0.002763635,0.00021282745,0.0028689732,0.051305432],"study_design_scores_gemma":[0.00006228592,0.0005373765,0.786283,0.00019669965,0.00026744057,0.00018141759,0.00039996806,0.19964014,0.006728831,0.00080329145,0.004839369,0.000060141057],"about_ca_topic_score_codex":0.019729385,"about_ca_topic_score_gemma":0.026601057,"teacher_disagreement_score":0.019729385,"about_ca_system_score_codex":0.0008536974,"about_ca_system_score_gemma":0.0010267617,"threshold_uncertainty_score":0.039229095},"labels":[],"label_agreement":null},{"id":"W3121864895","doi":"10.2196/14837","title":"A Mobile App and Dashboard for Early Detection of Infectious Disease Outbreaks: Development Study","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dashboard; Workflow; Infectious disease (medical specialty); Public health; Disease surveillance; Outbreak; Computer science; Web application; Public health surveillance; mHealth; Internet privacy; Computer security; Medicine; Data science; World Wide Web; Disease; Database; Nursing","score_opus":0.0207137022014415,"score_gpt":0.30953780849957013,"score_spread":0.28882410629812866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121864895","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":[0.9589651,0.0010574516,0.021279538,0.00052266615,0.00016382356,0.007633164,0.00080298743,0.0013113366,0.008263893],"genre_scores_gemma":[0.7885761,0.0035055075,0.18333797,0.00064480485,0.00008076769,0.008096752,0.0026821108,0.0002815754,0.012794434],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984378,0.00056498486,0.00014133219,0.00015038566,0.0005238214,0.00018170706],"domain_scores_gemma":[0.99309903,0.0037125642,0.00034833496,0.0002648776,0.0020065885,0.00056871196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040766783,0.000921586,0.0004038167,0.0012105522,0.00043392178,0.001327949,0.001084932,0.000808094,0.0025978533],"category_scores_gemma":[0.008825546,0.00041655527,0.00058547995,0.00046388173,0.0003259084,0.0018970005,0.0011101601,0.0007867938,0.00087964686],"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":[0.0015141602,0.016057309,0.09620739,0.007027345,0.00026734354,0.0056170253,0.016550057,0.0028610502,0.04590102,0.0026779382,0.018473333,0.786846],"study_design_scores_gemma":[0.0023843434,0.08124127,0.40805843,0.006969916,0.00212657,0.011736115,0.03282872,0.08099114,0.106880516,0.0017539437,0.2642963,0.00073281006],"about_ca_topic_score_codex":0.0015140495,"about_ca_topic_score_gemma":0.0017117327,"teacher_disagreement_score":0.0040766783,"about_ca_system_score_codex":0.00039246245,"about_ca_system_score_gemma":0.0014283321,"threshold_uncertainty_score":0.021559775},"labels":[],"label_agreement":null},{"id":"W3122121085","doi":"10.2196/20746","title":"Postcode based participatory disease surveillance systems : a comparison with traditional risk-based surveillance and its application in the COVID-19 pandemic (Preprint)","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Disease surveillance; Public health surveillance; Medicine; Public health; Preprint; Pandemic; Population; Environmental health; Medical emergency; Disease; Coronavirus disease 2019 (COVID-19); Computer science; Infectious disease (medical specialty); World Wide Web","score_opus":0.12506016414839033,"score_gpt":0.3489533709038155,"score_spread":0.22389320675542518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122121085","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":[0.9593102,0.00095014856,0.019521324,0.0016336032,0.00026249085,0.003330161,0.004259806,0.0004182891,0.01031412],"genre_scores_gemma":[0.96839595,0.00051807304,0.02626148,0.00028521646,0.000095707524,0.0017317606,0.0013460515,0.00002811441,0.0013375891],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9810983,0.014109427,0.0009214722,0.0013778706,0.0021352204,0.00035769262],"domain_scores_gemma":[0.958894,0.02175641,0.006873241,0.004238962,0.007014307,0.0012229324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016945714,0.00046370242,0.00038684483,0.0013234862,0.0005249537,0.0016901104,0.00085204124,0.0005249798,0.0024256988],"category_scores_gemma":[0.036699224,0.00022461503,0.0005984146,0.0014608213,0.00054749753,0.0017401569,0.0019068687,0.00036236443,0.00042393422],"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":[0.0040074117,0.0011048021,0.6035811,0.0024852736,0.0005398533,0.00019228687,0.010630662,0.003870221,0.0034116053,0.0032705623,0.0041076886,0.3627986],"study_design_scores_gemma":[0.00060078263,0.016327435,0.8722193,0.001143349,0.00073601527,0.0006257657,0.012091896,0.05316095,0.0063926675,0.0031387869,0.033312008,0.00025103206],"about_ca_topic_score_codex":0.008940756,"about_ca_topic_score_gemma":0.0075196754,"teacher_disagreement_score":0.016945714,"about_ca_system_score_codex":0.0016833977,"about_ca_system_score_gemma":0.0017405065,"threshold_uncertainty_score":0.08961862},"labels":[],"label_agreement":null},{"id":"W3122991949","doi":"","title":"Predicting Recessions in Real-Time: Mining Google Trends and Electronic Payments Data for Clues","year":2013,"lang":"en","type":"article","venue":"C.D. Howe Institute Commentary","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Recession; Payment; Context (archaeology); Business cycle; Snapshot (computer storage); Credit card; Order (exchange); Business; Economics; Computer science; Finance; Geography; Macroeconomics; Database","score_opus":0.03727596703599749,"score_gpt":0.3302935350314867,"score_spread":0.29301756799548917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122991949","genre_codex":"empirical","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78942204,0.027159888,0.010113093,0.060859464,0.0026344873,0.00033668624,0.07700804,0.0006791125,0.03178719],"genre_scores_gemma":[0.9212061,0.014012756,0.010364462,0.0027509069,0.0030169755,0.0001996014,0.0436935,0.00017467834,0.004581007],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985531,0.00035127482,0.00021412592,0.00020967735,0.0004953618,0.00017649753],"domain_scores_gemma":[0.9930154,0.003171235,0.0017185662,0.00042225127,0.0013853215,0.00028711278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016989253,0.00053414656,0.00053202716,0.015825905,0.0006687737,0.0037811603,0.00069095293,0.0013372214,0.0012650803],"category_scores_gemma":[0.020963164,0.0002502576,0.00072516914,0.016646171,0.00048751684,0.004030054,0.001262971,0.0012581701,0.001312897],"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":[0.00041982354,0.00013971982,0.51298004,0.001749626,0.00042189637,0.0025425367,0.005692037,0.006347286,0.001277237,0.021641606,0.24763131,0.19915691],"study_design_scores_gemma":[0.00006683333,0.0001577189,0.46712133,0.0022266253,0.00037190953,0.0013069473,0.023682766,0.091825634,0.0013237878,0.028474947,0.3832275,0.0002140117],"about_ca_topic_score_codex":0.02185093,"about_ca_topic_score_gemma":0.0356424,"teacher_disagreement_score":0.02185093,"about_ca_system_score_codex":0.00093692984,"about_ca_system_score_gemma":0.0012051096,"threshold_uncertainty_score":0.043447495},"labels":[],"label_agreement":null},{"id":"W3123334631","doi":"","title":"The Importance of Industry Classification in Estimating Concentration Ratios","year":2011,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Mining industry; Econometrics; Industrial organization; Business; Statistics; Mathematics; Engineering; Mining engineering","score_opus":0.027681816642701234,"score_gpt":0.2888656816311502,"score_spread":0.2611838649884489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123334631","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":[0.7562623,0.0071987286,0.18171264,0.0023023048,0.00061813917,0.0010790392,0.008581992,0.0006916188,0.041553188],"genre_scores_gemma":[0.95984066,0.00064654154,0.03431288,0.00030315798,0.00020125402,0.00016661998,0.0036308651,0.000089848654,0.0008081669],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.960204,0.028680598,0.0025434669,0.0019900277,0.0058120405,0.000769866],"domain_scores_gemma":[0.8793935,0.08804696,0.015194671,0.007949019,0.00858718,0.0008286741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029102448,0.00087523257,0.0012017762,0.0074684937,0.00065939425,0.002972605,0.001360525,0.0011459979,0.0015409192],"category_scores_gemma":[0.13588732,0.0002893498,0.00093156943,0.010646689,0.0008327219,0.001856998,0.0009646408,0.0010762274,0.0011258498],"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":[0.00028058322,0.000097944794,0.9013518,0.00021914973,0.00070759037,0.00011011274,0.00017308287,0.011253468,0.0005700462,0.0062773894,0.0032181316,0.0757407],"study_design_scores_gemma":[0.00019304767,0.0009475951,0.7972428,0.00036851928,0.00076120166,0.00080732984,0.0010680791,0.15012395,0.004988584,0.025157077,0.018191556,0.0001502687],"about_ca_topic_score_codex":0.010440518,"about_ca_topic_score_gemma":0.0072160675,"teacher_disagreement_score":0.029102448,"about_ca_system_score_codex":0.00091161474,"about_ca_system_score_gemma":0.00083657604,"threshold_uncertainty_score":0.15391028},"labels":[],"label_agreement":null},{"id":"W3123961437","doi":"10.22215/etd/2006-08123","title":"Spatial analysis of criminal incidents: implementation of GIS and spatial statistics","year":2006,"lang":"en","type":"dissertation","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Geography; Cartography; Library science; Humanities; Computer science; Art","score_opus":0.010975258626237648,"score_gpt":0.340513966474195,"score_spread":0.32953870784795736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123961437","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":[0.015420017,0.0003728048,0.92504144,0.0027548096,0.00017065008,0.000596983,0.008583886,0.030196987,0.016862385],"genre_scores_gemma":[0.05917043,0.000402216,0.9311904,0.00012732839,0.000047297468,0.00043084475,0.0057364563,0.0009284177,0.0019666445],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99455583,0.0025856746,0.00058683514,0.0007001599,0.0014145323,0.00015686193],"domain_scores_gemma":[0.98290354,0.008382567,0.0007905921,0.0033995293,0.004114757,0.00040906155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009620527,0.000731648,0.0007742993,0.006190666,0.00054505316,0.0033798572,0.0013238481,0.00043296875,0.00627566],"category_scores_gemma":[0.028725324,0.00082262146,0.0009819083,0.006245102,0.0006047552,0.0026705817,0.0022162031,0.00089970144,0.0027345042],"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":[0.00020297161,0.00023228106,0.026027802,0.000646894,0.00056538475,0.000106627216,0.00160846,0.023523046,0.00447675,0.03810112,0.045640405,0.85886824],"study_design_scores_gemma":[0.00024882075,0.00030103239,0.051102336,0.000498999,0.00043115136,0.00039655712,0.0030378932,0.5349674,0.029955978,0.07129635,0.3075687,0.00019472306],"about_ca_topic_score_codex":0.01551427,"about_ca_topic_score_gemma":0.012828519,"teacher_disagreement_score":0.01551427,"about_ca_system_score_codex":0.0010219744,"about_ca_system_score_gemma":0.003993818,"threshold_uncertainty_score":0.050878823},"labels":[],"label_agreement":null},{"id":"W3126088183","doi":"","title":"The Future of Prediction: How Google Searches Foreshadow Housing Prices and Quantities","year":2015,"lang":"en","type":"article","venue":"Journal of the Association for Information Systems","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Index (typography); The Internet; Set (abstract data type); Government (linguistics); Sample (material); Econometrics; Quarter (Canadian coin); Computer science; Business; Economics; World Wide Web","score_opus":0.02520806520368501,"score_gpt":0.26417792916614896,"score_spread":0.23896986396246395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126088183","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":[0.7918952,0.018237807,0.07258175,0.05502406,0.0009100742,0.00015131573,0.013018941,0.00083170336,0.0473491],"genre_scores_gemma":[0.9880546,0.0020234743,0.005845652,0.0005676974,0.0002524953,0.000022787819,0.001819287,0.00006591183,0.0013480628],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99842477,0.0006864634,0.00008500293,0.0003984338,0.00023810512,0.00016725935],"domain_scores_gemma":[0.9829107,0.012466079,0.0015934331,0.0010539587,0.0014522201,0.00052367203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062267133,0.0009151489,0.000738589,0.0034945835,0.0006536299,0.0055712746,0.0010303422,0.0013948188,0.0028487593],"category_scores_gemma":[0.028991142,0.0005797869,0.0007607736,0.00313825,0.001267438,0.011226518,0.0014581701,0.002027836,0.0011168061],"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":[0.00086713437,0.00022644768,0.58013946,0.00038216054,0.00045367674,0.00046500133,0.0017599985,0.15226384,0.00062840374,0.078053825,0.024186172,0.16057383],"study_design_scores_gemma":[0.0000578633,0.00015473212,0.09451223,0.00031533427,0.00014531468,0.00024315981,0.0015725929,0.6429487,0.00078636315,0.24095052,0.018132012,0.00018116157],"about_ca_topic_score_codex":0.04502121,"about_ca_topic_score_gemma":0.039665304,"teacher_disagreement_score":0.04502121,"about_ca_system_score_codex":0.0017971284,"about_ca_system_score_gemma":0.0014645695,"threshold_uncertainty_score":0.08951831},"labels":[],"label_agreement":null},{"id":"W3126823409","doi":"10.1177/0020702020985227","title":"Building a better global health security early-warning system post-COVID: The view from Canada","year":2021,"lang":"en","type":"article","venue":"International Journal Canada s Journal of Global Policy Analysis","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Centre for International Governance Innovation","funders":"","keywords":"Outbreak; Pandemic; Warning system; Public health; Global health; China; International Health Regulations; Middle East respiratory syndrome; Economic growth; Political science; Coronavirus disease 2019 (COVID-19); Development economics; Infectious disease (medical specialty); Disease; Medicine; Economics; Virology; Telecommunications; Engineering; Law","score_opus":0.0067915729826254255,"score_gpt":0.3140180281553917,"score_spread":0.30722645517276626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126823409","genre_codex":"commentary","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":[0.012107834,0.009942831,0.0049867546,0.9351942,0.0023077738,0.00011469705,0.0007430743,0.00023991226,0.03436291],"genre_scores_gemma":[0.59057397,0.049284678,0.070462376,0.23452643,0.001977905,0.00025279183,0.0021492469,0.0006037435,0.05016891],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9892084,0.0024050854,0.00030767536,0.0010144751,0.004021446,0.0030429089],"domain_scores_gemma":[0.958134,0.0047059534,0.0006807953,0.0013084046,0.022353223,0.012817623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015285275,0.0009186727,0.00072295073,0.003358736,0.017039118,0.01694897,0.0036016754,0.0052929623,0.004387591],"category_scores_gemma":[0.027786594,0.0006270231,0.0009569843,0.0036909778,0.01318663,0.0077245445,0.006759886,0.009514151,0.00079947314],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.00021356149,0.00017844963,0.023729593,0.0010155966,0.00018246476,0.00079851545,0.016338725,0.005649348,0.001335391,0.2937943,0.44985867,0.2069053],"study_design_scores_gemma":[0.00008620374,0.00010814352,0.015051795,0.0018830452,0.00012391634,0.00019149593,0.018423265,0.004047598,0.0012454123,0.034245953,0.92429525,0.00029794243],"about_ca_topic_score_codex":0.99308527,"about_ca_topic_score_gemma":0.9921598,"teacher_disagreement_score":0.8494116,"about_ca_system_score_codex":0.15058838,"about_ca_system_score_gemma":0.46671757,"threshold_uncertainty_score":0.98519707},"labels":[],"label_agreement":null},{"id":"W3128309088","doi":"","title":"Getting data from the Canada Covid-19 Tracker using R","year":2021,"lang":"en","type":"article","venue":"From the Bottom of the Heap","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Limnology; Paleoecology; Geography; Ecology; Physical geography; Biology; Medicine; Outbreak; Virology","score_opus":0.08566064619664382,"score_gpt":0.3256302553201266,"score_spread":0.2399696091234828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128309088","genre_codex":"dataset","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036313867,0.0014976785,0.0516834,0.0033858863,0.00046284858,0.00039377375,0.8309879,0.08882647,0.01913063],"genre_scores_gemma":[0.055479124,0.0021617431,0.16849704,0.0026633395,0.00023455042,0.0010664635,0.7286713,0.026565986,0.0146604115],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9925371,0.0014611019,0.000676618,0.0017422125,0.0029001005,0.0006827975],"domain_scores_gemma":[0.96144295,0.013544469,0.0018885966,0.009453197,0.011837931,0.0018328688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008054838,0.0016722463,0.0018495938,0.006607758,0.0016474302,0.005722148,0.003034785,0.0012795359,0.043452587],"category_scores_gemma":[0.09882626,0.0014791292,0.002456843,0.010578846,0.00089069374,0.00255729,0.002891229,0.0022174793,0.03817617],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052105356,0.00003582718,0.015678858,0.001010674,0.00041055994,0.00011105502,0.00026090085,0.0039901976,0.0009433603,0.007005499,0.9047606,0.0652714],"study_design_scores_gemma":[0.00048337254,0.00007590176,0.023267876,0.0010210994,0.00040205862,0.0001529901,0.00038421326,0.030351501,0.004924606,0.023573441,0.9150822,0.00028071],"about_ca_topic_score_codex":0.6883606,"about_ca_topic_score_gemma":0.71648836,"teacher_disagreement_score":0.31163943,"about_ca_system_score_codex":0.005848264,"about_ca_system_score_gemma":0.040674116,"threshold_uncertainty_score":0.6269494},"labels":[],"label_agreement":null},{"id":"W3129362847","doi":"10.2196/26719","title":"Automated Travel History Extraction From Clinical Notes for Informing the Detection of Emergent Infectious Disease Events: Algorithm Development and Validation","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"U.S. Department of Homeland Security","keywords":"Computer science; Unstructured data; Preparedness; Public health; Artificial intelligence; Veterans Affairs; Information extraction; Machine learning; Data extraction; Public health surveillance; Medical record; Infectious disease (medical specialty); Health care; MEDLINE; Data mining; Medicine; Disease; Big data","score_opus":0.053542316156824935,"score_gpt":0.35696000222065566,"score_spread":0.30341768606383074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129362847","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":[0.29243544,0.0012825333,0.6887492,0.0010919559,0.0001725982,0.0019845793,0.0031131841,0.0092045,0.0019660175],"genre_scores_gemma":[0.42847958,0.000483593,0.56137145,0.00031957877,0.00007867908,0.0011249023,0.0067090094,0.00015917607,0.0012740645],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977896,0.0008818878,0.0002819952,0.00061347394,0.00032614762,0.00010689476],"domain_scores_gemma":[0.9889387,0.007885328,0.00052531675,0.00058652734,0.0019131084,0.00015097181],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054637557,0.0011238679,0.00077502645,0.0023102257,0.00076902285,0.0016717504,0.0013574292,0.0016221674,0.0016346563],"category_scores_gemma":[0.017995447,0.00041610398,0.0010008314,0.0011789331,0.000518123,0.001503981,0.0011665523,0.0013406766,0.0014093549],"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":[0.0009763623,0.0009756233,0.050693948,0.0007384033,0.0004624266,0.0007386882,0.0007729697,0.20565228,0.021161249,0.0015581973,0.010266146,0.70600367],"study_design_scores_gemma":[0.00005879241,0.00013460836,0.004556112,0.0000588626,0.000049601604,0.00020061102,0.00019631645,0.984263,0.0076924623,0.0010652643,0.0017036219,0.000020691603],"about_ca_topic_score_codex":0.009894017,"about_ca_topic_score_gemma":0.009668965,"teacher_disagreement_score":0.009894017,"about_ca_system_score_codex":0.0010789797,"about_ca_system_score_gemma":0.002933904,"threshold_uncertainty_score":0.028895438},"labels":[],"label_agreement":null},{"id":"W3132086785","doi":"10.2196/24288","title":"Reporting and Availability of COVID-19 Demographic Data by US Health Departments (April to October 2020): Observational Study","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Observational study; Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Pandemic; Medicine; Environmental health; Geography; Virology; Outbreak; Disease; Infectious disease (medical specialty); Pathology","score_opus":0.16419808561314367,"score_gpt":0.42431914565923784,"score_spread":0.26012106004609414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132086785","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":[0.983511,0.00038555425,0.00026748134,0.00036217153,0.000032595282,0.00015667477,0.013342024,0.000022489128,0.0019200342],"genre_scores_gemma":[0.9916049,0.00020551446,0.00023614493,0.00027851327,0.00003092857,0.00021191871,0.007089803,0.000012191648,0.00032993243],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99282503,0.0022693295,0.0013626576,0.00109251,0.0014044745,0.0010460665],"domain_scores_gemma":[0.96210456,0.0062275613,0.022742165,0.0020865635,0.004483145,0.0023560468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059991297,0.00038994048,0.00049279706,0.003003639,0.0010922285,0.0014419169,0.0018014894,0.0008368583,0.0025764361],"category_scores_gemma":[0.021005297,0.0007291221,0.0010546156,0.0056526456,0.0007245865,0.0024167383,0.002513715,0.0024036453,0.00093470514],"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":[0.00004709468,0.00008837878,0.9982162,0.000019428375,0.000039242947,0.000014235317,0.00016182415,0.000021413767,0.000011717875,0.000012916439,0.00086746487,0.0004999732],"study_design_scores_gemma":[0.000008920931,0.00010829628,0.9967507,0.00005201551,0.000035637506,0.00008494599,0.0017897931,0.00021757933,0.000049914746,0.000017137721,0.00087441475,0.000010636721],"about_ca_topic_score_codex":0.07179991,"about_ca_topic_score_gemma":0.06618351,"teacher_disagreement_score":0.07179991,"about_ca_system_score_codex":0.0023286818,"about_ca_system_score_gemma":0.002986538,"threshold_uncertainty_score":0.14276397},"labels":[],"label_agreement":null},{"id":"W3134320576","doi":"10.23889/ijpds.v5i4.1393","title":"A commentary on the value of hospital data for covid-19 pandemic surveillance and planning","year":2021,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Ottawa Public Health; Ottawa Hospital","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; Public health; Medical emergency; Health care; Public hospital; Medicine; Unit (ring theory); Business; Data science; Computer science; Nursing; Psychology; Infectious disease (medical specialty); Disease; Political science","score_opus":0.12901159375738336,"score_gpt":0.4403597145464793,"score_spread":0.31134812078909596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134320576","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000051698746,0.0011163383,0.00010369062,0.966704,0.03139223,0.000013724904,0.00012471873,0.000015070106,0.00047845792],"genre_scores_gemma":[0.0008613517,0.0011562028,0.00029736647,0.9669438,0.02970552,0.00005430563,0.000061605395,0.000031492513,0.0008882804],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.96478856,0.014900073,0.0056517636,0.0039694044,0.008730992,0.0019591895],"domain_scores_gemma":[0.6748023,0.26044717,0.0061593917,0.004879236,0.043287743,0.010424087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04171007,0.0012921466,0.0023215592,0.0027305877,0.006319213,0.008988372,0.009489433,0.05628559,0.011094927],"category_scores_gemma":[0.2407459,0.0017099808,0.004054049,0.0035996723,0.009971319,0.013836503,0.005299951,0.07722794,0.005364761],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023285478,0.0000062191652,0.000111919195,0.00013665433,0.000012511538,0.00009980373,0.00016577664,0.00004163082,0.000021164195,0.002198185,0.99400055,0.003182186],"study_design_scores_gemma":[0.00009130063,0.000044681095,0.00089846493,0.0024859183,0.000047567948,0.00036462414,0.00095958763,0.000254951,0.0001551412,0.0072743297,0.9873046,0.00011884317],"about_ca_topic_score_codex":0.03612113,"about_ca_topic_score_gemma":0.03843343,"teacher_disagreement_score":0.05628559,"about_ca_system_score_codex":0.009481413,"about_ca_system_score_gemma":0.020468928,"threshold_uncertainty_score":0.22058654},"labels":[],"label_agreement":null},{"id":"W3134421977","doi":"10.3138/cart-2020-0025","title":"Schools as Vectors of Infectious Disease Transmission during the 1918 Influenza Pandemic","year":2021,"lang":"fr","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Humanities; Influenza pandemic; Political science; Coronavirus disease 2019 (COVID-19); Philosophy; Medicine; Infectious disease (medical specialty)","score_opus":0.010140049530809023,"score_gpt":0.305620375241514,"score_spread":0.2954803257107049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134421977","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":[0.97551984,0.0028832583,0.0022782783,0.001881837,0.00008951581,0.00010602058,0.0024291775,0.00006683078,0.0147452345],"genre_scores_gemma":[0.9900967,0.0022664692,0.00253719,0.000084601714,0.000044767043,0.00005772219,0.0009842197,0.000019439345,0.003908984],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99813914,0.001011198,0.0001386593,0.00020110648,0.00023466481,0.00027522963],"domain_scores_gemma":[0.9949469,0.0016954637,0.0016554793,0.00041363662,0.0009933932,0.000295034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019985836,0.00023228717,0.00026356228,0.0040095276,0.0010190896,0.0027456551,0.00052659726,0.00057179836,0.004986976],"category_scores_gemma":[0.008853721,0.00039217543,0.0003741864,0.0048799864,0.0009678344,0.0020622509,0.0019828123,0.0005008395,0.0005985224],"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":[0.00015338365,0.000046282945,0.91915524,0.00034512902,0.00007906085,0.00029609856,0.022570549,0.0005742832,0.00053001626,0.003342986,0.0025041297,0.050402734],"study_design_scores_gemma":[0.000014313132,0.000107067935,0.9273165,0.00049306685,0.00014170178,0.00022251463,0.041614108,0.0012179884,0.00052894896,0.0009875189,0.027320834,0.000035463403],"about_ca_topic_score_codex":0.08131211,"about_ca_topic_score_gemma":0.19552678,"teacher_disagreement_score":0.08131211,"about_ca_system_score_codex":0.0023144071,"about_ca_system_score_gemma":0.0022360191,"threshold_uncertainty_score":0.16167766},"labels":[],"label_agreement":null},{"id":"W3134471948","doi":"10.1038/s41746-021-00407-6","title":"Digital public health surveillance: a systematic scoping review","year":2021,"lang":"en","type":"article","venue":"npj Digital Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":96,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Health Services; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Cumming School of Medicine, University of Calgary; O'Brien Institute for Public Health, University of Calgary; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Public health; The Internet; Digital health; Public health surveillance; Systematic review; Underpinning; Medicine; Data science; Internet privacy; Public relations; Medical education; Computer science; MEDLINE; World Wide Web; Political science; Health care; Engineering; Pathology","score_opus":0.05479254040524195,"score_gpt":0.3476133980444081,"score_spread":0.29282085763916615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134471948","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":[0.0011907037,0.9852261,0.0021070102,0.0013118744,0.00054007425,0.0076093236,0.001017154,0.000047668957,0.0009500884],"genre_scores_gemma":[0.011512841,0.96520495,0.006497827,0.00091210834,0.00018980478,0.014395732,0.0009909907,0.000021715232,0.00027403925],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9318173,0.027561799,0.025730696,0.00337608,0.01023139,0.0012827653],"domain_scores_gemma":[0.834165,0.11462229,0.02374335,0.0050163693,0.021041187,0.0014118849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07621972,0.0030248784,0.012286496,0.04708819,0.002769884,0.008036096,0.004858833,0.004681598,0.005914836],"category_scores_gemma":[0.22002204,0.0029246355,0.010067978,0.040069308,0.0031925254,0.010053076,0.0065345922,0.002797996,0.0009810321],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000903293,0.00003084472,0.00071634,0.92468333,0.0024035748,0.00012820764,0.00089026,0.00020507246,0.00013320186,0.0010435292,0.0032144664,0.066460766],"study_design_scores_gemma":[0.00003554058,0.00004228279,0.00056111976,0.9834731,0.0045280955,0.00008937551,0.00046140453,0.00007036652,0.00006931736,0.0003274281,0.010324062,0.00001787551],"about_ca_topic_score_codex":0.013254622,"about_ca_topic_score_gemma":0.027961027,"teacher_disagreement_score":0.07621972,"about_ca_system_score_codex":0.014174845,"about_ca_system_score_gemma":0.06508285,"threshold_uncertainty_score":0.40309322},"labels":[],"label_agreement":null},{"id":"W3137174455","doi":"10.3138/cart-2020-0027","title":"Geovisualization of COVID-19: State of the Art and Opportunities","year":2021,"lang":"fr","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Humanities; Art","score_opus":0.02726653386274161,"score_gpt":0.32209104212744155,"score_spread":0.29482450826469997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137174455","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":[0.10980448,0.30668336,0.2879779,0.032153603,0.0050685075,0.0012359685,0.016251994,0.0142623335,0.22656195],"genre_scores_gemma":[0.39780036,0.24896222,0.28229833,0.0037896808,0.0040661274,0.0010813343,0.011120702,0.0043174103,0.046563778],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9965675,0.0016403078,0.00014060957,0.00042856575,0.0009863025,0.00023668933],"domain_scores_gemma":[0.978336,0.01491636,0.0006749445,0.0015716421,0.0038317987,0.00066931697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008178699,0.0010414732,0.000638904,0.00562626,0.00066320226,0.007354412,0.0022720916,0.001332845,0.018152865],"category_scores_gemma":[0.01998531,0.00056412304,0.0010711739,0.004285568,0.0020586622,0.004636069,0.0029775356,0.0013793868,0.0033075886],"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":[0.00074460276,0.00009792104,0.009118663,0.0064295745,0.0001729137,0.00027374827,0.0100402245,0.002934466,0.008273175,0.02095741,0.04652028,0.894437],"study_design_scores_gemma":[0.00006738013,0.00033352547,0.022132559,0.0077273524,0.00018820215,0.0011645425,0.0109409345,0.007149155,0.007805421,0.01617783,0.9260116,0.00030153766],"about_ca_topic_score_codex":0.013492708,"about_ca_topic_score_gemma":0.011783167,"teacher_disagreement_score":0.018152865,"about_ca_system_score_codex":0.0018755249,"about_ca_system_score_gemma":0.0021254113,"threshold_uncertainty_score":0.060727358},"labels":[],"label_agreement":null},{"id":"W3138019666","doi":"10.1016/s1473-3099(21)00151-1","title":"The effect of seasonal respiratory virus transmission on syndromic surveillance for COVID-19 in Ontario, Canada","year":2021,"lang":"en","type":"article","venue":"The Lancet Infectious Diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Impact; University of Toronto; University Health Network; Toronto Public Health; McMaster University; Women's College Hospital; Kingston Health Sciences Centre; Public Health Ontario","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; Department of Medicine, University of Toronto","keywords":"Declaration; Public health; Public health surveillance; Scopus; Coronavirus disease 2019 (COVID-19); Medicine; Phone; Family medicine; MEDLINE; Political science; Nursing","score_opus":0.017309493296597228,"score_gpt":0.2859379187145255,"score_spread":0.2686284254179283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138019666","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":[0.90698,0.0077914605,0.0010405217,0.025887901,0.0005176445,0.00033737277,0.040677384,0.0001612047,0.016606404],"genre_scores_gemma":[0.9888166,0.0018426263,0.00060078135,0.0016413449,0.000059704988,0.00006202377,0.0036203119,0.000039354985,0.0033171845],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9942198,0.0007908862,0.00047227566,0.0005924212,0.0019066273,0.002017943],"domain_scores_gemma":[0.9793881,0.0019996199,0.0033812448,0.00052361627,0.008894475,0.005812898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032765595,0.00046961775,0.00072289695,0.0010983177,0.0026265488,0.0025732962,0.00260716,0.0009085248,0.0027759112],"category_scores_gemma":[0.015191331,0.00070394424,0.0011937375,0.0031887917,0.0015052467,0.00089345925,0.0018948574,0.0018715032,0.00027173717],"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":[0.00039749726,0.00006349272,0.9574644,0.00040776996,0.00041968224,0.00017565912,0.0020833453,0.0015923843,0.0003879321,0.0010104435,0.019846682,0.016150763],"study_design_scores_gemma":[0.00003094853,0.000055580844,0.9936652,0.00016461087,0.00010361878,0.00004083227,0.0014779263,0.0010382066,0.00009561699,0.000053712334,0.003249471,0.000024358147],"about_ca_topic_score_codex":0.99917465,"about_ca_topic_score_gemma":0.99940276,"teacher_disagreement_score":0.10261759,"about_ca_system_score_codex":0.10261759,"about_ca_system_score_gemma":0.16102125,"threshold_uncertainty_score":0.74454623},"labels":[],"label_agreement":null},{"id":"W3138021152","doi":"10.2196/18593","title":"Estimation of Asthma Symptom Onset Using Internet Search Queries: Lag-Time Series Analysis","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Agence Nationale de la Recherche","keywords":"Series (stratigraphy); Lag; The Internet; Time series; Computer science; Time lag; Asthma; Statistics; Lag time; Data mining; Econometrics; Information retrieval; Medicine; Mathematics; World Wide Web; Machine learning; Biology","score_opus":0.02757917335255797,"score_gpt":0.33305611093131265,"score_spread":0.30547693757875466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138021152","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":[0.9640427,0.00085074553,0.030135049,0.0004404903,0.000045711844,0.00009428238,0.0030250342,0.00021479875,0.0011512171],"genre_scores_gemma":[0.9922191,0.00022078831,0.0049968758,0.000029376793,0.000030798394,0.000032704495,0.0021857168,0.000009058754,0.0002756818],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990282,0.00038984153,0.000105057305,0.00020972993,0.0001759894,0.00009121706],"domain_scores_gemma":[0.9909734,0.006757087,0.0013132739,0.00035286185,0.00043282754,0.0001705036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029724694,0.00061472354,0.0006687307,0.0027184892,0.00017401573,0.0010599176,0.00061853096,0.00060907536,0.0010170891],"category_scores_gemma":[0.011764242,0.00024322752,0.0007363457,0.0028723315,0.0002367384,0.0008406771,0.00050738064,0.00075862015,0.00030799548],"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":[0.00053260755,0.000418615,0.82467777,0.00023245256,0.0005216579,0.00026805158,0.00020015037,0.123818114,0.0015070046,0.0019682543,0.0017737007,0.044081654],"study_design_scores_gemma":[0.000029618977,0.0002371617,0.23474196,0.000044713637,0.00012863873,0.00014782594,0.00020432046,0.760821,0.00096790405,0.0018366638,0.00080405344,0.000036242684],"about_ca_topic_score_codex":0.02171692,"about_ca_topic_score_gemma":0.01278849,"teacher_disagreement_score":0.02171692,"about_ca_system_score_codex":0.0006640611,"about_ca_system_score_gemma":0.00080849184,"threshold_uncertainty_score":0.043181002},"labels":[],"label_agreement":null},{"id":"W3138088657","doi":"10.1186/s13031-021-00354-9","title":"Humanitarian led community-based surveillance: case study in Ekondo-titi, Cameroon","year":2021,"lang":"en","type":"article","venue":"Conflict and Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Reach Technologies (Canada)","funders":"UNICEF","keywords":"Outbreak; Medicine; Public health; Environmental health; Psychological intervention; Government (linguistics); Distrust; Public health surveillance; Medical emergency; Nursing; Political science","score_opus":0.11070224737472363,"score_gpt":0.395738554132092,"score_spread":0.2850363067573684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138088657","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":[0.9969029,0.0003844603,0.00042753064,0.0004887633,0.00003189953,0.00023777006,0.0001361612,0.000008141656,0.0013825025],"genre_scores_gemma":[0.9984018,0.00039122978,0.00044573532,0.00018969747,0.000025512334,0.000055328917,0.00005227642,0.0000027842666,0.000435786],"study_design_codex":"case_report","study_design_gemma":"observational","domain_scores_codex":[0.99910396,0.0004003487,0.00004603016,0.00010312828,0.000056408302,0.00029009563],"domain_scores_gemma":[0.9985026,0.00034453836,0.00031740178,0.000052745392,0.00014726541,0.0006354226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089726795,0.0010834917,0.0002131262,0.0010607061,0.0024952819,0.0009555557,0.0008652741,0.0013850519,0.0034669144],"category_scores_gemma":[0.0031485897,0.00038650972,0.00025234066,0.0009018802,0.0011901372,0.00069822103,0.0014101852,0.0010292949,0.00021260986],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004573663,0.0014975298,0.43357837,0.00075276906,0.000080470214,0.49632663,0.035823904,0.0012933532,0.0043447935,0.000937748,0.0025669215,0.022340128],"study_design_scores_gemma":[0.00013595427,0.003884051,0.56319493,0.0009899476,0.00013854135,0.21090733,0.20187762,0.0042031123,0.0030392574,0.0008812605,0.010591182,0.00015688436],"about_ca_topic_score_codex":0.028612703,"about_ca_topic_score_gemma":0.042881053,"teacher_disagreement_score":0.028612703,"about_ca_system_score_codex":0.0020697254,"about_ca_system_score_gemma":0.0016262766,"threshold_uncertainty_score":0.056892335},"labels":[],"label_agreement":null},{"id":"W3139180850","doi":"10.3390/ijerph18063099","title":"Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development","year":2021,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Data science; Thematic analysis; Health care; Thematic map; Big data; Knowledge management; Field (mathematics); Identification (biology); Computer science; Data mining; Qualitative research; Geography; Sociology; Political science; Social science; Cartography","score_opus":0.29790372692531986,"score_gpt":0.49582656366049377,"score_spread":0.1979228367351739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3139180850","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21703567,0.05533404,0.4477349,0.024723483,0.001115005,0.00095589616,0.009096385,0.0014388289,0.24256569],"genre_scores_gemma":[0.6716782,0.017410891,0.2994059,0.0006580428,0.00019403479,0.0006562128,0.0026648736,0.00015484223,0.0071770847],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9970471,0.0015986561,0.00034900228,0.00023466082,0.0006407496,0.00012985565],"domain_scores_gemma":[0.9905089,0.0067650042,0.0010058226,0.00044331953,0.0010253491,0.00025161827],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0064629694,0.0006262586,0.0003767395,0.023027383,0.0016506598,0.007015053,0.0007118159,0.0008823953,0.0036991776],"category_scores_gemma":[0.012967047,0.0003175424,0.00071288843,0.027776161,0.00193343,0.0077029006,0.0025837645,0.0008499969,0.00061788224],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017898255,0.00008985692,0.042384584,0.004666747,0.00035444935,0.0012708169,0.04093308,0.009920157,0.0030787196,0.47173783,0.014183452,0.41120136],"study_design_scores_gemma":[0.000033125798,0.00019237354,0.03476308,0.0038182524,0.0002619653,0.0016847304,0.05678306,0.025844028,0.002477833,0.35126942,0.52276427,0.00010794749],"about_ca_topic_score_codex":0.0044454327,"about_ca_topic_score_gemma":0.0075239753,"teacher_disagreement_score":0.993537,"about_ca_system_score_codex":0.0038443967,"about_ca_system_score_gemma":0.0033756627,"threshold_uncertainty_score":0.034179866},"labels":[],"label_agreement":null},{"id":"W3141939040","doi":"10.2196/22880","title":"The Causality Inference of Public Interest in Restaurants and Bars on Daily COVID-19 Cases in the United States: Google Trends Analysis","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Granger causality; Causality (physics); Pandemic; Test (biology); Demography; Geography; Statistics; Medicine; Mathematics; Sociology","score_opus":0.14936792210416241,"score_gpt":0.40771401588816636,"score_spread":0.25834609378400397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3141939040","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":[0.98414683,0.0011263419,0.002142232,0.0005530482,0.000050445185,0.0000565274,0.009654186,0.00016157521,0.0021087565],"genre_scores_gemma":[0.99297595,0.00032065788,0.001289153,0.000041346873,0.000032975102,0.000026743019,0.0050290255,0.000015489277,0.00026859684],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99818856,0.0006078817,0.00018378657,0.00035308264,0.00043620265,0.00023043517],"domain_scores_gemma":[0.9834077,0.0108063575,0.0032051615,0.0008273626,0.0013130982,0.00044038394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035580855,0.0005410121,0.0005884377,0.009993694,0.00048105046,0.0016454919,0.0005666378,0.00055062055,0.001663735],"category_scores_gemma":[0.015929382,0.00023619052,0.0022193308,0.011253456,0.00057704665,0.0014778178,0.00093297777,0.00076968747,0.00044177205],"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":[0.00017009577,0.000044292945,0.98118573,0.00013352766,0.0003171138,0.00032692307,0.0002296938,0.0042224196,0.00021390205,0.0006851674,0.001600594,0.0108705945],"study_design_scores_gemma":[0.000026823918,0.00016077569,0.8982512,0.0001211628,0.00046799038,0.0005992056,0.0014382779,0.093353845,0.00065356033,0.0016407738,0.003242823,0.00004363529],"about_ca_topic_score_codex":0.058966193,"about_ca_topic_score_gemma":0.03291377,"teacher_disagreement_score":0.058966193,"about_ca_system_score_codex":0.0008291024,"about_ca_system_score_gemma":0.0011418734,"threshold_uncertainty_score":0.11724591},"labels":[],"label_agreement":null},{"id":"W3151875318","doi":"10.2196/23305","title":"Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Research Foundation of Korea; National Research Foundation","keywords":"Computer science; Word embedding; Machine learning; Artificial intelligence; Microblogging; Social media; Data mining; Word (group theory); Embedding; World Wide Web","score_opus":0.05695722559282902,"score_gpt":0.40064171698820844,"score_spread":0.34368449139537943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3151875318","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":[0.45485133,0.0052155335,0.5310214,0.0022174316,0.00037229064,0.0002492676,0.0005311206,0.0015454191,0.0039962446],"genre_scores_gemma":[0.91596687,0.0011564265,0.07760409,0.000322181,0.00016414307,0.0001767951,0.00089264574,0.000049666116,0.0036672086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995523,0.00010623234,0.00004809832,0.0001394427,0.00007707722,0.00007678961],"domain_scores_gemma":[0.99706835,0.0018548296,0.00016314274,0.000106598905,0.0007274307,0.00007959349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018442773,0.0012158783,0.0010180213,0.0012754769,0.00042746085,0.00076753995,0.001173004,0.0012745538,0.0010303716],"category_scores_gemma":[0.0046307114,0.0004400786,0.00097164,0.00090319506,0.0003428416,0.001565472,0.000587676,0.0018245755,0.00034152274],"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":[0.0003708652,0.0012570956,0.017072136,0.00034714804,0.00029399566,0.0003529745,0.00024552093,0.6234098,0.00595289,0.0026191932,0.0056984723,0.34237993],"study_design_scores_gemma":[0.0000030363556,0.000018789684,0.00025234694,0.0000040782993,0.000010776617,0.0000068565582,0.000006359939,0.9990226,0.00042526226,0.00017467882,0.000072931216,0.0000022489003],"about_ca_topic_score_codex":0.013043293,"about_ca_topic_score_gemma":0.009958218,"teacher_disagreement_score":0.013043293,"about_ca_system_score_codex":0.0009774938,"about_ca_system_score_gemma":0.0010390541,"threshold_uncertainty_score":0.025934756},"labels":[],"label_agreement":null},{"id":"W3152541909","doi":"10.2196/26303","title":"Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE): Overview, Components, and Public Health Applications","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Centers for Disease Control and Prevention","keywords":"Disease surveillance; Computer science; Personalization; Public health; Data sharing; Public health surveillance; Tracking system; Data science; World Wide Web; Medicine","score_opus":0.08076132617561707,"score_gpt":0.34574446101138295,"score_spread":0.26498313483576585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152541909","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":[0.06993638,0.01556712,0.7368994,0.0033496602,0.00064421084,0.0053301523,0.00925925,0.11907065,0.03994326],"genre_scores_gemma":[0.4150723,0.017656935,0.48498243,0.0023033828,0.0011009052,0.004180556,0.029846674,0.003577088,0.041279744],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984143,0.00030578958,0.00023211542,0.00025417103,0.0006439318,0.0001497874],"domain_scores_gemma":[0.9980804,0.0005467031,0.00017758865,0.00022985713,0.000734157,0.00023130384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030036075,0.0009882697,0.00064213085,0.0029157985,0.0004091047,0.0023949165,0.001306758,0.00093463884,0.007463434],"category_scores_gemma":[0.0034961384,0.00058757747,0.00049833587,0.0017779819,0.00029903217,0.0028527852,0.0016119205,0.0008877804,0.004375956],"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":[0.0009140866,0.0006853483,0.024445111,0.0012749045,0.00012693976,0.00052215724,0.00086103374,0.006484547,0.01817546,0.00987431,0.1108924,0.8257436],"study_design_scores_gemma":[0.00026340238,0.0010337794,0.046012834,0.0010394128,0.000295031,0.0037944035,0.00057798653,0.11917695,0.03757401,0.007525731,0.7824079,0.0002986007],"about_ca_topic_score_codex":0.0019347712,"about_ca_topic_score_gemma":0.0010735292,"teacher_disagreement_score":0.007463434,"about_ca_system_score_codex":0.0006186842,"about_ca_system_score_gemma":0.0011163731,"threshold_uncertainty_score":0.02496767},"labels":[],"label_agreement":null},{"id":"W3153744412","doi":"10.3389/fpubh.2021.656635","title":"Prediction of COVID-19 Waves Using Social Media and Google Search: A Case Study of the US and Canada","year":2021,"lang":"en","type":"article","venue":"Frontiers in Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Sunnybrook Health Science Centre; University of Guelph","funders":"University of Guelph","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Outbreak; Social media; Public health surveillance; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Public health; Geography; Demography; Medicine; Computer science; Sociology; Virology; Pathology; World Wide Web","score_opus":0.10108707377754501,"score_gpt":0.3298031419109567,"score_spread":0.22871606813341172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153744412","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":[0.9930402,0.00022650952,0.00039378708,0.00082241907,0.000014454331,0.00007297999,0.0033718986,0.000038090955,0.0020197192],"genre_scores_gemma":[0.9935674,0.00032460678,0.0012496572,0.00016057758,0.0000115501225,0.000017420949,0.0034139417,0.000015233619,0.0012395894],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99928707,0.00009067902,0.000031152667,0.00011011608,0.00020567389,0.00027523312],"domain_scores_gemma":[0.99808997,0.00051627314,0.00017300891,0.00009046585,0.0007909184,0.00033934775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009006814,0.0007464595,0.00035734655,0.0018891619,0.001840384,0.0017956712,0.0010453864,0.0007695917,0.0008971368],"category_scores_gemma":[0.0037577657,0.00025157482,0.0005680117,0.0031221535,0.0006513362,0.0007134101,0.0008367052,0.00089792436,0.00021655232],"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":[0.00024048914,0.00030054376,0.95799124,0.000098479824,0.0001394483,0.0019571583,0.0024803,0.009193554,0.0008247656,0.00096236484,0.007447357,0.018364318],"study_design_scores_gemma":[0.00006214327,0.00013410165,0.793952,0.00011422193,0.00014299026,0.0005143255,0.024903947,0.16867162,0.001447744,0.0005912657,0.00932986,0.00013571294],"about_ca_topic_score_codex":0.98861593,"about_ca_topic_score_gemma":0.9890514,"teacher_disagreement_score":0.014093571,"about_ca_system_score_codex":0.014093571,"about_ca_system_score_gemma":0.013212025,"threshold_uncertainty_score":0.10225648},"labels":[],"label_agreement":null},{"id":"W3155235020","doi":"10.22122/johoe.2020.192232.1110","title":"Worldwide interest in silver diamine fluoride over the last decade: A longitudinal retrospective study","year":2021,"lang":"en","type":"article","venue":"Journal of Oral Health and Oral Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Autoregressive integrated moving average; Medicine; Fluoride; Demography; Time series; Mathematics; Statistics; Chemistry","score_opus":0.10679548456915618,"score_gpt":0.4148944716458961,"score_spread":0.30809898707673994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155235020","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":[0.99590707,0.0010389887,0.00008934048,0.00009289427,0.000008793538,0.000026766662,0.0020356714,0.0000054157904,0.0007950416],"genre_scores_gemma":[0.99778444,0.0004864306,0.00009598343,0.00006144351,0.000012563778,0.00003149774,0.0012263918,0.0000037276632,0.00029753472],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99925596,0.00011475791,0.00015723078,0.0001713564,0.00017216215,0.00012857583],"domain_scores_gemma":[0.99775946,0.00028734573,0.00123199,0.000118353244,0.0003670336,0.00023570958],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0009927767,0.0001941428,0.0004315764,0.0019424591,0.0005793593,0.00093048933,0.00031265875,0.0005044439,0.0020104025],"category_scores_gemma":[0.002738025,0.00031084038,0.00074993935,0.0030930308,0.00029134226,0.0011051492,0.0005828886,0.00075902365,0.0005225333],"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":[0.000046743546,0.000025904896,0.998611,0.00002603656,0.000033668144,0.000056867662,0.00014356303,0.0000100359575,0.000037716676,0.000015900308,0.00014299215,0.0008495853],"study_design_scores_gemma":[0.000004535796,0.0001434187,0.9973579,0.000043375145,0.000067554975,0.0004648637,0.0010129295,0.000113750284,0.00005000492,0.00002298598,0.00070961873,0.000009038747],"about_ca_topic_score_codex":0.016395833,"about_ca_topic_score_gemma":0.015072367,"teacher_disagreement_score":0.99805754,"about_ca_system_score_codex":0.0005378205,"about_ca_system_score_gemma":0.00059730007,"threshold_uncertainty_score":0.03260082},"labels":[],"label_agreement":null},{"id":"W3156510658","doi":"10.1038/d41586-021-01028-5","title":"Sharing COVID data? Check these recommendations and guidelines","year":2021,"lang":"en","type":"letter","venue":"Nature","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Data sharing; Computer science; MEDLINE; Data science; Virology; Medicine; Outbreak; Political science; Alternative medicine; Pathology; Infectious disease (medical specialty)","score_opus":0.10439807188414088,"score_gpt":0.3989064689899738,"score_spread":0.2945083971058329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156510658","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014028867,0.00027385162,0.00032445532,0.9644724,0.021531293,0.00010281274,0.0005324193,0.00015893043,0.012463553],"genre_scores_gemma":[0.0004274967,0.00015613754,0.0005236364,0.97616756,0.0081582805,0.0001849083,0.0001768452,0.00008759509,0.014117473],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9603362,0.00874637,0.007637921,0.0024068328,0.014835322,0.006037214],"domain_scores_gemma":[0.8097597,0.10519981,0.011827797,0.009080218,0.037897997,0.026234534],"candidate_categories":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.03824349,0.0012444906,0.0034048709,0.0035561605,0.014229601,0.018553248,0.0072965,0.16788828,0.06271505],"category_scores_gemma":[0.18996006,0.002835946,0.0049118623,0.0041091098,0.0073586786,0.011124436,0.010387834,0.077408984,0.05439777],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009856945,0.000022248969,0.00022182347,0.0000207529,0.000008175748,0.0001496442,0.00006580625,0.000021283611,0.000027834214,0.0012857498,0.99614125,0.0020256815],"study_design_scores_gemma":[0.00011279892,0.000026090638,0.0012382349,0.0006260923,0.000035071207,0.00020684625,0.00041927674,0.00038845846,0.00009499365,0.0073535587,0.9894051,0.0000934404],"about_ca_topic_score_codex":0.05381794,"about_ca_topic_score_gemma":0.09622407,"teacher_disagreement_score":0.9927035,"about_ca_system_score_codex":0.012907489,"about_ca_system_score_gemma":0.041756384,"threshold_uncertainty_score":0.20980275},"labels":[],"label_agreement":null},{"id":"W3156937620","doi":"10.1101/2021.04.13.21255449","title":"Physical activity, sedentary behaviour, and sleep on Twitter: A labelled dataset for public health research","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada; University of Calgary","funders":"","keywords":"Demographics; Social media; Public health; Public health surveillance; Computer science; Physical activity; Data science; Medicine; Internet privacy; Artificial intelligence; World Wide Web; Demography; Sociology","score_opus":0.2459479852636265,"score_gpt":0.4482997752509828,"score_spread":0.2023517899873563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156937620","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1142033,0.0011842154,0.0028218208,0.0025651888,0.00037248246,0.00035199974,0.8728857,0.0009122537,0.0047030975],"genre_scores_gemma":[0.11513014,0.00043439085,0.006568878,0.0004965661,0.00027570198,0.0006030986,0.87287116,0.00007159378,0.0035484205],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99891937,0.00035563644,0.00010499457,0.00026757663,0.00019776318,0.00015458908],"domain_scores_gemma":[0.9968893,0.0013292463,0.00044340861,0.00056628004,0.00044396,0.00032780383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010574944,0.0007246013,0.0006008745,0.0024944453,0.0008267538,0.00079764455,0.0009445008,0.0018750891,0.004439456],"category_scores_gemma":[0.0058659245,0.0002306373,0.00067423325,0.0024521411,0.0005995787,0.00071910484,0.0015719419,0.00088645,0.00426375],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017742952,0.0006885775,0.18628308,0.004112978,0.00044693792,0.0019480299,0.0015800288,0.009988576,0.009262077,0.0030209147,0.681683,0.099211454],"study_design_scores_gemma":[0.0004649301,0.00050485355,0.5263561,0.001165848,0.0002964872,0.001644287,0.003672392,0.04478893,0.006542531,0.0055513564,0.40875298,0.00025928268],"about_ca_topic_score_codex":0.031877723,"about_ca_topic_score_gemma":0.060512524,"teacher_disagreement_score":0.031877723,"about_ca_system_score_codex":0.0012252757,"about_ca_system_score_gemma":0.0011452705,"threshold_uncertainty_score":0.063384354},"labels":[],"label_agreement":null},{"id":"W3157041886","doi":"10.1016/j.patter.2021.100272","title":"Welcome to the revolution: COVID-19 and the democratization of spatial-temporal data","year":2021,"lang":"en","type":"review","venue":"Patterns","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Dashboard; Democratization; Context (archaeology); Coronavirus disease 2019 (COVID-19); Pandemic; Perspective (graphical); Presentation (obstetrics); Political science; Digital Revolution; China; Data science; Regional science; Geography; Computer science; Democracy; Politics; Artificial intelligence; Medicine","score_opus":0.11369231662974164,"score_gpt":0.3968501778524485,"score_spread":0.2831578612227069,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157041886","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":[0.00012234028,0.99146605,0.00013909121,0.0066452813,0.0006625313,0.000005196351,0.000013878719,0.0000065669556,0.0009390453],"genre_scores_gemma":[0.0026840784,0.9911942,0.00035974497,0.0043491987,0.00093606196,0.000013685502,0.000034655026,0.00000795408,0.0004204419],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99747795,0.0013518474,0.00023790085,0.00019323797,0.00057201204,0.00016696008],"domain_scores_gemma":[0.9909657,0.0068948697,0.00062590826,0.00017698872,0.00095359545,0.00038302332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057697576,0.0005448296,0.0009347287,0.0025709744,0.00059525867,0.0033190933,0.0009509483,0.0024273482,0.0030344345],"category_scores_gemma":[0.011870391,0.00031500685,0.00077237515,0.004087718,0.0021001578,0.005538305,0.0018098138,0.0054142335,0.00086067716],"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":[0.000080784856,0.000053659445,0.00035017225,0.025409916,0.00014664383,0.00015910281,0.001257485,0.00029473627,0.00026465405,0.06210961,0.102150805,0.8077224],"study_design_scores_gemma":[0.000013110553,0.000043256267,0.00070314814,0.0154339885,0.0000521236,0.0003513761,0.0006045746,0.00006683834,0.000120464036,0.0077171787,0.97487146,0.000022593607],"about_ca_topic_score_codex":0.0038672045,"about_ca_topic_score_gemma":0.0046291053,"teacher_disagreement_score":0.0057697576,"about_ca_system_score_codex":0.0024175225,"about_ca_system_score_gemma":0.0048945127,"threshold_uncertainty_score":0.030513763},"labels":[],"label_agreement":null},{"id":"W3157687642","doi":"10.2196/27712","title":"Google Trends in Dermatology: Scoping Review of the Literature","year":2021,"lang":"en","type":"article","venue":"JMIR Dermatology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Specialty; Social media; MEDLINE; Analytics; Medicine; Dermatology; Family medicine; Data science; Computer science; World Wide Web; Political science","score_opus":0.014840192584740843,"score_gpt":0.33882014356717555,"score_spread":0.3239799509824347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157687642","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":[0.0009292723,0.9916003,0.0005130471,0.0016071568,0.0005786873,0.0011056629,0.0019189608,0.000041212323,0.0017056055],"genre_scores_gemma":[0.00544074,0.9898201,0.0011987621,0.00071552995,0.00024640598,0.0012859864,0.0009810233,0.00001842948,0.00029289437],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9880927,0.0029626542,0.0055206562,0.000633643,0.002349503,0.00044066482],"domain_scores_gemma":[0.9266744,0.048807256,0.011818348,0.0013677613,0.010520287,0.0008119653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01736305,0.0018681143,0.00543789,0.060543045,0.0013696372,0.004446684,0.0025619452,0.002748525,0.010038163],"category_scores_gemma":[0.07158082,0.0013865273,0.0059687225,0.043805566,0.0014708815,0.0051482986,0.0036026065,0.0016807577,0.0014815609],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010059204,0.000024473815,0.00073554966,0.8826147,0.0011771066,0.00019989803,0.0005579558,0.00012918195,0.00018727443,0.00082692975,0.009883862,0.1035625],"study_design_scores_gemma":[0.00002229563,0.000043898657,0.00110943,0.96121645,0.0034958236,0.00024170942,0.00036645017,0.000052293573,0.000084891784,0.0003521204,0.03299754,0.000017120692],"about_ca_topic_score_codex":0.0115872435,"about_ca_topic_score_gemma":0.02910387,"teacher_disagreement_score":0.060543045,"about_ca_system_score_codex":0.005852701,"about_ca_system_score_gemma":0.032006606,"threshold_uncertainty_score":0.09182572},"labels":[],"label_agreement":null},{"id":"W3158142982","doi":"10.1101/2021.04.25.21256069","title":"Reliability of COVID-19 data: An evaluation and reflection","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"York University; Temple University; George Washington University","keywords":"Coronavirus disease 2019 (COVID-19); Reliability (semiconductor); Statistics; 2019-20 coronavirus outbreak; Case fatality rate; Medicine; Outbreak; Demography; Geography; Environmental health; Computer science; Mathematics; Disease","score_opus":0.13353820030014385,"score_gpt":0.43064885513012696,"score_spread":0.2971106548299831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158142982","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70240265,0.004092224,0.22796372,0.020508794,0.00093440444,0.0045216554,0.0053194957,0.0010551377,0.03320192],"genre_scores_gemma":[0.9521343,0.00034590779,0.04378676,0.0007963071,0.00015167771,0.0009449728,0.001107236,0.00018543047,0.000547415],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.44547117,0.41377687,0.027888497,0.014524163,0.09573138,0.0026079318],"domain_scores_gemma":[0.1141424,0.6962379,0.038335744,0.04263222,0.10710267,0.001549034],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.54762584,0.00088050263,0.0011486445,0.0055033676,0.0015942681,0.008031091,0.0034542095,0.0015810783,0.0015187612],"category_scores_gemma":[0.76174694,0.0008083139,0.002002976,0.005469588,0.0059567583,0.0053904573,0.0055027753,0.0027374798,0.00050457276],"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":[0.0022961,0.00069195835,0.69191927,0.0021839684,0.0020956502,0.00029065032,0.030344427,0.015283907,0.0017597633,0.028527327,0.01657159,0.20803542],"study_design_scores_gemma":[0.00075276307,0.0056213047,0.58424413,0.0075510023,0.0022228314,0.00122171,0.028588688,0.22140768,0.020080213,0.047280844,0.08031804,0.00071071845],"about_ca_topic_score_codex":0.0063487412,"about_ca_topic_score_gemma":0.0028932143,"teacher_disagreement_score":0.45237416,"about_ca_system_score_codex":0.007990768,"about_ca_system_score_gemma":0.0058186245,"threshold_uncertainty_score":0.55785793},"labels":[],"label_agreement":null},{"id":"W3158640552","doi":"10.2196/27976","title":"Public Discussion of Anthrax on Twitter: Using Machine Learning to Identify Relevant Topics and Events","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Categorization; Social media; Relevance (law); Bacillus anthracis; Data science; Information retrieval; Event (particle physics); Computer science; Artificial intelligence; Proxy (statistics); Machine learning; Natural language processing; World Wide Web; Biology","score_opus":0.07141352321373394,"score_gpt":0.3733272626778466,"score_spread":0.30191373946411265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158640552","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":[0.98417795,0.00026832547,0.0072416104,0.0007187586,0.000048259837,0.00028169146,0.0016869612,0.00019419951,0.0053823087],"genre_scores_gemma":[0.98593235,0.00018985465,0.011429844,0.000081238126,0.000078194134,0.00018299036,0.0012773112,0.00001409986,0.000814209],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987478,0.0005192513,0.00010866829,0.00021730186,0.00027920768,0.0001276777],"domain_scores_gemma":[0.99120784,0.00560579,0.001696549,0.00029562655,0.0008653274,0.00032889165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023018322,0.0004139265,0.0003668453,0.0048223725,0.0007405953,0.002054031,0.00039125734,0.0006403441,0.0012238689],"category_scores_gemma":[0.008196438,0.0001925496,0.00049754156,0.0026966045,0.00035630373,0.0019563744,0.0007402908,0.000713003,0.00071398687],"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":[0.00048534412,0.0007070105,0.79012805,0.00033848555,0.0001333975,0.00026274,0.00499866,0.0040266654,0.0052149813,0.0006869724,0.0036757067,0.18934204],"study_design_scores_gemma":[0.000031051608,0.00053030765,0.8115101,0.00020497071,0.00012476477,0.00027801958,0.009343857,0.1635176,0.0055711693,0.0026557974,0.006132072,0.0001003573],"about_ca_topic_score_codex":0.004677394,"about_ca_topic_score_gemma":0.0061988914,"teacher_disagreement_score":0.0048223725,"about_ca_system_score_codex":0.0009538279,"about_ca_system_score_gemma":0.00052744173,"threshold_uncertainty_score":0.012173355},"labels":[],"label_agreement":null},{"id":"W3158722241","doi":"10.2196/24348","title":"Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Overweight; Obesity; Medicine; Environmental health; Demography; Internal medicine","score_opus":0.13699957429789505,"score_gpt":0.3605111569688454,"score_spread":0.22351158267095034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158722241","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":[0.9963999,0.0002857725,0.00024360527,0.00004480383,0.0000038847343,0.00012628798,0.002582779,0.000003386061,0.00030957218],"genre_scores_gemma":[0.9963337,0.00027386125,0.00069907715,0.00005270747,0.000009598835,0.00017685707,0.002351362,0.000003055431,0.0000997952],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981061,0.0009443193,0.00027503425,0.00024931727,0.00019791204,0.00022726481],"domain_scores_gemma":[0.99463296,0.001427222,0.0027000539,0.0004440597,0.00047894457,0.00031676085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00226904,0.00039887207,0.0005811961,0.0022286165,0.0005950987,0.00065172673,0.00063556945,0.0005813362,0.0014437725],"category_scores_gemma":[0.008327434,0.00051210023,0.0007841902,0.0054805744,0.00041066474,0.0011557775,0.0009743497,0.0007205417,0.00033652887],"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":[0.0000999425,0.00015976904,0.9974533,0.00012173288,0.00011113119,0.00006135245,0.0002319245,0.00007477899,0.000063503154,0.000027340457,0.00022827923,0.001366989],"study_design_scores_gemma":[0.000049082286,0.00031149323,0.99524975,0.00014531927,0.00020311687,0.00031880348,0.001428575,0.001342739,0.00012518717,0.00004636407,0.00076623313,0.000013349083],"about_ca_topic_score_codex":0.017476324,"about_ca_topic_score_gemma":0.015880922,"teacher_disagreement_score":0.017476324,"about_ca_system_score_codex":0.0005415182,"about_ca_system_score_gemma":0.00080591656,"threshold_uncertainty_score":0.03474921},"labels":[],"label_agreement":null},{"id":"W3159374965","doi":"10.1136/bmjgh-2021-005542","title":"Challenges in reported COVID-19 data: best practices and recommendations for future epidemics","year":2021,"lang":"en","type":"review","venue":"BMJ Global Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Pandemic; MEDLINE; Public health; Betacoronavirus; Medicine; Coronavirus Infections; Virology; Environmental health; Political science; Nursing; Outbreak; Internal medicine; Infectious disease (medical specialty)","score_opus":0.6397646608566762,"score_gpt":0.6309359263521984,"score_spread":0.008828734504477742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159374965","genre_codex":"commentary","genre_gemma":"review","domain_codex":null,"domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003576655,0.027954381,0.14899725,0.77835476,0.0085726045,0.0030213967,0.009519718,0.0056259343,0.014377307],"genre_scores_gemma":[0.038240526,0.040545613,0.8301462,0.05241417,0.0045232633,0.005856804,0.020539388,0.002968997,0.004765048],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.63971883,0.21640973,0.07533988,0.018865407,0.043543693,0.00612244],"domain_scores_gemma":[0.2647649,0.34811947,0.03344618,0.09159503,0.24829233,0.013782052],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.43300343,0.003886294,0.005104817,0.023315351,0.0059149596,0.03964996,0.028940694,0.013934302,0.01159792],"category_scores_gemma":[0.6589048,0.004936254,0.0076242904,0.02521965,0.013114321,0.069773726,0.023980243,0.028610928,0.01083226],"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":[0.00029014907,0.00041853308,0.03354558,0.00946042,0.0006887216,0.0007033095,0.014797322,0.008018492,0.0007518925,0.059744604,0.4225054,0.44907558],"study_design_scores_gemma":[0.00016425193,0.00015327158,0.007200407,0.048133496,0.00038345993,0.0006993408,0.023027737,0.010437119,0.0017837033,0.14111385,0.76625687,0.00064650236],"about_ca_topic_score_codex":0.085788004,"about_ca_topic_score_gemma":0.059148803,"teacher_disagreement_score":0.5669966,"about_ca_system_score_codex":0.015207234,"about_ca_system_score_gemma":0.057159215,"threshold_uncertainty_score":0.6992077},"labels":[],"label_agreement":null},{"id":"W3160010777","doi":"10.2196/27621","title":"Performance of the Severe Acute Respiratory Illness Sentinel Surveillance System in Yemen: Mixed Methods Evaluation Study","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Disease surveillance; Environmental health; Disease control; Disease monitoring; Epidemiology; Medical emergency; Disease; Internal medicine","score_opus":0.04637033512563938,"score_gpt":0.38030365389877435,"score_spread":0.333933318773135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160010777","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":[0.99625313,0.00062221766,0.0006221702,0.00014262527,0.000013417655,0.0014844133,0.00028087062,0.0000075078256,0.0005736603],"genre_scores_gemma":[0.9927483,0.00041772387,0.0022897818,0.000120695535,0.000020793816,0.003843769,0.00033408284,0.000005851689,0.00021901124],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9438408,0.045773264,0.0043654838,0.0013230864,0.0029156955,0.0017816983],"domain_scores_gemma":[0.96689063,0.015671337,0.0075247055,0.0015281923,0.007280075,0.0011050975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05445991,0.0011797374,0.0011562465,0.0016751679,0.0011646543,0.0018524325,0.0009854964,0.0009360121,0.0012606598],"category_scores_gemma":[0.04383717,0.00055342127,0.0022025881,0.0019498225,0.0010245367,0.0020738987,0.0016316809,0.00062506663,0.00022654558],"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":[0.009481551,0.0074941893,0.8665911,0.0030097987,0.0029713337,0.00033052938,0.023170568,0.0012247638,0.001027177,0.00042076755,0.0010046626,0.08327356],"study_design_scores_gemma":[0.0015661682,0.057280272,0.8725229,0.0018721445,0.0020192,0.00031938605,0.05123379,0.007120803,0.0026196297,0.00032106508,0.0028882357,0.00023640259],"about_ca_topic_score_codex":0.006827954,"about_ca_topic_score_gemma":0.010839754,"teacher_disagreement_score":0.05445991,"about_ca_system_score_codex":0.005476952,"about_ca_system_score_gemma":0.004144354,"threshold_uncertainty_score":0.288015},"labels":[],"label_agreement":null},{"id":"W3160026548","doi":"10.1111/zph.12849","title":"Detection of spatial and spatio‐temporal <i>Salmonella</i> Heidelberg and <i>Salmonella</i> Typhimurium human case clusters focused around licensed abattoirs in Ontario in 2015, and their potential relation to known outbreaks","year":2021,"lang":"en","type":"article","venue":"Zoonoses and Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Guelph Wellington Seniors Association; Guelph General Hospital; Public Health Ontario; University of Guelph; Toronto Public Health","funders":"","keywords":"Salmonella; Outbreak; Cluster (spacecraft); Veterinary medicine; Serotype; Environmental health; Geography; Biology; Medicine; Virology","score_opus":0.023094047430316695,"score_gpt":0.26745127196412094,"score_spread":0.24435722453380423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160026548","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":[0.99733233,0.000111877234,0.000277853,0.00007295448,0.000004373576,0.0000424498,0.0013870985,0.000009752306,0.0007612885],"genre_scores_gemma":[0.9982667,0.00008346606,0.0002619471,0.000026787488,0.0000035658654,0.0000150564565,0.00096791354,0.0000024439776,0.00037194983],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989085,0.000103863786,0.000064236796,0.0002802352,0.00035976426,0.00028347527],"domain_scores_gemma":[0.997912,0.00017101663,0.0008921433,0.0001202322,0.0006085278,0.0002961905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004895012,0.00027999093,0.00026222758,0.0010260904,0.00097513956,0.00073047134,0.00067162566,0.00027987218,0.00083161774],"category_scores_gemma":[0.002321122,0.00029115682,0.00044099643,0.0015528937,0.0009002485,0.00028540008,0.00091020565,0.00021145226,0.00010283897],"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":[0.000100629455,0.000010818324,0.9942316,0.000032595624,0.000041600724,0.0001357289,0.0015020544,0.0001627832,0.0012242547,0.000045920453,0.00040515597,0.0021070896],"study_design_scores_gemma":[0.000001894867,0.000018761637,0.9986897,0.0000054656048,0.000009553835,0.00003899167,0.00075878436,0.00014788318,0.000092402275,0.000008578159,0.00022450359,0.0000034896036],"about_ca_topic_score_codex":0.9037418,"about_ca_topic_score_gemma":0.94451374,"teacher_disagreement_score":0.09625822,"about_ca_system_score_codex":0.006756855,"about_ca_system_score_gemma":0.007910412,"threshold_uncertainty_score":0.19365019},"labels":[],"label_agreement":null},{"id":"W3161221184","doi":"10.1089/fpd.2020.2918","title":"Area-Level Clustering of Shiga Toxin–Producing <i>Escherichia coli</i> Infections and Their Socioeconomic and Demographic Factors in Ontario, Canada: An Ecological Study","year":2021,"lang":"en","type":"article","venue":"Foodborne Pathogens and Disease","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Socioeconomic status; Cluster (spacecraft); Scan statistic; Demography; Logistic regression; Odds; Odds ratio; Poisson regression; Geography; Population; Environmental health; Medicine; Statistics; Computer science; Pathology; Internal medicine","score_opus":0.027061345036069908,"score_gpt":0.23278303700988012,"score_spread":0.2057216919738102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161221184","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":[0.99600714,0.00023615842,0.00018224437,0.00016368843,0.0000044615244,0.000058825583,0.0022794197,0.0000070966635,0.0010608503],"genre_scores_gemma":[0.9979773,0.00020009083,0.00026785253,0.00004919906,0.0000034031373,0.000023094415,0.00088712457,0.0000035577368,0.00058843073],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991708,0.000070270406,0.000066162684,0.00014972543,0.00027069016,0.0002723796],"domain_scores_gemma":[0.9976872,0.000120252706,0.0007509153,0.00010037107,0.00090209313,0.00043911752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005931202,0.00028772015,0.00029431892,0.001480482,0.0027009593,0.001006859,0.0010463184,0.00029141895,0.0012716431],"category_scores_gemma":[0.0019089216,0.0003551832,0.0005522788,0.0037395835,0.00086860405,0.00037520318,0.001074096,0.0003675992,0.0001466448],"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":[0.000025179626,0.000014722498,0.99638957,0.000025024548,0.000036260735,0.000061476276,0.0009970381,0.00010354064,0.0001764941,0.000057277663,0.00047165158,0.0016417245],"study_design_scores_gemma":[0.0000027763822,0.000012395873,0.99764746,0.000016827144,0.00001523416,0.000043836426,0.0013537061,0.00026330265,0.000040028448,0.000016437014,0.00058217696,0.000005919142],"about_ca_topic_score_codex":0.9944141,"about_ca_topic_score_gemma":0.99710685,"teacher_disagreement_score":0.02529069,"about_ca_system_score_codex":0.02529069,"about_ca_system_score_gemma":0.0301915,"threshold_uncertainty_score":0.18349767},"labels":[],"label_agreement":null},{"id":"W3162149128","doi":"10.1145/3404820.3404821","title":"Introduction to this special issue: Modeling and understanding the spread of COVID-19","year":2020,"lang":"en","type":"article","venue":"SIGSPATIAL Special","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Globe; 2019-20 coronavirus outbreak; German; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Data science; Position (finance); Pandemic; Geography; Computer science; Public relations; Regional science; Political science; Business; Infectious disease (medical specialty); Medicine; Disease; Virology","score_opus":0.04824369830473133,"score_gpt":0.29820721068652745,"score_spread":0.24996351238179612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162149128","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042124726,0.08355067,0.28259677,0.11522581,0.42578647,0.0005924024,0.0126188025,0.007995694,0.067420915],"genre_scores_gemma":[0.029813027,0.119140655,0.10811215,0.028756775,0.48234096,0.00071035314,0.030377213,0.005467612,0.19528121],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99867934,0.00027557905,0.00012460237,0.0003298759,0.0004991651,0.00009139293],"domain_scores_gemma":[0.9928886,0.0037612587,0.0003086546,0.00062422676,0.0015335949,0.0008836088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021541202,0.0016882272,0.0014239894,0.0026212328,0.0009282217,0.0045616035,0.0020763157,0.0028761374,0.04879069],"category_scores_gemma":[0.011094132,0.00074953894,0.0019545374,0.002179907,0.00084466377,0.0036307252,0.0028897172,0.005331525,0.020813342],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038713417,0.00007751413,0.0008146383,0.00039109835,0.000053832646,0.00016558361,0.000077130826,0.004180647,0.00047226058,0.006046827,0.91569465,0.07198708],"study_design_scores_gemma":[0.000014966499,0.000065200766,0.00085015164,0.00031342002,0.00003354433,0.0003660537,0.00007427529,0.008456267,0.00023541151,0.013320326,0.9762383,0.000032114323],"about_ca_topic_score_codex":0.004634854,"about_ca_topic_score_gemma":0.0054100524,"teacher_disagreement_score":0.04879069,"about_ca_system_score_codex":0.0010938789,"about_ca_system_score_gemma":0.0015992527,"threshold_uncertainty_score":0.16322112},"labels":[],"label_agreement":null},{"id":"W3162464467","doi":"10.22541/au.161918947.77588494/v2","title":"The Epidemic Volatility Index: an early warning tool for epidemics","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Impact","funders":"","keywords":"Volatility (finance); Coronavirus disease 2019 (COVID-19); Warning system; Pandemic; Outbreak; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Index (typography); Econometrics; Early warning system; Computer science; Actuarial science; Business; Economics; Medicine; Telecommunications; Virology; Infectious disease (medical specialty)","score_opus":0.039405513747548075,"score_gpt":0.34474552076791626,"score_spread":0.3053400070203682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162464467","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":[0.065869175,0.007407857,0.87143004,0.0047866777,0.0015459469,0.00042589282,0.021539854,0.012249401,0.014745199],"genre_scores_gemma":[0.4711551,0.0032544783,0.5079782,0.0006927004,0.001200165,0.00036840193,0.011464523,0.00064994104,0.003236465],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981906,0.00057112344,0.00019225766,0.00029584795,0.0006639278,0.00008612147],"domain_scores_gemma":[0.9937016,0.0036372028,0.0010113314,0.00051354256,0.000860719,0.00027571718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029768788,0.0012494291,0.0010167874,0.005294833,0.0003513418,0.0034600531,0.00094197266,0.00075490115,0.0030313144],"category_scores_gemma":[0.015124739,0.00037617824,0.000687666,0.00260334,0.00049277593,0.0021320162,0.0016630548,0.0017548266,0.0010643748],"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":[0.000505606,0.00028442536,0.10749919,0.0010045189,0.0005645539,0.00044563907,0.00056690734,0.08733325,0.008850074,0.052368447,0.07220125,0.6683762],"study_design_scores_gemma":[0.00013437784,0.00050597533,0.050504923,0.00055935426,0.00025950663,0.0017242194,0.0003477504,0.7016501,0.013749546,0.12958185,0.10053855,0.0004437602],"about_ca_topic_score_codex":0.0017865715,"about_ca_topic_score_gemma":0.0010826426,"teacher_disagreement_score":0.005294833,"about_ca_system_score_codex":0.00054418267,"about_ca_system_score_gemma":0.0009740406,"threshold_uncertainty_score":0.015743434},"labels":[],"label_agreement":null},{"id":"W3163685844","doi":"10.2196/25720","title":"Analysis of Keywords Used in Internet Searches for Melanoma Information: Observational Study","year":2021,"lang":"en","type":"article","venue":"JMIR Dermatology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Cancer Institute; National Institutes of Health","keywords":"Melanoma; The Internet; Observational study; Medicine; Skin cancer; Ranking (information retrieval); Cancer; World Wide Web; Internet privacy; Computer science; Information retrieval; Internal medicine; Cancer research","score_opus":0.09266731699894319,"score_gpt":0.3766171316791348,"score_spread":0.2839498146801916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163685844","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":[0.9947249,0.0011731423,0.0001459647,0.00006729476,0.0000070026176,0.00014980999,0.002896688,0.000008252084,0.0008268549],"genre_scores_gemma":[0.99604213,0.0007397118,0.00024567923,0.00008257659,0.000014536167,0.00014223991,0.0025388703,0.000009735324,0.0001846687],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9964193,0.00088672177,0.0011973312,0.00044397675,0.00075404096,0.00029869276],"domain_scores_gemma":[0.9715244,0.010667929,0.012859796,0.0008794816,0.0029638987,0.0011043681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026932736,0.00025843875,0.000868419,0.004101559,0.0005539202,0.0013781394,0.0005637925,0.0007174569,0.0021641508],"category_scores_gemma":[0.023189913,0.00039898843,0.001235805,0.0058094985,0.00038806853,0.0024147194,0.0011128882,0.0009198135,0.0007680887],"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":[0.000175552,0.00014229794,0.99638957,0.0002728544,0.000091060596,0.00007956418,0.0005322044,0.00001666071,0.00008474282,0.00001768173,0.00027835165,0.00191942],"study_design_scores_gemma":[0.00002849817,0.00030901315,0.99450487,0.00016860914,0.00018526746,0.000687376,0.0028855957,0.00024764493,0.00010192185,0.000038750823,0.00082457287,0.00001783382],"about_ca_topic_score_codex":0.006816003,"about_ca_topic_score_gemma":0.006399302,"teacher_disagreement_score":0.006816003,"about_ca_system_score_codex":0.0007748515,"about_ca_system_score_gemma":0.00094188075,"threshold_uncertainty_score":0.014243603},"labels":[],"label_agreement":null},{"id":"W3163853261","doi":"10.2196/23528","title":"Building an Interactive Geospatial Visualization Application for National Health Care–Associated Infection Surveillance: Development Study","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Geospatial analysis; Geocoding; Computer science; Visualization; Dashboard; Health care; Data science; Data visualization; World Wide Web; Data mining; Geography; Cartography","score_opus":0.031175035266803267,"score_gpt":0.39377664879428215,"score_spread":0.3626016135274789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163853261","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":[0.40951726,0.00046329305,0.4869137,0.0014630203,0.00019914379,0.008899426,0.008919221,0.06709393,0.016530981],"genre_scores_gemma":[0.21076934,0.00042481217,0.76777744,0.00022839941,0.000034866083,0.0037567078,0.0102501465,0.0026565425,0.0041018277],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984169,0.0007251306,0.00010432369,0.00021874411,0.00039446083,0.0001403569],"domain_scores_gemma":[0.9951427,0.002509693,0.00017305234,0.00042329569,0.001238221,0.0005130316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059804344,0.0013613312,0.00040119945,0.001192814,0.0006196192,0.0016251868,0.0023100807,0.00069214153,0.0077834893],"category_scores_gemma":[0.008682993,0.00068135577,0.001009472,0.0008181604,0.00052798394,0.00216015,0.0021993825,0.0009363703,0.0014806343],"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":[0.001658392,0.0075730826,0.07012161,0.0036968936,0.00048533594,0.0033690883,0.014123218,0.03523691,0.06340275,0.00889198,0.081649676,0.7097911],"study_design_scores_gemma":[0.0022032165,0.006075664,0.0952575,0.001793965,0.00095606846,0.0026164842,0.0058360174,0.45522857,0.12862101,0.008303739,0.29240713,0.00070058525],"about_ca_topic_score_codex":0.007935857,"about_ca_topic_score_gemma":0.0062755086,"teacher_disagreement_score":0.007935857,"about_ca_system_score_codex":0.00062078284,"about_ca_system_score_gemma":0.002286701,"threshold_uncertainty_score":0.031627953},"labels":[],"label_agreement":null},{"id":"W3165357450","doi":"10.2196/27052","title":"Internet Interest in Colon Cancer Following the Death of Chadwick Boseman: Infoveillance Study","year":2021,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Colorectal cancer; Demography; Medicine; Mortality rate; Demographics; Cancer; Gerontology; Oncology; Internal medicine; Sociology","score_opus":0.16554325186956553,"score_gpt":0.46978038485894796,"score_spread":0.30423713298938243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165357450","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":[0.9984012,0.000048100377,0.000025987558,0.00013832677,0.000004301313,0.000014260826,0.0005634914,0.0000020870727,0.00080216967],"genre_scores_gemma":[0.99842167,0.00007840415,0.00004615705,0.000071835115,0.000008874019,0.000013849546,0.00081209844,0.0000020789232,0.0005450513],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99973005,0.00005905774,0.000017844008,0.000051229188,0.00007429773,0.00006750835],"domain_scores_gemma":[0.99802375,0.0003661563,0.00076058763,0.00011472313,0.0003041712,0.00043053826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000522751,0.00014185256,0.00016547959,0.001058747,0.000670569,0.0006656148,0.00035512142,0.00034404357,0.001830975],"category_scores_gemma":[0.0030135408,0.00014467437,0.00026784878,0.0010683434,0.0003026062,0.00073396123,0.0006199589,0.000723134,0.0004150059],"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":[0.00007748676,0.00020175142,0.99449366,0.000010730086,0.000025144564,0.00012412085,0.00097465346,0.00005456863,0.00009540165,0.000053917152,0.0011010343,0.0027875595],"study_design_scores_gemma":[0.000003946666,0.00006901923,0.9970413,0.000008381747,0.000013331778,0.000100240875,0.0017755779,0.00035683083,0.00004806423,0.00001735091,0.0005599864,0.000006007407],"about_ca_topic_score_codex":0.17468148,"about_ca_topic_score_gemma":0.21392573,"teacher_disagreement_score":0.17468148,"about_ca_system_score_codex":0.0010751853,"about_ca_system_score_gemma":0.0007520223,"threshold_uncertainty_score":0.34732944},"labels":[],"label_agreement":null},{"id":"W3166663043","doi":"10.2196/21709","title":"Psoriasis Google Trends","year":2021,"lang":"en","type":"article","venue":"JMIR Dermatology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Psoriasis; Dermatology; Medicine","score_opus":0.015556746817256344,"score_gpt":0.3119070300344655,"score_spread":0.2963502832172092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166663043","genre_codex":"other","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":[0.004320459,0.00988935,0.0013454957,0.012706821,0.003695562,0.00020840944,0.40402442,0.012699477,0.55111],"genre_scores_gemma":[0.033272844,0.021931004,0.004163087,0.011705648,0.0069354447,0.0002331078,0.43245482,0.005160263,0.48414376],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9995461,0.000038609203,0.000046339173,0.000085350846,0.00021941986,0.000064198524],"domain_scores_gemma":[0.99807596,0.00040705706,0.00030295845,0.00015523177,0.0005944136,0.00046436198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029517178,0.00077646796,0.0005147868,0.0065909736,0.0005926215,0.003795878,0.00049988035,0.0008208049,0.26718467],"category_scores_gemma":[0.0039901417,0.000334018,0.00070999004,0.0051706047,0.00017778056,0.0021809868,0.0015686541,0.0011210862,0.23200971],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002803775,0.00000938462,0.00082782627,0.00026446165,0.000008075295,0.000038811686,0.000028994686,0.00002803914,0.000111251175,0.00082203804,0.9524108,0.045422297],"study_design_scores_gemma":[0.000011160896,0.000006186393,0.002446309,0.00015543612,0.000009836643,0.00012008184,0.000040766463,0.00008766761,0.0000895128,0.00039685023,0.9966298,0.000006379496],"about_ca_topic_score_codex":0.010081132,"about_ca_topic_score_gemma":0.01790172,"teacher_disagreement_score":0.26718467,"about_ca_system_score_codex":0.0006275429,"about_ca_system_score_gemma":0.0011597947,"threshold_uncertainty_score":0.8938218},"labels":[],"label_agreement":null},{"id":"W3167305779","doi":"10.1155/2021/6663559","title":"Seasonal Variation and Global Public Interest in the Internet Searches for Osteoporosis","year":2021,"lang":"en","type":"article","venue":"BioMed Research International","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Beijing Municipal Health Commission; Beijing Talents Fund; National Natural Science Foundation of China","keywords":"Osteoporosis; Medicine; Zoledronic acid; Demography; Public health; Gerontology; Internal medicine; Pathology","score_opus":0.19405126977749987,"score_gpt":0.4181085797983547,"score_spread":0.22405731002085483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167305779","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":[0.98202413,0.0033190534,0.00027063786,0.00084183255,0.0000355864,0.000024467667,0.009844649,0.000039388146,0.0036003613],"genre_scores_gemma":[0.9959857,0.00077882793,0.00015842327,0.00007550837,0.000049099664,0.000012490055,0.0026913187,0.000009609292,0.00023890763],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993025,0.00015482177,0.00013149017,0.0001372968,0.0001617662,0.000112064154],"domain_scores_gemma":[0.99301404,0.0022045835,0.0035501048,0.00022135476,0.00068626384,0.00032376917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009920076,0.00012780975,0.00026478662,0.003829519,0.00015957774,0.00097373064,0.00022287235,0.00024515647,0.0023408106],"category_scores_gemma":[0.005954365,0.000085549065,0.00046481812,0.008053526,0.0002998998,0.0011255479,0.0004943002,0.00028765708,0.00031859006],"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":[0.00013572423,0.000021218999,0.97999907,0.00032269058,0.00015140612,0.00012914314,0.0007331919,0.00028962898,0.0004375922,0.00025281362,0.0019271852,0.015600445],"study_design_scores_gemma":[0.0000022824916,0.000023450411,0.996914,0.000029661007,0.000031310763,0.00017999519,0.0006642677,0.00041813968,0.0000741247,0.000078315774,0.0015792855,0.000005066402],"about_ca_topic_score_codex":0.010863068,"about_ca_topic_score_gemma":0.020500654,"teacher_disagreement_score":0.010863068,"about_ca_system_score_codex":0.0004765222,"about_ca_system_score_gemma":0.00031676985,"threshold_uncertainty_score":0.02159965},"labels":[],"label_agreement":null},{"id":"W3167386324","doi":"10.1038/s41746-021-00459-8","title":"Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases","year":2021,"lang":"en","type":"review","venue":"npj Digital Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":123,"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":"Canadian Institutes of Health Research","keywords":"Grey literature; Preparedness; Operationalization; Triage; Pandemic; Public health; Psychological intervention; Preprint; Delphi method; Computer science; Medicine; Coronavirus disease 2019 (COVID-19); Artificial intelligence; Data science; MEDLINE; Medical emergency; Infectious disease (medical specialty); Disease; Nursing; Political science","score_opus":0.30572327218425255,"score_gpt":0.5010962774777272,"score_spread":0.19537300529347468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167386324","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":[0.00057031296,0.99387085,0.00082753395,0.0023907793,0.0004955844,0.0004836282,0.00026321807,0.000013982799,0.0010841216],"genre_scores_gemma":[0.0071000787,0.9868547,0.0020143767,0.0020273412,0.00024522725,0.001223311,0.0002771307,0.000014206076,0.0002435752],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9745815,0.008777432,0.010232321,0.0013176983,0.0043766596,0.0007142906],"domain_scores_gemma":[0.8190238,0.15314458,0.011979565,0.002150636,0.012958682,0.0007428098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029159918,0.002240868,0.004843996,0.025283804,0.0014655914,0.007803031,0.0031268261,0.0054733767,0.0054932325],"category_scores_gemma":[0.15274858,0.0015476163,0.0063315853,0.02060438,0.002462275,0.007428658,0.0042411364,0.0035047522,0.0010433736],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009191334,0.000026952597,0.0004937169,0.86215264,0.0014578095,0.00031726982,0.0012180344,0.00033011957,0.00028517735,0.003498459,0.006167802,0.12396011],"study_design_scores_gemma":[0.00001853428,0.000043199405,0.00042658777,0.95194423,0.0034774307,0.00019386645,0.0005971786,0.00012073923,0.00015798234,0.0016557744,0.041343026,0.000021445874],"about_ca_topic_score_codex":0.008555438,"about_ca_topic_score_gemma":0.019142114,"teacher_disagreement_score":0.029159918,"about_ca_system_score_codex":0.0072229574,"about_ca_system_score_gemma":0.029092366,"threshold_uncertainty_score":0.1542142},"labels":[],"label_agreement":null},{"id":"W3171559874","doi":"10.21203/rs.2.16121/v1","title":"Geographic access to optometry services across Canada: Mapping distribution, need and self-reported use","year":2019,"lang":"en","type":"preprint","venue":"Research Square","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Distribution (mathematics); Optometry; Geography; Regional science; Cartography; Medicine","score_opus":0.057551460876399466,"score_gpt":0.41981869102169483,"score_spread":0.3622672301452954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171559874","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":[0.93391544,0.0009641212,0.00086626585,0.000713446,0.000012824868,0.00013608333,0.059568267,0.00006830442,0.0037551832],"genre_scores_gemma":[0.9800071,0.0007896882,0.0020174128,0.000111445486,0.0000059392796,0.00009533419,0.013786292,0.000018349116,0.0031682926],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99912435,0.000070838505,0.000071558316,0.00016073819,0.00029222463,0.00028027437],"domain_scores_gemma":[0.9973124,0.00027261375,0.00039869666,0.000105549945,0.0014589464,0.0004516783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076016993,0.00034711143,0.00033156108,0.0033627222,0.001277912,0.0015508515,0.0009829815,0.0003245695,0.0022881897],"category_scores_gemma":[0.0040736184,0.0002886444,0.000705335,0.011927317,0.000483784,0.0005180772,0.0012956008,0.0005103547,0.00033286502],"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":[0.000058115318,0.00003274647,0.977521,0.00010427918,0.00010177868,0.00004098402,0.0014350803,0.00073629513,0.00023153512,0.00040145445,0.005286895,0.014049778],"study_design_scores_gemma":[0.000006053904,0.000010078171,0.9933803,0.000052673557,0.000028429891,0.000021326678,0.002384946,0.0012245493,0.00008761442,0.0000739198,0.0027176337,0.000012482095],"about_ca_topic_score_codex":0.9983107,"about_ca_topic_score_gemma":0.99872345,"teacher_disagreement_score":0.020813292,"about_ca_system_score_codex":0.020813292,"about_ca_system_score_gemma":0.04073618,"threshold_uncertainty_score":0.1510117},"labels":[],"label_agreement":null},{"id":"W3172179230","doi":"10.1126/sciadv.abb1237","title":"Toward the use of neural networks for influenza prediction at multiple spatial resolutions","year":2021,"lang":"en","type":"article","venue":"Science Advances","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":52,"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 Institute of General Medical Sciences; National Institutes of Health; Institute for Catastrophic Loss Reduction","keywords":"Artificial neural network; Computer science; Artificial intelligence; Machine learning; Seasonal influenza; Coronavirus disease 2019 (COVID-19); Medicine; Infectious disease (medical specialty)","score_opus":0.0717917850203235,"score_gpt":0.32717053980282573,"score_spread":0.2553787547825022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172179230","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":[0.4363876,0.0013517808,0.55461323,0.0024391378,0.00008064226,0.0000910788,0.0004893167,0.0007711947,0.0037760062],"genre_scores_gemma":[0.9296142,0.00035185346,0.068656035,0.00016375576,0.000050751376,0.00004570445,0.00026928482,0.00002482878,0.000823656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995121,0.0002473471,0.000024881598,0.0001039778,0.00007444648,0.00003723082],"domain_scores_gemma":[0.99638367,0.0027045496,0.0002903489,0.00019455179,0.00036387204,0.00006314077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030143934,0.00083677896,0.00055686355,0.00097593706,0.00026465085,0.00094481354,0.00094544387,0.00070406304,0.00067710807],"category_scores_gemma":[0.010404081,0.00043270757,0.000386006,0.00070032256,0.00042008917,0.0019460447,0.00087379507,0.0013592332,0.00017740768],"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":[0.00010834379,0.00008002755,0.010941736,0.000033449458,0.00006432026,0.00005698655,0.000051957155,0.94045496,0.0012089033,0.0039715357,0.00051768223,0.04251012],"study_design_scores_gemma":[0.0000015646759,0.0000072399853,0.0003084327,0.0000027470085,0.0000024173767,0.0000024602475,0.000005330932,0.99804735,0.0001515508,0.0014194251,0.00004950549,0.0000018444356],"about_ca_topic_score_codex":0.014447709,"about_ca_topic_score_gemma":0.017429586,"teacher_disagreement_score":0.014447709,"about_ca_system_score_codex":0.0008307026,"about_ca_system_score_gemma":0.000650766,"threshold_uncertainty_score":0.028727233},"labels":[],"label_agreement":null},{"id":"W3172890739","doi":"","title":"Forecasting Canadian GDP growth using XGBoost","year":2020,"lang":"en","type":"article","venue":"Carleton Economic Papers","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nowcasting; Gradient boosting; Computer science; Econometrics; Feature selection; Boosting (machine learning); Vector autoregression; Artificial intelligence; Machine learning; Economics; Geography","score_opus":0.05042131887868386,"score_gpt":0.24209908208591935,"score_spread":0.19167776320723548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172890739","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":[0.5619193,0.003750085,0.3865018,0.0037617509,0.000688884,0.0002640796,0.008215194,0.0053760125,0.029522937],"genre_scores_gemma":[0.89856565,0.0010555562,0.08538929,0.0002583919,0.00007197654,0.0000963158,0.005770608,0.0001268087,0.0086655095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995964,0.000066571585,0.000013370661,0.0000669494,0.0001712803,0.00008545108],"domain_scores_gemma":[0.9994929,0.00013752308,0.000049016944,0.00002926804,0.0002563911,0.00003494824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010903036,0.0008564876,0.0006043409,0.0014563522,0.00056941353,0.001098335,0.0009977211,0.00049960456,0.0016440323],"category_scores_gemma":[0.0025593853,0.00028248105,0.00050145405,0.0026549096,0.00041751712,0.0004318492,0.00038822446,0.0007296243,0.00039761848],"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":[0.00009733915,0.000040960233,0.009980622,0.000047244994,0.000058966536,0.000047377056,0.0000302524,0.8970718,0.00039800536,0.0025145875,0.00908043,0.08063244],"study_design_scores_gemma":[0.000006201594,0.000005722146,0.0019001777,0.0000047245967,0.000004605233,0.0000023100804,0.000011919619,0.9959317,0.00029299172,0.00047873304,0.0013559058,0.000005042091],"about_ca_topic_score_codex":0.78382593,"about_ca_topic_score_gemma":0.7295935,"teacher_disagreement_score":0.21617407,"about_ca_system_score_codex":0.006325963,"about_ca_system_score_gemma":0.006667614,"threshold_uncertainty_score":0.43489426},"labels":[],"label_agreement":null},{"id":"W3173615069","doi":"10.1007/978-3-030-70179-6_58","title":"Examining Geographical Visualizations of COVID-19","year":2021,"lang":"en","type":"book-chapter","venue":"Global perspectives on health geography","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; Disease; 2019-20 coronavirus outbreak; Big data; Data science; Geography; Cartography; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Visualization; History; Regional science; Computer science; Medicine; Data mining; Virology; Pathology; Outbreak; Infectious disease (medical specialty)","score_opus":0.04814953042357952,"score_gpt":0.3671007646445819,"score_spread":0.3189512342210024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173615069","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":[0.18885696,0.010135693,0.4157505,0.013912071,0.0011335756,0.00019780375,0.016342165,0.01069083,0.34298044],"genre_scores_gemma":[0.7042547,0.004975333,0.24605411,0.00045796155,0.00022762424,0.00013296503,0.008872921,0.002711399,0.032313034],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99945384,0.00026172667,0.000028356875,0.000076583936,0.00013882354,0.000040716463],"domain_scores_gemma":[0.9947129,0.0037325055,0.00031635968,0.00041997153,0.0006880045,0.0001302086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012040592,0.00062335207,0.00030230748,0.0038878527,0.0009909865,0.0073389933,0.00059834536,0.00067044026,0.01675899],"category_scores_gemma":[0.010559179,0.0004311124,0.0005061588,0.00549092,0.00084143,0.0047233696,0.0025142734,0.0008713856,0.0014956269],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014608007,0.000034095272,0.020030644,0.00091659266,0.0000838916,0.0009468656,0.024073953,0.026867714,0.0038058725,0.6406005,0.08306929,0.19942454],"study_design_scores_gemma":[0.000021179538,0.000035534766,0.018893255,0.00082878827,0.00008784998,0.0008350062,0.020440381,0.11424378,0.0029772185,0.2692467,0.5722879,0.00010229119],"about_ca_topic_score_codex":0.022582106,"about_ca_topic_score_gemma":0.039440174,"teacher_disagreement_score":0.022582106,"about_ca_system_score_codex":0.001574233,"about_ca_system_score_gemma":0.0008510379,"threshold_uncertainty_score":0.056064367},"labels":[],"label_agreement":null},{"id":"W3173732943","doi":"10.18617/liinc.v17i1.5711","title":"O nascimento do saber infodemiológico","year":2021,"lang":"pt","type":"article","venue":"Liinc em Revista","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Humanities; Political science; Philosophy","score_opus":0.01804611513749748,"score_gpt":0.29856169450230996,"score_spread":0.2805155793648125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173732943","genre_codex":"other","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":[0.07930147,0.10488299,0.05377169,0.29257435,0.013095178,0.00028033764,0.00075356,0.0011419018,0.45419863],"genre_scores_gemma":[0.8122887,0.06550331,0.032149743,0.027140465,0.01170518,0.00028484617,0.0004899166,0.0006698537,0.049767997],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9832314,0.008157425,0.00069513626,0.0021876302,0.0045699393,0.0011584946],"domain_scores_gemma":[0.94698554,0.029408062,0.0045880647,0.010769545,0.0054980014,0.0027507946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01739656,0.00081201683,0.0012538879,0.00830798,0.00581983,0.020798992,0.0022048382,0.005475703,0.009412856],"category_scores_gemma":[0.051221713,0.00062448665,0.0009862164,0.00588497,0.018407773,0.023044975,0.01127239,0.008757359,0.0027053992],"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":[0.00012595004,0.00011533306,0.0056673363,0.0011862875,0.000040492312,0.0008556145,0.027963808,0.0007584656,0.0012396822,0.69506305,0.032020405,0.23496355],"study_design_scores_gemma":[0.000019818,0.00007805782,0.002022036,0.0017504744,0.0000254197,0.00090369344,0.008515934,0.0006649021,0.0007757826,0.06558111,0.91961104,0.00005168585],"about_ca_topic_score_codex":0.0049674483,"about_ca_topic_score_gemma":0.005363942,"teacher_disagreement_score":0.020798992,"about_ca_system_score_codex":0.008647934,"about_ca_system_score_gemma":0.0106382435,"threshold_uncertainty_score":0.09200287},"labels":[],"label_agreement":null},{"id":"W3173756340","doi":"10.1186/s12889-021-11132-w","title":"Development and validation of an automated emergency department-based syndromic surveillance system to enhance public health surveillance in Yukon: a lower-resourced and remote setting","year":2021,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph; Public Health Agency of Canada; Yukon Health and Social Services","funders":"","keywords":"Medicine; Public health surveillance; Public health; Triage; Medical emergency; Disease surveillance; Emergency department; Diagnosis code; Environmental health; Population; Pathology","score_opus":0.030568474606683736,"score_gpt":0.33596790371316826,"score_spread":0.30539942910648454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173756340","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":[0.9638341,0.00012709922,0.023180764,0.00029919107,0.00004362857,0.0019446366,0.0064714337,0.0013923189,0.0027067699],"genre_scores_gemma":[0.90484285,0.00011743329,0.082954645,0.00018709159,0.000010565026,0.00094508764,0.009452045,0.00007373499,0.0014165937],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978574,0.0006855723,0.00041372376,0.0004366713,0.0004075503,0.00019908337],"domain_scores_gemma":[0.9921687,0.0009974601,0.00052730495,0.00061726675,0.0053893803,0.00029996203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004674129,0.00044549035,0.00037124244,0.0010688406,0.0007893254,0.001032808,0.0010569093,0.0005333966,0.0012286883],"category_scores_gemma":[0.0064879805,0.00021310546,0.00039360728,0.00083939714,0.00044298972,0.00060359895,0.0011551462,0.0002524612,0.0005689022],"study_design_candidate":"bench_or_experimental","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":[0.0017568023,0.00077162305,0.73194766,0.0009539802,0.00022123927,0.0011396456,0.005127014,0.011938573,0.04653213,0.0009194106,0.008715899,0.18997593],"study_design_scores_gemma":[0.00035770872,0.001502183,0.86087954,0.00042290924,0.0002820664,0.0006118607,0.007956713,0.08242807,0.028496891,0.00038989846,0.016529346,0.00014287943],"about_ca_topic_score_codex":0.20385088,"about_ca_topic_score_gemma":0.19796406,"teacher_disagreement_score":0.79614913,"about_ca_system_score_codex":0.0041043665,"about_ca_system_score_gemma":0.008473168,"threshold_uncertainty_score":0.40532863},"labels":[],"label_agreement":null},{"id":"W3174361409","doi":"10.1186/s12889-021-11303-9","title":"Development and assessment of a hospital admissions-based syndromic surveillance system for COVID-19 in Ontario, Canada: ACES Pandemic Tracker","year":2021,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Kingston Health Sciences Centre","funders":"","keywords":"Medicine; Biostatistics; Coronavirus disease 2019 (COVID-19); Pandemic; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Public health; Epidemiology; Coronavirus Infections; Medical emergency; Betacoronavirus; Virology; Environmental health; Outbreak; Infectious disease (medical specialty); Pathology","score_opus":0.06718425764802337,"score_gpt":0.3475846683423884,"score_spread":0.280400410694365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174361409","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":[0.9667965,0.0006007619,0.006941421,0.0014273704,0.000059376565,0.004141024,0.009401308,0.0005394378,0.010092929],"genre_scores_gemma":[0.96664166,0.0006222513,0.02222788,0.00029331355,0.00002656376,0.00078661815,0.0071082655,0.000029244226,0.002264175],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99795413,0.00036480284,0.00020372708,0.00024872005,0.0009239107,0.00030480596],"domain_scores_gemma":[0.9874664,0.0007553039,0.0011626837,0.00027077366,0.009066825,0.0012780962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039460384,0.00052791147,0.00029744956,0.0012248836,0.0017288193,0.0012827024,0.001248459,0.00027797982,0.0010146352],"category_scores_gemma":[0.007641588,0.00029300302,0.0003919222,0.0016803474,0.00062273035,0.0005924312,0.0010796825,0.0003487628,0.00023555857],"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":[0.00037418137,0.00021136834,0.9175301,0.00039758385,0.00007827166,0.00016969093,0.0028952232,0.0028944334,0.004437232,0.0002481199,0.005276409,0.065487385],"study_design_scores_gemma":[0.000077390316,0.00049389363,0.97313744,0.00014470113,0.00007374994,0.00006936667,0.00340402,0.012985574,0.0014384224,0.000050351064,0.008073345,0.000051817016],"about_ca_topic_score_codex":0.97843003,"about_ca_topic_score_gemma":0.9805265,"teacher_disagreement_score":0.029070707,"about_ca_system_score_codex":0.029070707,"about_ca_system_score_gemma":0.07472104,"threshold_uncertainty_score":0.21092373},"labels":[],"label_agreement":null},{"id":"W3177040000","doi":"10.2196/27670","title":"Social Media Monitoring of the COVID-19 Pandemic and Influenza Epidemic With Adaptation for Informal Language in Arabic Twitter Data: Qualitative Study","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Deanship of Scientific Research, King Saud University; King Saud University; University of Leeds","keywords":"Computer science; Social media; Natural language processing; Artificial intelligence; Naive Bayes classifier; Terminology; Sentiment analysis; Support vector machine; Information retrieval; Data science; World Wide Web; Linguistics","score_opus":0.1639337992724459,"score_gpt":0.46163208562677094,"score_spread":0.29769828635432505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177040000","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":[0.9957046,0.00015876206,0.0004557526,0.0010054804,0.000013491621,0.00008967807,0.00033234266,0.00000523923,0.0022345737],"genre_scores_gemma":[0.99762255,0.0002235021,0.0004581325,0.00031751452,0.0000097166485,0.00019313683,0.00013538502,0.0000107929145,0.0010292418],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99822277,0.0011089348,0.00007639927,0.000161442,0.00020076134,0.00022974046],"domain_scores_gemma":[0.9925553,0.004852897,0.001088502,0.00017339167,0.0010020103,0.00032776644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034782654,0.0002971508,0.0002670005,0.0013771462,0.0022868165,0.0014005962,0.00054299383,0.000572879,0.002614593],"category_scores_gemma":[0.011290406,0.00023495653,0.00020427752,0.0012191086,0.0017455211,0.0023376157,0.0021346929,0.0007833309,0.0003109127],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008343771,0.0000807295,0.11725843,0.00045868644,0.000018880019,0.00171461,0.85867566,0.00008862666,0.001953414,0.0013297835,0.0018940308,0.016443651],"study_design_scores_gemma":[0.0000029284247,0.00004381381,0.044172563,0.00015613469,0.000010034242,0.00026688696,0.94920945,0.00038280393,0.0003794591,0.00022452543,0.005132003,0.000019341598],"about_ca_topic_score_codex":0.010032434,"about_ca_topic_score_gemma":0.0155690685,"teacher_disagreement_score":0.010032434,"about_ca_system_score_codex":0.0023064415,"about_ca_system_score_gemma":0.0010720172,"threshold_uncertainty_score":0.019948125},"labels":[],"label_agreement":null},{"id":"W3178540907","doi":"10.1101/2021.07.12.21259660","title":"An Open Repository of Real-Time COVID-19 Indicators","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Centers for Disease Control and Prevention","keywords":"Python (programming language); Computer science; Phone; Coronavirus disease 2019 (COVID-19); Open data; Pandemic; Public health; Data science; Software; Public health surveillance; The Internet; Tracking (education); World Wide Web; Infectious disease (medical specialty); Medicine","score_opus":0.026417093690887867,"score_gpt":0.34039310699913483,"score_spread":0.313976013308247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3178540907","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022226917,0.00081048027,0.03812616,0.0016721449,0.0013715209,0.00028684092,0.85355276,0.09047262,0.01148487],"genre_scores_gemma":[0.015335627,0.00090730266,0.036028814,0.00054508675,0.00040790634,0.00049333845,0.9298199,0.011652647,0.0048094015],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99513716,0.00083703373,0.0008556789,0.001069464,0.0018265349,0.00027411076],"domain_scores_gemma":[0.9721938,0.009092698,0.0022297846,0.009403962,0.0048396345,0.0022401197],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.009084883,0.0018020623,0.0018181241,0.0070594987,0.00086864457,0.007640221,0.0033912433,0.0023554359,0.052061424],"category_scores_gemma":[0.04969452,0.001110969,0.0014182964,0.008616758,0.000779,0.0034563523,0.006037544,0.0028981888,0.06043114],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048902264,0.00016535276,0.010429995,0.0016646452,0.00021945326,0.0003719075,0.0002590812,0.00642132,0.001162768,0.012560782,0.89157426,0.07468149],"study_design_scores_gemma":[0.000415256,0.00008973349,0.009644947,0.000738865,0.000101506776,0.00031305375,0.00015985242,0.019229291,0.003630606,0.027932681,0.937489,0.00025534088],"about_ca_topic_score_codex":0.0046600215,"about_ca_topic_score_gemma":0.005066169,"teacher_disagreement_score":0.99660873,"about_ca_system_score_codex":0.0011990269,"about_ca_system_score_gemma":0.004893463,"threshold_uncertainty_score":0.1741628},"labels":[],"label_agreement":null},{"id":"W3180364617","doi":"10.1186/s12890-021-01602-7","title":"Internet search results correlate with seasonal variation of sarcoidosis","year":2021,"lang":"en","type":"article","venue":"BMC Pulmonary Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Medicine; Etiology; Sarcoidosis; Incidence (geometry); Demography; Seasonality; Epidemiology; Pathology; Statistics","score_opus":0.03543502254048267,"score_gpt":0.2812322432705365,"score_spread":0.2457972207300538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3180364617","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":[0.9957443,0.0008397838,0.00025023814,0.00020350756,0.000015519105,0.000010935373,0.0012240371,0.00001716685,0.0016945077],"genre_scores_gemma":[0.998973,0.00016712886,0.000112269125,0.000018309122,0.000016088674,0.000004247305,0.0005756863,0.000005797183,0.00012745087],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993488,0.00015969301,0.0001315985,0.00013907674,0.00011904092,0.0001017121],"domain_scores_gemma":[0.99028045,0.0036971988,0.0042000785,0.00038545925,0.00091268006,0.0005241493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071023806,0.00008988353,0.0002301988,0.0014827021,0.00014997191,0.0007474875,0.00023576687,0.00021806089,0.002651637],"category_scores_gemma":[0.00690395,0.00009732111,0.00031717538,0.002629007,0.00025111303,0.00064409734,0.00039223745,0.00034246626,0.00038049198],"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":[0.00009496588,0.000016011958,0.9932145,0.000064951426,0.00007020854,0.000082853156,0.00019791041,0.0001363815,0.00054350495,0.00004927371,0.00046621502,0.0050631757],"study_design_scores_gemma":[0.0000011133722,0.000016908365,0.99892247,0.000012874056,0.0000106977395,0.00019466359,0.00023931314,0.00022270961,0.000065830194,0.000030324994,0.00028082717,0.0000023122318],"about_ca_topic_score_codex":0.002936141,"about_ca_topic_score_gemma":0.0042424393,"teacher_disagreement_score":0.002936141,"about_ca_system_score_codex":0.00021625396,"about_ca_system_score_gemma":0.00017094499,"threshold_uncertainty_score":0.008870602},"labels":[],"label_agreement":null},{"id":"W3181216097","doi":"10.1038/s41597-021-00955-2","title":"A sub-national real-time epidemiological and vaccination database for the COVID-19 pandemic in Canada","year":2021,"lang":"en","type":"article","venue":"Scientific Data","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph; SickKids Foundation; Hospital for Sick Children; Public Health Ontario; University of Toronto","funders":"Medical Research Council; National Institute for Health and Care Research; Wellcome Trust","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Dashboard; Public health; Epidemiology; Outbreak; Vaccination; Open data; Geography; Political science; Medicine; Database; Computer science; World Wide Web; Nursing; Virology; Infectious disease (medical specialty)","score_opus":0.13854344895396647,"score_gpt":0.3767589396202392,"score_spread":0.23821549066627273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181216097","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009095409,0.0005220379,0.000738642,0.00064059737,0.00004062066,0.00019901012,0.9844514,0.0003557785,0.0039564893],"genre_scores_gemma":[0.041837122,0.0009373331,0.0050869933,0.00042041478,0.000028628965,0.00032790436,0.9484171,0.000110577996,0.0028339247],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99807966,0.000113875816,0.00023697683,0.00036052283,0.0007872395,0.00042161165],"domain_scores_gemma":[0.98861325,0.0007160615,0.000781998,0.00069796067,0.007537954,0.0016527954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014379936,0.00079609244,0.0009339458,0.006566461,0.0029523168,0.0023771815,0.0019759678,0.000770513,0.009421432],"category_scores_gemma":[0.009546505,0.0005687428,0.00089348655,0.013180251,0.0005366816,0.00073595846,0.001615409,0.0013297944,0.003187858],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005591601,0.00012489357,0.11544984,0.0015354096,0.00033693202,0.000346081,0.00084018597,0.003050226,0.0007509175,0.0032900572,0.8274021,0.046314232],"study_design_scores_gemma":[0.00027749027,0.000061390245,0.39610264,0.0017434017,0.00024777683,0.0002497421,0.0017962919,0.007248303,0.0013701105,0.0013127441,0.58928746,0.00030257076],"about_ca_topic_score_codex":0.9944325,"about_ca_topic_score_gemma":0.9952362,"teacher_disagreement_score":0.9647921,"about_ca_system_score_codex":0.035207946,"about_ca_system_score_gemma":0.10168437,"threshold_uncertainty_score":0.2554527},"labels":[],"label_agreement":null},{"id":"W3181493438","doi":"10.2196/25422","title":"Global Public Interests and Dynamic Trends in Osteoporosis From 2004 to 2019: Infodemiology Study","year":2021,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Osteoporosis; Public health; Medicine; Life expectancy; Demography; Gerontology; Population; Environmental health; Family medicine; Internal medicine; Pathology","score_opus":0.06707077571557814,"score_gpt":0.46508232547307005,"score_spread":0.3980115497574919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181493438","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":[0.8532141,0.02470037,0.0002795495,0.0041172397,0.00010874654,0.00021061092,0.10609479,0.000059090813,0.0112156],"genre_scores_gemma":[0.9686243,0.007402166,0.00029769045,0.00066608214,0.00014747684,0.00016730769,0.021533368,0.000025679057,0.0011359993],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998604,0.00023206323,0.00034812937,0.00020087634,0.00036094346,0.00025406858],"domain_scores_gemma":[0.9911322,0.0025012277,0.0043155807,0.00020402776,0.0011213761,0.0007255064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013458579,0.00024782523,0.00061428954,0.009925373,0.00040518693,0.0017346399,0.0005011098,0.0006319553,0.004069718],"category_scores_gemma":[0.0096279755,0.00018885237,0.0015584959,0.019515693,0.00038402106,0.0028318672,0.0013087946,0.0008619135,0.0008725479],"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":[0.00026747648,0.000083179584,0.96318465,0.0024824098,0.00043908393,0.00034997292,0.0019405901,0.00012797985,0.0000825425,0.0007487122,0.0090829935,0.02121054],"study_design_scores_gemma":[0.00001746396,0.00008943351,0.9817426,0.0007778671,0.00036448165,0.00054991146,0.0040926365,0.00035009225,0.000084913634,0.00016204236,0.011746849,0.000021694657],"about_ca_topic_score_codex":0.04647936,"about_ca_topic_score_gemma":0.052456968,"teacher_disagreement_score":0.04647936,"about_ca_system_score_codex":0.0016485002,"about_ca_system_score_gemma":0.002387684,"threshold_uncertainty_score":0.0924176},"labels":[],"label_agreement":null},{"id":"W3183729605","doi":"10.12927/hcq.2021.26553","title":"The Role of a Resilient Information Infrastructure in COVID-19 Vaccine Uptake in Ontario","year":2021,"lang":"en","type":"article","venue":"Healthcare Quarterly","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Hospital for Sick Children; Public Health Ontario; Toronto Public Health","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Government (linguistics); Vaccination; Business; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Public health; Public relations; Medicine; Virology; Political science; Nursing","score_opus":0.009286968698295935,"score_gpt":0.2828414050634834,"score_spread":0.27355443636518745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183729605","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":[0.83935356,0.0035383222,0.0024061883,0.07579066,0.0001149593,0.000522195,0.009179315,0.00023297568,0.06886187],"genre_scores_gemma":[0.9931613,0.0012023492,0.0011496368,0.00089693617,0.000024314031,0.000054779714,0.000775875,0.000017515162,0.0027172314],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99637395,0.0007153048,0.000266907,0.00032772313,0.0013407373,0.00097534864],"domain_scores_gemma":[0.98428506,0.0039157113,0.0024237595,0.0006221157,0.005354466,0.0033987958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003694824,0.0001226789,0.00023199547,0.0014069384,0.0027937894,0.0033051467,0.0008887296,0.00040933173,0.002699426],"category_scores_gemma":[0.019692484,0.00030570428,0.00023970492,0.0028073378,0.0016004819,0.0017004164,0.0025871128,0.00082494697,0.00021271306],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021067017,0.00012088623,0.76779187,0.00085537607,0.0001060253,0.00064834167,0.03596672,0.0028001443,0.001818541,0.015518382,0.03149192,0.14267117],"study_design_scores_gemma":[0.000044643064,0.00013818342,0.8581674,0.00073284935,0.000077011784,0.00014265142,0.020935873,0.004933177,0.0010551787,0.0023526635,0.11134124,0.00007903304],"about_ca_topic_score_codex":0.9845184,"about_ca_topic_score_gemma":0.9878883,"teacher_disagreement_score":0.93318117,"about_ca_system_score_codex":0.06681885,"about_ca_system_score_gemma":0.11380023,"threshold_uncertainty_score":0.48480695},"labels":[],"label_agreement":null},{"id":"W3185632910","doi":"10.2196/30444","title":"Detection of Spatiotemporal Clusters of COVID-19–Associated Symptoms and Prevention Using a Participatory Surveillance App: Protocol for the @choum Study","year":2021,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Pandemic; Cluster (spacecraft); Disease surveillance; Epidemiology; Data collection; Informed consent; Disease; Family medicine; Coronavirus disease 2019 (COVID-19); Infectious disease (medical specialty); Computer science; Alternative medicine","score_opus":0.41968581818753153,"score_gpt":0.5950986700667923,"score_spread":0.17541285187926076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185632910","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050316006,0.00007936871,0.003999278,0.000260615,0.00013738568,0.98617095,0.001992687,0.00009827252,0.002229761],"genre_scores_gemma":[0.0015437785,0.00003851133,0.0029295557,0.00008395716,0.000013283038,0.9949189,0.0001654979,0.000005208603,0.00030124278],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9864487,0.009068881,0.0014660641,0.0010033564,0.0006372541,0.0013757211],"domain_scores_gemma":[0.9846732,0.0054622893,0.0011824988,0.0023440889,0.004698405,0.0016394879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032630343,0.0025317096,0.0026378375,0.001796971,0.0047852127,0.0020185902,0.0024039277,0.0034545788,0.052915026],"category_scores_gemma":[0.02579213,0.0024169486,0.0023961256,0.0013043581,0.0020556892,0.0023138092,0.0034194281,0.0046913265,0.015324115],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.08896429,0.065645956,0.026789097,0.04553944,0.0006100089,0.0048847017,0.051622655,0.0067850365,0.013684367,0.020769257,0.26599783,0.40870747],"study_design_scores_gemma":[0.086610086,0.049787726,0.0775384,0.021118293,0.00054127764,0.0010796997,0.02937169,0.008999751,0.011092551,0.017167551,0.69578266,0.0009103675],"about_ca_topic_score_codex":0.0042375512,"about_ca_topic_score_gemma":0.0069897417,"teacher_disagreement_score":0.052915026,"about_ca_system_score_codex":0.0030591688,"about_ca_system_score_gemma":0.011708791,"threshold_uncertainty_score":0.1770184},"labels":[],"label_agreement":null},{"id":"W3185965133","doi":"10.2196/28975","title":"Association Between What People Learned About COVID-19 Using Web Searches and Their Behavior Toward Public Health Guidelines: Empirical Infodemiology Study","year":2021,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Misinformation; Public health; The Internet; Internet privacy; Social media; Pandemic; Information Dissemination; Public relations; Business; World Wide Web; Political science; Coronavirus disease 2019 (COVID-19); Medicine; Computer science; Disease; Infectious disease (medical specialty)","score_opus":0.5679338725568206,"score_gpt":0.5885825384508111,"score_spread":0.020648665893990503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185965133","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":[0.99823976,0.00014083476,0.00006561635,0.00016752526,0.0000073256224,0.00006929265,0.00033765822,0.0000027397357,0.0009691019],"genre_scores_gemma":[0.99781704,0.00020363463,0.00019550395,0.00037268642,0.000024327135,0.00023194273,0.00037836318,0.0000065767977,0.00076995493],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99669135,0.0013832811,0.00053264113,0.00043305854,0.0004919487,0.0004677226],"domain_scores_gemma":[0.9540343,0.023298372,0.015575777,0.002014582,0.0025557354,0.0025212816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037657516,0.00035162998,0.0007178409,0.0024223193,0.0016120556,0.0024741986,0.0007927053,0.0017941251,0.005794874],"category_scores_gemma":[0.02429084,0.0007169716,0.0012401338,0.0027484572,0.0016679134,0.0040867557,0.001982016,0.0029507184,0.0012004707],"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":[0.00030289948,0.0023358227,0.9737447,0.0002187347,0.00022077047,0.00008996994,0.016764078,0.000055764867,0.000075921846,0.00020517831,0.00062081276,0.0053654024],"study_design_scores_gemma":[0.00007286673,0.0008415372,0.96673524,0.00015197626,0.00018349904,0.00011723583,0.029599944,0.00063369476,0.00013100823,0.00021716655,0.0012824379,0.000033533288],"about_ca_topic_score_codex":0.010265283,"about_ca_topic_score_gemma":0.010475525,"teacher_disagreement_score":0.010265283,"about_ca_system_score_codex":0.0011328192,"about_ca_system_score_gemma":0.0013737208,"threshold_uncertainty_score":0.020411074},"labels":[],"label_agreement":null},{"id":"W3186327110","doi":"10.3390/ijerph18157841","title":"The Global Interest in Vaccines and Its Prediction and Perspectives in the Era of COVID-19. Real-Time Surveillance Using Google Trends","year":2021,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Autoregressive integrated moving average; Pandemic; Exponential smoothing; Quarter (Canadian coin); Medicine; Poliomyelitis; Econometrics; Time series; Virology; Statistics; Demography; Geography; Disease; Mathematics; Infectious disease (medical specialty)","score_opus":0.12780778459053146,"score_gpt":0.42245739408085237,"score_spread":0.29464960949032093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186327110","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":[0.8855739,0.009069062,0.0059604063,0.004527906,0.0001992961,0.000103483915,0.083462745,0.0004446568,0.010658395],"genre_scores_gemma":[0.9737317,0.001760996,0.0035479453,0.00014407956,0.00009225436,0.000037216483,0.019878838,0.000028433788,0.0007783614],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996203,0.000098965844,0.000041922438,0.00008656537,0.0001100836,0.00004207713],"domain_scores_gemma":[0.9977882,0.0007477967,0.000813671,0.00011933078,0.0004243805,0.0001065693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000994964,0.0003488689,0.00025084143,0.0029953213,0.00010172932,0.0009742726,0.00024745974,0.0003396586,0.00095636223],"category_scores_gemma":[0.004613497,0.00012359678,0.0006402044,0.0037255117,0.00017001225,0.0012171019,0.0004929789,0.0004522719,0.00046172075],"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":[0.00020178141,0.00004617596,0.91226095,0.0006570652,0.00022510385,0.000225729,0.0005335182,0.011493952,0.0010449167,0.0022496684,0.013105199,0.057955895],"study_design_scores_gemma":[0.000010062302,0.00014355769,0.91095626,0.00027887884,0.00016899651,0.00036253873,0.0013883733,0.05993648,0.0017952166,0.0019386888,0.022979915,0.00004105159],"about_ca_topic_score_codex":0.027413499,"about_ca_topic_score_gemma":0.02300178,"teacher_disagreement_score":0.027413499,"about_ca_system_score_codex":0.0006885372,"about_ca_system_score_gemma":0.0004938843,"threshold_uncertainty_score":0.05450785},"labels":[],"label_agreement":null},{"id":"W3187289091","doi":"","title":"Development of Hotzone Identification Models for Simultaneous Crime and Collision Reduction","year":2019,"lang":"en","type":"article","venue":"YorkSpace (York University)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Statistics; Akaike information criterion; Collision; Bayes' theorem; Residual; Negative binomial distribution; Bayesian probability; Mean squared error; Mathematics; Econometrics; Goodness of fit; Poisson distribution; Computer science; Algorithm","score_opus":0.01785126377934053,"score_gpt":0.2321731939307592,"score_spread":0.21432193015141865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3187289091","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":[0.063452885,0.00031212997,0.9309887,0.00024306179,0.000069129,0.00007430902,0.0003818141,0.00063343934,0.0038444982],"genre_scores_gemma":[0.8333159,0.000496068,0.15612112,0.000094572766,0.00006467929,0.0002996441,0.0010696471,0.00012429661,0.008414124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996513,0.00011674562,0.00002005235,0.00010550589,0.000054575452,0.0000518446],"domain_scores_gemma":[0.9991698,0.00041452522,0.00013131356,0.00006275308,0.0001798869,0.000041855063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010930597,0.0006101813,0.0008562278,0.00087972445,0.00041330644,0.0011080338,0.001678161,0.00067720586,0.0028371047],"category_scores_gemma":[0.0026784637,0.0005591023,0.0010886167,0.0006912658,0.00033454545,0.0013983287,0.0013979605,0.0011768439,0.00052830036],"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":[0.000051002273,0.000049823375,0.008778352,0.000052418207,0.000084767125,0.0000652385,0.00014790233,0.9354544,0.00059847464,0.012448891,0.00096733595,0.041301396],"study_design_scores_gemma":[0.0000019440618,0.000007895429,0.00062112435,0.0000053178946,0.0000075533385,0.0000069044227,0.00002580303,0.9966916,0.00009105372,0.0021512064,0.00038401844,0.0000055673795],"about_ca_topic_score_codex":0.019230036,"about_ca_topic_score_gemma":0.021241777,"teacher_disagreement_score":0.019230036,"about_ca_system_score_codex":0.0007550774,"about_ca_system_score_gemma":0.0011622986,"threshold_uncertainty_score":0.0382362},"labels":[],"label_agreement":null},{"id":"W3189274973","doi":"10.2196/31236","title":"Participatory Surveillance of COVID-19 in Lesotho via Weekly Calls: Protocol for Cell Phone Data Collection","year":2021,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Centers for Disease Control and Prevention","keywords":"Phone; Population; Medicine; Context (archaeology); Data collection; mHealth; Protocol (science); Internet privacy; Environmental health; Geography; Computer science; Psychological intervention; Nursing; Statistics","score_opus":0.5229664795707736,"score_gpt":0.6105348593277744,"score_spread":0.08756837975700082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189274973","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026194097,0.000115046205,0.006931002,0.0005265328,0.00020080569,0.97664446,0.008079465,0.000111473644,0.0047718515],"genre_scores_gemma":[0.0015688087,0.000092198905,0.007674118,0.00027538612,0.00002358522,0.9885965,0.0009831634,0.000009146828,0.00077714457],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98537713,0.00942931,0.0019402559,0.0010111124,0.00095203595,0.0012900839],"domain_scores_gemma":[0.98114955,0.0057849074,0.0018222925,0.002597366,0.0069365352,0.0017093192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031822108,0.0020732929,0.0013785155,0.0025409358,0.0056888596,0.002183558,0.0025397602,0.0031129862,0.07135376],"category_scores_gemma":[0.027577309,0.0021418068,0.0012258495,0.0021082156,0.0019844386,0.0020703773,0.002747289,0.0035834138,0.015578382],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.025736928,0.012558685,0.018129297,0.03297518,0.00035001454,0.0057743117,0.030651687,0.004760749,0.0209008,0.028245663,0.5482558,0.27166083],"study_design_scores_gemma":[0.02091072,0.007670718,0.05282041,0.0140810665,0.00025569595,0.00093026867,0.011792951,0.0044998494,0.0052887737,0.010488898,0.8708093,0.00045137902],"about_ca_topic_score_codex":0.009676109,"about_ca_topic_score_gemma":0.021830527,"teacher_disagreement_score":0.07135376,"about_ca_system_score_codex":0.004905182,"about_ca_system_score_gemma":0.022134386,"threshold_uncertainty_score":0.23870212},"labels":[],"label_agreement":null},{"id":"W3189604282","doi":"10.17269/s41997-021-00560-1","title":"Detection of COVID-19 case clusters in Québec, May–October 2020","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"McGill University; Université de Montréal; Université Laval; Institut National de Santé Publique du Québec","funders":"Ministère de la Santé; Ministère de la Santé et des Services sociaux","keywords":"Scan statistic; Public health; Coronavirus disease 2019 (COVID-19); Cluster (spacecraft); Statistic; Public health interventions; Compromise; Poisson distribution; Software deployment; Public health surveillance; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Geography; 2019-20 coronavirus outbreak; Computer science; Environmental health; Political science; Medicine; Statistics; Nursing; Virology","score_opus":0.05051174995579253,"score_gpt":0.32905731876977695,"score_spread":0.2785455688139844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189604282","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":[0.9242587,0.0017261027,0.005756376,0.001416217,0.00008199604,0.00075124024,0.05022407,0.00056943623,0.015215918],"genre_scores_gemma":[0.9793688,0.00039360038,0.0034585623,0.00019135464,0.000017123148,0.00014624718,0.012741731,0.000022045095,0.0036604065],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999321,0.00007677199,0.000034672754,0.00013615163,0.00027604052,0.00015545904],"domain_scores_gemma":[0.99793446,0.00020495271,0.00048206098,0.00006576672,0.0009889089,0.00032388064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011105742,0.00045118996,0.00024434042,0.0023676036,0.0009531788,0.0010234149,0.0010394071,0.0003186323,0.002698607],"category_scores_gemma":[0.0030203622,0.0001831183,0.00026574792,0.0019589867,0.00038341054,0.00028878674,0.00079116126,0.00037409703,0.00033340778],"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":[0.00019355037,0.000039129598,0.9576653,0.00011332701,0.00007760099,0.00028578306,0.0005225212,0.0015820656,0.00095765915,0.00025704817,0.013904098,0.024401907],"study_design_scores_gemma":[0.000009414226,0.000043023254,0.9898017,0.000049119062,0.00001773657,0.00007698649,0.00054223096,0.0040380536,0.00044299476,0.00004625345,0.0049201977,0.00001230551],"about_ca_topic_score_codex":0.96866447,"about_ca_topic_score_gemma":0.97549874,"teacher_disagreement_score":0.031335533,"about_ca_system_score_codex":0.013299018,"about_ca_system_score_gemma":0.011356231,"threshold_uncertainty_score":0.096491575},"labels":[],"label_agreement":null},{"id":"W3193136051","doi":"","title":"Analyse du marché du travail à l’aide des données de Google Trends","year":2021,"lang":"fr","type":"article","venue":"CIRANO Project Reports","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Geography; Philosophy","score_opus":0.047560886139886197,"score_gpt":0.2917462421438476,"score_spread":0.2441853560039614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193136051","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":[0.7684204,0.0094243325,0.026641143,0.003555992,0.00040344632,0.000307979,0.1598524,0.0039591608,0.027435213],"genre_scores_gemma":[0.87095696,0.0022778974,0.024584612,0.00016322623,0.0001604993,0.00014158503,0.091246255,0.00029168572,0.01017727],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9970974,0.0004078367,0.00020231589,0.00042068432,0.0016502192,0.00022160694],"domain_scores_gemma":[0.9896269,0.0036065602,0.00096433976,0.0009885085,0.004559147,0.00025454586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028485993,0.0010187824,0.00071641296,0.006615769,0.0005821163,0.00288853,0.000952886,0.00066262134,0.0024536827],"category_scores_gemma":[0.01604751,0.00035163417,0.0012253205,0.007926508,0.00035875442,0.0017935537,0.00070137257,0.0009837267,0.0017330288],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080933276,0.00017162978,0.63052374,0.0019829553,0.0011318706,0.0009820699,0.0027931859,0.054334763,0.0054747337,0.004564439,0.05200574,0.24522565],"study_design_scores_gemma":[0.00003981328,0.00013517983,0.65099084,0.00039946302,0.00033006718,0.000495563,0.0027691717,0.23776194,0.00502295,0.0019700548,0.09991439,0.00017055997],"about_ca_topic_score_codex":0.60678464,"about_ca_topic_score_gemma":0.63426197,"teacher_disagreement_score":0.60678464,"about_ca_system_score_codex":0.0029616442,"about_ca_system_score_gemma":0.0032488743,"threshold_uncertainty_score":0.7910622},"labels":[],"label_agreement":null},{"id":"W3193784510","doi":"10.3389/fevo.2021.669477","title":"Confronting Missing Ecological Data in the Age of Pandemic Lockdown","year":2021,"lang":"en","type":"article","venue":"Frontiers in Ecology and Evolution","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trent University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Missing data; Imputation (statistics); Inference; Computer science; Data collection; Bayesian inference; Environmental data; Data science; Bayesian probability; Environmental resource management; Ecology; Environmental science; Machine learning; Artificial intelligence; Statistics","score_opus":0.025777640064451265,"score_gpt":0.2831742483787029,"score_spread":0.25739660831425165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193784510","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048604935,0.027981615,0.086523406,0.81557715,0.0061028874,0.00018090983,0.0076932004,0.00048700746,0.0068489215],"genre_scores_gemma":[0.55014205,0.03898638,0.1783862,0.20646167,0.014060352,0.00091239624,0.007885321,0.0006049447,0.0025606935],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9339065,0.04776726,0.004909813,0.005061046,0.006471282,0.0018841054],"domain_scores_gemma":[0.6127655,0.30484712,0.028396038,0.020501085,0.022300437,0.0111898575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1386157,0.00078787503,0.0027147182,0.005152535,0.0032402384,0.0075234803,0.0041272715,0.008950137,0.0039507663],"category_scores_gemma":[0.36023438,0.001557711,0.0013487592,0.0050225104,0.00818535,0.011757979,0.009097374,0.016980851,0.0012516648],"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":[0.0012969974,0.00029912384,0.19469741,0.0048005637,0.0014809985,0.003772333,0.017054621,0.014440028,0.0015625375,0.09560898,0.23147094,0.4335154],"study_design_scores_gemma":[0.00014617623,0.00033018255,0.045619648,0.007938624,0.00037734682,0.0026570808,0.012821501,0.013808435,0.0012983714,0.7172546,0.19739437,0.00035359693],"about_ca_topic_score_codex":0.007109012,"about_ca_topic_score_gemma":0.012343405,"teacher_disagreement_score":0.1386157,"about_ca_system_score_codex":0.0038805706,"about_ca_system_score_gemma":0.009301753,"threshold_uncertainty_score":0.7330786},"labels":[],"label_agreement":null},{"id":"W3195330928","doi":"10.1038/s41598-021-95934-3","title":"Author Correction: Setting a baseline for global urban virome surveillance in sewage","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Horizon 2020 Framework Programme; Novo Nordisk; European Commission; Novo Nordisk Fonden; World Health Organization","keywords":"Erasmus+; European union; Political science; Library science; Public administration; Medicine; Business; International trade; History","score_opus":0.015195606335277284,"score_gpt":0.29827522968798637,"score_spread":0.2830796233527091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195330928","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00034863848,0.00069562154,0.0019238109,0.0828733,0.9080784,0.00006362544,0.003045626,0.000666815,0.0023043288],"genre_scores_gemma":[0.058064707,0.008679809,0.017699296,0.21767104,0.5040489,0.001045712,0.008857095,0.0052457387,0.17868781],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99210006,0.002016001,0.0014350129,0.0013093626,0.0026054455,0.0005340335],"domain_scores_gemma":[0.9322908,0.022432588,0.00346554,0.0050981576,0.034306053,0.002406919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007505518,0.0018654526,0.0016211324,0.0028694363,0.002104281,0.0034146397,0.0033189338,0.005380057,0.053614013],"category_scores_gemma":[0.15570815,0.0009198183,0.0016670924,0.0021212506,0.0022102252,0.0021458748,0.0026144115,0.009027093,0.028139396],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032597633,0.0000030805052,0.00014809873,0.00014548768,0.000014513131,0.00012613814,0.000055826327,0.00004173677,0.00003594419,0.0005489293,0.9951343,0.0037134115],"study_design_scores_gemma":[0.000106428,0.000025741014,0.0014028067,0.0009893768,0.000084091254,0.0007246676,0.00023246346,0.0005637719,0.0006749767,0.0028362416,0.99229455,0.00006490463],"about_ca_topic_score_codex":0.01225392,"about_ca_topic_score_gemma":0.012465833,"teacher_disagreement_score":0.053614013,"about_ca_system_score_codex":0.00276504,"about_ca_system_score_gemma":0.007330863,"threshold_uncertainty_score":0.17935675},"labels":[],"label_agreement":null},{"id":"W3195679265","doi":"10.2196/26523","title":"Novel Methods in the Surveillance of Influenza-Like Illness in Germany Using Data From a Symptom Assessment App (Ada): Observational Case Study","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Sore throat; Epidemiology; Population; Demography; Observational study; Internal medicine; Environmental health; Surgery","score_opus":0.21880260508460728,"score_gpt":0.48106176546823187,"score_spread":0.2622591603836246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195679265","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":[0.9847335,0.00154518,0.009335382,0.0007195386,0.00003835658,0.0007565186,0.0017338151,0.000021257716,0.0011163248],"genre_scores_gemma":[0.9833649,0.00065119565,0.013680957,0.00024468862,0.000045245706,0.00081714615,0.0009572604,0.000011005575,0.00022750896],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9820416,0.012094773,0.0015938182,0.001957621,0.00154038,0.0007718323],"domain_scores_gemma":[0.97370195,0.013765133,0.0066395914,0.0028322288,0.0023091678,0.00075193855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020675512,0.00055130245,0.00044265625,0.002468747,0.0010080491,0.0014327065,0.0011353889,0.0013677451,0.0009318526],"category_scores_gemma":[0.03389167,0.00049517665,0.001054264,0.002295033,0.0013463566,0.0013444999,0.002133846,0.0010830924,0.0002681807],"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":[0.0002523504,0.0004694888,0.97402203,0.00031931157,0.00022136974,0.0024034593,0.0060355403,0.00042394598,0.00037820268,0.0006537471,0.0010148978,0.013805672],"study_design_scores_gemma":[0.00022632106,0.001928198,0.921229,0.0011639555,0.00066933635,0.011895217,0.025206514,0.017174827,0.0020912583,0.0032501202,0.014999238,0.00016598542],"about_ca_topic_score_codex":0.015568044,"about_ca_topic_score_gemma":0.020112209,"teacher_disagreement_score":0.020675512,"about_ca_system_score_codex":0.001661333,"about_ca_system_score_gemma":0.002082961,"threshold_uncertainty_score":0.10934383},"labels":[],"label_agreement":null},{"id":"W3196279437","doi":"10.1016/j.infoandorg.2021.100366","title":"Engaging with uncertainty: Information practices in the context of disease surveillance in Burkina Faso","year":2021,"lang":"en","type":"article","venue":"Information and Organization","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Metis; Context (archaeology); Construct (python library); Dengue fever; Knowledge management; Medicine; Computer science; Geography; Database","score_opus":0.008588082192145614,"score_gpt":0.2479989332795137,"score_spread":0.2394108510873681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196279437","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":[0.955028,0.0027462705,0.004887905,0.021792697,0.000043125532,0.00011595291,0.000057132977,0.000012712324,0.01531627],"genre_scores_gemma":[0.9975575,0.0006813203,0.0009399199,0.00042930682,0.00001092826,0.000021143993,0.000011770222,0.0000023560972,0.00034571273],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9756884,0.020160899,0.0008238005,0.00078231597,0.0010794977,0.0014650187],"domain_scores_gemma":[0.9572565,0.034175463,0.0041623632,0.0009993789,0.0014649073,0.0019413191],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017600201,0.00028187278,0.00030504254,0.0017272293,0.010965781,0.009685448,0.0010132124,0.0019677372,0.0015355393],"category_scores_gemma":[0.037296835,0.00034213637,0.00017579082,0.0020013922,0.009607469,0.00683861,0.0069018733,0.002061824,0.00013948689],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053866086,0.000084577885,0.04079213,0.00018817344,0.000017410875,0.0017082026,0.91486114,0.0003647928,0.00054805505,0.015104063,0.0005853184,0.025692247],"study_design_scores_gemma":[0.000008229468,0.00009989794,0.014567537,0.0006949239,0.000014832156,0.00061787775,0.9494759,0.00079942547,0.00040483792,0.008453833,0.024825476,0.000037296144],"about_ca_topic_score_codex":0.047821008,"about_ca_topic_score_gemma":0.040676996,"teacher_disagreement_score":0.047821008,"about_ca_system_score_codex":0.010278682,"about_ca_system_score_gemma":0.011513938,"threshold_uncertainty_score":0.09508532},"labels":[],"label_agreement":null},{"id":"W3198144684","doi":"10.2196/32639","title":"Public Health Surveillance Systems in the Eastern Mediterranean Region: Bibliometric Analysis of Scientific Literature","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Public health; Environmental health; Public health surveillance; Geography; Data science; Political science; Computer science; Medicine","score_opus":0.09630747337210392,"score_gpt":0.34337462828470894,"score_spread":0.247067154912605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198144684","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":[0.8734592,0.07967654,0.0014317188,0.0027437457,0.00011731099,0.0004014748,0.022919914,0.00014232505,0.019107798],"genre_scores_gemma":[0.9632302,0.025026502,0.002793525,0.00013147022,0.00015350914,0.00023950396,0.007937922,0.000018414148,0.000468926],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9911992,0.0020003235,0.0026666338,0.00074696966,0.0030278855,0.0003589599],"domain_scores_gemma":[0.9259725,0.04661099,0.015673626,0.0014082082,0.009354438,0.000980281],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0097476635,0.00039916515,0.0012734303,0.14779401,0.0011308251,0.0051743616,0.00074927293,0.000641351,0.0025872851],"category_scores_gemma":[0.0548739,0.00019744804,0.0013835904,0.18026778,0.00088165904,0.0035503644,0.0022645947,0.00030658545,0.00031162667],"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":[0.00030499886,0.00014175866,0.76005936,0.021795869,0.0023416374,0.0010713519,0.009038512,0.0017609701,0.000786782,0.0044439607,0.009442281,0.18881252],"study_design_scores_gemma":[0.000040874747,0.00015229423,0.94211143,0.0062333145,0.0018009975,0.0015462018,0.016739128,0.00370559,0.00053573225,0.0028768277,0.024188913,0.000068767564],"about_ca_topic_score_codex":0.0059687616,"about_ca_topic_score_gemma":0.0059250332,"teacher_disagreement_score":0.852206,"about_ca_system_score_codex":0.002610432,"about_ca_system_score_gemma":0.004195485,"threshold_uncertainty_score":0.051551223},"labels":[],"label_agreement":null},{"id":"W3198167802","doi":"10.1080/13623699.2021.1955759","title":"Using Twitter to Understand the Effects of the Cameroon Anglophone Crisis on Social Determinants of Health","year":2021,"lang":"en","type":"article","venue":"Medicine Conflict & Survival","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Public health; Poverty; Oppression; Social determinants of health; Social media; Neglect; Government (linguistics); Political science; Economic growth; Health equity; Health policy; Public relations; Medicine; Economics; Nursing; Politics","score_opus":0.1175479595897158,"score_gpt":0.387022843523984,"score_spread":0.26947488393426816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198167802","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":[0.9658797,0.0020688483,0.0011654586,0.016740197,0.00021159694,0.00015110416,0.0020476393,0.00002457452,0.011710902],"genre_scores_gemma":[0.99559754,0.0014406288,0.00080706016,0.00089294405,0.00007206751,0.000118256605,0.0002698006,0.0000056590475,0.00079612475],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99901223,0.0006690609,0.000036611043,0.000051413255,0.00006468365,0.00016609544],"domain_scores_gemma":[0.997097,0.001895589,0.000494572,0.00006201414,0.00025637195,0.00019439164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013240585,0.0002885671,0.00018394744,0.0011404554,0.0011144791,0.001820027,0.00019041006,0.0004776207,0.002922848],"category_scores_gemma":[0.0048804497,0.0001519552,0.00021106671,0.0011829174,0.00050070253,0.002902349,0.0013320658,0.0008861345,0.0003393521],"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":[0.0005920225,0.0002672686,0.6961217,0.0012416373,0.00012681373,0.0024663548,0.13529575,0.0011738284,0.0036200313,0.005889731,0.015146394,0.13805857],"study_design_scores_gemma":[0.000037782436,0.0003588854,0.58887243,0.001216009,0.000118521435,0.00041688164,0.34255317,0.0067365495,0.0018165454,0.0040644226,0.0537223,0.00008643975],"about_ca_topic_score_codex":0.013007384,"about_ca_topic_score_gemma":0.019401345,"teacher_disagreement_score":0.013007384,"about_ca_system_score_codex":0.0009945997,"about_ca_system_score_gemma":0.0011466531,"threshold_uncertainty_score":0.02586335},"labels":[],"label_agreement":null},{"id":"W3198517269","doi":"10.21203/rs.3.rs-928545/v1","title":"Cohort Characteristics and Factors Associated With Cannabis Use Among Adolescents in Canada Using Pattern Discovery and Disentanglement Method","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"Institute of Population and Public Health; Institute of Nutrition, Metabolism and Diabetes; Ministère de la Santé et des Services sociaux; Ministère de la Santé; Canadian Institutes of Health Research; Health Canada","keywords":"Compass; Cannabis; Cohort; Set (abstract data type); Population; Class (philosophy); Psychology; Demography; Geography; Medicine; Computer science; Cartography; Artificial intelligence; Psychiatry; Environmental health","score_opus":0.06251550978987241,"score_gpt":0.36260600178224944,"score_spread":0.30009049199237703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198517269","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":[0.92416596,0.000901728,0.011918872,0.00064011005,0.000048402206,0.00029719193,0.06029664,0.00039407294,0.0013369895],"genre_scores_gemma":[0.9052847,0.00036778857,0.017920062,0.000109876535,0.00002281521,0.00020717071,0.0752527,0.00003278592,0.00080211647],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99836594,0.00022320879,0.00019570481,0.0006068373,0.00040763724,0.00020070169],"domain_scores_gemma":[0.99400383,0.0021791926,0.000847981,0.0010897412,0.0014814956,0.0003978146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002201001,0.0005151716,0.0006449377,0.0032131607,0.0016415212,0.001359419,0.0012854928,0.00044690393,0.0012018724],"category_scores_gemma":[0.010549106,0.00024961444,0.001295327,0.003968664,0.00058181514,0.00038949485,0.0010236601,0.0009223191,0.00021676075],"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":[0.00024396947,0.00009053744,0.96979415,0.00013387635,0.00037033643,0.00019149198,0.00028232345,0.0032704042,0.00072681886,0.00037678055,0.0042392663,0.020279994],"study_design_scores_gemma":[0.000091010515,0.000090850954,0.9308868,0.00009674576,0.0004111552,0.00031846386,0.0009212057,0.052238807,0.0012533541,0.0010864744,0.012541571,0.000063636],"about_ca_topic_score_codex":0.8246715,"about_ca_topic_score_gemma":0.8547371,"teacher_disagreement_score":0.1753285,"about_ca_system_score_codex":0.003944264,"about_ca_system_score_gemma":0.012399039,"threshold_uncertainty_score":0.35272205},"labels":[],"label_agreement":null},{"id":"W3198735263","doi":"10.1186/s12889-021-11588-w","title":"Analyzing the reach of public health campaigns based on multidimensional aspects: the case of the syphilis epidemic in Brazil","year":2021,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"Universidade Federal do Rio Grande do Norte; Ministério da Saúde; Athabasca University","keywords":"Syphilis; Public health; Biostatistics; Medicine; Population; Early syphilis; Environmental health; Family medicine; Nursing; Human immunodeficiency virus (HIV)","score_opus":0.07433743932990292,"score_gpt":0.3606393927796684,"score_spread":0.2863019534497655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198735263","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":[0.98927224,0.0011564686,0.002110755,0.0018243707,0.0000076010906,0.000105182604,0.00070609763,0.000017492255,0.0047999187],"genre_scores_gemma":[0.99833316,0.00024965787,0.0010567404,0.00004862133,0.0000045658016,0.00002794208,0.00019372227,0.0000034232137,0.00008218267],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99720854,0.0014870688,0.00019825369,0.0002832145,0.00042241393,0.00040054077],"domain_scores_gemma":[0.97630376,0.01701405,0.0038460868,0.00070073945,0.0015486869,0.0005866573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006918743,0.00033858718,0.00047093706,0.0045515895,0.0006978565,0.0018871902,0.0005155091,0.000768002,0.00079332874],"category_scores_gemma":[0.028497348,0.00025862115,0.0009114389,0.0051991823,0.001220668,0.0022659742,0.0020584527,0.00083088584,0.000072460105],"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":[0.000088811576,0.00013926606,0.9370539,0.00040783844,0.00020417791,0.0009533692,0.022036111,0.0022348948,0.00067227305,0.006471504,0.00067833095,0.029059477],"study_design_scores_gemma":[0.000015819049,0.00009271489,0.9494472,0.00038559816,0.00014665045,0.0003535953,0.023508847,0.017858222,0.00033074612,0.0037559397,0.0040638223,0.00004080126],"about_ca_topic_score_codex":0.09254654,"about_ca_topic_score_gemma":0.064764895,"teacher_disagreement_score":0.09254654,"about_ca_system_score_codex":0.0029954053,"about_ca_system_score_gemma":0.0023827483,"threshold_uncertainty_score":0.18401569},"labels":[],"label_agreement":null},{"id":"W3198925403","doi":"10.2196/31329","title":"Authors’ Response to Peer Reviews of “The Exchange of Informational Support in Online Health Communities at the Onset of the COVID-19 Pandemic: Content Analysis”","year":2021,"lang":"en","type":"article","venue":"JMIRx Med","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Content analysis; Content (measure theory); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Psychology; Medicine; Virology; Sociology; Mathematics; Social science; Disease","score_opus":0.23451230760951722,"score_gpt":0.42674737113286537,"score_spread":0.19223506352334815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198925403","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006590345,0.0007753159,0.0004961626,0.7643536,0.22815692,0.00014863885,0.0012679046,0.00032091435,0.003821527],"genre_scores_gemma":[0.0108063165,0.0017543543,0.0015481524,0.8040398,0.13881104,0.0008227651,0.0008765617,0.0005954639,0.040745534],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9625748,0.009268673,0.0047175046,0.0026964953,0.018168697,0.0025738885],"domain_scores_gemma":[0.66761035,0.1104419,0.017671814,0.006108537,0.18599226,0.0121751595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025439344,0.0011869966,0.0017043833,0.003128484,0.0073771304,0.008333702,0.0025393688,0.020160178,0.046103723],"category_scores_gemma":[0.35202453,0.0010261319,0.0016439541,0.0019032476,0.0028469788,0.003815302,0.005260917,0.017667389,0.028338026],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032922413,0.000011101884,0.00020195005,0.00011459021,0.000010598311,0.00006976279,0.0002532927,0.000016947348,0.00008091306,0.00016852637,0.99562657,0.0034128085],"study_design_scores_gemma":[0.00004801724,0.00003495518,0.0015951847,0.00069200696,0.000032216714,0.00015274057,0.0014838364,0.00030322807,0.00032075404,0.00055299717,0.99471563,0.00006849899],"about_ca_topic_score_codex":0.009536438,"about_ca_topic_score_gemma":0.015940277,"teacher_disagreement_score":0.046103723,"about_ca_system_score_codex":0.005083726,"about_ca_system_score_gemma":0.0140475035,"threshold_uncertainty_score":0.15423232},"labels":[],"label_agreement":null},{"id":"W3199165077","doi":"10.2196/31161","title":"Development and Actionability of the Dutch COVID-19 Dashboard: Descriptive Assessment and Expert Appraisal Study","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Amsterdam University Medical Centers","keywords":"Dashboard; Public health; Government (linguistics); Checklist; Medicine; Public relations; Environmental health; Data science; Computer science; Psychology; Political science; Nursing","score_opus":0.07410951959972743,"score_gpt":0.4093363678569918,"score_spread":0.33522684825726434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199165077","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":[0.9038487,0.006056797,0.018607588,0.0025511598,0.0003532312,0.04563128,0.0065491963,0.00016100921,0.016241075],"genre_scores_gemma":[0.88025165,0.005692818,0.04286891,0.00069286674,0.00011049485,0.06367573,0.0038653598,0.00017202072,0.0026702385],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.89323163,0.058855396,0.022699991,0.0036637366,0.018711388,0.0028379029],"domain_scores_gemma":[0.7204125,0.16625613,0.024881875,0.0074966704,0.07780833,0.003144433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13194305,0.0006937069,0.0022281066,0.0075012594,0.0018687698,0.0045055025,0.0016176499,0.0009270665,0.0024486247],"category_scores_gemma":[0.20489444,0.0008221662,0.0025884162,0.0075956476,0.001888121,0.0032836136,0.0038582194,0.0015056805,0.0004873447],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020246229,0.002244287,0.18859333,0.047614902,0.0009602179,0.0020805283,0.2749943,0.0023767822,0.0024288155,0.0035990204,0.01951841,0.4535648],"study_design_scores_gemma":[0.000922364,0.0058603343,0.40574777,0.043775734,0.0019180281,0.0011414925,0.39988238,0.010163049,0.0060238084,0.0026020654,0.12118702,0.0007759636],"about_ca_topic_score_codex":0.01083292,"about_ca_topic_score_gemma":0.018038997,"teacher_disagreement_score":0.13194305,"about_ca_system_score_codex":0.010794924,"about_ca_system_score_gemma":0.020969132,"threshold_uncertainty_score":0.6977899},"labels":[],"label_agreement":null},{"id":"W3199323206","doi":"10.14745/ccdr.v47i09a01","title":"Crowdsourced disease surveillance success story: The FluWatchers program","year":2021,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Public Health Agency of Canada","funders":"","keywords":"Preparedness; Disease surveillance; Pandemic; Coronavirus disease 2019 (COVID-19); Disease; Medicine; Computer science; Data science; Political science; Infectious disease (medical specialty); Pathology","score_opus":0.014872137371071422,"score_gpt":0.2773931183646199,"score_spread":0.2625209809935485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199323206","genre_codex":"commentary","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":[0.04615924,0.009802588,0.019258559,0.6674405,0.016452614,0.0011651439,0.104965635,0.009549925,0.12520584],"genre_scores_gemma":[0.5376569,0.009746302,0.051468257,0.12020591,0.00649379,0.0024771763,0.17633191,0.0031082474,0.09251153],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9929355,0.002778228,0.00015897262,0.00090301887,0.0024821656,0.0007421834],"domain_scores_gemma":[0.9595125,0.01697847,0.00082596263,0.004694169,0.01047147,0.007517406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017057616,0.0010580634,0.0005226436,0.0014409765,0.004758943,0.004184805,0.0022211855,0.0028614837,0.0067707933],"category_scores_gemma":[0.036573514,0.0003595563,0.00082027557,0.0021689816,0.0024851155,0.0047513493,0.0065939217,0.0051260875,0.0037517566],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021131751,0.000110235946,0.0052868393,0.00015490338,0.000059160175,0.00008885119,0.00095689297,0.0008508015,0.0003241792,0.004880745,0.9317141,0.05536192],"study_design_scores_gemma":[0.0002913301,0.00026578046,0.015993714,0.00047091558,0.00007214372,0.00012657054,0.005361473,0.0070813545,0.0015355847,0.013815842,0.9548345,0.00015072465],"about_ca_topic_score_codex":0.256364,"about_ca_topic_score_gemma":0.36728895,"teacher_disagreement_score":0.743636,"about_ca_system_score_codex":0.0051815063,"about_ca_system_score_gemma":0.014752471,"threshold_uncertainty_score":0.5097436},"labels":[],"label_agreement":null},{"id":"W3199555357","doi":"10.14745/ccdr.v47i09a03","title":"Representativeness of the FluWatchers Participatory Disease Surveillance Program 2015–2016 to 2018–2019: How do participants compare with the Canadian population?","year":2021,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Representativeness heuristic; Population; Demography; Census; Geography; Medicine; Vaccination; Disease surveillance; Public health surveillance; Environmental health; Epidemiology; Public health; Psychology; Immunology","score_opus":0.042324819397367035,"score_gpt":0.327054713489827,"score_spread":0.28472989409245997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199555357","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92210716,0.0051985937,0.004334914,0.005500175,0.0003388771,0.0018685404,0.04691066,0.00012858547,0.013612524],"genre_scores_gemma":[0.9886304,0.0006539955,0.0013331306,0.0006666194,0.000035416346,0.00087278144,0.006940725,0.000026072621,0.0008408611],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9807747,0.0041543106,0.0011207105,0.0028022216,0.007818618,0.0033295646],"domain_scores_gemma":[0.98493296,0.0014753765,0.00214755,0.0009946434,0.008909271,0.0015401262],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02256683,0.00055198494,0.0009205964,0.002650787,0.0035926602,0.002424634,0.003751892,0.00084206,0.002026956],"category_scores_gemma":[0.04055291,0.00035963338,0.0014423982,0.004891996,0.0013240498,0.0011994918,0.0022839285,0.00084270973,0.00029170167],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034053123,0.00005420856,0.9591977,0.00044952845,0.0006615092,0.00007393148,0.005992564,0.00026833024,0.0002668618,0.0005603993,0.007741679,0.024392769],"study_design_scores_gemma":[0.00002457913,0.00011390928,0.98691654,0.0002008417,0.0001388372,0.00004045533,0.00439917,0.0005246544,0.00019548922,0.00011828717,0.0072887023,0.000038436556],"about_ca_topic_score_codex":0.964718,"about_ca_topic_score_gemma":0.96225435,"teacher_disagreement_score":0.9803685,"about_ca_system_score_codex":0.019631524,"about_ca_system_score_gemma":0.032690313,"threshold_uncertainty_score":0.14243734},"labels":[],"label_agreement":null},{"id":"W3200269178","doi":"10.2196/26644","title":"Understanding Health Communication Through Google Trends and News Coverage for COVID-19: Multinational Study in Eight Countries","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"National Social Science Fund of China","keywords":"Online search; Pandemic; Public health; Popularity; Web search query; Coronavirus disease 2019 (COVID-19); The Internet; Geography; Business; Medicine; Political science; Computer science; Search engine; Information retrieval; World Wide Web","score_opus":0.14268310995454953,"score_gpt":0.40702768061154515,"score_spread":0.2643445706569956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200269178","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":[0.9976047,0.00014020689,0.000042183063,0.00012243276,0.000004725723,0.000025220723,0.0013620191,0.0000024616768,0.00069600064],"genre_scores_gemma":[0.9977246,0.0002237186,0.00011121379,0.000081341546,0.000011662116,0.00004041287,0.001546778,0.000005066181,0.0002552236],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99845564,0.0005575245,0.00021951643,0.00023923325,0.0002145862,0.00031345745],"domain_scores_gemma":[0.9915173,0.0029054745,0.0034132472,0.00043300336,0.00095339085,0.0007776983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024966162,0.00046707576,0.00052090763,0.0035930153,0.0009268474,0.0018108565,0.0005114507,0.0006632904,0.0017500984],"category_scores_gemma":[0.0076410384,0.00037249865,0.0009621039,0.006606364,0.00073314307,0.0021697753,0.001826255,0.000856902,0.00044558247],"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":[0.000040025025,0.00006489402,0.9945167,0.000042907875,0.000051010662,0.00012482124,0.0029579015,0.000064638734,0.00003776325,0.00004758264,0.00032339134,0.0017284363],"study_design_scores_gemma":[0.0000072227144,0.00008072284,0.9849924,0.000054622356,0.00005914297,0.0001614162,0.013006418,0.00072979886,0.000048927686,0.000020744808,0.0008244046,0.000014158094],"about_ca_topic_score_codex":0.09578678,"about_ca_topic_score_gemma":0.07049322,"teacher_disagreement_score":0.09578678,"about_ca_system_score_codex":0.0011244768,"about_ca_system_score_gemma":0.0010649668,"threshold_uncertainty_score":0.19045848},"labels":[],"label_agreement":null},{"id":"W3201122600","doi":"10.14745/ccdr.v47i09a02","title":"FluWatchers: Evaluation of a crowdsourced influenza-like illness surveillance application for Canadian influenza seasons 2015–2016 to 2018–2019","year":2021,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Medicine; Influenza-like illness; Environmental health; Disease surveillance; Population; Influenza season; Respiratory infection; Epidemiology; Vaccination; Influenza vaccine; Virology; Virus; Internal medicine","score_opus":0.03338065725895174,"score_gpt":0.3333065358893709,"score_spread":0.2999258786304192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201122600","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":[0.84737515,0.0013275986,0.03961429,0.0028152235,0.00066860404,0.019426879,0.040007796,0.019715725,0.029048672],"genre_scores_gemma":[0.8229816,0.00076456135,0.117726214,0.00092538807,0.00007524797,0.007222579,0.040958926,0.0005833667,0.008762088],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9968978,0.0011722549,0.00020156888,0.00059465657,0.00079309737,0.0003406099],"domain_scores_gemma":[0.9919407,0.0023313204,0.00029615057,0.00050526886,0.0039846683,0.00094208686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008021823,0.0014101914,0.00051879627,0.0012704912,0.0018855884,0.001148808,0.0019914906,0.00082482083,0.0030777098],"category_scores_gemma":[0.017646356,0.00035567742,0.0010517752,0.00093000237,0.00062070676,0.0009575143,0.0016787754,0.00080265163,0.0007993802],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.014692745,0.0053917533,0.14623882,0.0075726006,0.001325624,0.0012167614,0.019107703,0.050331436,0.037646163,0.002914247,0.14600627,0.56755584],"study_design_scores_gemma":[0.0036185079,0.0094967345,0.4283048,0.0023628252,0.0011334122,0.0003771507,0.014163789,0.29002222,0.021114,0.0024179122,0.22602117,0.00096746156],"about_ca_topic_score_codex":0.75562793,"about_ca_topic_score_gemma":0.81672984,"teacher_disagreement_score":0.24437207,"about_ca_system_score_codex":0.009463566,"about_ca_system_score_gemma":0.015837578,"threshold_uncertainty_score":0.49162245},"labels":[],"label_agreement":null},{"id":"W3201313776","doi":"10.2196/27385","title":"Using Google Trends to Inform the Population Size Estimation and Spatial Distribution of Gay, Bisexual, and Other Men Who Have Sex With Men: Proof-of-concept Study","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Victoria; Simon Fraser University","funders":"Canadian Institutes of Health Research; Michael Smith Health Research BC","keywords":"Men who have sex with men; Population; Population size; Estimation; Sexual orientation; Public health; Geography; Demography; Psychology; Sociology; Medicine; Social psychology; Engineering","score_opus":0.05316985699409201,"score_gpt":0.36730187481447457,"score_spread":0.3141320178203826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201313776","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":[0.7480675,0.0021260034,0.12691848,0.0039491532,0.00077008316,0.055127043,0.027523609,0.0010777242,0.034440443],"genre_scores_gemma":[0.72448206,0.0015773658,0.22528449,0.0012634846,0.00021241925,0.03875865,0.005466252,0.00015058227,0.0028047566],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9840366,0.010009694,0.0007716093,0.001318941,0.0031997268,0.00066333043],"domain_scores_gemma":[0.9502823,0.028413303,0.0048469375,0.003623983,0.011780401,0.0010531738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.035289526,0.0010853129,0.00082411926,0.0035046807,0.0009028861,0.0020051072,0.0024386228,0.0012536842,0.0065451534],"category_scores_gemma":[0.066396065,0.00069993426,0.0024433543,0.0016562508,0.0011646093,0.0032110477,0.0029313301,0.0012084916,0.0015663825],"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":[0.0031454288,0.007607119,0.35611877,0.010982947,0.0016486874,0.0007280311,0.012814066,0.011659484,0.004658606,0.013558448,0.026140735,0.55093765],"study_design_scores_gemma":[0.008360056,0.04075464,0.4532918,0.010512117,0.006655384,0.002614849,0.049291734,0.14895792,0.03281762,0.022982882,0.22265598,0.0011049678],"about_ca_topic_score_codex":0.012838836,"about_ca_topic_score_gemma":0.010535974,"teacher_disagreement_score":0.035289526,"about_ca_system_score_codex":0.001540823,"about_ca_system_score_gemma":0.0061583244,"threshold_uncertainty_score":0.18663108},"labels":[],"label_agreement":null},{"id":"W3202125022","doi":"10.2196/31423","title":"Peer Review of “The Exchange of Informational Support in Online Health Communities at the Onset of the COVID-19 Pandemic: Content Analysis”","year":2021,"lang":"en","type":"article","venue":"JMIRx Med","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Content (measure theory); Content analysis; Psychology; Medicine; Virology; Sociology; Infectious disease (medical specialty); Social science; Mathematics","score_opus":0.19973352324686072,"score_gpt":0.41114220817645425,"score_spread":0.21140868492959353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202125022","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073163738,0.007281688,0.010393895,0.49944878,0.39165607,0.0023867255,0.0048573087,0.0015639598,0.075095154],"genre_scores_gemma":[0.18828532,0.02291093,0.024283122,0.14349928,0.27084857,0.004201802,0.011285859,0.00574756,0.32893756],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9339429,0.024925724,0.0045211646,0.002889716,0.032136917,0.0015835932],"domain_scores_gemma":[0.43956622,0.09509259,0.011799663,0.017034722,0.4242437,0.012263099],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.038901336,0.0007293591,0.00134427,0.0063679228,0.0061930344,0.0068824086,0.0023369964,0.0032522317,0.07046245],"category_scores_gemma":[0.38803965,0.00037994827,0.0008904011,0.004259505,0.0036605194,0.0053701503,0.005713246,0.0032345455,0.03127237],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004927051,0.000026827105,0.0006380321,0.0008927657,0.000025483001,0.00009426549,0.0010365304,0.000033258075,0.00036250995,0.0010116249,0.950043,0.045786433],"study_design_scores_gemma":[0.000025456906,0.000062302395,0.0017170185,0.0012870225,0.000025956399,0.000116811214,0.001344721,0.00033085825,0.0005525717,0.0010427716,0.9934576,0.000036922174],"about_ca_topic_score_codex":0.0034526112,"about_ca_topic_score_gemma":0.005307221,"teacher_disagreement_score":0.9610987,"about_ca_system_score_codex":0.0034716292,"about_ca_system_score_gemma":0.01730491,"threshold_uncertainty_score":0.2357204},"labels":[],"label_agreement":null},{"id":"W3204153977","doi":"10.22091/stim.2020.5959.1444","title":"رفتار اطلاعیابی گردشگران سلولهای بنیادی براساس تحلیل گوگل ترندز","year":2020,"lang":"fa","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.35040939164635676,"score_gpt":0.5845582238034264,"score_spread":0.2341488321570696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204153977","genre_codex":"other","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":[0.29353172,0.014560468,0.036247,0.018756347,0.0034233276,0.0009787647,0.003526476,0.00080616865,0.6281698],"genre_scores_gemma":[0.6985173,0.026646335,0.056914803,0.0042660898,0.0016128613,0.0020636674,0.0035271314,0.0009803881,0.20547138],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99618727,0.0008624089,0.00042647208,0.00036577578,0.0018164888,0.0003416227],"domain_scores_gemma":[0.9916419,0.0031873612,0.0016363907,0.00038130133,0.0027393962,0.00041367632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034082457,0.00040610056,0.00032281913,0.0023314352,0.0015771841,0.0051717754,0.00044130176,0.0008306769,0.053052194],"category_scores_gemma":[0.011159431,0.00043591886,0.00041806337,0.0035325713,0.0020048155,0.0030516593,0.0014034985,0.0018886826,0.030710906],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010979925,0.0003377117,0.027990477,0.0046324627,0.00010578087,0.005564708,0.03648521,0.00071370875,0.04457397,0.1145663,0.07815243,0.6857793],"study_design_scores_gemma":[0.000056825917,0.00027651762,0.07661523,0.002301587,0.00007669608,0.0031691461,0.03322162,0.0009014958,0.014497035,0.019064987,0.84963846,0.00018031291],"about_ca_topic_score_codex":0.005850576,"about_ca_topic_score_gemma":0.008116216,"teacher_disagreement_score":0.053052194,"about_ca_system_score_codex":0.0022102979,"about_ca_system_score_gemma":0.0050166706,"threshold_uncertainty_score":0.17747724},"labels":[],"label_agreement":null},{"id":"W3205435495","doi":"10.2196/32355","title":"Physical Activity, Sedentary Behavior, and Sleep on Twitter: Multicountry and Fully Labeled Public Data Set for Digital Public Health Surveillance Research","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada; University of Calgary","funders":"","keywords":"Public health; Sedentary behavior; Public health surveillance; Physical activity; Set (abstract data type); Data set; Computer science; Sleep (system call); Environmental health; Internet privacy; Medicine; Physical medicine and rehabilitation; Artificial intelligence","score_opus":0.19328640655245052,"score_gpt":0.4376932876392101,"score_spread":0.2444068810867596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205435495","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69094783,0.0005055701,0.0125476755,0.0039389785,0.0003408068,0.0013014555,0.28398427,0.0008432242,0.0055901557],"genre_scores_gemma":[0.606897,0.00040769577,0.039235745,0.0008978439,0.00041546277,0.004665871,0.3443927,0.00012406628,0.0029636496],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99812037,0.0007141054,0.00023587063,0.00038638795,0.00034362104,0.00019962413],"domain_scores_gemma":[0.99346364,0.0020273284,0.001243501,0.0016590855,0.0010502591,0.0005561893],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0020647123,0.00054040883,0.0004928356,0.002280875,0.001016066,0.0008514289,0.0010979798,0.0011185297,0.0025557324],"category_scores_gemma":[0.008783151,0.0002499219,0.00062928547,0.0023352115,0.0006588874,0.0015928562,0.0026867944,0.001116211,0.0016880778],"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":[0.001031249,0.0015127106,0.70837724,0.0015314735,0.00037506217,0.0014855579,0.0045964867,0.006965021,0.008605675,0.0036066298,0.13771339,0.12419952],"study_design_scores_gemma":[0.00013982516,0.0005069527,0.84658045,0.00046678673,0.00021660987,0.00089970167,0.009915779,0.03580154,0.005499785,0.0037910808,0.0960027,0.00017878863],"about_ca_topic_score_codex":0.013462891,"about_ca_topic_score_gemma":0.024644697,"teacher_disagreement_score":0.998902,"about_ca_system_score_codex":0.00092141965,"about_ca_system_score_gemma":0.0012455994,"threshold_uncertainty_score":0.026769102},"labels":[],"label_agreement":null},{"id":"W3207441079","doi":"10.2196/29387","title":"Public Interest and Behavior Change in the United States Regarding Colorectal Cancer Following the Death of Chadwick Boseman: Infodemiology Investigation of Internet Search Trends Nationally and in At-Risk Areas","year":2021,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"American Cancer Society","keywords":"Colorectal cancer; Demography; Medicine; Population; Cancer; Mortality rate; Gerontology; Environmental health; Internal medicine; Sociology","score_opus":0.11580753107800265,"score_gpt":0.3681103137665818,"score_spread":0.2523027826885792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207441079","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":[0.99642664,0.0001987744,0.000098743774,0.0009610939,0.000015121895,0.000012761362,0.0010171195,0.000005177017,0.00126459],"genre_scores_gemma":[0.9980009,0.00018483204,0.00010141927,0.00012161494,0.000019462175,0.000012883368,0.0009306192,0.0000017553433,0.000626365],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999739,0.00008294974,0.000019810324,0.0000572408,0.00005928523,0.000041782605],"domain_scores_gemma":[0.99668103,0.0010557069,0.0012803868,0.00010489066,0.00046456905,0.0004134505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007671648,0.00011383425,0.00014844941,0.0009762876,0.00038377917,0.0007319245,0.00031758688,0.0004151167,0.002304416],"category_scores_gemma":[0.0050511598,0.00012697994,0.0003668591,0.0011435808,0.00024519503,0.00050896924,0.00049391633,0.0006992079,0.00022815401],"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":[0.000038465652,0.000104888866,0.9948112,0.000012965658,0.00003575767,0.000027805905,0.0002612979,0.00018244002,0.000046938065,0.00008884348,0.00082764373,0.003561735],"study_design_scores_gemma":[0.0000025874028,0.000039118448,0.9973218,0.000014459354,0.000019542545,0.000022781864,0.0005089227,0.0014343534,0.000034735873,0.000035995217,0.0005616269,0.000004110655],"about_ca_topic_score_codex":0.15357782,"about_ca_topic_score_gemma":0.17356674,"teacher_disagreement_score":0.15357782,"about_ca_system_score_codex":0.0012005767,"about_ca_system_score_gemma":0.0009241729,"threshold_uncertainty_score":0.30536777},"labels":[],"label_agreement":null},{"id":"W3208011707","doi":"10.1016/j.yebeh.2021.108377","title":"Patterns of Google search behavior for epilepsy and seizures in the Philippines: An infodemiological study","year":2021,"lang":"en","type":"article","venue":"Epilepsy & Behavior","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"","keywords":"Epilepsy; Search and seizure; Etiology; Public health; Psychiatry; Medicine; Psychology; Political science; Pathology","score_opus":0.06638615576185804,"score_gpt":0.37643384814158,"score_spread":0.31004769237972196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208011707","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":[0.99497414,0.00019218851,0.000035397064,0.00011484354,0.0000021629198,0.000010862021,0.003935001,0.000010885416,0.00072455447],"genre_scores_gemma":[0.99794227,0.00014463432,0.000057392805,0.00002810163,0.000004394548,0.000008838329,0.0015482121,0.0000037012474,0.0002623974],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996196,0.00007669686,0.00007006957,0.00006696857,0.00008335057,0.000083310195],"domain_scores_gemma":[0.996748,0.0006163282,0.0017259704,0.000112900874,0.0004566552,0.00034018574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037888865,0.00023976662,0.00023503181,0.0034541767,0.00037706987,0.00091314776,0.00040198062,0.00040042913,0.0017025492],"category_scores_gemma":[0.0028193668,0.00017124777,0.0003676775,0.0056025214,0.00032783957,0.0010109079,0.0007690551,0.00040295225,0.00037114494],"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":[0.000019826159,0.000019615016,0.9974057,0.000028411581,0.000031425217,0.00008666931,0.00058867526,0.00004389139,0.000076613156,0.000028684379,0.00028771898,0.0013827637],"study_design_scores_gemma":[0.0000010354238,0.000023414945,0.99707186,0.000017029684,0.000017910656,0.00019555198,0.0017982859,0.00040286852,0.000052725998,0.0000131312745,0.00040085212,0.0000054555535],"about_ca_topic_score_codex":0.08959742,"about_ca_topic_score_gemma":0.104962796,"teacher_disagreement_score":0.08959742,"about_ca_system_score_codex":0.0007546641,"about_ca_system_score_gemma":0.00062755815,"threshold_uncertainty_score":0.17815179},"labels":[],"label_agreement":null},{"id":"W3208164294","doi":"10.2196/32856","title":"Impact of the World Inflammatory Bowel Disease Day and Crohn’s and Colitis Awareness Week on Population Interest Between 2016 and 2020: Google Trends Analysis","year":2021,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ulcerative colitis; Inflammatory bowel disease; Medicine; Public health; Disease; Colitis; Population; Crohn's disease; Environmental health; Internal medicine; Pathology","score_opus":0.02674995156575241,"score_gpt":0.3460247965061518,"score_spread":0.31927484494039937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208164294","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":[0.88717633,0.006000239,0.0005147243,0.0019282541,0.00013780894,0.00008749508,0.09887534,0.00020693488,0.0050728945],"genre_scores_gemma":[0.96491367,0.0012616575,0.00050662487,0.00012600601,0.00007277266,0.00006376022,0.032412212,0.000041276136,0.00060210493],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99807906,0.00031672482,0.00025275213,0.00030913216,0.0007075789,0.00033479743],"domain_scores_gemma":[0.9933141,0.0021353283,0.0025268737,0.000262287,0.0013001665,0.0004611871],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0016998621,0.00045481836,0.00062861684,0.0067726374,0.0003072655,0.0019563586,0.0005860165,0.00046489213,0.002123243],"category_scores_gemma":[0.010414367,0.00018154475,0.0023377312,0.014511453,0.00040606334,0.002113003,0.0014510219,0.0010409887,0.0007746421],"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":[0.00022974197,0.000035348654,0.98104525,0.00052417716,0.0005693977,0.00014211259,0.00043296636,0.0012516319,0.000115926334,0.0005693955,0.0055292277,0.009554811],"study_design_scores_gemma":[0.000014695383,0.00010246188,0.9814388,0.00017780694,0.00036314357,0.0002980283,0.0017911616,0.0069705285,0.00020539558,0.00022832758,0.008381267,0.000028329874],"about_ca_topic_score_codex":0.07007278,"about_ca_topic_score_gemma":0.06015017,"teacher_disagreement_score":0.99322736,"about_ca_system_score_codex":0.0011081821,"about_ca_system_score_gemma":0.0013384689,"threshold_uncertainty_score":0.13932979},"labels":[],"label_agreement":null},{"id":"W3209642233","doi":"10.3390/jrfm14110512","title":"Google Search in Exchange Rate Models: Hype or Hope?","year":2021,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Exchange rate; Currency; Econometrics; Online search; Predictive power; Economics; Value (mathematics); Panel data; Computer science; Monetary economics; Information retrieval; Machine learning","score_opus":0.02575675000228881,"score_gpt":0.27093860246059487,"score_spread":0.24518185245830607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209642233","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":[0.9062412,0.008674512,0.06408428,0.009157852,0.00020138439,0.00007811115,0.0012542406,0.00054269296,0.009765847],"genre_scores_gemma":[0.98901165,0.0013761405,0.0074000885,0.00027952794,0.0002950104,0.00002671277,0.0006421479,0.00004178124,0.0009268778],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976956,0.0016409263,0.00012641591,0.00023440557,0.00018203525,0.00012053191],"domain_scores_gemma":[0.9490284,0.04495581,0.0030350052,0.0018092789,0.0006117898,0.0005596749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011515865,0.0010191037,0.0015196645,0.0024982875,0.00052370003,0.0036622344,0.0013769744,0.0012579251,0.0022946084],"category_scores_gemma":[0.051167645,0.00050199806,0.0009715668,0.0021884046,0.0010887271,0.003747389,0.001709167,0.0017434567,0.0005710639],"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":[0.0010504029,0.00034913115,0.47611365,0.0004791744,0.0015891793,0.0006751368,0.0010952747,0.37086856,0.00036766636,0.037688654,0.0078783035,0.10184481],"study_design_scores_gemma":[0.000101915575,0.00019162551,0.027293593,0.00016469823,0.00019352742,0.00023666205,0.00056261773,0.9234436,0.00030580434,0.044593718,0.0028442827,0.00006785605],"about_ca_topic_score_codex":0.013881352,"about_ca_topic_score_gemma":0.015420548,"teacher_disagreement_score":0.013881352,"about_ca_system_score_codex":0.00056280376,"about_ca_system_score_gemma":0.00073599815,"threshold_uncertainty_score":0.060902476},"labels":[],"label_agreement":null},{"id":"W3210547737","doi":"10.5281/zenodo.4434972","title":"A Geo-Tagged COVID-19 Twitter Dataset for 10 North American Metropolitan Areas","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Metropolitan area; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Geography; 2019-20 coronavirus outbreak; Cartography; Outbreak; Biology; Virology; Medicine; Archaeology","score_opus":0.055115464838458895,"score_gpt":0.3239826475311255,"score_spread":0.2688671826926666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210547737","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020813965,0.000086259475,0.00021435866,0.00018549587,0.00006628051,0.00003592512,0.99574226,0.00061161735,0.0009763662],"genre_scores_gemma":[0.0014108439,0.000045167733,0.00038159522,0.00004439036,0.000009958892,0.00006589106,0.9974469,0.000025052917,0.0005703073],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994254,0.00009806518,0.000061109364,0.00014477097,0.00017847921,0.00009218628],"domain_scores_gemma":[0.99907553,0.00015984221,0.00008781464,0.00018174665,0.00034572574,0.00014935494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005607784,0.0016298665,0.0008217319,0.0024061003,0.0009094302,0.0009882405,0.0015609716,0.0013836187,0.01107686],"category_scores_gemma":[0.0019232185,0.00039660203,0.0007714825,0.003995091,0.00033873666,0.0009076072,0.0014300839,0.0011596306,0.016910838],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008251372,0.00006488465,0.0034298454,0.0003947579,0.00003791577,0.00007631301,0.000059583417,0.0012734815,0.0005911291,0.0005096948,0.98833746,0.0051424457],"study_design_scores_gemma":[0.0001981232,0.00006648401,0.038625263,0.00027322627,0.00006492033,0.00021495266,0.0005302985,0.010528288,0.0017960626,0.0017184671,0.94589335,0.00009057404],"about_ca_topic_score_codex":0.09605044,"about_ca_topic_score_gemma":0.17775074,"teacher_disagreement_score":0.09605044,"about_ca_system_score_codex":0.0016330532,"about_ca_system_score_gemma":0.0021172483,"threshold_uncertainty_score":0.19098276},"labels":[],"label_agreement":null},{"id":"W3210735461","doi":"10.2196/31961","title":"Analyzing Citizens’ and Health Care Professionals’ Searches for Smell/Taste Disorders and Coronavirus in Finland During the COVID-19 Pandemic: Infodemiological Approach Using Database Logs","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pandemic; Database; Medicine; Biostatistics; Outbreak; The Internet; Epidemiology; Public health; Negative binomial distribution; Disease; Infectious disease (medical specialty); Coronavirus disease 2019 (COVID-19); Family medicine; Computer science; World Wide Web; Statistics; Virology; Pathology","score_opus":0.19662760775462407,"score_gpt":0.4352277487741369,"score_spread":0.23860014101951282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210735461","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":[0.993664,0.00012836812,0.00095057307,0.00012548348,0.0000052493574,0.00005485027,0.004643359,0.00003711008,0.00039102844],"genre_scores_gemma":[0.9919241,0.00008328679,0.0020674388,0.000028385537,0.000015548814,0.000087532295,0.0056143957,0.000004402649,0.00017488912],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99806637,0.000721977,0.0003282222,0.00036602776,0.00029942772,0.00021786863],"domain_scores_gemma":[0.9830062,0.011616991,0.0032503426,0.0006763054,0.0008008224,0.0006493981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028880688,0.00043601068,0.00041907525,0.00702161,0.0004136198,0.0015016312,0.00066824077,0.00091573416,0.0008057864],"category_scores_gemma":[0.01175465,0.00026182312,0.0007093803,0.0046054106,0.00046001398,0.001559714,0.0010611917,0.0005133929,0.00029499538],"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":[0.00014197394,0.0002074771,0.9865211,0.00010883362,0.000089918794,0.00019778241,0.00085913134,0.0028011266,0.0004054638,0.00019594553,0.00038209895,0.008089105],"study_design_scores_gemma":[0.000014841579,0.00027018736,0.9425679,0.000056944784,0.000088698085,0.00034593264,0.0045079975,0.049719773,0.0007675934,0.00041799335,0.001202715,0.000039437953],"about_ca_topic_score_codex":0.024349554,"about_ca_topic_score_gemma":0.018146882,"teacher_disagreement_score":0.024349554,"about_ca_system_score_codex":0.0013188457,"about_ca_system_score_gemma":0.0009580527,"threshold_uncertainty_score":0.0484156},"labels":[],"label_agreement":null},{"id":"W3211178287","doi":"10.1136/bmjopen-2021-053962","title":"Digital technology and disease surveillance in the COVID-19 pandemic: a scoping review protocol","year":2021,"lang":"en","type":"review","venue":"BMJ Open","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Fanshawe College; Western University","funders":"","keywords":"Medicine; Public health; Disease surveillance; CINAHL; Infographic; Public health surveillance; Pandemic; Thematic analysis; Digital health; PsycINFO; Public relations; MEDLINE; Grey literature; Health care; Infectious disease (medical specialty); Coronavirus disease 2019 (COVID-19); Disease; Political science; Nursing; Qualitative research; Computer science","score_opus":0.22091726635733908,"score_gpt":0.5401791690671898,"score_spread":0.31926190270985066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211178287","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00043605585,0.0051949583,0.0011424124,0.00071891735,0.00025030813,0.9856711,0.0052826856,0.00006182458,0.0012417093],"genre_scores_gemma":[0.0005818131,0.0056931516,0.0031550198,0.00036197866,0.000038231305,0.9884101,0.0010302807,0.00000828763,0.00072111626],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9741975,0.008781567,0.009537143,0.0019936003,0.004231355,0.0012589479],"domain_scores_gemma":[0.95510656,0.015903002,0.008757748,0.0031317223,0.014975084,0.0021259764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06623726,0.004074282,0.010451756,0.023513526,0.0044771787,0.00784058,0.005303872,0.00799638,0.073044494],"category_scores_gemma":[0.076038204,0.0042400346,0.011215361,0.017623268,0.005337526,0.008520776,0.0063906787,0.0051974435,0.011875644],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001478165,0.00020449299,0.0006660309,0.9122933,0.0008006706,0.0006429557,0.002344335,0.0006270508,0.0010265706,0.0058673136,0.027463162,0.046585854],"study_design_scores_gemma":[0.0033270284,0.0007068745,0.0035551034,0.7838441,0.003369913,0.000634739,0.0028371357,0.0004462753,0.0009089103,0.0055337027,0.19461343,0.00022280966],"about_ca_topic_score_codex":0.007442136,"about_ca_topic_score_gemma":0.015712047,"teacher_disagreement_score":0.073044494,"about_ca_system_score_codex":0.015229837,"about_ca_system_score_gemma":0.08060749,"threshold_uncertainty_score":0.3503003},"labels":[],"label_agreement":null},{"id":"W3213057249","doi":"10.3233/shti210628","title":"Opportunities to Improve COVID-19 Dashboard Designs for the Public","year":2021,"lang":"en","type":"book-chapter","venue":"Studies in health technology and informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Dashboard; Heuristics; Test (biology); Health information technology; Design elements and principles","score_opus":0.25693164500316484,"score_gpt":0.41571106152453985,"score_spread":0.158779416521375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213057249","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":[0.02127594,0.00359822,0.8444106,0.01607441,0.0014364063,0.0010401742,0.0016510057,0.015336373,0.09517687],"genre_scores_gemma":[0.045244824,0.0031740621,0.9189405,0.0015841784,0.00016877061,0.00047614722,0.0028849305,0.0027718688,0.024754725],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9934627,0.003200816,0.0005443204,0.00054118317,0.0020095988,0.00024139423],"domain_scores_gemma":[0.97096485,0.016270371,0.00088027614,0.0040889373,0.007002466,0.000793178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017791227,0.0013215408,0.0005488701,0.0022235466,0.0011417891,0.0072005847,0.0025424445,0.0015842181,0.013271215],"category_scores_gemma":[0.033776633,0.0010337038,0.00073509145,0.0029459693,0.0010304954,0.013625436,0.0023258573,0.0033785196,0.004905557],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012791053,0.0006446484,0.0051985946,0.0018290653,0.00005647438,0.00028031183,0.0072660674,0.014954139,0.00860206,0.11662462,0.14717008,0.69724596],"study_design_scores_gemma":[0.000062550556,0.0003805825,0.0019550573,0.0016502893,0.00006212015,0.00036957356,0.0036854304,0.034245666,0.008324157,0.06907669,0.880039,0.0001488614],"about_ca_topic_score_codex":0.0029335595,"about_ca_topic_score_gemma":0.006243282,"teacher_disagreement_score":0.017791227,"about_ca_system_score_codex":0.0017909757,"about_ca_system_score_gemma":0.0022393654,"threshold_uncertainty_score":0.094090104},"labels":[],"label_agreement":null},{"id":"W3213460695","doi":"10.2196/28857","title":"Understanding the Predictors of Missing Location Data to Inform Smartphone Study Design: Observational Study","year":2021,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Manchester Biomedical Research Centre; Versus Arthritis; Centre for Epidemiology Versus Arthritis, University of Manchester; Medical Research Council; National Institute for Health and Care Research","keywords":"Observational study; Activity tracker; mHealth; Missing data; Mobile phone; Logistic regression; Geolocation; Android (operating system); Medicine; Data collection; Computer science; Psychological intervention; Statistics; Physical therapy; Mathematics; Physical activity","score_opus":0.5154515002366681,"score_gpt":0.4591363209669944,"score_spread":0.056315179269673665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213460695","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7917066,0.003560784,0.18530746,0.0057910336,0.00038066402,0.0036520471,0.006052992,0.00018331067,0.003365208],"genre_scores_gemma":[0.95314944,0.0005341828,0.04020748,0.0009862348,0.00013978088,0.0032548404,0.0013863143,0.000038626276,0.00030320266],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.8932046,0.0808384,0.01128528,0.005055604,0.0077959485,0.0018201683],"domain_scores_gemma":[0.63513064,0.23307897,0.08329094,0.029163344,0.016865777,0.0024703334],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.09521197,0.00067579135,0.0010802689,0.0019211208,0.0013431725,0.0022787123,0.0021549966,0.001838848,0.0030612708],"category_scores_gemma":[0.30760163,0.00091018097,0.0017729494,0.002925683,0.0017912134,0.0039430745,0.0023774144,0.0022051812,0.0005034087],"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":[0.00039473767,0.00036484227,0.9705104,0.00083425426,0.0005428824,0.00013201556,0.0028935038,0.0009128162,0.00024291223,0.0012538967,0.0020610474,0.019856565],"study_design_scores_gemma":[0.0003431137,0.0029190641,0.93841237,0.003506886,0.0011680494,0.0008380952,0.005891045,0.026833279,0.0017779706,0.00895818,0.009196945,0.00015497053],"about_ca_topic_score_codex":0.0064086197,"about_ca_topic_score_gemma":0.006739041,"teacher_disagreement_score":0.904788,"about_ca_system_score_codex":0.0011315752,"about_ca_system_score_gemma":0.0037873155,"threshold_uncertainty_score":0.50353503},"labels":[],"label_agreement":null},{"id":"W3216086416","doi":"10.2196/34016","title":"Predicting the Number of Suicides in Japan Using Internet Search Queries: Vector Autoregression Time Series Model","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Vector autoregression; Econometrics; Autoregressive model; Heteroscedasticity; Statistics; Mathematics; Computer science","score_opus":0.04285619926828595,"score_gpt":0.3483404124701982,"score_spread":0.30548421320191227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216086416","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":[0.9751879,0.0013316675,0.015844023,0.0015356999,0.0001472502,0.00014649483,0.0031888674,0.00021891977,0.0023990497],"genre_scores_gemma":[0.99122745,0.00079758896,0.002654285,0.00009493601,0.000082044775,0.000105846164,0.002847749,0.000014436574,0.0021757353],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905723,0.00029051502,0.00011465863,0.0002484305,0.00014255392,0.00014653764],"domain_scores_gemma":[0.9967781,0.001904802,0.00062433665,0.00010816063,0.0004493312,0.00013540247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020818799,0.00087048847,0.0009989628,0.0023645756,0.0003672549,0.0016475306,0.0010441531,0.0010418186,0.0021265794],"category_scores_gemma":[0.0056206975,0.00040273968,0.0013188946,0.0026481673,0.00041311065,0.0011635914,0.0006425783,0.0013183891,0.0006897815],"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":[0.0006089464,0.0008074514,0.75007135,0.00042715378,0.0007855883,0.0011862634,0.00070096215,0.17959316,0.0010015988,0.0054873973,0.007611064,0.051719144],"study_design_scores_gemma":[0.000039311424,0.00018921022,0.09414796,0.00007110197,0.00024882148,0.00010395199,0.00032799738,0.90155184,0.00020359606,0.0017509417,0.0013129045,0.00005239421],"about_ca_topic_score_codex":0.048467692,"about_ca_topic_score_gemma":0.01875506,"teacher_disagreement_score":0.048467692,"about_ca_system_score_codex":0.00082564255,"about_ca_system_score_gemma":0.0012672498,"threshold_uncertainty_score":0.096371174},"labels":[],"label_agreement":null},{"id":"W35014891","doi":"10.1177/07067437211065726","title":"在宅看護実習指導内容・方法の検討 : 新カリ体制における在宅看護実習学習状況から(看護科)","year":2003,"lang":"en","type":"article","venue":"足利短期大学研究紀要","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"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":"Canadian Institutes of Health Research","keywords":"Computer science","score_opus":0.012165053390302235,"score_gpt":0.27066096529111316,"score_spread":0.25849591190081095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W35014891","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":[0.6425642,0.012458692,0.06681017,0.037594233,0.0009684157,0.0014275573,0.00214461,0.00053813827,0.23549399],"genre_scores_gemma":[0.9475733,0.0023335118,0.03222,0.0018046959,0.00018142817,0.00028926853,0.00062792713,0.000049281443,0.01492054],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9960368,0.0017283929,0.0004692641,0.00046707658,0.0009700057,0.00032856534],"domain_scores_gemma":[0.987217,0.005240522,0.0022312389,0.0012033624,0.0032089418,0.0008989565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01133472,0.00018578999,0.00021219124,0.0012091876,0.0010779944,0.004232548,0.0006442377,0.0005813667,0.0063608875],"category_scores_gemma":[0.016483234,0.00026866747,0.00031769834,0.0013138715,0.0020578867,0.0022094839,0.0013630852,0.0013747847,0.0013344124],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003246698,0.00029017215,0.21573761,0.0010851544,0.00010371094,0.0007650079,0.008313354,0.00085004006,0.0038077754,0.058961514,0.016378284,0.69338274],"study_design_scores_gemma":[0.00015224912,0.0012879072,0.44088775,0.0025641543,0.000349832,0.004635675,0.022679301,0.0054858737,0.031225568,0.06975021,0.42076552,0.00021604013],"about_ca_topic_score_codex":0.0112326285,"about_ca_topic_score_gemma":0.02295212,"teacher_disagreement_score":0.01133472,"about_ca_system_score_codex":0.0030398434,"about_ca_system_score_gemma":0.005458617,"threshold_uncertainty_score":0.05994445},"labels":[],"label_agreement":null},{"id":"W36968027","doi":"10.1039/d3ra00512g","title":"Evaluation des systèmes d'intelligence épidémiologique appliqués à la détection précoce des maladies infectieuses au niveau mondial.","year":2014,"lang":"en","type":"dissertation","venue":"RSC Advances","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"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":"Khon Kaen University; European Food Safety Authority; Public Health Agency of Canada; European Centre for Disease Prevention and Control; Georgetown University; Centers for Disease Control and Prevention; Helsingin Yliopisto","keywords":"Humanities; Political science; Art","score_opus":0.022145294277781755,"score_gpt":0.3377473426141056,"score_spread":0.3156020483363238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W36968027","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":[0.44148517,0.01354616,0.5048535,0.0017135362,0.00074348086,0.0012859754,0.008005852,0.016043153,0.012323227],"genre_scores_gemma":[0.78407097,0.001840173,0.20519067,0.00039800367,0.00017593408,0.00083359337,0.0038212088,0.00009182133,0.003577673],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99816626,0.0006284572,0.00014940611,0.0004749611,0.00047251736,0.000108334716],"domain_scores_gemma":[0.9966779,0.0014489617,0.00036502545,0.00017484366,0.0012144706,0.00011877905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004673515,0.0012897268,0.0011143713,0.0031102041,0.00031280515,0.0018766375,0.0007545674,0.0011728504,0.0018262725],"category_scores_gemma":[0.008625369,0.00025439085,0.0005476494,0.0010601686,0.00024677176,0.0010160969,0.0006892298,0.0004362747,0.0010320427],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018181226,0.0005335319,0.14250225,0.0011643771,0.0008632264,0.00018122203,0.00034692461,0.032348096,0.038836047,0.001358248,0.0071862377,0.7728617],"study_design_scores_gemma":[0.00020455426,0.0019684206,0.16992077,0.0002632334,0.00065378443,0.00058421295,0.0005003603,0.74676824,0.056082055,0.0028865351,0.020006418,0.00016144673],"about_ca_topic_score_codex":0.005480813,"about_ca_topic_score_gemma":0.0045281914,"teacher_disagreement_score":0.005480813,"about_ca_system_score_codex":0.00077099854,"about_ca_system_score_gemma":0.00064438384,"threshold_uncertainty_score":0.024716198},"labels":[],"label_agreement":null},{"id":"W379217718","doi":"10.20381/ruor-19392","title":"Development of a Methodology to Use Geographical Information Systems and Administrative Data to Measure and Improve Inequity in Health Service Distribution","year":2010,"lang":"en","type":"article","venue":"uO Research (University of Ottawa)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Measure (data warehouse); Distribution (mathematics); Information system; Data science; Computer science; Regional science; Geography; Data mining; Political science; Mathematics","score_opus":0.2794081767400526,"score_gpt":0.41906912993089734,"score_spread":0.13966095319084476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W379217718","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":[0.030637508,0.0000786304,0.9542597,0.0002737105,0.00006257076,0.0064940723,0.004799147,0.0009706888,0.0024239547],"genre_scores_gemma":[0.040203538,0.000029056353,0.9518585,0.000030027712,0.000009926265,0.0059603537,0.0015188967,0.000039122722,0.00035066067],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9835217,0.00990882,0.0017976628,0.001658227,0.002780203,0.00033336226],"domain_scores_gemma":[0.97477186,0.01505946,0.0027823735,0.0026551008,0.0043378198,0.00039341464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022757841,0.0010837746,0.0011433138,0.010812194,0.0012389341,0.0029713602,0.0017165443,0.0006328419,0.002920445],"category_scores_gemma":[0.044800516,0.0009985984,0.0018043135,0.009905955,0.00072153116,0.001971781,0.0040742513,0.0012368548,0.00058024627],"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":[0.00020522965,0.0012558677,0.22962892,0.0012260252,0.0011694514,0.0002692614,0.0057700174,0.016694086,0.004105544,0.04430242,0.008085318,0.6872878],"study_design_scores_gemma":[0.00059223705,0.0021242935,0.22329095,0.0008757302,0.0007766416,0.00076003064,0.017671797,0.55089796,0.017912678,0.080831036,0.103844866,0.0004217404],"about_ca_topic_score_codex":0.013376403,"about_ca_topic_score_gemma":0.013915243,"teacher_disagreement_score":0.022757841,"about_ca_system_score_codex":0.0021617345,"about_ca_system_score_gemma":0.0057578515,"threshold_uncertainty_score":0.12035644},"labels":[],"label_agreement":null},{"id":"W38504875","doi":"10.14745/ccdr.v49i1112a06","title":"249 教育実習における日誌の指導について(4) : 2年次の実習における指導教諭の助言(口頭発表II(保育者の専門職性・養成7))","year":2004,"lang":"en","type":"article","venue":"日本保育学会大会発表論文集","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.01030658610477219,"score_gpt":0.2714812772300625,"score_spread":0.2611746911252903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W38504875","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":[0.5251937,0.0060974155,0.040094364,0.0065790913,0.00037597376,0.0014351656,0.009205927,0.0012756333,0.4097428],"genre_scores_gemma":[0.9054257,0.0035031491,0.030576883,0.0012229157,0.00009330165,0.00021315817,0.004183995,0.00005948473,0.054721523],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9986039,0.00020014985,0.000115089286,0.00021519112,0.00064389716,0.00022173593],"domain_scores_gemma":[0.99843556,0.000225155,0.00027000465,0.0000942385,0.00079006585,0.00018486264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018360531,0.0004039328,0.00024621806,0.00096631405,0.0012231941,0.002297163,0.0006790076,0.00047235528,0.008667263],"category_scores_gemma":[0.002673465,0.00016403462,0.0004260454,0.0010144665,0.0012290365,0.0005410059,0.0007545423,0.00041894524,0.0036859543],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004929437,0.0003711192,0.3494587,0.00074560085,0.00016872359,0.001820254,0.0023604701,0.0030716392,0.018063892,0.014393915,0.034495614,0.5745571],"study_design_scores_gemma":[0.00007468993,0.00073935423,0.48357376,0.000713687,0.0003077195,0.0046064397,0.0056236,0.010265751,0.06721586,0.008662404,0.41804984,0.00016701798],"about_ca_topic_score_codex":0.21638863,"about_ca_topic_score_gemma":0.36891624,"teacher_disagreement_score":0.21638863,"about_ca_system_score_codex":0.006185634,"about_ca_system_score_gemma":0.009360584,"threshold_uncertainty_score":0.4302582},"labels":[],"label_agreement":null},{"id":"W4200044653","doi":"10.1159/000520692","title":"Googling Alzheimer Disease: An Infodemiological and Ecological Study","year":2021,"lang":"en","type":"article","venue":"Dementia and Geriatric Cognitive Disorders Extra","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"University of the Philippines","keywords":"Dementia; Socioeconomic status; Disease; Psychology; Gerontology; Public health; Demography; Medicine; Environmental health; Population; Internal medicine; Pathology","score_opus":0.023861375880725785,"score_gpt":0.30836389731248515,"score_spread":0.28450252143175936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200044653","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":[0.99791926,0.00034541916,0.00009684532,0.0001393489,0.0000062259046,0.00002830734,0.0004872473,0.0000025453826,0.00097493205],"genre_scores_gemma":[0.99880195,0.00028743182,0.00019692938,0.0000739247,0.00001581362,0.00003216369,0.00037521744,0.0000023594457,0.00021421726],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992957,0.00032687612,0.00006372696,0.000080390506,0.000096916854,0.00013635869],"domain_scores_gemma":[0.9980234,0.0006052968,0.000622161,0.00016324015,0.00018561094,0.0004004336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00151553,0.00026386953,0.00037576858,0.002996126,0.0014646708,0.0012972066,0.00035877718,0.00055233913,0.002180744],"category_scores_gemma":[0.0036619161,0.00022454542,0.0005824496,0.004119997,0.0006969788,0.0015897827,0.0012860163,0.00054763036,0.00034880821],"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":[0.00006706499,0.00016990524,0.9943281,0.00005649819,0.00005025235,0.00016260489,0.0019582624,0.000021869979,0.000054385153,0.0001576897,0.00019609927,0.0027772812],"study_design_scores_gemma":[0.000005522318,0.00019404136,0.9910165,0.000039177274,0.00004247343,0.00029459444,0.007166541,0.00015091492,0.00002565058,0.00013147376,0.0009265304,0.0000065839963],"about_ca_topic_score_codex":0.037248824,"about_ca_topic_score_gemma":0.05696611,"teacher_disagreement_score":0.037248824,"about_ca_system_score_codex":0.00072431477,"about_ca_system_score_gemma":0.0009232448,"threshold_uncertainty_score":0.07406402},"labels":[],"label_agreement":null},{"id":"W4200055690","doi":"10.1155/2021/4178161","title":"Classification of Public Health Centres in Accra through a Web-Based Portal Integrated with Geographical Information System (GIS)","year":2021,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Public health; Geographic information system; Health care; Information system; Population; Categorization; Business; Health information; Location; Knowledge management; Medicine; World Wide Web; Environmental health; Computer science; Geography; Nursing; Economic growth; Political science; Cartography","score_opus":0.022748581651365233,"score_gpt":0.27334914586657133,"score_spread":0.2506005642152061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200055690","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":[0.9237903,0.001032044,0.026043575,0.0019574875,0.00006906646,0.0013203582,0.0145667475,0.001196012,0.030024467],"genre_scores_gemma":[0.92443806,0.0007012216,0.063168846,0.00009057945,0.000021558357,0.00048118614,0.0066655227,0.00003064662,0.0044021807],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993755,0.00018010108,0.00009129839,0.00008640035,0.00015637086,0.00011032313],"domain_scores_gemma":[0.9985845,0.00037142407,0.00049339054,0.000111922665,0.0002691864,0.00016955819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008184864,0.0002226504,0.00017196347,0.0056293015,0.0007338599,0.0018575018,0.00045718715,0.00043010997,0.0041990587],"category_scores_gemma":[0.0021365026,0.00018001324,0.000244408,0.008205435,0.00039880397,0.0013742913,0.0010745898,0.00030704384,0.00078428356],"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":[0.00020416478,0.00039959498,0.7146132,0.0007439969,0.000048268917,0.0014516765,0.0130582815,0.0052323085,0.0037867436,0.008665322,0.020186825,0.23160958],"study_design_scores_gemma":[0.0000608097,0.00017038717,0.7604151,0.0005335693,0.000082130486,0.0013159803,0.060992107,0.032536738,0.0040509854,0.0032441164,0.13649967,0.00009837478],"about_ca_topic_score_codex":0.0333781,"about_ca_topic_score_gemma":0.03638779,"teacher_disagreement_score":0.0333781,"about_ca_system_score_codex":0.0010630423,"about_ca_system_score_gemma":0.002567641,"threshold_uncertainty_score":0.066367626},"labels":[],"label_agreement":null},{"id":"W4200139013","doi":"10.2196/preprints.35662","title":"Using the EISTL Model to Structure Citation Trends of Research in JMIR mHealth and uHealth: Bibliometric Analysis (Preprint)","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Citation; Plot (graphics); Chart; Information retrieval; Data science; Data mining; World Wide Web; Statistics; Mathematics","score_opus":0.2756745658248081,"score_gpt":0.5103307137768209,"score_spread":0.23465614795201284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200139013","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5832658,0.0064740553,0.19032589,0.013574414,0.00061076303,0.0022269152,0.14449824,0.011631611,0.047392324],"genre_scores_gemma":[0.90214556,0.0013753746,0.06494438,0.00032140722,0.00016978175,0.0012363595,0.02572391,0.00034526546,0.0037379286],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9943439,0.0023328,0.00089278934,0.0007895924,0.001315916,0.00032509046],"domain_scores_gemma":[0.9325023,0.049160343,0.0073125083,0.0024343017,0.0080572255,0.00053331954],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.012395494,0.0007113476,0.0008834208,0.041742533,0.0006371375,0.00544742,0.00117684,0.0010956874,0.008043396],"category_scores_gemma":[0.07816852,0.00027975664,0.0024000246,0.04545958,0.0007203398,0.006428258,0.002151854,0.0008624385,0.0024785863],"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":[0.00073158636,0.00031992048,0.62361944,0.0045159888,0.0015574546,0.00033153497,0.003879644,0.04573074,0.0016345255,0.0296824,0.048476778,0.23951998],"study_design_scores_gemma":[0.00019531969,0.0006775599,0.40684596,0.0014478927,0.001178614,0.0005335018,0.00816274,0.47738612,0.0027133015,0.05581693,0.04482257,0.00021955096],"about_ca_topic_score_codex":0.013682378,"about_ca_topic_score_gemma":0.0115828095,"teacher_disagreement_score":0.9876045,"about_ca_system_score_codex":0.0033191817,"about_ca_system_score_gemma":0.0031065957,"threshold_uncertainty_score":0.06555438},"labels":[],"label_agreement":null},{"id":"W4200257572","doi":"10.1101/2021.12.06.21267360","title":"Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Cytodiagnostics (Canada); Western University","funders":"Ministère de la Défense Nationale","keywords":"Geography; Geospatial analysis; Spatial epidemiology; Cartography; Spatial analysis; Geographic information system; Pandemic; Coronavirus disease 2019 (COVID-19); Epidemiology; Infectious disease (medical specialty); Remote sensing; Medicine; Disease","score_opus":0.051611731351499456,"score_gpt":0.2971147413299203,"score_spread":0.2455030099784208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200257572","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":[0.9821535,0.0007915133,0.0010065447,0.00054340286,0.000019486928,0.00015372408,0.008707357,0.00005050083,0.006573999],"genre_scores_gemma":[0.9956671,0.0003358351,0.0009944759,0.000051497318,0.0000040799764,0.00003198325,0.0013512437,0.0000058933874,0.0015580028],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995024,0.000048396047,0.000024060842,0.0000958283,0.00013576372,0.00019351757],"domain_scores_gemma":[0.99896467,0.00008415478,0.00020349237,0.000030678453,0.0005312544,0.00018570897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004424253,0.00026008152,0.00024441545,0.001260197,0.0018830693,0.0012922025,0.00078913925,0.00017102665,0.0023388937],"category_scores_gemma":[0.0016019837,0.00019095441,0.00030733203,0.0019855553,0.0004928727,0.00028834242,0.0009110316,0.00021093352,0.00014550265],"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":[0.00010505249,0.000022616854,0.9732257,0.0001847601,0.00006446371,0.0003539508,0.0026005218,0.0013730578,0.001060991,0.0005120797,0.00370901,0.016787853],"study_design_scores_gemma":[0.00001116922,0.00002615111,0.98843235,0.00011069387,0.000034978602,0.00009797492,0.0050721993,0.0019665412,0.0002593121,0.00010843283,0.0038600732,0.000020084184],"about_ca_topic_score_codex":0.99630004,"about_ca_topic_score_gemma":0.99804676,"teacher_disagreement_score":0.026239593,"about_ca_system_score_codex":0.026239593,"about_ca_system_score_gemma":0.03381573,"threshold_uncertainty_score":0.19038242},"labels":[],"label_agreement":null},{"id":"W4200401135","doi":"10.2196/32680","title":"An Evaluation of the Text Illness Monitoring (TIM) Platform for COVID-19: Cross-sectional Online Survey of Public Health Users","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Cross-sectional study; Public health; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Public health surveillance; Medicine; Environmental health; Computer science; Virology; Outbreak; Nursing; Disease; Pathology; Infectious disease (medical specialty)","score_opus":0.22893643280605985,"score_gpt":0.45908811364348434,"score_spread":0.2301516808374245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200401135","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":[0.9961914,0.000092575996,0.00019272187,0.000279095,0.000012024929,0.0006507466,0.0013831146,0.000018788234,0.0011795314],"genre_scores_gemma":[0.9940685,0.00024048942,0.0012596635,0.0006609979,0.000031196392,0.001699309,0.0014761606,0.000019239014,0.0005443542],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9951782,0.0022644142,0.00063868304,0.00044245974,0.0009698001,0.0005063968],"domain_scores_gemma":[0.9812107,0.0064186756,0.0059379633,0.00086353405,0.0034025959,0.0021664486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01245557,0.00034992298,0.00051010353,0.001419977,0.0010175606,0.0018352912,0.00085933733,0.0009414168,0.0027052283],"category_scores_gemma":[0.025216667,0.0006031449,0.0008736445,0.0012833196,0.00054290815,0.003519257,0.00205911,0.001228157,0.0010129136],"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":[0.00038276933,0.0017830986,0.97095156,0.00030671788,0.00009025447,0.00010819191,0.0072807916,0.000084077896,0.0002550501,0.000070217226,0.0026565709,0.016030656],"study_design_scores_gemma":[0.00006910617,0.001655619,0.9795562,0.00014716183,0.000054917728,0.00020808757,0.013984129,0.0012557595,0.00020591212,0.000046741294,0.002774604,0.00004172171],"about_ca_topic_score_codex":0.0061824396,"about_ca_topic_score_gemma":0.006640103,"teacher_disagreement_score":0.01245557,"about_ca_system_score_codex":0.001231949,"about_ca_system_score_gemma":0.001127596,"threshold_uncertainty_score":0.06587213},"labels":[],"label_agreement":null},{"id":"W4200537071","doi":"10.2196/32364","title":"United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundação para a Ciência e a Tecnologia; Centro de Investigação em Tecnologias e Serviços de Saúde","keywords":"Coronavirus disease 2019 (COVID-19); Seasonal influenza; Confidence interval; Demography; Pandemic; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Influenza A virus; Medicine; Geography; Virology; Disease; Infectious disease (medical specialty); Internal medicine; Virus","score_opus":0.08340358027983351,"score_gpt":0.40650605449331634,"score_spread":0.3231024742134828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200537071","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":[0.9930489,0.00020125056,0.00010549417,0.00011165168,0.000006806757,0.000030204645,0.005699799,0.000009456836,0.0007864803],"genre_scores_gemma":[0.99323976,0.00014108625,0.00013338681,0.000084800944,0.000020344696,0.00003923724,0.006001792,0.0000058347982,0.00033382743],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994686,0.00013190618,0.000091952665,0.0001228405,0.00009377477,0.00009086657],"domain_scores_gemma":[0.99686074,0.0004570792,0.0016312163,0.0002479957,0.0004334259,0.0003695269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008875424,0.00027421353,0.0002951273,0.0015294848,0.00039478592,0.0007178283,0.00042812398,0.0005216338,0.0015203774],"category_scores_gemma":[0.003207765,0.00026573072,0.0006619705,0.0027042364,0.00020407098,0.0010555328,0.0007578127,0.00067521643,0.0006289048],"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":[0.00005202535,0.000043883832,0.9981798,0.000015355918,0.000045933626,0.000031683212,0.00013275944,0.0000532848,0.000050221548,0.000020983009,0.00040350994,0.0009705332],"study_design_scores_gemma":[0.000002259344,0.00004454869,0.9989085,0.00000743137,0.00001808833,0.000070377486,0.00023884673,0.00028647453,0.00003627521,0.000005661808,0.00037901034,0.0000023785065],"about_ca_topic_score_codex":0.044274222,"about_ca_topic_score_gemma":0.052720677,"teacher_disagreement_score":0.044274222,"about_ca_system_score_codex":0.0007662628,"about_ca_system_score_gemma":0.0006279716,"threshold_uncertainty_score":0.08803302},"labels":[],"label_agreement":null},{"id":"W4200586000","doi":"10.3389/fmed.2021.778930","title":"Global Public Interest and Seasonal Variations in Alzheimer's Disease: Evidence From Google Trends","year":2021,"lang":"en","type":"article","venue":"Frontiers in Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Disease; Demography; Population; Public health; Etiology; Medicine; Southern Hemisphere; Geography; Population ageing; Environmental health; Biology; Pathology; Ecology","score_opus":0.04796383100305094,"score_gpt":0.3210160789484453,"score_spread":0.27305224794539434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200586000","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":[0.8766627,0.057652213,0.00042576916,0.005197671,0.00024312174,0.00008858466,0.046077907,0.00007919978,0.013573004],"genre_scores_gemma":[0.9726862,0.016552014,0.00024571846,0.0003168173,0.00019893762,0.000024988265,0.009490122,0.00002182519,0.0004633891],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99870205,0.00029966244,0.00025878154,0.00018420724,0.00041997904,0.00013523019],"domain_scores_gemma":[0.9793554,0.008568009,0.0077037946,0.0006630274,0.0029735307,0.0007362473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017357102,0.00030519962,0.00044153276,0.008570793,0.00024793917,0.001448541,0.00048124496,0.00045284047,0.0024507141],"category_scores_gemma":[0.01360232,0.00017403712,0.0014725113,0.019689295,0.00050238625,0.002688782,0.0010138636,0.0005552044,0.00047982796],"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":[0.00035796818,0.00003968717,0.9456411,0.0028637466,0.00078366336,0.00028618888,0.0013866676,0.00027012327,0.00010578693,0.00069136056,0.007690764,0.03988296],"study_design_scores_gemma":[0.000013014752,0.000066566965,0.989184,0.00074867374,0.00040440724,0.00028971653,0.0017744978,0.0007207032,0.00007956005,0.00021055287,0.0064905277,0.000017764476],"about_ca_topic_score_codex":0.041584708,"about_ca_topic_score_gemma":0.047046352,"teacher_disagreement_score":0.041584708,"about_ca_system_score_codex":0.00060280156,"about_ca_system_score_gemma":0.0008202693,"threshold_uncertainty_score":0.08268529},"labels":[],"label_agreement":null},{"id":"W4205221394","doi":"10.2196/preprints.29602","title":"Digital healthcare tools for the world’s poorest people and places: A new framework for syndromic surveillance forged in the fight against COVID-19 in Somalia. (Preprint)","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Vector Institute; McMaster University; Cambridge Memorial Hospital; University of Toronto","funders":"","keywords":"Psychological intervention; Health care; Sanitation; Digital health; Population; Government (linguistics); Pandemic; Disease surveillance; Public relations; Medicine; Business; Political science; Public health; Environmental health; Coronavirus disease 2019 (COVID-19); Infectious disease (medical specialty); Nursing; Disease","score_opus":0.05136288145669951,"score_gpt":0.34894687562020166,"score_spread":0.2975839941635021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205221394","genre_codex":"commentary","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":[0.0350132,0.019100418,0.3004175,0.49487388,0.005698829,0.002037183,0.002744039,0.0029552914,0.1371597],"genre_scores_gemma":[0.3288275,0.017657654,0.6036408,0.02111822,0.0020909386,0.0019830714,0.002510349,0.00068927184,0.0214821],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9909394,0.0062027154,0.0006988901,0.00064045924,0.00089629355,0.0006223287],"domain_scores_gemma":[0.9779823,0.013234962,0.001048769,0.0019834947,0.0029654722,0.0027849611],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02150911,0.0013408497,0.00077493046,0.009968745,0.008087603,0.03235213,0.0041639498,0.0075807525,0.0057650604],"category_scores_gemma":[0.023572812,0.0011583329,0.0014355226,0.0071524973,0.028191846,0.03489921,0.02032205,0.0074319174,0.001339378],"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":[0.000052531414,0.00015665792,0.006334374,0.0010607408,0.0000497821,0.0010912643,0.08622532,0.0008440638,0.00063529104,0.7449762,0.04863544,0.10993836],"study_design_scores_gemma":[0.000020725483,0.00008983408,0.0030836973,0.0036190883,0.00007128323,0.00082134234,0.14295945,0.0033662347,0.00057306077,0.19256496,0.6527441,0.000086138294],"about_ca_topic_score_codex":0.013983942,"about_ca_topic_score_gemma":0.017938416,"teacher_disagreement_score":0.03235213,"about_ca_system_score_codex":0.0067942226,"about_ca_system_score_gemma":0.015807593,"threshold_uncertainty_score":0.113752425},"labels":[],"label_agreement":null},{"id":"W4205287484","doi":"10.2196/preprints.19357","title":"A Global Digital Citizen Science Policy to Tackle Pandemics Like COVID-19 (Preprint)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; University of Regina","funders":"","keywords":"Pandemic; Preprint; Civil liberties; Coronavirus disease 2019 (COVID-19); Big data; Political science; Citizen science; Democracy; Civil society; Public relations; Existentialism; Internet privacy; Law; Computer science; Politics; Infectious disease (medical specialty)","score_opus":0.0383566862381046,"score_gpt":0.36766903562135217,"score_spread":0.32931234938324755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205287484","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025877743,0.0013337602,0.020327784,0.716837,0.016440045,0.00033850645,0.0009576816,0.0010948845,0.24008259],"genre_scores_gemma":[0.1995663,0.007547044,0.08788191,0.37735176,0.024893543,0.0018452703,0.0025575506,0.0020380307,0.29631856],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9869828,0.007527676,0.0004544088,0.00095187465,0.00325631,0.00082697027],"domain_scores_gemma":[0.96051437,0.025197618,0.0015295411,0.004383962,0.005876273,0.0024983115],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.027213393,0.00071808073,0.00070610776,0.0018541181,0.003934224,0.021311149,0.0016235358,0.020831123,0.052868027],"category_scores_gemma":[0.062138043,0.00047787683,0.0010937123,0.002133042,0.008522356,0.01598452,0.008252391,0.008876912,0.022091677],"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":[0.000038086884,0.00009562211,0.00038041163,0.00015930735,0.000011924238,0.0001228133,0.0005677736,0.0008492171,0.00033532365,0.6323951,0.3471464,0.017898187],"study_design_scores_gemma":[0.000042815584,0.000021546488,0.0003891424,0.0003278147,0.000011255081,0.000053416323,0.00083106547,0.0012515053,0.0007516405,0.19668067,0.79960924,0.000029889809],"about_ca_topic_score_codex":0.0025930062,"about_ca_topic_score_gemma":0.0023121126,"teacher_disagreement_score":0.9983765,"about_ca_system_score_codex":0.0068572764,"about_ca_system_score_gemma":0.017457666,"threshold_uncertainty_score":0.17686117},"labels":[],"label_agreement":null},{"id":"W4205324194","doi":"10.2196/29894","title":"Misinformation About and Interest in Chlorine Dioxide During the COVID-19 Pandemic in Mexico Identified Using Google Trends Data: Infodemiology Study","year":2021,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Pandemic; Misinformation; Coronavirus disease 2019 (COVID-19); Chlorine dioxide; Consumption (sociology); Environmental health; Medicine; Popularity; Business; Political science; Computer science; Disease; Chemistry; Computer security","score_opus":0.19484945027817877,"score_gpt":0.4266809176341666,"score_spread":0.23183146735598784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205324194","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":[0.9583028,0.002549365,0.00011284653,0.0008299494,0.000028841208,0.00006545248,0.034894634,0.000032450305,0.0031836855],"genre_scores_gemma":[0.981895,0.002018792,0.00033165287,0.00016839583,0.000061361316,0.000074828095,0.0147592025,0.000014273309,0.0006765557],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999373,0.000104202154,0.000112032736,0.00012010351,0.0001626115,0.0001281283],"domain_scores_gemma":[0.99578494,0.0014281974,0.0019599062,0.00013324006,0.00050715444,0.0001866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007942231,0.00023501522,0.00041985878,0.005405018,0.000392763,0.0013546364,0.00037322004,0.0005737722,0.0020701175],"category_scores_gemma":[0.0051795407,0.00013618637,0.00068072235,0.0073877177,0.00024282967,0.0015471353,0.00069883914,0.00041015647,0.0003010879],"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":[0.00023262763,0.000052919906,0.98061174,0.00092949416,0.0001244799,0.00035439813,0.001663035,0.000114442686,0.00024973374,0.00020461727,0.0044746874,0.010987814],"study_design_scores_gemma":[0.000012046419,0.000049991628,0.9864768,0.0003056275,0.00017384098,0.00038017536,0.0048573283,0.00067654636,0.00024794295,0.00004802433,0.0067544435,0.000017200331],"about_ca_topic_score_codex":0.058859162,"about_ca_topic_score_gemma":0.049453344,"teacher_disagreement_score":0.058859162,"about_ca_system_score_codex":0.001007749,"about_ca_system_score_gemma":0.0009916935,"threshold_uncertainty_score":0.117033124},"labels":[],"label_agreement":null},{"id":"W4205330435","doi":"10.1186/s12942-022-00300-9","title":"Reconciling public health common good and individual privacy: new methods and issues in geoprivacy","year":2022,"lang":"en","type":"editorial","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"European Commission","keywords":"Health informatics; Public health; Health geography; Human geography; Internet privacy; Environmental health; Public relations; Computer science; Political science; Health policy; Sociology; Medicine; International health; Social science","score_opus":0.07252203084652697,"score_gpt":0.4467221776923018,"score_spread":0.37420014684577485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205330435","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009679876,0.05698344,0.0031687238,0.25759935,0.6777768,0.000041769432,0.000055243236,0.00008222039,0.004195672],"genre_scores_gemma":[0.0015189378,0.028366996,0.0014026018,0.041058525,0.9226643,0.00005315708,0.00001964454,0.000069185844,0.0048466334],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9837202,0.007825189,0.0012075766,0.0015905458,0.0052848225,0.00037164523],"domain_scores_gemma":[0.8391514,0.14417003,0.0019543339,0.0026650603,0.009539912,0.0025192285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022878004,0.0022373057,0.0025587429,0.0038936387,0.0041219243,0.015776066,0.003724342,0.02159402,0.0044912957],"category_scores_gemma":[0.07634141,0.0009275639,0.0021218613,0.0020504168,0.015321239,0.014142297,0.0035134712,0.02979768,0.0022527848],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029427012,0.000016905557,0.00006338396,0.000779419,0.000034766308,0.00015530057,0.0005002358,0.0001365362,0.000073689895,0.029822258,0.93577135,0.032616746],"study_design_scores_gemma":[0.000019913774,0.000022985652,0.00011484061,0.00143732,0.0000311365,0.00020038607,0.00031821837,0.00039942501,0.00008357549,0.031793334,0.9655456,0.00003316196],"about_ca_topic_score_codex":0.0018173644,"about_ca_topic_score_gemma":0.0041097887,"teacher_disagreement_score":0.022878004,"about_ca_system_score_codex":0.005162757,"about_ca_system_score_gemma":0.0050069382,"threshold_uncertainty_score":0.120991886},"labels":[],"label_agreement":null},{"id":"W4205347469","doi":"10.2196/preprints.21820","title":"How to Fight an Infodemic: The Four Pillars of Infodemic Management (Preprint)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"JMIR Publications","funders":"","keywords":"Public relations; Political science; Literacy; Health literacy; The Internet; Sociology; Health care; Computer science; Law","score_opus":0.045342844505357395,"score_gpt":0.29781796072110434,"score_spread":0.2524751162157469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205347469","genre_codex":"commentary","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":[0.0012632842,0.015219933,0.0056152903,0.79168135,0.07501606,0.00010824446,0.00028279648,0.0010501909,0.109762914],"genre_scores_gemma":[0.05317846,0.056367587,0.020383548,0.27321425,0.15162182,0.00040518606,0.0011063309,0.002337467,0.44138536],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9959512,0.0014745328,0.00025848675,0.0003124351,0.0015766141,0.00042679044],"domain_scores_gemma":[0.9858803,0.0048325155,0.0011490338,0.00094509154,0.004275761,0.0029173107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005702243,0.0005699424,0.0005324188,0.0019414731,0.005084898,0.025108315,0.0013470005,0.0059551974,0.044399798],"category_scores_gemma":[0.02052045,0.00046051154,0.0005455803,0.001768723,0.00702136,0.01795008,0.0049304697,0.007044887,0.029648306],"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":[0.0000056526615,0.000015910671,0.00017376363,0.00009581518,0.0000045060797,0.000030885847,0.0005860834,0.00003652542,0.0000842651,0.020202134,0.94552183,0.033242546],"study_design_scores_gemma":[0.0000034936693,0.000009855385,0.0003359302,0.0002446242,0.000004535901,0.000047870642,0.0013298702,0.000081192884,0.00012895635,0.012715907,0.9850845,0.000013151465],"about_ca_topic_score_codex":0.0029982603,"about_ca_topic_score_gemma":0.0059890384,"teacher_disagreement_score":0.044399798,"about_ca_system_score_codex":0.0030187415,"about_ca_system_score_gemma":0.008153287,"threshold_uncertainty_score":0.1485321},"labels":[{"model":"gemma","categories":["metaresearch"],"domain":"evaluation","study_design":"theoretical_or_conceptual","genre":"commentary","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"commentary","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"split"},{"id":"W4205434253","doi":"10.2196/preprints.21390","title":"Web searches related to insomnia correlate with the escalation/de-escalation measures enforced during the COVID-19-induced quarantine and lockdown in Italy: insights from a Big Data-based infodemiological survey (Preprint)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Quarantine; Psychological intervention; Social distance; Coronavirus disease 2019 (COVID-19); Pandemic; Social media; Internet privacy; Business; Psychology; Medicine; Computer science; World Wide Web; Psychiatry","score_opus":0.12735361853670268,"score_gpt":0.32901842586610874,"score_spread":0.20166480732940606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205434253","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":[0.9876572,0.0005264995,0.00016240287,0.00071014115,0.000021839534,0.000044226224,0.009014469,0.000015718673,0.0018476628],"genre_scores_gemma":[0.9934604,0.00041781185,0.0003167775,0.0001958693,0.000054352986,0.000061362705,0.0050305463,0.000008588826,0.00045428282],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994343,0.0001765253,0.00009731746,0.00010084011,0.00008233879,0.000108700675],"domain_scores_gemma":[0.995116,0.00096626097,0.0028539926,0.0002289597,0.00043003212,0.00040466952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074834103,0.00016078542,0.00021377683,0.0010717675,0.00022996165,0.0007574152,0.00035341887,0.0003989685,0.0018650432],"category_scores_gemma":[0.0048829536,0.000153214,0.0005252836,0.0021663082,0.00027819723,0.00055743544,0.00082813005,0.00046145316,0.00039800035],"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":[0.00009272075,0.000039396247,0.9924725,0.00011858746,0.00006307936,0.000054908927,0.0006544401,0.000055063414,0.00010856013,0.00004636676,0.001677309,0.004617142],"study_design_scores_gemma":[0.0000014926384,0.000019681427,0.99872714,0.00002715519,0.00001478001,0.000026021515,0.00060085964,0.00008270307,0.000024579578,0.000011795783,0.00046113637,0.0000026582957],"about_ca_topic_score_codex":0.028158003,"about_ca_topic_score_gemma":0.03866023,"teacher_disagreement_score":0.028158003,"about_ca_system_score_codex":0.00047945237,"about_ca_system_score_gemma":0.00066890375,"threshold_uncertainty_score":0.055988252},"labels":[],"label_agreement":null},{"id":"W4205586261","doi":"10.1097/qai.0000000000002903","title":"Visualizing the Geography of HIV Observational Cohorts With Density-Adjusted Cartograms","year":2022,"lang":"en","type":"article","venue":"JAIDS Journal of Acquired Immune Deficiency Syndromes","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"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 Center for Advancing Translational Sciences; National Institute on Minority Health and Health Disparities; National Center for Research Resources; National Institute of Allergy and Infectious Diseases; National Institute on Aging; National Eye Institute; National Institute on Drug Abuse; National Institute of General Medical Sciences; National Institute of Mental Health; National Heart, Lung, and Blood Institute; National Cancer Institute; National Institute on Alcohol Abuse and Alcoholism; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Human immunodeficiency virus (HIV); Public health; Observational study; Representation (politics); Cohort; Health geography; Cohort study; Distribution (mathematics)","score_opus":0.028611543865927666,"score_gpt":0.2719850120938942,"score_spread":0.24337346822796652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205586261","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":[0.5390543,0.001986454,0.3786301,0.0023567697,0.00047838624,0.0011414454,0.05012692,0.007832765,0.01839282],"genre_scores_gemma":[0.7972468,0.0006694112,0.18939643,0.00016010355,0.00008062836,0.00068482885,0.010187555,0.00058984617,0.0009844508],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9962274,0.0028285466,0.00021249057,0.00033917188,0.00028766863,0.000104858125],"domain_scores_gemma":[0.9805116,0.0127773145,0.0024167967,0.0023649489,0.0016329519,0.00029639786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006608831,0.0006466496,0.00043743761,0.006049093,0.0005466231,0.0030660874,0.0008177474,0.0004411276,0.005565946],"category_scores_gemma":[0.035936918,0.00054760353,0.0011758623,0.0048331055,0.00067248486,0.0012115118,0.0022535105,0.00084333075,0.0005223627],"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":[0.0009440842,0.00017662188,0.55838627,0.0013255468,0.0015993624,0.0006367681,0.009903976,0.12274607,0.0015384255,0.041544184,0.040626433,0.22057223],"study_design_scores_gemma":[0.00035597407,0.00030995387,0.44237334,0.0009172093,0.0005938678,0.0008715433,0.010105837,0.3201954,0.0027174577,0.073033534,0.14805198,0.00047391764],"about_ca_topic_score_codex":0.03379322,"about_ca_topic_score_gemma":0.03606049,"teacher_disagreement_score":0.03379322,"about_ca_system_score_codex":0.00096028193,"about_ca_system_score_gemma":0.0013052629,"threshold_uncertainty_score":0.06719303},"labels":[],"label_agreement":null},{"id":"W4205697470","doi":"10.1073/pnas.2111452118","title":"An open repository of real-time COVID-19 indicators","year":2021,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Center for Machine Learning and Health, School of Computer Science, Carnegie Mellon University; Centers for Disease Control and Prevention; Amazon Web Services; National Science Foundation","keywords":"Coronavirus disease 2019 (COVID-19); Social distance; Pandemic; Public health; Internet privacy; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; The Internet; Population; Data science; Computer science; Actuarial science; Medicine; Business; Environmental health; World Wide Web; Nursing","score_opus":0.04320088745258147,"score_gpt":0.3684871504465092,"score_spread":0.3252862629939277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205697470","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019066988,0.0006007492,0.031009154,0.0012408919,0.0009220623,0.00027602,0.8897889,0.06331088,0.010944563],"genre_scores_gemma":[0.0124560725,0.0007577738,0.03395467,0.00048232166,0.0002634894,0.00050520495,0.9390438,0.008264195,0.004272424],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99511564,0.00079473184,0.0009929773,0.0010601797,0.0017608565,0.00027548222],"domain_scores_gemma":[0.9677126,0.009449772,0.003238023,0.009563831,0.0075859185,0.0024498994],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.007837261,0.0017204005,0.0017498295,0.008467089,0.00094670575,0.0070255836,0.0038701908,0.0022810583,0.042047184],"category_scores_gemma":[0.052028794,0.0010710072,0.0014300321,0.012001695,0.00066176115,0.0047928942,0.005691868,0.003364746,0.048180375],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038352393,0.00015906777,0.012702591,0.0016883865,0.000218178,0.00026802078,0.00026964548,0.0050688516,0.0009425798,0.008919899,0.88734645,0.08203281],"study_design_scores_gemma":[0.00025986548,0.00007976707,0.011140084,0.00078378926,0.00011517765,0.00022527647,0.00020206405,0.014804938,0.0028206045,0.018653417,0.9506514,0.00026350905],"about_ca_topic_score_codex":0.008491383,"about_ca_topic_score_gemma":0.010323392,"teacher_disagreement_score":0.9961298,"about_ca_system_score_codex":0.0013825893,"about_ca_system_score_gemma":0.005944142,"threshold_uncertainty_score":0.14066184},"labels":[],"label_agreement":null},{"id":"W4205744884","doi":"10.2196/preprints.18993","title":"When “Corona” Goes Viral – Online Search Interests in Google for Coronavirus versus Its Incidence in Australia, Canada, the United Kingdom, and the United States: An Infodemiological Analysis (Preprint)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Public health; Geography; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus; Online search; Political science; Demography; Medicine; World Wide Web; Computer science; Disease; Sociology; Infectious disease (medical specialty)","score_opus":0.21400590148653542,"score_gpt":0.41782009257979535,"score_spread":0.20381419109325993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205744884","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":[0.9682387,0.0012857676,0.000063309126,0.00056915736,0.000017758386,0.000016606793,0.025992092,0.000026532973,0.0037901073],"genre_scores_gemma":[0.98445374,0.00059652893,0.00010961655,0.000096836135,0.0000246783,0.0000126225095,0.013505497,0.000014132226,0.0011864929],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993042,0.00007942006,0.00008495797,0.000104454055,0.00023103289,0.00019604234],"domain_scores_gemma":[0.99390185,0.0019369056,0.0017088781,0.00012977987,0.0016561687,0.0006665016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066936004,0.00018657447,0.00034857288,0.0046841027,0.00038799935,0.0022602978,0.0004068181,0.00038404888,0.0029141158],"category_scores_gemma":[0.005723502,0.00011560276,0.00070078595,0.010551987,0.00048996595,0.0013424258,0.0006880042,0.0005109057,0.00050188275],"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":[0.0002552843,0.000035622026,0.98022044,0.0003160251,0.00024891755,0.00017596359,0.00164213,0.0007417531,0.00023910994,0.00083554327,0.008462277,0.0068269274],"study_design_scores_gemma":[0.0000035053279,0.000020831134,0.9936056,0.00006042071,0.00006880489,0.000082425744,0.0024904266,0.0013404115,0.000105687985,0.000053284588,0.0021554735,0.0000129510445],"about_ca_topic_score_codex":0.7210464,"about_ca_topic_score_gemma":0.7241678,"teacher_disagreement_score":0.2789536,"about_ca_system_score_codex":0.0030771247,"about_ca_system_score_gemma":0.0027315838,"threshold_uncertainty_score":0.56119287},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":true,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":true,"confidence":"high"}],"label_agreement":"agree"},{"id":"W4205772760","doi":"10.2196/preprints.18195","title":"An App for Classifying the Trend Magnitudes and the Multiple Infection Rates(MIR) of Novel Coronavirus (2019-nCoV) in Countries/Regions: A Data-Driven Analysis (Preprint)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Outbreak; Coronavirus disease 2019 (COVID-19); Dashboard; Geography; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); China; Diagram; Computer science; Cartography; Data science; Database; Medicine; Virology; Disease; Infectious disease (medical specialty)","score_opus":0.11327879413826239,"score_gpt":0.37723576929902347,"score_spread":0.2639569751607611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205772760","genre_codex":"dataset","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":[0.16696684,0.0012347992,0.06299009,0.0019024509,0.0014367453,0.0028349084,0.52787894,0.2166168,0.01813837],"genre_scores_gemma":[0.30897096,0.00134976,0.2300614,0.0016713075,0.00072219316,0.0055740564,0.42051888,0.005474148,0.025657244],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957234,0.00009239525,0.000052175594,0.00011276066,0.00013074804,0.00003960224],"domain_scores_gemma":[0.99724776,0.0017362119,0.00015169014,0.00015194512,0.0005556496,0.00015667078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000922992,0.0012024868,0.00065415585,0.00319666,0.0003747776,0.0012904826,0.00062421564,0.0007985663,0.016959494],"category_scores_gemma":[0.004048635,0.00036595494,0.0010916678,0.0015295226,0.00014861044,0.0011337838,0.0009789692,0.0006688069,0.008497443],"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":[0.0018498882,0.0010376275,0.094551615,0.0039071133,0.00045686687,0.0019806614,0.0025327632,0.008183961,0.0061447034,0.0018229277,0.6299105,0.24762142],"study_design_scores_gemma":[0.00079406827,0.0013473128,0.31889656,0.0015391588,0.0006655976,0.0021708019,0.004196863,0.26034415,0.015287316,0.0109408125,0.38321868,0.0005987186],"about_ca_topic_score_codex":0.0046306583,"about_ca_topic_score_gemma":0.0077670566,"teacher_disagreement_score":0.016959494,"about_ca_system_score_codex":0.00037654364,"about_ca_system_score_gemma":0.00050996424,"threshold_uncertainty_score":0.056735158},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"software","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"split"},{"id":"W4205783516","doi":"10.2196/preprints.31510","title":"Global Research on Coronaviruses: Metadata-Based Analysis for Public Health Policies (Preprint)","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Metadata; Preprint; Public health; Context (archaeology); Data science; Computer science; Open science; Global health; Pandemic; Political science; Public relations; Coronavirus disease 2019 (COVID-19); World Wide Web; Medicine; Geography","score_opus":0.35576087111811555,"score_gpt":0.49944688167695594,"score_spread":0.1436860105588404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205783516","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028459655,0.0147327,0.35595503,0.22127518,0.0058804373,0.0022493345,0.23786725,0.0067228293,0.12685761],"genre_scores_gemma":[0.30046597,0.022230277,0.5193608,0.010948007,0.005156924,0.0040565096,0.11675726,0.0024425942,0.018581683],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9804366,0.012337903,0.0023556813,0.0013826166,0.003174844,0.00031235855],"domain_scores_gemma":[0.84379286,0.11689512,0.010935476,0.011552378,0.015433852,0.0013903361],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.032006864,0.00043896123,0.00075631175,0.012174011,0.0014397242,0.011480198,0.0014149297,0.0018059004,0.03404644],"category_scores_gemma":[0.13416244,0.000433671,0.0012297924,0.024724316,0.0020162363,0.0081562605,0.003643024,0.001585411,0.0078035807],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019205063,0.00011068247,0.028619,0.0047427216,0.00022985713,0.00015460471,0.0034277076,0.006766145,0.0010208637,0.39079222,0.3104666,0.25347754],"study_design_scores_gemma":[0.000080542544,0.000068149486,0.02312565,0.0060602752,0.00019202869,0.000117011594,0.007227527,0.012609889,0.0028282856,0.27823275,0.66932535,0.00013253007],"about_ca_topic_score_codex":0.009407132,"about_ca_topic_score_gemma":0.0075380565,"teacher_disagreement_score":0.96799314,"about_ca_system_score_codex":0.005670591,"about_ca_system_score_gemma":0.012523065,"threshold_uncertainty_score":0.16927046},"labels":[],"label_agreement":null},{"id":"W4206022193","doi":"10.2196/preprints.24445","title":"Telehealth Demand Trends During the COVID-19 Pandemic in the Top 50 Most Affected Countries: Infodemiological Evaluation (Preprint)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Telehealth; Coronavirus disease 2019 (COVID-19); Pandemic; Information and Communications Technology; Index (typography); Business; Demography; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Geography; Medicine; 2019-20 coronavirus outbreak; Telemedicine; Political science; Economic growth; Health care; Computer science; Outbreak; Sociology; Virology; Economics; World Wide Web","score_opus":0.08048447748165313,"score_gpt":0.38880875377555946,"score_spread":0.30832427629390635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206022193","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":[0.866621,0.00091649976,0.00024906595,0.0008735348,0.00003729537,0.00026008335,0.12639861,0.00005810809,0.0045857816],"genre_scores_gemma":[0.92161214,0.0008576759,0.0005824161,0.00022680833,0.00009231091,0.00033846477,0.075284295,0.000022408885,0.0009834344],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99824,0.00063596136,0.00035729716,0.00021149249,0.0003799158,0.0001752084],"domain_scores_gemma":[0.98896664,0.0060379147,0.0025920067,0.00020280898,0.0017490872,0.00045158042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028191041,0.00030916877,0.00038979368,0.0051299627,0.00021084746,0.0015631276,0.00037255316,0.00046996956,0.0039019552],"category_scores_gemma":[0.010339145,0.00015940696,0.0012809371,0.008089039,0.0002862623,0.0019184722,0.00063803565,0.00040761256,0.0009340175],"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":[0.00096979085,0.00022787975,0.96822685,0.00089496514,0.0003968892,0.00010363639,0.00055153976,0.0018853641,0.00014202966,0.00030383316,0.010783991,0.015513396],"study_design_scores_gemma":[0.00004248743,0.00051197695,0.990112,0.00015171619,0.00014569935,0.00010554904,0.0025314817,0.0033330233,0.0002512633,0.00007363404,0.0027158393,0.00002528193],"about_ca_topic_score_codex":0.016813118,"about_ca_topic_score_gemma":0.010737356,"teacher_disagreement_score":0.016813118,"about_ca_system_score_codex":0.0009027029,"about_ca_system_score_gemma":0.00062953564,"threshold_uncertainty_score":0.033430517},"labels":[],"label_agreement":null},{"id":"W4206069189","doi":"10.1093/infdis/185.11.1696-b","title":"Errata","year":2002,"lang":"en","type":"article","venue":"The Journal of Infectious Diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Legend; Incidence (geometry); Sentence; Demography; Medicine; Geography; Mathematics; Philosophy; Linguistics; Sociology; Archaeology","score_opus":0.015377310460137074,"score_gpt":0.2549360889313298,"score_spread":0.23955877847119272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206069189","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006629941,0.004217292,0.0033053628,0.08182122,0.85136473,0.00019892999,0.015564561,0.0019501398,0.040914748],"genre_scores_gemma":[0.012387881,0.014858527,0.013451836,0.13179734,0.15972352,0.00065407576,0.041134328,0.006328846,0.6196636],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9933553,0.00095974136,0.0012677216,0.00066967116,0.0034696108,0.00027790075],"domain_scores_gemma":[0.95559734,0.010062603,0.0023536044,0.0031385145,0.027800562,0.0010474353],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004587194,0.0012997091,0.0009750034,0.0049323114,0.0033737796,0.004247488,0.0020676877,0.0028966446,0.20731404],"category_scores_gemma":[0.07038081,0.0008076948,0.0013967761,0.0037661025,0.0010421869,0.0028016702,0.0022552095,0.004904121,0.10400865],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025214424,0.0000051548673,0.0000746035,0.000093302755,0.0000035604812,0.00007789554,0.000032053318,0.000015053779,0.000025439655,0.00067941734,0.9901992,0.008769085],"study_design_scores_gemma":[0.00001098402,0.0000073016836,0.00022143402,0.00020638485,0.000009126522,0.00014180361,0.00006716104,0.000027677339,0.00009977872,0.00045362164,0.99874425,0.000010449728],"about_ca_topic_score_codex":0.015709156,"about_ca_topic_score_gemma":0.019123768,"teacher_disagreement_score":0.792686,"about_ca_system_score_codex":0.003302369,"about_ca_system_score_gemma":0.0059801,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4206232427","doi":"10.2196/36631","title":"Evaluation of a Dengue Surveillance Control Program, Yemen, Hodeiadah (2021)","year":2022,"lang":"en","type":"article","venue":"Iproceedings","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dengue fever; Representativeness heuristic; Medicine; Data quality; Disease surveillance; Case fatality rate; Environmental health; Outbreak; Data collection; Preparedness; Disease control; Medical emergency; Epidemiology; Operations management; Statistics; Population; Metric (unit); Virology","score_opus":0.026397682490310413,"score_gpt":0.3198583908108051,"score_spread":0.29346070832049465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206232427","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":[0.9738482,0.0012342915,0.0021861717,0.0021691867,0.00011679848,0.0070742145,0.0055147917,0.000117811236,0.007738594],"genre_scores_gemma":[0.97987056,0.00094287196,0.0070764306,0.00045570184,0.000035051893,0.0041308138,0.0060473313,0.000008179034,0.001433047],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9961229,0.0022720282,0.00039225636,0.0002213399,0.0004644692,0.00052705896],"domain_scores_gemma":[0.9958592,0.0006997328,0.0007175137,0.00010978441,0.0018387805,0.0007749854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013147911,0.0008409222,0.00029185327,0.0011160919,0.00079185585,0.0010575262,0.0006599177,0.00042907635,0.0021397572],"category_scores_gemma":[0.009808766,0.00026468188,0.00053173234,0.00090657134,0.0003817551,0.0009258823,0.0011299116,0.0003191342,0.00018574714],"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":[0.0027187096,0.0025987597,0.71995074,0.0027416248,0.00051833724,0.0011302759,0.0046547363,0.008443341,0.005439357,0.0008472334,0.0096218055,0.24133514],"study_design_scores_gemma":[0.00068378774,0.011968092,0.9398032,0.0012831944,0.0003912475,0.00034897943,0.014066756,0.008553292,0.0051487037,0.00018055155,0.017511854,0.000060335482],"about_ca_topic_score_codex":0.03906405,"about_ca_topic_score_gemma":0.041283045,"teacher_disagreement_score":0.03906405,"about_ca_system_score_codex":0.005751534,"about_ca_system_score_gemma":0.009767656,"threshold_uncertainty_score":0.077673316},"labels":[],"label_agreement":null},{"id":"W4206351543","doi":"10.2196/36554","title":"Electronic Integrated Disease Early Warning System Surveillance System Evaluation, Sana'a Capital, Yemen, 2021","year":2022,"lang":"en","type":"article","venue":"Iproceedings","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Representativeness heuristic; Disease surveillance; Psychological intervention; Interview; Data collection; Medicine; Environmental health; Warning system; Strengths and weaknesses; Business; Medical emergency; Public health; Operations management; Computer science; Psychology; Nursing; Statistics; Engineering","score_opus":0.009674476965703225,"score_gpt":0.24458125538443293,"score_spread":0.2349067784187297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206351543","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":[0.9723488,0.0020461755,0.0015704882,0.0038614885,0.00008645988,0.002438249,0.007404999,0.000091036796,0.010152219],"genre_scores_gemma":[0.98996323,0.00061494624,0.0025161316,0.00031286196,0.000014755491,0.0013940601,0.0037796113,0.000005831084,0.0013985797],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99682164,0.0020348455,0.0002650773,0.00012380091,0.00034550318,0.00040902157],"domain_scores_gemma":[0.9966581,0.00074818777,0.0006498462,0.00007049148,0.0015977046,0.00027574456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009254253,0.00080075744,0.00028005365,0.0009906839,0.00049522525,0.0011893586,0.00037797468,0.0004046807,0.0026548114],"category_scores_gemma":[0.009456998,0.00021433177,0.00037632402,0.0011056646,0.0005604422,0.0011487264,0.0008790009,0.00038772848,0.00027906988],"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":[0.0030405347,0.0008167634,0.8345201,0.0015066689,0.00038965562,0.0005826724,0.0023012266,0.0069238595,0.002271179,0.0014595242,0.015196868,0.13099092],"study_design_scores_gemma":[0.00034221786,0.0043245205,0.9542244,0.0010513815,0.00033866856,0.00024246547,0.0073919795,0.015215943,0.0032177102,0.0004422367,0.013145565,0.00006288347],"about_ca_topic_score_codex":0.02782748,"about_ca_topic_score_gemma":0.031760633,"teacher_disagreement_score":0.02782748,"about_ca_system_score_codex":0.005849345,"about_ca_system_score_gemma":0.004635494,"threshold_uncertainty_score":0.05533099},"labels":[],"label_agreement":null},{"id":"W4206382523","doi":"10.2196/31131","title":"An Association of Influenza Epidemics in Children With Mobile App Data: Population-Based Observational Study in Osaka, Japan","year":2021,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Linear regression; Regression analysis; Observational study; Medicine; Statistics; Spearman's rank correlation coefficient; Demography; Population; Correlation coefficient; Regression; Environmental health; Mathematics","score_opus":0.13019826548021574,"score_gpt":0.4687345505210102,"score_spread":0.33853628504079447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206382523","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":[0.99925584,0.00013617937,0.000050579336,0.000035090892,0.0000047813555,0.000009731563,0.000376944,0.0000022206423,0.00012865335],"genre_scores_gemma":[0.9991787,0.000119463926,0.00010052112,0.000040587336,0.0000063086277,0.00002279811,0.000449833,0.000002166722,0.00007970158],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990194,0.00018448697,0.00017928748,0.00027984005,0.00017797455,0.00015910415],"domain_scores_gemma":[0.99837166,0.00015537301,0.0007068204,0.00017014677,0.00025849152,0.00033748502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087264396,0.00044426305,0.00051908736,0.00081563054,0.0006962755,0.0007736112,0.000611001,0.00051046297,0.0008575893],"category_scores_gemma":[0.0018744621,0.00065616233,0.00083397503,0.001183083,0.0004482705,0.0007737678,0.00082152715,0.00082118553,0.00020021781],"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":[0.000017516948,0.000015707139,0.9994837,0.000008145073,0.000032157113,0.000040478404,0.00011973939,0.0000083950945,0.000055065433,0.0000031311804,0.000051142764,0.00016484938],"study_design_scores_gemma":[0.0000024344658,0.000037737744,0.9992126,0.0000059824574,0.000027318718,0.00009836657,0.00043840244,0.00007130488,0.000014950975,0.0000025811457,0.00008560927,0.0000027759156],"about_ca_topic_score_codex":0.04726005,"about_ca_topic_score_gemma":0.04957275,"teacher_disagreement_score":0.04726005,"about_ca_system_score_codex":0.0009585722,"about_ca_system_score_gemma":0.0008599071,"threshold_uncertainty_score":0.09396994},"labels":[],"label_agreement":null},{"id":"W4206453029","doi":"10.2196/33941","title":"Exploring Relationships Between Tweet Numbers and Over-the-counter Drug Sales for Allergic Rhinitis: Retrospective Analysis","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Medicine; Nose; Over-the-counter; Disease; Social media; Family medicine; Dermatology; Internal medicine; Surgery; Medical prescription","score_opus":0.16010583539947362,"score_gpt":0.399178268057314,"score_spread":0.23907243265784037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206453029","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":[0.9696398,0.0010208734,0.0011017913,0.00018298783,0.000058386446,0.0001446344,0.026542636,0.00003550813,0.0012735021],"genre_scores_gemma":[0.9830509,0.0004921691,0.001079688,0.00009009597,0.00006734215,0.0002647651,0.014337412,0.000014483827,0.0006031094],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985228,0.00023449007,0.00043813468,0.00039926643,0.00026100452,0.00014426635],"domain_scores_gemma":[0.99391526,0.0016242944,0.0027679,0.0004972328,0.0008960254,0.00029931025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016034929,0.0003927628,0.0004321824,0.0024445052,0.00048626208,0.00079645397,0.0004669088,0.00041689927,0.0012919344],"category_scores_gemma":[0.0066023446,0.00039746688,0.00096900563,0.0034741755,0.00022706046,0.001071667,0.0006324695,0.0006768852,0.0004950204],"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":[0.000107907836,0.000030407384,0.9948449,0.0001360017,0.00021761717,0.0001454978,0.00019805144,0.00016209562,0.00029695054,0.000059220656,0.001040628,0.0027606694],"study_design_scores_gemma":[0.000011253288,0.00008555472,0.99233305,0.000053682543,0.00035819624,0.0005082569,0.0006041931,0.002841959,0.00043616784,0.000083037536,0.0026578717,0.000026936188],"about_ca_topic_score_codex":0.008117518,"about_ca_topic_score_gemma":0.010822251,"teacher_disagreement_score":0.008117518,"about_ca_system_score_codex":0.000391384,"about_ca_system_score_gemma":0.0005326455,"threshold_uncertainty_score":0.01614058},"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":"W4206563739","doi":"10.2196/36630","title":"Evaluation of the Dengue Surveillance System in Islamabad (2019)","year":2022,"lang":"en","type":"article","venue":"Iproceedings","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Representativeness heuristic; Dengue fever; Flexibility (engineering); Public health surveillance; Quality (philosophy); Business; Environmental health; Disease surveillance; Population; Public health; Data collection; Operations management; Medicine; Statistics; Engineering; Nursing","score_opus":0.021297908037648562,"score_gpt":0.2758952519190927,"score_spread":0.2545973438814441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206563739","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":[0.99026734,0.00008161788,0.0012817136,0.0005526649,0.00005459889,0.001189902,0.0023892003,0.00010376109,0.0040790928],"genre_scores_gemma":[0.9942477,0.00007718832,0.002980067,0.00008375382,0.000017326733,0.00030403232,0.00191984,0.0000043163473,0.00036593262],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99343675,0.0035270955,0.00081835664,0.00039679068,0.0011821092,0.0006388722],"domain_scores_gemma":[0.98980963,0.0028527942,0.001221087,0.00036135683,0.004827775,0.0009273758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011587543,0.00049523916,0.00028100878,0.0010609698,0.00070994673,0.0012060055,0.00052400964,0.0003847721,0.0018304599],"category_scores_gemma":[0.0140887005,0.00020508353,0.00044726045,0.0007796405,0.00040038352,0.0011065924,0.0010669607,0.00030380124,0.0003740514],"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":[0.004501579,0.0015041446,0.80184156,0.0009946844,0.00022008737,0.0008212477,0.005804101,0.006257201,0.006704302,0.0008942846,0.0067101,0.1637467],"study_design_scores_gemma":[0.0003883727,0.008837813,0.9445787,0.00023719542,0.00016753684,0.0004961342,0.01292899,0.01683211,0.0067011993,0.00022764657,0.00851358,0.00009071154],"about_ca_topic_score_codex":0.02601978,"about_ca_topic_score_gemma":0.017555775,"teacher_disagreement_score":0.02601978,"about_ca_system_score_codex":0.0039040775,"about_ca_system_score_gemma":0.0027988248,"threshold_uncertainty_score":0.061281502},"labels":[],"label_agreement":null},{"id":"W4206634069","doi":"10.1002/9780470131886.index","title":"Index","year":2006,"lang":"en","type":"paratext","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"McGill University","funders":"","keywords":"Index (typography); Library science; Citation; Computer science; World Wide Web","score_opus":0.011189844022656955,"score_gpt":0.28635964008949477,"score_spread":0.27516979606683784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206634069","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014549455,0.0006167881,0.009461062,0.0028699688,0.0018877327,0.0005557903,0.13229898,0.0109385,0.8399162],"genre_scores_gemma":[0.0029959965,0.000526629,0.0035159597,0.00055280165,0.00041835516,0.00018628546,0.059718326,0.0012678041,0.9308179],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992442,0.00013939002,0.00006731307,0.00010622198,0.0003913217,0.00005158257],"domain_scores_gemma":[0.9948909,0.0011527971,0.00021215148,0.00061378634,0.0024822792,0.00064804807],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010214258,0.0008718241,0.0006896395,0.0040314435,0.00096464035,0.0035988654,0.0012399843,0.00095305627,0.69528246],"category_scores_gemma":[0.010377476,0.0003557895,0.0003469004,0.004435123,0.00034794322,0.0021655343,0.0012136191,0.0009090915,0.6197464],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023168448,0.000026634902,0.00030659875,0.000052559415,0.0000015969467,0.000015063954,0.00001208168,0.00017235096,0.00010549428,0.0015108312,0.9545115,0.04326204],"study_design_scores_gemma":[0.000023434886,0.000019148514,0.001169838,0.00007975103,0.0000041297194,0.000049066828,0.00003427874,0.0012840567,0.0003100841,0.0021955087,0.9948214,0.000009298767],"about_ca_topic_score_codex":0.009565017,"about_ca_topic_score_gemma":0.01009781,"teacher_disagreement_score":0.30471754,"about_ca_system_score_codex":0.0015824082,"about_ca_system_score_gemma":0.0015894459,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4206669656","doi":"10.2196/preprints.32127","title":"Online Search Behavior Related to COVID-19 Vaccines: Infodemiology Study (Preprint)","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Vaccination; Coronavirus disease 2019 (COVID-19); Medicine; Quarter (Canadian coin); Pandemic; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Family medicine; Internet privacy; Virology; Computer science; Geography; Internal medicine","score_opus":0.06696507503991739,"score_gpt":0.4181661520329927,"score_spread":0.3512010769930753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206669656","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":[0.98987025,0.00030884525,0.0000437528,0.00043344902,0.000012563105,0.000052353662,0.006894845,0.00000857044,0.002375404],"genre_scores_gemma":[0.99004465,0.0006918433,0.0001280591,0.00040462305,0.00007199993,0.00014897947,0.005757463,0.000016072327,0.0027361917],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992937,0.0002070606,0.00013624961,0.000102360194,0.00012240728,0.00013814983],"domain_scores_gemma":[0.99023926,0.0039390363,0.003983865,0.0003825387,0.00062327547,0.0008321109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010157857,0.00017372642,0.0003335108,0.0023215804,0.00065241253,0.0015803477,0.00039050798,0.0006967524,0.010022154],"category_scores_gemma":[0.007901167,0.0003128812,0.0006957445,0.004337824,0.00030119988,0.0022576067,0.00077419827,0.00088066625,0.0030091414],"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":[0.00016892598,0.00043352367,0.98727906,0.00018368437,0.00008676958,0.000086824955,0.0018498235,0.000039701543,0.000078482095,0.00013338671,0.0042645205,0.0053954693],"study_design_scores_gemma":[0.000015910971,0.00015297953,0.99467975,0.000067145156,0.000050803406,0.0001240534,0.0029992512,0.00019560446,0.000053435062,0.000034581928,0.0016182385,0.000008271067],"about_ca_topic_score_codex":0.022703836,"about_ca_topic_score_gemma":0.022974882,"teacher_disagreement_score":0.022703836,"about_ca_system_score_codex":0.000575636,"about_ca_system_score_gemma":0.00075356354,"threshold_uncertainty_score":0.045143366},"labels":[],"label_agreement":null},{"id":"W4206772354","doi":"10.2196/preprints.26644","title":"Understanding Health Communication Through Google Trends and News Coverage for COVID-19: Multinational Study in Eight Countries (Preprint)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Social Science Fund of China","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Popularity; Public health; Online search; The Internet; Geography; Political science; Medicine; Business; Computer science; Information retrieval; World Wide Web; Disease","score_opus":0.19222989085596034,"score_gpt":0.41455203755740433,"score_spread":0.22232214670144398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206772354","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":[0.9943613,0.00035184727,0.000060591115,0.0005807961,0.0000151931645,0.000035617162,0.0032539794,0.0000044333187,0.0013362757],"genre_scores_gemma":[0.9958734,0.00044829104,0.00014915095,0.00022091779,0.000035046596,0.00006652496,0.002754278,0.00000955428,0.0004428653],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99809784,0.000747119,0.00028988504,0.00025188192,0.00026595156,0.00034737503],"domain_scores_gemma":[0.98399204,0.0069248755,0.0057598483,0.0005854934,0.001619562,0.0011181465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033687092,0.000353154,0.00046530375,0.002757996,0.0007740917,0.0021681706,0.0005043393,0.0006334842,0.0030901178],"category_scores_gemma":[0.013021423,0.00033501844,0.00117665,0.007249694,0.0006653687,0.0026697158,0.0017424753,0.000993894,0.0006951659],"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":[0.000041415784,0.0000629746,0.99257386,0.00008912557,0.00008587854,0.00007636863,0.0034845022,0.000048137448,0.000022825077,0.00007981092,0.00096606504,0.002468954],"study_design_scores_gemma":[0.000007102603,0.00007071503,0.98517644,0.000108861306,0.000068527006,0.000088965324,0.012942089,0.00044347649,0.000032487387,0.000026001759,0.0010230051,0.000012177146],"about_ca_topic_score_codex":0.098449595,"about_ca_topic_score_gemma":0.061095886,"teacher_disagreement_score":0.098449595,"about_ca_system_score_codex":0.0011091707,"about_ca_system_score_gemma":0.0013772694,"threshold_uncertainty_score":0.1957531},"labels":[],"label_agreement":null},{"id":"W4207002735","doi":"10.1007/s10072-022-05902-6","title":"Low online search interest in teleneurology before and during COVID-19 pandemic: an infodemiological study","year":2022,"lang":"en","type":"article","venue":"Neurological Sciences","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Michael's Hospital","funders":"","keywords":"Telemedicine; Medicine; Telehealth; Pandemic; The Internet; Coronavirus disease 2019 (COVID-19); Proxy (statistics); Health care; Medical emergency; Public health; Nursing; World Wide Web; Pathology","score_opus":0.15755815795952674,"score_gpt":0.40092910637378043,"score_spread":0.2433709484142537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4207002735","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":[0.9884792,0.0008357032,0.00009610719,0.0010524808,0.00004791318,0.000025699255,0.0062711975,0.000009036864,0.003182674],"genre_scores_gemma":[0.99625754,0.000251728,0.00005822545,0.0005111592,0.00005315967,0.000019190518,0.001951418,0.000011567897,0.00088597176],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987091,0.00025342446,0.0002572801,0.00025193504,0.00016488222,0.0003633924],"domain_scores_gemma":[0.9864482,0.0045577083,0.0055609816,0.00047255357,0.0009638548,0.0019966564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010158757,0.0001999393,0.000419915,0.0022513254,0.0007352466,0.0018906659,0.0008029894,0.0011987117,0.009424166],"category_scores_gemma":[0.013046566,0.0002698988,0.00087411946,0.0032860206,0.0004729498,0.0026444786,0.0013116922,0.001638513,0.0014130815],"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":[0.00018130757,0.00014287606,0.99602425,0.00007303737,0.00010287306,0.00009051759,0.00036493482,0.000021359003,0.00004356626,0.00010974355,0.0010349819,0.0018105262],"study_design_scores_gemma":[0.000011013126,0.00011315199,0.99580044,0.00009560985,0.00010469241,0.00020479372,0.0022188975,0.00024780634,0.000043626183,0.00011499012,0.0010327918,0.000012277889],"about_ca_topic_score_codex":0.02424413,"about_ca_topic_score_gemma":0.02449307,"teacher_disagreement_score":0.02424413,"about_ca_system_score_codex":0.0006342134,"about_ca_system_score_gemma":0.00094265013,"threshold_uncertainty_score":0.04820603},"labels":[],"label_agreement":null},{"id":"W4210563571","doi":"10.2196/36514","title":"Evaluation of the National Electronic Disease Surveillance System Amid the COVID-19 Pandemic in Elsahel District, Cairo Governorate, Egypt, 2020","year":2022,"lang":"en","type":"article","venue":"Iproceedings","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Disease surveillance; Pandemic; Medicine; Public health surveillance; Public health; Population; Interview; Environmental health; Medical emergency; Disease; Coronavirus disease 2019 (COVID-19); Infectious disease (medical specialty); Political science; Nursing","score_opus":0.04185800537110097,"score_gpt":0.31701136840081057,"score_spread":0.2751533630297096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210563571","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":[0.9953153,0.00007992122,0.00044313623,0.00039587426,0.000012870284,0.0010073411,0.00045983357,0.000049719998,0.002236116],"genre_scores_gemma":[0.9956235,0.00008836201,0.0024193279,0.000103498925,0.000008690506,0.00038847094,0.0008523627,0.0000035985072,0.00051218836],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9929469,0.0042726924,0.00058338186,0.00029311827,0.0012721468,0.0006317922],"domain_scores_gemma":[0.9895327,0.00304483,0.0009225925,0.0003998706,0.0051495926,0.0009504401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021770637,0.00039327648,0.00033185518,0.0009617681,0.00066356454,0.0011281838,0.00070152193,0.0006416573,0.0014882499],"category_scores_gemma":[0.01891513,0.00016700466,0.00049726776,0.00075656694,0.0003904382,0.00096317934,0.00085114007,0.00029145618,0.0002102797],"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":[0.0060756076,0.006343048,0.7845449,0.0014341654,0.00038564607,0.0011213734,0.0093169045,0.019397305,0.0063639856,0.0008298271,0.005328566,0.15885863],"study_design_scores_gemma":[0.0006930514,0.018674407,0.90481037,0.00043487837,0.0003155274,0.00023377294,0.0243183,0.03690134,0.0053378893,0.00013994769,0.008055189,0.00008533213],"about_ca_topic_score_codex":0.03303055,"about_ca_topic_score_gemma":0.029602097,"teacher_disagreement_score":0.03303055,"about_ca_system_score_codex":0.0047362475,"about_ca_system_score_gemma":0.0044465996,"threshold_uncertainty_score":0.11513555},"labels":[],"label_agreement":null},{"id":"W4210605126","doi":"10.2196/36458","title":"Surveillance Evaluation for Severe Acute Respiratory Infection, Sana'a city, Yemen, 2021","year":2022,"lang":"en","type":"article","venue":"Iproceedings","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Guideline; Health surveillance; Ranking (information retrieval); Environmental health; Medical emergency; Computer science","score_opus":0.04144565420378109,"score_gpt":0.3377884334711299,"score_spread":0.2963427792673488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210605126","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":[0.89660186,0.019541439,0.0026712383,0.009448004,0.0003896053,0.0042579747,0.046565395,0.00021125904,0.020313254],"genre_scores_gemma":[0.96043307,0.0074810185,0.005419513,0.0009157189,0.00008806,0.004364961,0.017746223,0.000012879392,0.0035384654],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988054,0.0004825466,0.00025860482,0.00006883706,0.000164025,0.00022053228],"domain_scores_gemma":[0.9982785,0.00014819535,0.0003798605,0.000025835932,0.00098416,0.00018344047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003154795,0.00078082643,0.0003781053,0.001410947,0.0006583249,0.00070954155,0.0004493919,0.00036497973,0.002924975],"category_scores_gemma":[0.0039948346,0.000212389,0.0005327414,0.0014664297,0.00033562887,0.0008045312,0.0010112191,0.00047994687,0.00036884588],"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":[0.00049922726,0.0002112218,0.88301796,0.0029498278,0.00017062137,0.0007548585,0.0020506307,0.000901697,0.0009690015,0.00042269446,0.02018269,0.087869555],"study_design_scores_gemma":[0.00006454714,0.0005909843,0.9738367,0.0016681438,0.00015120133,0.00033885255,0.006548282,0.0012987285,0.0007645236,0.00015694449,0.014543489,0.0000375887],"about_ca_topic_score_codex":0.04902707,"about_ca_topic_score_gemma":0.058756452,"teacher_disagreement_score":0.04902707,"about_ca_system_score_codex":0.0036107858,"about_ca_system_score_gemma":0.0051992238,"threshold_uncertainty_score":0.097483456},"labels":[],"label_agreement":null},{"id":"W4210739057","doi":"10.6000/1929-6029.2022.11.01","title":"Multiple Imputation of Missing Race and Ethnicity in CDC COVID-19 Case-Level Surveillance Data","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institutes of Health","keywords":"Imputation (statistics); Coronavirus disease 2019 (COVID-19); Missing data; Ethnic group; Race (biology); Statistics; Computer science; Political science; Sociology; Medicine; Mathematics; Gender studies","score_opus":0.21614269870612388,"score_gpt":0.5172899677975115,"score_spread":0.30114726909138756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210739057","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":[0.26988465,0.0067410646,0.64188576,0.0058510667,0.0018881737,0.0021517605,0.061020367,0.0015608968,0.009016327],"genre_scores_gemma":[0.71035355,0.0023062439,0.23431687,0.0032307722,0.0004368002,0.003290981,0.042494256,0.00034044828,0.0032300695],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.94702595,0.034567766,0.006566262,0.005355237,0.0046485965,0.00183614],"domain_scores_gemma":[0.92616564,0.03742551,0.012311627,0.014047345,0.009439471,0.0006104172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06124321,0.0011851962,0.0024849596,0.0031501001,0.0013919272,0.0024520422,0.0031497092,0.0013923044,0.002869177],"category_scores_gemma":[0.15409361,0.0012730919,0.0031659142,0.0071698283,0.00070446276,0.0014323551,0.0022263518,0.003345574,0.0010250801],"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":[0.0010994802,0.0004568563,0.693222,0.0018629296,0.0034893688,0.0012975115,0.0023291807,0.036297318,0.001101251,0.014935403,0.056876186,0.18703258],"study_design_scores_gemma":[0.0008052703,0.0012720277,0.39693993,0.0050669597,0.004556836,0.0021947687,0.003343602,0.38632146,0.0089653805,0.074300855,0.115755916,0.00047698242],"about_ca_topic_score_codex":0.023449022,"about_ca_topic_score_gemma":0.021819726,"teacher_disagreement_score":0.06124321,"about_ca_system_score_codex":0.0016071621,"about_ca_system_score_gemma":0.00396381,"threshold_uncertainty_score":0.3238889},"labels":[],"label_agreement":null},{"id":"W4210833614","doi":"10.2196/36119","title":"Identifying COVID-19 Outbreaks From Contact-Tracing Interview Forms for Public Health Departments: Development of a Natural Language Processing Pipeline","year":2022,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Institute on Drug Abuse; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; U.S. National Library of Medicine; National Institute on Alcohol Abuse and Alcoholism; National Institutes of Health","keywords":"Outbreak; Pipeline (software); Computer science; Recall; Precision and recall; Named-entity recognition; Contact tracing; Coronavirus disease 2019 (COVID-19); Natural language processing; Artificial intelligence; Data mining; Infectious disease (medical specialty); Medicine; Disease; Psychology; Engineering; Virology; Pathology","score_opus":0.08075919963231584,"score_gpt":0.3833390910134271,"score_spread":0.3025798913811113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210833614","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":[0.23257948,0.00068724336,0.6682335,0.0030069987,0.00022350773,0.00439941,0.045709882,0.037783995,0.0073759],"genre_scores_gemma":[0.29941022,0.00036296784,0.64217573,0.00040311398,0.0000756126,0.0014061179,0.05248943,0.00040985024,0.003267008],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979049,0.00071716635,0.00031371653,0.0005867484,0.00035465747,0.00012287087],"domain_scores_gemma":[0.990417,0.0055394717,0.0008071633,0.00075196946,0.002293071,0.00019142742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047635455,0.0010384138,0.00043770624,0.004614257,0.0006165269,0.0014582125,0.0011106996,0.0008015899,0.0033351283],"category_scores_gemma":[0.016259458,0.0005441456,0.001077883,0.0019530079,0.00037908938,0.0025512753,0.0017141267,0.0014536711,0.0027705124],"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":[0.0005133061,0.00060598843,0.09987921,0.0014895292,0.00027666608,0.0010495919,0.0028563836,0.037770513,0.04122081,0.0034512756,0.055869773,0.7550169],"study_design_scores_gemma":[0.00012058501,0.00036390088,0.0832717,0.00037603808,0.0002469002,0.00088096317,0.0033794485,0.804731,0.046773575,0.010372758,0.049316235,0.00016694491],"about_ca_topic_score_codex":0.025585666,"about_ca_topic_score_gemma":0.032000817,"teacher_disagreement_score":0.025585666,"about_ca_system_score_codex":0.0016281356,"about_ca_system_score_gemma":0.00320712,"threshold_uncertainty_score":0.05087346},"labels":[],"label_agreement":null},{"id":"W4210862213","doi":"10.2196/37115","title":"Advancing Infodemiology in a Digital Intensive Era","year":2022,"lang":"en","type":"editorial","venue":"JMIR Infodemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"JMIR Publications; University of Victoria","funders":"","keywords":"Computer science","score_opus":0.009465989163628109,"score_gpt":0.3255671513137631,"score_spread":0.316101162150135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210862213","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000030545238,0.011884566,0.00022564859,0.07244168,0.91424793,0.000018666695,0.00004453423,0.000039258055,0.0010671711],"genre_scores_gemma":[0.0004446715,0.009570538,0.00020346568,0.03119921,0.9568185,0.000018048275,0.000015037648,0.000026289203,0.0017041344],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98591983,0.004240537,0.002119089,0.0009405002,0.0062734764,0.0005066522],"domain_scores_gemma":[0.875827,0.08354541,0.0058625876,0.0019199462,0.023347279,0.009497723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021047972,0.003054076,0.0033687525,0.0076355953,0.0035607321,0.014347735,0.0032684028,0.018192418,0.010707204],"category_scores_gemma":[0.09531276,0.0011847758,0.0022312966,0.002870236,0.004289082,0.010022773,0.003095756,0.024438456,0.0054010567],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004644758,0.00001833494,0.000076238306,0.00088516116,0.00004652281,0.00011439345,0.000074722666,0.000026482252,0.000047953985,0.002045826,0.9789297,0.017688315],"study_design_scores_gemma":[0.00009467866,0.000050908067,0.00020298584,0.0020521197,0.00010631351,0.00021793325,0.000102348415,0.00016969809,0.000093102324,0.0026517212,0.9942321,0.000026042839],"about_ca_topic_score_codex":0.0016029923,"about_ca_topic_score_gemma":0.0046629794,"teacher_disagreement_score":0.021047972,"about_ca_system_score_codex":0.004040506,"about_ca_system_score_gemma":0.007181273,"threshold_uncertainty_score":0.11131358},"labels":[],"label_agreement":null},{"id":"W4210946727","doi":"10.2196/36584","title":"Evaluation of the Dengue Surveillance System in Khyber Pakhtunkhwa Province, Pakistan, in 2020","year":2022,"lang":"en","type":"article","venue":"Iproceedings","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dengue fever; Disease surveillance; Medicine; Representativeness heuristic; Data quality; Public health surveillance; Preparedness; Environmental health; Medical emergency; Public health; Operations management; Statistics; Engineering; Virology","score_opus":0.016591663712746507,"score_gpt":0.2875873191183093,"score_spread":0.2709956554055628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210946727","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":[0.997652,0.00003798269,0.00030698438,0.00014146425,0.0000077633895,0.00028635957,0.0005617047,0.000017034115,0.0009887978],"genre_scores_gemma":[0.99799335,0.00006173634,0.001076567,0.000027577411,0.000003579306,0.00009078135,0.0005785167,0.0000012511658,0.0001667131],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971693,0.000996183,0.00028439096,0.00020318612,0.0009769159,0.00037004234],"domain_scores_gemma":[0.9939107,0.0010748277,0.0008001336,0.00015211015,0.0033211221,0.0007412143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008063361,0.00033631752,0.00025022612,0.0007607551,0.00090256485,0.00085302297,0.00043500768,0.00026821933,0.00063921005],"category_scores_gemma":[0.010122855,0.00021887975,0.00028979202,0.0007753275,0.0005329509,0.00095400703,0.0006216366,0.00029228095,0.00013706431],"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":[0.0006266979,0.0004555536,0.9580435,0.00022955082,0.000048921054,0.000488589,0.0031952967,0.00298519,0.0025755574,0.00017143051,0.000881687,0.030298023],"study_design_scores_gemma":[0.000047691778,0.0025974172,0.98136824,0.000056091154,0.000031256634,0.00020037671,0.008100968,0.004710151,0.001394385,0.000051957482,0.0014157532,0.000025658932],"about_ca_topic_score_codex":0.084173895,"about_ca_topic_score_gemma":0.057567157,"teacher_disagreement_score":0.084173895,"about_ca_system_score_codex":0.0040322617,"about_ca_system_score_gemma":0.006641569,"threshold_uncertainty_score":0.16736788},"labels":[],"label_agreement":null},{"id":"W4211088701","doi":"10.1016/j.jad.2022.02.021","title":"Suicide and resilience-related Google searches during the first 12 months of the COVID-19 pandemic","year":2022,"lang":"en","type":"letter","venue":"Journal of Affective Disorders","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"University of Toronto; Vienna Science and Technology Fund; Australian Government; Australian Research Council; Sunnybrook Research Institute","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Proxy (statistics); 2019-20 coronavirus outbreak; Suicide prevention; Poison control; Resilience (materials science); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Human factors and ergonomics; Injury prevention; Occupational safety and health; Psychological resilience; Psychology; Medicine; Medical emergency; Psychiatry; Virology; Outbreak; Statistics; Social psychology; Internal medicine; Infectious disease (medical specialty)","score_opus":0.018557809575527835,"score_gpt":0.2861852099823669,"score_spread":0.2676274004068391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211088701","genre_codex":"commentary","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":[0.09606913,0.0025409772,0.00013280961,0.86502844,0.004267752,0.000101052225,0.008617943,0.00013383759,0.023108138],"genre_scores_gemma":[0.4323811,0.006529961,0.00066092156,0.52980006,0.011345056,0.00025102432,0.0036416163,0.00012016915,0.015270048],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99814427,0.00046226266,0.0004563776,0.00017106379,0.0003824548,0.0003837108],"domain_scores_gemma":[0.98671794,0.0047524613,0.0035480536,0.0003804428,0.0016678692,0.002933242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015455354,0.0002945446,0.0004606024,0.0015718201,0.0024858804,0.0023655917,0.0007093141,0.007791245,0.0046388507],"category_scores_gemma":[0.016388444,0.00036229688,0.00057536084,0.0017726556,0.00087129173,0.002126879,0.0019077465,0.0048555136,0.001802172],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027995757,0.00014343808,0.18095647,0.00036167479,0.0000891438,0.008371583,0.0031488352,0.00016774987,0.0003751534,0.0015329863,0.76832414,0.036248922],"study_design_scores_gemma":[0.00027655685,0.0004020795,0.4118686,0.004132403,0.00026637057,0.013271376,0.04280745,0.0030397635,0.0009588769,0.0052073086,0.51747054,0.0002987437],"about_ca_topic_score_codex":0.064049065,"about_ca_topic_score_gemma":0.15168396,"teacher_disagreement_score":0.064049065,"about_ca_system_score_codex":0.002666465,"about_ca_system_score_gemma":0.0039440463,"threshold_uncertainty_score":0.12735254},"labels":[],"label_agreement":null},{"id":"W4212866152","doi":"10.14745/ccdr.v48i01a02f","title":"Divergences entre les données administratives sur les infections associées aux soins de santé et les données issues de la surveillance active au Canada","year":2022,"lang":"fr","type":"article","venue":"Relevé des maladies transmissibles au Canada","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Sante Montreal; Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Political science; Humanities; Philosophy","score_opus":0.022268412645291582,"score_gpt":0.2796644210764035,"score_spread":0.25739600843111193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212866152","genre_codex":"review","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":[0.31119645,0.41764334,0.016795307,0.060155045,0.0021136992,0.0035172072,0.1003581,0.00028026247,0.08794056],"genre_scores_gemma":[0.73501897,0.19751532,0.02438647,0.009531606,0.0004233373,0.0029943385,0.023292916,0.00015307323,0.006683962],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.91241187,0.024837296,0.022110868,0.004885724,0.032153077,0.0036010933],"domain_scores_gemma":[0.6034715,0.24219964,0.027939,0.007060034,0.11578953,0.0035403827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06575035,0.00068129285,0.0021115125,0.02057431,0.005368836,0.009595652,0.00309747,0.0012327683,0.0042820596],"category_scores_gemma":[0.26749778,0.0007041043,0.0024550625,0.04767617,0.0041593714,0.0031774456,0.004122365,0.0019924499,0.00042340337],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013130504,0.000110139226,0.33548614,0.10995607,0.0036722212,0.0015337681,0.11354879,0.0010019261,0.0021652817,0.01555144,0.032058716,0.38360247],"study_design_scores_gemma":[0.00009773879,0.00016460572,0.587061,0.08505183,0.004431009,0.00080988865,0.07070496,0.0005506767,0.003189701,0.0032989269,0.24428584,0.00035386375],"about_ca_topic_score_codex":0.90066546,"about_ca_topic_score_gemma":0.9224931,"teacher_disagreement_score":0.09933454,"about_ca_system_score_codex":0.052575078,"about_ca_system_score_gemma":0.20475225,"threshold_uncertainty_score":0.38146073},"labels":[],"label_agreement":null},{"id":"W4212924273","doi":"10.1101/2022.02.17.22271099","title":"Timeliness of reporting of SARS-CoV-2 seroprevalence results and their utility for infectious disease surveillance","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"McGill University Health Centre; McMaster University; University of New Brunswick; University of Toronto; Hamilton Health Sciences; Canada Research Chairs; McGill University; University of Calgary","funders":"Canadian Medical Association; Robert Koch Institut; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology; Public Health Agency; Public Health Agency of Canada; World Health Organization","keywords":"Seroprevalence; Representativeness heuristic; Medicine; Population; Sampling frame; Sample (material); Univariate; Demography; Environmental health; Family medicine; Statistics; Multivariate statistics; Serology","score_opus":0.07209403389258424,"score_gpt":0.35385629999282336,"score_spread":0.28176226610023913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212924273","genre_codex":"review","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":[0.40268835,0.46191636,0.03116208,0.01266276,0.002837567,0.0038499944,0.076040514,0.00059708237,0.008245301],"genre_scores_gemma":[0.9272009,0.03647347,0.01600807,0.0011092542,0.0010666251,0.0041174707,0.013103227,0.00023109595,0.0006898962],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.5787602,0.18263382,0.18551329,0.014820847,0.035762563,0.0025093171],"domain_scores_gemma":[0.11650111,0.5506537,0.2641404,0.03205234,0.035191093,0.0014614327],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.25422293,0.0009646082,0.0024559502,0.018484494,0.000949218,0.00524516,0.002385975,0.0012804649,0.003283988],"category_scores_gemma":[0.60660857,0.0012229338,0.007462893,0.025645167,0.0016028365,0.00636131,0.0035470105,0.0014729039,0.0004758545],"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":[0.0033492036,0.000087084074,0.75202596,0.07400657,0.023820685,0.0003712438,0.0044717565,0.0020128728,0.00083471095,0.0015407843,0.008125199,0.12935396],"study_design_scores_gemma":[0.00071212667,0.0015300189,0.8456904,0.06958081,0.018795932,0.0024643943,0.004052933,0.004756947,0.002735295,0.0031646166,0.04610136,0.0004151628],"about_ca_topic_score_codex":0.0050018597,"about_ca_topic_score_gemma":0.0062524155,"teacher_disagreement_score":0.25422293,"about_ca_system_score_codex":0.0026200388,"about_ca_system_score_gemma":0.006011047,"threshold_uncertainty_score":0.9196759},"labels":[],"label_agreement":null},{"id":"W4212938086","doi":"10.21203/rs.3.rs-54086/v1","title":"Development and Validation of an Automated Emergency Department-Based Syndromic Surveillance System for Use at a Mass Gathering Event (2020 Arctic Winter Games, Yukon Canada) During a Global Pandemic: Implications for Lower Resourced and Remote Settings","year":2020,"lang":"en","type":"preprint","venue":"Research Square (Research Square)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Mass gathering; Arctic; Pandemic; Event (particle physics); The arctic; Emergency department; Meteorology; Coronavirus disease 2019 (COVID-19); Aeronautics; Climatology; Geography; History; Psychology; Oceanography; Engineering; Medicine","score_opus":0.06473486475459818,"score_gpt":0.39152283274890015,"score_spread":0.326787967994302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212938086","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":[0.9260825,0.00013099532,0.047209393,0.00048272294,0.00008790158,0.0045920974,0.009528246,0.008108406,0.0037777652],"genre_scores_gemma":[0.85451007,0.000106890984,0.12812608,0.0002484425,0.00002206104,0.0013888973,0.01337897,0.00015520764,0.002063291],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99783427,0.0006251851,0.0003983081,0.00055143825,0.00046427787,0.00012643673],"domain_scores_gemma":[0.9899165,0.0018856487,0.0005556707,0.00058069994,0.0067502824,0.00031122688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049480377,0.00055617926,0.00037659516,0.0013533578,0.0009862178,0.0014744729,0.0010299942,0.00054915954,0.000968463],"category_scores_gemma":[0.009576651,0.00024505384,0.00040693564,0.00067260023,0.00049256225,0.0006404363,0.0007302371,0.00030328348,0.0006411304],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030628645,0.0020995154,0.4619941,0.0014955751,0.0005592706,0.0019161954,0.007038299,0.03283031,0.11748013,0.0012720246,0.024938796,0.34531295],"study_design_scores_gemma":[0.000606225,0.0018993658,0.48056093,0.00047332025,0.0004644692,0.000718119,0.0055893064,0.41251513,0.072490826,0.00058273517,0.023844814,0.0002547694],"about_ca_topic_score_codex":0.24329996,"about_ca_topic_score_gemma":0.20731027,"teacher_disagreement_score":0.75670004,"about_ca_system_score_codex":0.0037673605,"about_ca_system_score_gemma":0.0075268243,"threshold_uncertainty_score":0.48376757},"labels":[],"label_agreement":null},{"id":"W4213182176","doi":"10.2196/preprints.20913","title":"Trends in Web Searches About the Causes and Treatments of Autism Over the Past 15 Years: Exploratory Infodemiology Study (Preprint)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Surrey Place Centre; Hospital for Sick Children; University of Toronto; McMaster University","funders":"","keywords":"The Internet; Autism; Autism spectrum disorder; World Wide Web; Preprint; Informatics; Inclusion (mineral); MEDLINE; Medicine; Internet privacy; Analytics; Psychology; Computer science; Data science; Psychiatry; Political science; Social psychology","score_opus":0.07427267040169355,"score_gpt":0.34978769058705333,"score_spread":0.2755150201853598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213182176","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":[0.7846577,0.0020898385,0.00025116227,0.0010318257,0.000040116538,0.0001250377,0.20593607,0.00015544741,0.0057126964],"genre_scores_gemma":[0.8692878,0.0029581776,0.0011357057,0.00046123663,0.00011370662,0.00043384288,0.12174315,0.00011563369,0.0037508344],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99829954,0.0003482233,0.00047692054,0.0003102189,0.00039565263,0.0001693862],"domain_scores_gemma":[0.97952783,0.00859411,0.008084106,0.00064946373,0.0023975098,0.00074690394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016496755,0.00026528744,0.0004949709,0.01029206,0.00048263368,0.0017983827,0.0005770007,0.000559541,0.006127461],"category_scores_gemma":[0.014490179,0.0002472661,0.0014374924,0.016260518,0.00028641385,0.0025301038,0.0011553433,0.0007251823,0.0021087073],"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":[0.00041937042,0.0001438275,0.9516252,0.0014571815,0.00047437943,0.00020207283,0.002277309,0.0001916096,0.00019206459,0.00038547872,0.018883752,0.023747688],"study_design_scores_gemma":[0.000017127433,0.00009753963,0.987105,0.00033237896,0.00017246454,0.00030541615,0.003262674,0.00039319662,0.00019184331,0.00007412162,0.008025725,0.000022466143],"about_ca_topic_score_codex":0.025575114,"about_ca_topic_score_gemma":0.02713382,"teacher_disagreement_score":0.025575114,"about_ca_system_score_codex":0.00084599195,"about_ca_system_score_gemma":0.0011335663,"threshold_uncertainty_score":0.050852478},"labels":[],"label_agreement":null},{"id":"W4220692319","doi":"10.1016/j.inpa.2022.03.004","title":"Fusion of spatiotemporal and thematic features of textual data for animal disease surveillance","year":2022,"lang":"en","type":"article","venue":"Information Processing in Agriculture","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"European Regional Development Fund; Région Occitanie Pyrénées-Méditerranée; Centre de Coopération Internationale en Recherche Agronomique pour le Développement; Agence Nationale de la Recherche; European Commission","keywords":"Context (archaeology); Computer science; Representation (politics); Information retrieval; Selection (genetic algorithm); Feature selection; Disease surveillance; Feature (linguistics); Sensor fusion; Data mining; Artificial intelligence; Natural language processing; Disease; Geography; Medicine; Pathology; Linguistics","score_opus":0.016586692334417848,"score_gpt":0.2756903477295713,"score_spread":0.25910365539515345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220692319","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":[0.745789,0.0029536816,0.23028222,0.0007303718,0.00020006062,0.00052081834,0.012551752,0.0028439648,0.004128093],"genre_scores_gemma":[0.8105601,0.00066814787,0.17678905,0.000056625882,0.00012540951,0.00016796954,0.01094887,0.00008810182,0.00059573486],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883085,0.0003458112,0.00017855718,0.00018328712,0.00039212307,0.00006935887],"domain_scores_gemma":[0.9941749,0.00335001,0.0006601671,0.0003824248,0.0012870099,0.000145661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016416688,0.00052372424,0.0005944374,0.0065230494,0.0002796073,0.0010410061,0.0004024925,0.00047555772,0.0007215611],"category_scores_gemma":[0.010224465,0.00018584421,0.000923353,0.004557338,0.00027135608,0.0018510813,0.0005972861,0.00051058974,0.00041938972],"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":[0.0033873806,0.0006450699,0.04818996,0.0022342536,0.00062469183,0.0011315716,0.001242245,0.029399658,0.13805619,0.0021537826,0.0048018694,0.7681333],"study_design_scores_gemma":[0.00019456167,0.0014990085,0.22605285,0.00037220653,0.0013828622,0.0018050136,0.0027718996,0.6485869,0.08687709,0.008141831,0.022030525,0.00028520133],"about_ca_topic_score_codex":0.002571438,"about_ca_topic_score_gemma":0.0033333234,"teacher_disagreement_score":0.0065230494,"about_ca_system_score_codex":0.0005050229,"about_ca_system_score_gemma":0.000470531,"threshold_uncertainty_score":0.008682132},"labels":[],"label_agreement":null},{"id":"W4220700923","doi":"10.2196/35253","title":"Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology Study","year":2022,"lang":"en","type":"article","venue":"JMIR Mental Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Depression (economics); Mental health; Population; Epidemiology; Feeling; Medicine; Demography; Psychiatry; Psychology; Environmental health; Social psychology","score_opus":0.13770527036708757,"score_gpt":0.41887943087915586,"score_spread":0.2811741605120683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220700923","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":[0.9888809,0.00018116663,0.0012754108,0.00038152534,0.000008377883,0.00013248759,0.00814347,0.000051869876,0.0009447883],"genre_scores_gemma":[0.9886535,0.00020306848,0.003530858,0.00007635772,0.000017212984,0.00021293943,0.007046389,0.00001949966,0.0002402392],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990164,0.00048342935,0.000086070904,0.00015766872,0.00013270606,0.00012371148],"domain_scores_gemma":[0.99603903,0.0021345578,0.0007400002,0.0004777912,0.00038333595,0.00022535672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017864838,0.00034520862,0.00031422699,0.0053862203,0.00053953513,0.0013731511,0.00050714205,0.0003489547,0.0009279833],"category_scores_gemma":[0.0074602263,0.0002531226,0.0014737662,0.008308872,0.00046095904,0.0012640872,0.0014644426,0.000520394,0.00022201613],"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":[0.000079605896,0.00016654006,0.9796561,0.00014664166,0.00026686426,0.00031199356,0.0022521708,0.0014446165,0.0001850229,0.00088370265,0.0024401557,0.01216652],"study_design_scores_gemma":[0.00003240552,0.00018621396,0.9455136,0.0001273534,0.00022196915,0.0004755095,0.01751368,0.029759336,0.0004233757,0.0012444379,0.0044610384,0.00004108057],"about_ca_topic_score_codex":0.05239977,"about_ca_topic_score_gemma":0.05480394,"teacher_disagreement_score":0.05239977,"about_ca_system_score_codex":0.0009236443,"about_ca_system_score_gemma":0.0012626811,"threshold_uncertainty_score":0.104189515},"labels":[],"label_agreement":null},{"id":"W4220736037","doi":"10.21203/rs.3.rs-1398923/v1","title":"Infodemiology of autoimmune encephalitis: An analysis of online search behavior using Google Trends","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Autoimmune encephalitis; Encephalitis; Computer science; Data science; Information retrieval; Virology; Medicine; Virus","score_opus":0.1726091040423019,"score_gpt":0.5106030218578026,"score_spread":0.3379939178155007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220736037","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":[0.98775315,0.0011627303,0.0002660904,0.0001914747,0.000009371618,0.000057473313,0.008632742,0.000024558713,0.0019025044],"genre_scores_gemma":[0.9931156,0.00056751847,0.0005691472,0.00004244584,0.000020250232,0.000046393976,0.0053360197,0.0000076208175,0.0002950695],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985864,0.00038265303,0.00037260336,0.00017901864,0.00035179107,0.00012753319],"domain_scores_gemma":[0.98897505,0.0042330516,0.005086226,0.00030905212,0.0010192804,0.0003773547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014338149,0.00022086923,0.00035186452,0.009362491,0.00021923479,0.0014390456,0.0003382597,0.00031266082,0.0011328458],"category_scores_gemma":[0.00869769,0.000112418005,0.0007334876,0.011177663,0.00021476483,0.001613989,0.0006150564,0.00032034927,0.00034678364],"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":[0.000076120305,0.000038453727,0.9887898,0.0002171398,0.00012335823,0.00009183472,0.00045443542,0.0001986805,0.00010905831,0.00017010065,0.00067877874,0.00905214],"study_design_scores_gemma":[0.000004869531,0.00007816211,0.99186665,0.000120230754,0.000083469226,0.0004438294,0.0019010946,0.0030106334,0.00021198516,0.00013512971,0.0021301778,0.000013812494],"about_ca_topic_score_codex":0.011662345,"about_ca_topic_score_gemma":0.014523836,"teacher_disagreement_score":0.011662345,"about_ca_system_score_codex":0.00056323735,"about_ca_system_score_gemma":0.00060275587,"threshold_uncertainty_score":0.023188889},"labels":[],"label_agreement":null},{"id":"W4220739098","doi":"10.1109/iccrd54409.2022.9730439","title":"Research on the Relationship between Mobile Network Data and COVID-19 Cases","year":2022,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Computer science; Medicine","score_opus":0.44093453919641606,"score_gpt":0.49010157769463647,"score_spread":0.049167038498220406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220739098","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":[0.91757303,0.0134542715,0.021313973,0.019530965,0.00070390065,0.0002817996,0.016810318,0.00020332311,0.010128381],"genre_scores_gemma":[0.97857624,0.0033388876,0.007676673,0.0007700878,0.00038613367,0.00012108602,0.008114194,0.00002415525,0.0009926455],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9908805,0.0048562814,0.0008261499,0.0014102057,0.0015849263,0.00044184888],"domain_scores_gemma":[0.879274,0.09563507,0.013143233,0.0036459416,0.00687538,0.0014263593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007913522,0.00062773435,0.00051026425,0.0058970535,0.0006908719,0.002153328,0.0010812723,0.0011357724,0.0018867799],"category_scores_gemma":[0.07525073,0.0004519202,0.00105402,0.008077916,0.0008201689,0.0028116982,0.0008916967,0.0022239983,0.0005989344],"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":[0.000043542765,0.000073399104,0.9768594,0.00012807515,0.0002123859,0.00009629965,0.00015097145,0.0026361896,0.00008620094,0.0010903775,0.002183853,0.016439475],"study_design_scores_gemma":[0.000031052743,0.00024414092,0.83950996,0.0006971309,0.0005555898,0.001160732,0.0032282877,0.12672767,0.0013930517,0.008068495,0.018292475,0.00009133073],"about_ca_topic_score_codex":0.017323473,"about_ca_topic_score_gemma":0.014747433,"teacher_disagreement_score":0.017323473,"about_ca_system_score_codex":0.0017357823,"about_ca_system_score_gemma":0.0011003101,"threshold_uncertainty_score":0.041851163},"labels":[],"label_agreement":null},{"id":"W4220761027","doi":"10.1038/d41586-022-00792-2","title":"Lessons from the COVID data wizards","year":2022,"lang":"en","type":"article","venue":"Nature","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"World Federation of Science Journalists","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; Data science; 2019-20 coronavirus outbreak; Computer science; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); World Wide Web; Medicine; Virology","score_opus":0.04642288869653496,"score_gpt":0.3623665615561117,"score_spread":0.31594367285957675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220761027","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01358515,0.007020864,0.05044916,0.7884656,0.012024245,0.00022543827,0.008647629,0.013602612,0.10597926],"genre_scores_gemma":[0.26951122,0.02270302,0.27235588,0.1819926,0.008878049,0.001060087,0.034333084,0.022463573,0.18670253],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98302436,0.006098502,0.0013098947,0.0016413137,0.006663087,0.0012628679],"domain_scores_gemma":[0.92445385,0.031741396,0.0016681486,0.014822891,0.01967874,0.0076350286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.035768628,0.0013242434,0.0006936028,0.0023356336,0.0034521287,0.015157632,0.006066405,0.0071607814,0.025121264],"category_scores_gemma":[0.102291636,0.0013794486,0.0015208661,0.0032448405,0.004376388,0.029930953,0.008438777,0.013407255,0.0155241415],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019966403,0.00014254995,0.0041694753,0.0002603824,0.00003686484,0.00044381715,0.0018734292,0.0020058604,0.000457864,0.06720642,0.8273103,0.09589336],"study_design_scores_gemma":[0.00004873863,0.000052165396,0.00068520673,0.00033737594,0.000015945425,0.00023773812,0.0011216836,0.0016341364,0.00060535286,0.019535976,0.9756699,0.000055824843],"about_ca_topic_score_codex":0.039004453,"about_ca_topic_score_gemma":0.039951067,"teacher_disagreement_score":0.039004453,"about_ca_system_score_codex":0.0040696794,"about_ca_system_score_gemma":0.008401132,"threshold_uncertainty_score":0.18916482},"labels":[],"label_agreement":null},{"id":"W4220782249","doi":"10.1002/9781118786352.wbieg2043","title":"GeoAI and Its Implications","year":2022,"lang":"en","type":"other","venue":"International Encyclopedia of Geography","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Geospatial analysis; Data science; Geographic information system; Transferability; Big data; Geomatics; Computer science; Software; Inclusion (mineral); Geography; Predictive power; Cartography; Sociology; Data mining; Social science; Machine learning; Epistemology","score_opus":0.007423577779617358,"score_gpt":0.2680215817836383,"score_spread":0.2605980040040209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220782249","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022290507,0.03944897,0.003184878,0.42075872,0.010231962,0.000014682341,0.00052011915,0.000092218375,0.5235194],"genre_scores_gemma":[0.32246542,0.11059312,0.0049208226,0.18137585,0.025091683,0.00016672809,0.0012676785,0.0006077271,0.353511],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99793917,0.00089787785,0.0000711958,0.00033929633,0.00050483673,0.00024763544],"domain_scores_gemma":[0.99674904,0.0015054737,0.00013254663,0.00036979854,0.00079304026,0.00045008663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032486182,0.0004757266,0.0004042444,0.0020572785,0.0035062083,0.008247468,0.0011534084,0.0041302633,0.04426812],"category_scores_gemma":[0.0100669665,0.00015547215,0.00032743817,0.002419172,0.012462176,0.009421018,0.0051037124,0.006071203,0.0071049877],"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":[0.000007217677,0.0000045941747,0.00016802397,0.00003891104,0.000001629268,0.000053476568,0.000416071,0.00009206234,0.000013021528,0.89454496,0.088864684,0.015795385],"study_design_scores_gemma":[0.0000020046148,0.0000028827967,0.00021470641,0.00021041869,0.0000013810352,0.00009789467,0.00071427727,0.00007792379,0.000022865002,0.19281597,0.8058344,0.0000052972],"about_ca_topic_score_codex":0.013673707,"about_ca_topic_score_gemma":0.012325508,"teacher_disagreement_score":0.04426812,"about_ca_system_score_codex":0.005286035,"about_ca_system_score_gemma":0.004354821,"threshold_uncertainty_score":0.14809167},"labels":[],"label_agreement":null},{"id":"W4220988972","doi":"10.24095/hpcdp.42.3.02","title":"Development and formative evaluation of the Canadian Armed Forces Surveillance and Outbreak Management System (CAF SOMS): applications for COVID-19 and beyond","year":2022,"lang":"en","type":"article","venue":"Health Promotion and Chronic Disease Prevention in Canada","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Canadian Armed Forces; Department of National Defence","funders":"Canadian Armed Forces","keywords":"Formative assessment; Coronavirus disease 2019 (COVID-19); Outbreak; Pandemic; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Business; Public health; Computer science; Process management; Medicine; Disease; Infectious disease (medical specialty); Virology; Nursing; Psychology","score_opus":0.036248920719967846,"score_gpt":0.3348322868155888,"score_spread":0.298583366095621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220988972","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":[0.7568094,0.0009006435,0.07851704,0.005751783,0.0008626272,0.040805645,0.045932703,0.01968657,0.0507335],"genre_scores_gemma":[0.62406653,0.0010040972,0.29547566,0.0008776872,0.00012428121,0.012882577,0.049969483,0.0017030564,0.013896541],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9858401,0.0062087975,0.0010335027,0.0011671666,0.0045541865,0.0011963291],"domain_scores_gemma":[0.9079059,0.032024577,0.0016619798,0.005325385,0.048243534,0.004838614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039538108,0.0014367015,0.00060945976,0.0029796162,0.0028598418,0.0034714642,0.002693909,0.0007970838,0.0039015473],"category_scores_gemma":[0.08751493,0.0006095172,0.0008271103,0.0030833636,0.0012787945,0.0023975177,0.002728359,0.0019395918,0.0007997185],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0034830137,0.006672129,0.10491234,0.0037499547,0.00043492002,0.0007878998,0.026112443,0.020137237,0.018411813,0.005982233,0.12601429,0.6833017],"study_design_scores_gemma":[0.003241127,0.009145716,0.35227886,0.0035050001,0.00079351413,0.00040477052,0.031989343,0.1142058,0.055878576,0.003644363,0.42360228,0.0013105682],"about_ca_topic_score_codex":0.74769413,"about_ca_topic_score_gemma":0.7754414,"teacher_disagreement_score":0.98092294,"about_ca_system_score_codex":0.019077044,"about_ca_system_score_gemma":0.045856252,"threshold_uncertainty_score":0.5075835},"labels":[],"label_agreement":null},{"id":"W4221001452","doi":"10.2196/29819","title":"Searching for HIV and AIDS Health Information in South Africa, 2004-2019: Analysis of Google and Wikipedia Search Trends","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pandemic; Medicine; Public health; Developing country; Demography; Environmental health; Gerontology; Economic growth; Disease; Sociology; Coronavirus disease 2019 (COVID-19); Nursing; Infectious disease (medical specialty); Pathology","score_opus":0.04715650768730088,"score_gpt":0.40407842924826193,"score_spread":0.35692192156096103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221001452","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":[0.94933385,0.002662961,0.00008172895,0.00052404637,0.000018890874,0.00004750497,0.045325436,0.000033314638,0.0019722492],"genre_scores_gemma":[0.98221785,0.0015650959,0.00018625888,0.000058029374,0.000015918285,0.000054211712,0.015146337,0.000016180069,0.00074007426],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990808,0.00015218288,0.00020937064,0.00014433126,0.0002381852,0.00017517277],"domain_scores_gemma":[0.99166954,0.0024734973,0.0035771306,0.00016298566,0.0015263144,0.00059052615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012901115,0.0003490963,0.00051592424,0.013871773,0.00053370354,0.002004242,0.0006037928,0.0004602355,0.0021134878],"category_scores_gemma":[0.009385161,0.00021457265,0.0008689272,0.022549015,0.0004507616,0.0022744741,0.0012261056,0.00053462613,0.00064833433],"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":[0.00019645477,0.000035162255,0.98305327,0.0009962866,0.00019660807,0.00042293593,0.0029090883,0.0003602906,0.00026085216,0.00025435406,0.002682413,0.008632205],"study_design_scores_gemma":[0.0000035272367,0.000036785474,0.9894963,0.00016158234,0.000058449965,0.0003947373,0.0058571273,0.0007002162,0.00013783223,0.000036467824,0.0031031517,0.000013819017],"about_ca_topic_score_codex":0.09536275,"about_ca_topic_score_gemma":0.087423705,"teacher_disagreement_score":0.09536275,"about_ca_system_score_codex":0.0015900059,"about_ca_system_score_gemma":0.0016005336,"threshold_uncertainty_score":0.18961537},"labels":[],"label_agreement":null},{"id":"W4221004914","doi":"10.2196/35073","title":"Investigating Health Context Using a Spatial Data Analytical Tool: Development of a Geospatial Big Data Ecosystem","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"U.S. National Library of Medicine","keywords":"Geospatial analysis; Data science; Computer science; Geographic information system; Big data; Context (archaeology); Spatial data infrastructure; Spatial contextual awareness; Spatial epidemiology; Health geography; Spatial analysis; Knowledge management; Geography; Data mining; Health care; Cartography; Remote sensing; Health policy","score_opus":0.1512190503759598,"score_gpt":0.3730215576694672,"score_spread":0.2218025072935074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221004914","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":[0.013611258,0.0013520909,0.94175684,0.017137757,0.000523429,0.0014278188,0.0050034355,0.005259682,0.013927636],"genre_scores_gemma":[0.041728795,0.0006956441,0.95245606,0.00063372584,0.000102945756,0.0005119376,0.0028023564,0.00030217296,0.00076646183],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9909627,0.004041205,0.0011147812,0.0011933738,0.0023695824,0.00031832542],"domain_scores_gemma":[0.9714669,0.0124178035,0.0016200225,0.0048391004,0.007431777,0.0022244009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01946783,0.0011094332,0.00086435,0.009454787,0.0024288173,0.011934444,0.0035884532,0.0015997245,0.0030268822],"category_scores_gemma":[0.039285235,0.00096602325,0.0022821191,0.009028522,0.0029637797,0.019043202,0.011471268,0.0031285642,0.0012353848],"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":[0.00017279728,0.00033124423,0.041934367,0.002549898,0.00055378885,0.0016558557,0.016291745,0.017818524,0.0050903377,0.38629416,0.061510943,0.46579632],"study_design_scores_gemma":[0.00006842368,0.00019235392,0.010073618,0.0031373708,0.0003562558,0.0010258205,0.018080542,0.13293472,0.005609463,0.38662636,0.4416414,0.00025373945],"about_ca_topic_score_codex":0.0062679183,"about_ca_topic_score_gemma":0.0067766802,"teacher_disagreement_score":0.01946783,"about_ca_system_score_codex":0.0022784295,"about_ca_system_score_gemma":0.007663035,"threshold_uncertainty_score":0.10295695},"labels":[],"label_agreement":null},{"id":"W4223584058","doi":"10.1186/s12911-022-01831-8","title":"The use of electronic health records to inform cancer surveillance efforts: a scoping review and test of indicators for public health surveillance of cancer prevention and control","year":2022,"lang":"en","type":"review","venue":"BMC Medical Informatics and Decision Making","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":28,"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":"Centers for Disease Control and Prevention; National Center for Chronic Disease Prevention and Health Promotion; York University; New York State Department of Health","keywords":"Health informatics; Public health; Public health surveillance; Medicine; Health records; Test (biology); Environmental health; Cancer prevention; Cancer; Family medicine; Health care; Nursing; Political science","score_opus":0.11274301898840985,"score_gpt":0.44831639391058586,"score_spread":0.335573374922176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223584058","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":[0.017326405,0.922015,0.021873422,0.010901873,0.0015991823,0.015343609,0.004081963,0.00013746564,0.0067210747],"genre_scores_gemma":[0.18113261,0.71214354,0.068031676,0.0032908802,0.0005114549,0.030050194,0.0043381513,0.00007537919,0.00042616407],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.6167563,0.19044875,0.12750944,0.009210957,0.053735062,0.0023394406],"domain_scores_gemma":[0.31218594,0.5213936,0.06385892,0.018540084,0.08279522,0.0012262112],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.3248535,0.0019310546,0.005688957,0.048507366,0.0022747037,0.009851103,0.0041268915,0.0035448526,0.0017680201],"category_scores_gemma":[0.6296799,0.0020340995,0.011895031,0.047479883,0.003814945,0.010944268,0.006025703,0.0029665444,0.00031156372],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003655812,0.00010364043,0.030444983,0.63196516,0.016147757,0.00016777415,0.00594436,0.001374132,0.00021043693,0.007293584,0.0047488934,0.30123374],"study_design_scores_gemma":[0.000104339124,0.0001760732,0.009363841,0.951386,0.018541772,0.00011575797,0.0016134573,0.0009801763,0.00035635754,0.0016357958,0.01566933,0.0000571378],"about_ca_topic_score_codex":0.019688664,"about_ca_topic_score_gemma":0.026369954,"teacher_disagreement_score":0.3248535,"about_ca_system_score_codex":0.013758739,"about_ca_system_score_gemma":0.054344352,"threshold_uncertainty_score":0.8325758},"labels":[],"label_agreement":null},{"id":"W4224220726","doi":"10.2196/32386","title":"Integrating Google Trends Search Engine Query Data Into Adult Emergency Department Volume Forecasting: Infodemiology Study","year":2022,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Web search query; Emergency department; Baseline (sea); Medicine; Computer science; Health care; Mean absolute error; Volume (thermodynamics); Medical emergency; Emergency medicine; Information retrieval; Data mining; Mean squared error; Search engine; Statistics; Mathematics","score_opus":0.08298358006252113,"score_gpt":0.382464308479162,"score_spread":0.29948072841664086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224220726","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":[0.9962447,0.00044252077,0.00089725904,0.00024334814,0.000016751483,0.00006303172,0.0014269137,0.000041390907,0.00062410644],"genre_scores_gemma":[0.9953499,0.00028303865,0.0013463106,0.00006816718,0.00005949958,0.00004245088,0.0026481408,0.000011582493,0.00019103632],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984268,0.00076427683,0.00013275126,0.00027567023,0.00029643122,0.00010409337],"domain_scores_gemma":[0.9908879,0.005392416,0.001308119,0.0007645177,0.0013006806,0.0003464171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039033846,0.0006463687,0.00049415836,0.0021337892,0.00022764922,0.0009236201,0.000781791,0.0006108365,0.0008434834],"category_scores_gemma":[0.013375822,0.00026070792,0.0015307895,0.0028615461,0.0002978772,0.0016141867,0.00063301105,0.0006988298,0.0003385465],"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":[0.00037775812,0.0005215608,0.96728826,0.00014257441,0.00039768906,0.00015200782,0.00022399728,0.007644658,0.00016092137,0.0003122748,0.0015682311,0.02121011],"study_design_scores_gemma":[0.000094201314,0.0011763592,0.7770814,0.00012472698,0.0005696794,0.00041938882,0.000979948,0.21498258,0.0008948762,0.00048734096,0.0031408975,0.000048566537],"about_ca_topic_score_codex":0.053446338,"about_ca_topic_score_gemma":0.030291164,"teacher_disagreement_score":0.053446338,"about_ca_system_score_codex":0.0013238519,"about_ca_system_score_gemma":0.001433165,"threshold_uncertainty_score":0.10627049},"labels":[],"label_agreement":null},{"id":"W4224231470","doi":"10.2196/38695","title":"Authors’ Response to Peer Reviews of “Google Trends as a Predictive Tool for COVID-19 Vaccinations in Italy: Retrospective Infodemiological Analysis”","year":2022,"lang":"en","type":"article","venue":"JMIRx Med","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Vaccination; Mass vaccination; Virology; Medicine; Data science; Computer science; Outbreak; Internal medicine","score_opus":0.04767542709799373,"score_gpt":0.4134635204293434,"score_spread":0.3657880933313497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224231470","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005843531,0.003447307,0.00077827036,0.6405876,0.34598213,0.000132864,0.003018704,0.00038261732,0.0050860825],"genre_scores_gemma":[0.015169268,0.0085037,0.0020807586,0.56024534,0.3474127,0.00059715396,0.002913487,0.0009121415,0.06216544],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9794923,0.0052145035,0.0030887234,0.0015230243,0.00978488,0.0008965976],"domain_scores_gemma":[0.73708963,0.056058165,0.01279692,0.005144853,0.18109114,0.0078193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017659867,0.000817042,0.0016359299,0.002563586,0.0021235177,0.006130702,0.0018071703,0.007220513,0.057054847],"category_scores_gemma":[0.26264375,0.000543464,0.0014465434,0.0020473571,0.0014189685,0.002927098,0.0029954424,0.008704618,0.037160832],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023569786,0.000003759488,0.00016439674,0.00015687925,0.000010470846,0.00003355307,0.000044290733,0.000009895094,0.000024605704,0.00010598664,0.9960068,0.0034157876],"study_design_scores_gemma":[0.000033562104,0.00002409539,0.0011221166,0.0007802905,0.000030611787,0.00017159354,0.00033568568,0.0001685049,0.00016484797,0.00036617182,0.9967624,0.000040061725],"about_ca_topic_score_codex":0.0038112411,"about_ca_topic_score_gemma":0.0058140364,"teacher_disagreement_score":0.057054847,"about_ca_system_score_codex":0.0024294748,"about_ca_system_score_gemma":0.007140297,"threshold_uncertainty_score":0.19086748},"labels":[],"label_agreement":null},{"id":"W4224251926","doi":"10.2196/38724","title":"Peer Review of “Google Trends as a Predictive Tool for COVID-19 Vaccinations in Italy: Retrospective Infodemiological Analysis”","year":2022,"lang":"en","type":"article","venue":"JMIRx Med","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Vaccination; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Virology; Medicine; Internal medicine; Outbreak","score_opus":0.037528542538483436,"score_gpt":0.3969654696747506,"score_spread":0.3594369271362672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224251926","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015315227,0.013101774,0.012282999,0.40165007,0.42971802,0.0034872761,0.04642445,0.0044662384,0.07355395],"genre_scores_gemma":[0.19852455,0.023024878,0.022335691,0.0763645,0.2447002,0.004877105,0.093634054,0.0092052175,0.3273338],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.97789586,0.006765987,0.0022806944,0.0014962417,0.010725804,0.0008354559],"domain_scores_gemma":[0.6258598,0.04909338,0.010215636,0.01591603,0.28908607,0.009829052],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.026490003,0.0007809518,0.0017914625,0.0072179358,0.0025301906,0.0048398254,0.0018381701,0.0022341253,0.12445545],"category_scores_gemma":[0.24809547,0.00038589028,0.0014220454,0.004196075,0.0018197006,0.0028527335,0.003462048,0.0019070139,0.051471334],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007917428,0.000013644226,0.0010397292,0.0005848765,0.00003390651,0.00006912207,0.00008884146,0.000026729746,0.00012097877,0.00027560926,0.97864974,0.019017652],"study_design_scores_gemma":[0.000084893574,0.000081787235,0.006652478,0.001687599,0.0000843211,0.00017833608,0.0003926347,0.00066092337,0.00069702644,0.0007585759,0.988675,0.000046465477],"about_ca_topic_score_codex":0.004767234,"about_ca_topic_score_gemma":0.0055774227,"teacher_disagreement_score":0.97351,"about_ca_system_score_codex":0.0025058198,"about_ca_system_score_gemma":0.009339486,"threshold_uncertainty_score":0.41634494},"labels":[],"label_agreement":null},{"id":"W4225250067","doi":"10.2196/35356","title":"Google Trends as a Predictive Tool for COVID-19 Vaccinations in Italy: Retrospective Infodemiological Analysis","year":2022,"lang":"en","type":"article","venue":"JMIRx Med","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Vaccination; Statistics; Confounding; Medicine; Demography; Econometrics; Mathematics; Internal medicine; Virology","score_opus":0.026302210244839534,"score_gpt":0.36858681084941636,"score_spread":0.3422846006045768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225250067","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":[0.9798655,0.00116961,0.0006597054,0.00020284836,0.000021049194,0.00017385339,0.014412616,0.000057718236,0.0034370865],"genre_scores_gemma":[0.9906255,0.00040227332,0.000511309,0.000044757864,0.0000387849,0.00014748484,0.007605851,0.000026037435,0.0005980608],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9973423,0.0007770105,0.00041512473,0.0004887676,0.00064319995,0.00033351593],"domain_scores_gemma":[0.98190033,0.008282714,0.005564942,0.0015836746,0.002028553,0.0006397965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044399956,0.00042950007,0.00059897953,0.006183448,0.000330921,0.0016461313,0.00044179944,0.00047626838,0.002566121],"category_scores_gemma":[0.019034162,0.0002799869,0.0014640884,0.0064200065,0.0005170484,0.001292783,0.00088731566,0.0007699497,0.0010644159],"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":[0.00015841046,0.000058951933,0.99278826,0.00016166245,0.000104464554,0.00008631977,0.00045008684,0.00017215428,0.00006566266,0.00011152831,0.0009617936,0.004880729],"study_design_scores_gemma":[0.0000055738437,0.00012394054,0.995204,0.00008317689,0.00015538897,0.0002074986,0.00067506905,0.0015753576,0.00014432124,0.000055335044,0.0017574601,0.000012929232],"about_ca_topic_score_codex":0.02122289,"about_ca_topic_score_gemma":0.011890868,"teacher_disagreement_score":0.02122289,"about_ca_system_score_codex":0.0007471766,"about_ca_system_score_gemma":0.0010829619,"threshold_uncertainty_score":0.042198718},"labels":[],"label_agreement":null},{"id":"W4225887541","doi":"10.1007/s11069-021-05081-1","title":"Early detection of emergency events from social media: a new text clustering approach","year":2022,"lang":"en","type":"article","venue":"Natural Hazards","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":62,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"GreenField Specialty Alcolhols (Canada)","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Cluster analysis; Event (particle physics); Computer science; Social media; Big data; Data mining; Emergency management; Similarity (geometry); Natural disaster; Data science; Artificial intelligence; World Wide Web; Geography","score_opus":0.019196304291842377,"score_gpt":0.27475480021835974,"score_spread":0.25555849592651736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225887541","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":[0.13289341,0.003375045,0.8313997,0.0015123312,0.00077994535,0.0019226347,0.016863795,0.0063192295,0.0049339775],"genre_scores_gemma":[0.47277877,0.0011758636,0.48796517,0.00049239997,0.001168708,0.0011407859,0.026994394,0.0003210808,0.007962761],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99802065,0.00030082962,0.0002528487,0.00065597444,0.0005724291,0.00019734741],"domain_scores_gemma":[0.9967997,0.001474603,0.0003029109,0.00027838684,0.00094974466,0.00019465345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001410992,0.0018824115,0.0018488633,0.0128417015,0.0011044055,0.001934518,0.0020816093,0.001655739,0.0015951989],"category_scores_gemma":[0.004231037,0.00041886474,0.002288243,0.0052497545,0.00047536514,0.0014134295,0.001662697,0.0012844414,0.0019591239],"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":[0.0011341817,0.0026223005,0.0451016,0.0011836668,0.00132975,0.00086762506,0.00061035383,0.051829755,0.045206483,0.003742497,0.029276986,0.8170948],"study_design_scores_gemma":[0.00006030572,0.00031502885,0.023273092,0.00008221766,0.00046340568,0.00049371447,0.00039229254,0.9489441,0.010331379,0.007442335,0.008090074,0.0001119732],"about_ca_topic_score_codex":0.010047065,"about_ca_topic_score_gemma":0.014461635,"teacher_disagreement_score":0.0128417015,"about_ca_system_score_codex":0.0007773545,"about_ca_system_score_gemma":0.0015691462,"threshold_uncertainty_score":0.019977212},"labels":[],"label_agreement":null},{"id":"W4226078050","doi":"10.1097/pec.0000000000002442","title":"Seasonal Trends in Pediatric Respiratory Illnesses: Using Google Trends to Inform Precision Outreach.","year":2022,"lang":"en","type":"article","venue":"PubMed","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Croup; Bronchiolitis; Medicine; Outreach; Incidence (geometry); Epidemiology; Public health; Environmental health; Pediatrics; Family medicine; Pathology; Internal medicine; Respiratory system","score_opus":0.04784489616450874,"score_gpt":0.2991696639892926,"score_spread":0.25132476782478386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226078050","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":[0.5615159,0.029918907,0.011581,0.013067219,0.0006849571,0.0015272617,0.33776978,0.0021279715,0.041806914],"genre_scores_gemma":[0.9106957,0.008012283,0.017692098,0.0011371465,0.0004679977,0.0005531109,0.059861675,0.00017037413,0.0014097147],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9964012,0.00096237124,0.00073017064,0.00064970844,0.0010047453,0.00025179863],"domain_scores_gemma":[0.9706123,0.012776488,0.008626474,0.0017852688,0.005028904,0.0011706036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005173699,0.0004824085,0.0006604081,0.015266704,0.00049903226,0.0027771231,0.000876813,0.00061545044,0.002081764],"category_scores_gemma":[0.045280542,0.00032278034,0.000789354,0.017167382,0.00040471734,0.0036870823,0.0019746996,0.00072807673,0.00073757477],"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":[0.0002566108,0.000055351633,0.8184188,0.0038152947,0.00046099906,0.0002789042,0.003980839,0.0013080101,0.00034397253,0.0019051285,0.03728545,0.13189062],"study_design_scores_gemma":[0.00005080358,0.00015831979,0.9183179,0.00207659,0.0004047691,0.0003916734,0.006419227,0.009641108,0.0007729593,0.0029111928,0.05874258,0.00011289621],"about_ca_topic_score_codex":0.16437629,"about_ca_topic_score_gemma":0.23510705,"teacher_disagreement_score":0.16437629,"about_ca_system_score_codex":0.0024610877,"about_ca_system_score_gemma":0.0039303415,"threshold_uncertainty_score":0.32683903},"labels":[],"label_agreement":null},{"id":"W4226149220","doi":"10.2196/38665","title":"Peer Review of “Google Trends as a Predictive Tool for COVID-19 Vaccinations in Italy: Retrospective Infodemiological Analysis”","year":2022,"lang":"en","type":"article","venue":"JMIRx Med","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Medicine; Virology; Data science; Computer science; Outbreak; Internal medicine; Infectious disease (medical specialty)","score_opus":0.037528542538483436,"score_gpt":0.3969654696747506,"score_spread":0.3594369271362672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226149220","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09170727,0.017794162,0.010443747,0.39748436,0.18520154,0.006695759,0.17659388,0.003739775,0.11033948],"genre_scores_gemma":[0.5116722,0.01603572,0.01852763,0.07792498,0.06983387,0.005926205,0.13484628,0.005172187,0.16006099],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9777109,0.007638715,0.0036759032,0.0015608456,0.00838744,0.001026205],"domain_scores_gemma":[0.66557914,0.06732737,0.017438782,0.0174739,0.2236484,0.008532409],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.028440448,0.0004885349,0.0012988404,0.0046631657,0.0017752859,0.004022201,0.0016019173,0.0020809567,0.04881999],"category_scores_gemma":[0.26917607,0.00037361734,0.0016332811,0.0029845298,0.0013741154,0.0023057153,0.0024650192,0.0016475086,0.020476243],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000390874,0.000042519277,0.013728494,0.002295134,0.00024956695,0.00025129173,0.0003612357,0.000073828225,0.0003354155,0.00061301235,0.9411175,0.040541142],"study_design_scores_gemma":[0.00033456733,0.0001834152,0.05076201,0.004875501,0.0005102876,0.0004983318,0.0009106199,0.0012834116,0.0017053074,0.0011247293,0.93771404,0.00009780558],"about_ca_topic_score_codex":0.0073302207,"about_ca_topic_score_gemma":0.008260864,"teacher_disagreement_score":0.9715595,"about_ca_system_score_codex":0.0018558211,"about_ca_system_score_gemma":0.008983578,"threshold_uncertainty_score":0.16331911},"labels":[],"label_agreement":null},{"id":"W4226200319","doi":"10.34105/j.kmel.2021.13.022","title":"Automated thematic analysis of health information technology (HIT) related incident reports","year":2021,"lang":"en","type":"article","venue":"Knowledge Management & E-Learning An International Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Thematic analysis; Computer science; Thematic map; Key (lock); Process (computing); Data science; Patient safety; Artificial intelligence; Natural language processing; Information retrieval; Qualitative research; Health care; Computer security","score_opus":0.010770398996092138,"score_gpt":0.32515768488400437,"score_spread":0.31438728588791226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226200319","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":[0.16349469,0.0005101559,0.8117905,0.0016535897,0.00020907406,0.0033144052,0.007040924,0.0023906732,0.009596042],"genre_scores_gemma":[0.24856761,0.00032018163,0.7413172,0.00017535606,0.000083154475,0.002218135,0.0052049225,0.00027352627,0.0018398376],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9815593,0.009695853,0.002092648,0.0017403107,0.0045091542,0.00040280263],"domain_scores_gemma":[0.86986613,0.09658384,0.008992425,0.0076857414,0.0164144,0.0004574251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022420194,0.000961266,0.0007649088,0.015332234,0.0016851199,0.0030200707,0.0019261487,0.0006232749,0.0022595162],"category_scores_gemma":[0.074751966,0.0004803577,0.0011654883,0.009427322,0.0011904814,0.0036616758,0.0027985293,0.0009774468,0.00058660377],"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":[0.0006819938,0.0005451956,0.03584309,0.0039422866,0.00036058464,0.0012900871,0.076339304,0.011405317,0.032772023,0.024708012,0.01274191,0.7993702],"study_design_scores_gemma":[0.0003365716,0.00073650834,0.08688579,0.0027136898,0.00062799675,0.0024719762,0.12909113,0.37814587,0.10342064,0.1408998,0.15398765,0.00068235165],"about_ca_topic_score_codex":0.0037193368,"about_ca_topic_score_gemma":0.0038318536,"teacher_disagreement_score":0.022420194,"about_ca_system_score_codex":0.0023002503,"about_ca_system_score_gemma":0.0035932714,"threshold_uncertainty_score":0.118570745},"labels":[],"label_agreement":null},{"id":"W4226236871","doi":"10.2196/38726","title":"Peer Review of “Google Trends as a Predictive Tool for COVID-19 Vaccinations in Italy: Retrospective Infodemiological Analysis”","year":2022,"lang":"en","type":"article","venue":"JMIRx Med","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Vaccination; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Virology; Medicine; Internal medicine; Outbreak","score_opus":0.037528542538483436,"score_gpt":0.3969654696747506,"score_spread":0.3594369271362672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226236871","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016211823,0.025684118,0.012689819,0.55625117,0.28640664,0.003792366,0.036918245,0.0022832395,0.05976262],"genre_scores_gemma":[0.27678946,0.050091155,0.028741103,0.16261531,0.255456,0.0050121476,0.048918456,0.006675113,0.16570126],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.96257055,0.014845558,0.0049591805,0.0020547323,0.014522353,0.0010476975],"domain_scores_gemma":[0.6091936,0.07686797,0.020336632,0.020851145,0.26443154,0.008319132],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.040981818,0.00060899986,0.0017329999,0.005811209,0.0022263646,0.004866849,0.0018631092,0.0023989906,0.06306228],"category_scores_gemma":[0.34602278,0.00044288504,0.0014957692,0.0037975663,0.0022508993,0.0028351678,0.00313214,0.002331555,0.026763903],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015518624,0.000019046745,0.002968861,0.0016240858,0.00010471275,0.00015573122,0.00023565629,0.00003589341,0.00016062948,0.00051942794,0.96078485,0.033235837],"study_design_scores_gemma":[0.00009862595,0.000113697184,0.009470629,0.0045911283,0.00017122814,0.00044365873,0.0005882682,0.00060959166,0.0007530285,0.0010059898,0.9820858,0.00006820547],"about_ca_topic_score_codex":0.0042635575,"about_ca_topic_score_gemma":0.004646534,"teacher_disagreement_score":0.9590182,"about_ca_system_score_codex":0.0024795437,"about_ca_system_score_gemma":0.010135371,"threshold_uncertainty_score":0.21673512},"labels":[],"label_agreement":null},{"id":"W4229069564","doi":"10.2196/35266","title":"Enhancing COVID-19 Epidemic Forecasting Accuracy by Combining Real-time and Historical Data From Multiple Internet-Based Sources: Analysis of Social Media Data, Online News Articles, and Search Queries","year":2022,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"City University of Hong Kong","keywords":"The Internet; Computer science; Social media; Data science; Big data; Data mining; World Wide Web","score_opus":0.16032406123076146,"score_gpt":0.369769992526947,"score_spread":0.20944593129618552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229069564","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":[0.9726333,0.003256686,0.008829113,0.0016572042,0.00017602001,0.00013512021,0.010755195,0.0003067049,0.0022505918],"genre_scores_gemma":[0.9691622,0.001065731,0.010682858,0.0001290196,0.0002477777,0.00007158696,0.018132174,0.00002764093,0.00048098652],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982413,0.0005673715,0.00026414948,0.0003470875,0.00038054725,0.0001995105],"domain_scores_gemma":[0.9860487,0.010163047,0.0014431692,0.0007176971,0.0013161746,0.00031125563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004572699,0.0012339583,0.0008755161,0.01151574,0.00041994403,0.0018707623,0.00065353396,0.0010640884,0.00059427036],"category_scores_gemma":[0.014291671,0.0003198305,0.0017192829,0.0061842827,0.00037027505,0.0028459367,0.001075783,0.0011726589,0.0004411251],"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":[0.00055568904,0.00072078244,0.83440024,0.00064968364,0.0009410723,0.0007855192,0.0005248657,0.0526873,0.0018818027,0.00088055065,0.0065992884,0.09937312],"study_design_scores_gemma":[0.00005152015,0.0002133692,0.3671526,0.00018736368,0.00048718206,0.0004245361,0.0012501011,0.6209393,0.001971925,0.0021207542,0.0051000547,0.00010124482],"about_ca_topic_score_codex":0.016675442,"about_ca_topic_score_gemma":0.014238512,"teacher_disagreement_score":0.016675442,"about_ca_system_score_codex":0.00076683925,"about_ca_system_score_gemma":0.0007795165,"threshold_uncertainty_score":0.033156753},"labels":[],"label_agreement":null},{"id":"W4231447289","doi":"10.3402/ehtj.v3i0.7096","title":"The landscape of international event-based biosurveillance","year":2010,"lang":"en","type":"article","venue":"Emerging Health Threats Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Agency of Canada","funders":"","keywords":"Infectious disease (medical specialty); Disease surveillance; Warning system; Event (particle physics); Global health; Data science; Computer security; The Internet; Public health; Computer science; Disease; Internet privacy; Medicine; World Wide Web","score_opus":0.01551501567414431,"score_gpt":0.3466348546947698,"score_spread":0.3311198390206255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231447289","genre_codex":"commentary","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":[0.015843682,0.13563427,0.30256596,0.32698658,0.009051532,0.00040364935,0.0017831614,0.002153396,0.20557772],"genre_scores_gemma":[0.5116769,0.14554675,0.2748263,0.030485084,0.014759626,0.000837896,0.0030670394,0.0007643043,0.01803615],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99258727,0.0038848072,0.0005117937,0.0008717394,0.0017819144,0.0003625646],"domain_scores_gemma":[0.96911275,0.015737064,0.0019245086,0.0047464767,0.006447142,0.0020321668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030610356,0.0009029016,0.0011877307,0.0036990372,0.0012553888,0.010754037,0.0024176545,0.0030633395,0.00547162],"category_scores_gemma":[0.029652366,0.00044358894,0.00081877457,0.0025565007,0.005047744,0.0100330645,0.006331168,0.0053252224,0.0013925908],"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":[0.000108124696,0.000091780435,0.005608997,0.000737107,0.000102822545,0.00016187846,0.0014039071,0.0045767054,0.0011919725,0.2407385,0.04917696,0.69610125],"study_design_scores_gemma":[0.000021011976,0.00016017718,0.004705748,0.0016411843,0.000050886705,0.00068553264,0.0017488295,0.009226421,0.0014479879,0.1951681,0.7850402,0.000103964216],"about_ca_topic_score_codex":0.0022993607,"about_ca_topic_score_gemma":0.0010964538,"teacher_disagreement_score":0.030610356,"about_ca_system_score_codex":0.0024490594,"about_ca_system_score_gemma":0.0028526045,"threshold_uncertainty_score":0.16188496},"labels":[],"label_agreement":null},{"id":"W4231604135","doi":"10.32920/14639427","title":"Exploring the Boundaries of Web Map Services: The Example of the Online Injury Atlas for Ontario","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"St. Michael's Hospital; University of Toronto; Toronto Metropolitan University","funders":"Natural Resources Canada","keywords":"Geospatial analysis; License; Business; Geocoding; Confidentiality; Government (linguistics); Public health; Private sector; Data science; Computer security; Geography; Computer science; Cartography; Political science; Medicine","score_opus":0.08813342284313312,"score_gpt":0.29192873723981394,"score_spread":0.2037953143966808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231604135","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":[0.5110652,0.0017904863,0.06534279,0.033851035,0.00067144516,0.0007985503,0.05434285,0.0105772475,0.3215604],"genre_scores_gemma":[0.87034273,0.0015191904,0.05808906,0.00069577585,0.00009923494,0.00012225608,0.023731507,0.00082782307,0.044572335],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99898,0.00017133505,0.00004111512,0.000116424584,0.00044320012,0.0002478512],"domain_scores_gemma":[0.9973672,0.0005432648,0.00010047649,0.00042536797,0.0010528244,0.0005109279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010488863,0.00030625678,0.0002253044,0.0013533346,0.003728086,0.0032727614,0.00074847933,0.0007928987,0.004325386],"category_scores_gemma":[0.0033766748,0.00021524476,0.00040796574,0.0052234572,0.0019729468,0.001798101,0.002074404,0.0005084058,0.0008460359],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008454857,0.00016958339,0.13660093,0.0012515003,0.00013766649,0.006243262,0.05626169,0.027767716,0.014609436,0.16464135,0.32859513,0.26287633],"study_design_scores_gemma":[0.000043710013,0.000043100245,0.06069921,0.00017930301,0.0000488495,0.00038604517,0.01549699,0.02760128,0.0035383273,0.009671926,0.882173,0.000118295255],"about_ca_topic_score_codex":0.9549215,"about_ca_topic_score_gemma":0.97463995,"teacher_disagreement_score":0.045078516,"about_ca_system_score_codex":0.016746283,"about_ca_system_score_gemma":0.018983023,"threshold_uncertainty_score":0.12150335},"labels":[],"label_agreement":null},{"id":"W4232504079","doi":"10.2196/preprints.13347","title":"Lifestyle Disease Surveillance Using Population Search Behavior: Feasibility Study (Preprint)","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Overfitting; Generalizability theory; Covariate; Multivariate statistics; Computer science; Population; Proxy (statistics); Baseline (sea); Statistics; Medicine; Econometrics; Artificial intelligence; Machine learning; Environmental health; Mathematics","score_opus":0.07353565567406418,"score_gpt":0.3828333291003248,"score_spread":0.30929767342626063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232504079","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":[0.97890764,0.00010178036,0.0041230093,0.001217352,0.00007859699,0.008311181,0.004469004,0.00014546962,0.002645914],"genre_scores_gemma":[0.96096814,0.00023078422,0.015573636,0.00086608034,0.00020965483,0.015845472,0.004815767,0.00003730559,0.0014531239],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9927395,0.005573468,0.0004046137,0.00043709867,0.00050793285,0.00033743298],"domain_scores_gemma":[0.9639794,0.02454865,0.002130761,0.0032019094,0.0040260474,0.002113225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02097812,0.00055785803,0.000494287,0.0009793548,0.0008397087,0.00095851644,0.0011016363,0.0011075157,0.0055258516],"category_scores_gemma":[0.04918781,0.00047650342,0.0010477713,0.0010244059,0.00057179533,0.002137308,0.001232833,0.0009982958,0.002865128],"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":[0.0076587186,0.045661002,0.7822223,0.0016957613,0.00048373258,0.000858901,0.006186323,0.0036808946,0.0016005713,0.0013953829,0.017977366,0.13057901],"study_design_scores_gemma":[0.0047460194,0.09052797,0.85176134,0.00041694174,0.000527933,0.00064008473,0.0109780915,0.025477866,0.0024627121,0.0016147603,0.010616395,0.00022993845],"about_ca_topic_score_codex":0.016589956,"about_ca_topic_score_gemma":0.009655537,"teacher_disagreement_score":0.02097812,"about_ca_system_score_codex":0.0010169588,"about_ca_system_score_gemma":0.0042682686,"threshold_uncertainty_score":0.11094427},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"split"},{"id":"W4233794349","doi":"10.32920/14639427.v1","title":"Exploring the Boundaries of Web Map Services: The Example of the Online Injury Atlas for Ontario","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"St. Michael's Hospital; University of Toronto; Toronto Metropolitan University","funders":"Natural Resources Canada","keywords":"Geospatial analysis; License; Business; Confidentiality; Government (linguistics); Geocoding; Public health; Data science; Private sector; Computer security; Computer science; Geography; Cartography; Political science; Medicine","score_opus":0.08813342284313312,"score_gpt":0.29192873723981394,"score_spread":0.2037953143966808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233794349","genre_codex":"empirical","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5110652,0.0017904863,0.06534279,0.033851035,0.00067144516,0.0007985503,0.05434285,0.0105772475,0.3215604],"genre_scores_gemma":[0.87034273,0.0015191904,0.05808906,0.00069577585,0.00009923494,0.00012225608,0.023731507,0.00082782307,0.044572335],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99898,0.00017133505,0.00004111512,0.000116424584,0.00044320012,0.0002478512],"domain_scores_gemma":[0.9973672,0.0005432648,0.00010047649,0.00042536797,0.0010528244,0.0005109279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010488863,0.00030625678,0.0002253044,0.0013533346,0.003728086,0.0032727614,0.00074847933,0.0007928987,0.004325386],"category_scores_gemma":[0.0033766748,0.00021524476,0.00040796574,0.0052234572,0.0019729468,0.001798101,0.002074404,0.0005084058,0.0008460359],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008454857,0.00016958339,0.13660093,0.0012515003,0.00013766649,0.006243262,0.05626169,0.027767716,0.014609436,0.16464135,0.32859513,0.26287633],"study_design_scores_gemma":[0.000043710013,0.000043100245,0.06069921,0.00017930301,0.0000488495,0.00038604517,0.01549699,0.02760128,0.0035383273,0.009671926,0.882173,0.000118295255],"about_ca_topic_score_codex":0.9549215,"about_ca_topic_score_gemma":0.97463995,"teacher_disagreement_score":0.045078516,"about_ca_system_score_codex":0.016746283,"about_ca_system_score_gemma":0.018983023,"threshold_uncertainty_score":0.12150335},"labels":[],"label_agreement":null},{"id":"W4234046897","doi":"10.4018/978-1-5225-6915-2.ch069","title":"Notifiable Disease Databases for Client Management and Surveillance","year":2018,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Notifiable disease; Infectious disease (medical specialty); Database; Public health; Data collection; Disease; Medicine; Environmental health; Computer science; Pathology","score_opus":0.027091621932455297,"score_gpt":0.28459629497926414,"score_spread":0.25750467304680885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234046897","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001952087,0.019576702,0.3519366,0.015623489,0.003087378,0.0015535915,0.08956659,0.014788489,0.50191516],"genre_scores_gemma":[0.016884735,0.03148655,0.50876,0.0068397042,0.0018314727,0.0018952244,0.1484734,0.0075849188,0.27624404],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976688,0.0004693669,0.00034123176,0.00030571807,0.0011297252,0.000085133855],"domain_scores_gemma":[0.99465746,0.0021459092,0.00029190304,0.0012155555,0.0014419929,0.00024713363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003816725,0.0008294612,0.0007318729,0.004343547,0.0010324888,0.006864584,0.0029825454,0.0015002049,0.053221308],"category_scores_gemma":[0.011930694,0.00068507827,0.0006461174,0.011096506,0.0006600894,0.007230354,0.0034715761,0.001910878,0.04997301],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019617379,0.00003193624,0.0004985279,0.00048991997,0.000012452285,0.000072352945,0.00047761854,0.000837756,0.00045466388,0.12791021,0.5628469,0.30634815],"study_design_scores_gemma":[0.0000035479177,0.000002250967,0.00021585455,0.00021906167,0.0000023087819,0.00007385351,0.00008322878,0.00046976708,0.00016051262,0.015766973,0.9829952,0.000007413383],"about_ca_topic_score_codex":0.0051482446,"about_ca_topic_score_gemma":0.0054656733,"teacher_disagreement_score":0.053221308,"about_ca_system_score_codex":0.0021866872,"about_ca_system_score_gemma":0.0035057336,"threshold_uncertainty_score":0.17804301},"labels":[],"label_agreement":null},{"id":"W4234804330","doi":"10.23889/ijpds.v5i4.1389","title":"Developing a Data Integrated COVID-19 Tracking System for Decision-Making and Public Use","year":2020,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Dashboard; Computer science; Government (linguistics); Data science; Benchmarking; World Wide Web; Business; Marketing","score_opus":0.3197080733320304,"score_gpt":0.47189515171622576,"score_spread":0.15218707838419537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234804330","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":[0.023007961,0.0006541768,0.5359651,0.0059206206,0.0013106484,0.0043001766,0.078413084,0.32158348,0.028844876],"genre_scores_gemma":[0.107611835,0.00068875385,0.72598565,0.0017419581,0.0003644112,0.0029078256,0.13813336,0.009167502,0.013398726],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99149346,0.002547773,0.0011652625,0.0020529686,0.002310687,0.00042980863],"domain_scores_gemma":[0.9755284,0.008295403,0.0012441336,0.0058523184,0.0068138125,0.0022660014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016882805,0.0014739324,0.001408252,0.0045286296,0.0018693144,0.007533977,0.0035176089,0.0022076608,0.026440589],"category_scores_gemma":[0.03894015,0.001189412,0.0017868164,0.004124685,0.00072036457,0.009032034,0.0073863054,0.0027914576,0.014719641],"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":[0.0019648394,0.0010061268,0.03626776,0.0017994185,0.0005115969,0.0008714299,0.0037544344,0.021947369,0.010040156,0.022735167,0.46471328,0.4343883],"study_design_scores_gemma":[0.0005012672,0.00048626206,0.013771974,0.00112852,0.00021090808,0.0003550636,0.0015978204,0.28940362,0.017459225,0.036562167,0.6380044,0.00051875],"about_ca_topic_score_codex":0.014408477,"about_ca_topic_score_gemma":0.010029177,"teacher_disagreement_score":0.026440589,"about_ca_system_score_codex":0.0024505323,"about_ca_system_score_gemma":0.0052414914,"threshold_uncertainty_score":0.08928591},"labels":[],"label_agreement":null},{"id":"W4235435351","doi":"10.2196/preprints.13130","title":"Using Google Trends to track walk-in clinic and emergency department searches over time and across provinces in Canada (Preprint)","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Emergency department; Primary care; Geography; Medicine; Demography; Medical emergency; Family medicine; Sociology; Nursing","score_opus":0.04434157894135854,"score_gpt":0.35849989355785494,"score_spread":0.3141583146164964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235435351","genre_codex":"dataset","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":[0.14539969,0.0035500648,0.0009699689,0.002447784,0.00013637274,0.0002900445,0.82949907,0.00070150587,0.017005468],"genre_scores_gemma":[0.5662573,0.005845986,0.008539952,0.0011609457,0.00010785139,0.00054813985,0.40432695,0.0003320596,0.012880779],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983352,0.00009494869,0.00024753343,0.00027447785,0.0007060656,0.00034180962],"domain_scores_gemma":[0.9897494,0.0015311001,0.0012637198,0.00031519207,0.006307263,0.00083331205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013124701,0.00045004778,0.0005622732,0.011214171,0.0014315997,0.00233775,0.0010534507,0.00040412208,0.005321112],"category_scores_gemma":[0.012481256,0.0003586108,0.0012333796,0.029864248,0.00041374896,0.0012338967,0.0011744609,0.0005214208,0.0015254363],"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":[0.00038093765,0.000048892023,0.66748,0.0028877358,0.00040534404,0.00024459165,0.002441761,0.0016947193,0.0004094224,0.0015409535,0.23661588,0.08584976],"study_design_scores_gemma":[0.00005027952,0.000041210373,0.9260662,0.000844525,0.00021262123,0.00011072836,0.003574057,0.0030740064,0.00043128975,0.00026317965,0.065245725,0.00008609884],"about_ca_topic_score_codex":0.99540776,"about_ca_topic_score_gemma":0.99643046,"teacher_disagreement_score":0.020507703,"about_ca_system_score_codex":0.020507703,"about_ca_system_score_gemma":0.049507227,"threshold_uncertainty_score":0.14879447},"labels":[],"label_agreement":null},{"id":"W4235610789","doi":"10.2196/29795","title":"Correction: Early Detection of Dengue Fever Outbreaks Using a Surveillance App (Mozzify): Cross-sectional Mixed Methods Usability Study","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dengue fever; Usability; Outbreak; Environmental health; Cross-sectional study; Computer science; Medicine; Virology; Human–computer interaction","score_opus":0.05318538789939024,"score_gpt":0.3991770548444749,"score_spread":0.34599166694508465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235610789","genre_codex":"editorial","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":[0.00043971097,0.0010197407,0.0013299229,0.16695844,0.82185006,0.00018483443,0.005445507,0.00079156965,0.0019802493],"genre_scores_gemma":[0.037310895,0.009667925,0.013572259,0.47028282,0.3186282,0.0034693624,0.006050304,0.0038575614,0.1371607],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.97999644,0.006446432,0.004586956,0.0020831604,0.0057287817,0.0011581946],"domain_scores_gemma":[0.7502253,0.08261526,0.009900084,0.010912178,0.14029951,0.0060477033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019601766,0.002552993,0.0027435056,0.0037495028,0.0031615726,0.005406569,0.0047992333,0.009940425,0.06854014],"category_scores_gemma":[0.34532776,0.0016224417,0.002670367,0.0029096208,0.003136186,0.0034792174,0.0032310283,0.012044005,0.030144373],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006242339,0.000010837983,0.00021673238,0.00048382345,0.000033854794,0.00010383069,0.000119446624,0.000032615924,0.000030082549,0.00024233323,0.9943508,0.0043132356],"study_design_scores_gemma":[0.00060130516,0.00011542802,0.0061261775,0.0048855445,0.0003066127,0.000680468,0.00094976125,0.0008513361,0.00057898357,0.0016247228,0.9830382,0.00024146125],"about_ca_topic_score_codex":0.046195716,"about_ca_topic_score_gemma":0.042672165,"teacher_disagreement_score":0.06854014,"about_ca_system_score_codex":0.006715959,"about_ca_system_score_gemma":0.014415095,"threshold_uncertainty_score":0.22928959},"labels":[],"label_agreement":null},{"id":"W4235808886","doi":"10.1017/s0021900200008949","title":"Continuous, Discrete, and Conditional Scan Statistics","year":2012,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scan statistic; Mathematics; Bernoulli trial; Conditional probability distribution; Discrete phase-type distribution; Sufficient statistic; Statistics; Statistic; Markov chain; Discrete-time stochastic process; Bernoulli's principle; Normal-gamma distribution; Conditional expectation; Applied mathematics; Markov property; Stochastic process; Asymptotic distribution; Markov model; Continuous-time stochastic process; Estimator","score_opus":0.012753173341647458,"score_gpt":0.2705407487586908,"score_spread":0.25778757541704334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235808886","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":[0.04647078,0.0009041207,0.9456495,0.0008599616,0.00007256397,0.00011159487,0.00066476426,0.0003409723,0.0049257553],"genre_scores_gemma":[0.80447227,0.0017087852,0.18468037,0.00033260757,0.00028889618,0.00070955936,0.0012403174,0.0002460234,0.0063212155],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99338704,0.0027751168,0.00030966476,0.0012687213,0.0017968392,0.0004627264],"domain_scores_gemma":[0.911084,0.073074244,0.004800905,0.0061362362,0.003604821,0.001299861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014335945,0.0007012469,0.001339688,0.0030802153,0.00050078076,0.003469352,0.00303611,0.0018244844,0.0065163653],"category_scores_gemma":[0.09640113,0.00069757295,0.001196893,0.0032086677,0.0058480143,0.0058500827,0.0025624144,0.003961143,0.000716919],"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":[0.0001012749,0.000035470977,0.0056834677,0.00017771029,0.000050035025,0.00032820003,0.00022486033,0.087968275,0.00093180063,0.87758386,0.0013448303,0.025570285],"study_design_scores_gemma":[0.00002548286,0.0000679364,0.00247989,0.00010121298,0.000029177485,0.0006095768,0.000101639744,0.46513954,0.000873146,0.5281759,0.0023456095,0.0000508909],"about_ca_topic_score_codex":0.0018311625,"about_ca_topic_score_gemma":0.0014360489,"teacher_disagreement_score":0.014335945,"about_ca_system_score_codex":0.0021037653,"about_ca_system_score_gemma":0.0017490117,"threshold_uncertainty_score":0.07581663},"labels":[],"label_agreement":null},{"id":"W4236195259","doi":"10.32920/14639586","title":"Online Map Design for Public Health Decision-Makers","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"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; Canadian Institutes of Health Research","keywords":"Variety (cybernetics); Public health; Space (punctuation); Set (abstract data type); Data science; Computer science; Component (thermodynamics); Geography; Decision support system; Data mining; Medicine; Artificial intelligence","score_opus":0.122216932610592,"score_gpt":0.3755598826093082,"score_spread":0.2533429499987162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236195259","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":[0.009816417,0.0002107299,0.9513945,0.0014281998,0.00022966736,0.00068614545,0.002371504,0.010782068,0.02308076],"genre_scores_gemma":[0.1089182,0.0003434313,0.8774391,0.00024240614,0.00008708539,0.0015788502,0.0027330006,0.0015602835,0.0070977393],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957681,0.0027606685,0.0003361771,0.00035695508,0.0006164538,0.00016169013],"domain_scores_gemma":[0.984011,0.010971584,0.00048789824,0.00216603,0.0018431167,0.0005204692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074575334,0.0011165019,0.0010644626,0.0020059252,0.001130572,0.008020158,0.0022493054,0.0016469534,0.044860244],"category_scores_gemma":[0.024500774,0.00088603573,0.001156544,0.0019352767,0.00093371404,0.0055437675,0.003961954,0.001341512,0.008404334],"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":[0.001144643,0.0010676585,0.0045668595,0.0021529545,0.0002494544,0.000953761,0.00962663,0.099870086,0.008513996,0.2098482,0.08178304,0.5802228],"study_design_scores_gemma":[0.00047803717,0.00031769232,0.0010832998,0.00051781815,0.00014291437,0.00027026222,0.0049713077,0.3193475,0.007736224,0.29919654,0.3658115,0.00012686956],"about_ca_topic_score_codex":0.0019488009,"about_ca_topic_score_gemma":0.002655357,"teacher_disagreement_score":0.044860244,"about_ca_system_score_codex":0.0010617125,"about_ca_system_score_gemma":0.0017777035,"threshold_uncertainty_score":0.15007246},"labels":[],"label_agreement":null},{"id":"W4236228090","doi":"10.1007/978-1-4939-7131-2_101238","title":"Spatial Scan Statistic","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","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 Calgary","funders":"","keywords":"Scan statistic; Statistic; Geography; Computer science; Statistics; Mathematics","score_opus":0.01854078549799776,"score_gpt":0.2699747934407618,"score_spread":0.25143400794276405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236228090","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":[0.0011186549,0.0048101023,0.81791013,0.006089267,0.0019611071,0.000074175055,0.0042392286,0.0038922874,0.15990514],"genre_scores_gemma":[0.0677688,0.014641952,0.58828145,0.005237262,0.0053167674,0.00051235926,0.012252514,0.0067720558,0.29921684],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99864,0.00043678694,0.00006322704,0.0002758306,0.00053208065,0.000052163377],"domain_scores_gemma":[0.9958216,0.0027597544,0.00009385697,0.00053741265,0.0007137229,0.00007363413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021571438,0.0010186707,0.0008875051,0.0017285412,0.0003286112,0.0019886247,0.0012425144,0.00085140497,0.043224394],"category_scores_gemma":[0.013254915,0.0004571274,0.00059666904,0.0023978667,0.0010616345,0.0020325775,0.0012085732,0.00220719,0.01892329],"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":[0.000031905085,0.000021102074,0.0006478345,0.00014441652,0.00003263532,0.00006482779,0.000054108496,0.00974352,0.0003403282,0.31375805,0.3253601,0.34980118],"study_design_scores_gemma":[0.000014287977,0.000020175774,0.0004915654,0.000117151285,0.000026880527,0.0002922177,0.000066813336,0.050106853,0.0008976919,0.53798175,0.40995345,0.00003117042],"about_ca_topic_score_codex":0.0035520687,"about_ca_topic_score_gemma":0.004080275,"teacher_disagreement_score":0.043224394,"about_ca_system_score_codex":0.0010377553,"about_ca_system_score_gemma":0.0019465125,"threshold_uncertainty_score":0.14460003},"labels":[],"label_agreement":null},{"id":"W4237327036","doi":"10.2196/preprints.9749","title":"Methodology for establishing demographic, development and environmental geospatial data surveillance platform in the context of a resource constrained environment: lessons from SOMAARTH DDESS, Palwal (India) (Preprint)","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Geospatial analysis; Sanitation; Geographic information system; Context (archaeology); Geography; Data quality; Population; Business; Environmental planning; Environmental resource management; Engineering; Environmental health; Cartography; Service (business); Environmental engineering","score_opus":0.09306518464090531,"score_gpt":0.31450308683725037,"score_spread":0.22143790219634507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237327036","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":[0.009710077,0.00042302863,0.97142136,0.0026944925,0.0002884706,0.0044064894,0.0025584523,0.0007220628,0.0077755954],"genre_scores_gemma":[0.018359568,0.00028638434,0.9762042,0.00016124811,0.000026875863,0.0020801339,0.0009297968,0.00008545124,0.0018662123],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9893112,0.0070248065,0.0012147487,0.0008485761,0.0013897408,0.00021096307],"domain_scores_gemma":[0.98361236,0.0064922837,0.0012617472,0.0022037078,0.005944646,0.00048524153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017292144,0.0008437925,0.00061182264,0.0053957207,0.0013565511,0.004330102,0.0024327522,0.0010013402,0.005154833],"category_scores_gemma":[0.025705157,0.0007918047,0.0010906382,0.0066337776,0.0017822388,0.0029550719,0.0039728396,0.0019335295,0.0022755738],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000931806,0.0003928711,0.049756467,0.0023102583,0.00019014788,0.002278374,0.025486648,0.016577903,0.0050694426,0.22104326,0.0459297,0.6308718],"study_design_scores_gemma":[0.00013342133,0.0004377342,0.060755678,0.0031448347,0.00017516925,0.002940656,0.0639837,0.099953905,0.008978105,0.23602398,0.5230225,0.00045030733],"about_ca_topic_score_codex":0.014040783,"about_ca_topic_score_gemma":0.01906489,"teacher_disagreement_score":0.017292144,"about_ca_system_score_codex":0.0024806384,"about_ca_system_score_gemma":0.010659212,"threshold_uncertainty_score":0.09145069},"labels":[],"label_agreement":null},{"id":"W4238359992","doi":"10.1109/bigdatase53435.2021.00011","title":"Spatial-Temporal Data Science of COVID-19 Data","year":2021,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Big data; Data science; Computer science; Spatial analysis; Variety (cybernetics); Spatial epidemiology; Coronavirus disease 2019 (COVID-19); Pandemic; Disease; Data mining; Data type; Epidemiology; Geography; Infectious disease (medical specialty); Artificial intelligence; Medicine; Remote sensing","score_opus":0.13691816431155385,"score_gpt":0.40366022044998184,"score_spread":0.266742056138428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238359992","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":[0.029051559,0.0027541986,0.8981508,0.0077351327,0.00070446276,0.0012062442,0.034609064,0.011088226,0.014700325],"genre_scores_gemma":[0.22559465,0.002920165,0.7205085,0.001998823,0.0005931968,0.0006261142,0.043428674,0.00086545484,0.0034644005],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9908949,0.0015199545,0.0011576246,0.0019966015,0.0041164635,0.00031447533],"domain_scores_gemma":[0.9786709,0.007343724,0.002249494,0.005485402,0.0055943816,0.0006561811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008573662,0.0009927583,0.0011485454,0.008131575,0.0015182508,0.006389595,0.002586718,0.0014448672,0.002423943],"category_scores_gemma":[0.032209065,0.0007517815,0.0026901723,0.011983163,0.001809335,0.00862389,0.0050629666,0.0024958313,0.0018710294],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057887385,0.00043924985,0.13468835,0.0026840626,0.000984469,0.0021816385,0.0028317617,0.08651649,0.009925982,0.31138483,0.07780728,0.36997706],"study_design_scores_gemma":[0.00006068606,0.00016756133,0.017333124,0.000549728,0.00021367524,0.0013869554,0.002270702,0.44790694,0.016935777,0.2540075,0.25900877,0.0001585867],"about_ca_topic_score_codex":0.007860886,"about_ca_topic_score_gemma":0.0072963648,"teacher_disagreement_score":0.008573662,"about_ca_system_score_codex":0.001767914,"about_ca_system_score_gemma":0.0047584753,"threshold_uncertainty_score":0.045342445},"labels":[],"label_agreement":null},{"id":"W4240064625","doi":"10.17975/sfj-2017-008","title":"What can big data tell us about past, current, and future patterns of infection?","year":2017,"lang":"en","type":"article","venue":"STEM Fellowship Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Current (fluid); Big data; Data science; History; Computer science; Data mining; Geology; Oceanography","score_opus":0.0669557529102045,"score_gpt":0.33293440877155395,"score_spread":0.26597865586134944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240064625","genre_codex":"commentary","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":[0.102381565,0.23465948,0.057033863,0.4368919,0.009467481,0.00044114538,0.1329776,0.0013428422,0.024804221],"genre_scores_gemma":[0.6062809,0.1594415,0.060910016,0.06963252,0.01805917,0.0006077555,0.08238699,0.00047346676,0.0022076904],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99223423,0.0034093575,0.0010885714,0.0010375824,0.0019150114,0.00031522184],"domain_scores_gemma":[0.90300715,0.064112514,0.010272938,0.009878502,0.00957084,0.0031580052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019260902,0.0013449179,0.002147926,0.0069862115,0.00091871514,0.008471114,0.002526906,0.0033339937,0.0028792433],"category_scores_gemma":[0.11159669,0.0010730717,0.0014805327,0.008261396,0.0021197794,0.0167147,0.0028621857,0.0053608725,0.0022029192],"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":[0.00077786925,0.0002619126,0.50019854,0.0064285,0.0032625343,0.0003276328,0.0016049516,0.0046004173,0.0007060925,0.02174526,0.1487633,0.31132302],"study_design_scores_gemma":[0.00020762914,0.0004172839,0.27851748,0.012057992,0.0018054242,0.0013465363,0.0068253353,0.020687921,0.0016978857,0.29533294,0.38053444,0.0005691184],"about_ca_topic_score_codex":0.008585631,"about_ca_topic_score_gemma":0.009508845,"teacher_disagreement_score":0.019260902,"about_ca_system_score_codex":0.0013743672,"about_ca_system_score_gemma":0.0025769607,"threshold_uncertainty_score":0.10186261},"labels":[],"label_agreement":null},{"id":"W4240816621","doi":"10.4018/978-1-5225-7113-1.ch039","title":"Notifiable Disease Databases for Client Management and Surveillance","year":2018,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Notifiable disease; Infectious disease (medical specialty); Database; Public health; Disease surveillance; Data collection; Disease; Environmental health; Medicine; Data science; Computer science; Pathology","score_opus":0.027091621932455297,"score_gpt":0.28459629497926414,"score_spread":0.25750467304680885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240816621","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001952087,0.019576702,0.3519366,0.015623489,0.003087378,0.0015535915,0.08956659,0.014788489,0.50191516],"genre_scores_gemma":[0.016884735,0.03148655,0.50876,0.0068397042,0.0018314727,0.0018952244,0.1484734,0.0075849188,0.27624404],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976688,0.0004693669,0.00034123176,0.00030571807,0.0011297252,0.000085133855],"domain_scores_gemma":[0.99465746,0.0021459092,0.00029190304,0.0012155555,0.0014419929,0.00024713363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003816725,0.0008294612,0.0007318729,0.004343547,0.0010324888,0.006864584,0.0029825454,0.0015002049,0.053221308],"category_scores_gemma":[0.011930694,0.00068507827,0.0006461174,0.011096506,0.0006600894,0.007230354,0.0034715761,0.001910878,0.04997301],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019617379,0.00003193624,0.0004985279,0.00048991997,0.000012452285,0.000072352945,0.00047761854,0.000837756,0.00045466388,0.12791021,0.5628469,0.30634815],"study_design_scores_gemma":[0.0000035479177,0.000002250967,0.00021585455,0.00021906167,0.0000023087819,0.00007385351,0.00008322878,0.00046976708,0.00016051262,0.015766973,0.9829952,0.000007413383],"about_ca_topic_score_codex":0.0051482446,"about_ca_topic_score_gemma":0.0054656733,"teacher_disagreement_score":0.053221308,"about_ca_system_score_codex":0.0021866872,"about_ca_system_score_gemma":0.0035057336,"threshold_uncertainty_score":0.17804301},"labels":[],"label_agreement":null},{"id":"W4241397209","doi":"10.1145/3357729","title":"Proceedings of the 9th International Conference on Digital Public Health","year":2019,"lang":"en","type":"paratext","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public health; Digital health; Library science; Political science; Solidarity; Public relations; Health care; Medicine; Computer science; Politics; Law","score_opus":0.060565120854093504,"score_gpt":0.32553737316681935,"score_spread":0.26497225231272586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241397209","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004488536,0.095433146,0.037756193,0.10405599,0.13770264,0.0007004444,0.0069333487,0.0058339373,0.6070957],"genre_scores_gemma":[0.037266873,0.075132005,0.026227595,0.030262703,0.03947058,0.0006715889,0.018931089,0.0017106778,0.7703269],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9956591,0.0013377946,0.00030826151,0.0006819756,0.0015212023,0.0004917626],"domain_scores_gemma":[0.9914231,0.0024619065,0.00032872945,0.001416368,0.0023036909,0.0020663154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004873137,0.0010746133,0.0011682459,0.0023233604,0.0015385828,0.012012776,0.0021556427,0.0033084906,0.24237493],"category_scores_gemma":[0.0127353985,0.0004574694,0.0009977592,0.0019701724,0.0019028309,0.009167279,0.006777412,0.0049248794,0.1456972],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008069628,0.00007267033,0.0005668952,0.00041822757,0.000033292137,0.0001243374,0.00027607905,0.00009751222,0.000718994,0.011388914,0.8215169,0.16470549],"study_design_scores_gemma":[0.0000046104433,0.000011770666,0.0003021245,0.00018605276,0.000007672638,0.00009246645,0.00014888635,0.0001139063,0.00011961598,0.0024309922,0.9965738,0.000008231595],"about_ca_topic_score_codex":0.0018495569,"about_ca_topic_score_gemma":0.002263076,"teacher_disagreement_score":0.24237493,"about_ca_system_score_codex":0.0016233982,"about_ca_system_score_gemma":0.0030468658,"threshold_uncertainty_score":0.8108249},"labels":[],"label_agreement":null},{"id":"W4241587302","doi":"10.2196/preprints.25422","title":"Global Public Interests and Dynamic Trends in Osteoporosis From 2004 to 2019: Infodemiology Study (Preprint)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Osteoporosis; Medicine; Demography; Public health; Gerontology; Life expectancy; Population; Family medicine; Environmental health; Internal medicine; Pathology","score_opus":0.031664827490726445,"score_gpt":0.3452777260080025,"score_spread":0.31361289851727603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241587302","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":[0.82577467,0.008682886,0.00014442309,0.0042916615,0.00013186705,0.00011265545,0.14992824,0.000059356702,0.010874163],"genre_scores_gemma":[0.9582404,0.0044172197,0.00018580843,0.0006467292,0.00019517008,0.0001509169,0.03331107,0.000030821226,0.0028217155],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991689,0.00014685078,0.00018624323,0.00011801851,0.00018165914,0.00019831539],"domain_scores_gemma":[0.99317217,0.0022114278,0.0028593699,0.00017624695,0.0009670627,0.00061371527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009502685,0.00017522987,0.0003835643,0.005177929,0.00032002755,0.0016590932,0.0003785417,0.0005786835,0.009299384],"category_scores_gemma":[0.00797946,0.0001583903,0.0012902042,0.012644401,0.00027705825,0.002406312,0.0009499798,0.00074982113,0.002067612],"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":[0.00033110206,0.000081478975,0.9569548,0.0014092672,0.00034346554,0.00021335353,0.0016566584,0.00011088699,0.00007580059,0.00066688564,0.0208496,0.01730679],"study_design_scores_gemma":[0.000013063185,0.000074680815,0.9877878,0.00037494852,0.0001759864,0.00020501231,0.0033733014,0.00021030319,0.000060555703,0.00007763162,0.007633118,0.000013604798],"about_ca_topic_score_codex":0.05946737,"about_ca_topic_score_gemma":0.054506462,"teacher_disagreement_score":0.05946737,"about_ca_system_score_codex":0.0012890532,"about_ca_system_score_gemma":0.0017860483,"threshold_uncertainty_score":0.1182425},"labels":[],"label_agreement":null},{"id":"W4243765514","doi":"10.2196/31416","title":"Peer Review of “The Exchange of Informational Support in Online Health Communities at the Onset of the COVID-19 Pandemic: Content Analysis”","year":2021,"lang":"en","type":"article","venue":"JMIRx Med","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Content (measure theory); Virology; Psychology; Medicine; Mathematics; Pathology; Disease; Infectious disease (medical specialty)","score_opus":0.19973352324686072,"score_gpt":0.41114220817645425,"score_spread":0.21140868492959353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243765514","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023083435,0.0029998675,0.011934014,0.51199967,0.29857853,0.0070013152,0.013824626,0.0031475225,0.12743104],"genre_scores_gemma":[0.2819605,0.0078119277,0.033742934,0.12607421,0.12118431,0.011667303,0.021344762,0.0076287747,0.3885853],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.93387395,0.027201049,0.005075646,0.002506567,0.029536668,0.0018060926],"domain_scores_gemma":[0.3223384,0.1423312,0.010634353,0.017464105,0.4959101,0.0113219],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04685527,0.00054866204,0.0011114167,0.006888969,0.0058324244,0.006604782,0.0021779942,0.003828435,0.07702627],"category_scores_gemma":[0.50075537,0.00041170538,0.0010418355,0.0038693596,0.0029097523,0.004574455,0.0050068884,0.0032489826,0.025890958],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000097331256,0.00004863647,0.00093476067,0.0009839104,0.000035173372,0.00012932807,0.0017654409,0.000050711278,0.0005999179,0.0010770358,0.9456415,0.048636228],"study_design_scores_gemma":[0.00008480493,0.000098944685,0.005858492,0.0017470465,0.0000629741,0.00012938029,0.0029151936,0.00082474825,0.0012224233,0.0017452978,0.98524,0.000070734895],"about_ca_topic_score_codex":0.0063865143,"about_ca_topic_score_gemma":0.011240517,"teacher_disagreement_score":0.9531447,"about_ca_system_score_codex":0.003872817,"about_ca_system_score_gemma":0.020985607,"threshold_uncertainty_score":0.25767857},"labels":[],"label_agreement":null},{"id":"W4244154685","doi":"10.32920/14639586.v1","title":"Online Map Design for Public Health Decision-Makers","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"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; Canadian Institutes of Health Research","keywords":"Variety (cybernetics); Public health; Space (punctuation); Set (abstract data type); Data science; Computer science; Component (thermodynamics); Geography; World Wide Web; Medicine; Artificial intelligence","score_opus":0.122216932610592,"score_gpt":0.3755598826093082,"score_spread":0.2533429499987162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244154685","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":[0.009816417,0.0002107299,0.9513945,0.0014281998,0.00022966736,0.00068614545,0.002371504,0.010782068,0.02308076],"genre_scores_gemma":[0.1089182,0.0003434313,0.8774391,0.00024240614,0.00008708539,0.0015788502,0.0027330006,0.0015602835,0.0070977393],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9957681,0.0027606685,0.0003361771,0.00035695508,0.0006164538,0.00016169013],"domain_scores_gemma":[0.984011,0.010971584,0.00048789824,0.00216603,0.0018431167,0.0005204692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074575334,0.0011165019,0.0010644626,0.0020059252,0.001130572,0.008020158,0.0022493054,0.0016469534,0.044860244],"category_scores_gemma":[0.024500774,0.00088603573,0.001156544,0.0019352767,0.00093371404,0.0055437675,0.003961954,0.001341512,0.008404334],"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":[0.001144643,0.0010676585,0.0045668595,0.0021529545,0.0002494544,0.000953761,0.00962663,0.099870086,0.008513996,0.2098482,0.08178304,0.5802228],"study_design_scores_gemma":[0.00047803717,0.00031769232,0.0010832998,0.00051781815,0.00014291437,0.00027026222,0.0049713077,0.3193475,0.007736224,0.29919654,0.3658115,0.00012686956],"about_ca_topic_score_codex":0.0019488009,"about_ca_topic_score_gemma":0.002655357,"teacher_disagreement_score":0.044860244,"about_ca_system_score_codex":0.0010617125,"about_ca_system_score_gemma":0.0017777035,"threshold_uncertainty_score":0.15007246},"labels":[],"label_agreement":null},{"id":"W4244210227","doi":"10.2196/preprints.27052","title":"Internet Interest in Colon Cancer Following the Death of Chadwick Boseman: Infoveillance Study (Preprint)","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Colorectal cancer; Demography; Medicine; Cancer; Mortality rate; Gerontology; Oncology; Internal medicine; Sociology","score_opus":0.0725554241117124,"score_gpt":0.3435846376222685,"score_spread":0.2710292135105561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244210227","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":[0.9955279,0.00008598337,0.00002436021,0.00044796657,0.000013780252,0.00001612532,0.0022568223,0.0000042976308,0.0016228344],"genre_scores_gemma":[0.9952297,0.0001743871,0.000040361585,0.00017993303,0.00003196661,0.000022080689,0.0029040773,0.000005569546,0.001411922],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99981624,0.000039531893,0.000014303842,0.000035902114,0.00005009357,0.00004398534],"domain_scores_gemma":[0.9980379,0.00046968117,0.0006863028,0.00010561051,0.0003125503,0.00038805962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045025698,0.00011139552,0.00013496497,0.00088712934,0.00043868157,0.00071165006,0.0003034825,0.0002878889,0.0027595821],"category_scores_gemma":[0.0037059903,0.000119025615,0.00023114495,0.0011252981,0.00020366648,0.0006358378,0.00046174318,0.0005708356,0.0006463998],"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":[0.00011848737,0.00014907765,0.98762286,0.000022294988,0.00003899303,0.00012319913,0.0010084016,0.000046143934,0.000101276026,0.00007447548,0.006023555,0.0046712877],"study_design_scores_gemma":[0.0000037084665,0.00004751973,0.9975305,0.000009690037,0.00001561092,0.000059200942,0.0013152591,0.00015556296,0.00004194418,0.000011389899,0.0008052014,0.0000044116937],"about_ca_topic_score_codex":0.11416808,"about_ca_topic_score_gemma":0.10521563,"teacher_disagreement_score":0.11416808,"about_ca_system_score_codex":0.0006030736,"about_ca_system_score_gemma":0.0004503398,"threshold_uncertainty_score":0.22700709},"labels":[],"label_agreement":null},{"id":"W4244440127","doi":"10.3138/carto.43.1.003","title":"Visualizing and Analysing Public-Health Data Using Value-by-Area Cartograms: Toward a New Synthetic Framework","year":2008,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Public health; Situated; Data science; Visualization; Computer science; Data mining; Artificial intelligence; Medicine","score_opus":0.1034883399732306,"score_gpt":0.37042510948294366,"score_spread":0.26693676950971307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244440127","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":[0.028543336,0.0010775386,0.95143765,0.0039807404,0.00008243449,0.00018399017,0.0015298373,0.0011378065,0.012026655],"genre_scores_gemma":[0.22832724,0.0009238952,0.76875025,0.00011276115,0.000052236854,0.00020811852,0.0009303988,0.0001977931,0.0004973346],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99629045,0.002626295,0.00021797992,0.00029650555,0.00046746692,0.00010136853],"domain_scores_gemma":[0.98428637,0.010824325,0.001079995,0.0019357657,0.0014882028,0.0003854143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007924341,0.00094993954,0.00072918186,0.01313796,0.0011531744,0.012203171,0.0017119641,0.00082895986,0.002692028],"category_scores_gemma":[0.025472924,0.0005860042,0.0011783952,0.010857223,0.005316324,0.00813449,0.0028971473,0.0012552518,0.00038357178],"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":[0.000052435487,0.0000440729,0.0081399195,0.00041976103,0.00009379605,0.00017315423,0.008697916,0.032433063,0.0009320459,0.852311,0.0042154943,0.09248739],"study_design_scores_gemma":[0.000032324315,0.00004725807,0.0049984828,0.00052739773,0.000082022525,0.00028598635,0.01263207,0.15252666,0.0012946143,0.7461987,0.08125828,0.00011617764],"about_ca_topic_score_codex":0.010689254,"about_ca_topic_score_gemma":0.009310263,"teacher_disagreement_score":0.01313796,"about_ca_system_score_codex":0.0021460413,"about_ca_system_score_gemma":0.0026694199,"threshold_uncertainty_score":0.041908383},"labels":[],"label_agreement":null},{"id":"W4247917985","doi":"10.12688/gatesopenres.12891.1","title":"Automated verbal autopsy classification: using one-against-all ensemble method and Naïve Bayes classifier","year":2018,"lang":"en","type":"preprint","venue":"Gates Open Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Centre for Global Health Research; St. Michael's Hospital; Toronto Metropolitan University","funders":"Bill and Melinda Gates Foundation","keywords":"Verbal autopsy; Concordance; Classifier (UML); Bayes' theorem; Medical diagnosis; Population; Artificial intelligence; Statistics; Naive Bayes classifier; Cause of death; Medicine; Machine learning; Computer science; Mathematics; Internal medicine; Bayesian probability; Pathology; Environmental health","score_opus":0.3803490711206243,"score_gpt":0.524560363374478,"score_spread":0.14421129225385365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247917985","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":[0.3606609,0.0036855221,0.6147294,0.0014148174,0.0012490442,0.0007708687,0.003107288,0.007758459,0.0066236295],"genre_scores_gemma":[0.74003255,0.00048875174,0.24728405,0.0005104728,0.00046539848,0.00029852302,0.0068780524,0.00020995286,0.0038322418],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99650806,0.0012105095,0.00039827256,0.00089813845,0.00065421674,0.00033074146],"domain_scores_gemma":[0.99504745,0.0023093647,0.00029488912,0.00051301526,0.0016473229,0.00018795794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00631636,0.0022006838,0.0021402938,0.0034122681,0.0011680751,0.0015151087,0.0018025691,0.0016338269,0.0018810754],"category_scores_gemma":[0.009251404,0.0003585679,0.0017186711,0.0013955542,0.00035708584,0.0010662656,0.0010777396,0.0015730642,0.0011520891],"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":[0.0008122555,0.0006168221,0.035813056,0.00020997445,0.00075311324,0.00031605273,0.00018476925,0.117493734,0.0041110595,0.001056316,0.012773309,0.82585955],"study_design_scores_gemma":[0.000044570304,0.00023333046,0.004779278,0.000057367924,0.00021476147,0.00014541206,0.00011833919,0.9861921,0.003678057,0.0023577267,0.0021369674,0.000042085183],"about_ca_topic_score_codex":0.012891752,"about_ca_topic_score_gemma":0.0124097625,"teacher_disagreement_score":0.012891752,"about_ca_system_score_codex":0.0007632934,"about_ca_system_score_gemma":0.0016746497,"threshold_uncertainty_score":0.03340453},"labels":[],"label_agreement":null},{"id":"W4248311009","doi":"10.2196/preprints.12974","title":"Seasonality Patterns of Internet Searches on Mental Health: Exploratory Infodemiology Study (Preprint)","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University; St. Joseph’s Healthcare Hamilton","funders":"","keywords":"Mental health; The Internet; Public health; Psychiatry; Anxiety; Population; Depression (economics); Psychology; Medicine; Geography; Demography; Environmental health; Computer science; World Wide Web; Sociology; Pathology","score_opus":0.10015318073813397,"score_gpt":0.38374342709411785,"score_spread":0.2835902463559839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248311009","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":[0.9927631,0.0000697528,0.00006405879,0.00011600786,0.0000029731366,0.000037606645,0.0061612725,0.000012453432,0.0007727086],"genre_scores_gemma":[0.9933262,0.00009188324,0.0001509895,0.000047061847,0.000011283011,0.000064262334,0.005453478,0.000012287826,0.00084248313],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999382,0.00011553717,0.00008902684,0.00011626291,0.00014072671,0.00015641842],"domain_scores_gemma":[0.9948472,0.001709529,0.001907101,0.0003181765,0.00073785614,0.00048007147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008089507,0.00015106355,0.00025002603,0.0026888254,0.0006730462,0.001059696,0.00042649722,0.00037267306,0.0038151443],"category_scores_gemma":[0.0052247336,0.00020698419,0.00059687183,0.0058685117,0.00041592357,0.0007501742,0.0007651278,0.00035554776,0.00075956597],"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":[0.00006276729,0.00004791506,0.99426925,0.000034873112,0.00004601105,0.00007655819,0.001827855,0.000039231334,0.0001341093,0.000058750164,0.0012949653,0.0021076635],"study_design_scores_gemma":[0.0000020929965,0.000018078246,0.99741334,0.0000068607283,0.000007957915,0.000038262307,0.0020144293,0.0001183632,0.000026157755,0.000008179382,0.00034280177,0.0000034419957],"about_ca_topic_score_codex":0.24035159,"about_ca_topic_score_gemma":0.27150255,"teacher_disagreement_score":0.24035159,"about_ca_system_score_codex":0.001399577,"about_ca_system_score_gemma":0.0019300892,"threshold_uncertainty_score":0.47790515},"labels":[],"label_agreement":null},{"id":"W4249012171","doi":"10.1007/978-1-4614-6170-8_100088","title":"Spatial Scan Statistic","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Scan statistic; Statistic; Computer science; Geography; Statistics; Mathematics","score_opus":0.01425209146112818,"score_gpt":0.2544697614197098,"score_spread":0.2402176699585816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249012171","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":[0.001074927,0.0048335567,0.79751766,0.005957349,0.0017833129,0.00007384686,0.0037252242,0.0036267566,0.18140747],"genre_scores_gemma":[0.061143473,0.01394412,0.5649045,0.0049385494,0.0045854934,0.00047963762,0.009955758,0.0059987474,0.33404973],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99876416,0.00040911415,0.000055483077,0.00023828897,0.00048545303,0.000047615635],"domain_scores_gemma":[0.9965018,0.0023127906,0.00007946258,0.00043580477,0.00060616306,0.00006392041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019719107,0.0009468366,0.0008403683,0.0017234752,0.00031819,0.0017730877,0.001171169,0.0008019823,0.043914337],"category_scores_gemma":[0.011378105,0.00045341538,0.0005459941,0.0023616517,0.001038125,0.0018215732,0.0011014264,0.0021191672,0.018197132],"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":[0.000027717908,0.000019026436,0.00057355716,0.00012210019,0.0000283825,0.00006150378,0.00005275453,0.008272434,0.00032738651,0.2927075,0.3374177,0.36038998],"study_design_scores_gemma":[0.000013901481,0.000019544605,0.00051858946,0.00011100973,0.000025432355,0.00030651147,0.0000697539,0.044158697,0.00086415303,0.5037553,0.4501255,0.000031673902],"about_ca_topic_score_codex":0.003932315,"about_ca_topic_score_gemma":0.0048410385,"teacher_disagreement_score":0.043914337,"about_ca_system_score_codex":0.0010262751,"about_ca_system_score_gemma":0.0019163232,"threshold_uncertainty_score":0.1469081},"labels":[],"label_agreement":null},{"id":"W4250063794","doi":"10.32920/14639652.v1","title":"Naive Bayes classifiers for verbal autopsies: comparison to physician-based classification for 21,000 child and adult deaths","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario; University of Toronto; Centre for Global Health Research; St. Michael's Hospital","funders":"Medical Research Council; University of Toronto; University of the Witwatersrand, Johannesburg","keywords":"Naive Bayes classifier; Medicine; Population; Bayes' theorem; Verbal autopsy; Cause of death; Statistics; Demography; Artificial intelligence; Computer science; Mathematics; Internal medicine; Environmental health; Disease","score_opus":0.0344629680575352,"score_gpt":0.329187281080795,"score_spread":0.2947243130232598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250063794","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":[0.89931107,0.012599028,0.05427839,0.0023283288,0.0012980377,0.000814005,0.016177395,0.002280168,0.01091346],"genre_scores_gemma":[0.9285378,0.0015223675,0.046384096,0.0004792206,0.0004958396,0.0002991208,0.020524485,0.00014256156,0.0016145308],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99007285,0.0048713293,0.0010384594,0.0017638116,0.0019031905,0.00035029792],"domain_scores_gemma":[0.96334755,0.027943606,0.0022255804,0.0019415634,0.003872526,0.00066918845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018958759,0.0012451783,0.0013516878,0.0043739765,0.000652383,0.0016898304,0.0014432322,0.0016692962,0.0025384366],"category_scores_gemma":[0.04853365,0.00032531764,0.0012872074,0.0018160846,0.0005073473,0.001731341,0.0011738513,0.0014641546,0.00122941],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0058004316,0.0008889699,0.4240238,0.001408984,0.0015607177,0.00022329623,0.00065052015,0.09976733,0.0013377244,0.0022840644,0.030533146,0.43152103],"study_design_scores_gemma":[0.0005524122,0.001658523,0.108969584,0.00091313024,0.0004353734,0.0006543207,0.0010989419,0.8656169,0.0024731345,0.008805654,0.00866139,0.00016069291],"about_ca_topic_score_codex":0.0130757615,"about_ca_topic_score_gemma":0.008232663,"teacher_disagreement_score":0.018958759,"about_ca_system_score_codex":0.0013403562,"about_ca_system_score_gemma":0.0016833856,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4251443514","doi":"10.22541/au.159569198.82432694/v2","title":"The value of hospital data for covid-19 pandemic surveillance and planning","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Health care; Medical emergency; Business; Computer science; Medicine; Virology; Economics; Economic growth","score_opus":0.08032945101860059,"score_gpt":0.3793894041256198,"score_spread":0.29905995310701916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251443514","genre_codex":"commentary","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":[0.20681572,0.018858047,0.23525254,0.25392428,0.0075595994,0.00089847913,0.15545729,0.0053539597,0.11588015],"genre_scores_gemma":[0.76133025,0.009303891,0.15631783,0.0030216246,0.0021076533,0.00020100965,0.062916756,0.0010196368,0.0037812819],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9887904,0.007574514,0.00059192564,0.00066672603,0.0020285668,0.00034784325],"domain_scores_gemma":[0.91193515,0.06036511,0.0032899412,0.012184851,0.009034397,0.003190492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013732605,0.00091192307,0.00094484544,0.004120848,0.00073824637,0.007337236,0.0012945099,0.0016715028,0.0050896113],"category_scores_gemma":[0.09509429,0.0007575005,0.00061538274,0.0052882098,0.0011230968,0.0071841697,0.0030369528,0.0026701537,0.0018329177],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007419509,0.0002512883,0.12812804,0.00093411334,0.0005916248,0.0005925723,0.00059786276,0.17129956,0.00098381,0.12202863,0.21451898,0.3593316],"study_design_scores_gemma":[0.00019472414,0.00017953476,0.03898768,0.0018796907,0.00018480135,0.00026032154,0.0031210016,0.378237,0.0032356258,0.38970208,0.1837529,0.00026454663],"about_ca_topic_score_codex":0.021235624,"about_ca_topic_score_gemma":0.012151009,"teacher_disagreement_score":0.021235624,"about_ca_system_score_codex":0.002798395,"about_ca_system_score_gemma":0.004506305,"threshold_uncertainty_score":0.072625756},"labels":[],"label_agreement":null},{"id":"W4251940844","doi":"10.4018/978-1-4666-2794-9.ch015","title":"Affordable System for Rapid Detection and Mitigation of Emerging Diseases","year":2012,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Disease surveillance; Notifiable disease; Data science; Mobile phone; Health informatics; Communicable disease; Computer science; Digitization; Computer security; Geography; Disease; Medicine; Public health; Telecommunications; Pathology","score_opus":0.012482082628382424,"score_gpt":0.2478759210664165,"score_spread":0.23539383843803408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251940844","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004380806,0.07474598,0.17120449,0.009050573,0.006757834,0.0007014064,0.007071999,0.009204456,0.71688247],"genre_scores_gemma":[0.02426491,0.051062167,0.12950645,0.0044589685,0.0022293823,0.00042820346,0.0077834874,0.0012117244,0.7790547],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994611,0.000063376625,0.000024420353,0.00010667816,0.00031102978,0.000033277593],"domain_scores_gemma":[0.99958974,0.0001342346,0.000024825435,0.00007957195,0.0001317258,0.000039976967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058585394,0.00084224856,0.0004916185,0.0016481236,0.00039524536,0.0026628342,0.0014803672,0.0016402754,0.066919744],"category_scores_gemma":[0.0009089815,0.00029564294,0.00046422277,0.001998345,0.00041702206,0.0030647088,0.0016148138,0.0010808645,0.042970337],"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":[0.000047216385,0.000050720657,0.00039745725,0.0008215259,0.000019032514,0.00013465727,0.00021840082,0.0007599861,0.006004872,0.03536319,0.35168597,0.60449696],"study_design_scores_gemma":[0.000006161788,0.00003498653,0.00046574735,0.00019166493,0.000007762563,0.00021437202,0.000054417505,0.00090493937,0.0013841355,0.005316816,0.9914076,0.000011497581],"about_ca_topic_score_codex":0.0011589803,"about_ca_topic_score_gemma":0.0015130644,"teacher_disagreement_score":0.066919744,"about_ca_system_score_codex":0.000688738,"about_ca_system_score_gemma":0.0007461111,"threshold_uncertainty_score":0.22386885},"labels":[],"label_agreement":null},{"id":"W4252876123","doi":"10.2196/preprints.32355","title":"Physical Activity, Sedentary Behavior, and Sleep on Twitter: Multicountry and Fully Labeled Public Data Set for Digital Public Health Surveillance Research (Preprint)","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Social media; Computer science; Metadata; Data set; Public health; Public health surveillance; Set (abstract data type); Data quality; Data science; World Wide Web; Medicine; Engineering; Artificial intelligence","score_opus":0.25700503669810304,"score_gpt":0.44358962938003205,"score_spread":0.186584592681929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252876123","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64788836,0.00060255354,0.0060462607,0.0048524817,0.00054405245,0.0004539031,0.33487177,0.0007192239,0.0040213745],"genre_scores_gemma":[0.45073417,0.00038330362,0.018779842,0.0007287669,0.0005058797,0.001311992,0.52446365,0.000112520625,0.0029798655],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99800545,0.000767368,0.00020359461,0.00038636685,0.0003664958,0.0002707122],"domain_scores_gemma":[0.9912986,0.0031201902,0.001194408,0.0022538297,0.0014456288,0.00068734324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002471331,0.0005542672,0.0006540215,0.0021589294,0.00107133,0.0010218039,0.00115387,0.0015344226,0.0030189352],"category_scores_gemma":[0.008671807,0.0002745803,0.00091565656,0.0025960577,0.0007443265,0.0015930649,0.0023630194,0.0014468073,0.0024753944],"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":[0.0013977619,0.0020099736,0.6042425,0.0015816619,0.0005596283,0.0015706012,0.0027109114,0.010575637,0.0068198177,0.003451364,0.24697755,0.11810265],"study_design_scores_gemma":[0.0001699806,0.0004929625,0.8405517,0.0003777927,0.00022670015,0.00078940333,0.0073738396,0.052144207,0.0044602416,0.0027902524,0.09041163,0.00021123906],"about_ca_topic_score_codex":0.023626849,"about_ca_topic_score_gemma":0.034964446,"teacher_disagreement_score":0.023626849,"about_ca_system_score_codex":0.0010766012,"about_ca_system_score_gemma":0.001335671,"threshold_uncertainty_score":0.046978652},"labels":[],"label_agreement":null},{"id":"W4253346999","doi":"10.2196/preprints.26798","title":"Development and Application of Mini App \"Travel Health Guide\" (Preprint)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Upload; China; Preprint; Public health; Disease control; Business; Commission; Tourism; Environmental health; Public relations; World Wide Web; Medicine; Political science; Computer science; Nursing","score_opus":0.02996941442635019,"score_gpt":0.323897645982269,"score_spread":0.2939282315559188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253346999","genre_codex":"other","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":[0.058347248,0.002501375,0.24805039,0.008471639,0.008319803,0.015369304,0.124275014,0.24619943,0.28846583],"genre_scores_gemma":[0.08796376,0.0025454196,0.29286543,0.0045527644,0.0018015403,0.011915554,0.09757839,0.013444207,0.487333],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993129,0.00014312773,0.00008115171,0.00014845902,0.00024254218,0.00007186421],"domain_scores_gemma":[0.9969823,0.0009638343,0.00010755384,0.00022922838,0.001427955,0.00028912697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013087502,0.0009366414,0.0005539205,0.00213802,0.000545966,0.0011907496,0.0010618333,0.0008622696,0.16663657],"category_scores_gemma":[0.0046834005,0.00039566535,0.0006096427,0.0010913075,0.00022446386,0.0015819557,0.0013623572,0.0006968078,0.087642394],"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":[0.0003620909,0.0002297899,0.0030777878,0.0011478872,0.000024994379,0.0005857008,0.0010267069,0.00033857103,0.0041245157,0.0012291371,0.6904917,0.2973612],"study_design_scores_gemma":[0.00017329048,0.00042981296,0.01702855,0.00049969665,0.00006310591,0.00066544354,0.0013514577,0.0041811783,0.006327538,0.001425694,0.96774274,0.00011148999],"about_ca_topic_score_codex":0.0031010793,"about_ca_topic_score_gemma":0.0031588103,"teacher_disagreement_score":0.16663657,"about_ca_system_score_codex":0.00033375443,"about_ca_system_score_gemma":0.001429467,"threshold_uncertainty_score":0.5574548},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"software","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W4253581622","doi":"10.2196/13755","title":"Metadata Correction: Performance, Acceptability, and Usability of Respiratory Rate Timers and Pulse Oximeters When Used by Frontline Health Workers to Detect Symptoms of Pneumonia in Sub-Saharan Africa and Southeast Asia: Protocol for a Two-Phase, Multisite, Mixed-Methods Trial","year":2019,"lang":"en","type":"erratum","venue":"JMIR Research Protocols","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Usability; Metadata; Protocol (science); Southeast asia; Medicine; Pneumonia; South asia; Nursing; Computer science; World Wide Web; Alternative medicine; Internal medicine","score_opus":0.11766385058842742,"score_gpt":0.5137645947173756,"score_spread":0.39610074412894813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253581622","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021472638,0.0028680698,0.031383082,0.15808879,0.42496678,0.11911526,0.2008539,0.009976219,0.031275272],"genre_scores_gemma":[0.11922006,0.0028462466,0.12476197,0.087093845,0.019644177,0.45639297,0.03060853,0.00637075,0.15306146],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.941304,0.024019552,0.02200164,0.002221009,0.008430329,0.0020234266],"domain_scores_gemma":[0.6550599,0.18250328,0.022659192,0.030619577,0.106299825,0.0028582423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.060112845,0.00200412,0.002734152,0.003365597,0.003826613,0.0028969464,0.0029297683,0.00636541,0.12176929],"category_scores_gemma":[0.49413937,0.0018547102,0.004160359,0.0021093856,0.0024714484,0.003078608,0.0025085015,0.005872667,0.025684772],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011813692,0.00037878027,0.0011244615,0.009297157,0.00038186522,0.00015877697,0.0006658876,0.00022868376,0.0003797987,0.0015374647,0.91989875,0.05413477],"study_design_scores_gemma":[0.049197394,0.0033305031,0.034501426,0.029956652,0.0026582452,0.0005540755,0.001853369,0.0029012563,0.006813728,0.012170874,0.85532206,0.00074049586],"about_ca_topic_score_codex":0.009020509,"about_ca_topic_score_gemma":0.022624781,"teacher_disagreement_score":0.12176929,"about_ca_system_score_codex":0.004873019,"about_ca_system_score_gemma":0.011379393,"threshold_uncertainty_score":0.40735883},"labels":[],"label_agreement":null},{"id":"W4253658449","doi":"10.4018/978-1-4666-2940-0.ch013","title":"A Real-Time 3D Visualization Framework for Multimedia Data Management, Simulation, and Prediction","year":2013,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Government of Alberta; University of Alberta","funders":"","keywords":"Geospatial analysis; Computer science; Visualization; Geographic information system; Data science; Geovisualization; Event (particle physics); Data visualization; Data mining; Information visualization; Cartography; Geography","score_opus":0.0312233141938132,"score_gpt":0.32523489691009044,"score_spread":0.29401158271627725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253658449","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":[0.00086018303,0.0011359837,0.9746405,0.000408026,0.00017649363,0.000105896834,0.0012485768,0.007945854,0.013478452],"genre_scores_gemma":[0.02567762,0.0034146805,0.9440558,0.00030995332,0.0001530652,0.00042933435,0.0038299195,0.0019236973,0.020205956],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997117,0.000060075057,0.000022401871,0.00004215452,0.00014053329,0.000023187718],"domain_scores_gemma":[0.9996019,0.00017213258,0.000021195812,0.000059309787,0.00009478853,0.00005074985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078425143,0.001323553,0.00067035464,0.0015643359,0.0005356172,0.003282289,0.002258639,0.0013779681,0.020891871],"category_scores_gemma":[0.0012146519,0.0006508268,0.0013175474,0.0014228041,0.00062883465,0.0017431057,0.0021095183,0.0016754932,0.0049352585],"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":[0.00012470505,0.00013990178,0.00073089334,0.0011833436,0.00011559219,0.0007313152,0.0012596936,0.101229005,0.019227259,0.15834424,0.17979954,0.53711456],"study_design_scores_gemma":[0.000043836255,0.000048033813,0.0005212542,0.0003735193,0.00003844256,0.0006571793,0.00014638822,0.30934992,0.0063292445,0.068820015,0.6135668,0.0001052978],"about_ca_topic_score_codex":0.0062855934,"about_ca_topic_score_gemma":0.0077061774,"teacher_disagreement_score":0.020891871,"about_ca_system_score_codex":0.0007417578,"about_ca_system_score_gemma":0.0009316946,"threshold_uncertainty_score":0.06989026},"labels":[],"label_agreement":null},{"id":"W4255155938","doi":"10.20944/preprints201811.0260.v1","title":"&lt;em&gt;Real-Time Inferential Analytics Based on Online Databases of Trends: A Breakthrough within the Discipline of Digital Epidemiology in Dentistry and Dental Anatomy&lt;/em&gt;","year":2018,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Analytics; Computer science; Data science; Statistical inference; Microsoft excel; Data analysis; Database; Data mining; Statistics","score_opus":0.09219551152416798,"score_gpt":0.388766670767801,"score_spread":0.29657115924363303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255155938","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":[0.03813471,0.26770613,0.56660914,0.03052638,0.0020773634,0.0032207868,0.04671713,0.013411885,0.03159646],"genre_scores_gemma":[0.18258557,0.13726985,0.6466514,0.0033396774,0.0025555745,0.0034927696,0.018950913,0.0015049683,0.0036493107],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.98138857,0.009742351,0.0030049535,0.001787768,0.0037972715,0.00027915274],"domain_scores_gemma":[0.8545718,0.12025133,0.009348935,0.006565264,0.008169862,0.0010926662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022735678,0.0009861416,0.0018556585,0.013465083,0.00044957484,0.0075229215,0.0018133223,0.0013421114,0.007859681],"category_scores_gemma":[0.09278526,0.00063741504,0.0025025953,0.019386994,0.0015248138,0.007997772,0.00345103,0.0018403272,0.002494743],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034794176,0.00010297633,0.0118250465,0.03479916,0.0011913262,0.00033124126,0.0014980233,0.0030017162,0.0021018242,0.027359273,0.031875093,0.8855665],"study_design_scores_gemma":[0.0003852719,0.000901263,0.059753913,0.05396422,0.0028342882,0.0020096216,0.005459874,0.052263953,0.011958637,0.19326277,0.6166315,0.00057469046],"about_ca_topic_score_codex":0.0024271335,"about_ca_topic_score_gemma":0.003112081,"teacher_disagreement_score":0.022735678,"about_ca_system_score_codex":0.0014853581,"about_ca_system_score_gemma":0.0048760446,"threshold_uncertainty_score":0.12023914},"labels":[],"label_agreement":null},{"id":"W4255674091","doi":"10.32920/14639652","title":"Naive Bayes classifiers for verbal autopsies: comparison to physician-based classification for 21,000 child and adult deaths","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Public Health Ontario; University of Toronto; Centre for Global Health Research; St. Michael's Hospital","funders":"Medical Research Council; University of Toronto; University of the Witwatersrand, Johannesburg","keywords":"Naive Bayes classifier; Medicine; Population; Bayes' theorem; Cause of death; Verbal autopsy; Statistics; Demography; Artificial intelligence; Mathematics; Computer science; Internal medicine; Environmental health; Disease","score_opus":0.0344629680575352,"score_gpt":0.329187281080795,"score_spread":0.2947243130232598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255674091","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":[0.89931107,0.012599028,0.05427839,0.0023283288,0.0012980377,0.000814005,0.016177395,0.002280168,0.01091346],"genre_scores_gemma":[0.9285378,0.0015223675,0.046384096,0.0004792206,0.0004958396,0.0002991208,0.020524485,0.00014256156,0.0016145308],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99007285,0.0048713293,0.0010384594,0.0017638116,0.0019031905,0.00035029792],"domain_scores_gemma":[0.96334755,0.027943606,0.0022255804,0.0019415634,0.003872526,0.00066918845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018958759,0.0012451783,0.0013516878,0.0043739765,0.000652383,0.0016898304,0.0014432322,0.0016692962,0.0025384366],"category_scores_gemma":[0.04853365,0.00032531764,0.0012872074,0.0018160846,0.0005073473,0.001731341,0.0011738513,0.0014641546,0.00122941],"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":[0.0058004316,0.0008889699,0.4240238,0.001408984,0.0015607177,0.00022329623,0.00065052015,0.09976733,0.0013377244,0.0022840644,0.030533146,0.43152103],"study_design_scores_gemma":[0.0005524122,0.001658523,0.108969584,0.00091313024,0.0004353734,0.0006543207,0.0010989419,0.8656169,0.0024731345,0.008805654,0.00866139,0.00016069291],"about_ca_topic_score_codex":0.0130757615,"about_ca_topic_score_gemma":0.008232663,"teacher_disagreement_score":0.018958759,"about_ca_system_score_codex":0.0013403562,"about_ca_system_score_gemma":0.0016833856,"threshold_uncertainty_score":0.10026473},"labels":[],"label_agreement":null},{"id":"W4280508510","doi":"10.2196/30371","title":"The Associations Between Racially/Ethnically Stratified COVID-19 Tweets and COVID-19 Cases and Deaths: Cross-sectional Study","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institutes of Health","keywords":"Ethnic group; Pandemic; Demography; Coronavirus disease 2019 (COVID-19); Medicine; Ethnically diverse; Health equity; Population; Geography; Gerontology; Public health; Environmental health; Disease; Political science; Infectious disease (medical specialty); Pathology; Sociology","score_opus":0.18311432097319166,"score_gpt":0.5200430171503114,"score_spread":0.33692869617711974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280508510","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":[0.9898442,0.00049108895,0.0005400416,0.00017641587,0.00004674096,0.00009285307,0.0073103453,0.000014886941,0.0014834993],"genre_scores_gemma":[0.9944443,0.0002577223,0.00055217533,0.00021033117,0.000046528712,0.00017020959,0.0036324416,0.00000951765,0.0006769233],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990189,0.000246543,0.00014068431,0.00031199885,0.00013165157,0.00015027828],"domain_scores_gemma":[0.997726,0.0004727231,0.0009737203,0.00024002545,0.00037466187,0.00021279031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010370703,0.00025692338,0.00030529362,0.0009252786,0.0007176259,0.0006912101,0.00039150732,0.00044586757,0.002831455],"category_scores_gemma":[0.004095016,0.0003922457,0.00057777995,0.0012927627,0.0002662191,0.0009103865,0.0008600115,0.00075462426,0.00074711716],"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":[0.00009191268,0.00006624068,0.99665976,0.000056690766,0.00013540783,0.000043526008,0.0003774522,0.00004041623,0.00017274042,0.00006955749,0.0008967798,0.0013895222],"study_design_scores_gemma":[0.000007556148,0.00013522476,0.9956655,0.000052054304,0.00012840865,0.00020253696,0.0014108123,0.00047504518,0.00017850214,0.00009107595,0.0016394367,0.000014013326],"about_ca_topic_score_codex":0.007673169,"about_ca_topic_score_gemma":0.012894083,"teacher_disagreement_score":0.007673169,"about_ca_system_score_codex":0.00034116753,"about_ca_system_score_gemma":0.00044097204,"threshold_uncertainty_score":0.015257001},"labels":[],"label_agreement":null},{"id":"W4280585809","doi":"10.2196/35973","title":"A Standard-Based Citywide Health Information Exchange for Public Health in Response to COVID-19: Development Study","year":2022,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Public health; Public health informatics; Health information exchange; Public health surveillance; Health care; Health informatics; Business; Data governance; International Health Regulations; Medicine; Environmental health; Computer science; Health policy; HRHIS; Data quality; Disease; Nursing; Political science; Infectious disease (medical specialty)","score_opus":0.06897556953694717,"score_gpt":0.37343363418565506,"score_spread":0.3044580646487079,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280585809","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":[0.9472635,0.00007672833,0.023839552,0.0013656139,0.000036640817,0.006552859,0.0029969518,0.0008217265,0.01704652],"genre_scores_gemma":[0.845059,0.00017436838,0.13546339,0.00033024265,0.000014973582,0.0031817926,0.01167359,0.0001136521,0.0039889254],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9905297,0.0054858914,0.00046600375,0.0007048997,0.0017617939,0.0010518194],"domain_scores_gemma":[0.97886425,0.0056198947,0.0012471878,0.0029985062,0.009124247,0.0021459728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025306657,0.00056967436,0.00022714277,0.0015762511,0.0010132273,0.0026302175,0.0020368437,0.0009285348,0.0036139884],"category_scores_gemma":[0.01785961,0.00039544454,0.00067863334,0.0020136242,0.0010883259,0.0031758754,0.0021750666,0.00089410314,0.00080290565],"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":[0.0030082797,0.024178294,0.4491638,0.0009894586,0.00026876418,0.0012527908,0.008842758,0.049946968,0.013678215,0.04290103,0.02294344,0.3828263],"study_design_scores_gemma":[0.0018818205,0.019284066,0.58053255,0.00063346303,0.00040054138,0.0008080136,0.025960017,0.25148508,0.03086036,0.004587284,0.08325378,0.00031302733],"about_ca_topic_score_codex":0.047510937,"about_ca_topic_score_gemma":0.025867295,"teacher_disagreement_score":0.047510937,"about_ca_system_score_codex":0.007217451,"about_ca_system_score_gemma":0.016844321,"threshold_uncertainty_score":0.13383597},"labels":[],"label_agreement":null},{"id":"W4280625057","doi":"10.2196/33184","title":"Exploring Google Searches for Out-of-Clinic Medication Abortion in the United States During 2020: Infodemiology Approach Using Multiple Samples","year":2022,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Abortion; The Internet; Population; Popularity; Online search; Index (typography); Pill; Information retrieval; Computer science; Internet privacy; World Wide Web; Medicine; Family medicine; Psychology; Pregnancy; Nursing","score_opus":0.3419736466900529,"score_gpt":0.407736119466774,"score_spread":0.06576247277672109,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280625057","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":[0.9889376,0.00031209734,0.0011086,0.00013487348,0.000010238363,0.0002963912,0.007909685,0.000040606585,0.0012498361],"genre_scores_gemma":[0.98816955,0.00019829493,0.003502733,0.00010420221,0.000026982269,0.0007069638,0.0067633046,0.000024038589,0.0005039964],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99715555,0.0011685727,0.00036021616,0.0005449944,0.00048354833,0.00028719567],"domain_scores_gemma":[0.9906685,0.005201139,0.0019955381,0.0006223129,0.001139981,0.00037264335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034314722,0.000395274,0.0006733808,0.008354609,0.0007747056,0.0017627204,0.0007109755,0.0006121288,0.0018560429],"category_scores_gemma":[0.016938955,0.00029176255,0.0014718558,0.007048641,0.0005072414,0.0016712061,0.0016492876,0.0007548763,0.0005375174],"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":[0.00033406465,0.0003120451,0.9725769,0.00043219837,0.0006014439,0.00024083951,0.004523804,0.000979375,0.00045916482,0.00056705263,0.0020495758,0.01692365],"study_design_scores_gemma":[0.000040874304,0.00040001603,0.9695533,0.00011856463,0.00041411983,0.00038119193,0.00947998,0.014018575,0.0007145908,0.000529776,0.004298523,0.000050486462],"about_ca_topic_score_codex":0.028384613,"about_ca_topic_score_gemma":0.028363429,"teacher_disagreement_score":0.028384613,"about_ca_system_score_codex":0.0009176802,"about_ca_system_score_gemma":0.0007909106,"threshold_uncertainty_score":0.056438804},"labels":[],"label_agreement":null},{"id":"W4281553282","doi":"10.3233/shti220480","title":"A Conceptual Framework for Representing Events Under Public Health Surveillance","year":2022,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Conceptual framework; Computer science; Conceptual model; Domain (mathematical analysis); Public health; Data science; Public health surveillance; Event (particle physics); The Conceptual Framework; Data mining; Medicine; Sociology; Pathology","score_opus":0.11996088798715578,"score_gpt":0.4191216313538564,"score_spread":0.2991607433667006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281553282","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":[0.0052582733,0.00088770484,0.9746236,0.004649664,0.00023059206,0.00041089958,0.0012254228,0.00050671824,0.012207188],"genre_scores_gemma":[0.111177735,0.0010764741,0.8805682,0.0008815133,0.00017969515,0.0009837542,0.0028276271,0.00008034425,0.0022247056],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9917902,0.00393731,0.0013266485,0.0012936379,0.0011118622,0.00054031686],"domain_scores_gemma":[0.98811835,0.0059876465,0.0013583185,0.0012356626,0.0022040394,0.0010960693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01277915,0.0013645835,0.000803526,0.006503641,0.00260347,0.010019216,0.004451738,0.0041238354,0.0047465013],"category_scores_gemma":[0.014111219,0.0008054766,0.003286622,0.006836573,0.0064455583,0.013938109,0.0047361352,0.0038531155,0.0010172442],"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":[0.000020461124,0.00003564871,0.00059492904,0.00014017809,0.000026701737,0.00015728107,0.0017617613,0.0028234844,0.00028325638,0.98348665,0.0016188707,0.009050704],"study_design_scores_gemma":[0.000080066166,0.0001176747,0.0009753999,0.00069622754,0.00013944955,0.0006285658,0.003715491,0.03941328,0.0011197529,0.8064029,0.14661103,0.00010010837],"about_ca_topic_score_codex":0.016264517,"about_ca_topic_score_gemma":0.010063758,"teacher_disagreement_score":0.016264517,"about_ca_system_score_codex":0.0035131157,"about_ca_system_score_gemma":0.006918713,"threshold_uncertainty_score":0.06758338},"labels":[],"label_agreement":null},{"id":"W4281620402","doi":"10.1016/j.patter.2022.100507","title":"Disease mapping and innovation: A history from wood-block prints to Web 3.0","year":2022,"lang":"en","type":"review","venue":"Patterns","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Presentation (obstetrics); Dashboard; Computer science; World Wide Web; Point (geometry); Data science; Medicine","score_opus":0.12140413191801963,"score_gpt":0.3348838773241334,"score_spread":0.21347974540611375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281620402","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":[0.0001572577,0.99184436,0.00028995596,0.003834474,0.0005498193,0.000006929932,0.000026760375,0.000020239457,0.0032701578],"genre_scores_gemma":[0.0020245828,0.993196,0.00055732555,0.002477912,0.0007375742,0.00001564254,0.000034399298,0.000014884538,0.00094175775],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977082,0.00084636797,0.00029119774,0.00026195537,0.0007507016,0.00014155456],"domain_scores_gemma":[0.9851216,0.011849992,0.0007475867,0.0003602052,0.0014060965,0.00051450776],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0049165133,0.0007534928,0.0014082615,0.005997788,0.0007791306,0.004714564,0.001063446,0.0026815443,0.0077948985],"category_scores_gemma":[0.010546751,0.0004235786,0.00072399323,0.00757747,0.0032833405,0.00718566,0.0024680651,0.0039255484,0.0027240654],"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":[0.000039890292,0.000048434547,0.00020617658,0.015110658,0.000054289245,0.000110303976,0.000644128,0.00016454222,0.0002526579,0.028264612,0.037332088,0.91777223],"study_design_scores_gemma":[0.000004746362,0.000040856055,0.00044741997,0.009781381,0.000029137393,0.00033409917,0.00032128452,0.000033856297,0.00013527815,0.0051594395,0.983694,0.0000184619],"about_ca_topic_score_codex":0.0038872452,"about_ca_topic_score_gemma":0.0041761296,"teacher_disagreement_score":0.99922085,"about_ca_system_score_codex":0.0024337661,"about_ca_system_score_gemma":0.004337021,"threshold_uncertainty_score":0.026076496},"labels":[],"label_agreement":null},{"id":"W4281680075","doi":"10.1371/journal.pone.0268669","title":"Dynamic topic modeling of twitter data during the COVID-19 pandemic","year":2022,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":43,"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":"York University; UK Research and Innovation; New York University Shanghai","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Data science; Social media; Computer science; Biology; Medicine; Virology; World Wide Web; Outbreak; Infectious disease (medical specialty)","score_opus":0.16911437864694318,"score_gpt":0.32421531303495693,"score_spread":0.15510093438801376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281680075","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":[0.8942796,0.0040364284,0.065411165,0.009306944,0.00094060693,0.0002462478,0.019439738,0.0006930146,0.0056463336],"genre_scores_gemma":[0.9656028,0.0010837982,0.0143569205,0.000394078,0.00072058063,0.00021471754,0.014650463,0.00009062463,0.0028859354],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987293,0.0006525762,0.00006912686,0.00026901474,0.000110644374,0.00016933665],"domain_scores_gemma":[0.99578756,0.003197876,0.0003363575,0.00024611963,0.000276001,0.0001560398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038941125,0.0007108099,0.0007384539,0.0016874984,0.0007277993,0.0017183463,0.0008738213,0.0013307306,0.001190584],"category_scores_gemma":[0.010257883,0.00034520862,0.0008991731,0.0017931612,0.0006253991,0.0021646628,0.0011679668,0.0016732154,0.0008855277],"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":[0.0021440892,0.00063677086,0.36642346,0.0013013764,0.0005707898,0.0013073772,0.007019733,0.35522577,0.008780326,0.04045644,0.0671987,0.14893517],"study_design_scores_gemma":[0.00004915625,0.000065921704,0.035283126,0.000084649415,0.000052477426,0.00013101309,0.0017360753,0.9387557,0.0007444998,0.011008157,0.012036531,0.000052806357],"about_ca_topic_score_codex":0.01567373,"about_ca_topic_score_gemma":0.019008799,"teacher_disagreement_score":0.01567373,"about_ca_system_score_codex":0.0010877568,"about_ca_system_score_gemma":0.00060051837,"threshold_uncertainty_score":0.031165004},"labels":[],"label_agreement":null},{"id":"W4281860697","doi":"10.1007/s10680-022-09619-2","title":"Harnessing the Potential of Google Searches for Understanding Dynamics of Intimate Partner Violence Before and After the COVID-19 Outbreak","year":2022,"lang":"en","type":"article","venue":"European Journal of Population / Revue européenne de Démographie","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Max-Planck-Institut für demografische Forschung; Università degli Studi di Trento; McGill University","keywords":"Predictive power; Domestic violence; Leverage (statistics); Coronavirus disease 2019 (COVID-19); Psychology; Pandemic; Social media; Socioeconomic status; Internet privacy; Data science; Poison control; Social psychology; Human factors and ergonomics; Computer science; Sociology; Medicine; Demography; Environmental health; World Wide Web","score_opus":0.03807526686599054,"score_gpt":0.2902015271678601,"score_spread":0.2521262603018695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281860697","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":[0.9740562,0.0018492021,0.0028523263,0.0016792959,0.00006924279,0.000082247054,0.009373965,0.0001891162,0.009848402],"genre_scores_gemma":[0.99288225,0.0005186977,0.0023888522,0.00010956599,0.000090795904,0.000034984776,0.0033243923,0.00002780439,0.00062254595],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99916947,0.00034180077,0.00006441888,0.00016660041,0.00014049848,0.000117186726],"domain_scores_gemma":[0.9878727,0.007823954,0.00213496,0.0009839202,0.0006965332,0.0004878811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012457027,0.0005225679,0.00053216866,0.009461122,0.00047172396,0.0037266465,0.0005111244,0.00095838815,0.002377478],"category_scores_gemma":[0.014736836,0.0002676749,0.00054030964,0.006966761,0.00056947157,0.004871951,0.0019509855,0.00083291443,0.0011737138],"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":[0.00026245377,0.00013080252,0.9305733,0.0005150021,0.00019537177,0.0005257982,0.0046686805,0.0031982625,0.00074984704,0.0035438917,0.0036462261,0.051990293],"study_design_scores_gemma":[0.000022066179,0.00021101962,0.8785374,0.00075201155,0.00023493216,0.0007895205,0.01630083,0.06739478,0.0009984754,0.013949682,0.020687984,0.00012127086],"about_ca_topic_score_codex":0.010901746,"about_ca_topic_score_gemma":0.018215936,"teacher_disagreement_score":0.010901746,"about_ca_system_score_codex":0.00053223997,"about_ca_system_score_gemma":0.00044727963,"threshold_uncertainty_score":0.0216766},"labels":[],"label_agreement":null},{"id":"W4282578770","doi":"10.1016/j.xpro.2022.101463","title":"EpiTopics: A dynamic machine learning model to predict and inform non-pharmacological public health interventions from global news reports","year":2022,"lang":"en","type":"article","venue":"STAR Protocols","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research; McGill University","keywords":"Computer science; Psychological intervention; Protocol (science); Public health interventions; Machine learning; Data science; Transfer of learning; Artificial intelligence; Coronavirus disease 2019 (COVID-19); Infectious disease (medical specialty); Medicine; Alternative medicine; Disease","score_opus":0.06258587671138065,"score_gpt":0.40443834361748315,"score_spread":0.3418524669061025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282578770","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":[0.036503248,0.0006972977,0.8491898,0.009684751,0.0005462475,0.004108007,0.05633767,0.01907216,0.023860833],"genre_scores_gemma":[0.2786634,0.0008233477,0.6273741,0.0019153288,0.0003089255,0.006509836,0.06453124,0.0008065385,0.01906723],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99805546,0.0011076534,0.0001256661,0.00032122634,0.00030654555,0.00008339032],"domain_scores_gemma":[0.9931218,0.0047327634,0.00033435505,0.00087004475,0.0007646011,0.0001765046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007299757,0.0008651081,0.0005847271,0.0013529017,0.00055753614,0.0013803508,0.0019838437,0.0012709432,0.010746785],"category_scores_gemma":[0.020418508,0.00048830075,0.0010446899,0.00081671897,0.0006081278,0.002743644,0.0025281468,0.0020201479,0.0028809125],"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":[0.001756284,0.00076739985,0.021857632,0.0009370932,0.000512603,0.00023771044,0.0006934354,0.22023936,0.0047333785,0.08172755,0.21995933,0.44657815],"study_design_scores_gemma":[0.00022469052,0.00021380416,0.0026333712,0.000097348864,0.00007422542,0.000053150576,0.00009351411,0.90002185,0.0033701796,0.047997437,0.045155853,0.00006452563],"about_ca_topic_score_codex":0.009979828,"about_ca_topic_score_gemma":0.015341826,"teacher_disagreement_score":0.010746785,"about_ca_system_score_codex":0.0012564276,"about_ca_system_score_gemma":0.0037371258,"threshold_uncertainty_score":0.038605213},"labels":[],"label_agreement":null},{"id":"W4282944813","doi":"10.1016/j.yebeh.2022.108730","title":"Infodemiology of autoimmune encephalitis, autoimmune seizures, and autoimmune epilepsy: An analysis of online search behavior using Google Trends","year":2022,"lang":"fi","type":"article","venue":"Epilepsy & Behavior","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"","keywords":"Autoimmune encephalitis; Epilepsy; Medicine; Encephalitis; Autoimmune disease; Immunology; Psychiatry; Virus; Antibody","score_opus":0.04395595282388923,"score_gpt":0.3513923839501672,"score_spread":0.30743643112627794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282944813","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":[0.9779546,0.0011245816,0.0001932155,0.00029081665,0.000015369125,0.00002172891,0.018179562,0.000032792534,0.0021873014],"genre_scores_gemma":[0.9880624,0.00057034305,0.0004005046,0.00006776154,0.000031590665,0.000013552462,0.01033009,0.0000107378555,0.00051309],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99911433,0.00018609157,0.00018011156,0.00014655986,0.0002514697,0.00012145397],"domain_scores_gemma":[0.9920432,0.0028729117,0.0034100441,0.00035332819,0.0007420517,0.0005784925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076143065,0.00023447449,0.00037500865,0.005684522,0.0002703237,0.001237856,0.00037298724,0.00048034688,0.0013382279],"category_scores_gemma":[0.0053897672,0.0001139497,0.00093744724,0.006932566,0.00019822559,0.001319893,0.0006082098,0.00048775016,0.00046649997],"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":[0.0000750241,0.00003133856,0.9932609,0.00006641229,0.00012571953,0.00006823769,0.00012456677,0.00014148834,0.00012006495,0.000119323784,0.0006626953,0.005204217],"study_design_scores_gemma":[0.000002972763,0.000054101805,0.9946321,0.000039161197,0.0000897781,0.0004285697,0.0005557821,0.0021047052,0.00018058666,0.00009530789,0.0018080733,0.000008924171],"about_ca_topic_score_codex":0.020542352,"about_ca_topic_score_gemma":0.03314237,"teacher_disagreement_score":0.020542352,"about_ca_system_score_codex":0.0004148989,"about_ca_system_score_gemma":0.0005042862,"threshold_uncertainty_score":0.040845573},"labels":[],"label_agreement":null},{"id":"W4282961100","doi":"10.2196/39180","title":"News Coverage of Colorectal Cancer on Google News: Descriptive Study","year":2022,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Colorectal cancer; Descriptive statistics; Medicine; Content analysis; Disease; Cancer; Internal medicine; Sociology; Statistics","score_opus":0.025941070525048095,"score_gpt":0.3309438117286465,"score_spread":0.30500274120359844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282961100","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":[0.98334587,0.0018348781,0.00011514624,0.00022929742,0.000016363012,0.00031512138,0.01082255,0.00001598732,0.0033047246],"genre_scores_gemma":[0.98730636,0.0032850474,0.0005202341,0.00029668078,0.000074641146,0.00067212345,0.006793786,0.00002140935,0.0010297035],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99824405,0.0003256508,0.0004931691,0.00016793307,0.000564255,0.00020498189],"domain_scores_gemma":[0.9762083,0.012942399,0.0067326124,0.00038198667,0.0030407952,0.0006937814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018359867,0.00021227624,0.00056936493,0.010741804,0.0006682994,0.0017493464,0.00042611564,0.000433635,0.003352816],"category_scores_gemma":[0.014505052,0.00036324325,0.0007141519,0.011898153,0.0006116865,0.0022355916,0.0011906362,0.0005136337,0.00083016884],"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":[0.00023056031,0.00021061377,0.9670479,0.0024026542,0.00011926685,0.0007201091,0.0141608985,0.000037738613,0.00036576315,0.00017375463,0.0024589663,0.012071801],"study_design_scores_gemma":[0.000015285823,0.00023408128,0.9585071,0.00076020975,0.00016293078,0.0018795526,0.031745538,0.00022117393,0.00022220016,0.000059143993,0.0061674695,0.000025278316],"about_ca_topic_score_codex":0.0050172517,"about_ca_topic_score_gemma":0.0077972,"teacher_disagreement_score":0.010741804,"about_ca_system_score_codex":0.0010237625,"about_ca_system_score_gemma":0.0009853579,"threshold_uncertainty_score":0.011216283},"labels":[],"label_agreement":null},{"id":"W4283023946","doi":"10.1038/s41598-022-13663-7","title":"Infodemiological study on the impact of the COVID-19 pandemic on increased headache incidences at the world level","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Incidence (geometry); The Internet; Outbreak; Coronavirus disease 2019 (COVID-19); Population; Web search query; Demography; Medicine; Computer science; Artificial intelligence; Geography; Environmental health; Search engine; World Wide Web; Internal medicine; Mathematics; Disease; Pathology","score_opus":0.16615287792268238,"score_gpt":0.3991693236752305,"score_spread":0.23301644575254812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283023946","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":[0.9961358,0.00022589894,0.00018033075,0.00014222493,0.000014912799,0.000014487102,0.001616083,0.000009040172,0.001661166],"genre_scores_gemma":[0.99810886,0.0001191244,0.000112550915,0.000040386738,0.000023649214,0.00001210012,0.0011645543,0.0000025829854,0.0004161476],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994097,0.00019977355,0.000052831332,0.0000962427,0.00012466995,0.00011686843],"domain_scores_gemma":[0.99594265,0.0019832395,0.0011208229,0.00023492097,0.00042340695,0.00029504017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009414859,0.00015439959,0.00018685807,0.0016711116,0.00020195259,0.00066837965,0.00020263462,0.00033598888,0.0027498573],"category_scores_gemma":[0.003964002,0.000086120475,0.00061430654,0.0018138981,0.00027439947,0.00065059104,0.00039861226,0.0004969422,0.0003740562],"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":[0.0002610885,0.00014931623,0.988861,0.000089693734,0.00016584151,0.00019200396,0.00036673355,0.0007528695,0.00048748532,0.00026027087,0.0006228367,0.0077908477],"study_design_scores_gemma":[0.000003202102,0.00020080044,0.99609935,0.000018713472,0.000060667793,0.00007471574,0.0008400416,0.001680242,0.0002603474,0.00006900501,0.00068797043,0.000005023126],"about_ca_topic_score_codex":0.010368323,"about_ca_topic_score_gemma":0.009609911,"teacher_disagreement_score":0.010368323,"about_ca_system_score_codex":0.0004273961,"about_ca_system_score_gemma":0.00043710705,"threshold_uncertainty_score":0.020615935},"labels":[],"label_agreement":null},{"id":"W4283032704","doi":"10.31390/gradschool_theses.5270","title":"Mapping Louisiana's Missing: Spatiotemporal Profiling of Louisiana's Missing Persons- An Experimental Application of Geographic Information Systems and Forensic Anthropology","year":2021,"lang":"en","type":"dissertation","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Forensic anthropology; Missing data; Profiling (computer programming); Data science; Population; Geography; Criminology; Genealogy; Sociology; History; Computer science; Demography; Archaeology","score_opus":0.014201073103441602,"score_gpt":0.30015179041898954,"score_spread":0.28595071731554794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283032704","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":[0.99101734,0.00006757531,0.0030646059,0.00017842205,0.000006746838,0.00018572908,0.0022188695,0.000058605667,0.0032021257],"genre_scores_gemma":[0.9814889,0.00012736047,0.012841321,0.00006825573,0.000006076127,0.0003991628,0.0028871954,0.000024326702,0.0021574597],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99877673,0.0005578692,0.000065518354,0.00027898926,0.0002045582,0.000116307354],"domain_scores_gemma":[0.9965783,0.0009235912,0.0006107393,0.0005224799,0.001209156,0.00015568297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015252866,0.00026325037,0.00023693124,0.0021545987,0.001913885,0.001195647,0.000821717,0.00047732942,0.0020333603],"category_scores_gemma":[0.004873218,0.00020491779,0.00034550525,0.0034726704,0.0007105414,0.0008752838,0.0017021617,0.0006303681,0.00037000526],"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":[0.00047702092,0.0009592535,0.8180574,0.00025025487,0.00016520059,0.0010089811,0.03354852,0.0063472213,0.011455454,0.0048668687,0.006960417,0.115903385],"study_design_scores_gemma":[0.000040436134,0.0010072591,0.85908127,0.00016428994,0.00015483648,0.00048466408,0.07637872,0.02619761,0.00706768,0.0022319588,0.027075792,0.00011548982],"about_ca_topic_score_codex":0.13006414,"about_ca_topic_score_gemma":0.28130114,"teacher_disagreement_score":0.13006414,"about_ca_system_score_codex":0.0024627235,"about_ca_system_score_gemma":0.002258971,"threshold_uncertainty_score":0.25861412},"labels":[],"label_agreement":null},{"id":"W4283067046","doi":"10.2196/37412","title":"Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series","year":2022,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Cancer Institute","keywords":"Autoregressive integrated moving average; Social media; Advertising; Social marketing; Population; Psychology; Medicine; Time series; Business; Environmental health; Marketing; Statistics; Computer science; Mathematics","score_opus":0.16450947113410663,"score_gpt":0.30809553293612063,"score_spread":0.143586061802014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283067046","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":[0.9693066,0.00050331844,0.022239564,0.0019620557,0.0001104691,0.00009789017,0.0024034542,0.00012237158,0.0032542236],"genre_scores_gemma":[0.9925194,0.00021572215,0.004895109,0.00006439798,0.000064167594,0.00006180396,0.0012991374,0.000012940811,0.0008673492],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99939144,0.0002375165,0.000054779986,0.00015482801,0.00008602434,0.00007541642],"domain_scores_gemma":[0.99365544,0.0047155996,0.00093814253,0.00021925442,0.00026807643,0.00020348583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019886682,0.00055834715,0.0003895101,0.002758152,0.00043132776,0.0015754243,0.00050094095,0.0008011574,0.0032587317],"category_scores_gemma":[0.008237305,0.00021390345,0.001673655,0.0021012072,0.0004079206,0.0013077995,0.00055804045,0.0011486384,0.0006072052],"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":[0.0001606393,0.0003266871,0.93435436,0.000091375674,0.0005070637,0.00025986775,0.0005058982,0.018025292,0.00070544257,0.0042233435,0.0018780746,0.03896198],"study_design_scores_gemma":[0.000019217387,0.00027031873,0.43190205,0.00010771093,0.00033390286,0.00018390764,0.0010771673,0.55544287,0.00069348025,0.0061324826,0.0037801655,0.000056667774],"about_ca_topic_score_codex":0.013397588,"about_ca_topic_score_gemma":0.009486945,"teacher_disagreement_score":0.013397588,"about_ca_system_score_codex":0.0007976723,"about_ca_system_score_gemma":0.00082095515,"threshold_uncertainty_score":0.026639163},"labels":[],"label_agreement":null},{"id":"W4283578151","doi":"10.2196/37756","title":"Twenty Years of the Health Insurance Portability and Accountability Act Safe Harbor Provision: Unsolved Challenges and Ways Forward","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","keywords":"Health Insurance Portability and Accountability Act; Accountability; Data sharing; Medicine; Population; Software portability; Confidentiality; Internet privacy; Public relations; Computer security; Computer science; Business; Political science; Environmental health; Law; Alternative medicine","score_opus":0.023954616836031856,"score_gpt":0.30278330679145504,"score_spread":0.2788286899554232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283578151","genre_codex":"commentary","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":[0.0022014838,0.027047193,0.004077543,0.9528259,0.0041735983,0.000017826465,0.00005519962,0.000051395993,0.009549919],"genre_scores_gemma":[0.16339575,0.08935502,0.022501098,0.6891185,0.02095591,0.0002868989,0.00028540046,0.00031976384,0.013781665],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9270441,0.034983404,0.0044205687,0.0062180287,0.021305896,0.0060279565],"domain_scores_gemma":[0.7906411,0.16182514,0.009550467,0.009218198,0.01996562,0.008799486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1141593,0.0010751294,0.002122048,0.0032463956,0.01382821,0.029120171,0.0076778033,0.03412625,0.004992729],"category_scores_gemma":[0.16808864,0.0014373453,0.0022625646,0.0044326982,0.06512425,0.04305756,0.015254043,0.052480463,0.0015396042],"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":[0.000032635606,0.00006720805,0.0007851163,0.00023621388,0.000024376268,0.00024695206,0.007938307,0.0005525137,0.000077285935,0.84182274,0.10416627,0.04405035],"study_design_scores_gemma":[0.000025243038,0.000066557375,0.0008639362,0.0031362488,0.000030792944,0.00017877894,0.009458591,0.0007780251,0.00022767135,0.47721767,0.5078643,0.00015211411],"about_ca_topic_score_codex":0.03832033,"about_ca_topic_score_gemma":0.024390152,"teacher_disagreement_score":0.1141593,"about_ca_system_score_codex":0.01922843,"about_ca_system_score_gemma":0.054517467,"threshold_uncertainty_score":0.60373926},"labels":[],"label_agreement":null},{"id":"W4283640798","doi":"10.2196/38551","title":"The Landscape of Participatory Surveillance Systems Across the One Health Spectrum: Systematic Review","year":2022,"lang":"en","type":"review","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Tides Foundation; Emory University","keywords":"Citizen journalism; Digital health; One Health; Information system; Environmental resource management; Environmental health; Geography; Business; Data science; Environmental planning; Public health; Medicine; Health care; Computer science; Political science; World Wide Web; Nursing","score_opus":0.10862548408789689,"score_gpt":0.39894318324580236,"score_spread":0.29031769915790545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283640798","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":[0.0018488722,0.989465,0.0017773573,0.0043593603,0.0003320626,0.0009247288,0.00039388586,0.000022944536,0.00087579794],"genre_scores_gemma":[0.07065861,0.90304744,0.015194865,0.0044573434,0.00032768585,0.0054535456,0.00063790276,0.000039931936,0.00018279113],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.739246,0.16972345,0.056297958,0.009153269,0.023257023,0.0023222968],"domain_scores_gemma":[0.37186027,0.5292772,0.047639336,0.011605251,0.037396807,0.00222109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.17633326,0.0016383171,0.007396237,0.028918834,0.0025202443,0.010969598,0.0043762657,0.0035110782,0.0027522708],"category_scores_gemma":[0.42343706,0.0024781546,0.0059123915,0.03038293,0.0056536077,0.0146159865,0.0066588367,0.003176259,0.0003132913],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013399315,0.00003827033,0.002547836,0.8431716,0.004248874,0.00017733745,0.0054052947,0.00045705104,0.00019072,0.006590146,0.004353042,0.13268577],"study_design_scores_gemma":[0.00004532941,0.00012040187,0.0018964063,0.96179336,0.00405253,0.00018500704,0.0032444273,0.00018222391,0.00012379985,0.0021421523,0.026176378,0.000038052156],"about_ca_topic_score_codex":0.014995592,"about_ca_topic_score_gemma":0.03196711,"teacher_disagreement_score":0.17633326,"about_ca_system_score_codex":0.01816595,"about_ca_system_score_gemma":0.062231395,"threshold_uncertainty_score":0.93255055},"labels":[],"label_agreement":null},{"id":"W4284973546","doi":"10.1016/j.cjca.2022.07.001","title":"Big Data, Big Expectations, and Big Judgements","year":2022,"lang":"en","type":"letter","venue":"Canadian Journal of Cardiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McGill University; McGill University Health Centre","funders":"Fonds de Recherche du Québec - Santé","keywords":"Medicine; Big data; Data science; Data mining","score_opus":0.06016978306376249,"score_gpt":0.27425168429050373,"score_spread":0.21408190122674123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4284973546","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000081244856,0.00028439355,0.000045646266,0.99588686,0.0027997456,0.0000026280313,0.000025873087,0.000004588656,0.00086899323],"genre_scores_gemma":[0.0035757571,0.0005723334,0.00025102452,0.97425336,0.01890315,0.000024694178,0.00002472387,0.000019063558,0.0023758707],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9838717,0.007378231,0.0011938352,0.0013584162,0.00494417,0.0012535506],"domain_scores_gemma":[0.8704672,0.09855729,0.0039618392,0.002604621,0.010343955,0.014065088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020736368,0.0006525857,0.0014577501,0.0014408564,0.007975365,0.010963113,0.002222198,0.046945404,0.009563644],"category_scores_gemma":[0.10426979,0.00076097803,0.0012913948,0.001716283,0.011945121,0.008746488,0.0037881734,0.060426705,0.003833314],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022806202,0.0000128072315,0.0005110321,0.000023941393,0.0000128349875,0.00037761076,0.00029337988,0.000036817928,0.000023383582,0.008013844,0.98636717,0.004304413],"study_design_scores_gemma":[0.00014913242,0.000023837532,0.001756324,0.0006199616,0.0000283934,0.00093417696,0.0029283697,0.00089307927,0.00008321275,0.09030939,0.9021546,0.00011961719],"about_ca_topic_score_codex":0.026418302,"about_ca_topic_score_gemma":0.050185204,"teacher_disagreement_score":0.046945404,"about_ca_system_score_codex":0.010808635,"about_ca_system_score_gemma":0.01584244,"threshold_uncertainty_score":0.10966569},"labels":[],"label_agreement":null},{"id":"W4285339906","doi":"10.2196/34583","title":"Linguistic Pattern–Infused Dual-Channel Bidirectional Long Short-term Memory With Attention for Dengue Case Summary Generation From the Program for Monitoring Emerging Diseases–Mail Database: Algorithm Development Study","year":2022,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dengue fever; Term (time); Computer science; Algorithm; Dual (grammatical number); Long short term memory; Channel (broadcasting); Data mining; Database; Machine learning; Medicine; Linguistics; Telecommunications","score_opus":0.05185555039542284,"score_gpt":0.34393098402782646,"score_spread":0.2920754336324036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285339906","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":[0.30094963,0.0020973405,0.6807085,0.0010655832,0.00018495915,0.00071090896,0.0013548642,0.009994808,0.0029334424],"genre_scores_gemma":[0.6867793,0.00037296527,0.30600235,0.00027813177,0.0001084622,0.0005863638,0.0025717798,0.0000846414,0.0032159423],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996165,0.00009014002,0.000049484406,0.00012509344,0.000058071197,0.00006075957],"domain_scores_gemma":[0.99852717,0.000984261,0.00008091702,0.000074994765,0.00026292482,0.000069709014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011347525,0.0008256642,0.00074111874,0.0014597219,0.0004611467,0.000836946,0.0013061017,0.0009899754,0.0030879534],"category_scores_gemma":[0.0038357766,0.0002482412,0.0007087671,0.00095976476,0.00019787587,0.0010229899,0.00085839786,0.00082674593,0.0008692742],"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":[0.0005286546,0.00034535016,0.0073153246,0.00025857295,0.00015557159,0.0003817239,0.0002325374,0.05096078,0.009491951,0.0009804087,0.004576573,0.92477244],"study_design_scores_gemma":[0.000040445466,0.00013581822,0.0018313483,0.00001269491,0.00007045679,0.00009996811,0.00010784376,0.9913948,0.0047067897,0.0007505735,0.0008374531,0.000011718936],"about_ca_topic_score_codex":0.008400464,"about_ca_topic_score_gemma":0.009966307,"teacher_disagreement_score":0.008400464,"about_ca_system_score_codex":0.0006967394,"about_ca_system_score_gemma":0.0010997296,"threshold_uncertainty_score":0.016703129},"labels":[],"label_agreement":null},{"id":"W4285364837","doi":"10.2196/33678","title":"Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"Computer science; Artificial intelligence; Deep learning; Machine learning; Relevance (law); Classifier (UML); Sentiment analysis; Random forest; Natural language processing; Language model; Transfer of learning; Transformer; Information retrieval","score_opus":0.07220711212445817,"score_gpt":0.3981763846864982,"score_spread":0.32596927256204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285364837","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":[0.98324203,0.0011080138,0.0085423505,0.0006162939,0.00012198983,0.000086886546,0.001702504,0.0002806873,0.004299158],"genre_scores_gemma":[0.98698366,0.00041414672,0.0056577194,0.00010658939,0.00006059019,0.000049506634,0.0040491023,0.000040647195,0.0026379693],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993487,0.00023269744,0.000045084904,0.0001406332,0.00012790275,0.000104964805],"domain_scores_gemma":[0.99718297,0.0017749991,0.00019790915,0.00020244946,0.0005201552,0.00012154436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016944968,0.00075835816,0.00043513646,0.0019059152,0.00042261786,0.0011090053,0.0008222759,0.0009711097,0.0019332726],"category_scores_gemma":[0.005851564,0.00017698175,0.0005558939,0.0011666662,0.00045823577,0.002232384,0.0009556868,0.0013475715,0.000986106],"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":[0.002190085,0.002486455,0.26967177,0.0009004297,0.000521245,0.0008160758,0.0029702606,0.08011302,0.009180678,0.0036462706,0.03060834,0.59689534],"study_design_scores_gemma":[0.000036332232,0.00046463206,0.07539706,0.00013778388,0.00008492246,0.00015150446,0.0028605626,0.9088029,0.0047651636,0.0024882348,0.00474885,0.00006198241],"about_ca_topic_score_codex":0.012667359,"about_ca_topic_score_gemma":0.015231197,"teacher_disagreement_score":0.012667359,"about_ca_system_score_codex":0.0012372178,"about_ca_system_score_gemma":0.0005169489,"threshold_uncertainty_score":0.025187254},"labels":[],"label_agreement":null},{"id":"W4285386161","doi":"10.1007/s10708-022-10707-3","title":"Comparing geospatial clustering methods to study spatial patterns of lung cancer rates in urban areas: A case study in Mashhad, Iran","year":2022,"lang":"en","type":"article","venue":"GeoJournal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Geospatial analysis; Lung cancer; Health geography; Cluster analysis; Spatial analysis; Geography; Spatial epidemiology; Cancer registry; Cartography; Population; Epidemiology; Environmental health; Demography; Medicine; Statistics; Pathology; Public health; Mathematics","score_opus":0.05138924453451683,"score_gpt":0.3989882490840987,"score_spread":0.34759900454958187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285386161","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":[0.9928455,0.00042055294,0.0048654247,0.00025450432,0.00002298276,0.00011638041,0.0006260074,0.00004628291,0.0008024151],"genre_scores_gemma":[0.98968303,0.00023212179,0.009272429,0.000018050108,0.000013690439,0.00005511,0.0005736936,0.000007976406,0.00014390136],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985215,0.00078914774,0.00008194521,0.00017667128,0.0002808162,0.00015005343],"domain_scores_gemma":[0.99597317,0.002327835,0.00043207555,0.00023105797,0.00089129945,0.00014459185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003214641,0.00048611636,0.00044389267,0.0051478343,0.0006701347,0.0010885467,0.00095342955,0.00048012284,0.00051300944],"category_scores_gemma":[0.0070638987,0.00019341629,0.0010169356,0.0052940673,0.00043739425,0.0005619702,0.00067053235,0.000398301,0.000078826604],"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":[0.0002760248,0.00042631614,0.91084814,0.00020872655,0.00048389143,0.0003563129,0.0018814337,0.021097066,0.00041556338,0.0016029968,0.0015242919,0.060879238],"study_design_scores_gemma":[0.000058788148,0.00030936766,0.82812697,0.00008476165,0.00039930712,0.00048276645,0.017004484,0.14877297,0.0008355031,0.0016160716,0.0022455084,0.00006344783],"about_ca_topic_score_codex":0.14785933,"about_ca_topic_score_gemma":0.15906829,"teacher_disagreement_score":0.14785933,"about_ca_system_score_codex":0.0018815205,"about_ca_system_score_gemma":0.0020663803,"threshold_uncertainty_score":0.2939974},"labels":[],"label_agreement":null},{"id":"W4285465067","doi":"10.32920/ryerson.14636922.v1","title":"User Task Scenarios for Map-Based Decision Support in Community Health Planning","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Usability; Task (project management); Thematic map; Computer science; Think aloud protocol; Thematic analysis; Process (computing); Human–computer interaction; Data science; Geography; Qualitative research; Cartography; Engineering","score_opus":0.0463037572837384,"score_gpt":0.3720117633182882,"score_spread":0.3257080060345498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285465067","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":[0.59043574,0.00035267617,0.3757596,0.0009705877,0.00011158284,0.0061444705,0.003100465,0.012416926,0.010707913],"genre_scores_gemma":[0.69202936,0.00014590583,0.2987659,0.00016798206,0.000031278498,0.004785395,0.0019081419,0.00048631098,0.0016797751],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9924075,0.006155044,0.00040430774,0.00030156638,0.00047591006,0.00025561187],"domain_scores_gemma":[0.934921,0.05975761,0.0006551926,0.0018708995,0.0014755843,0.0013196259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010725712,0.0018254989,0.00084258337,0.0014086973,0.0010081956,0.0022809044,0.002049415,0.0021779516,0.011994827],"category_scores_gemma":[0.037922945,0.0006954528,0.0009162805,0.00082119764,0.0007081432,0.0024147157,0.003084985,0.0008964647,0.0020402786],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.01810347,0.012169895,0.040720552,0.0115287015,0.00057592924,0.010533833,0.11914385,0.12014413,0.0742743,0.014556201,0.046575267,0.5316739],"study_design_scores_gemma":[0.0034631528,0.009332918,0.040244613,0.0030751466,0.00049330894,0.004936998,0.032482933,0.642668,0.05801812,0.036525432,0.16742079,0.00133852],"about_ca_topic_score_codex":0.0015039251,"about_ca_topic_score_gemma":0.0016531771,"teacher_disagreement_score":0.011994827,"about_ca_system_score_codex":0.00082822435,"about_ca_system_score_gemma":0.00069742865,"threshold_uncertainty_score":0.056723654},"labels":[],"label_agreement":null},{"id":"W4285576275","doi":"10.33423/jabe.v22i10.3723","title":"Predicting Amazon’s Choice of HQ2 From Social Media: Evidence From the Tweets of Informed Sources","year":2020,"lang":"en","type":"article","venue":"Journal of Applied Business and Economics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Popularity; Variety (cybernetics); Affect (linguistics); Social media; Amazon rainforest; Negotiation; Set (abstract data type); Advertising; Sociology; Psychology; Social psychology; Computer science; Business; Artificial intelligence; Social science; World Wide Web; Communication","score_opus":0.04005210134251319,"score_gpt":0.2506740878769024,"score_spread":0.21062198653438918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285576275","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":[0.9957268,0.00012241142,0.00065219257,0.00056293333,0.000022692953,0.000013829233,0.0015378848,0.000014862091,0.0013464339],"genre_scores_gemma":[0.9964277,0.000090778645,0.0007049566,0.00008308743,0.00006987934,0.0000107601545,0.0021681979,0.000006914902,0.00043772103],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99941516,0.00027568455,0.00003435771,0.00010275244,0.000115289935,0.00005676147],"domain_scores_gemma":[0.99168265,0.0058150273,0.0012765256,0.0003489529,0.00057673047,0.00029999125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012690701,0.0003142126,0.00021656946,0.0013824946,0.0003693385,0.0012933445,0.00034147795,0.000721318,0.0010759707],"category_scores_gemma":[0.009311249,0.00017098963,0.00025385997,0.0013930332,0.00025093163,0.001342968,0.0005521723,0.00069548725,0.00062500045],"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":[0.00049592485,0.00030370956,0.95654553,0.00008893835,0.00019730367,0.00031885607,0.0010517881,0.0066910954,0.0013153127,0.0013553882,0.0054340544,0.026202058],"study_design_scores_gemma":[0.00005824591,0.00023042066,0.7409663,0.00007134315,0.00014074228,0.00023651654,0.0032218823,0.23979819,0.002674093,0.0032056787,0.009318675,0.00007792707],"about_ca_topic_score_codex":0.0115747005,"about_ca_topic_score_gemma":0.018354028,"teacher_disagreement_score":0.0115747005,"about_ca_system_score_codex":0.00039799127,"about_ca_system_score_gemma":0.00025297733,"threshold_uncertainty_score":0.023014605},"labels":[],"label_agreement":null},{"id":"W4286221696","doi":"10.2196/34464","title":"Confounding Effect of Undergraduate Semester–Driven “Academic\" Internet Searches on the Ability to Detect True Disease Seasonality in Google Trends Data: Fourier Filter Method Development and Demonstration","year":2022,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"The Internet; Filter (signal processing); Variety (cybernetics); Computer science; Confounding; Information retrieval; Medicine; Data science; Statistics; Mathematics; Artificial intelligence; World Wide Web","score_opus":0.08482455523747844,"score_gpt":0.40421644742612783,"score_spread":0.3193918921886494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286221696","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4967732,0.00033978125,0.49678957,0.0007088698,0.00012970935,0.0004519937,0.00044646458,0.0013550833,0.003005376],"genre_scores_gemma":[0.6882103,0.00025153885,0.3084851,0.00019201566,0.000067646804,0.00048909994,0.00059798354,0.00016016724,0.0015461127],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827015,0.00082959136,0.0001233067,0.00020955279,0.00043743206,0.00012998283],"domain_scores_gemma":[0.98650694,0.010781148,0.00047366234,0.0009931116,0.0010877805,0.00015727962],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007828779,0.0004684341,0.00031153936,0.00076904043,0.0005232221,0.000764284,0.00060307386,0.00076534075,0.001823539],"category_scores_gemma":[0.029515652,0.0002351654,0.0006883548,0.000801548,0.00047499867,0.00060772395,0.0007383547,0.00071047636,0.00058145897],"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":[0.0015180794,0.00070761755,0.14682445,0.0003843938,0.0003245656,0.000726189,0.0010224063,0.035368565,0.07751889,0.009824086,0.0046155574,0.7211652],"study_design_scores_gemma":[0.000104905834,0.0008094918,0.12183739,0.00009483981,0.00013557645,0.0009349705,0.00045934255,0.80405426,0.06166897,0.0033594253,0.0064479997,0.00009281272],"about_ca_topic_score_codex":0.0063003777,"about_ca_topic_score_gemma":0.0063504204,"teacher_disagreement_score":0.9921712,"about_ca_system_score_codex":0.00042978476,"about_ca_system_score_gemma":0.0011113808,"threshold_uncertainty_score":0.041402996},"labels":[],"label_agreement":null},{"id":"W4286379249","doi":"10.2196/preprints.34464","title":"Confounding Effect of Undergraduate Semester–Driven “Academic\" Internet Searches on the Ability to Detect True Disease Seasonality in Google Trends Data: Fourier Filter Method Development and Demonstration (Preprint)","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"The Internet; Filter (signal processing); Variety (cybernetics); Computer science; Medicine; Information retrieval; World Wide Web; Artificial intelligence","score_opus":0.08094119713989044,"score_gpt":0.3840589304696644,"score_spread":0.30311773332977393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286379249","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":[0.518853,0.00039774738,0.47357297,0.001045932,0.00023077716,0.00040460343,0.00069492724,0.0015854482,0.0032146184],"genre_scores_gemma":[0.7088454,0.00031350568,0.28633967,0.00030692384,0.00012429497,0.00044758848,0.0010245793,0.00025196525,0.0023460684],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982418,0.00093225815,0.00010810748,0.00020996272,0.00036490642,0.0001430765],"domain_scores_gemma":[0.98140395,0.015233185,0.00055457064,0.0012935414,0.0013120371,0.0002027157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008962567,0.00047914463,0.00034097163,0.0006779233,0.00051184645,0.0009104246,0.00067189324,0.0009163521,0.0022336184],"category_scores_gemma":[0.0368977,0.00023833233,0.00088399,0.0007081024,0.0005543391,0.0007005882,0.00076970656,0.0008261419,0.00074903516],"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":[0.0027282767,0.00091895775,0.1719702,0.0005384179,0.0005662976,0.00083263783,0.0010200958,0.05661057,0.084298596,0.013490913,0.0076638446,0.65936124],"study_design_scores_gemma":[0.00011338261,0.0006803045,0.114647165,0.00008720824,0.00016845808,0.0006339046,0.00038241062,0.8169769,0.058798827,0.0029350584,0.004477357,0.00009903999],"about_ca_topic_score_codex":0.009050832,"about_ca_topic_score_gemma":0.006324113,"teacher_disagreement_score":0.009050832,"about_ca_system_score_codex":0.0004569168,"about_ca_system_score_gemma":0.0013314126,"threshold_uncertainty_score":0.047399163},"labels":[],"label_agreement":null},{"id":"W4288429604","doi":"10.1093/bioinformatics/btac497","title":"BioCaster in 2021: automatic disease outbreaks detection from global news media","year":2022,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; McGill University","funders":"Economic and Social Research Council","keywords":"Computer science; Upgrade; Outbreak; Infectious disease (medical specialty); Dashboard; Machine translation; Visualization; Artificial neural network; Ontology; Data science; Cloud computing; Semantics (computer science); World Wide Web; Artificial intelligence; Disease; Medicine; Programming language","score_opus":0.01273902401072915,"score_gpt":0.24822163088394172,"score_spread":0.23548260687321257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288429604","genre_codex":"dataset","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":[0.038256127,0.0028746964,0.028500749,0.0071860286,0.0012810883,0.0007977949,0.7482047,0.14999256,0.022906197],"genre_scores_gemma":[0.04885078,0.00090415403,0.085390374,0.0011114223,0.00040617224,0.0005415199,0.85165554,0.003938824,0.0072012823],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982967,0.0003605964,0.00014840087,0.00050141924,0.0005244045,0.00016845395],"domain_scores_gemma":[0.9927428,0.0019985868,0.00067838083,0.0013109045,0.0023804698,0.0008888637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029551394,0.0014930313,0.00094429066,0.0073569617,0.0013229129,0.003477512,0.0020402914,0.0012140272,0.019762846],"category_scores_gemma":[0.012975638,0.00059119845,0.0011126879,0.0061223633,0.0004980227,0.0019297666,0.0026889571,0.0011170591,0.019880014],"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":[0.00062192953,0.00010109638,0.012708754,0.0010805655,0.00021769865,0.0003174894,0.000250545,0.0015323961,0.0043201274,0.0014916072,0.9087945,0.06856316],"study_design_scores_gemma":[0.0009646087,0.00024290067,0.059580814,0.00064738933,0.00046229316,0.0006140946,0.00082857173,0.073517986,0.023939312,0.010886183,0.82807225,0.00024349117],"about_ca_topic_score_codex":0.0511282,"about_ca_topic_score_gemma":0.08726595,"teacher_disagreement_score":0.0511282,"about_ca_system_score_codex":0.0023027183,"about_ca_system_score_gemma":0.0030206104,"threshold_uncertainty_score":0.101661205},"labels":[],"label_agreement":null},{"id":"W4288884738","doi":"10.1101/2022.07.29.22278191","title":"Has the pandemic enhanced and sustained digital health-seeking behaviour? A big data interrupted time-series analysis of Google Trends","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Digital health; Pandemic; Proxy (statistics); Mainstream; Health care; Telehealth; Telemedicine; Political science; Business; Coronavirus disease 2019 (COVID-19); Medicine; Economic growth; Computer science; Economics","score_opus":0.07039955859805598,"score_gpt":0.33343628097870615,"score_spread":0.2630367223806502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288884738","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":[0.9629836,0.0008257869,0.005006896,0.0024007708,0.000103289,0.00010279373,0.025259402,0.00014466932,0.0031727732],"genre_scores_gemma":[0.9864253,0.00016472678,0.0015473394,0.00011406972,0.000049471033,0.0000831966,0.010798859,0.000027751077,0.0007892537],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9963864,0.0017237162,0.00038540873,0.0005680961,0.00054779404,0.00038851195],"domain_scores_gemma":[0.98140746,0.011283935,0.0041905493,0.0015556474,0.0011722558,0.00039005547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005343142,0.0003146575,0.0005278423,0.00196642,0.00028546242,0.0018281109,0.0007502532,0.0008797475,0.0031410155],"category_scores_gemma":[0.023841137,0.00020015579,0.0009493211,0.004403724,0.0006659736,0.0011408905,0.0010382007,0.0011102728,0.0007918163],"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":[0.0005054684,0.0001484221,0.95725495,0.00043566865,0.00060986326,0.0003296586,0.0018582875,0.008277803,0.00041128925,0.0043421914,0.0084461095,0.01738026],"study_design_scores_gemma":[0.00003520898,0.00021904965,0.9246533,0.00020665956,0.00026623596,0.0002116225,0.0034507967,0.0551909,0.00071887963,0.0025949322,0.012383993,0.000068339396],"about_ca_topic_score_codex":0.026674207,"about_ca_topic_score_gemma":0.0135479625,"teacher_disagreement_score":0.026674207,"about_ca_system_score_codex":0.00085237494,"about_ca_system_score_gemma":0.00062747387,"threshold_uncertainty_score":0.05303788},"labels":[],"label_agreement":null},{"id":"W4289782555","doi":"10.1186/s12942-022-00308-1","title":"The role of geographic information system and global positioning system in dementia care and research: a scoping review","year":2022,"lang":"en","type":"review","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"University of New South Wales","keywords":"Global Positioning System; Geographic information system; Dementia; Health informatics; Information system; Medicine; Knowledge management; Public health; Computer science; Nursing; Geography; Cartography; Engineering","score_opus":0.049657028105445214,"score_gpt":0.4198981493217896,"score_spread":0.3702411212163444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289782555","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":[0.00031105257,0.99707234,0.0003263744,0.0010422714,0.00022035989,0.000088011446,0.00006458428,0.0000060707052,0.00086892146],"genre_scores_gemma":[0.0052104904,0.99288696,0.0009971703,0.00035924002,0.00011465216,0.00026944015,0.00008405099,0.0000051996262,0.00007288756],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.96229166,0.020349566,0.00928427,0.0016858802,0.0056273933,0.00076117925],"domain_scores_gemma":[0.8180908,0.15847789,0.009157482,0.0020704404,0.01147839,0.0007250636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.035776637,0.0014917075,0.0043954984,0.029322421,0.0019679226,0.009413367,0.0027803353,0.0045463685,0.0029988948],"category_scores_gemma":[0.12477942,0.001361741,0.0040628496,0.03990271,0.0034773299,0.008697852,0.0044302535,0.0027932713,0.00061212014],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008197693,0.000050381364,0.0020265514,0.5892438,0.0010760527,0.0002662865,0.0041506817,0.00057935837,0.00015049994,0.010580251,0.009312035,0.38248214],"study_design_scores_gemma":[0.000018992137,0.00006370946,0.0022456262,0.9062701,0.0020137131,0.00028939964,0.0024380635,0.00020768162,0.00012260006,0.002199478,0.08409354,0.000037174235],"about_ca_topic_score_codex":0.018701911,"about_ca_topic_score_gemma":0.020318102,"teacher_disagreement_score":0.035776637,"about_ca_system_score_codex":0.009002978,"about_ca_system_score_gemma":0.031946216,"threshold_uncertainty_score":0.18920714},"labels":[],"label_agreement":null},{"id":"W4290420447","doi":"10.1101/2022.08.05.22278478","title":"Improving estimates of pertussis burden in Ontario, Canada 2010-2017 by combining validation and capture-recapture methodologies","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"St. Michael's Hospital; Vector Institute; Institute for Clinical Evaluative Sciences; Public Health Ontario; University of Toronto","funders":"","keywords":"False positive paradox; Mark and recapture; Statistics; Abundance (ecology); Sensitivity (control systems); Medicine; Econometrics; Demography; Environmental health; Mathematics; Biology; Ecology; Population","score_opus":0.03916059390209296,"score_gpt":0.29316968976226243,"score_spread":0.25400909586016945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290420447","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":[0.84615463,0.0086389035,0.058123637,0.0032601012,0.0002117446,0.00067841617,0.068861656,0.0006400414,0.013430874],"genre_scores_gemma":[0.95835614,0.0013859519,0.02072181,0.0004311839,0.000044503686,0.000181424,0.016159631,0.00006820166,0.002651002],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99569964,0.0013772367,0.00027789857,0.0006343741,0.0015887616,0.00042210755],"domain_scores_gemma":[0.98480994,0.0021031355,0.0023020313,0.001169491,0.0092220055,0.0003933308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009385113,0.0007300302,0.00063112524,0.0018479589,0.0015674984,0.0013857147,0.0018027098,0.00038895334,0.0012673881],"category_scores_gemma":[0.017035015,0.00050976715,0.0010856242,0.0032905592,0.0005612687,0.00058651937,0.0011926759,0.0005938513,0.0002565352],"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":[0.00019500386,0.000040109593,0.9284427,0.0006310267,0.0012878143,0.000101175734,0.0008520749,0.015234976,0.0011823381,0.001116939,0.011286172,0.039629664],"study_design_scores_gemma":[0.000044611603,0.000057818816,0.9509577,0.0003231318,0.00043499473,0.0000707208,0.0005634404,0.03377026,0.001316898,0.0003674678,0.012037141,0.000055885197],"about_ca_topic_score_codex":0.98953784,"about_ca_topic_score_gemma":0.9921218,"teacher_disagreement_score":0.025210572,"about_ca_system_score_codex":0.025210572,"about_ca_system_score_gemma":0.033007666,"threshold_uncertainty_score":0.1829164},"labels":[],"label_agreement":null},{"id":"W4292485398","doi":"10.2196/37177","title":"Pilot Influenza Syndromic Surveillance System Based on Absenteeism and Temperature in China: Development and Usability Study","year":2022,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Absenteeism; Medicine; Attendance; China; Public health; Workload; Family medicine; Biostatistics; Environmental health; Demography; Nursing; Psychology; Geography; Computer science","score_opus":0.026025493723338886,"score_gpt":0.3002684511390839,"score_spread":0.274242957415745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292485398","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":[0.9956564,0.000053673317,0.0028161725,0.000067939174,0.000009767853,0.00071357656,0.00013697651,0.00015070406,0.00039487044],"genre_scores_gemma":[0.9804945,0.00015723998,0.017149167,0.00007046493,0.000012703716,0.00077564036,0.000585344,0.000023604282,0.00073130813],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99731797,0.0013103364,0.00034652383,0.0003718218,0.0004307361,0.00022262646],"domain_scores_gemma":[0.99497235,0.0017019152,0.00022933265,0.00038557316,0.0022481342,0.0004626013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074120034,0.00071264594,0.000594039,0.0009447532,0.000590787,0.0007952311,0.00076299155,0.0005195428,0.001240608],"category_scores_gemma":[0.00767853,0.000314047,0.00066951796,0.00049573625,0.00042811924,0.0012815264,0.0008691988,0.00029233837,0.00025562008],"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":[0.0022739517,0.0092945,0.56351584,0.0028113276,0.00044483633,0.0020045012,0.024383033,0.0048980345,0.055655744,0.0005603798,0.005271109,0.32888672],"study_design_scores_gemma":[0.000775229,0.026077736,0.8420488,0.00047606952,0.0008377861,0.0011031353,0.022860166,0.06512718,0.027742296,0.0002858239,0.012388167,0.00027753855],"about_ca_topic_score_codex":0.0066526546,"about_ca_topic_score_gemma":0.005625793,"teacher_disagreement_score":0.0074120034,"about_ca_system_score_codex":0.0008449998,"about_ca_system_score_gemma":0.0014596727,"threshold_uncertainty_score":0.039198875},"labels":[],"label_agreement":null},{"id":"W4292551131","doi":"10.1007/s11205-022-02984-9","title":"Forecasting National and Regional Youth Unemployment in Spain Using Google Trends","year":2022,"lang":"en","type":"article","venue":"Social Indicators Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Academia Româna; Universidad de Murcia; European Commission","keywords":"Unemployment; Quarter (Canadian coin); Economics; Bayesian probability; Coronavirus disease 2019 (COVID-19); Demographic economics; Autoregressive model; Econometrics; Youth unemployment; Geography; Statistics; Economic growth","score_opus":0.2879149257474314,"score_gpt":0.44210509307080936,"score_spread":0.15419016732337798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292551131","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":[0.97678906,0.0013625924,0.002217319,0.00045966732,0.00006822793,0.000033674158,0.016039062,0.000245468,0.0027848652],"genre_scores_gemma":[0.98410493,0.00058148324,0.0022163885,0.00003298146,0.00004346712,0.000023813178,0.012433774,0.000018449633,0.0005447401],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997048,0.0000914259,0.0000310515,0.000056876877,0.00006457699,0.000051228577],"domain_scores_gemma":[0.99914205,0.00025905215,0.00022937446,0.00005621776,0.00023835865,0.00007489053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008300115,0.00033769183,0.0003997994,0.0016446977,0.00010680056,0.000759199,0.00031329071,0.00038232343,0.000523741],"category_scores_gemma":[0.0024120994,0.00012076268,0.0005384106,0.0016247049,0.000108320266,0.00056017254,0.00057232886,0.00029520132,0.00033728013],"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":[0.00057969673,0.00012427349,0.8682617,0.00034242138,0.00026472614,0.0003259361,0.00047312112,0.065563045,0.0006688211,0.001229931,0.010614692,0.051551588],"study_design_scores_gemma":[0.000043656753,0.00015305077,0.60860133,0.00022940348,0.0001436666,0.00013137226,0.0017507733,0.37698036,0.0009353072,0.0010150535,0.009971545,0.000044444503],"about_ca_topic_score_codex":0.06327102,"about_ca_topic_score_gemma":0.04529653,"teacher_disagreement_score":0.06327102,"about_ca_system_score_codex":0.0005160147,"about_ca_system_score_gemma":0.00037170335,"threshold_uncertainty_score":0.1258055},"labels":[],"label_agreement":null},{"id":"W4292940196","doi":"10.21203/rs.3.rs-1871614/v1","title":"SARS-CoV-2 Genomic Contextual Data Harmonization: Recommendations from a Mixed Methods Analysis of COVID-19 Case Report Forms Across Canada","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"Genome British Columbia; Genome Canada","keywords":"Harmonization; Pandemic; Identification (biology); Data sharing; Data science; Standardization; Data integration; Data collection; Public health; Coronavirus disease 2019 (COVID-19); Computer science; Data mining; Medicine; Infectious disease (medical specialty); Disease; Biology; Pathology; Sociology","score_opus":0.2568860167304794,"score_gpt":0.5453981565113779,"score_spread":0.2885121397808985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292940196","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1173846,0.015705807,0.36337876,0.2117876,0.003931002,0.04771317,0.2040069,0.0032327247,0.032859486],"genre_scores_gemma":[0.27045798,0.00351875,0.5893911,0.029769607,0.0007262723,0.030146329,0.069781676,0.0020323016,0.004176018],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.5871235,0.32554433,0.030528316,0.013089225,0.035733495,0.007981109],"domain_scores_gemma":[0.3568724,0.29454917,0.025232356,0.06462241,0.24855617,0.010167477],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.38980445,0.0020607363,0.0038982376,0.008015925,0.0052824263,0.014182103,0.011608485,0.0035011356,0.007672639],"category_scores_gemma":[0.6439328,0.0028906544,0.008491909,0.016728554,0.0037433463,0.005341001,0.010778582,0.0054705585,0.0015633784],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003648025,0.0005425944,0.33838388,0.008370473,0.010057697,0.00022137798,0.021004084,0.012717792,0.001535768,0.019978484,0.29983366,0.2837061],"study_design_scores_gemma":[0.0029659965,0.0008857203,0.52286947,0.03375348,0.010768073,0.0003166961,0.025006035,0.038176425,0.0055563613,0.025464121,0.33341014,0.000827468],"about_ca_topic_score_codex":0.88918775,"about_ca_topic_score_gemma":0.8931863,"teacher_disagreement_score":0.9626962,"about_ca_system_score_codex":0.037303798,"about_ca_system_score_gemma":0.24164028,"threshold_uncertainty_score":0.7524798},"labels":[],"label_agreement":null},{"id":"W4293093366","doi":"10.23889/ijpds.v7i3.2095","title":"A large linked data platform to inform the COVID-19 response in British Columbia: The BC COVID-19 Cohort.","year":2022,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Provincial Health Services Authority; BC Centre for Disease Control","funders":"","keywords":"Pandemic; Public health; Medicine; Coronavirus disease 2019 (COVID-19); Public health surveillance; Population; Health care; Psychological intervention; Epidemiology; Family medicine; Medical emergency; Environmental health; Nursing; Political science","score_opus":0.10922815609529045,"score_gpt":0.4249872716149249,"score_spread":0.3157591155196344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293093366","genre_codex":"dataset","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":[0.050669402,0.0011899228,0.009178102,0.01314606,0.0005046023,0.0017482494,0.8896669,0.006765824,0.027130807],"genre_scores_gemma":[0.11236539,0.0011594158,0.04408062,0.003984962,0.00014752532,0.0027367722,0.81451195,0.0013889214,0.019624429],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9963045,0.0009042825,0.0003057592,0.00073024107,0.0012481484,0.00050717086],"domain_scores_gemma":[0.976543,0.0033975113,0.0009735248,0.004107482,0.009599678,0.0053787413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074101966,0.000739408,0.0005433,0.0038396316,0.0029500646,0.0033998324,0.0021285294,0.0009298628,0.010506992],"category_scores_gemma":[0.023475947,0.00066435646,0.00060745206,0.005878519,0.000664233,0.0015412302,0.005798877,0.0017454795,0.004915683],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00088318967,0.00020537006,0.1328813,0.0004900864,0.00025133425,0.00054466305,0.0015961796,0.0016502845,0.0017860133,0.0044937595,0.7701078,0.085109934],"study_design_scores_gemma":[0.0004963144,0.00009275597,0.18513852,0.001011128,0.00015445615,0.00027106272,0.0029651904,0.010521836,0.0015583207,0.004497173,0.79306096,0.00023222664],"about_ca_topic_score_codex":0.88620776,"about_ca_topic_score_gemma":0.9254388,"teacher_disagreement_score":0.88620776,"about_ca_system_score_codex":0.013703405,"about_ca_system_score_gemma":0.039214324,"threshold_uncertainty_score":0.22892475},"labels":[],"label_agreement":null},{"id":"W4293462858","doi":"10.2196/37656","title":"Google Trends on Human Papillomavirus Vaccine Searches in the United States From 2010 to 2021: Infodemiology Study","year":2022,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Cancer Institute","keywords":"Medicine; Human papillomavirus; Genital warts; Demography; Vaccination; Cervical cancer; Human papillomavirus vaccine; Virology; Cancer; Internal medicine; Gardasil","score_opus":0.09058190090256449,"score_gpt":0.3906912030771038,"score_spread":0.30010930217453935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293462858","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":[0.9391517,0.0020825686,0.00009747347,0.00057543995,0.000027289505,0.000058134174,0.054205954,0.000051357565,0.003750111],"genre_scores_gemma":[0.97232234,0.00129114,0.00019765465,0.00021776666,0.000050038827,0.000083096325,0.024747228,0.000023187675,0.001067522],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989987,0.00015857401,0.00018416358,0.00020843922,0.00029299207,0.00015718282],"domain_scores_gemma":[0.99241275,0.0014192719,0.004033567,0.00026930377,0.0012281516,0.0006369853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009906461,0.00026743463,0.00051778305,0.005593926,0.00040769647,0.0015188082,0.0005620699,0.00049491075,0.0023503352],"category_scores_gemma":[0.0059359404,0.00025925908,0.0012314538,0.010803227,0.00029041545,0.0020750358,0.001164479,0.000851177,0.00081210036],"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":[0.0001536371,0.000080002574,0.9885844,0.0003382956,0.00024073981,0.00007497599,0.00059805525,0.00017042606,0.00007809113,0.00013097262,0.0038808722,0.0056695063],"study_design_scores_gemma":[0.00000687091,0.0000719933,0.9955811,0.00009672765,0.000102146245,0.00014723571,0.00112175,0.00043756876,0.00006778552,0.000023422672,0.0023352897,0.0000080606105],"about_ca_topic_score_codex":0.077598765,"about_ca_topic_score_gemma":0.095579125,"teacher_disagreement_score":0.077598765,"about_ca_system_score_codex":0.0010128394,"about_ca_system_score_gemma":0.0012955884,"threshold_uncertainty_score":0.15429413},"labels":[],"label_agreement":null},{"id":"W4294685782","doi":"10.1080/03610918.2022.2116748","title":"OTL web interface: outlier testing in lifetime data","year":2022,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Interface (matter); Outlier; Computer science; Database; World Wide Web; Operating system; Artificial intelligence","score_opus":0.21863756661819939,"score_gpt":0.4710614577907027,"score_spread":0.2524238911725033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294685782","genre_codex":"software","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":[0.00391581,0.00023335953,0.37918386,0.0006480846,0.00024142535,0.0005176081,0.044689056,0.5590899,0.01148085],"genre_scores_gemma":[0.13087314,0.0008273638,0.49177936,0.0028712873,0.00072859385,0.004176195,0.1400964,0.18154965,0.047098026],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819165,0.00043883498,0.00022124735,0.00024654754,0.00074355473,0.00015813223],"domain_scores_gemma":[0.98068,0.014114498,0.00075798266,0.0019056563,0.0020133695,0.0005285305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048205378,0.001757131,0.0014500121,0.0033486646,0.00047086278,0.002261367,0.002350476,0.0015242363,0.19301051],"category_scores_gemma":[0.028609967,0.0009008444,0.0012574819,0.0018572456,0.000530281,0.0034802656,0.0031479537,0.00162066,0.06625524],"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":[0.0014999037,0.0002912894,0.0072060185,0.0010346073,0.000173085,0.0008472448,0.00041105333,0.006800905,0.004926139,0.008250603,0.70897454,0.25958455],"study_design_scores_gemma":[0.0021919087,0.0006449832,0.013489701,0.00088553096,0.00013886715,0.0018161271,0.00032267784,0.37891996,0.027852563,0.057064593,0.51620394,0.00046918],"about_ca_topic_score_codex":0.0026511122,"about_ca_topic_score_gemma":0.0026826398,"teacher_disagreement_score":0.19301051,"about_ca_system_score_codex":0.00075704214,"about_ca_system_score_gemma":0.0010681058,"threshold_uncertainty_score":0.6456845},"labels":[],"label_agreement":null},{"id":"W4294770080","doi":"10.2196/36211","title":"Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis","year":2022,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McGill University; McGill University Health Centre","funders":"Université de Bordeaux; Agence Nationale de la Recherche","keywords":"Outbreak; Disease surveillance; Medicine; Public health; Infectious disease (medical specialty); Environmental health; Public health surveillance; Disease; Virology; Pathology","score_opus":0.01544351248642537,"score_gpt":0.3043140402252896,"score_spread":0.2888705277388642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294770080","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":[0.8253684,0.0034319803,0.16173977,0.0009963583,0.00013388082,0.00021331203,0.004480609,0.00057939946,0.003056255],"genre_scores_gemma":[0.98625237,0.00037786,0.010645933,0.00005071294,0.000054167234,0.0000736366,0.0023501185,0.000037002235,0.00015828242],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9947667,0.0030545576,0.0003995714,0.0010241381,0.00059637567,0.00015877598],"domain_scores_gemma":[0.97604567,0.015910001,0.0042918916,0.0025757896,0.00093392044,0.00024265905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012952883,0.00063609757,0.0006229455,0.003469894,0.00018278963,0.0011818386,0.0006901556,0.00050881883,0.0011136825],"category_scores_gemma":[0.032125015,0.00023262981,0.0011373499,0.004077398,0.0005875052,0.001201085,0.0008718591,0.00070011534,0.00020413967],"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":[0.00014328749,0.000080280166,0.8780535,0.00019235909,0.0016534093,0.00017866529,0.00027727854,0.0600465,0.0006718514,0.002933905,0.0016489042,0.054120068],"study_design_scores_gemma":[0.000016600683,0.00020550894,0.6632826,0.000107632724,0.00035292053,0.0004609211,0.0003605647,0.32295188,0.00081494544,0.007925139,0.00346349,0.00005788503],"about_ca_topic_score_codex":0.0035568005,"about_ca_topic_score_gemma":0.0018055119,"teacher_disagreement_score":0.012952883,"about_ca_system_score_codex":0.00067477516,"about_ca_system_score_gemma":0.00032711617,"threshold_uncertainty_score":0.06850219},"labels":[],"label_agreement":null},{"id":"W4294990689","doi":"10.1111/irv.13037","title":"Influenza surveillance systems using traditional and alternative sources of data: A scoping review","year":2022,"lang":"en","type":"review","venue":"Influenza and Other Respiratory Viruses","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"World Health Organization","keywords":"Medicine; Computer science; Virology","score_opus":0.5626952246983624,"score_gpt":0.485437094938268,"score_spread":0.07725812976009444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294990689","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":[0.00068261503,0.99714726,0.00037075032,0.00057856063,0.00016856725,0.00033016677,0.00021441949,0.000008153443,0.0004994163],"genre_scores_gemma":[0.008457233,0.988117,0.0015764354,0.00041145628,0.000116517695,0.0009447853,0.00030222101,0.0000071262625,0.000067194014],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.94059426,0.024320519,0.023768254,0.0025203887,0.007894746,0.0009017986],"domain_scores_gemma":[0.7070521,0.24378324,0.025256502,0.0032819067,0.019789863,0.00083628466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.054290757,0.0022297886,0.0071124495,0.04026794,0.0015461132,0.007403298,0.003864516,0.0044219894,0.0033647553],"category_scores_gemma":[0.21018866,0.0023324147,0.008273225,0.032794185,0.0023813567,0.008926862,0.004088153,0.0024272385,0.00063145853],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015556392,0.000041505733,0.0018173301,0.8345612,0.0043041934,0.00015607334,0.0011058379,0.0002593745,0.00018373612,0.0010923455,0.0022413398,0.15408146],"study_design_scores_gemma":[0.00003317183,0.00007600302,0.0017950977,0.97402567,0.007717752,0.00017869475,0.00076778996,0.00013157843,0.00016438664,0.0003772844,0.0147050675,0.000027523136],"about_ca_topic_score_codex":0.011702165,"about_ca_topic_score_gemma":0.017999789,"teacher_disagreement_score":0.054290757,"about_ca_system_score_codex":0.008542933,"about_ca_system_score_gemma":0.026715644,"threshold_uncertainty_score":0.2871204},"labels":[],"label_agreement":null},{"id":"W4295162296","doi":"10.2196/35034","title":"Worldwide Evolution of Vaccinable and Nonvaccinable Viral Skin Infections: Google Trends Analysis","year":2022,"lang":"en","type":"article","venue":"JMIR Dermatology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Measles; Rubella; Incidence (geometry); Population; Virology; Medicine; Immunology; Environmental health; Vaccination","score_opus":0.005287489497741547,"score_gpt":0.265266374597195,"score_spread":0.25997888509945344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295162296","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":[0.95590484,0.0022752795,0.0008916612,0.0005224562,0.000027887792,0.000047775204,0.036934756,0.00013892437,0.0032564017],"genre_scores_gemma":[0.97687405,0.0010186367,0.001489493,0.00004429421,0.000026805707,0.000031689357,0.019951694,0.000022783232,0.00054061896],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993612,0.000118154865,0.000100968035,0.00012941711,0.00020531309,0.00008496484],"domain_scores_gemma":[0.9972485,0.0009199084,0.0010763863,0.0001422984,0.00047863534,0.00013427956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077243254,0.00027278927,0.00032838792,0.009889137,0.00018815768,0.0013016267,0.00028117065,0.00023394525,0.0011632981],"category_scores_gemma":[0.0037030703,0.00009982555,0.0009443266,0.013050975,0.00017433825,0.0010400673,0.00055774505,0.00030609625,0.00042394397],"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":[0.00013336884,0.000028134556,0.9633336,0.00042604178,0.00030147424,0.00020875342,0.00053167786,0.0018285154,0.00058794266,0.0007823059,0.0033431319,0.028495032],"study_design_scores_gemma":[0.000005294443,0.00007103546,0.9803241,0.00010351559,0.00013368919,0.0005378009,0.001332018,0.010159521,0.0005020433,0.0003189794,0.0064947675,0.000017161785],"about_ca_topic_score_codex":0.026967505,"about_ca_topic_score_gemma":0.02307983,"teacher_disagreement_score":0.026967505,"about_ca_system_score_codex":0.0006423925,"about_ca_system_score_gemma":0.0005559508,"threshold_uncertainty_score":0.053621054},"labels":[],"label_agreement":null},{"id":"W4296130589","doi":"10.1101/2022.09.12.22279856","title":"A Simulation Study of Sampling in Difficult Settings: Statistical Superiority of Little-Used method","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"McMaster University","funders":"Canadian Institutes of Health Research","keywords":"Simple random sample; Cluster sampling; Statistics; Sampling (signal processing); Population; Sample (material); Census; Sampling design; Sample size determination; Systematic sampling; Stratified sampling; Computer science; Demography; Mathematics; Geography; Telecommunications","score_opus":0.0567592206547533,"score_gpt":0.40528333794128457,"score_spread":0.34852411728653127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296130589","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78743875,0.0022439728,0.19687614,0.0024450177,0.00042184722,0.0019597271,0.00089213124,0.00038879455,0.0073336065],"genre_scores_gemma":[0.940595,0.0003201139,0.056249093,0.00043488722,0.000086028915,0.0011782066,0.00037652612,0.00007572335,0.000684513],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9236882,0.07059774,0.001007665,0.0019809436,0.0017305505,0.0009950163],"domain_scores_gemma":[0.21258311,0.75473404,0.010546797,0.011656496,0.008609843,0.0018697352],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08876367,0.0012667261,0.0016177593,0.0017372486,0.0011810771,0.0018047893,0.0035391084,0.002476671,0.0046484047],"category_scores_gemma":[0.31476614,0.00063151814,0.002171348,0.0017140083,0.0025944333,0.003498084,0.0023097917,0.0037277993,0.00030858244],"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":[0.0080171805,0.0027224442,0.058116056,0.0012389873,0.0016636596,0.00054380746,0.0014283557,0.81049234,0.0005693594,0.06973849,0.0057300846,0.03973926],"study_design_scores_gemma":[0.0007948848,0.0010261883,0.0023865951,0.00011226335,0.0001760767,0.00008111475,0.00025191848,0.9802399,0.00023922577,0.014023735,0.0006210858,0.000047020214],"about_ca_topic_score_codex":0.011655884,"about_ca_topic_score_gemma":0.004979663,"teacher_disagreement_score":0.91123635,"about_ca_system_score_codex":0.00287314,"about_ca_system_score_gemma":0.0024564632,"threshold_uncertainty_score":0.46943277},"labels":[],"label_agreement":null},{"id":"W4296261087","doi":"10.1503/cmaj.220577","title":"A better normal in Canada will need a better detection system for emerging and re-emerging respiratory pathogens","year":2022,"lang":"en","type":"article","venue":"Canadian Medical Association Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Outbreak; Public health; Coronavirus disease 2019 (COVID-19); Middle East respiratory syndrome; Public health surveillance; 2019-20 coronavirus outbreak; Infectious disease (medical specialty); Disease surveillance; Medicine; Computer science; Data science; Environmental health; Disease; Virology; Pathology","score_opus":0.005995978724998947,"score_gpt":0.2141826170129924,"score_spread":0.20818663828799347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296261087","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03801491,0.017767306,0.048564307,0.6643424,0.010828611,0.0017583336,0.07710068,0.012866747,0.12875661],"genre_scores_gemma":[0.29302204,0.02100129,0.19347252,0.31223363,0.004859199,0.0011308836,0.05913289,0.0024306679,0.11271687],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9935427,0.0009703767,0.00032186354,0.0007599878,0.002994236,0.0014108183],"domain_scores_gemma":[0.93450487,0.0035553076,0.0025635776,0.001669011,0.046890292,0.010816991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006868395,0.0013059318,0.0010300124,0.0032828331,0.0059327306,0.0072194943,0.0044946508,0.0023682523,0.042389154],"category_scores_gemma":[0.021216862,0.00061911717,0.0015288783,0.0031392346,0.0018337397,0.0045069866,0.0039804773,0.0053321146,0.010574854],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016756431,0.00023770785,0.06401748,0.00065361965,0.0001093367,0.0001875853,0.00057206984,0.0012400446,0.0014931306,0.007342875,0.73777866,0.18620004],"study_design_scores_gemma":[0.00016030313,0.00027057255,0.10813262,0.0029072736,0.00021433774,0.00035069196,0.004589421,0.008793413,0.002768521,0.01100035,0.86033857,0.00047392817],"about_ca_topic_score_codex":0.97466135,"about_ca_topic_score_gemma":0.9798623,"teacher_disagreement_score":0.93659115,"about_ca_system_score_codex":0.06340886,"about_ca_system_score_gemma":0.17321473,"threshold_uncertainty_score":0.4600656},"labels":[],"label_agreement":null},{"id":"W4297627112","doi":"10.1007/bf03405115","title":"Modern Information Technologies in Environmental Health Surveillance","year":2002,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Emerging technologies; Context (archaeology); Information technology; Data science; Environmental monitoring; Telecommunications; Engineering; Geography","score_opus":0.033906126641679135,"score_gpt":0.25827583728062553,"score_spread":0.2243697106389464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297627112","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":[0.018372051,0.13183334,0.7514288,0.035621643,0.0020060984,0.00019826775,0.0013719873,0.0016249497,0.057542924],"genre_scores_gemma":[0.41511604,0.117057554,0.44546938,0.0033865026,0.0041932785,0.00027975882,0.0017226601,0.0002445353,0.012530379],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965473,0.0014288872,0.0003555225,0.00035730837,0.0011693131,0.0001416206],"domain_scores_gemma":[0.9863193,0.00982852,0.0005599665,0.0016956718,0.0013067047,0.0002899324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006275318,0.0005327056,0.0008295568,0.0049922178,0.00074861915,0.0072947624,0.0014912729,0.0022302563,0.0040976675],"category_scores_gemma":[0.012855199,0.00082353246,0.00083542493,0.005439221,0.0029193345,0.011586489,0.0022772038,0.002524537,0.00096891937],"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":[0.000100038334,0.00011415435,0.0034123387,0.0008647748,0.00012537105,0.00027634512,0.0006730873,0.012124894,0.0029595653,0.45959112,0.016597105,0.5031612],"study_design_scores_gemma":[0.000043604796,0.000077341225,0.0030077198,0.0007263714,0.00011855929,0.00047308666,0.00053257204,0.062481727,0.003863689,0.66001165,0.26857442,0.00008923749],"about_ca_topic_score_codex":0.005903478,"about_ca_topic_score_gemma":0.0043153046,"teacher_disagreement_score":0.0072947624,"about_ca_system_score_codex":0.0025155495,"about_ca_system_score_gemma":0.0017521415,"threshold_uncertainty_score":0.03318739},"labels":[],"label_agreement":null},{"id":"W4297728781","doi":"","title":"A discrete time sequential method for analyzing censored survival data, Hamilton, Canada","year":2002,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Statistics; Computer science; Geography; Econometrics; Mathematics","score_opus":0.02661017550384648,"score_gpt":0.28793439037080687,"score_spread":0.2613242148669604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297728781","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":[0.013330597,0.0006624125,0.9779237,0.00059465,0.00013930227,0.0003302636,0.0031052139,0.000720739,0.0031931521],"genre_scores_gemma":[0.19744258,0.00154437,0.76894397,0.0002168402,0.000118365555,0.00094972044,0.006066504,0.000234687,0.024482956],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99897313,0.0002817503,0.00005451513,0.00015886255,0.00040911353,0.0001226187],"domain_scores_gemma":[0.9976955,0.00092888926,0.0001065593,0.00020589656,0.00094791624,0.00011525247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025850723,0.00053484755,0.00077544904,0.0016119557,0.0010468285,0.0009724049,0.0016205119,0.0003975061,0.005736129],"category_scores_gemma":[0.007122825,0.0005117296,0.00070546306,0.0029600242,0.00077441667,0.00043137977,0.00078515627,0.00078579504,0.0005316057],"study_design_candidate":"theoretical_or_conceptual","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":[0.00072811556,0.00013692079,0.013793761,0.00044525007,0.0003217855,0.0004230876,0.00041845645,0.34554252,0.0035750298,0.14391914,0.030333318,0.46036258],"study_design_scores_gemma":[0.00023991872,0.0000562164,0.008231193,0.0000587751,0.00011793742,0.00006821595,0.00012142254,0.9143796,0.0015090643,0.04807077,0.02708792,0.000059085214],"about_ca_topic_score_codex":0.8296497,"about_ca_topic_score_gemma":0.853024,"teacher_disagreement_score":0.8296497,"about_ca_system_score_codex":0.006425566,"about_ca_system_score_gemma":0.027086219,"threshold_uncertainty_score":0.34270704},"labels":[],"label_agreement":null},{"id":"W4297774607","doi":"10.1007/bf03405213","title":"The Global Public Health Intelligence Network and Early Warning Outbreak Detection","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":153,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"York University","funders":"University of Toronto","keywords":"Public health; Outbreak; Global health; Warning system; Credibility; Infectious disease (medical specialty); International Health Regulations; Global network; The Internet; Public health surveillance; Political science; Public relations; Environmental health; Medicine; Disease; Coronavirus disease 2019 (COVID-19); Computer science; Telecommunications; Virology; Pathology","score_opus":0.038606138096359446,"score_gpt":0.2939615298437185,"score_spread":0.25535539174735905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297774607","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":[0.25689104,0.02536811,0.28210846,0.15838867,0.0037043428,0.0009550278,0.029070288,0.002729966,0.24078406],"genre_scores_gemma":[0.92168516,0.004054635,0.060024504,0.0025309524,0.00076841883,0.00024961177,0.005261421,0.000063650776,0.0053616515],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969586,0.001873979,0.00013296105,0.00026279083,0.0006347351,0.00013686116],"domain_scores_gemma":[0.98533106,0.009387086,0.0013431173,0.0012615686,0.0021896898,0.0004874061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00669299,0.0004976298,0.00046031046,0.0035635077,0.0005673073,0.0028838702,0.0007639574,0.0011376229,0.0027109964],"category_scores_gemma":[0.028481076,0.00029638942,0.00031680363,0.0027736789,0.000719995,0.0030428302,0.0016505183,0.0010651108,0.00039215092],"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":[0.00045378215,0.00017742094,0.22734047,0.00041145363,0.00027594282,0.0004300088,0.00032524118,0.03878963,0.00073238375,0.09185391,0.105521046,0.53368866],"study_design_scores_gemma":[0.0001997536,0.00035791026,0.14706723,0.0007615622,0.00043497168,0.0010683108,0.0015020433,0.38646168,0.0040146997,0.23089144,0.22710724,0.00013318329],"about_ca_topic_score_codex":0.023810904,"about_ca_topic_score_gemma":0.025615102,"teacher_disagreement_score":0.023810904,"about_ca_system_score_codex":0.00174303,"about_ca_system_score_gemma":0.0027593789,"threshold_uncertainty_score":0.047344625},"labels":[],"label_agreement":null},{"id":"W4298209543","doi":"10.5281/zenodo.6967001","title":"https://www.outlookindia.com/outlook-spotlight/keto-blast-gummies-canada-shark-tank-update-is-blast-keto-gummies-legit-and-effective-check-price-and-official-website-reviews--news-213014","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Library science; Computer science","score_opus":0.020230401480113236,"score_gpt":0.24753559169466174,"score_spread":0.2273051902145485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298209543","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005045108,0.0068160524,0.0011828702,0.020189548,0.0068812873,0.00024935018,0.045855775,0.011664773,0.9066559],"genre_scores_gemma":[0.0048715915,0.010066414,0.0021185125,0.011934496,0.002808158,0.00022007323,0.031285867,0.0034951528,0.93319976],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988487,0.00007506643,0.000034975277,0.00008339804,0.0008124807,0.00014537762],"domain_scores_gemma":[0.99383295,0.00091338967,0.00045455762,0.00032586552,0.002764563,0.0017086316],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002248675,0.00085132587,0.000512842,0.0017268676,0.0008450715,0.005829923,0.0010521354,0.0027304124,0.52647984],"category_scores_gemma":[0.0056513217,0.00043622503,0.00057396636,0.0026038154,0.00046243935,0.0019740693,0.0011652695,0.0019941537,0.4775903],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014887439,0.0000068020186,0.000043356602,0.00007969209,0.000001252517,0.000010316133,0.000011449101,0.000012556894,0.0001170673,0.0004336266,0.9819624,0.01730659],"study_design_scores_gemma":[0.0000039230395,0.0000039067127,0.00022437405,0.000032787648,0.0000012053318,0.0000058835208,0.00001137152,0.000007738045,0.0000643084,0.000060829614,0.99958044,0.0000032031835],"about_ca_topic_score_codex":0.028894385,"about_ca_topic_score_gemma":0.058416028,"teacher_disagreement_score":0.47352016,"about_ca_system_score_codex":0.003610549,"about_ca_system_score_gemma":0.004594206,"threshold_uncertainty_score":0.67541903},"labels":[],"label_agreement":null},{"id":"W4306168025","doi":"10.2196/37669","title":"Digitalizing and Upgrading Severe Acute Respiratory Infections Surveillance in Malta: System Development","year":2022,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"European Centre for Disease Prevention and Control","keywords":"Medicine; Public health; Public health surveillance; Incidence (geometry); Pandemic; Medical emergency; Disease surveillance; Environmental health; Pediatrics; Epidemiology; Coronavirus disease 2019 (COVID-19); Disease; Family medicine; Emergency medicine; Internal medicine; Infectious disease (medical specialty); Pathology","score_opus":0.02979473083371505,"score_gpt":0.29913081325246627,"score_spread":0.26933608241875123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306168025","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":[0.83439934,0.0055136224,0.051859003,0.047842473,0.00021895739,0.007296558,0.011264369,0.0045726974,0.037033003],"genre_scores_gemma":[0.8920388,0.0038776507,0.09350166,0.0010342379,0.00008102709,0.0014355192,0.0055884407,0.00010302986,0.002339628],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98957807,0.0056301057,0.0012833236,0.0011094728,0.0013860742,0.0010128247],"domain_scores_gemma":[0.96720314,0.0082839625,0.005927902,0.0042295638,0.011691416,0.0026640922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016902175,0.00043606723,0.00030411073,0.005208419,0.001299826,0.006632015,0.0021892898,0.0005259928,0.0021282455],"category_scores_gemma":[0.036198374,0.00041803144,0.0003754037,0.0067202738,0.0015292626,0.004800716,0.0060062823,0.0006895682,0.0005669357],"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":[0.00027715904,0.00035973787,0.4330222,0.000993297,0.00009391459,0.00026956561,0.007594281,0.005061121,0.0034676895,0.0047526653,0.011763034,0.5323454],"study_design_scores_gemma":[0.00015950503,0.00091586256,0.76720345,0.0023288096,0.00021412941,0.00050298084,0.025405614,0.052112464,0.012472229,0.0038779248,0.1345794,0.0002276244],"about_ca_topic_score_codex":0.1360244,"about_ca_topic_score_gemma":0.08532813,"teacher_disagreement_score":0.1360244,"about_ca_system_score_codex":0.015283876,"about_ca_system_score_gemma":0.019562168,"threshold_uncertainty_score":0.2704653},"labels":[],"label_agreement":null},{"id":"W4306816466","doi":"10.3389/fonc.2022.833265","title":"Small area disease mapping of cancer incidence in British Columbia using Bayesian spatial models and the smallareamapp R Package","year":2022,"lang":"en","type":"article","venue":"Frontiers in Oncology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Women's Health Research Institute; Simon Fraser University; University of British Columbia; Government of British Columbia; BC Centre for Disease Control; Provincial Health Services Authority","funders":"Canadian Institutes of Health Research","keywords":"Bayesian probability; Incidence (geometry); Spatial epidemiology; Spatial analysis; R package; Medicine; Cancer incidence; Cancer; Cancer registry; Disease; Cartography; Geography; Computer science; Statistics; Epidemiology; Internal medicine; Artificial intelligence; Mathematics","score_opus":0.026967520291961207,"score_gpt":0.26889135297290667,"score_spread":0.24192383268094547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306816466","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":[0.113479964,0.0024769157,0.60519594,0.00210379,0.00017306975,0.00065853336,0.20349826,0.055327676,0.017085802],"genre_scores_gemma":[0.4057014,0.0018202395,0.4889081,0.0003141626,0.00006333882,0.0018278996,0.08301645,0.0074100746,0.010938298],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977714,0.0009307042,0.00012924733,0.000562728,0.00045936645,0.00014650657],"domain_scores_gemma":[0.9932243,0.00367108,0.0005218858,0.00091801333,0.0014470157,0.000217692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00404935,0.0012499272,0.0009652191,0.0038198323,0.0009804434,0.002832989,0.002181891,0.00046348106,0.014447289],"category_scores_gemma":[0.019981075,0.00095074356,0.0017838675,0.0057257684,0.00048768017,0.0006096857,0.0020467883,0.001104626,0.0033909564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039725425,0.00011597988,0.23347116,0.0021079874,0.0024077941,0.0006830745,0.0016293987,0.20640029,0.002048307,0.021519504,0.2793253,0.24989392],"study_design_scores_gemma":[0.00018793179,0.000064413056,0.14311816,0.00075883523,0.0006890806,0.00060105545,0.0007427095,0.63963634,0.0018216518,0.025091378,0.1870482,0.00024023032],"about_ca_topic_score_codex":0.6421226,"about_ca_topic_score_gemma":0.62106115,"teacher_disagreement_score":0.35787737,"about_ca_system_score_codex":0.0028190932,"about_ca_system_score_gemma":0.011663978,"threshold_uncertainty_score":0.71997},"labels":[],"label_agreement":null},{"id":"W4307405414","doi":"10.2196/preprints.43630","title":"Development of an Automated Drug Detection System on Social Media (Preprint)","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Social media; Enforcement; Internet privacy; Law enforcement; Warning system; Tracking (education); Business; Public health; Preprint; Medicine; Public relations; Computer science; Political science; Psychology; World Wide Web; Pathology","score_opus":0.02702705013240267,"score_gpt":0.30707476384137117,"score_spread":0.2800477137089685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307405414","genre_codex":"software","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":[0.11057498,0.0009730845,0.20652285,0.0041168877,0.0021144985,0.004017492,0.13444047,0.49935263,0.037887145],"genre_scores_gemma":[0.38274083,0.00063553336,0.42203617,0.002271768,0.00091498217,0.0024159134,0.14011164,0.0030143315,0.045858797],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917185,0.00009152839,0.00008775847,0.00028541492,0.00028362437,0.000079805686],"domain_scores_gemma":[0.9977629,0.0005272515,0.00017354918,0.00026363568,0.0010948696,0.00017778072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008655442,0.0012520518,0.0008500622,0.0033565145,0.0009684243,0.0015008907,0.0011802776,0.00096810603,0.010664152],"category_scores_gemma":[0.0035751332,0.00045391917,0.0008775995,0.0011700445,0.00020622044,0.0019679824,0.0010707149,0.0007799582,0.012036319],"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":[0.0009826664,0.0007850943,0.035298984,0.0009298828,0.00038923108,0.0015556762,0.0004912198,0.007949326,0.032907106,0.002009252,0.41372365,0.50297797],"study_design_scores_gemma":[0.0003155583,0.0005316428,0.042766377,0.00017140416,0.0002468559,0.000698737,0.0008496123,0.6702326,0.060841706,0.0057026646,0.21739858,0.00024420343],"about_ca_topic_score_codex":0.021168776,"about_ca_topic_score_gemma":0.023887789,"teacher_disagreement_score":0.021168776,"about_ca_system_score_codex":0.0011548481,"about_ca_system_score_gemma":0.0015111113,"threshold_uncertainty_score":0.04209113},"labels":[],"label_agreement":null},{"id":"W4307565510","doi":"10.1038/s41597-022-01743-2","title":"Elaboration of a new framework for fine-grained epidemiological annotation","year":2022,"lang":"en","type":"article","venue":"Scientific Data","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université de Sherbrooke","funders":"Horizon 2020 Framework Programme; Fonds De La Recherche Scientifique - FNRS; FOD Volksgezondheid, Veiligheid van de Voedselketen en Leefmilieu; Centre de Coopération Internationale en Recherche Agronomique pour le Développement; Agence Nationale de la Recherche; European Commission","keywords":"Annotation; Computer science; Variety (cybernetics); Context (archaeology); Data science; Process (computing); Information retrieval; World Wide Web; Artificial intelligence; Geography","score_opus":0.1471525771918431,"score_gpt":0.40116059898123296,"score_spread":0.25400802178938986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307565510","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":[0.000888128,0.00020495622,0.9892812,0.0008616097,0.00014484643,0.00035961607,0.001768546,0.0046539595,0.0018371588],"genre_scores_gemma":[0.010726128,0.0001893162,0.9795415,0.0003516041,0.00015763612,0.00038878073,0.0061542066,0.00081009656,0.0016808618],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9888975,0.0033455547,0.0019337413,0.002855084,0.0024846736,0.0004834173],"domain_scores_gemma":[0.97695166,0.010363368,0.0014254655,0.0044962512,0.0058003226,0.00096291804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016172586,0.0020608068,0.0017841749,0.010555179,0.0034805422,0.008554767,0.0057348884,0.003448615,0.007406596],"category_scores_gemma":[0.024707993,0.0020133848,0.004505492,0.0071002766,0.0037815198,0.009745414,0.010376668,0.005839479,0.004457493],"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":[0.0003089092,0.0005242084,0.0045945146,0.0029697905,0.0004061774,0.0018982501,0.009145775,0.027463622,0.02405851,0.4444767,0.080726616,0.40342695],"study_design_scores_gemma":[0.00008599353,0.000121172634,0.0019150147,0.0010682321,0.00029424558,0.0010489898,0.0015747299,0.24266312,0.017099747,0.33348978,0.40035093,0.00028806244],"about_ca_topic_score_codex":0.024244592,"about_ca_topic_score_gemma":0.031430695,"teacher_disagreement_score":0.024244592,"about_ca_system_score_codex":0.0031750663,"about_ca_system_score_gemma":0.0075910725,"threshold_uncertainty_score":0.085529864},"labels":[],"label_agreement":null},{"id":"W4308554578","doi":"10.1111/jnu.12844","title":"Global utilization of online information for substance use disorder: An infodemiological study of Google and Wikipedia from 2004 to 2022","year":2022,"lang":"en","type":"article","venue":"Journal of Nursing Scholarship","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Autoregressive integrated moving average; The Internet; Page view; Social media; Psychoactive substance; Business; World Wide Web; Psychology; Medicine; Computer science; Psychiatry; Web navigation","score_opus":0.10189901352116806,"score_gpt":0.3884236833387307,"score_spread":0.28652466981756264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308554578","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":[0.9902334,0.0009945561,0.00005591526,0.0003516133,0.000017319033,0.00003827094,0.0058753616,0.0000139665435,0.0024196086],"genre_scores_gemma":[0.9952302,0.0006130853,0.0001370399,0.00008369575,0.00002515774,0.00004035768,0.003383127,0.000011868679,0.00047552292],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987895,0.00033904583,0.00021583705,0.00015384647,0.00033108512,0.00017068484],"domain_scores_gemma":[0.9814589,0.005561347,0.009229779,0.00038531932,0.0021683644,0.0011962716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017763256,0.000267654,0.0003641465,0.010037072,0.000609895,0.0026413074,0.00052775955,0.0005482767,0.0016893648],"category_scores_gemma":[0.010676785,0.00019693119,0.00070879346,0.013391571,0.0007735463,0.0037472341,0.0015908115,0.0005862969,0.00051271636],"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":[0.00015020002,0.0001216749,0.97855294,0.00034745337,0.00010875468,0.00026287942,0.007888,0.00013788282,0.000093547336,0.00033800982,0.0021008237,0.009897804],"study_design_scores_gemma":[0.0000039666843,0.000092361035,0.98524797,0.00013631019,0.000046326237,0.00021817733,0.011151934,0.00036265136,0.00009125654,0.000055124947,0.0025785284,0.00001542213],"about_ca_topic_score_codex":0.034962058,"about_ca_topic_score_gemma":0.043392405,"teacher_disagreement_score":0.034962058,"about_ca_system_score_codex":0.0012846788,"about_ca_system_score_gemma":0.00088281406,"threshold_uncertainty_score":0.069517136},"labels":[],"label_agreement":null},{"id":"W4308726062","doi":"10.2196/38037","title":"Modeling the Potential Impact of Missing Race and Ethnicity Data in Infectious Disease Surveillance Systems on Disparity Measures: Scenario Analysis of Different Imputation Strategies","year":2022,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention; Centers for Disease Control and Prevention; New York State Department of Health","keywords":"Gonorrhea; Imputation (statistics); Ethnic group; Chlamydia; Demography; Missing data; Medicine; Population; Medical diagnosis; Environmental health; Statistics; Family medicine; Immunology; Political science","score_opus":0.04843104351523602,"score_gpt":0.3529417469302488,"score_spread":0.3045107034150128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308726062","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8924498,0.0010043411,0.09315497,0.003159385,0.00011770295,0.0007735309,0.004201294,0.00018858271,0.0049504703],"genre_scores_gemma":[0.97596574,0.00020120577,0.020859066,0.0002946326,0.000028705786,0.00045481612,0.0014599129,0.000021950504,0.00071386824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9805635,0.015856512,0.0005258008,0.0014493038,0.0005582195,0.0010466148],"domain_scores_gemma":[0.90794533,0.07845271,0.006355506,0.0030211706,0.0032939243,0.00093137403],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.041153856,0.001258592,0.0012245587,0.0017664623,0.0010018889,0.0024002506,0.003076835,0.0025142615,0.0030407016],"category_scores_gemma":[0.071700364,0.00097570365,0.004467652,0.0018056168,0.0011192599,0.003256281,0.002410771,0.0031671156,0.0002493081],"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":[0.0006232478,0.00028595916,0.08473105,0.0002088018,0.0011348508,0.00020578371,0.00041138357,0.88508683,0.000182608,0.015188221,0.0014803645,0.01046096],"study_design_scores_gemma":[0.00011131415,0.0003638465,0.011789864,0.00011965121,0.00037982126,0.00010219768,0.0003588244,0.97158855,0.00026180028,0.014115135,0.0007488439,0.000060136757],"about_ca_topic_score_codex":0.02784088,"about_ca_topic_score_gemma":0.015822519,"teacher_disagreement_score":0.95884615,"about_ca_system_score_codex":0.0043649445,"about_ca_system_score_gemma":0.0026007353,"threshold_uncertainty_score":0.21764505},"labels":[],"label_agreement":null},{"id":"W4308782123","doi":"10.1186/s12940-022-00938-9","title":"Correction: Toolkit for detecting misused epidemiological methods","year":2022,"lang":"en","type":"erratum","venue":"Environmental Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","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 Alberta","funders":"","keywords":"Epidemiology; Computer science; Data science; Environmental health; Environmental epidemiology; Medicine; Pathology","score_opus":0.05351592208366875,"score_gpt":0.39871768874694613,"score_spread":0.3452017666632774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308782123","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003452756,0.00059819553,0.009281789,0.09599083,0.832277,0.00021590234,0.03486504,0.014093042,0.012332921],"genre_scores_gemma":[0.019037923,0.003378998,0.07929593,0.21542378,0.17851664,0.0018628184,0.039387364,0.043468367,0.41962817],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9788994,0.004768368,0.005199016,0.0023587446,0.0074546617,0.0013198168],"domain_scores_gemma":[0.7695313,0.093674965,0.007917071,0.018847665,0.10610199,0.003927069],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.011918401,0.0035426063,0.0028302628,0.008773547,0.0050857426,0.008291446,0.005503561,0.010640341,0.23371637],"category_scores_gemma":[0.28677464,0.002566127,0.002726209,0.007435928,0.003627963,0.004741733,0.0047740145,0.012386083,0.14588723],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021668497,0.0000040276427,0.00008277892,0.000107993095,0.000010036927,0.000081515136,0.000029144112,0.000034276614,0.000019914436,0.0004201337,0.9965049,0.0026836474],"study_design_scores_gemma":[0.00011223763,0.000011927658,0.00081262697,0.0006099941,0.000052759915,0.00042966424,0.00013021506,0.0006045416,0.00045932672,0.0042561777,0.9924454,0.00007509201],"about_ca_topic_score_codex":0.044492666,"about_ca_topic_score_gemma":0.055270076,"teacher_disagreement_score":0.9880816,"about_ca_system_score_codex":0.0042983512,"about_ca_system_score_gemma":0.013306154,"threshold_uncertainty_score":0.78185916},"labels":[],"label_agreement":null},{"id":"W4309442889","doi":"10.2196/43253","title":"Leveraging Symptom Search Data to Understand Disparities in US Mental Health Care: Demographic Analysis of Search Engine Trace Data","year":2022,"lang":"en","type":"article","venue":"JMIR Mental Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Division of Information and Intelligent Systems; National Institute of Mental Health; National Institutes of Health; National Science Foundation","keywords":"Mental health; Medicine; Psychiatry; Anxiety; Poverty; Psychology","score_opus":0.08250677817147742,"score_gpt":0.40141271794175726,"score_spread":0.3189059397702798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309442889","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":[0.9774226,0.0005063782,0.0010442745,0.0003816413,0.000011984443,0.000083172345,0.019550182,0.000058045825,0.00094177213],"genre_scores_gemma":[0.98282295,0.000165156,0.001479609,0.000080469195,0.00001611926,0.00010304924,0.015144645,0.000023840359,0.00016413382],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9968382,0.0011465042,0.0007043208,0.00045890093,0.000528523,0.00032346626],"domain_scores_gemma":[0.97118497,0.016442034,0.007881736,0.0016656707,0.0020141068,0.00081155624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059580123,0.00029291114,0.00042251492,0.010689496,0.0003672428,0.0012989071,0.00048216837,0.000417384,0.0016290031],"category_scores_gemma":[0.035283282,0.00021298723,0.0010834541,0.009635226,0.00034614798,0.0026725128,0.0017901117,0.0006288098,0.0003321087],"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":[0.00005982023,0.000031616346,0.99389356,0.00007673126,0.0001547146,0.000022171937,0.00031329782,0.00014666311,0.00011440275,0.00010573766,0.00064315996,0.0044382485],"study_design_scores_gemma":[0.000010267898,0.00006686564,0.9919457,0.000064000975,0.000110630186,0.00012593644,0.001645954,0.0037036138,0.0002205341,0.00030245085,0.001788243,0.000015890468],"about_ca_topic_score_codex":0.020046627,"about_ca_topic_score_gemma":0.026861323,"teacher_disagreement_score":0.020046627,"about_ca_system_score_codex":0.0006458425,"about_ca_system_score_gemma":0.0009338444,"threshold_uncertainty_score":0.03985989},"labels":[],"label_agreement":null},{"id":"W4310088355","doi":"10.5281/zenodo.7369365","title":"[A^I^R™]** TEXANS vs DOLPHINS LIVE FREE","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Medicine","score_opus":0.028760501082029308,"score_gpt":0.2510208505583492,"score_spread":0.2222603494763199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310088355","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025722191,0.00020896425,0.00021247468,0.00037121505,0.000654755,0.00003873454,0.981642,0.0022121768,0.012087426],"genre_scores_gemma":[0.006132866,0.00015672631,0.000668895,0.00029896412,0.00008236148,0.00010368682,0.9793514,0.0007941185,0.01241112],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992513,0.00008826378,0.000061068895,0.00027909203,0.00017139483,0.00014876819],"domain_scores_gemma":[0.9985846,0.00034429337,0.00014025041,0.0003419511,0.00044484352,0.00014409843],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00049616053,0.0012219545,0.0008821506,0.0019761473,0.00079938176,0.0021665064,0.0012043884,0.00075134286,0.16693394],"category_scores_gemma":[0.0042203707,0.0003336905,0.0008971397,0.0026938003,0.0003182816,0.0021260027,0.0014829696,0.0014755727,0.13648959],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011323848,0.000016910733,0.0017128342,0.00019883753,0.000015687614,0.000009679562,0.000028293263,0.000068766174,0.00011967407,0.00035523527,0.99242264,0.0049383403],"study_design_scores_gemma":[0.00014794103,0.000051318246,0.019724187,0.00020845149,0.000018864188,0.000065532644,0.00035946275,0.00042272662,0.0004080118,0.0008933522,0.9776684,0.00003171812],"about_ca_topic_score_codex":0.0329261,"about_ca_topic_score_gemma":0.053104054,"teacher_disagreement_score":0.83306605,"about_ca_system_score_codex":0.00081682677,"about_ca_system_score_gemma":0.0006312672,"threshold_uncertainty_score":0.5584496},"labels":[],"label_agreement":null},{"id":"W4310088728","doi":"10.5281/zenodo.7369359","title":"[W-A-T-C-H™]** RAVENS VS JAGUARS LIVE FREE WEEK-12","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Art","score_opus":0.033174672972972316,"score_gpt":0.24591893440588372,"score_spread":0.21274426143291142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310088728","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010517362,0.00013746224,0.00052922097,0.000410818,0.0008836482,0.00007234576,0.9677815,0.0087709185,0.020362303],"genre_scores_gemma":[0.0019550803,0.00009822622,0.0009638963,0.00026394692,0.00008384389,0.00012581982,0.98021495,0.0026271052,0.013667053],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988943,0.00015545967,0.00009511909,0.0004351409,0.00020857771,0.00021133463],"domain_scores_gemma":[0.9975776,0.00037952414,0.00015925105,0.0008435185,0.00078048423,0.0002596898],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00083331333,0.0018213695,0.0010720254,0.0023236787,0.001199501,0.0039975625,0.001979256,0.00091053353,0.2807828],"category_scores_gemma":[0.005757489,0.00073935033,0.0011274569,0.0038458654,0.00038543678,0.0032515097,0.0024744775,0.0017188755,0.37469253],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050851308,0.000008458955,0.0005019213,0.00009661678,0.000007733357,0.000004760895,0.000016059688,0.00002969275,0.000054356897,0.00022176058,0.9957278,0.0032802029],"study_design_scores_gemma":[0.00007072535,0.000023163835,0.0050237854,0.00009518108,0.000007375544,0.000023692262,0.000111906906,0.00021872815,0.00022528705,0.0005615309,0.9936138,0.000024929785],"about_ca_topic_score_codex":0.031299505,"about_ca_topic_score_gemma":0.048361562,"teacher_disagreement_score":0.7192172,"about_ca_system_score_codex":0.0008105703,"about_ca_system_score_gemma":0.0008231893,"threshold_uncertainty_score":0.939312},"labels":[],"label_agreement":null},{"id":"W4310435601","doi":"10.5281/zenodo.7381186","title":"[\"LIVE@STREAM\"]]* Mosconi Cup 2022 Live Coverage TV","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Environmental science","score_opus":0.023309556619034614,"score_gpt":0.251900098006938,"score_spread":0.2285905413879034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310435601","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019039348,0.000092595896,0.00028638143,0.0003831679,0.0004473588,0.00004169407,0.98157436,0.009788974,0.007195081],"genre_scores_gemma":[0.00046815752,0.00008278884,0.0005034375,0.00020978412,0.00007126439,0.000086631495,0.99187684,0.0021193004,0.0045816894],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985441,0.00020876824,0.00011441595,0.0004995577,0.0003656052,0.00026762538],"domain_scores_gemma":[0.99672866,0.00040942008,0.00014957672,0.0010819281,0.0012426551,0.00038783858],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012901422,0.0032492667,0.0018436507,0.0028036374,0.0016103412,0.0064919866,0.0031569612,0.0020056132,0.2595021],"category_scores_gemma":[0.006891413,0.0010620999,0.0014800254,0.006328367,0.0004550742,0.006043198,0.0042487388,0.002668279,0.48038402],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020115245,0.0000034088675,0.00007095539,0.00005963777,0.0000029994887,0.0000022784616,0.000005674058,0.000014226933,0.000027701863,0.00007826769,0.998723,0.0009916403],"study_design_scores_gemma":[0.00005510681,0.000011092189,0.0011744942,0.00008320222,0.000004987498,0.000014783191,0.00005442923,0.00029214987,0.00021267636,0.00045307024,0.99762064,0.00002326998],"about_ca_topic_score_codex":0.024308087,"about_ca_topic_score_gemma":0.032867417,"teacher_disagreement_score":0.7404979,"about_ca_system_score_codex":0.0012303161,"about_ca_system_score_gemma":0.0016547148,"threshold_uncertainty_score":0.868121},"labels":[],"label_agreement":null},{"id":"W4310457196","doi":"10.2196/40825","title":"State-Level COVID-19 Symptom Searches and Case Data: Quantitative Analysis of Political Affiliation as a Predictor for Lag Time Using Google Trends and Centers for Disease Control and Prevention Data","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Operationalization; Politics; Lag; State (computer science); Coronavirus disease 2019 (COVID-19); Pandemic; Demography; Political science; Demographic economics; Psychology; Statistics; Medicine; Disease; Economics; Computer science; Mathematics; Sociology; Law","score_opus":0.2514002726675043,"score_gpt":0.509529806523304,"score_spread":0.25812953385579973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310457196","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":[0.9814627,0.0002637152,0.0020338704,0.0005531333,0.000026229416,0.00010376844,0.013751084,0.0000658053,0.001739693],"genre_scores_gemma":[0.9921525,0.00006244061,0.0011513527,0.00005262742,0.000017968425,0.00015111097,0.0061116307,0.000015787458,0.00028460592],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99510044,0.0018643341,0.0005676627,0.0009980389,0.0009820716,0.00048752496],"domain_scores_gemma":[0.93318766,0.04272811,0.016364517,0.0030518842,0.0033363495,0.0013314418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006457099,0.00025452112,0.00039878956,0.003424884,0.00056427874,0.0019181982,0.0006733811,0.00061124796,0.002781454],"category_scores_gemma":[0.049944464,0.0002689686,0.0009456474,0.0073417453,0.00076159753,0.0023655072,0.0015325156,0.0013454715,0.0007174525],"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":[0.00008957268,0.000054601373,0.9928665,0.00006453838,0.00010104329,0.00003676294,0.0009177623,0.0006920086,0.0001171965,0.00044498197,0.0012370163,0.0033780015],"study_design_scores_gemma":[0.0000092379705,0.00010954717,0.98305124,0.000070654336,0.00006095462,0.00012358383,0.004087151,0.009293016,0.00026193078,0.0006777452,0.002222913,0.000032001244],"about_ca_topic_score_codex":0.016304841,"about_ca_topic_score_gemma":0.018340314,"teacher_disagreement_score":0.016304841,"about_ca_system_score_codex":0.0007964332,"about_ca_system_score_gemma":0.0009968167,"threshold_uncertainty_score":0.034148872},"labels":[],"label_agreement":null},{"id":"W4311827969","doi":"10.1371/journal.pdig.0000150","title":"Using Primary Care Clinical Text Data and Natural Language Processing to Identify Indicators of COVID-19 in Toronto, Canada","year":2022,"lang":"en","type":"article","venue":"PLOS Digital Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"North York General Hospital; University of Toronto","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Medicine; Medical record; Retrospective cohort study; Severe acute respiratory syndrome; Primary care; Artificial intelligence; Family medicine; Pediatrics; Natural language processing; Computer science; Internal medicine; Disease","score_opus":0.05818390352170421,"score_gpt":0.43011884752479224,"score_spread":0.371934944003088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311827969","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":[0.9611228,0.0011290957,0.0013473884,0.0008510354,0.000023159364,0.0002616225,0.03126223,0.00011730603,0.0038853039],"genre_scores_gemma":[0.9860036,0.0006018349,0.0024192429,0.00018876827,0.000012105739,0.000056948717,0.00959275,0.000011874697,0.0011128809],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99850106,0.000185621,0.00019414736,0.00032255342,0.0005454404,0.0002512101],"domain_scores_gemma":[0.99192315,0.0016701547,0.0012956883,0.00025163798,0.0042063696,0.0006529582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010787973,0.00031371514,0.0002609031,0.002778555,0.000995303,0.001640187,0.0006857775,0.00030659276,0.0012254063],"category_scores_gemma":[0.008579294,0.00023278224,0.00030277215,0.005129179,0.0006014142,0.00043643778,0.00067924935,0.00031948127,0.00023259856],"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":[0.00016190039,0.000033816275,0.97559386,0.00025384472,0.000065009124,0.00040556455,0.0016277505,0.0012587215,0.001131164,0.00021647237,0.0027252126,0.016526673],"study_design_scores_gemma":[0.000012612018,0.000040682906,0.99045813,0.00006835819,0.000046312078,0.00011384945,0.0020030357,0.0035661843,0.00061965553,0.000062114595,0.0029880758,0.000021026495],"about_ca_topic_score_codex":0.991789,"about_ca_topic_score_gemma":0.99207485,"teacher_disagreement_score":0.026342748,"about_ca_system_score_codex":0.026342748,"about_ca_system_score_gemma":0.03496978,"threshold_uncertainty_score":0.19113094},"labels":[],"label_agreement":null},{"id":"W4312189790","doi":"10.2196/37122","title":"The Role of Heterogenous Real-world Data for Dengue Surveillance in Martinique: Observational Retrospective Study","year":2022,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Martinique; Observational study; Dengue fever; Medicine; Virology; Environmental health; History; West indies; Internal medicine","score_opus":0.0768604925647218,"score_gpt":0.36727020031403823,"score_spread":0.2904097077493164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312189790","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":[0.9968681,0.0005112145,0.0002590511,0.000074531046,0.0000054401007,0.000069591995,0.0019705533,0.000005266445,0.00023624333],"genre_scores_gemma":[0.99799806,0.00016681073,0.00022202701,0.00003258015,0.0000071150143,0.00008435187,0.0014233076,0.0000028578654,0.000062840605],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99660975,0.001477651,0.00046007932,0.0005248036,0.0004750203,0.00045264722],"domain_scores_gemma":[0.986013,0.0035946304,0.0070109544,0.0013258836,0.001481318,0.00057435734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044605364,0.0004480375,0.00057600776,0.0023895032,0.00057134614,0.0011312981,0.00074416015,0.0004640131,0.00073177076],"category_scores_gemma":[0.013055084,0.00050405023,0.0007608229,0.0029398748,0.0005929704,0.0008715803,0.0009682707,0.00058014976,0.00012672048],"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":[0.0000502557,0.000019837098,0.9988777,0.000041475923,0.000060152845,0.00007204635,0.0002124648,0.00004327642,0.000057201796,0.000014034597,0.000086394546,0.00046503867],"study_design_scores_gemma":[0.000006367906,0.000084538275,0.99803096,0.000055464876,0.000052918193,0.00019095649,0.0008446784,0.0003024771,0.000042164997,0.000008168556,0.0003721041,0.000009051655],"about_ca_topic_score_codex":0.087347746,"about_ca_topic_score_gemma":0.07831555,"teacher_disagreement_score":0.087347746,"about_ca_system_score_codex":0.0021682945,"about_ca_system_score_gemma":0.0022466327,"threshold_uncertainty_score":0.17367864},"labels":[],"label_agreement":null},{"id":"W4312242765","doi":"10.2196/42292","title":"Big Data and Infectious Disease Epidemiology: Bibliometric Analysis and Research Agenda","year":2022,"lang":"en","type":"article","venue":"Interactive Journal of Medical Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Big data; Disease surveillance; Data science; Infectious disease (medical specialty); Informatics; Epidemiology; Disease; Pandemic; Health informatics; The Internet; Public health; Medicine; Computer science; Political science; Coronavirus disease 2019 (COVID-19); World Wide Web; Data mining; Pathology","score_opus":0.3080709112029464,"score_gpt":0.5562182349421962,"score_spread":0.24814732373924986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312242765","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10448576,0.5501314,0.027964314,0.23053235,0.002873237,0.0009052551,0.0075425724,0.00036296097,0.075202145],"genre_scores_gemma":[0.5430804,0.4105746,0.03041271,0.0047285124,0.005101154,0.00067628024,0.003501538,0.00006650939,0.0018583178],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98153573,0.0096236225,0.0015430393,0.0008787066,0.005851567,0.0005673483],"domain_scores_gemma":[0.8814325,0.09058195,0.01021761,0.0035401988,0.01255723,0.0016705707],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.025818802,0.00096536084,0.0023888713,0.11122602,0.0027881914,0.016103169,0.0012955638,0.0020443276,0.0032753837],"category_scores_gemma":[0.080375865,0.00048953434,0.0013323816,0.16651312,0.004243781,0.014561708,0.0033368166,0.0014649107,0.00042641882],"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":[0.00015226308,0.00023740214,0.16749832,0.021904785,0.0011880624,0.0004768042,0.005717061,0.004274429,0.00048289303,0.21175563,0.04778981,0.53852254],"study_design_scores_gemma":[0.000078248595,0.0002328227,0.19222473,0.036239784,0.001125485,0.0015034085,0.043748286,0.01955951,0.0014208137,0.3833643,0.32020608,0.00029651576],"about_ca_topic_score_codex":0.007826701,"about_ca_topic_score_gemma":0.008778997,"teacher_disagreement_score":0.9741812,"about_ca_system_score_codex":0.0076642134,"about_ca_system_score_gemma":0.014232049,"threshold_uncertainty_score":0.13654453},"labels":[],"label_agreement":null},{"id":"W4312336284","doi":"10.1007/978-3-031-12240-8_2","title":"Alternative Data","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"HEC Montréal","funders":"","keywords":"Data science; Computer science; Big data; Context (archaeology); Variety (cybernetics); The Internet; Multitude; Credit card; World Wide Web; Data mining; Geography","score_opus":0.0777190235464245,"score_gpt":0.32082985065825276,"score_spread":0.24311082711182824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312336284","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011667705,0.0026090962,0.19399354,0.010791552,0.002709093,0.00020726508,0.004688077,0.0010410385,0.7827936],"genre_scores_gemma":[0.04179556,0.005585301,0.15807505,0.0065909973,0.0019199045,0.0004967132,0.010278125,0.0013577681,0.77390057],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9958593,0.0011710081,0.00017663636,0.00066856365,0.0019644406,0.00016005669],"domain_scores_gemma":[0.99358255,0.002502445,0.00015153934,0.0026701463,0.0009330336,0.00016034437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004448889,0.0010368174,0.0007030215,0.002500148,0.0011033977,0.007045631,0.0032058705,0.0018784809,0.15274253],"category_scores_gemma":[0.01385205,0.0006919295,0.0010601176,0.0038316206,0.0020458263,0.011156053,0.0046304283,0.003995749,0.047423307],"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":[0.000040741365,0.000032875516,0.00016175123,0.00017495616,0.000017536802,0.00008711299,0.00017928545,0.0013694436,0.0002776545,0.68964654,0.16154225,0.14646982],"study_design_scores_gemma":[0.000007969328,0.000009030905,0.00007860833,0.0001297322,0.0000067889746,0.00017566196,0.000105131694,0.0030705167,0.00042000882,0.25014722,0.7458355,0.000013876611],"about_ca_topic_score_codex":0.002419629,"about_ca_topic_score_gemma":0.0031495087,"teacher_disagreement_score":0.15274253,"about_ca_system_score_codex":0.0019729582,"about_ca_system_score_gemma":0.0018541093,"threshold_uncertainty_score":0.51097465},"labels":[],"label_agreement":null},{"id":"W4312729483","doi":"10.2196/40403","title":"Development of a COVID-19–Related Anti-Asian Tweet Data Set: Quantitative Study","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Western University; Institute for Christian Studies; University of Toronto","funders":"","keywords":"Social media; Set (abstract data type); Computer science; Stigma (botany); Data set; Coronavirus disease 2019 (COVID-19); Information retrieval; Internet privacy; Artificial intelligence; Psychology; World Wide Web; Medicine","score_opus":0.22349808099952279,"score_gpt":0.5090776074569722,"score_spread":0.2855795264574494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312729483","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":[0.8450147,0.0003836589,0.02447043,0.002206324,0.00024402804,0.004259185,0.11689934,0.00091160496,0.0056106225],"genre_scores_gemma":[0.6538019,0.00026655672,0.08486684,0.00065560156,0.00014616134,0.00954235,0.24768369,0.00027009877,0.002766861],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99194545,0.003937108,0.00060412753,0.0010615375,0.0021491575,0.00030262853],"domain_scores_gemma":[0.96473634,0.020418908,0.0019841236,0.0028280313,0.008754885,0.0012777174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00898036,0.00045107966,0.00044988416,0.0035850008,0.0014819158,0.0012707018,0.0015901808,0.0012852908,0.0021970242],"category_scores_gemma":[0.030463476,0.0002746984,0.00045726972,0.003698009,0.0011340955,0.0023607835,0.0018044825,0.0019475558,0.0018186842],"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":[0.0011885066,0.006654189,0.5123479,0.004845901,0.00034528392,0.001742692,0.021272711,0.014949882,0.018504838,0.011456025,0.17518623,0.23150596],"study_design_scores_gemma":[0.00036221158,0.001846918,0.60194075,0.0011004627,0.00020758437,0.0011555398,0.043792013,0.11102895,0.022601312,0.0055126376,0.21002948,0.00042210927],"about_ca_topic_score_codex":0.010711125,"about_ca_topic_score_gemma":0.015245321,"teacher_disagreement_score":0.010711125,"about_ca_system_score_codex":0.0018287497,"about_ca_system_score_gemma":0.0016314611,"threshold_uncertainty_score":0.04749328},"labels":[],"label_agreement":null},{"id":"W4312865864","doi":"10.2196/41269","title":"Comparative Performance Evaluation of the Public Health Surveillance Systems in 6 Gulf Cooperation Countries: Cross-sectional Study","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Public health surveillance; Representativeness heuristic; Public health; Medicine; Environmental health; Disease surveillance; Global health; Statistics","score_opus":0.17879502549855358,"score_gpt":0.4826628817445512,"score_spread":0.3038678562459976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312865864","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":[0.9987973,0.00021863596,0.00013180374,0.000030071751,0.000005921332,0.00007769491,0.000335054,0.0000053757385,0.00039807076],"genre_scores_gemma":[0.9988661,0.00012798212,0.0003022793,0.000012517984,0.000005060859,0.000057919187,0.0005673507,0.0000015911842,0.000059132777],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98814243,0.006408095,0.0019804505,0.00076519983,0.0021099946,0.0005938278],"domain_scores_gemma":[0.95705414,0.014597611,0.013065194,0.0017964907,0.011484806,0.0020017626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025412282,0.0006608426,0.00057395874,0.004108054,0.0005898142,0.0013426847,0.000606325,0.0005810222,0.0009176653],"category_scores_gemma":[0.034084536,0.00038814417,0.0010905603,0.0045197,0.0009117211,0.0012618377,0.0013180954,0.000587504,0.00019670691],"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":[0.00018088908,0.00010877483,0.9949504,0.00006759002,0.00011823566,0.000036539255,0.001336611,0.00020306741,0.00006740444,0.000035910245,0.00013888995,0.0027557628],"study_design_scores_gemma":[0.000012951043,0.00066903606,0.9949681,0.00004479974,0.000046495235,0.00010823474,0.003181912,0.0005095688,0.00013405162,0.000015225099,0.0002983206,0.000011389422],"about_ca_topic_score_codex":0.0080367075,"about_ca_topic_score_gemma":0.004407765,"teacher_disagreement_score":0.025412282,"about_ca_system_score_codex":0.0020775057,"about_ca_system_score_gemma":0.0015528005,"threshold_uncertainty_score":0.13439459},"labels":[],"label_agreement":null},{"id":"W4312979111","doi":"10.2196/34387","title":"Architecture Assessment of the Chilean Epidemiological Surveillance System for Notifiable Diseases (EPIVIGILA): Qualitative Study","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Architecture; Software deployment; Information system; Computer science; Pandemic; Epidemiology; Medicine; Data science; Business; Environmental health; Disease; Coronavirus disease 2019 (COVID-19); Political science; Geography; Infectious disease (medical specialty)","score_opus":0.0766638175276662,"score_gpt":0.4775288154947652,"score_spread":0.400864997967099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312979111","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":[0.9675166,0.0077491906,0.0036184546,0.005772588,0.00005899848,0.0011680212,0.00074528885,0.000022625716,0.013348136],"genre_scores_gemma":[0.9927268,0.0029759158,0.0024238864,0.000461971,0.000009722122,0.00054318603,0.00015869777,0.000010274905,0.00068954506],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9786606,0.01584782,0.0017018347,0.0007860762,0.002139965,0.00086370227],"domain_scores_gemma":[0.931408,0.05306864,0.0053293486,0.0008304972,0.007792152,0.0015713709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04211065,0.0002929544,0.00067746924,0.0034942331,0.0022147633,0.0039820485,0.0010576335,0.00069715554,0.001674562],"category_scores_gemma":[0.058135614,0.00038064708,0.00051883585,0.003307821,0.0030501569,0.0041145906,0.0030955295,0.0008728415,0.00008248261],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000118323456,0.00007294238,0.061764196,0.010948643,0.00011409883,0.0009907847,0.876938,0.00018346352,0.0009548584,0.006116031,0.0017114573,0.040087305],"study_design_scores_gemma":[0.000016637574,0.00013079178,0.02496167,0.0057301563,0.00010238969,0.00029428265,0.9424535,0.0003124814,0.00038523923,0.00067901175,0.024902876,0.00003106524],"about_ca_topic_score_codex":0.01837628,"about_ca_topic_score_gemma":0.019081527,"teacher_disagreement_score":0.04211065,"about_ca_system_score_codex":0.013086442,"about_ca_system_score_gemma":0.018893426,"threshold_uncertainty_score":0.22270507},"labels":[],"label_agreement":null},{"id":"W4313268822","doi":"10.1101/2022.12.15.520583","title":"A schema for digitized surface swab site metadata in open-source DNA sequence databases","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Institute of Food and Agriculture; U.S. Department of Agriculture","keywords":"Metadata; Computer science; Information retrieval; Controlled vocabulary; World Wide Web; Database","score_opus":0.05559735390608114,"score_gpt":0.30677772134651027,"score_spread":0.25118036744042915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313268822","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":[0.02490504,0.00047790477,0.89161813,0.0017359357,0.00019197626,0.0029900772,0.05877541,0.009007595,0.010298048],"genre_scores_gemma":[0.051092975,0.00062535354,0.853477,0.00046296866,0.000049478567,0.0014567067,0.08959857,0.000981995,0.00225492],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9926563,0.0014600009,0.0031530322,0.0009851045,0.0014861305,0.0002593676],"domain_scores_gemma":[0.97820854,0.007595171,0.0033330016,0.004494601,0.0056824726,0.00068620837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012982993,0.0006562339,0.00083169655,0.012640722,0.0016963837,0.00790517,0.0019863576,0.0017948099,0.0035971077],"category_scores_gemma":[0.02116718,0.0009549896,0.0016442493,0.012860242,0.0017629082,0.0096271215,0.0037542935,0.0017413642,0.003103958],"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":[0.00036923803,0.0005558705,0.03836496,0.0029617418,0.0001958096,0.002434785,0.013316017,0.016557923,0.029339861,0.5797162,0.056516025,0.25967148],"study_design_scores_gemma":[0.00010379585,0.00018979337,0.011750955,0.0023181133,0.00014258952,0.0015446265,0.0060435887,0.049424548,0.016960278,0.10067148,0.81063277,0.00021759521],"about_ca_topic_score_codex":0.010626074,"about_ca_topic_score_gemma":0.008807686,"teacher_disagreement_score":0.012982993,"about_ca_system_score_codex":0.0035808554,"about_ca_system_score_gemma":0.0067109982,"threshold_uncertainty_score":0.06866145},"labels":[],"label_agreement":null},{"id":"W4313536866","doi":"10.2196/42401","title":"The Effect of the COVID-19 Pandemic on Digital Health–Seeking Behavior: Big Data Interrupted Time-Series Analysis of Google Trends","year":2023,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Interrupted Time Series Analysis; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Time series; Series (stratigraphy); Interrupted time series; Big data; Computer science; Psychology; Medicine; Virology; Statistics; Data mining; Psychological intervention; Mathematics; Outbreak; Biology; Psychiatry","score_opus":0.18983780894771202,"score_gpt":0.4971471565111147,"score_spread":0.30730934756340267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313536866","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":[0.9748814,0.00063532,0.002408716,0.00070178095,0.00006421879,0.00007204548,0.019625207,0.000107327476,0.0015039722],"genre_scores_gemma":[0.9799168,0.0001925527,0.0015413526,0.00007599917,0.00004853609,0.00009824224,0.01769295,0.00002341322,0.00041025464],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976095,0.0008585224,0.00029473854,0.00049219176,0.0004431995,0.0003018947],"domain_scores_gemma":[0.9881265,0.006239516,0.0031186708,0.0011471757,0.00093365676,0.00043451777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003093641,0.0004219108,0.0006269663,0.003010654,0.00035502206,0.0015397003,0.00073598337,0.0008592657,0.0016505294],"category_scores_gemma":[0.015771372,0.0002452909,0.0013415318,0.005168376,0.0005933419,0.0011889022,0.0010048685,0.0011571822,0.00061633566],"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":[0.0003376803,0.00015176713,0.97306675,0.0002503291,0.00046255847,0.00031868462,0.0007684395,0.008965139,0.00035364972,0.0014440486,0.0042652916,0.009615659],"study_design_scores_gemma":[0.00002471913,0.0001751112,0.9381525,0.000096868134,0.0002012028,0.00020553877,0.0013127857,0.052739684,0.00043904298,0.00096105546,0.0056369347,0.000054590273],"about_ca_topic_score_codex":0.030902676,"about_ca_topic_score_gemma":0.017924739,"teacher_disagreement_score":0.030902676,"about_ca_system_score_codex":0.00079013285,"about_ca_system_score_gemma":0.0006296515,"threshold_uncertainty_score":0.061445594},"labels":[],"label_agreement":null},{"id":"W4313598873","doi":"10.1109/jbhi.2023.3234818","title":"Spatio-Temporal Clustering of Multi-Location Time Series to Model Seasonal Influenza Spread","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cluster analysis; Time series; Proxy (statistics); Temporal database; Computer science; Geography; Cluster (spacecraft); Spatial analysis; Spatial ecology; Hierarchical clustering; Data mining; Spatial epidemiology; Outbreak; Artificial intelligence; Medicine; Machine learning; Epidemiology; Remote sensing","score_opus":0.06185673431165625,"score_gpt":0.362287283472997,"score_spread":0.3004305491613407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313598873","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":[0.48385084,0.0018673452,0.49832934,0.0015569323,0.00030468445,0.000267586,0.0075805737,0.0014422777,0.0048005166],"genre_scores_gemma":[0.95781153,0.00038296235,0.036274653,0.00008127141,0.00006614759,0.00010083235,0.0030744653,0.00006221396,0.0021460096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993499,0.00023021776,0.00005853912,0.00018761231,0.00010053624,0.000073199095],"domain_scores_gemma":[0.99794334,0.0010746708,0.0003482586,0.00020120111,0.00033571006,0.000096913434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018069724,0.00076632085,0.00056583766,0.0018799164,0.00041808025,0.0007309246,0.001294844,0.00078708597,0.0013269774],"category_scores_gemma":[0.0056834,0.00033640076,0.0010215027,0.0019680574,0.0004076215,0.0007579669,0.0006393395,0.001099132,0.0004236236],"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":[0.000060673705,0.000037136873,0.012130593,0.000037388574,0.00011664876,0.000052877058,0.00006528437,0.97212607,0.00032689754,0.0033621702,0.0012523426,0.010431985],"study_design_scores_gemma":[0.0000019805977,0.000006229385,0.0016824553,0.0000035956493,0.000006371013,0.0000068124004,0.000012044347,0.9967616,0.000048501384,0.001171021,0.000295947,0.0000034675052],"about_ca_topic_score_codex":0.11637797,"about_ca_topic_score_gemma":0.08718016,"teacher_disagreement_score":0.11637797,"about_ca_system_score_codex":0.0019674553,"about_ca_system_score_gemma":0.00095264235,"threshold_uncertainty_score":0.23140115},"labels":[],"label_agreement":null},{"id":"W4313886805","doi":"10.32920/ryerson.14636922.v2","title":"User Task Scenarios for Map-Based Decision Support in Community Health Planning","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Usability; Task (project management); Thematic map; Computer science; Think aloud protocol; Process (computing); Thematic analysis; Human–computer interaction; Data science; Geography; Cartography; Qualitative research; Engineering","score_opus":0.07685828195431071,"score_gpt":0.3933611794337189,"score_spread":0.3165028974794082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313886805","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":[0.5938499,0.00035264168,0.3725772,0.0009731318,0.00011130235,0.0061867326,0.003071129,0.012272189,0.010605668],"genre_scores_gemma":[0.69332033,0.00014621858,0.29748747,0.0001700819,0.00003133619,0.0048386552,0.001874697,0.00048039958,0.0016508413],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9923706,0.0061856136,0.00040840235,0.0003026491,0.00047548042,0.0002573095],"domain_scores_gemma":[0.934109,0.06050782,0.00066856755,0.0018959335,0.0014795007,0.0013392536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010744784,0.0018328715,0.00085027114,0.0014180108,0.001011922,0.002278048,0.002064743,0.0021971776,0.01192294],"category_scores_gemma":[0.038246147,0.0006996582,0.00091967196,0.00082490215,0.0007099508,0.0024155336,0.0031002744,0.00089982833,0.0020315254],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.018240605,0.01228024,0.040969916,0.011576603,0.00058193,0.010600837,0.118840665,0.12049511,0.07442175,0.014250976,0.046145815,0.5315955],"study_design_scores_gemma":[0.003504809,0.009571263,0.040312152,0.003101706,0.0005011887,0.0049856203,0.032394625,0.64433503,0.05824713,0.036323942,0.16536853,0.0013541016],"about_ca_topic_score_codex":0.0014959894,"about_ca_topic_score_gemma":0.0016588058,"teacher_disagreement_score":0.01192294,"about_ca_system_score_codex":0.0008264734,"about_ca_system_score_gemma":0.000693696,"threshold_uncertainty_score":0.056824505},"labels":[],"label_agreement":null},{"id":"W4317494834","doi":"10.2196/preprints.45767","title":"Using Social Media to Help Understand Patient-Reported Health Outcomes of Post–COVID-19 Condition: Natural Language Processing Approach (Preprint)","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Preprint; Terminology; Social media; Normalization (sociology); Computer science; Natural language processing; Artificial intelligence; Named-entity recognition; F1 score; Information retrieval; Data science; Task (project management); World Wide Web; Linguistics","score_opus":0.11792255037041499,"score_gpt":0.40419502369474264,"score_spread":0.28627247332432765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317494834","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":[0.4344157,0.001552298,0.42613095,0.0101662,0.00068535126,0.0024369776,0.100480236,0.012480895,0.011651387],"genre_scores_gemma":[0.5891762,0.0007413797,0.35301104,0.00095289043,0.0003234303,0.0013696997,0.049960032,0.0003061715,0.00415914],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999153,0.00034727302,0.00009734752,0.00021152792,0.00013799951,0.000052787804],"domain_scores_gemma":[0.9942392,0.004384252,0.0004821755,0.00024139245,0.00057356164,0.000079418525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020274413,0.00073847396,0.00026710794,0.0020670127,0.00032541662,0.0012014821,0.00044038455,0.0005823294,0.0032199656],"category_scores_gemma":[0.008404783,0.0001767465,0.0009272405,0.0009439403,0.00032910187,0.001386413,0.00082917354,0.0008729546,0.0015290078],"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":[0.0015018043,0.0008357716,0.10267627,0.0028457048,0.000402076,0.0023956986,0.0058852322,0.018549053,0.04713388,0.007825229,0.06304086,0.7469084],"study_design_scores_gemma":[0.00017037455,0.00076792325,0.16306041,0.000700366,0.0004519415,0.0013828072,0.0080245575,0.6577452,0.048255462,0.029432708,0.08968469,0.00032356332],"about_ca_topic_score_codex":0.0066849655,"about_ca_topic_score_gemma":0.01251367,"teacher_disagreement_score":0.0066849655,"about_ca_system_score_codex":0.00082115654,"about_ca_system_score_gemma":0.0008444243,"threshold_uncertainty_score":0.013292074},"labels":[],"label_agreement":null},{"id":"W4318027682","doi":"10.2196/43819","title":"Web-Based COVID-19 Dashboards and Trackers in the United States: Survey Study","year":2023,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"BitTorrent tracker; Visualization; Computer science; Transparency (behavior); Data science; Data visualization; Data quality; Standardization; Web application; World Wide Web; Data mining; Business; Computer security; Eye tracking","score_opus":0.09105086991300165,"score_gpt":0.3936033691272216,"score_spread":0.30255249921421995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318027682","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":[0.9969494,0.0002287142,0.00027364786,0.00030938597,0.0000062519675,0.00012905024,0.00077154575,0.000013105418,0.0013188287],"genre_scores_gemma":[0.9959538,0.00079655054,0.0009992411,0.00042650185,0.000008658406,0.00020899498,0.0009255101,0.000015240733,0.0006655045],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9968765,0.0013664806,0.0004160857,0.00030249162,0.0006911927,0.0003473598],"domain_scores_gemma":[0.98453736,0.005997228,0.0033539706,0.000698502,0.0042231134,0.001189801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061886157,0.00023573067,0.000327918,0.0020101403,0.0010828009,0.0016529701,0.00052587176,0.00068476464,0.0012738081],"category_scores_gemma":[0.015557583,0.00050589786,0.00035948824,0.0033198553,0.0006386775,0.0030790998,0.0015951609,0.0011626668,0.0003520103],"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":[0.00006563045,0.00055931794,0.9536035,0.00026716272,0.000043547363,0.00027867296,0.021683307,0.000073442505,0.00029131043,0.00018627376,0.0026085572,0.020339193],"study_design_scores_gemma":[0.000008753548,0.00048448576,0.88318986,0.00034713827,0.000041670814,0.0003750216,0.10556777,0.0007191459,0.00036561248,0.00006958477,0.008801103,0.000029793966],"about_ca_topic_score_codex":0.030323992,"about_ca_topic_score_gemma":0.0468814,"teacher_disagreement_score":0.030323992,"about_ca_system_score_codex":0.0014329391,"about_ca_system_score_gemma":0.001969997,"threshold_uncertainty_score":0.060294926},"labels":[],"label_agreement":null},{"id":"W4318669713","doi":"10.2196/34982","title":"Gastroenteritis Forecasting Assessing the Use of Web and Electronic Health Record Data With a Linear and a Nonlinear Approach: Comparison Study","year":2023,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Institutes of Health; Agence Nationale de la Recherche","keywords":"Nonlinear system; Computer science; Electronic health record; Data mining; Statistics; Econometrics; Mathematics; Health care","score_opus":0.1782873133369915,"score_gpt":0.38581912222842923,"score_spread":0.20753180889143774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318669713","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":[0.99023193,0.0005134458,0.005937117,0.00030250128,0.000040097366,0.00014308542,0.0011875841,0.000092546805,0.0015516834],"genre_scores_gemma":[0.992871,0.0002716436,0.004766612,0.00004449897,0.000027859069,0.00007436159,0.0014950625,0.000018442453,0.00043064717],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995943,0.0023187401,0.00028148456,0.00065512187,0.00058213953,0.00021954991],"domain_scores_gemma":[0.9584517,0.03504894,0.0019113307,0.0009904149,0.0031299822,0.00046769655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010703302,0.00091687834,0.00075237284,0.0024200238,0.00027465937,0.0016101566,0.00094475545,0.0011966417,0.0015997773],"category_scores_gemma":[0.03303314,0.00033085758,0.0021481304,0.0017231021,0.0003323035,0.0027856657,0.00096861576,0.00087288476,0.0005500391],"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":[0.0033644428,0.0014346867,0.8571451,0.00042825274,0.0013700395,0.00013463658,0.00040658817,0.06239568,0.00068471837,0.00046766695,0.0010038423,0.07116441],"study_design_scores_gemma":[0.0001604998,0.0020904036,0.40369532,0.0001022244,0.00044680794,0.00011469682,0.0010321281,0.5899562,0.0008775101,0.00058227795,0.0008501268,0.00009180954],"about_ca_topic_score_codex":0.036397688,"about_ca_topic_score_gemma":0.01833498,"teacher_disagreement_score":0.036397688,"about_ca_system_score_codex":0.0017695155,"about_ca_system_score_gemma":0.0012407374,"threshold_uncertainty_score":0.07237166},"labels":[],"label_agreement":null},{"id":"W4319322100","doi":"10.2196/44055","title":"Tracking Population-Level Anxiety Using Search Engine Data: Ecological Study","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute for Health and Care Research","keywords":"Anxiety; Population; Pandemic; Psychology; Socioeconomic status; Demography; Medicine; Psychiatry; Coronavirus disease 2019 (COVID-19); Environmental health; Sociology; Disease","score_opus":0.3728223296336388,"score_gpt":0.5195721144486782,"score_spread":0.14674978481503942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319322100","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":[0.9954495,0.00012487815,0.00033352393,0.000095677206,0.000006176763,0.00020326576,0.0032982363,0.00001575276,0.00047305622],"genre_scores_gemma":[0.9941706,0.00014474946,0.0014097008,0.00013420782,0.000010604424,0.00041867973,0.0034959242,0.000012793535,0.00020276898],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9956501,0.0024741793,0.00032787473,0.0005857242,0.00042060905,0.000541515],"domain_scores_gemma":[0.99068916,0.003595073,0.0022395742,0.0011863282,0.0015127308,0.00077714847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004690256,0.0003824645,0.00047314636,0.0038089987,0.0011849384,0.0012692339,0.0008572106,0.00075125735,0.0013383216],"category_scores_gemma":[0.0128648495,0.0005507754,0.0014396886,0.0062415637,0.000883027,0.0019833897,0.002148396,0.0011004237,0.00055821013],"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":[0.00018364907,0.00044561658,0.9882139,0.0002111793,0.00031560924,0.0001362961,0.0039046071,0.000335007,0.00018093087,0.0002195645,0.0013550548,0.004498491],"study_design_scores_gemma":[0.000040518924,0.00041994592,0.9864564,0.00007420769,0.0001243123,0.00021440926,0.008947793,0.0018942036,0.00009611536,0.0001587247,0.0015324083,0.00004098177],"about_ca_topic_score_codex":0.09319527,"about_ca_topic_score_gemma":0.11659685,"teacher_disagreement_score":0.09319527,"about_ca_system_score_codex":0.0016278512,"about_ca_system_score_gemma":0.0021497428,"threshold_uncertainty_score":0.1853056},"labels":[],"label_agreement":null},{"id":"W4319339169","doi":"10.1016/j.annepidem.2023.02.001","title":"Current ethical and social issues in epidemiology","year":2023,"lang":"en","type":"article","venue":"Annals of Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Hamilton Health Sciences; Impact","funders":"","keywords":"Epidemiology; Public health; Medicine; Bioethics; Research ethics; Engineering ethics; Public relations; Political science; Law; Nursing; Pathology; Psychiatry","score_opus":0.30477415358950294,"score_gpt":0.5245122310206519,"score_spread":0.21973807743114898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319339169","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00084506563,0.036840566,0.002888569,0.944929,0.004073706,0.000029994553,0.000035055553,0.000019639943,0.010338539],"genre_scores_gemma":[0.19495781,0.13556664,0.014903138,0.5830955,0.062452797,0.00069334067,0.00017665951,0.00015471951,0.007999347],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7871563,0.16030091,0.012439781,0.008141532,0.026613802,0.0053476947],"domain_scores_gemma":[0.43921298,0.48755443,0.015984725,0.0142791895,0.030288722,0.0126798935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.18712844,0.00094448816,0.0016168623,0.0048700143,0.01119606,0.022671906,0.0039552758,0.023554385,0.007927855],"category_scores_gemma":[0.29414463,0.0012333198,0.0014131208,0.0058114817,0.07764983,0.022137146,0.013198176,0.024711907,0.0017893915],"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":[0.000038026665,0.00011104306,0.002398799,0.0017075026,0.000036461995,0.000498332,0.017949648,0.00045019432,0.000107219195,0.71374303,0.16143636,0.10152341],"study_design_scores_gemma":[0.000019904828,0.000024137777,0.00091425085,0.005619528,0.0000132685445,0.0007335928,0.01426303,0.0003596966,0.00006983245,0.6010206,0.3769085,0.000053622727],"about_ca_topic_score_codex":0.0029900598,"about_ca_topic_score_gemma":0.0023605148,"teacher_disagreement_score":0.18712844,"about_ca_system_score_codex":0.012987557,"about_ca_system_score_gemma":0.03844627,"threshold_uncertainty_score":0.9896416},"labels":[],"label_agreement":null},{"id":"W4319344942","doi":"10.1136/bmjopen-2022-066418","title":"Canadians’ opinions towards COVID-19 data-sharing: a national cross-sectional survey","year":2023,"lang":"en","type":"article","venue":"BMJ Open","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University; University of British Columbia; McGill University Health Centre; Simon Fraser University","funders":"Simon Fraser University; Genome Canada","keywords":"Medicine; Public health; Pandemic; Coronavirus disease 2019 (COVID-19); Data collection; Survey data collection; Data sharing; Cross-sectional study; Public opinion; Psychological intervention; Health care; Family medicine; Nursing; Alternative medicine; Political science; Politics; Sociology","score_opus":0.4606976379612705,"score_gpt":0.5416581456107274,"score_spread":0.08096050764945695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319344942","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":[0.9845893,0.0006682881,0.00019275554,0.004552323,0.00002360633,0.00022384035,0.0042658797,0.0000115709245,0.0054724757],"genre_scores_gemma":[0.99563134,0.00070547516,0.0004528523,0.0012423861,0.000011310607,0.000090699774,0.0011134361,0.0000063193015,0.0007462005],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9930367,0.0011753455,0.0005596844,0.00054012163,0.0033385737,0.0013495813],"domain_scores_gemma":[0.98153555,0.0021594365,0.003984766,0.00048747822,0.0083114,0.0035213965],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.008503214,0.00021506831,0.00035884712,0.0015779427,0.0055697192,0.0023213562,0.0011408019,0.00077635405,0.00270827],"category_scores_gemma":[0.01671144,0.00043633272,0.00057602697,0.003914125,0.0017780386,0.0010073027,0.0016614881,0.0014576993,0.00027912497],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012675495,0.00008461354,0.9478157,0.00025208446,0.00006769496,0.0001735619,0.031119205,0.000090038084,0.00028339349,0.00032774766,0.008043849,0.011615322],"study_design_scores_gemma":[0.000017916222,0.00008653856,0.9395582,0.00026147364,0.000029557063,0.00012275213,0.051264897,0.00030833608,0.00008872596,0.000054701068,0.008155023,0.000051865205],"about_ca_topic_score_codex":0.97818106,"about_ca_topic_score_gemma":0.98094004,"teacher_disagreement_score":0.9988592,"about_ca_system_score_codex":0.024171064,"about_ca_system_score_gemma":0.039707076,"threshold_uncertainty_score":0.17537415},"labels":[],"label_agreement":null},{"id":"W4319439818","doi":"10.2196/42683","title":"Predicting Social Determinants of Health in Patient Navigation: Case Study","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Minority Health and Health Disparities; National Center for Advancing Translational Sciences; National Cancer Institute; National Institutes of Health","keywords":"Machine learning; Artificial intelligence; Naive Bayes classifier; Random forest; Logistic regression; Computer science; Formative assessment; Health care; Social determinants of health; Support vector machine; Medicine; Medical education; Psychology; Nursing; Public health","score_opus":0.11333590721724428,"score_gpt":0.496672330829117,"score_spread":0.3833364236118727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319439818","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":[0.99711514,0.00011654365,0.0014239796,0.000412393,0.0000057699363,0.00017408577,0.00022942315,0.000012728961,0.00050987105],"genre_scores_gemma":[0.99178547,0.00020137976,0.0070267636,0.00018042186,0.000024517523,0.00022837345,0.0002784211,0.000006505896,0.00026810294],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99624723,0.0028359576,0.00015650805,0.00027230958,0.0002624461,0.00022552059],"domain_scores_gemma":[0.96852064,0.025187023,0.0025775922,0.0011571366,0.0015934317,0.00096422306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005315729,0.00045561983,0.00034875833,0.0015389961,0.0015754655,0.00079887983,0.00084495754,0.0014526374,0.0013800478],"category_scores_gemma":[0.021649024,0.00033410112,0.0007758563,0.0016610816,0.00093564205,0.00090847583,0.0011022772,0.0011776205,0.00019556335],"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":[0.00048198993,0.004161934,0.9330371,0.00030364172,0.00018468335,0.00676603,0.009426413,0.0073423795,0.0003739476,0.00062786654,0.0029034219,0.034390472],"study_design_scores_gemma":[0.00074770267,0.0071100476,0.7357195,0.0005960955,0.00044873267,0.013233809,0.05639776,0.16883999,0.0032517558,0.003925663,0.009440096,0.00028890386],"about_ca_topic_score_codex":0.023858543,"about_ca_topic_score_gemma":0.044519715,"teacher_disagreement_score":0.023858543,"about_ca_system_score_codex":0.0014794159,"about_ca_system_score_gemma":0.0017947372,"threshold_uncertainty_score":0.047439337},"labels":[],"label_agreement":null},{"id":"W4319836120","doi":"10.1002/9781119672333.ch36","title":"Integrating Alternative and Administrative Data into the Monthly Business Statistics: Some Applications from Statistics <scp>C</scp> anada","year":2023,"lang":"en","type":"other","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Context (archaeology); Survey data collection; Data quality; Data collection; Commodity; Business statistics; Quality (philosophy); Business; Computer science; Marketing; Statistics; Economics; Geography; Econometrics; Finance; Mathematics","score_opus":0.037744563768292785,"score_gpt":0.3291468615830939,"score_spread":0.29140229781480115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319836120","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023735952,0.101638064,0.23424913,0.23787823,0.0061339946,0.0013159309,0.0190045,0.0030327083,0.37301144],"genre_scores_gemma":[0.13206843,0.15044864,0.61598885,0.013323767,0.0062394026,0.0011000514,0.016904721,0.0012524106,0.06267374],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98084426,0.01103799,0.000875763,0.00041278833,0.0064588515,0.00037028],"domain_scores_gemma":[0.9491018,0.033943705,0.001516195,0.002787327,0.011688465,0.0009624434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017138457,0.0005882633,0.0005153863,0.0066199885,0.0012568622,0.004195628,0.0010399459,0.0010332853,0.007825842],"category_scores_gemma":[0.041411836,0.00042367307,0.00061872136,0.025975943,0.0016126949,0.0025841184,0.0023731072,0.0019437956,0.0021905964],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064129905,0.00015023054,0.017246347,0.00058282766,0.00003804575,0.00020048369,0.0010479408,0.0023018725,0.00027097217,0.12352643,0.24182943,0.61274135],"study_design_scores_gemma":[0.00002247364,0.000063043255,0.017964955,0.0007444519,0.000017751463,0.00019297085,0.0011242614,0.009074562,0.000525373,0.03441682,0.9357672,0.00008625484],"about_ca_topic_score_codex":0.16828053,"about_ca_topic_score_gemma":0.22037561,"teacher_disagreement_score":0.83171946,"about_ca_system_score_codex":0.0069032824,"about_ca_system_score_gemma":0.008759781,"threshold_uncertainty_score":0.33460206},"labels":[],"label_agreement":null},{"id":"W4320855024","doi":"10.48550/arxiv.2302.06567","title":"Poisson Cluster Process Models for Detecting Ultra-Diffuse Galaxies","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Galaxy; Computer science; Poisson distribution; Point process; Cluster (spacecraft); Construct (python library); Physics; Astrophysics; Data mining; Artificial intelligence; Statistics; Mathematics","score_opus":0.11306140469330249,"score_gpt":0.24126669652494068,"score_spread":0.1282052918316382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320855024","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":[0.007887718,0.00063399976,0.9027551,0.0018947491,0.0004382538,0.00046251804,0.067321435,0.013205803,0.005400393],"genre_scores_gemma":[0.22108065,0.0017912415,0.609367,0.001586492,0.0005931494,0.003526893,0.12889445,0.007431567,0.025728583],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983261,0.0006280722,0.00011410747,0.00032109136,0.0005020147,0.00010859092],"domain_scores_gemma":[0.9798222,0.012854693,0.0010432254,0.0027625146,0.0029822856,0.0005350846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066652903,0.0009518429,0.0012023018,0.002884919,0.000670346,0.0019556938,0.0045167273,0.0022567003,0.031552732],"category_scores_gemma":[0.044575904,0.00083686627,0.0019635258,0.003376614,0.00066032726,0.0019454272,0.0016869417,0.0026460348,0.012466332],"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":[0.00051414187,0.00020759544,0.021982776,0.0008381074,0.000378964,0.00039045291,0.00033855895,0.243675,0.001816977,0.18944883,0.43489817,0.10551038],"study_design_scores_gemma":[0.00013224977,0.000031747422,0.0036963648,0.00012282876,0.00007077726,0.00022245913,0.000053792,0.855101,0.0012508702,0.10432589,0.034922067,0.000070046684],"about_ca_topic_score_codex":0.016470099,"about_ca_topic_score_gemma":0.014422873,"teacher_disagreement_score":0.031552732,"about_ca_system_score_codex":0.001136819,"about_ca_system_score_gemma":0.0023778456,"threshold_uncertainty_score":0.1055544},"labels":[],"label_agreement":null},{"id":"W4321083903","doi":"10.4018/ijagr.318138","title":"IJAGR at the International Medical Symposium Geography 2022 Edinburgh, Scotland","year":2023,"lang":"en","type":"article","venue":"International Journal of Applied Geospatial Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"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":"University of Toronto; Queen's University; University of North Carolina at Chapel Hill; Appalachian State University","keywords":"Library science; Geospatial analysis; Health geography; Geography; State (computer science); Event (particle physics); History; Media studies; Medicine; Cartography; Sociology; Public health; International health; Pathology; Health policy","score_opus":0.02816438395122246,"score_gpt":0.3755435599942818,"score_spread":0.34737917604305935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321083903","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002162106,0.04748243,0.0016705842,0.1960853,0.58194053,0.00029251128,0.003257859,0.00057457533,0.16653399],"genre_scores_gemma":[0.020780653,0.04747047,0.0019363059,0.026595365,0.22184728,0.00017197538,0.0021880413,0.00043336937,0.6785765],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99733883,0.000494251,0.00022101132,0.0003510853,0.0011162274,0.00047855114],"domain_scores_gemma":[0.9930113,0.0012368286,0.00042666213,0.000325175,0.0020266345,0.0029734345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004184477,0.0013196216,0.0010862387,0.0033951132,0.0011233408,0.006433468,0.0010897466,0.0035884466,0.21513559],"category_scores_gemma":[0.009681095,0.00052674336,0.00091335055,0.0021554376,0.0010025731,0.0027292504,0.0027512005,0.0032333033,0.07550264],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046305948,0.000011449444,0.0002752745,0.00013217656,0.0000079289075,0.00017137118,0.000052813353,0.00002427007,0.00015089008,0.0007756759,0.9774835,0.02086841],"study_design_scores_gemma":[0.00000815967,0.000016227381,0.0006643142,0.00014779405,0.0000040363147,0.0000957753,0.000094323805,0.000015923124,0.000043028573,0.00021617883,0.99868864,0.000005647222],"about_ca_topic_score_codex":0.004127891,"about_ca_topic_score_gemma":0.014178097,"teacher_disagreement_score":0.21513559,"about_ca_system_score_codex":0.0024199341,"about_ca_system_score_gemma":0.0032399213,"threshold_uncertainty_score":0.7197002},"labels":[],"label_agreement":null},{"id":"W4321481740","doi":"10.1371/journal.pone.0279299","title":"Investigating the distribution of calls to a North American animal poison control call center by veterinarians and the public in space, time, and space-time","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","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 Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scan statistic; Context (archaeology); Triage; Demography; Phone; Geography; Public health; Cluster (spacecraft); Medicine; Distribution (mathematics); Medical emergency; Statistics; Computer science; Pathology; Sociology","score_opus":0.017248954460571488,"score_gpt":0.23096941558276776,"score_spread":0.21372046112219628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321481740","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":[0.9988213,0.000052222476,0.00023806273,0.000026314588,0.000001881524,0.00001611553,0.00029369755,0.0000051947322,0.000545141],"genre_scores_gemma":[0.9986858,0.00006767905,0.00036204665,0.00002055573,0.0000061455835,0.000024627621,0.00048003698,0.000003966124,0.0003491411],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989128,0.0003138924,0.00008590986,0.0002725558,0.00026112935,0.00015378564],"domain_scores_gemma":[0.9957968,0.0010285925,0.0020420724,0.0002213658,0.000616139,0.00029514355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009235303,0.00015898496,0.00021143572,0.0015923732,0.0005425833,0.000595987,0.00043923417,0.00031191146,0.0015784416],"category_scores_gemma":[0.0029113144,0.00019181755,0.00025304029,0.0017546212,0.00044109687,0.0006359354,0.0006694187,0.00034829666,0.00020709493],"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":[0.000052100764,0.000024052913,0.996176,0.000010856531,0.00003237683,0.00004174167,0.0005281261,0.000077892735,0.00044203288,0.00002424346,0.00010258207,0.0024879875],"study_design_scores_gemma":[9.842497e-7,0.000042700136,0.9982748,0.0000028052307,0.000009120979,0.000053505333,0.0011498242,0.00024774944,0.000077371435,0.000012520243,0.00012571662,0.000002938026],"about_ca_topic_score_codex":0.032913905,"about_ca_topic_score_gemma":0.07532684,"teacher_disagreement_score":0.032913905,"about_ca_system_score_codex":0.0007520839,"about_ca_system_score_gemma":0.0007609113,"threshold_uncertainty_score":0.06544465},"labels":[],"label_agreement":null},{"id":"W4322711402","doi":"10.2196/42710","title":"Online Health Information Seeking for Mpox in Endemic and Nonendemic Countries: Google Trends Study","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute for Health and Care Research","keywords":"Public health; The Internet; Pandemic; Political science; Medicine; Geography; Coronavirus disease 2019 (COVID-19); Economic growth; Disease; Infectious disease (medical specialty)","score_opus":0.08020581847453298,"score_gpt":0.4712652638666453,"score_spread":0.39105944539211235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322711402","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":[0.9939774,0.00040862538,0.00004365755,0.0001685472,0.0000054297293,0.000041654712,0.004366709,0.000007343762,0.0009805252],"genre_scores_gemma":[0.9960731,0.00035529505,0.00012706054,0.000062789135,0.000012062929,0.000041090007,0.0030707626,0.00000697331,0.00025082045],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99895084,0.00028112024,0.00014559062,0.00016692207,0.0002359134,0.0002195652],"domain_scores_gemma":[0.9918555,0.0023156875,0.0038321426,0.0004380791,0.00087279815,0.0006857659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014443534,0.00031276696,0.0005121854,0.004942643,0.00066074025,0.0018174433,0.0005416253,0.0006078563,0.0026048745],"category_scores_gemma":[0.00736311,0.0003035638,0.0012168695,0.009238628,0.00051711814,0.0027107333,0.0014785596,0.0009976774,0.00072049774],"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":[0.000093402385,0.00008966081,0.99401313,0.00015373537,0.00008817736,0.0001490104,0.0017653452,0.000052576033,0.000042058742,0.00012624574,0.00075033284,0.0026764341],"study_design_scores_gemma":[0.000009165417,0.00008611857,0.9922347,0.00009700789,0.00007317841,0.0003624756,0.005246726,0.0006270942,0.000048294965,0.000051063143,0.0011509764,0.000013282193],"about_ca_topic_score_codex":0.04041139,"about_ca_topic_score_gemma":0.030236578,"teacher_disagreement_score":0.04041139,"about_ca_system_score_codex":0.0006269036,"about_ca_system_score_gemma":0.00084846385,"threshold_uncertainty_score":0.08035231},"labels":[],"label_agreement":null},{"id":"W4323035821","doi":"10.1099/mgen.0.000929","title":"Epidemiological cluster identification using multiple data sources: an approach using logistic regression","year":2023,"lang":"en","type":"article","venue":"Microbial Genomics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"European Commission","keywords":"Cluster analysis; Pairwise comparison; Spatial epidemiology; Cluster (spacecraft); Data mining; Computational biology; Epidemiology; Logistic regression; Biology; Computer science; Statistics; Mathematics; Medicine","score_opus":0.2749071077727216,"score_gpt":0.38850209636470295,"score_spread":0.11359498859198136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323035821","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":[0.02247619,0.0007944709,0.97244734,0.0014627307,0.00011343888,0.00041598387,0.0005453652,0.00111473,0.0006297916],"genre_scores_gemma":[0.29132724,0.00072282634,0.7021579,0.00036386828,0.0003285599,0.0010358789,0.0017596182,0.000400333,0.0019037616],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.95165247,0.034195423,0.0022769442,0.007116095,0.0037614915,0.0009974781],"domain_scores_gemma":[0.91007024,0.07059736,0.0063983877,0.0060907668,0.0058730533,0.0009702832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.055075504,0.002880214,0.0046947883,0.0138802035,0.0026257632,0.0060129906,0.009475983,0.0037729107,0.002875927],"category_scores_gemma":[0.093096405,0.002254408,0.006492586,0.014097838,0.0017172195,0.0054491484,0.006882819,0.0061117383,0.0016623283],"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":[0.0013227442,0.00131599,0.20343153,0.0012579571,0.00794606,0.0017408342,0.002991494,0.31956232,0.0015121262,0.026987486,0.009280547,0.422651],"study_design_scores_gemma":[0.000052032196,0.00008421651,0.003995524,0.00009702666,0.00021841183,0.00015912135,0.00038462505,0.9756821,0.0003362998,0.016617093,0.0022927576,0.00008086095],"about_ca_topic_score_codex":0.015594813,"about_ca_topic_score_gemma":0.009798156,"teacher_disagreement_score":0.055075504,"about_ca_system_score_codex":0.0023228065,"about_ca_system_score_gemma":0.003838594,"threshold_uncertainty_score":0.2912706},"labels":[],"label_agreement":null},{"id":"W4323305631","doi":"10.2196/preprints.46810","title":"Digital Health Dashboards for Decision-Making to Enable Rapid Responses During Public Health Crises: Replicable and Scalable Methodology (Preprint)","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lawson Health Research Institute; Western University","funders":"","keywords":"Digital health; Big data; Public health; Dashboard; Analytics; Public health surveillance; Health care; Business; Public relations; Computer science; Knowledge management; Data science; Political science; Medicine; Nursing","score_opus":0.19018150660001196,"score_gpt":0.43714940231854216,"score_spread":0.2469678957185302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323305631","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":[0.04148141,0.00037269195,0.9080996,0.007403303,0.0010196866,0.010898581,0.00628808,0.006210354,0.01822631],"genre_scores_gemma":[0.1020049,0.00025246578,0.88559467,0.0005159042,0.00012799201,0.006254607,0.0028980314,0.00046581618,0.0018855843],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.960645,0.028232202,0.0028236953,0.0029724615,0.004694453,0.0006321883],"domain_scores_gemma":[0.868521,0.07472331,0.006910143,0.027217243,0.020616788,0.0020115802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05542527,0.0014060583,0.0006589445,0.00398244,0.0016042863,0.010048037,0.0028299855,0.0017430965,0.012881889],"category_scores_gemma":[0.15550973,0.0010385821,0.002138658,0.0039893785,0.002620996,0.007819965,0.006993801,0.002786344,0.0027353417],"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":[0.0008674161,0.0020626008,0.03266274,0.0028056803,0.00070347224,0.00071276515,0.019673957,0.03397897,0.0075769117,0.15090793,0.054184433,0.69386315],"study_design_scores_gemma":[0.0016107543,0.002953154,0.025104221,0.006431475,0.0007867953,0.00049841794,0.033185706,0.30214602,0.029254258,0.26324552,0.33397564,0.00080805947],"about_ca_topic_score_codex":0.0045170127,"about_ca_topic_score_gemma":0.0042932173,"teacher_disagreement_score":0.05542527,"about_ca_system_score_codex":0.0023974217,"about_ca_system_score_gemma":0.00709088,"threshold_uncertainty_score":0.29312038},"labels":[],"label_agreement":null},{"id":"W4323529170","doi":"10.2196/44695","title":"Exploring Cancer Incidence, Risk Factors, and Mortality in the Lleida Region: Interactive, Open-source R Shiny Application for Cancer Data Analysis","year":2023,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministerio de Economía y Competitividad; Generalitat de Catalunya; Universidad Técnica Federico Santa María","keywords":"Computer science; Population; Descriptive statistics; Analytics; Exploit; Web application; Cancer registry; Cloud computing; Data science; Database; World Wide Web; Medicine; Statistics; Computer security; Environmental health; Operating system","score_opus":0.22252933545137582,"score_gpt":0.4368407264154111,"score_spread":0.2143113909640353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323529170","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038683806,0.0010556125,0.23970231,0.0011486799,0.00025763398,0.001177924,0.059375983,0.64678913,0.011808986],"genre_scores_gemma":[0.2659459,0.0017276908,0.51095223,0.001611348,0.0002355169,0.00448743,0.112678975,0.084636554,0.017724322],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99894756,0.00024930268,0.00010344909,0.00031510904,0.00028161198,0.00010299135],"domain_scores_gemma":[0.99713,0.0016630308,0.00022106092,0.00040455686,0.0003494543,0.00023190927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024498818,0.0013912626,0.0009223792,0.001728746,0.00039861546,0.0016017823,0.0019259701,0.00064745534,0.018616103],"category_scores_gemma":[0.0072325617,0.0006559395,0.0018805249,0.0012174492,0.00037668314,0.001269402,0.003197999,0.001105997,0.008287122],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033125354,0.0006193469,0.05589664,0.004314582,0.0011707246,0.0029535824,0.003404179,0.027611058,0.025633123,0.009513164,0.5215365,0.34403446],"study_design_scores_gemma":[0.0015151271,0.0006435907,0.08870119,0.0010190881,0.0005885286,0.0021892178,0.0009311826,0.31277597,0.02938146,0.021569507,0.5400383,0.00064686546],"about_ca_topic_score_codex":0.0052259527,"about_ca_topic_score_gemma":0.0058342884,"teacher_disagreement_score":0.018616103,"about_ca_system_score_codex":0.00048818698,"about_ca_system_score_gemma":0.0014405786,"threshold_uncertainty_score":0.06227708},"labels":[],"label_agreement":null},{"id":"W4323529219","doi":"10.2196/44517","title":"Participatory Surveillance for COVID-19 Trend Detection in Brazil: Cross-sectional Study","year":2023,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"HORIZON EUROPE Framework Programme; Institute for Scientific Interchange; Fondazione CRT","keywords":"Disease surveillance; Data collection; Citizen journalism; Environmental health; Pandemic; Coronavirus disease 2019 (COVID-19); Population; Public health surveillance; Public health; Medicine; Outbreak; Business; Computer science; Geography; Data science; Disease; Statistics; Infectious disease (medical specialty); World Wide Web; Virology; Nursing","score_opus":0.11062891625472204,"score_gpt":0.43546541126964156,"score_spread":0.3248364950149195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323529219","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":[0.98688877,0.0014519084,0.0037097547,0.0008074408,0.00003886288,0.0009050073,0.0022732546,0.000028458244,0.0038965289],"genre_scores_gemma":[0.99587005,0.00053955516,0.0020528822,0.00024603488,0.000014608418,0.0004358638,0.0005777011,0.000008155457,0.0002552979],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974669,0.0011155097,0.00024902652,0.0005016774,0.00039578963,0.00027111938],"domain_scores_gemma":[0.99462986,0.0014359477,0.0017246638,0.00065955525,0.0011205678,0.0004292817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00514696,0.000376023,0.00050757005,0.0011093302,0.00092773896,0.0007933087,0.0007209847,0.00055448123,0.0016317726],"category_scores_gemma":[0.012893839,0.0004519734,0.0007162167,0.0009705284,0.00063353044,0.0008629189,0.0014863202,0.00070099835,0.00022110943],"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":[0.00007689035,0.00021674242,0.9770635,0.0004058423,0.00012751197,0.00027490733,0.0072356737,0.00009491015,0.0006000117,0.0005139438,0.0010773512,0.012312709],"study_design_scores_gemma":[0.000037569193,0.0006024873,0.9766508,0.00078150973,0.00015982133,0.0008245706,0.01191788,0.0015387877,0.00039549428,0.0005642889,0.006488441,0.000038385773],"about_ca_topic_score_codex":0.043522473,"about_ca_topic_score_gemma":0.04776884,"teacher_disagreement_score":0.043522473,"about_ca_system_score_codex":0.0011184898,"about_ca_system_score_gemma":0.002528107,"threshold_uncertainty_score":0.086538315},"labels":[],"label_agreement":null},{"id":"W4323542695","doi":"10.14745/ccdr.v49i23a01","title":"Event-based surveillance: Providing early warning for communicable disease threats","year":2023,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Public Health Agency of Canada","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Public health; Communicable disease; Warning system; Pandemic; Context (archaeology); Environmental health; Disease surveillance; Public health surveillance; Disease; Population; Non-communicable disease; Medicine; Coronavirus disease 2019 (COVID-19); Infectious disease (medical specialty); Geography; Computer science","score_opus":0.03650952109167818,"score_gpt":0.3158148498787824,"score_spread":0.27930532878710423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323542695","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":[0.028040798,0.09221905,0.3218057,0.20033026,0.015345324,0.003734503,0.086182386,0.009777411,0.24256456],"genre_scores_gemma":[0.25072688,0.12456941,0.4785086,0.034914922,0.007770742,0.0021410086,0.07255124,0.0007901473,0.028026938],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9914743,0.0030960748,0.0009207241,0.0007056102,0.0032651254,0.00053828425],"domain_scores_gemma":[0.9655289,0.011097801,0.0026112709,0.0026754471,0.015711443,0.002374999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015664067,0.0011399123,0.00073894596,0.0053948127,0.0008733378,0.00407559,0.0025825915,0.0013978167,0.004184266],"category_scores_gemma":[0.030753013,0.00045353165,0.000798335,0.0036814755,0.00081681204,0.0027617589,0.003028533,0.002234032,0.001672392],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022803678,0.00014391786,0.054496985,0.0037634843,0.00027283552,0.00020000194,0.0013408819,0.0034238806,0.0021735001,0.027941762,0.25814947,0.64786524],"study_design_scores_gemma":[0.000089315916,0.00025477706,0.04698889,0.0045542517,0.0002016408,0.00034688148,0.0013234292,0.008780246,0.0031911696,0.021094294,0.912979,0.00019614799],"about_ca_topic_score_codex":0.22413962,"about_ca_topic_score_gemma":0.323214,"teacher_disagreement_score":0.77586037,"about_ca_system_score_codex":0.005154794,"about_ca_system_score_gemma":0.019670574,"threshold_uncertainty_score":0.44567},"labels":[],"label_agreement":null},{"id":"W4324093654","doi":"10.3390/math11061359","title":"COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models","year":2023,"lang":"en","type":"article","venue":"Mathematics","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Poisson distribution; Bayesian probability; Kriging; Coronavirus disease 2019 (COVID-19); Statistics; Geostatistics; Spatial analysis; Econometrics; Pandemic; Poisson regression; Spatial dependence; Count data; Computer science; Nonparametric statistics; Geography; Data mining; Cartography; Spatial variability; Mathematics; Medicine","score_opus":0.18559600528225914,"score_gpt":0.40145217569660696,"score_spread":0.21585617041434782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324093654","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":[0.06653814,0.0005056231,0.90152216,0.0013897446,0.00008108209,0.00078122265,0.025341226,0.0014425576,0.0023982732],"genre_scores_gemma":[0.49262044,0.0006837168,0.46597463,0.0002595001,0.00010275749,0.0019425746,0.03542597,0.00041288685,0.0025775079],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99441504,0.0031052285,0.00049825216,0.00086492504,0.00086447975,0.00025199266],"domain_scores_gemma":[0.9804821,0.014072469,0.0019534715,0.0015303361,0.0017229415,0.00023858328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012885017,0.0007939954,0.0009940834,0.0035938467,0.0006484752,0.0019180549,0.0022026259,0.00074310886,0.0027690886],"category_scores_gemma":[0.050486874,0.0005964433,0.0017608947,0.003960754,0.00087576517,0.0011689306,0.0016770222,0.0016446109,0.0006325892],"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":[0.0003603016,0.00027121828,0.14563055,0.0007038551,0.0011106281,0.00054166914,0.0008877407,0.59605074,0.0013602588,0.091407254,0.026221648,0.13545409],"study_design_scores_gemma":[0.00004462161,0.00006559671,0.020137329,0.000099673474,0.00006129758,0.00012397137,0.00024943563,0.92448,0.00057675113,0.038969185,0.015136223,0.00005592935],"about_ca_topic_score_codex":0.10302289,"about_ca_topic_score_gemma":0.089580886,"teacher_disagreement_score":0.10302289,"about_ca_system_score_codex":0.0022600032,"about_ca_system_score_gemma":0.004541938,"threshold_uncertainty_score":0.20484644},"labels":[],"label_agreement":null},{"id":"W4362572158","doi":"10.1101/2022.06.20.22276542","title":"The Pandemic Response Commons","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Response Biomedical (Canada)","funders":"Centers for Disease Control and Prevention; National Institutes of Health; Johns Hopkins University; Chicago Community Trust","keywords":"Commons; Pandemic; Data sharing; Computer science; Data science; Political science; Coronavirus disease 2019 (COVID-19); Medicine; Law","score_opus":0.03691488863569863,"score_gpt":0.3278472914922619,"score_spread":0.29093240285656324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362572158","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04577413,0.00069013523,0.4360754,0.033712234,0.004199094,0.002953947,0.09494386,0.075465746,0.30618545],"genre_scores_gemma":[0.3651985,0.00086236454,0.32280084,0.015478627,0.0015660204,0.0031696446,0.16875157,0.019440213,0.102732286],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99055946,0.0027858908,0.0006593653,0.0018945683,0.0033349856,0.0007657125],"domain_scores_gemma":[0.9672405,0.008800631,0.0016273796,0.015308388,0.004262253,0.0027608494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013715545,0.00063801586,0.00073047203,0.0024014327,0.0017581342,0.006252706,0.0027705862,0.0022768723,0.03044805],"category_scores_gemma":[0.027430516,0.0007971579,0.0011401589,0.0023620685,0.0025406373,0.0064095184,0.011868624,0.00309519,0.012602815],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006141732,0.0002332089,0.012461127,0.00044233355,0.00010764631,0.0005406691,0.0015737686,0.012835669,0.0058957958,0.27773356,0.57268816,0.11487393],"study_design_scores_gemma":[0.00009128661,0.000053088657,0.0034877155,0.0002547473,0.00001582564,0.00022026808,0.00056303217,0.020292852,0.005581093,0.07921005,0.8901428,0.0000872304],"about_ca_topic_score_codex":0.0057883663,"about_ca_topic_score_gemma":0.0039717304,"teacher_disagreement_score":0.03044805,"about_ca_system_score_codex":0.0025693767,"about_ca_system_score_gemma":0.008568387,"threshold_uncertainty_score":0.101858854},"labels":[],"label_agreement":null},{"id":"W4365145757","doi":"10.3390/bdcc7020072","title":"A Review of Social Media Data Utilization for the Prediction of Disease Outbreaks and Understanding Public Perception","year":2023,"lang":"en","type":"review","venue":"Big Data and Cognitive Computing","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Public health; Disease; Social media; Infectious disease (medical specialty); Outbreak; Predictive modelling; Data science; Computer science; Disease surveillance; Population health; Medicine; Machine learning; Pathology","score_opus":0.6318823150115893,"score_gpt":0.44498291633996956,"score_spread":0.18689939867161975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365145757","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":[0.0026662261,0.97184753,0.011382035,0.0040523326,0.0016480946,0.00014439912,0.0020626907,0.00020876608,0.0059879664],"genre_scores_gemma":[0.014806308,0.96969867,0.00926845,0.0012787825,0.0016622907,0.00015772937,0.002018114,0.000040108545,0.0010696526],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9985967,0.0004490401,0.00023798061,0.0002511992,0.00040732362,0.000057643618],"domain_scores_gemma":[0.9866129,0.010121845,0.00078739895,0.000302193,0.0019581069,0.00021755311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003105756,0.0012148654,0.0011282422,0.00630573,0.00039922222,0.0018818831,0.001122716,0.0011953288,0.0024277095],"category_scores_gemma":[0.01094482,0.0005422992,0.0014612775,0.0058999807,0.00043389882,0.003165287,0.00076127576,0.0012510123,0.001441923],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008807758,0.00009098358,0.0051181642,0.02388626,0.00038121064,0.00018260386,0.00021952624,0.0022624475,0.00086819177,0.0047626956,0.04916611,0.9129738],"study_design_scores_gemma":[0.000024157094,0.00025992855,0.012945737,0.033102445,0.0009937182,0.0007913827,0.0005004402,0.011269202,0.0017194925,0.0075713764,0.93063647,0.00018569315],"about_ca_topic_score_codex":0.0049223704,"about_ca_topic_score_gemma":0.0062852483,"teacher_disagreement_score":0.00630573,"about_ca_system_score_codex":0.00081941223,"about_ca_system_score_gemma":0.0017865492,"threshold_uncertainty_score":0.016425014},"labels":[],"label_agreement":null},{"id":"W4365802030","doi":"10.2196/45188","title":"Novel Location-Based Survey Using Cognitive Interviews to Assess Geographic Networks and Hotspots of Sex and Drug Use: Implementation and Validation Study","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Drug Abuse; National Institute of Mental Health; University of California, Los Angeles; National Institutes of Health","keywords":"Geospatial analysis; Usability; Geography; Geographic information system; Survey data collection; Data science; Computer science; Cartography; Statistics","score_opus":0.21122099359072324,"score_gpt":0.4969322266115784,"score_spread":0.2857112330208551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365802030","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":[0.8929132,0.00014139427,0.05993166,0.0009466785,0.00018433342,0.036185477,0.0011322241,0.00023738934,0.008327617],"genre_scores_gemma":[0.7603217,0.0004021414,0.15850954,0.0014418045,0.00011504021,0.0729504,0.0015756881,0.000087903674,0.004595804],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.97319406,0.018840795,0.0014087617,0.0018139197,0.0027410171,0.002001416],"domain_scores_gemma":[0.9508316,0.022904009,0.002633835,0.0051151114,0.016611602,0.0019038477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05285829,0.0012002295,0.0006154173,0.0020013303,0.0027114025,0.001717566,0.0026448194,0.0012903683,0.0032894239],"category_scores_gemma":[0.05210154,0.0008982906,0.0015354581,0.0010893081,0.0018884607,0.0020559025,0.0037123042,0.0020581412,0.0010621627],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014498932,0.024907012,0.18444942,0.0027944865,0.00031005387,0.0022361455,0.3186459,0.0027903547,0.012462564,0.004010292,0.008635802,0.43730807],"study_design_scores_gemma":[0.002701923,0.039611988,0.3353211,0.003095998,0.00067172176,0.0017069213,0.47100052,0.017040491,0.022443999,0.0058522723,0.09998358,0.0005695433],"about_ca_topic_score_codex":0.007947542,"about_ca_topic_score_gemma":0.014854547,"teacher_disagreement_score":0.05285829,"about_ca_system_score_codex":0.00380354,"about_ca_system_score_gemma":0.010617005,"threshold_uncertainty_score":0.2795447},"labels":[],"label_agreement":null},{"id":"W4366962278","doi":"10.2196/45885","title":"Precision Public Health Approach in Outbreak Management: Protocol for a Scoping Review (Preprint)","year":2023,"lang":"en","type":"review","venue":"JMIR Research Protocols","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprint; Protocol (science); Public health; Computer science; Medicine; World Wide Web; Nursing; Alternative medicine","score_opus":0.6943827167252263,"score_gpt":0.679195291228281,"score_spread":0.015187425496945295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366962278","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003964143,0.0024370623,0.0037599374,0.0012813733,0.0010136444,0.97124916,0.017852167,0.00040856746,0.0016016357],"genre_scores_gemma":[0.00047409823,0.00064073404,0.007880171,0.00040515626,0.00006662189,0.98896503,0.0009885848,0.00003729096,0.0005422733],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.8573562,0.06621635,0.054368027,0.006451955,0.011390789,0.0042165965],"domain_scores_gemma":[0.7535057,0.12226884,0.042093854,0.025389425,0.05161735,0.0051249103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.15081361,0.005470932,0.014880639,0.020213768,0.0071134763,0.0113895675,0.004892999,0.010355233,0.1526103],"category_scores_gemma":[0.29132053,0.008911661,0.019415168,0.01983653,0.005897207,0.0071205255,0.01169242,0.014437265,0.031970084],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008006586,0.0004585296,0.0010982981,0.74478084,0.004362366,0.00040963467,0.0031568466,0.001183107,0.0026931528,0.0076331096,0.15919377,0.06702383],"study_design_scores_gemma":[0.044948295,0.0016139208,0.007381363,0.5486533,0.010686648,0.00041992046,0.003195158,0.0015903432,0.0033213533,0.018709823,0.35859025,0.00088969385],"about_ca_topic_score_codex":0.0085557215,"about_ca_topic_score_gemma":0.020742664,"teacher_disagreement_score":0.1526103,"about_ca_system_score_codex":0.015263215,"about_ca_system_score_gemma":0.082719885,"threshold_uncertainty_score":0.7975881},"labels":[],"label_agreement":null},{"id":"W4367312293","doi":"10.2196/42721","title":"Estimating Rare Disease Incidences With Large-scale Internet Search Data: Development and Evaluation of a Two-step Machine Learning Method","year":2023,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Peking Union Medical College; Peking Union Medical College Hospital; Tsinghua University; National Natural Science Foundation of China","keywords":"Computer science; Search engine indexing; Matching (statistics); The Internet; Online search; Scale (ratio); Session (web analytics); Information retrieval; Web search query; Big data; Search engine; Medicine; Machine learning; Data mining; Statistics; Geography; World Wide Web; Mathematics","score_opus":0.10359497613192552,"score_gpt":0.44114653239950186,"score_spread":0.3375515562675763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367312293","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":[0.17678426,0.002187695,0.8108879,0.0014212515,0.0002821843,0.001428486,0.0029718718,0.0023855737,0.0016508587],"genre_scores_gemma":[0.5638955,0.0009627932,0.42458165,0.0005434218,0.00036431354,0.0016850724,0.006050661,0.000101087426,0.0018154614],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99472445,0.0024422538,0.0006873239,0.001186584,0.00072055665,0.00023876148],"domain_scores_gemma":[0.9739691,0.020302935,0.0014378879,0.0010666675,0.0027332415,0.0004902173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012264284,0.001640572,0.0020151264,0.0056662476,0.00078494713,0.0017423774,0.0030478626,0.0020712733,0.0019452012],"category_scores_gemma":[0.029123979,0.00068234355,0.0022339448,0.003634094,0.00067897234,0.0023183012,0.0018188951,0.0026859909,0.00088338536],"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":[0.00088528986,0.001508806,0.2548714,0.0008559822,0.0014313678,0.00053216395,0.00034759723,0.28957266,0.0017621195,0.0032464394,0.007564433,0.43742174],"study_design_scores_gemma":[0.000030196163,0.00007978736,0.0053523546,0.000028529097,0.00006519081,0.00006071424,0.00005149121,0.992665,0.00025522872,0.001052253,0.00033867883,0.000020525666],"about_ca_topic_score_codex":0.014030257,"about_ca_topic_score_gemma":0.009251268,"teacher_disagreement_score":0.014030257,"about_ca_system_score_codex":0.0010509829,"about_ca_system_score_gemma":0.0024506021,"threshold_uncertainty_score":0.06486052},"labels":[],"label_agreement":null},{"id":"W4367597837","doi":"10.1371/journal.pdig.0000241","title":"Changes in digital healthcare search behavior during the early months of the COVID-19 pandemic: A study of six English-speaking countries","year":2023,"lang":"en","type":"article","venue":"PLOS Digital Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Declaration; Health care; Digital health; Coronavirus disease 2019 (COVID-19); Public healthcare; Public health; Telemedicine; Political science; Business; Public relations; Medicine; Economic growth; Nursing; Economics","score_opus":0.07611502691905617,"score_gpt":0.35722305476220734,"score_spread":0.28110802784315114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367597837","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":[0.9994454,0.000042736345,0.000026483267,0.000037771344,0.000002288916,0.000013922508,0.00015981312,6.013006e-7,0.00027088827],"genre_scores_gemma":[0.9992106,0.0000724849,0.00009211234,0.000099602505,0.0000043910154,0.000030901563,0.0002746478,0.0000029132125,0.00021221215],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987519,0.0005076638,0.00015331295,0.00018289426,0.000079898986,0.0003243389],"domain_scores_gemma":[0.9954145,0.0012891223,0.0015655862,0.00025386718,0.0006559924,0.0008208739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016862964,0.00027366375,0.0005121678,0.0010825524,0.0011117561,0.0013204461,0.0004084847,0.00059170864,0.0015656644],"category_scores_gemma":[0.0053840517,0.00039967176,0.00063802896,0.0012208167,0.0008483418,0.0014957757,0.0018103039,0.0007317676,0.00035002656],"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":[0.00031983148,0.00022794111,0.9703532,0.00008428081,0.00007042213,0.00037927864,0.024524298,0.00008019645,0.0003463644,0.000077285855,0.0003114917,0.0032254213],"study_design_scores_gemma":[0.000017377633,0.00025774966,0.9508951,0.000043213444,0.000030804043,0.00017347571,0.047579743,0.00020161568,0.00012510749,0.000025839561,0.0006260538,0.000023914512],"about_ca_topic_score_codex":0.06154573,"about_ca_topic_score_gemma":0.07569273,"teacher_disagreement_score":0.06154573,"about_ca_system_score_codex":0.0009725156,"about_ca_system_score_gemma":0.00090777886,"threshold_uncertainty_score":0.12237501},"labels":[],"label_agreement":null},{"id":"W4368361811","doi":"10.1371/journal.pgph.0001714","title":"Progression of a large syphilis outbreak in rural North Carolina through space and time: Application of a Bayesian Maximum Entropy graphical user interface","year":2023,"lang":"en","type":"article","venue":"PLOS Global Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"National Institutes of Health; American Sexually Transmitted Diseases Association; Ohio State University","keywords":"Geography; Rural area; Outbreak; Syphilis; Cartography; Socioeconomics; Sociology; Medicine","score_opus":0.014996390105166875,"score_gpt":0.3228048985495702,"score_spread":0.3078085084444033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4368361811","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":[0.29671514,0.00012963732,0.64019203,0.001244799,0.000078147124,0.0006234263,0.007000032,0.046701472,0.007315333],"genre_scores_gemma":[0.6261738,0.000109862325,0.3657599,0.00022316488,0.000023072358,0.0005016881,0.0032891077,0.0011570894,0.0027623074],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9993703,0.00033061008,0.000036951136,0.000109376364,0.00012228423,0.00003062714],"domain_scores_gemma":[0.99475646,0.0041132495,0.0002595161,0.00019148845,0.0005498464,0.00012946948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031457369,0.0005211994,0.00034088676,0.0013859853,0.00033679415,0.0010140588,0.00084724155,0.00059498416,0.005860919],"category_scores_gemma":[0.012737846,0.00038637966,0.0006055075,0.0005509999,0.0002491641,0.0005043282,0.0013449448,0.00047419433,0.00053206924],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026737282,0.0008321512,0.1326436,0.00078089995,0.0004053886,0.0013531373,0.0044401917,0.3000545,0.016790172,0.011151052,0.036843017,0.4920322],"study_design_scores_gemma":[0.00010952052,0.00016684238,0.017601812,0.00007096463,0.000046252753,0.00014383919,0.0001625041,0.969158,0.0036906241,0.0031828946,0.0055976836,0.0000690485],"about_ca_topic_score_codex":0.015939746,"about_ca_topic_score_gemma":0.025181275,"teacher_disagreement_score":0.015939746,"about_ca_system_score_codex":0.0006467227,"about_ca_system_score_gemma":0.000720138,"threshold_uncertainty_score":0.031693935},"labels":[],"label_agreement":null},{"id":"W4376132251","doi":"10.1186/s12889-023-15747-z","title":"ATQ: alert time quality, an evaluation metric for assessing timely epidemic detection models within a school absenteeism-based surveillance system","year":2023,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Guelph General Hospital; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metric (unit); Medicine; Absenteeism; Biostatistics; Public health; Logistic regression; Quality (philosophy); Statistics; Environmental health; Medical emergency; Operations management; Nursing; Psychology","score_opus":0.2064241410222613,"score_gpt":0.43512513143791787,"score_spread":0.22870099041565656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376132251","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7175639,0.00093715725,0.27613324,0.00072949275,0.00010649285,0.00040988912,0.0014953724,0.00088019477,0.0017442806],"genre_scores_gemma":[0.97579205,0.00009398858,0.02300278,0.000048260626,0.000024570962,0.000079389974,0.00078075373,0.00002119095,0.0001569866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957354,0.0018727037,0.00050955656,0.0005834495,0.0010854261,0.00021344067],"domain_scores_gemma":[0.9635252,0.024157358,0.0047262083,0.0013726146,0.0051869806,0.0010316566],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.012629978,0.0014128139,0.0010259823,0.0030204956,0.0004974133,0.0014264827,0.0013122701,0.0010232696,0.0006504101],"category_scores_gemma":[0.036843788,0.00035477016,0.0010689114,0.0010801876,0.0005334998,0.0017029211,0.0012479828,0.0010687659,0.0000974463],"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":[0.00055104145,0.00027260702,0.07697544,0.0002117089,0.00042174393,0.00008839322,0.00012846956,0.89225113,0.0018208978,0.0011368922,0.0009627322,0.025178876],"study_design_scores_gemma":[0.000016289678,0.00053608656,0.008683439,0.000012409187,0.00004032065,0.000045674038,0.000042879336,0.9891803,0.0006725148,0.0006016882,0.00015409211,0.000014304477],"about_ca_topic_score_codex":0.015803862,"about_ca_topic_score_gemma":0.007106098,"teacher_disagreement_score":0.98737,"about_ca_system_score_codex":0.0027325256,"about_ca_system_score_gemma":0.002157272,"threshold_uncertainty_score":0.066794455},"labels":[],"label_agreement":null},{"id":"W4376132888","doi":"10.2196/39105","title":"Impact of COVID-19 on Public Interest in Breast Cancer Screening and Related Symptoms: Google Trends Analysis","year":2023,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Breast cancer; Medicine; Mammography; Public health; Breast cancer screening; Health care; Oncology; Family medicine; Cancer; Internal medicine; Pathology","score_opus":0.054547011204447965,"score_gpt":0.39710577822711707,"score_spread":0.3425587670226691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376132888","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":[0.8227867,0.009540946,0.00043839976,0.002052616,0.0000977154,0.00017740675,0.15866786,0.00034350058,0.0058949715],"genre_scores_gemma":[0.9232745,0.0029730634,0.0011828302,0.00025641936,0.0000807717,0.00013110177,0.071159706,0.000071809416,0.0008697298],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.996734,0.00068888784,0.0004897008,0.00044942877,0.0012208401,0.0004171601],"domain_scores_gemma":[0.98288786,0.009604227,0.0039353264,0.00058601727,0.002383625,0.00060287834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023735745,0.0005567444,0.00094582856,0.0125701735,0.00043381113,0.0024934225,0.00067902124,0.00062294974,0.0017460954],"category_scores_gemma":[0.017518435,0.00019682944,0.0022244693,0.021177977,0.0004384836,0.002636724,0.0012444053,0.00085625466,0.00078806723],"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":[0.00078841473,0.00014864863,0.9383938,0.004475342,0.0010228546,0.0007219971,0.0010074769,0.0028100174,0.0007342969,0.0013120836,0.019958671,0.028626336],"study_design_scores_gemma":[0.00005369237,0.0002516637,0.9585011,0.0004998624,0.00073889317,0.0011931581,0.0029403155,0.015658421,0.0006650996,0.00048045858,0.018936666,0.00008065385],"about_ca_topic_score_codex":0.07188859,"about_ca_topic_score_gemma":0.06411218,"teacher_disagreement_score":0.07188859,"about_ca_system_score_codex":0.0013581353,"about_ca_system_score_gemma":0.001704083,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4376274788","doi":"10.1016/j.prevetmed.2023.105932","title":"Animal disease surveillance: How to represent textual data for classifying epidemiological information","year":2023,"lang":"en","type":"article","venue":"Preventive Veterinary Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Centre de Coopération Internationale en Recherche Agronomique pour le Développement; Agence Nationale de la Recherche; European Commission","keywords":"Relevance (law); Context (archaeology); Computer science; Representation (politics); Word (group theory); Natural language processing; Artificial intelligence; Binary classification; Information retrieval; Data science; Mathematics; Support vector machine; Geography","score_opus":0.1931269320748563,"score_gpt":0.419078500008632,"score_spread":0.22595156793377572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376274788","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":[0.23512074,0.0043427036,0.7252939,0.005774897,0.0006597419,0.0010148785,0.016009865,0.006711492,0.005071807],"genre_scores_gemma":[0.54083866,0.0012790018,0.44388652,0.0003676629,0.00030021137,0.00059426826,0.011281383,0.00014150409,0.0013107174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985494,0.00076377904,0.00018971085,0.00023996523,0.00019069307,0.00006639704],"domain_scores_gemma":[0.99059147,0.005884113,0.0009772488,0.0010323931,0.0013316515,0.00018300931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029563294,0.0010332282,0.00060628576,0.004630586,0.0002846422,0.0024364314,0.0008522669,0.0011235222,0.0016146646],"category_scores_gemma":[0.019125082,0.00023180818,0.00068005314,0.0025159218,0.00038247564,0.004781574,0.00080023764,0.00094304763,0.0015789594],"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":[0.00058064685,0.00066968583,0.020086285,0.0012533931,0.0002165476,0.00014643151,0.0006603866,0.028607603,0.022352878,0.005258277,0.011492721,0.908675],"study_design_scores_gemma":[0.00011187111,0.0005648857,0.014795463,0.00056220446,0.00021467078,0.00033597345,0.0010728132,0.90805507,0.021981884,0.03380363,0.018366039,0.0001355669],"about_ca_topic_score_codex":0.0027325074,"about_ca_topic_score_gemma":0.0029343702,"teacher_disagreement_score":0.004630586,"about_ca_system_score_codex":0.00074568996,"about_ca_system_score_gemma":0.000740367,"threshold_uncertainty_score":0.015634775},"labels":[],"label_agreement":null},{"id":"W4376638904","doi":"10.18280/isi.280202","title":"Clustering for Moroccan Prefecture-Provinces and World Countries Based COVID-19 Dataset","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Cluster analysis; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Geography; Computer science; Virology; Medicine; Artificial intelligence; Outbreak","score_opus":0.027894163924814364,"score_gpt":0.29959626664927624,"score_spread":0.2717021027244619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376638904","genre_codex":"dataset","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":[0.122162566,0.0014138881,0.0047936845,0.0010104588,0.0002984903,0.0005554721,0.86298555,0.0028794876,0.0039003852],"genre_scores_gemma":[0.060525563,0.00021461245,0.0119539,0.00008753349,0.00004789318,0.00032576727,0.92572206,0.00007169023,0.0010509182],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9987722,0.00025358336,0.00016295124,0.00031118732,0.00024396807,0.0002560248],"domain_scores_gemma":[0.9984132,0.00022783394,0.0001843722,0.0003810602,0.0006143057,0.00017928604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016073908,0.0011152453,0.00063670654,0.004246629,0.001133054,0.0007683442,0.0013502912,0.00092194777,0.002770979],"category_scores_gemma":[0.005176601,0.00019998295,0.0011439017,0.003316022,0.00030117002,0.0005147865,0.0011470891,0.0008154487,0.0021262355],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011040093,0.00064756564,0.19450751,0.001788948,0.0005206573,0.00097370794,0.0008673736,0.019179529,0.005220902,0.002987143,0.66625136,0.10595132],"study_design_scores_gemma":[0.00035213874,0.00026469197,0.4584033,0.0005211058,0.0002261596,0.0008913131,0.0023199837,0.07753147,0.007146056,0.0022711472,0.4498863,0.00018632697],"about_ca_topic_score_codex":0.11541879,"about_ca_topic_score_gemma":0.1426545,"teacher_disagreement_score":0.11541879,"about_ca_system_score_codex":0.0022609872,"about_ca_system_score_gemma":0.0028479293,"threshold_uncertainty_score":0.22949398},"labels":[],"label_agreement":null},{"id":"W4377091609","doi":"10.3233/shti230294","title":"Modelling Information Needs and Sources in a COVID-19 Designated Hospital","year":2023,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Health informatics; Outbreak; Coronavirus disease 2019 (COVID-19); Infectious disease (medical specialty); Public health; Stakeholder; Information needs; Health care; Informatics; Information system; Medicine; Disease; Knowledge management; Nursing; Public relations; Computer science; World Wide Web; Pathology; Political science","score_opus":0.05449711243471971,"score_gpt":0.360361891969461,"score_spread":0.3058647795347413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377091609","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":[0.94144887,0.00019593911,0.038226787,0.0029799168,0.0000109117655,0.00060957053,0.0019097821,0.00012350881,0.014494651],"genre_scores_gemma":[0.95698357,0.00022482924,0.03886402,0.00009457494,0.0000046768964,0.00042310025,0.0013014774,0.000033678058,0.0020700127],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941286,0.0038470654,0.00039933375,0.0005335378,0.00062270655,0.00046875697],"domain_scores_gemma":[0.9701578,0.024765518,0.0013374923,0.0008475203,0.0020792626,0.0008123077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071500125,0.00056570204,0.00042150842,0.0035422966,0.002080908,0.0053551523,0.0015968874,0.0024505765,0.0024630304],"category_scores_gemma":[0.023800593,0.0009608599,0.0009795914,0.0032315506,0.0015532684,0.0071113952,0.00338571,0.0013674983,0.00037494695],"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":[0.0014284913,0.001171513,0.29550126,0.0016042965,0.00024523906,0.0047581927,0.2555438,0.273025,0.0057259244,0.106866695,0.0030895635,0.05103999],"study_design_scores_gemma":[0.00016619924,0.00043107898,0.036112413,0.0010421547,0.00020800022,0.00089437823,0.2073848,0.66340786,0.004910681,0.04605483,0.03920354,0.00018401281],"about_ca_topic_score_codex":0.054888006,"about_ca_topic_score_gemma":0.04832722,"teacher_disagreement_score":0.054888006,"about_ca_system_score_codex":0.0075841574,"about_ca_system_score_gemma":0.004905091,"threshold_uncertainty_score":0.10913706},"labels":[],"label_agreement":null},{"id":"W4377092931","doi":"10.3233/shti230290","title":"Digital Disease Surveillance for Emerging Infectious Diseases: An Early Warning System Using the Internet and Social Media Data for COVID-19 Forecasting in Canada","year":2023,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Anosmia; Social media; Coronavirus disease 2019 (COVID-19); Incidence (geometry); The Internet; Correlation; Pandemic; Medicine; Population; Computer science; Demography; Infectious disease (medical specialty); Environmental health; Disease; Internal medicine; Mathematics; World Wide Web","score_opus":0.11384137858312442,"score_gpt":0.3802366522467181,"score_spread":0.26639527366359367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377092931","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":[0.88493794,0.0018553231,0.009257199,0.0038256391,0.00010868634,0.00040316497,0.08890385,0.00051300274,0.010195295],"genre_scores_gemma":[0.959455,0.0009825238,0.010026389,0.00017044877,0.00002653133,0.00006509777,0.026517062,0.000036814385,0.0027200682],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994974,0.0000457051,0.000040307503,0.000092473645,0.00018943642,0.00013462729],"domain_scores_gemma":[0.99752337,0.0002356212,0.00029492984,0.00006504833,0.0016280579,0.00025305315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010389453,0.00056689646,0.00038665716,0.0035059883,0.0010618424,0.0017079212,0.0010005111,0.00031572784,0.0013871553],"category_scores_gemma":[0.0044046585,0.0002884513,0.0007301126,0.0048819757,0.00035073387,0.00072586467,0.0010563955,0.00062688295,0.00026094067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018004434,0.000096845644,0.91486543,0.0003270583,0.00021351677,0.00018386173,0.0005097154,0.020746332,0.000916907,0.001577953,0.013703467,0.046678804],"study_design_scores_gemma":[0.000037980502,0.00006200363,0.72137886,0.00022566736,0.0002013805,0.000055844706,0.0021788126,0.26095027,0.0014856616,0.0005946031,0.012743126,0.00008581376],"about_ca_topic_score_codex":0.9953881,"about_ca_topic_score_gemma":0.9936127,"teacher_disagreement_score":0.022993324,"about_ca_system_score_codex":0.022993324,"about_ca_system_score_gemma":0.038411483,"threshold_uncertainty_score":0.16682905},"labels":[],"label_agreement":null},{"id":"W4378470119","doi":"10.2196/44286","title":"Convergence in Mobility Data Sets From Apple, Google, and Meta","year":2023,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Izaak Walton Killam Health Centre; Dalhousie University","funders":"","keywords":"Pandemic; Public health; Business; Download; Robustness (evolution); Convergence (economics); Meta-analysis; Geography; Coronavirus disease 2019 (COVID-19); Internet privacy; Infectious disease (medical specialty); Medicine; Computer science; Disease; World Wide Web; Economics; Economic growth; Biology","score_opus":0.1096488974195229,"score_gpt":0.37189454439673325,"score_spread":0.26224564697721037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378470119","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":[0.9477145,0.0007006154,0.0018411272,0.00042071656,0.00009295452,0.00016297962,0.044537246,0.00035819376,0.004171644],"genre_scores_gemma":[0.9099979,0.00044065548,0.0055389465,0.00009339911,0.000066647815,0.00023604489,0.08282699,0.000056290697,0.00074324064],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99741095,0.00062794064,0.0004240358,0.00046727533,0.0007876325,0.00028224566],"domain_scores_gemma":[0.99433374,0.00195088,0.0011138073,0.0009791517,0.0013164083,0.00030594115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022087155,0.0006636771,0.0005191589,0.008314217,0.0006267387,0.0016407766,0.00086481957,0.0007678958,0.0005324099],"category_scores_gemma":[0.01190891,0.00021121408,0.0007023983,0.008897446,0.0005203908,0.0018761952,0.0018548011,0.00052746787,0.00058080896],"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":[0.00058707065,0.00024377406,0.8989381,0.0008081712,0.00059787824,0.0010206844,0.004169061,0.012156386,0.0021366416,0.0031473462,0.020354155,0.055840805],"study_design_scores_gemma":[0.00003547472,0.00021011391,0.9251078,0.0003309363,0.00015781788,0.0010810865,0.008678523,0.027014665,0.0026251907,0.0019645537,0.032649264,0.0001446646],"about_ca_topic_score_codex":0.023915758,"about_ca_topic_score_gemma":0.031093977,"teacher_disagreement_score":0.023915758,"about_ca_system_score_codex":0.00082842004,"about_ca_system_score_gemma":0.00077153876,"threshold_uncertainty_score":0.047553122},"labels":[],"label_agreement":null},{"id":"W4378905583","doi":"10.14745/ccdr.v49i05a04","title":"Innovative technology and established partnerships—a recipe for rapid adaptability under emerging pandemic conditions","year":2023,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Canadian Paediatric Society; Public Health Agency of Canada","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Data collection; Pandemic; Public health; Intervention (counseling); Flexibility (engineering); Business; General partnership; Public health surveillance; Coronavirus disease 2019 (COVID-19); Environmental health; Medicine; Medical emergency; Psychology; Nursing; Disease; Finance; Sociology","score_opus":0.06989834703645766,"score_gpt":0.335942007698452,"score_spread":0.26604366066199436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378905583","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029463783,0.0083495965,0.10431435,0.568667,0.008762971,0.0020678649,0.00042170662,0.0018644065,0.2760883],"genre_scores_gemma":[0.60157484,0.014663936,0.21786669,0.087397166,0.0040596016,0.004097914,0.00077561993,0.0010471792,0.06851704],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9643559,0.021219505,0.0008918845,0.0027284704,0.0061315135,0.004672695],"domain_scores_gemma":[0.95872325,0.009889846,0.00257622,0.0059832684,0.0071311714,0.015696315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04315712,0.0011773233,0.00062128593,0.002361227,0.0101018,0.017909124,0.0039357045,0.0071830037,0.0286896],"category_scores_gemma":[0.052673273,0.0007828559,0.0012596288,0.0017726464,0.01703301,0.027634801,0.029036658,0.009362474,0.007672022],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019545158,0.0006385492,0.009971889,0.0012489221,0.00014048765,0.0021471889,0.039407417,0.0016967886,0.0023785832,0.4406011,0.19067876,0.31089488],"study_design_scores_gemma":[0.00009038049,0.0002489564,0.002092645,0.00089902646,0.000019577585,0.0005180548,0.021651885,0.0005648126,0.00042895778,0.14850186,0.82491577,0.00006818446],"about_ca_topic_score_codex":0.008332578,"about_ca_topic_score_gemma":0.010428042,"teacher_disagreement_score":0.99166745,"about_ca_system_score_codex":0.009667988,"about_ca_system_score_gemma":0.043463193,"threshold_uncertainty_score":0.22823936},"labels":[],"label_agreement":null},{"id":"W4378953239","doi":"10.2196/44603","title":"Reddit and Google Activity Related to Non-COVID Epidemic Diseases Surged at Start of COVID-19 Pandemic: Retrospective Study","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Vanderbilt University Medical Center; Vanderbilt University","keywords":"Popularity; Public health; Pandemic; Biostatistics; Coronavirus disease 2019 (COVID-19); Demography; Medicine; Infectious disease (medical specialty); Environmental health; Disease; Political science; Sociology; Pathology; Law","score_opus":0.10073007155468922,"score_gpt":0.46342557464692963,"score_spread":0.3626955030922404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378953239","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":[0.9820886,0.0010529702,0.00041525194,0.00007227701,0.000020257032,0.0001995659,0.013714578,0.000027263704,0.0024093005],"genre_scores_gemma":[0.9897843,0.00059400476,0.00038611653,0.000098504184,0.000038343576,0.00022865283,0.008142239,0.000021752026,0.0007061727],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982987,0.00026455318,0.00034802497,0.00043610207,0.00038119062,0.00027148108],"domain_scores_gemma":[0.99370295,0.0012797497,0.0025676396,0.00063525536,0.0012938931,0.000520496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014765173,0.00031899003,0.00053800485,0.0033270386,0.00069733,0.0010246714,0.0006199639,0.00052151945,0.0033519717],"category_scores_gemma":[0.006263741,0.00049396174,0.0010314642,0.0035122142,0.00053635665,0.0014056253,0.0010459268,0.0009796153,0.0015336201],"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":[0.00012816,0.000050490093,0.9969013,0.00014877401,0.000053143114,0.00011130105,0.00022459599,0.00003196617,0.00008824729,0.0000614572,0.0007610625,0.0014395202],"study_design_scores_gemma":[0.000010748486,0.00023012023,0.99441636,0.00009755656,0.00013353847,0.00094608415,0.001296361,0.0003514276,0.00021147249,0.000051582152,0.0022340803,0.000020662643],"about_ca_topic_score_codex":0.01110672,"about_ca_topic_score_gemma":0.010747389,"teacher_disagreement_score":0.01110672,"about_ca_system_score_codex":0.0006557248,"about_ca_system_score_gemma":0.0010374399,"threshold_uncertainty_score":0.022084177},"labels":[],"label_agreement":null},{"id":"W4380079799","doi":"10.3390/tropicalmed8060314","title":"Spatial and Temporal Data Visualisation for Mass Dissemination: Advances in the Era of COVID-19","year":2023,"lang":"en","type":"article","venue":"Tropical Medicine and Infectious Disease","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"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":"Keio University; Universidad de Murcia; University of Toronto; Johns Hopkins University","keywords":"Transparency (behavior); Public health; Accountability; Government (linguistics); Data quality; Pandemic; Disease surveillance; Data sharing; Quality (philosophy); Public relations; Data science; Business; Computer science; Medicine; Political science; Disease; Infectious disease (medical specialty); Computer security; Marketing; Coronavirus disease 2019 (COVID-19)","score_opus":0.03982800731532188,"score_gpt":0.3896390912490103,"score_spread":0.3498110839336884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380079799","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":[0.017873945,0.24870107,0.40832716,0.19989055,0.013509562,0.00074163446,0.008439447,0.024670001,0.07784664],"genre_scores_gemma":[0.11615693,0.18996824,0.6398394,0.010695292,0.012023506,0.0006743744,0.009004737,0.007931347,0.013706159],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9824592,0.009226867,0.0011959444,0.0013132964,0.0052206027,0.00058425363],"domain_scores_gemma":[0.88840866,0.07729734,0.002835684,0.009650097,0.016625261,0.005183025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032153983,0.0016636216,0.0016533391,0.008924019,0.0012464285,0.017581923,0.0054905345,0.0046004094,0.018396651],"category_scores_gemma":[0.10064888,0.0012144991,0.0025624852,0.0079043815,0.0040829126,0.02155312,0.01123792,0.007460905,0.004858171],"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":[0.00044644703,0.00014602511,0.0049232505,0.0063166763,0.00019903362,0.00046823316,0.009089575,0.0046201497,0.0028659026,0.09342142,0.15963744,0.7178659],"study_design_scores_gemma":[0.00007127375,0.000105009596,0.0026953886,0.004641166,0.00006683428,0.0007977589,0.0021737826,0.0087253675,0.0016811986,0.06216144,0.9166669,0.00021382156],"about_ca_topic_score_codex":0.006456056,"about_ca_topic_score_gemma":0.0050226934,"teacher_disagreement_score":0.032153983,"about_ca_system_score_codex":0.0028605736,"about_ca_system_score_gemma":0.0042191464,"threshold_uncertainty_score":0.17004853},"labels":[],"label_agreement":null},{"id":"W4380592302","doi":"10.2139/ssrn.4478794","title":"A Horizon Scanning Exercise of Large-Scale Data Aggregation Systems to Support the Creation and Sustainability of the Global Burden of Animal Diseases (Gbads) Knowledge Engine","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Sustainability; Scale (ratio); Horizon; Knowledge management; Business; Computer science; Data science; Environmental economics; Environmental resource management; Environmental science; Economics; Geography; Ecology; Mathematics; Biology; Cartography","score_opus":0.015477277569527595,"score_gpt":0.31980824850177614,"score_spread":0.30433097093224853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380592302","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":[0.24434844,0.000794484,0.61551,0.0035186897,0.00042499005,0.0014888595,0.022105178,0.07612237,0.035687],"genre_scores_gemma":[0.48038048,0.00030418136,0.5036516,0.0002450229,0.00004062421,0.00024640147,0.010306604,0.00180584,0.0030193555],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9978503,0.00065512606,0.00026549006,0.00037124124,0.00070693856,0.00015096557],"domain_scores_gemma":[0.9862347,0.008901411,0.0004958354,0.00241036,0.0016049126,0.0003527898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008391734,0.00076386315,0.00060461025,0.0025433905,0.001210035,0.002923853,0.0011779879,0.0009376616,0.008414752],"category_scores_gemma":[0.025089659,0.0005201175,0.00092607865,0.0024771376,0.0005789094,0.0032172042,0.0028357129,0.0012756889,0.001377585],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021527102,0.0011273039,0.045229513,0.001750035,0.0006835273,0.0017888427,0.00880123,0.12380634,0.034223158,0.056166977,0.08664684,0.6376236],"study_design_scores_gemma":[0.00017094894,0.00027066335,0.01156066,0.00023108225,0.000188622,0.0002777081,0.0020362418,0.85776097,0.030186253,0.035008196,0.062198583,0.000110157795],"about_ca_topic_score_codex":0.010778689,"about_ca_topic_score_gemma":0.012586034,"teacher_disagreement_score":0.010778689,"about_ca_system_score_codex":0.00088837655,"about_ca_system_score_gemma":0.0033044163,"threshold_uncertainty_score":0.044380307},"labels":[],"label_agreement":null},{"id":"W4380985505","doi":"10.1007/s40264-023-01324-1","title":"Serially Combining Epidemiological Designs Does Not Improve Overall Signal Detection in Vaccine Safety Surveillance","year":2023,"lang":"en","type":"article","venue":"Drug Safety","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Institute of Allergy and Infectious Diseases; National Institutes of Health","keywords":"Comparator; Medicine; Calibration; Type I and type II errors; Sensitivity (control systems); Clinical study design; SIGNAL (programming language); Statistics; Computer science; Clinical trial; Internal medicine; Electronic engineering; Mathematics","score_opus":0.022609838332286584,"score_gpt":0.28569730298624707,"score_spread":0.2630874646539605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380985505","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54893297,0.006101171,0.4310706,0.0016922915,0.00048151758,0.0026009378,0.0011408058,0.0010031038,0.00697653],"genre_scores_gemma":[0.8180981,0.00061319966,0.17816009,0.00071304105,0.0001716249,0.0009590635,0.0005981472,0.00006491244,0.00062173745],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.704862,0.26214227,0.008478646,0.011328185,0.012224752,0.0009640836],"domain_scores_gemma":[0.4964023,0.35507768,0.06424575,0.06643741,0.016121458,0.0017153615],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2558762,0.0017865449,0.0019213258,0.0023946543,0.00068544695,0.002421204,0.0028407248,0.0021182105,0.002403197],"category_scores_gemma":[0.36513183,0.0011132191,0.003149338,0.0021228874,0.0019936715,0.0027880764,0.00259289,0.0016008271,0.0006304147],"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":[0.011178877,0.0013988062,0.50967854,0.0020879265,0.008633553,0.00021797263,0.0014794656,0.026899388,0.005164443,0.007775961,0.0019715214,0.4235135],"study_design_scores_gemma":[0.0039863004,0.07172164,0.565639,0.0023184856,0.0122773405,0.0019729026,0.0006588172,0.22199762,0.051102195,0.04592458,0.021848446,0.00055277545],"about_ca_topic_score_codex":0.0008376709,"about_ca_topic_score_gemma":0.001093344,"teacher_disagreement_score":0.7441238,"about_ca_system_score_codex":0.0012641709,"about_ca_system_score_gemma":0.0024757711,"threshold_uncertainty_score":0.9176371},"labels":[],"label_agreement":null},{"id":"W4381747357","doi":"10.1007/978-981-99-0981-0_12","title":"Analysis of Online Health-Related Private Data using Named Entity Recognition by Deep Correlation Techniques","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trent University","funders":"","keywords":"Social media; Computer science; Artificial intelligence; Correlation; Private information retrieval; Machine learning; Internet privacy; Data mining; World Wide Web; Computer security; Mathematics","score_opus":0.04922887267697669,"score_gpt":0.3058935751828577,"score_spread":0.256664702505881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381747357","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":[0.24729457,0.0030868943,0.72570854,0.0016309973,0.0004006831,0.00015248022,0.012210464,0.005283104,0.0042322935],"genre_scores_gemma":[0.8176068,0.001218188,0.15283471,0.0002530595,0.00024769935,0.00013235371,0.022049395,0.00019859271,0.0054592974],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99916303,0.00015959742,0.00007263797,0.00024712118,0.00019962904,0.00015801583],"domain_scores_gemma":[0.9982205,0.0009596637,0.00019289741,0.00031218864,0.00024849147,0.00006621319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011972716,0.00093446695,0.000980927,0.0023928143,0.00035341433,0.001379354,0.0009869641,0.0008261698,0.001789648],"category_scores_gemma":[0.0025897154,0.00035689733,0.0014200128,0.0031822657,0.00027979852,0.0018667254,0.0011039701,0.0014801938,0.0019691216],"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":[0.0006102872,0.0013001604,0.06478673,0.00031158974,0.0006269152,0.0011127853,0.00022467787,0.12824582,0.01920652,0.006801422,0.028526023,0.74824715],"study_design_scores_gemma":[0.000005023939,0.00005179663,0.01014646,0.000015594258,0.0000518924,0.00015793848,0.00007153479,0.9781295,0.0043947413,0.004543489,0.0024128766,0.000019185858],"about_ca_topic_score_codex":0.006314104,"about_ca_topic_score_gemma":0.0082783485,"teacher_disagreement_score":0.006314104,"about_ca_system_score_codex":0.0006091653,"about_ca_system_score_gemma":0.00081527315,"threshold_uncertainty_score":0.012554705},"labels":[],"label_agreement":null},{"id":"W4381955477","doi":"10.1016/b978-0-443-15299-3.00010-5","title":"Early warning for emerging infectious disease outbreaks: Digital disease surveillance for public health preparedness and response","year":2023,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Data-Driven Disease Surveillance","field":"Medicine","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 Waterloo","funders":"","keywords":"Public health; Preparedness; Social media; Disease surveillance; Infectious disease (medical specialty); Outbreak; Emerging infectious disease; Environmental health; Warning system; Public health surveillance; The Internet; Pandemic; Disease; Internet privacy; Medicine; Computer science; Political science; Coronavirus disease 2019 (COVID-19); Virology; Pathology; World Wide Web","score_opus":0.033062383113180244,"score_gpt":0.2952637057157309,"score_spread":0.2622013226025507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381955477","genre_codex":"other","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":[0.0010626365,0.15170068,0.061369043,0.022855172,0.012056453,0.00013332517,0.0014833593,0.0020275107,0.7473119],"genre_scores_gemma":[0.013769416,0.17635718,0.054386195,0.010416062,0.0070014647,0.00013909064,0.0015783198,0.0008597559,0.73549247],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9995597,0.000084897794,0.000021402913,0.000048958686,0.00025806736,0.000026920641],"domain_scores_gemma":[0.99849236,0.001086376,0.00004657803,0.00007374576,0.00021345598,0.000087561646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001024403,0.0009669454,0.00046289386,0.0018863165,0.00041515965,0.005458972,0.000807554,0.0018571536,0.059136767],"category_scores_gemma":[0.00246373,0.00037395727,0.00038411008,0.002007948,0.0007217254,0.005126311,0.0018081676,0.0018161804,0.026988609],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008565277,0.00002274347,0.000147832,0.00037902404,0.00000816548,0.000047217563,0.0001405264,0.00064884947,0.0005475424,0.03672562,0.4439703,0.5173536],"study_design_scores_gemma":[0.0000022965303,0.000007594246,0.00018288384,0.0004853627,0.0000044405465,0.000110774236,0.00010270484,0.0007717286,0.00020117685,0.020278318,0.97784233,0.000010385015],"about_ca_topic_score_codex":0.002861723,"about_ca_topic_score_gemma":0.00561266,"teacher_disagreement_score":0.059136767,"about_ca_system_score_codex":0.001006718,"about_ca_system_score_gemma":0.0013213307,"threshold_uncertainty_score":0.19783217},"labels":[],"label_agreement":null},{"id":"W4382583924","doi":"10.3389/fdgth.2023.1203874","title":"Comparison of pretrained transformer-based models for influenza and COVID-19 detection using social media text data in Saskatchewan, Canada","year":2023,"lang":"en","type":"article","venue":"Frontiers in Digital Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Social media; Coronavirus disease 2019 (COVID-19); Computer science; Transformer; Artificial intelligence; Context (archaeology); Language model; Outbreak; Natural language processing; Machine learning; Medicine; Geography; World Wide Web; Virology; Infectious disease (medical specialty); Disease; Internal medicine","score_opus":0.1175481210410137,"score_gpt":0.39133407715466934,"score_spread":0.27378595611365564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382583924","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":[0.92603046,0.0035445984,0.035879035,0.0027642585,0.0005195954,0.0003759583,0.017260008,0.004510663,0.009115505],"genre_scores_gemma":[0.9415723,0.0009706334,0.016518466,0.0004863817,0.00005662262,0.00014640516,0.033078,0.00016904279,0.0070022033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991041,0.0002313184,0.00006740769,0.00028999246,0.00012672556,0.00018045046],"domain_scores_gemma":[0.99683017,0.0015323987,0.0000818022,0.00018588812,0.0012293117,0.00014051201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022032955,0.001983538,0.0007106237,0.0010888319,0.0009648399,0.0012124417,0.001734345,0.0008670597,0.0019681912],"category_scores_gemma":[0.006864623,0.00045833888,0.0009660996,0.0009971714,0.00059647,0.001153114,0.00095676736,0.0015898069,0.0012925984],"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":[0.002735843,0.00080913794,0.107373364,0.00055781205,0.00078802794,0.00050046266,0.000516895,0.5500696,0.0059719956,0.0023104192,0.043519784,0.2848466],"study_design_scores_gemma":[0.000059732476,0.0000865549,0.010308202,0.000036205194,0.00008534369,0.000033262146,0.00033105953,0.98479503,0.0015075683,0.00068502536,0.0020366309,0.000035355606],"about_ca_topic_score_codex":0.85922796,"about_ca_topic_score_gemma":0.85988957,"teacher_disagreement_score":0.14077204,"about_ca_system_score_codex":0.009341675,"about_ca_system_score_gemma":0.0102272695,"threshold_uncertainty_score":0.2832021},"labels":[],"label_agreement":null},{"id":"W4382866899","doi":"10.1609/socs.v16i1.27311","title":"Structural Bias in Heuristic Search (Student Abstract)","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Queen's University","funders":"Queen's University","keywords":"Satisficing; Heuristic; Computer science; Sampling bias; Sampling (signal processing); Variable (mathematics); Greedy algorithm; Selection (genetic algorithm); Selection bias; Omitted-variable bias; Variation (astronomy); Machine learning; Mathematical optimization; Artificial intelligence; Algorithm; Mathematics; Statistics; Sample size determination","score_opus":0.04267614711094431,"score_gpt":0.3356768622818528,"score_spread":0.2930007151709085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382866899","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":[0.011526279,0.0005295059,0.98008096,0.0017859784,0.00021417387,0.0000812156,0.000059400132,0.0002092321,0.005513404],"genre_scores_gemma":[0.36371958,0.0009673952,0.6247317,0.001866491,0.0004714483,0.0004302286,0.00018571843,0.00034315753,0.007284254],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9943205,0.0036715653,0.00023442294,0.0007126936,0.0007838961,0.00027691352],"domain_scores_gemma":[0.94857913,0.042432133,0.0013694846,0.0040987595,0.0028820508,0.00063841895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010539161,0.0006346557,0.0011503691,0.0014060696,0.0009636979,0.0022022529,0.001947313,0.0012806162,0.007835515],"category_scores_gemma":[0.053260453,0.0005716204,0.0016044924,0.0023044376,0.0036465917,0.0035999306,0.0020964595,0.0031208582,0.0010059845],"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":[0.00018043522,0.00015009791,0.0030401093,0.000352843,0.00017590247,0.00010497047,0.00032611287,0.20817502,0.0011197283,0.6532091,0.0070536686,0.12611191],"study_design_scores_gemma":[0.00006804311,0.00013620887,0.00040926095,0.000083005216,0.00004047566,0.00009158097,0.0000797853,0.51679397,0.0017703652,0.47324216,0.007249479,0.000035677705],"about_ca_topic_score_codex":0.0032995231,"about_ca_topic_score_gemma":0.003690298,"teacher_disagreement_score":0.010539161,"about_ca_system_score_codex":0.0022525052,"about_ca_system_score_gemma":0.002413415,"threshold_uncertainty_score":0.05573708},"labels":[],"label_agreement":null},{"id":"W4383958439","doi":"10.5772/intechopen.1001594","title":"Pandemic Open Data: Blessing or Curse?","year":2023,"lang":"en","type":"book-chapter","venue":"IntechOpen eBooks","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Blessing; Metadata; Narrative; Pandemic; Misinformation; Data science; Social media; Disinformation; Open data; Curse; Political science; Coronavirus disease 2019 (COVID-19); Internet privacy; Public relations; Computer science; Geography; World Wide Web; Sociology; Medicine; Law","score_opus":0.3144242678237758,"score_gpt":0.42036259907972573,"score_spread":0.10593833125594992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383958439","genre_codex":"commentary","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":[0.0015919005,0.22417441,0.20095423,0.2755201,0.03595176,0.00043826643,0.0069816145,0.0062621594,0.24812557],"genre_scores_gemma":[0.016567495,0.22957,0.27597913,0.10247999,0.0281644,0.0008394805,0.01147134,0.009843655,0.32508457],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9944886,0.0019557667,0.00033815586,0.0006986338,0.0023577597,0.00016111304],"domain_scores_gemma":[0.9701226,0.021713538,0.00056085666,0.0038928716,0.0030735545,0.0006366087],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.010386035,0.0011400225,0.0011997118,0.0044225124,0.001977743,0.012385142,0.0030475901,0.0040078084,0.020922268],"category_scores_gemma":[0.031260397,0.0010590879,0.0011392246,0.007482391,0.0044378145,0.026692642,0.0068664663,0.009973781,0.019419275],"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":[0.000013217861,0.000020976606,0.0001919866,0.0008192917,0.00002403842,0.000102367565,0.001612515,0.0003447201,0.00027085375,0.16926812,0.59865516,0.22867678],"study_design_scores_gemma":[0.0000019971221,0.0000028419038,0.00006224733,0.0006817744,0.0000036317424,0.00012807058,0.0003156354,0.00018214049,0.00011275287,0.042211257,0.9562864,0.000011277862],"about_ca_topic_score_codex":0.0036920076,"about_ca_topic_score_gemma":0.0051188483,"teacher_disagreement_score":0.9969524,"about_ca_system_score_codex":0.0025225072,"about_ca_system_score_gemma":0.0041994634,"threshold_uncertainty_score":0.069992006},"labels":[],"label_agreement":null},{"id":"W4385256793","doi":"10.2196/46644","title":"Enabling Multicentric Participatory Disease Surveillance for Global Health Enhancement: Viewpoint on Global Flu View","year":2023,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Disease surveillance; Public health; Data sharing; Digital health; Globe; Global health; Citizen journalism; Computer science; Population; Environmental health; Medicine; Internet privacy; Data science; Business; World Wide Web; Political science; Health care; Nursing; Pathology; Alternative medicine","score_opus":0.06975449642434098,"score_gpt":0.3951944952013301,"score_spread":0.32543999877698915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385256793","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047369897,0.012618029,0.36457327,0.32283077,0.003878969,0.0012262689,0.0013809875,0.0021013028,0.2440205],"genre_scores_gemma":[0.6992367,0.008654761,0.23792723,0.027173242,0.0016496846,0.0019344239,0.0011827818,0.0009110059,0.02133018],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.97815645,0.01759613,0.00035232856,0.0014742791,0.0012254141,0.001195338],"domain_scores_gemma":[0.97979647,0.012642161,0.0006670631,0.0030245646,0.0016530633,0.0022166595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031993218,0.0009773639,0.00049431715,0.0014581257,0.0030462341,0.009434284,0.0026452937,0.0037103945,0.009256059],"category_scores_gemma":[0.01859174,0.00066795014,0.0010597031,0.0012805948,0.008663155,0.016307311,0.024386793,0.004453867,0.0016993621],"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":[0.00019456909,0.00016667409,0.007642589,0.0023388048,0.00010172427,0.00080940634,0.08322371,0.0032706468,0.0038302112,0.5933016,0.06555432,0.23956574],"study_design_scores_gemma":[0.00006933523,0.0002129135,0.0029068028,0.0011797148,0.00005043648,0.00042601486,0.025650345,0.0030668143,0.0017428537,0.159136,0.80548877,0.000070040616],"about_ca_topic_score_codex":0.00405715,"about_ca_topic_score_gemma":0.0037404117,"teacher_disagreement_score":0.031993218,"about_ca_system_score_codex":0.0035140573,"about_ca_system_score_gemma":0.0061389767,"threshold_uncertainty_score":0.1691984},"labels":[],"label_agreement":null},{"id":"W4385501833","doi":"10.2196/47547","title":"Implementation of a Surveillance System for Severe Acute Respiratory Infections at a Tertiary Care Hospital in Austria: Protocol for a Retrospective Longitudinal Feasibility Study","year":2023,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"European Centre for Disease Prevention and Control; European Commission","keywords":"Medicine; Incidence (geometry); Health care; Emergency medicine; Pandemic; Medical emergency; Pediatrics; Disease; Coronavirus disease 2019 (COVID-19); Infectious disease (medical specialty); Internal medicine","score_opus":0.21296564268138243,"score_gpt":0.5772592620738831,"score_spread":0.3642936193925007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385501833","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070023066,0.00007310954,0.0031074784,0.0000967761,0.000044639168,0.98746246,0.0015185924,0.000030361565,0.0006643084],"genre_scores_gemma":[0.0057617663,0.00006902356,0.0047666947,0.00011743486,0.000021041402,0.9883569,0.0007844372,0.0000040420723,0.000118589676],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.94891375,0.036415223,0.0072679725,0.0023408346,0.0024229588,0.0026394092],"domain_scores_gemma":[0.95869607,0.008383989,0.008143998,0.0070253164,0.01391343,0.00383718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07961183,0.002949999,0.0029257666,0.0037606515,0.0040061357,0.002480118,0.002742007,0.0035348616,0.015058739],"category_scores_gemma":[0.041208476,0.0025940705,0.0024808084,0.003455104,0.00249498,0.002914921,0.0028596583,0.003005806,0.0055572568],"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":[0.23869202,0.10319692,0.26239386,0.032917265,0.0022378843,0.006132748,0.017109819,0.020141192,0.01866737,0.014899984,0.07813741,0.20547363],"study_design_scores_gemma":[0.12949249,0.1459473,0.43424246,0.0114942575,0.001245828,0.0016741073,0.014579803,0.014342404,0.009842693,0.0052694124,0.23061503,0.0012541689],"about_ca_topic_score_codex":0.005829251,"about_ca_topic_score_gemma":0.0053026117,"teacher_disagreement_score":0.07961183,"about_ca_system_score_codex":0.0063156844,"about_ca_system_score_gemma":0.024598433,"threshold_uncertainty_score":0.4210326},"labels":[],"label_agreement":null},{"id":"W4385516495","doi":"10.1136/bmjopen-2023-072761","title":"Using community-based geographical information system (GIS) to recruit older Asian Americans in an Alzheimer’s disease study","year":2023,"lang":"en","type":"review","venue":"BMJ Open","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Institute on Aging","keywords":"Ethnic group; Medicine; Vietnamese; Socioeconomic status; Gerontology; Outreach; Dementia; Epidemiology; Disease; Population; Environmental health; Economic growth; Pathology","score_opus":0.453093965919909,"score_gpt":0.5315443324826294,"score_spread":0.07845036656272042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385516495","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":[0.9762597,0.0002039469,0.0072959336,0.0015274487,0.000060664057,0.0051990515,0.00272794,0.00008615287,0.006639182],"genre_scores_gemma":[0.94863963,0.0004112489,0.039575797,0.0006289729,0.000037873393,0.0078689875,0.0017262973,0.000025477508,0.0010857928],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9955148,0.0033363092,0.00034484756,0.00017313173,0.0004704212,0.00016046678],"domain_scores_gemma":[0.99409014,0.0018234962,0.0010024649,0.0008313568,0.001543588,0.00070886075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011114265,0.00032601185,0.00025170943,0.002005216,0.0011686038,0.0013855281,0.0007584929,0.00040296954,0.0031018886],"category_scores_gemma":[0.017500684,0.00031315038,0.0004955222,0.0026801813,0.0003464131,0.0014706292,0.0022705074,0.00049098325,0.00066845416],"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":[0.000404318,0.001110506,0.84353036,0.00083693187,0.0002222478,0.00048391888,0.044674464,0.0007267376,0.0017527569,0.0011201795,0.0076048775,0.09753264],"study_design_scores_gemma":[0.00052966893,0.0032029483,0.8080591,0.0013542272,0.00031766892,0.0003131891,0.1361843,0.0052175396,0.0015770087,0.0028393036,0.040215593,0.00018937781],"about_ca_topic_score_codex":0.020036194,"about_ca_topic_score_gemma":0.03762853,"teacher_disagreement_score":0.020036194,"about_ca_system_score_codex":0.00066679827,"about_ca_system_score_gemma":0.0036677157,"threshold_uncertainty_score":0.058778584},"labels":[],"label_agreement":null},{"id":"W4385619932","doi":"10.17269/s41997-023-00811-3","title":"Tracking emergency response actions during COVID-19 leads to development of an innovative public health evaluation tool","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Canadian Institute for Health Information; Kingston Health Sciences Centre","funders":"","keywords":"Timeline; Public health; Computer science; Preparedness; Public health informatics; Data science; HRHIS; Health policy; Medicine; Political science; Geography; Nursing","score_opus":0.2849896489219572,"score_gpt":0.4512684180474814,"score_spread":0.1662787691255242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385619932","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":[0.15182766,0.0007765372,0.4986536,0.013677225,0.0015521867,0.016838467,0.10589837,0.10866245,0.1021135],"genre_scores_gemma":[0.34038153,0.0004909985,0.6113214,0.0014405693,0.00018133037,0.010048536,0.024307026,0.002908694,0.008919957],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.97900724,0.012374617,0.002937167,0.0018129017,0.0031196224,0.00074847805],"domain_scores_gemma":[0.91143876,0.05274024,0.0071527287,0.008261479,0.016905542,0.0035011945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029099066,0.0012762144,0.0010619664,0.006422699,0.0013471491,0.00585413,0.0019296132,0.0009917686,0.028025167],"category_scores_gemma":[0.07769261,0.00080335606,0.0012604899,0.0042960313,0.0005750314,0.0053902757,0.0032016218,0.0017736886,0.0058410103],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020100742,0.0016668504,0.115506575,0.003802962,0.0005114588,0.00039155525,0.011324101,0.014214235,0.004014647,0.015872966,0.22774297,0.6029416],"study_design_scores_gemma":[0.0017206556,0.0035073147,0.15527202,0.0069362363,0.0008819133,0.0005159705,0.018764649,0.1819959,0.03370134,0.037942372,0.5575922,0.0011694367],"about_ca_topic_score_codex":0.013236318,"about_ca_topic_score_gemma":0.016306875,"teacher_disagreement_score":0.029099066,"about_ca_system_score_codex":0.0031099322,"about_ca_system_score_gemma":0.0065978086,"threshold_uncertainty_score":0.1538924},"labels":[],"label_agreement":null},{"id":"W4386083867","doi":"10.1080/03007995.2023.2247968","title":"Enhancement and external validation of algorithms using diagnosis codes to identify invasive <i>Escherichia coli</i> disease","year":2023,"lang":"en","type":"article","venue":"Current Medical Research and Opinion","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis","funders":"","keywords":"Algorithm; Medicine; Diagnosis code; Bacteremia; Predictive value; Sepsis; Disease; Internal medicine; Computer science","score_opus":0.15565135152360093,"score_gpt":0.48168646841257445,"score_spread":0.3260351168889735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386083867","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":[0.9186349,0.002178762,0.06671515,0.0006119161,0.0003633073,0.0013120571,0.0030207965,0.001505261,0.005657858],"genre_scores_gemma":[0.94139946,0.00032344062,0.052419543,0.00036464838,0.00011102956,0.00042209058,0.004464162,0.00018545835,0.00031034564],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9515392,0.025461787,0.0061046486,0.008165709,0.007610113,0.001118532],"domain_scores_gemma":[0.78694797,0.13703991,0.020006213,0.013741503,0.040533397,0.0017309731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08178789,0.0016972682,0.0011603589,0.0041244463,0.0007301258,0.0042913286,0.0020466237,0.001939483,0.0009290981],"category_scores_gemma":[0.19127902,0.0005549655,0.0015376045,0.0025343988,0.0012016549,0.002282612,0.0022489703,0.0014314567,0.0006919302],"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":[0.0023729503,0.0006193803,0.8818401,0.00047792858,0.0014167229,0.000098000295,0.0006346753,0.023377467,0.003215852,0.0006964923,0.0025631003,0.08268735],"study_design_scores_gemma":[0.0009768915,0.0023062741,0.53007853,0.0007309646,0.0012788828,0.0010473981,0.00050963974,0.4314477,0.020638864,0.0019343317,0.008893294,0.00015720089],"about_ca_topic_score_codex":0.0037221683,"about_ca_topic_score_gemma":0.0023187546,"teacher_disagreement_score":0.08178789,"about_ca_system_score_codex":0.0015061966,"about_ca_system_score_gemma":0.0027808563,"threshold_uncertainty_score":0.4325409},"labels":[],"label_agreement":null},{"id":"W4386228223","doi":"10.2196/48827","title":"Assessing Public Interest in Mpox via Google Trends, YouTube, and TikTok","year":2023,"lang":"en","type":"article","venue":"JMIR Dermatology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Public interest; Internet privacy; World Wide Web; Advertising; Geography; Business; Computer science; Political science; Law","score_opus":0.08377668053887982,"score_gpt":0.36298367856240693,"score_spread":0.2792069980235271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386228223","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":[0.95896417,0.00042053047,0.0002864915,0.00063174515,0.00003192612,0.0001677808,0.031793125,0.00008020838,0.0076240846],"genre_scores_gemma":[0.97633934,0.0004908501,0.0013475325,0.0001566253,0.000059434202,0.00019499175,0.018091446,0.000023273924,0.0032965215],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991441,0.00018339077,0.00012982741,0.00012871982,0.00027172474,0.00014226207],"domain_scores_gemma":[0.99401146,0.0017117417,0.0023484174,0.00018211594,0.0011736365,0.0005725674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012280084,0.0002586986,0.00023118417,0.0060446532,0.0004633097,0.0013193564,0.00034391353,0.0004477988,0.0024837293],"category_scores_gemma":[0.0065396368,0.0001250436,0.00039171433,0.0042939736,0.00020390234,0.0015041799,0.001043454,0.0005966904,0.00091533293],"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":[0.00022746755,0.0001304757,0.966151,0.0003019799,0.00009794122,0.00020744443,0.0014787937,0.00030513827,0.00053529226,0.00032739257,0.005949604,0.024287455],"study_design_scores_gemma":[0.000013462698,0.00023587572,0.9804786,0.00012767498,0.000077949895,0.00025073998,0.005301018,0.0035391147,0.0006877369,0.00014133088,0.0091200145,0.000026482548],"about_ca_topic_score_codex":0.032839067,"about_ca_topic_score_gemma":0.05287664,"teacher_disagreement_score":0.032839067,"about_ca_system_score_codex":0.0006042612,"about_ca_system_score_gemma":0.00085419544,"threshold_uncertainty_score":0.065295815},"labels":[],"label_agreement":null},{"id":"W4386271983","doi":"10.1093/aje/kwad176","title":"Comparing Location Data From Smartphone and Dedicated Global Positioning System Devices: Implications for Epidemiologic Research","year":2023,"lang":"en","type":"article","venue":"American Journal of Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan; Simon Fraser University; University of Victoria; Université de Montréal","funders":"Université de Montréal","keywords":"Global Positioning System; Standard deviation; Data collection; Geography; Medicine; Computer science; Statistics; Telecommunications; Mathematics","score_opus":0.3354619894983343,"score_gpt":0.4908124069489871,"score_spread":0.1553504174506528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386271983","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8166542,0.039545402,0.1018573,0.015456685,0.0034110611,0.0011296765,0.008806362,0.00014646193,0.012992784],"genre_scores_gemma":[0.95161206,0.0043549943,0.03548018,0.0029677753,0.00075073045,0.0011353694,0.0029586756,0.00007746324,0.0006627052],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.8982693,0.07327909,0.009981539,0.0074446048,0.009786906,0.0012386092],"domain_scores_gemma":[0.7331868,0.19843392,0.034023035,0.014503263,0.018202065,0.0016509157],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.065468274,0.0009501803,0.001263684,0.005061432,0.00105511,0.004234862,0.0020061838,0.0018063061,0.0016813123],"category_scores_gemma":[0.30619633,0.0006444735,0.0017605729,0.012330205,0.0017269924,0.005794698,0.0032144424,0.002020972,0.0003752688],"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":[0.00037563578,0.00011322904,0.93832785,0.0010784138,0.0019567073,0.0001884693,0.0027525001,0.0007052272,0.00029766717,0.0021057178,0.0021860225,0.04991252],"study_design_scores_gemma":[0.00007809634,0.0008724511,0.95730835,0.0022629595,0.0014749602,0.0006980511,0.0110252295,0.0048082066,0.0005370199,0.007205136,0.013592372,0.00013706242],"about_ca_topic_score_codex":0.016568834,"about_ca_topic_score_gemma":0.017987873,"teacher_disagreement_score":0.93453175,"about_ca_system_score_codex":0.0011756321,"about_ca_system_score_gemma":0.0023847315,"threshold_uncertainty_score":0.3462335},"labels":[],"label_agreement":null},{"id":"W4386453955","doi":"10.1371/journal.pone.0285341","title":"Dissemination of information in event-based surveillance, a case study of Avian Influenza","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"European Commission","keywords":"Outbreak; News aggregator; Influenza A virus subtype H5N1; Event (particle physics); Biosecurity; Disease surveillance; Information Dissemination; Geography; Public health; Medicine; World Wide Web; Computer science; Virology","score_opus":0.03447157620274092,"score_gpt":0.3090294492530482,"score_spread":0.27455787305030727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386453955","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":[0.9831077,0.0011186458,0.00863678,0.00078571227,0.000025697942,0.00016931132,0.00086155266,0.00005114834,0.005243438],"genre_scores_gemma":[0.9914253,0.0004753232,0.0066524995,0.00006058351,0.000024128556,0.00006650878,0.000519934,0.000018697989,0.0007571538],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9950493,0.0033386375,0.00030714748,0.0005241423,0.00045564468,0.00032520722],"domain_scores_gemma":[0.9680739,0.024641994,0.0036739626,0.001350457,0.0015299603,0.0007297699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005126732,0.00037032727,0.00026688748,0.003911982,0.0013344436,0.0022255236,0.0007555086,0.0012702002,0.0013299937],"category_scores_gemma":[0.022769857,0.0003271608,0.00042083793,0.004622861,0.0011662979,0.004369998,0.0015964714,0.000769971,0.00021597226],"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":[0.0005735585,0.00048858754,0.76308006,0.0011679198,0.0002577053,0.009807142,0.09570738,0.012273411,0.0026558028,0.018418051,0.0043191384,0.091251135],"study_design_scores_gemma":[0.00011040049,0.0007058819,0.6824547,0.001070324,0.00035195812,0.0072557773,0.1598244,0.0759963,0.004284717,0.014486813,0.053283118,0.00017567705],"about_ca_topic_score_codex":0.021981869,"about_ca_topic_score_gemma":0.018543903,"teacher_disagreement_score":0.021981869,"about_ca_system_score_codex":0.001702973,"about_ca_system_score_gemma":0.00085182005,"threshold_uncertainty_score":0.043707848},"labels":[],"label_agreement":null},{"id":"W4386498393","doi":"10.2196/42446","title":"Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19","year":2023,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Education University of Hong Kong; Hong Kong University of Science and Technology","keywords":"Computer science; Data mining; Cluster analysis; Volume (thermodynamics); Normalization (sociology); Artificial intelligence","score_opus":0.08308901748638865,"score_gpt":0.40267037698722735,"score_spread":0.3195813595008387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386498393","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":[0.99174213,0.00047582644,0.005214861,0.0004910717,0.000027529331,0.000039369017,0.0009458865,0.000056845573,0.0010065592],"genre_scores_gemma":[0.99480903,0.00022697684,0.0033840826,0.000025704825,0.00003621099,0.000019190984,0.0012974488,0.000009960463,0.00019145985],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992624,0.00026365218,0.000054583,0.00017775624,0.00013811838,0.00010357115],"domain_scores_gemma":[0.996151,0.0021647203,0.0006278595,0.00033789017,0.0005023549,0.00021631869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020809488,0.0005476964,0.000515459,0.0035451287,0.00043106306,0.001030785,0.0006704962,0.00054494233,0.0004883894],"category_scores_gemma":[0.008979656,0.00022916429,0.0008623452,0.0028426298,0.000372564,0.0021408377,0.0007376127,0.0007798631,0.00016143477],"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":[0.00025153547,0.00031184818,0.8743919,0.00016262452,0.00035466402,0.00063461147,0.000623965,0.06808879,0.0010493037,0.0040733498,0.0023362776,0.047721293],"study_design_scores_gemma":[0.000011230155,0.00013816165,0.21042672,0.00003751186,0.000113992995,0.0002162815,0.0009502212,0.7839558,0.000712965,0.0019286504,0.0014773253,0.00003122038],"about_ca_topic_score_codex":0.032053716,"about_ca_topic_score_gemma":0.02210449,"teacher_disagreement_score":0.032053716,"about_ca_system_score_codex":0.00094748894,"about_ca_system_score_gemma":0.0008127375,"threshold_uncertainty_score":0.06373429},"labels":[],"label_agreement":null},{"id":"W4386499040","doi":"10.1117/12.2692210","title":"Analysis on the development and application of disease surveillance","year":2023,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Disease surveillance; Disease; Globe; Population; Outbreak; Computer science; Disease monitoring; Data science; Risk analysis (engineering); Computer security; Environmental health; Medicine; Pathology","score_opus":0.015855313130153488,"score_gpt":0.2798012553158668,"score_spread":0.2639459421857133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386499040","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":[0.1545729,0.03295829,0.48433363,0.037479118,0.0015393212,0.0020799,0.0039149784,0.0007695438,0.28235233],"genre_scores_gemma":[0.7796225,0.018332133,0.18208167,0.0019403395,0.00054511055,0.0005761783,0.0024320811,0.00012943092,0.014340568],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9826785,0.0091597,0.0011548845,0.0012370554,0.005217985,0.00055193616],"domain_scores_gemma":[0.9247106,0.037915483,0.0035970146,0.0048483205,0.027865577,0.0010630955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020359987,0.0006428852,0.0004098948,0.007832991,0.00096048054,0.004890487,0.001303898,0.00086918066,0.0026083358],"category_scores_gemma":[0.061663575,0.0004953414,0.00082272576,0.006139273,0.0017914625,0.0034025756,0.0017475985,0.0014673119,0.00067765877],"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":[0.00013577762,0.00017463046,0.09702278,0.0011546229,0.0001605891,0.00042364668,0.0018973405,0.0150869,0.0016711855,0.4128831,0.009370197,0.46001923],"study_design_scores_gemma":[0.000043785727,0.0005332927,0.121635824,0.0028983527,0.00028983355,0.0012825656,0.0039347433,0.12919542,0.012245475,0.16007368,0.56769836,0.00016860895],"about_ca_topic_score_codex":0.011008067,"about_ca_topic_score_gemma":0.0038460789,"teacher_disagreement_score":0.020359987,"about_ca_system_score_codex":0.0063712304,"about_ca_system_score_gemma":0.0061783916,"threshold_uncertainty_score":0.107675195},"labels":[],"label_agreement":null},{"id":"W4386818066","doi":"10.1111/idh.12755","title":"Digital surveillance: The interest in mouthwash‐related information","year":2023,"lang":"en","type":"article","venue":"International Journal of Dental Hygiene","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Medicine; Autoregressive integrated moving average; Coronavirus disease 2019 (COVID-19); Outbreak; Demography; Environmental health; Statistics; Internal medicine; Pathology; Disease; Time series; Infectious disease (medical specialty)","score_opus":0.016785533931247654,"score_gpt":0.2842898466126946,"score_spread":0.2675043126814469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386818066","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":[0.98699504,0.001376328,0.0003942678,0.00032904596,0.000014507697,0.000050749288,0.0046092835,0.000020397121,0.006210407],"genre_scores_gemma":[0.99707425,0.0006732407,0.00034635342,0.00006711131,0.000025648967,0.0000217027,0.00133653,0.0000037376637,0.00045139302],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99930847,0.00019214925,0.00011964306,0.0001056888,0.00020483286,0.00006932735],"domain_scores_gemma":[0.99344665,0.0018218762,0.0035751115,0.00028187013,0.0006031258,0.0002713005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007771231,0.00015637209,0.0001819168,0.0021695732,0.00015726463,0.0010368036,0.00031406345,0.00030943268,0.0022140793],"category_scores_gemma":[0.0058906446,0.00007850255,0.0002527071,0.0023756316,0.00024001436,0.0010906534,0.00059767533,0.00032789644,0.0004098465],"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":[0.000088733126,0.000038441252,0.97504675,0.00027403998,0.00005289651,0.00014363028,0.0010340138,0.00010310238,0.0005310838,0.00020558693,0.0008381744,0.021643477],"study_design_scores_gemma":[0.0000017318371,0.000061197185,0.9940242,0.00010062229,0.000030819516,0.00053334166,0.0015144183,0.0005259467,0.00039535057,0.00010005847,0.002704573,0.000007824012],"about_ca_topic_score_codex":0.0039984565,"about_ca_topic_score_gemma":0.0031526755,"teacher_disagreement_score":0.0039984565,"about_ca_system_score_codex":0.00039293978,"about_ca_system_score_gemma":0.00023805279,"threshold_uncertainty_score":0.007950366},"labels":[],"label_agreement":null},{"id":"W4386922014","doi":"10.1371/journal.pcbi.1011285","title":"Fourteen quick tips for crowdsourcing geographically linked data for public health advocacy","year":2023,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Ontario Institute for Cancer Research; University of Toronto","funders":"","keywords":"Crowdsourcing; Outreach; Citizen science; Crowd sourcing; Team building; Data science; Public health; Social media; Business; World Wide Web; Public relations; Computer science; Medicine; Engineering; Political science; Engineering management; Biology","score_opus":0.1783028597419542,"score_gpt":0.3841743094517602,"score_spread":0.20587144970980598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386922014","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":[0.008528313,0.006253372,0.68558925,0.12791009,0.026244238,0.005943698,0.017521167,0.050370947,0.071638875],"genre_scores_gemma":[0.029066319,0.0035863859,0.8739226,0.022074921,0.0038711086,0.006412904,0.009030871,0.01370532,0.03832948],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9795482,0.010603315,0.0018982687,0.0015735566,0.0050070947,0.0013695053],"domain_scores_gemma":[0.9314291,0.035187308,0.0022014375,0.008489116,0.016811728,0.005881226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026800161,0.002786014,0.0010255084,0.00465852,0.004374857,0.008308212,0.003004667,0.0053270236,0.079482526],"category_scores_gemma":[0.10897064,0.0025016216,0.0027751287,0.0039557884,0.0022207017,0.0097197145,0.01593265,0.0069523826,0.056474913],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037492995,0.00016377227,0.0016707894,0.000922636,0.00012927437,0.00036416476,0.0018042393,0.0012203426,0.0027091634,0.008225495,0.64626014,0.33615506],"study_design_scores_gemma":[0.00034493222,0.00021919928,0.0018390309,0.0014442402,0.00007683576,0.00036814043,0.0027096788,0.0025012358,0.0030107826,0.04321962,0.94402415,0.00024214514],"about_ca_topic_score_codex":0.003389493,"about_ca_topic_score_gemma":0.011281185,"teacher_disagreement_score":0.079482526,"about_ca_system_score_codex":0.0020562352,"about_ca_system_score_gemma":0.004398325,"threshold_uncertainty_score":0.26589555},"labels":[],"label_agreement":null},{"id":"W4387040158","doi":"10.32920/24200505.v1","title":"Pandemic Open Data: Blessing or Curse?","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Blessing; Metadata; Narrative; Pandemic; Misinformation; Curse; Open data; Disinformation; Political science; Social media; Data science; Coronavirus disease 2019 (COVID-19); Internet privacy; Public relations; Computer science; Sociology; World Wide Web; Geography; Law; Medicine","score_opus":0.5483937212482134,"score_gpt":0.5082777298098663,"score_spread":0.0401159914383471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387040158","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005553841,0.01382056,0.2946191,0.6085754,0.022450764,0.00060125464,0.014924562,0.008396201,0.031058272],"genre_scores_gemma":[0.10125156,0.022630319,0.64277685,0.13973843,0.019955574,0.001980158,0.031113781,0.015001959,0.025551343],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9066116,0.0526709,0.006036116,0.0074718413,0.02562684,0.0015826467],"domain_scores_gemma":[0.583072,0.22736217,0.012118685,0.12749714,0.04367783,0.0062721507],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.09772049,0.0014468231,0.0021933487,0.006746293,0.004332263,0.020114906,0.0045461203,0.006926387,0.013019719],"category_scores_gemma":[0.35977423,0.0018591965,0.0018129798,0.008901429,0.011947294,0.03235378,0.017530002,0.015449932,0.012116316],"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":[0.00010230807,0.00006798413,0.0029017685,0.0012123914,0.000156926,0.00029392118,0.004780561,0.0010079386,0.0008815315,0.2190783,0.59703904,0.17247733],"study_design_scores_gemma":[0.000030635732,0.00001361392,0.0007484662,0.0019657703,0.000036602727,0.00024764624,0.00402617,0.0020531018,0.0014016641,0.23048596,0.7588973,0.000093075],"about_ca_topic_score_codex":0.007346264,"about_ca_topic_score_gemma":0.0066666417,"teacher_disagreement_score":0.9954539,"about_ca_system_score_codex":0.0039039035,"about_ca_system_score_gemma":0.014579558,"threshold_uncertainty_score":0.5168015},"labels":[],"label_agreement":null},{"id":"W4387040304","doi":"10.32920/24200505","title":"Pandemic Open Data: Blessing or Curse?","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Blessing; Metadata; Narrative; Pandemic; Misinformation; Curse; Open data; Disinformation; Political science; Coronavirus disease 2019 (COVID-19); Social media; Data science; Internet privacy; Public relations; Computer science; Sociology; World Wide Web; Geography; Law; Medicine","score_opus":0.5483937212482134,"score_gpt":0.5082777298098663,"score_spread":0.0401159914383471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387040304","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005553841,0.01382056,0.2946191,0.6085754,0.022450764,0.00060125464,0.014924562,0.008396201,0.031058272],"genre_scores_gemma":[0.10125156,0.022630319,0.64277685,0.13973843,0.019955574,0.001980158,0.031113781,0.015001959,0.025551343],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9066116,0.0526709,0.006036116,0.0074718413,0.02562684,0.0015826467],"domain_scores_gemma":[0.583072,0.22736217,0.012118685,0.12749714,0.04367783,0.0062721507],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.09772049,0.0014468231,0.0021933487,0.006746293,0.004332263,0.020114906,0.0045461203,0.006926387,0.013019719],"category_scores_gemma":[0.35977423,0.0018591965,0.0018129798,0.008901429,0.011947294,0.03235378,0.017530002,0.015449932,0.012116316],"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":[0.00010230807,0.00006798413,0.0029017685,0.0012123914,0.000156926,0.00029392118,0.004780561,0.0010079386,0.0008815315,0.2190783,0.59703904,0.17247733],"study_design_scores_gemma":[0.000030635732,0.00001361392,0.0007484662,0.0019657703,0.000036602727,0.00024764624,0.00402617,0.0020531018,0.0014016641,0.23048596,0.7588973,0.000093075],"about_ca_topic_score_codex":0.007346264,"about_ca_topic_score_gemma":0.0066666417,"teacher_disagreement_score":0.9954539,"about_ca_system_score_codex":0.0039039035,"about_ca_system_score_gemma":0.014579558,"threshold_uncertainty_score":0.5168015},"labels":[],"label_agreement":null},{"id":"W4387114743","doi":"10.1111/ped.15623","title":"Philippine immunization coverage and dengvaxia: An infodemiological study","year":2023,"lang":"en","type":"article","venue":"Pediatrics International","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"","keywords":"Medicine; Dengue vaccine; Immunization; Dengue fever; Demography; Environmental health; Virology; Dengue virus; Immunology","score_opus":0.030975391742269168,"score_gpt":0.34854774828020313,"score_spread":0.317572356537934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387114743","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":[0.9926248,0.0006357358,0.000090381734,0.0001612366,0.0000036280073,0.000022134871,0.004235386,0.000013356758,0.002213194],"genre_scores_gemma":[0.9982041,0.0002716035,0.00008573691,0.00002832925,0.000009109745,0.000020604211,0.0012065092,0.0000037499872,0.0001702125],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994168,0.00016056336,0.00010678244,0.000084224586,0.00014688582,0.00008470945],"domain_scores_gemma":[0.99291915,0.0022402303,0.003480312,0.0001810344,0.00061283115,0.0005664541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011044246,0.00020726855,0.00020646893,0.003760205,0.00026848773,0.0009997479,0.0003502412,0.0002454329,0.003806734],"category_scores_gemma":[0.006237256,0.00014586926,0.00042501852,0.0067907097,0.00029437922,0.0010494125,0.0008127712,0.00047635374,0.00039096948],"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":[0.00002857695,0.000018480672,0.99737287,0.00006133926,0.000041346706,0.00004808416,0.0003780347,0.000041913558,0.000026879015,0.000031447715,0.0001543524,0.0017966214],"study_design_scores_gemma":[0.000001938219,0.000053174204,0.9979564,0.000032945718,0.00003412707,0.00014071989,0.00090762274,0.0003079426,0.000056253153,0.000017241584,0.00048816775,0.0000035561225],"about_ca_topic_score_codex":0.013513245,"about_ca_topic_score_gemma":0.010375048,"teacher_disagreement_score":0.013513245,"about_ca_system_score_codex":0.0006547684,"about_ca_system_score_gemma":0.0007135702,"threshold_uncertainty_score":0.026869178},"labels":[],"label_agreement":null},{"id":"W4387299380","doi":"10.21203/rs.3.rs-3406042/v1","title":"A Joint Model for Disease Mapping with Spatially Correlated Count Data and Covariate Measurement Error","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan; University of Waterloo","funders":"","keywords":"Covariate; Joint (building); Count data; Statistics; Observational error; Computer science; Econometrics; Mathematics; Engineering","score_opus":0.4553935188566719,"score_gpt":0.43439729938253663,"score_spread":0.02099621947413527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387299380","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":[0.0982786,0.0008078388,0.8942896,0.0017928834,0.00013263902,0.00023473985,0.0020645538,0.00042343372,0.0019757438],"genre_scores_gemma":[0.879203,0.0012584028,0.09923538,0.00042816103,0.00021618954,0.0012718543,0.0027326834,0.00012384802,0.015530408],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99372536,0.0030412846,0.00024102117,0.0018489896,0.0005993429,0.00054400385],"domain_scores_gemma":[0.9833739,0.011768432,0.0019080194,0.0009220057,0.001578794,0.00044888814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014321331,0.0017754812,0.0033678384,0.0027442246,0.0010284911,0.0028870006,0.005359113,0.0033217617,0.005073315],"category_scores_gemma":[0.022387834,0.0013769265,0.002737507,0.0032214767,0.0031782538,0.0027601519,0.0030022142,0.0026154013,0.0006607711],"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":[0.00019207054,0.000093965646,0.015674794,0.000118960335,0.00028939484,0.0002885752,0.00036420958,0.89307827,0.0003495022,0.0761718,0.0011843492,0.01219412],"study_design_scores_gemma":[0.000062720545,0.00006516645,0.0022732795,0.000027760394,0.000086581436,0.000055253488,0.000047052323,0.9714119,0.0000840613,0.02506013,0.00079322467,0.000032972664],"about_ca_topic_score_codex":0.082210094,"about_ca_topic_score_gemma":0.030276088,"teacher_disagreement_score":0.082210094,"about_ca_system_score_codex":0.003613758,"about_ca_system_score_gemma":0.0033449524,"threshold_uncertainty_score":0.16346318},"labels":[],"label_agreement":null},{"id":"W4387300261","doi":"10.1007/s10654-023-01049-6","title":"Slow data public health","year":2023,"lang":"en","type":"article","venue":"European Journal of Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre","funders":"Université de Fribourg","keywords":"Public health; Data quality; Medicine; Public health surveillance; Data science; Quality (philosophy); Data collection; Population health; Pandemic; Identification (biology); Information Dissemination; Internet privacy; Public relations; Computer science; Coronavirus disease 2019 (COVID-19); Business; World Wide Web; Nursing; Political science; Disease","score_opus":0.39516156214847287,"score_gpt":0.4311527721253957,"score_spread":0.035991209976922844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387300261","genre_codex":"commentary","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":[0.021672366,0.023185307,0.2848364,0.5250287,0.015769666,0.0032588018,0.005904004,0.004804201,0.115540594],"genre_scores_gemma":[0.40462658,0.0208867,0.22926006,0.2357983,0.01606843,0.006426182,0.0097364215,0.003756418,0.07344098],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.82474715,0.08725018,0.015431817,0.020219253,0.046725176,0.005626421],"domain_scores_gemma":[0.44089043,0.31667057,0.03900365,0.107736476,0.07947897,0.016219858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.16257747,0.00189346,0.0022432725,0.007157514,0.004128965,0.021625046,0.0059634484,0.0077201957,0.022872686],"category_scores_gemma":[0.39830554,0.0021540534,0.002104335,0.0057982397,0.013616506,0.028190559,0.022273378,0.015655534,0.012095074],"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":[0.0006118248,0.00022796927,0.01613025,0.004203459,0.0004079225,0.00042957376,0.0056655365,0.0017117821,0.0019105464,0.38345033,0.26980102,0.31544974],"study_design_scores_gemma":[0.00018238483,0.0002611503,0.004569668,0.0031592804,0.00010686041,0.00058484887,0.0023498242,0.0026454274,0.0023199033,0.1997951,0.78387713,0.00014837584],"about_ca_topic_score_codex":0.004444821,"about_ca_topic_score_gemma":0.002156444,"teacher_disagreement_score":0.16257747,"about_ca_system_score_codex":0.008927853,"about_ca_system_score_gemma":0.030887228,"threshold_uncertainty_score":0.8598021},"labels":[],"label_agreement":null},{"id":"W4387669078","doi":"10.1186/s12911-023-02315-z","title":"Leveraging machine learning approaches for predicting potential Lyme disease cases and incidence rates in the United States using Twitter","year":2023,"lang":"en","type":"article","venue":"BMC Medical Informatics and Decision Making","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université de Montréal","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Université de Montréal","keywords":"Lyme disease; Incidence (geometry); Health informatics; Computer science; Machine learning; Artificial intelligence; Data science; Medicine; Public health; Virology; Nursing; Mathematics","score_opus":0.10933009862813056,"score_gpt":0.3594793083299754,"score_spread":0.25014920970184484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387669078","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":[0.9366539,0.0014615222,0.037665505,0.002131696,0.00026440818,0.0002177943,0.012428027,0.0016040386,0.007573128],"genre_scores_gemma":[0.96902734,0.00043908326,0.019378327,0.00015837386,0.00019846052,0.00009115433,0.009295588,0.00003233772,0.0013794166],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995097,0.0001510506,0.00006189262,0.00012789298,0.00009553608,0.000053993484],"domain_scores_gemma":[0.99783415,0.0013377923,0.00029330765,0.0001009209,0.0003346936,0.000099113626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010479345,0.0006442644,0.00038656537,0.0037255508,0.00031786892,0.00090939994,0.0004057091,0.00061841897,0.0010325207],"category_scores_gemma":[0.0038522005,0.00016421254,0.00052018545,0.0016168562,0.00016831924,0.0013253745,0.000640483,0.0006432925,0.0012625013],"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":[0.0007456322,0.0008526105,0.5321524,0.0004714595,0.00038238819,0.0004600024,0.0004674603,0.11203588,0.006123897,0.00093274005,0.015410745,0.3299648],"study_design_scores_gemma":[0.000015088089,0.00014896141,0.06268768,0.000072921204,0.00005867911,0.00011764705,0.00044394942,0.9290812,0.0024873968,0.001659861,0.0031952122,0.000031411808],"about_ca_topic_score_codex":0.007364187,"about_ca_topic_score_gemma":0.012380239,"teacher_disagreement_score":0.007364187,"about_ca_system_score_codex":0.0005897077,"about_ca_system_score_gemma":0.000409911,"threshold_uncertainty_score":0.014642656},"labels":[],"label_agreement":null},{"id":"W4387715173","doi":"10.2196/47981","title":"Evaluation of a Targeted COVID-19 Community Outreach Intervention: Case Report for Precision Public Health","year":2023,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Outreach; Psychological intervention; Environmental health; Public health; Geography; Intervention (counseling); Medicine; Poisson regression; Census; Population; Nursing; Political science","score_opus":0.21137775332275033,"score_gpt":0.46106779346722115,"score_spread":0.24969004014447083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387715173","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":[0.8441083,0.0033833643,0.029964544,0.04399254,0.0008819142,0.032022603,0.0014199162,0.00041452976,0.043812215],"genre_scores_gemma":[0.9542726,0.0017904345,0.025714502,0.006972005,0.00021289985,0.0082077,0.0004231202,0.000039528582,0.0023670774],"study_design_codex":"design_other","study_design_gemma":"case_report","domain_scores_codex":[0.9850573,0.009479835,0.0006499852,0.0009651899,0.0016510658,0.0021967],"domain_scores_gemma":[0.98884684,0.0056088828,0.0014300502,0.0010276098,0.0014806985,0.0016060249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012097057,0.00088344014,0.00047148642,0.0010044886,0.004108538,0.0017290819,0.0025423393,0.0040582023,0.004407717],"category_scores_gemma":[0.02567267,0.00045503344,0.0010238806,0.0007136236,0.0013971832,0.0011422819,0.003363547,0.0021406198,0.00040498827],"study_design_candidate":"case_report","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":[0.002755205,0.037898503,0.19741936,0.004577363,0.0004019401,0.12817319,0.022130778,0.008803224,0.00786552,0.016595535,0.042019464,0.53135985],"study_design_scores_gemma":[0.007194139,0.10827202,0.32965818,0.01944829,0.0016713407,0.10101722,0.10562891,0.06750571,0.03546634,0.032535143,0.19078322,0.0008195321],"about_ca_topic_score_codex":0.020746319,"about_ca_topic_score_gemma":0.029499665,"teacher_disagreement_score":0.020746319,"about_ca_system_score_codex":0.006932451,"about_ca_system_score_gemma":0.017820574,"threshold_uncertainty_score":0.06397611},"labels":[],"label_agreement":null},{"id":"W4387765386","doi":"10.2196/49901","title":"The Impact of Temperature, Humidity, and Sunshine on Internet Search Volumes Related to Psoriasis","year":2023,"lang":"en","type":"article","venue":"JMIR Dermatology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Humidity; Psoriasis; Volume (thermodynamics); Sunshine duration; Relative humidity; The Internet; Environmental science; Medicine; Meteorology; Geography; Physics; Computer science; World Wide Web; Dermatology; Thermodynamics","score_opus":0.019438755910609826,"score_gpt":0.35530700961030737,"score_spread":0.3358682536996975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387765386","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":[0.99646425,0.0006982232,0.000083293984,0.00013159122,0.000010612238,0.0000060677376,0.0019327049,0.000008208341,0.00066510466],"genre_scores_gemma":[0.99921703,0.00010234873,0.00003888808,0.000013581036,0.000009757475,0.0000027313718,0.0005474894,0.0000025844329,0.00006547158],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987645,0.000486592,0.00018375939,0.00019304488,0.00019200037,0.00018005709],"domain_scores_gemma":[0.9918032,0.0035208324,0.0032133546,0.00035367825,0.00040025794,0.0007087634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001025125,0.0002250635,0.00035484697,0.0015202934,0.00023362004,0.000997589,0.00028292686,0.00032561738,0.0017110402],"category_scores_gemma":[0.008220299,0.00017597263,0.0006893875,0.0023657558,0.0002777157,0.0008449499,0.0005708247,0.0005585513,0.00024618683],"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":[0.0002191016,0.000041968407,0.99542326,0.000053607968,0.00019060292,0.000039571863,0.00015733585,0.00029971896,0.0001141232,0.000038689996,0.00023046134,0.0031915521],"study_design_scores_gemma":[0.0000014573445,0.000029369723,0.9992667,0.000010438844,0.000031069405,0.00003355254,0.00014066874,0.00031391915,0.00004342278,0.0000139123595,0.00011219209,0.0000032108933],"about_ca_topic_score_codex":0.016045129,"about_ca_topic_score_gemma":0.018089475,"teacher_disagreement_score":0.016045129,"about_ca_system_score_codex":0.00050657557,"about_ca_system_score_gemma":0.00028884812,"threshold_uncertainty_score":0.031903446},"labels":[],"label_agreement":null},{"id":"W4387770229","doi":"10.7189/jogh.13.04130","title":"What are the characteristics of participatory surveillance systems for influenza-like-illness?","year":2023,"lang":"en","type":"review","venue":"Journal of Global Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Global Health Research","funders":"","keywords":"Influenza-like illness; Medicine; MEDLINE; Citizen journalism; Environmental health; Respiratory illness; Virology; Medical emergency; Computer science; World Wide Web; Biology; Internal medicine","score_opus":0.14037871640990984,"score_gpt":0.4617340997868912,"score_spread":0.32135538337698133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387770229","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":[0.0225332,0.90852034,0.014913221,0.030428017,0.0018723144,0.0057924073,0.003039365,0.00007225933,0.012828896],"genre_scores_gemma":[0.45389938,0.47301057,0.042122137,0.00986115,0.00121721,0.017709903,0.0013974485,0.00008461364,0.0006975601],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.6398917,0.26715118,0.06094441,0.0070094643,0.023014994,0.0019882962],"domain_scores_gemma":[0.31267056,0.604888,0.047866844,0.0079262955,0.025409034,0.00123915],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.23361084,0.0011671904,0.004218383,0.009336892,0.0018304661,0.011033928,0.0032990181,0.0032394116,0.0033118497],"category_scores_gemma":[0.5161523,0.0012399347,0.006486284,0.010840516,0.0055676624,0.013631436,0.0047723884,0.002549867,0.0003448297],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007609027,0.00006576224,0.009907385,0.7222392,0.0052522295,0.00028902208,0.023412202,0.0006416331,0.00036792835,0.011043774,0.003910184,0.22210984],"study_design_scores_gemma":[0.00051092374,0.00089291285,0.014532977,0.84952575,0.010033375,0.00065775437,0.02079352,0.00060317415,0.0009512072,0.012569719,0.08871314,0.0002156203],"about_ca_topic_score_codex":0.0064517115,"about_ca_topic_score_gemma":0.0090778535,"teacher_disagreement_score":0.23361084,"about_ca_system_score_codex":0.0094964085,"about_ca_system_score_gemma":0.023599146,"threshold_uncertainty_score":0.9450943},"labels":[],"label_agreement":null},{"id":"W4387869738","doi":"10.1007/978-3-031-46813-1_7","title":"From Naive Interest to Shortage During COVID-19: A Google Trends and News Analysis","year":2023,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Popularity; Pandemic; Scarcity; Economic shortage; Coronavirus disease 2019 (COVID-19); Period (music); Tracking (education); Computer science; Advertising; World Wide Web; Geography; Political science; Business; Medicine; Psychology; Economics","score_opus":0.07452087295758843,"score_gpt":0.3613502059208051,"score_spread":0.28682933296321667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387869738","genre_codex":"other","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":[0.32707608,0.017053006,0.04140017,0.041386634,0.0030832305,0.0002334246,0.11576778,0.0051223948,0.44887727],"genre_scores_gemma":[0.7701667,0.008264494,0.034696188,0.0035433422,0.0021594716,0.00014634259,0.056099422,0.0031943726,0.12172969],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9994435,0.00009876627,0.00003324855,0.00007796539,0.00027069796,0.00007579191],"domain_scores_gemma":[0.9963881,0.002348124,0.0002546672,0.00016194579,0.000663563,0.00018363642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001219306,0.00053607556,0.00034242694,0.004428025,0.0009745813,0.0043642465,0.00060850556,0.0006954594,0.0122555755],"category_scores_gemma":[0.007728404,0.0003081791,0.00046824507,0.009861061,0.0006831979,0.0049247392,0.0008029416,0.001464331,0.0047578025],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002913975,0.00009617494,0.0739939,0.00041530243,0.000089591485,0.00040512637,0.0035078342,0.002683575,0.00059602904,0.06999742,0.51416975,0.33375385],"study_design_scores_gemma":[0.000043533375,0.0001376996,0.1587442,0.00051278394,0.00023951317,0.0010011244,0.0136207715,0.06497091,0.0028170822,0.096650325,0.66109586,0.00016626397],"about_ca_topic_score_codex":0.05969146,"about_ca_topic_score_gemma":0.08742506,"teacher_disagreement_score":0.05969146,"about_ca_system_score_codex":0.0017746,"about_ca_system_score_gemma":0.0014194166,"threshold_uncertainty_score":0.11868805},"labels":[],"label_agreement":null},{"id":"W4388089339","doi":"10.4103/hmj.hmj_65_23","title":"Infodemiology of cataract in India: An analysis of online search behaviour using google trends from 2011 to 2022","year":2023,"lang":"en","type":"article","venue":"Hamdan Medical Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Medicine; Cataract surgery; Quarter (Canadian coin); Cataracts; Blindness; Demography; Optometry; Ophthalmology; Geography","score_opus":0.05004266884393872,"score_gpt":0.4029183721211724,"score_spread":0.35287570327723367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388089339","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":[0.9887839,0.00065204425,0.00004961212,0.0003519674,0.000009812101,0.000015470438,0.0071104523,0.000017323015,0.0030094802],"genre_scores_gemma":[0.99599516,0.0005607371,0.000093789844,0.00005667404,0.00001166732,0.0000108840195,0.002688238,0.000006116513,0.00057682313],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99944013,0.00008810072,0.00009106924,0.000075175805,0.00015508693,0.00015038137],"domain_scores_gemma":[0.99644715,0.00078472437,0.0017805268,0.00013405949,0.00055603724,0.0002976291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046237608,0.00016791494,0.00022533082,0.0036360715,0.00033785863,0.0014253672,0.0004216949,0.00031595668,0.0015888278],"category_scores_gemma":[0.0029029248,0.00014956747,0.0007574178,0.007326491,0.00032523082,0.0010748975,0.00079388643,0.0005626332,0.00051694294],"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":[0.00006687338,0.00003601804,0.98652136,0.00020669075,0.000056496297,0.00026862576,0.0020781113,0.00016934809,0.00015790277,0.00020867292,0.0015725223,0.008657294],"study_design_scores_gemma":[7.935544e-7,0.000024986544,0.9946554,0.000037702335,0.000020709003,0.0002589649,0.0031199765,0.00026263818,0.00007184163,0.000020308706,0.0015185742,0.000008127911],"about_ca_topic_score_codex":0.06122552,"about_ca_topic_score_gemma":0.07394213,"teacher_disagreement_score":0.06122552,"about_ca_system_score_codex":0.00090747833,"about_ca_system_score_gemma":0.0012246659,"threshold_uncertainty_score":0.121738315},"labels":[],"label_agreement":null},{"id":"W4388225911","doi":"10.1186/s12961-023-01053-y","title":"Connections between health research systems and decision-making spaces: lessons from the COVID-19 pandemic in the province of Québec, Canada","year":2023,"lang":"en","type":"article","venue":"Health Research Policy and Systems","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal; Université Laval","funders":"","keywords":"Public health; Pandemic; Public relations; Health services research; Health informatics; Health administration; Health policy; Coronavirus disease 2019 (COVID-19); Medicine; Knowledge management; Political science; Computer science; Nursing","score_opus":0.4743880925327874,"score_gpt":0.5635455880511286,"score_spread":0.08915749551834123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388225911","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06038677,0.027826497,0.0028206778,0.85927564,0.0014818325,0.0001291412,0.0009321394,0.00006138927,0.04708585],"genre_scores_gemma":[0.9027043,0.019497212,0.004589456,0.061176397,0.00048774783,0.00010927273,0.00035517215,0.000081959355,0.010998425],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9818268,0.008154181,0.0005633504,0.0010752269,0.0026788583,0.0057016117],"domain_scores_gemma":[0.9355277,0.0261279,0.0025386498,0.0016567778,0.023975234,0.010173862],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.018891398,0.00053756934,0.00091036264,0.002077319,0.030987116,0.017109888,0.0048966403,0.004544338,0.0044992105],"category_scores_gemma":[0.041746825,0.00050869054,0.000956195,0.005856542,0.023776408,0.0046032313,0.004604807,0.009348469,0.00031653797],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.0003590208,0.00012932054,0.06042705,0.0016101617,0.00027323767,0.0031260794,0.20259495,0.0056553353,0.0004913334,0.35169977,0.2605894,0.1130443],"study_design_scores_gemma":[0.00016804591,0.00007771562,0.073199846,0.005882041,0.00033161015,0.00043226223,0.32798892,0.0039951885,0.0006263673,0.06375778,0.5230524,0.00048775482],"about_ca_topic_score_codex":0.9985879,"about_ca_topic_score_gemma":0.9993042,"teacher_disagreement_score":0.9811086,"about_ca_system_score_codex":0.36285698,"about_ca_system_score_gemma":0.518178,"threshold_uncertainty_score":0.73899555},"labels":[],"label_agreement":null},{"id":"W4388326273","doi":"10.1016/j.puhe.2023.10.008","title":"The state of integrated disease surveillance in seven countries: a synthesis report","year":2023,"lang":"en","type":"article","venue":"Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University; Public Health Agency of Canada","funders":"World Health Organization; Bill and Melinda Gates Foundation","keywords":"Conceptualization; Operationalization; CLARITY; Workforce; Focus group; Corporate governance; Public health; Maturity (psychological); Disease surveillance; Public relations; Qualitative property; Conceptual framework; Business; Political science; Process management; Medicine; Economic growth; Computer science; Sociology; Nursing; Marketing; Finance; Economics","score_opus":0.03225082971092832,"score_gpt":0.32300904991705354,"score_spread":0.29075822020612524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388326273","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":[0.18531077,0.7176689,0.017631676,0.029214805,0.0016330561,0.0035839474,0.0063677565,0.0001657801,0.03842335],"genre_scores_gemma":[0.7104475,0.25690868,0.024201596,0.002143033,0.00013389614,0.0031578767,0.0023466551,0.000059079463,0.0006017015],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9603888,0.024117524,0.0077158245,0.0023102253,0.0036493582,0.0018182015],"domain_scores_gemma":[0.9124784,0.06493287,0.0067813895,0.002934263,0.01154743,0.001325661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06479637,0.00092059775,0.0016537997,0.013793095,0.002288292,0.010869571,0.0014735934,0.0013565918,0.002292605],"category_scores_gemma":[0.08072487,0.0010669332,0.0013881061,0.029804675,0.004455775,0.009909145,0.008216021,0.0024640125,0.00014959286],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002294361,0.00008756306,0.04709153,0.12989916,0.0010113344,0.0009803217,0.35714632,0.0017085379,0.00070191314,0.084847346,0.009747506,0.36654896],"study_design_scores_gemma":[0.000060228344,0.00025546053,0.054861944,0.20785807,0.0013513133,0.00069194165,0.53749466,0.00091368804,0.0011441824,0.010041972,0.18521051,0.00011604159],"about_ca_topic_score_codex":0.017021494,"about_ca_topic_score_gemma":0.01294328,"teacher_disagreement_score":0.06479637,"about_ca_system_score_codex":0.023618193,"about_ca_system_score_gemma":0.033559557,"threshold_uncertainty_score":0.3426801},"labels":[],"label_agreement":null},{"id":"W4388432073","doi":"10.61737/ytfm1783","title":"Recension des solutions technologiques développées dans le monde afin de limiter la propagation de la COVID-19 et typologie des applications de traçage","year":2020,"lang":"fr","type":"report","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.09271038761895221,"score_gpt":0.37152010437269795,"score_spread":0.2788097167537457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388432073","genre_codex":"other","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":[0.20611607,0.038896758,0.34938884,0.036590405,0.0028464627,0.0022885618,0.0024560953,0.004566115,0.35685065],"genre_scores_gemma":[0.59203804,0.03110491,0.2703751,0.0043219747,0.00047868234,0.0014083755,0.002466485,0.0007943644,0.09701207],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99413437,0.0015470147,0.0003619062,0.0007919155,0.0022658543,0.0008989082],"domain_scores_gemma":[0.9923713,0.002380964,0.00070296775,0.0011189864,0.0027627423,0.0006629978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004670595,0.0012406778,0.00060870213,0.0023374744,0.0023685023,0.009660339,0.0017534656,0.003487192,0.008924279],"category_scores_gemma":[0.009472488,0.00063848577,0.0011651086,0.002473889,0.0017340397,0.008488012,0.0039813546,0.0031873353,0.003752487],"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":[0.00060522393,0.0004510279,0.020782484,0.0051109595,0.00021380706,0.0017062979,0.0127851395,0.009212844,0.059286706,0.17640118,0.053749736,0.6596946],"study_design_scores_gemma":[0.000049685612,0.0006560718,0.01152689,0.0024617987,0.00020390205,0.001955082,0.013626373,0.016832875,0.031101912,0.027753849,0.8936208,0.00021069022],"about_ca_topic_score_codex":0.008554479,"about_ca_topic_score_gemma":0.009162469,"teacher_disagreement_score":0.009660339,"about_ca_system_score_codex":0.0025510094,"about_ca_system_score_gemma":0.004721279,"threshold_uncertainty_score":0.029854655},"labels":[],"label_agreement":null},{"id":"W4388566788","doi":"10.2196/44401","title":"Thermometer-based fever surveillance and COVID-19 lead time in California, New York, Ohio, Pennsylvania, and Texas, November 2021 - March 2022: Ecological Study (Preprint)","year":2022,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprint; Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Environmental health; Geography; Ecology; Gerontology; Medicine; Virology; Biology; Outbreak; Infectious disease (medical specialty); Computer science","score_opus":0.04360177698709445,"score_gpt":0.32010554720661133,"score_spread":0.2765037702195169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388566788","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":[0.97141975,0.0001440281,0.00015639904,0.00040799458,0.000045481258,0.000023646864,0.026381576,0.000035542827,0.0013855249],"genre_scores_gemma":[0.9786103,0.00014661452,0.00020230103,0.00011380204,0.000032144064,0.00005889031,0.018510288,0.000016033615,0.002309565],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99953866,0.000084295374,0.000035571797,0.00014051191,0.000066426444,0.00013452262],"domain_scores_gemma":[0.9976343,0.00041855694,0.0007062199,0.00012359477,0.0005443758,0.0005730756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007803519,0.00025435202,0.00021857566,0.00090068014,0.0005969532,0.00083654973,0.00076207414,0.0004914871,0.003142452],"category_scores_gemma":[0.0029978314,0.0003602971,0.00040323046,0.001531714,0.00025859894,0.00082868536,0.0007700367,0.0009987607,0.0005123177],"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":[0.00021322514,0.000090519534,0.9896431,0.000023821114,0.00006106697,0.000027457525,0.00031767454,0.0003565942,0.0000799451,0.00007821071,0.007314358,0.001794016],"study_design_scores_gemma":[0.000018239134,0.00003958895,0.99734807,0.000016931635,0.000025670011,0.000023223569,0.0012104437,0.00047323923,0.00005095334,0.000022714668,0.000765276,0.0000055575147],"about_ca_topic_score_codex":0.39686164,"about_ca_topic_score_gemma":0.537262,"teacher_disagreement_score":0.39686164,"about_ca_system_score_codex":0.001433834,"about_ca_system_score_gemma":0.0021818017,"threshold_uncertainty_score":0.78910327},"labels":[],"label_agreement":null},{"id":"W4388653797","doi":"10.2196/40216","title":"Decreased Seasonal Influenza Rates Detected in a Crowdsourced Influenza-Like Illness Surveillance System During the COVID-19 Pandemic: Prospective Cohort Study","year":2023,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Influenza-like illness; Virology; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Human mortality from H5N1; Prospective cohort study; Medicine; Seasonal influenza; Outbreak; Environmental health; Disease; Virus; Infectious disease (medical specialty); Internal medicine","score_opus":0.03887727983739424,"score_gpt":0.34943239167125156,"score_spread":0.3105551118338573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388653797","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":[0.9939646,0.00011092493,0.0003081448,0.00010043218,0.000035629244,0.00032775576,0.0045303428,0.000011906894,0.000610251],"genre_scores_gemma":[0.9952053,0.00008996272,0.00039152015,0.00016942948,0.000022150844,0.0006157347,0.0030043235,0.000010453174,0.00049101136],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977983,0.0005614603,0.00020705658,0.0007736132,0.00032227876,0.00033728557],"domain_scores_gemma":[0.99618006,0.00046837048,0.001093316,0.00069078914,0.0009906788,0.0005767391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00352182,0.0006245908,0.00047749508,0.0009097028,0.0013418939,0.001281485,0.0010096461,0.00093643536,0.0018973732],"category_scores_gemma":[0.0070994664,0.00079424516,0.0014955892,0.0009964923,0.00048075276,0.0014480602,0.0016244968,0.0014700672,0.0009434386],"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":[0.000601637,0.000391614,0.9930616,0.00008879971,0.00031646527,0.00008857363,0.001514284,0.00007923517,0.00034915996,0.000044005854,0.0012875483,0.0021771728],"study_design_scores_gemma":[0.0001008718,0.00075894676,0.99431723,0.00006568357,0.00017007312,0.00014806085,0.0023803385,0.00068630185,0.0001628115,0.000052783005,0.0011172878,0.00003960487],"about_ca_topic_score_codex":0.02349671,"about_ca_topic_score_gemma":0.025212517,"teacher_disagreement_score":0.02349671,"about_ca_system_score_codex":0.00094754365,"about_ca_system_score_gemma":0.0010368461,"threshold_uncertainty_score":0.04671991},"labels":[],"label_agreement":null},{"id":"W4388658787","doi":"10.1038/d41586-023-03537-x","title":"Use space technology to help tackle public-health events","year":2023,"lang":"en","type":"letter","venue":"Nature","topic":"Data-Driven Disease Surveillance","field":"Medicine","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 Toronto","funders":"","keywords":"Space (punctuation); Public health; Internet privacy; Business; Computer science; Data science; Medicine; Nursing","score_opus":0.031877814110715254,"score_gpt":0.3390343583675884,"score_spread":0.3071565442568731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388658787","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038781491,0.00040025372,0.00030463084,0.98136467,0.008624786,0.0000277229,0.00010726294,0.000051128067,0.008731742],"genre_scores_gemma":[0.002712322,0.0003740096,0.00037179972,0.9714947,0.008459238,0.0000642719,0.000040432697,0.000029946616,0.01645334],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99472934,0.0013109911,0.00048435,0.0005917799,0.0017049032,0.0011785262],"domain_scores_gemma":[0.9847002,0.008341152,0.00091730757,0.0004535607,0.0024167732,0.0031710828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058107707,0.0011206078,0.0010010512,0.0011319516,0.0066951434,0.007851508,0.0022225217,0.10224351,0.012640103],"category_scores_gemma":[0.028126018,0.0010032519,0.0017463862,0.0009574676,0.004420644,0.006494854,0.0048201033,0.04716011,0.011064618],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032387445,0.000049243536,0.0011332615,0.000043200864,0.00001839335,0.000770993,0.00027137855,0.00008753412,0.0003982814,0.005855407,0.9827116,0.00862832],"study_design_scores_gemma":[0.000085261796,0.00006630709,0.0015963331,0.00022948589,0.000028621369,0.00046286834,0.0009601681,0.0007914832,0.00033741666,0.010581,0.9847991,0.00006202146],"about_ca_topic_score_codex":0.03293756,"about_ca_topic_score_gemma":0.057526138,"teacher_disagreement_score":0.10224351,"about_ca_system_score_codex":0.005075561,"about_ca_system_score_gemma":0.010491754,"threshold_uncertainty_score":0.06549168},"labels":[],"label_agreement":null},{"id":"W4388722389","doi":"10.2196/47101","title":"Web-Based Content on Diet and Nutrition Written in Japanese: Infodemiology Study Based on Google Trends and Google Search","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Tokyo; Ministry of Health, Labour and Welfare","keywords":"Identification (biology); Scrutiny; The Internet; World Wide Web; Computer science; Information retrieval; Biology; Botany; Political science","score_opus":0.112588726108784,"score_gpt":0.42885554927005776,"score_spread":0.31626682316127375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388722389","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":[0.9952247,0.0006580018,0.00009681741,0.00011872763,0.0000069892926,0.00031882164,0.0020791297,0.0000049591717,0.0014917935],"genre_scores_gemma":[0.99221486,0.0019411463,0.00060176244,0.000291748,0.000034962897,0.0007710066,0.003134804,0.000014277544,0.0009953807],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986632,0.00024976113,0.00044485347,0.00015238542,0.00033236138,0.00015743377],"domain_scores_gemma":[0.993587,0.0021705038,0.0025100163,0.00016913156,0.0011533006,0.00041011185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017981841,0.0003336905,0.0007247895,0.00903416,0.0011536238,0.0017428767,0.00033520054,0.0005061957,0.0023598806],"category_scores_gemma":[0.0082452325,0.0004930771,0.00082505035,0.011026782,0.0007200629,0.0033584614,0.0016268317,0.0004750605,0.0006252924],"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":[0.00020573426,0.000314726,0.904328,0.002995488,0.000100550504,0.0012373546,0.06892703,0.000032624488,0.0008852559,0.00019469224,0.0015462936,0.019232124],"study_design_scores_gemma":[0.000018011624,0.00020350031,0.9350037,0.0004684917,0.0001381503,0.00058690616,0.06009807,0.00016869175,0.00020014138,0.00005774228,0.0030299504,0.000026651562],"about_ca_topic_score_codex":0.016600385,"about_ca_topic_score_gemma":0.027262848,"teacher_disagreement_score":0.016600385,"about_ca_system_score_codex":0.0014388326,"about_ca_system_score_gemma":0.0020433841,"threshold_uncertainty_score":0.033007503},"labels":[],"label_agreement":null},{"id":"W4388828744","doi":"10.2196/46087","title":"A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Public Health Ontario; University of Toronto; Artificial Intelligence in Medicine (Canada); York University","funders":"Natural Sciences and Engineering Research Council of Canada; International Development Research Centre","keywords":"Sentiment analysis; Granger causality; Resource (disambiguation); Social media; Pandemic; Coronavirus disease 2019 (COVID-19); Health care; Business; Causality (physics); Medical emergency; Computer science; Medicine; Economics; Econometrics; Artificial intelligence; Economic growth; World Wide Web","score_opus":0.10457073894611395,"score_gpt":0.45040251573459794,"score_spread":0.345831776788484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388828744","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":[0.8825726,0.002147277,0.068466656,0.003954814,0.00094818627,0.0010816314,0.015924955,0.00060895755,0.024295019],"genre_scores_gemma":[0.9644975,0.0005215943,0.028304445,0.0002492861,0.00038335685,0.00034729636,0.0029313432,0.000036180514,0.0027289921],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995172,0.00011715587,0.0000512453,0.00013186329,0.00011367283,0.000068916735],"domain_scores_gemma":[0.9971976,0.0014346386,0.0005786679,0.00012609942,0.0005116232,0.0001513138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096339214,0.00052306824,0.00035073172,0.00454293,0.0005530305,0.0017222638,0.0003384634,0.0006173248,0.0017700612],"category_scores_gemma":[0.0043202187,0.0001466571,0.0005382639,0.0025391986,0.0003432779,0.001794681,0.0007320818,0.00063739985,0.0009006165],"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":[0.0013411796,0.00088142365,0.52041936,0.0011883315,0.0003814143,0.0019862014,0.0070332256,0.006868375,0.042526964,0.013774268,0.02579981,0.3777995],"study_design_scores_gemma":[0.00006157711,0.00064460875,0.6122385,0.0003688427,0.0003138714,0.0013029604,0.021546822,0.29000345,0.01391329,0.012027056,0.0474292,0.00014970225],"about_ca_topic_score_codex":0.0035024914,"about_ca_topic_score_gemma":0.0043651247,"teacher_disagreement_score":0.00454293,"about_ca_system_score_codex":0.00064209197,"about_ca_system_score_gemma":0.0005971145,"threshold_uncertainty_score":0.0069642067},"labels":[],"label_agreement":null},{"id":"W4388858655","doi":"10.32920/24593247","title":"Mapping COVID-19 in Context: Promoting a Proportionate Perspective on the Pandemic","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Perspective (graphical); Context (archaeology); Thematic map; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Field (mathematics); Thematic analysis; Geography; Cartography; Political science; Data science; Public relations; Regional science; Sociology; Computer science; Medicine; Virology; Infectious disease (medical specialty); Disease; Qualitative research; Social science; Artificial intelligence","score_opus":0.10203636542381003,"score_gpt":0.3724701976973965,"score_spread":0.2704338322735864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388858655","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":[0.066839375,0.007646004,0.52936023,0.23046817,0.0040189223,0.00039065882,0.0027958667,0.0015615053,0.15691935],"genre_scores_gemma":[0.6015671,0.008504686,0.37065846,0.006830804,0.0016802038,0.0007271987,0.0014288026,0.0013282928,0.007274468],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.97174567,0.023729017,0.0008349226,0.0014118177,0.0018080843,0.00047060166],"domain_scores_gemma":[0.9600464,0.027415344,0.0018423222,0.0057330043,0.0040008393,0.00096204336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033281878,0.0010836631,0.0009629793,0.0062402925,0.0043594977,0.024971502,0.0025876039,0.003402166,0.005978267],"category_scores_gemma":[0.076161325,0.0010513269,0.0008371909,0.0086134095,0.01663964,0.026968813,0.015894223,0.0049458006,0.0013242308],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009867959,0.000044577824,0.007606027,0.0010840836,0.00010657351,0.0004889615,0.1391402,0.0061657135,0.0012151028,0.68260086,0.031723607,0.12972564],"study_design_scores_gemma":[0.000021532132,0.000030926036,0.0023132872,0.0009767272,0.000059001617,0.00030122424,0.06992802,0.0033235354,0.0010607806,0.53509504,0.38682866,0.00006126965],"about_ca_topic_score_codex":0.0068630856,"about_ca_topic_score_gemma":0.0087562455,"teacher_disagreement_score":0.033281878,"about_ca_system_score_codex":0.0050604427,"about_ca_system_score_gemma":0.0060527236,"threshold_uncertainty_score":0.17601347},"labels":[],"label_agreement":null},{"id":"W4388858771","doi":"10.32920/24593247.v1","title":"Mapping COVID-19 in Context: Promoting a Proportionate Perspective on the Pandemic","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Perspective (graphical); Context (archaeology); Thematic map; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Field (mathematics); Thematic analysis; Geography; Political science; Cartography; Public relations; Data science; Sociology; Computer science; Medicine; Virology; Infectious disease (medical specialty); Disease; Qualitative research; Social science; Artificial intelligence","score_opus":0.10203636542381003,"score_gpt":0.3724701976973965,"score_spread":0.2704338322735864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388858771","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":[0.066839375,0.007646004,0.52936023,0.23046817,0.0040189223,0.00039065882,0.0027958667,0.0015615053,0.15691935],"genre_scores_gemma":[0.6015671,0.008504686,0.37065846,0.006830804,0.0016802038,0.0007271987,0.0014288026,0.0013282928,0.007274468],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.97174567,0.023729017,0.0008349226,0.0014118177,0.0018080843,0.00047060166],"domain_scores_gemma":[0.9600464,0.027415344,0.0018423222,0.0057330043,0.0040008393,0.00096204336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033281878,0.0010836631,0.0009629793,0.0062402925,0.0043594977,0.024971502,0.0025876039,0.003402166,0.005978267],"category_scores_gemma":[0.076161325,0.0010513269,0.0008371909,0.0086134095,0.01663964,0.026968813,0.015894223,0.0049458006,0.0013242308],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009867959,0.000044577824,0.007606027,0.0010840836,0.00010657351,0.0004889615,0.1391402,0.0061657135,0.0012151028,0.68260086,0.031723607,0.12972564],"study_design_scores_gemma":[0.000021532132,0.000030926036,0.0023132872,0.0009767272,0.000059001617,0.00030122424,0.06992802,0.0033235354,0.0010607806,0.53509504,0.38682866,0.00006126965],"about_ca_topic_score_codex":0.0068630856,"about_ca_topic_score_gemma":0.0087562455,"teacher_disagreement_score":0.033281878,"about_ca_system_score_codex":0.0050604427,"about_ca_system_score_gemma":0.0060527236,"threshold_uncertainty_score":0.17601347},"labels":[],"label_agreement":null},{"id":"W4388896474","doi":"10.2196/preprints.54650","title":"Infoveillance of COVID-19 Infections in Dentistry Using Platform X: Descriptive Study (Preprint)","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Medicine; Pandemic; Family medicine; Dentistry; Pathology; Disease; Infectious disease (medical specialty)","score_opus":0.13769696886303134,"score_gpt":0.3951123474864315,"score_spread":0.25741537862340014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388896474","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":[0.9742449,0.0013083073,0.000280887,0.0005290465,0.000042501488,0.00034806345,0.019845603,0.000028586552,0.0033721137],"genre_scores_gemma":[0.9841413,0.0026844717,0.0006075712,0.0005627441,0.000108584536,0.0007848892,0.008662065,0.00003289212,0.0024154983],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999113,0.00019547282,0.00024669018,0.00012226755,0.00017904644,0.00014352087],"domain_scores_gemma":[0.9957917,0.0021997364,0.0011491867,0.00014122768,0.0005219928,0.00019613394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009605808,0.00017235005,0.00028784692,0.0019585001,0.000490436,0.0008978042,0.0002881514,0.00040212972,0.006710916],"category_scores_gemma":[0.0070672645,0.00021881839,0.00046273196,0.0021104459,0.00028691208,0.001430658,0.0010157252,0.0004373357,0.0017719453],"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":[0.0005098593,0.00021981518,0.91711825,0.003279123,0.00010592691,0.001021024,0.019542817,0.00007663144,0.00085709005,0.00057565223,0.018624013,0.03806994],"study_design_scores_gemma":[0.000015073175,0.00027185422,0.9402687,0.000942144,0.00009166758,0.0016381091,0.03817482,0.00023282813,0.00041544516,0.00011536616,0.017792499,0.000041420077],"about_ca_topic_score_codex":0.005543996,"about_ca_topic_score_gemma":0.0044153566,"teacher_disagreement_score":0.006710916,"about_ca_system_score_codex":0.00048144866,"about_ca_system_score_gemma":0.000869275,"threshold_uncertainty_score":0.022450268},"labels":[],"label_agreement":null},{"id":"W4389086927","doi":"10.2196/50379","title":"Generating Contextual Variables From Web-Based Data for Health Research: Tutorial on Web Scraping, Text Mining, and Spatial Overlay Analysis","year":2023,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; St. Michael's Hospital; Public Health Ontario; University of Toronto","funders":"","keywords":"Computer science; Preprint; Python (programming language); Data mining; World Wide Web; Data science; Information retrieval","score_opus":0.16510108244643254,"score_gpt":0.41493739771211524,"score_spread":0.2498363152656827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389086927","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":[0.0030802996,0.008602969,0.9642242,0.0022167976,0.00045428553,0.0012924604,0.0059218197,0.0074347486,0.006772418],"genre_scores_gemma":[0.010011942,0.008629213,0.96881616,0.00072087115,0.00041016834,0.0012537596,0.005043009,0.00092868996,0.004186259],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99841344,0.00063222734,0.00018794772,0.0002891204,0.00042495268,0.000052390074],"domain_scores_gemma":[0.9934493,0.0049387957,0.0002758473,0.00034539495,0.0008112646,0.00017950033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030198223,0.0016334031,0.00079855794,0.006283354,0.0005689917,0.001988659,0.0013230477,0.00087573216,0.011436077],"category_scores_gemma":[0.010055415,0.0007416741,0.0017542896,0.0056759682,0.0006305376,0.0028981504,0.0016713,0.0018327893,0.007695199],"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":[0.00005170686,0.00022809762,0.0037134564,0.002432735,0.00015294047,0.00045817977,0.0013016055,0.0058407807,0.0065841423,0.01726874,0.079663604,0.88230395],"study_design_scores_gemma":[0.00006167251,0.00021352219,0.019616755,0.00282429,0.00012624195,0.0019916934,0.0014092989,0.06868438,0.014074159,0.090840355,0.7999306,0.00022711007],"about_ca_topic_score_codex":0.0034545118,"about_ca_topic_score_gemma":0.004827895,"teacher_disagreement_score":0.011436077,"about_ca_system_score_codex":0.00073775015,"about_ca_system_score_gemma":0.0011951306,"threshold_uncertainty_score":0.03825748},"labels":[],"label_agreement":null},{"id":"W4389128295","doi":"10.1101/2023.11.28.23298985","title":"Establishing syndromic surveillance of gastrointestinal infections in emergency departments using routine emergency department data and validating it against laboratory-based surveillance, Germany, January 2019 – June 2023","year":2023,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Medicine; Emergency department; Emergency medicine; Confidence interval; Medical diagnosis; Medical emergency; Outbreak; Pediatrics; Internal medicine","score_opus":0.047919105503407826,"score_gpt":0.33114872817000723,"score_spread":0.2832296226665994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389128295","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":[0.9738535,0.0009329055,0.0037033681,0.00041424326,0.000083375664,0.0003875116,0.01849969,0.00010441785,0.002020987],"genre_scores_gemma":[0.9722428,0.00043195515,0.0066595064,0.00008984728,0.000039363866,0.00023277845,0.01996006,0.000015221882,0.00032847346],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9968569,0.0010847098,0.00068205583,0.00043183414,0.00056995533,0.00037443943],"domain_scores_gemma":[0.99443865,0.00071958953,0.0025746378,0.0003571455,0.001561231,0.00034879104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004793943,0.00039947272,0.0003048899,0.0028320933,0.00022377686,0.0009721029,0.0004802299,0.00024921494,0.00055564835],"category_scores_gemma":[0.008343693,0.00027096726,0.00041648516,0.0025954084,0.00030939403,0.00053236657,0.0010767285,0.00026999068,0.00021948054],"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":[0.00011802675,0.00002892185,0.9792941,0.0001841035,0.0000801062,0.00010210263,0.00019140338,0.00070497603,0.0006907757,0.00019857887,0.002502918,0.015903987],"study_design_scores_gemma":[0.000009279877,0.000057371548,0.99567646,0.00006370511,0.000028628367,0.00007682522,0.00019556645,0.0020816145,0.00055923394,0.000030002959,0.0012154565,0.000005895786],"about_ca_topic_score_codex":0.045216035,"about_ca_topic_score_gemma":0.04918429,"teacher_disagreement_score":0.045216035,"about_ca_system_score_codex":0.0019002805,"about_ca_system_score_gemma":0.002679981,"threshold_uncertainty_score":0.08990568},"labels":[],"label_agreement":null},{"id":"W4389131482","doi":"10.1117/12.3013588","title":"Comparative study of machine learning methods for influenza outbreak forecasting","year":2023,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Outbreak; Computer science; Artificial intelligence; Machine learning; Virology; Medicine","score_opus":0.1773916580454234,"score_gpt":0.4637059570519858,"score_spread":0.2863142990065624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389131482","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":[0.38840193,0.022504209,0.5729836,0.0027816927,0.00071737816,0.00030981548,0.0009588108,0.0014468238,0.00989569],"genre_scores_gemma":[0.87617075,0.0041565266,0.11709764,0.00019318181,0.00027628164,0.00013165956,0.00073460204,0.00007670002,0.0011626391],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99540883,0.0028222457,0.00037557373,0.00047122236,0.0007687041,0.00015347348],"domain_scores_gemma":[0.9549605,0.040239867,0.00080430834,0.00093737117,0.0028380735,0.00021994155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014051606,0.0011387706,0.0012230496,0.0045003532,0.00051738584,0.001262158,0.0009356915,0.0011897213,0.0009629297],"category_scores_gemma":[0.030392034,0.00030258426,0.0013196593,0.0023243786,0.0003394691,0.002071076,0.0005917351,0.0012356585,0.0002568749],"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":[0.0005632641,0.00036887976,0.023798542,0.0003292369,0.0005852062,0.00008587085,0.00012371666,0.67609984,0.00059101474,0.00621567,0.001804762,0.28943405],"study_design_scores_gemma":[0.0000080105465,0.00007427607,0.0016252289,0.000018768687,0.000025142599,0.000013960918,0.000027416207,0.9963952,0.00020002584,0.0013513113,0.00025204598,0.00000863285],"about_ca_topic_score_codex":0.011064368,"about_ca_topic_score_gemma":0.0064877993,"teacher_disagreement_score":0.014051606,"about_ca_system_score_codex":0.0014005542,"about_ca_system_score_gemma":0.0012600044,"threshold_uncertainty_score":0.074312866},"labels":[],"label_agreement":null},{"id":"W4389234955","doi":"10.1371/journal.pone.0273205","title":"Improving estimates of pertussis burden in Ontario, Canada 2010–2017 by combining validation and capture-recapture methodologies","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University; Vector Institute; Institute for Clinical Evaluative Sciences; Public Health Ontario; University of Toronto","funders":"Canadian Institutes of Health Research; World Health Organization","keywords":"Mark and recapture; False positive paradox; Statistics; Medicine; Demography; Environmental health; Mathematics; Population","score_opus":0.07039522811402428,"score_gpt":0.2758204301386783,"score_spread":0.20542520202465403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389234955","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":[0.81834906,0.012118832,0.04521612,0.004206632,0.00023696473,0.0007094046,0.099144794,0.00060296827,0.019415291],"genre_scores_gemma":[0.94816774,0.002832149,0.017672058,0.0006415096,0.000057527315,0.00023978199,0.02545076,0.00008010738,0.004858441],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99621105,0.00093717,0.00030011527,0.00052774535,0.001585393,0.00043858116],"domain_scores_gemma":[0.9894782,0.0011629971,0.0016690318,0.0007515168,0.0066310754,0.0003072039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006581178,0.0006381375,0.00058283145,0.0017969992,0.0017218105,0.0013762028,0.0016820744,0.00034557775,0.0014028063],"category_scores_gemma":[0.013564675,0.0005443858,0.0010885244,0.0038236862,0.0005783188,0.00064179645,0.0011177224,0.00062386855,0.00026325136],"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":[0.00020735958,0.000039726332,0.91548914,0.00077426,0.001162098,0.00013444104,0.0012935994,0.012437951,0.0012326601,0.0016141597,0.016097179,0.049517456],"study_design_scores_gemma":[0.000040255658,0.000045116627,0.96188724,0.00034308998,0.000358218,0.00008415756,0.000638025,0.016095487,0.0010869843,0.00039292042,0.018975876,0.000052649586],"about_ca_topic_score_codex":0.99394184,"about_ca_topic_score_gemma":0.9956493,"teacher_disagreement_score":0.02921841,"about_ca_system_score_codex":0.02921841,"about_ca_system_score_gemma":0.0448572,"threshold_uncertainty_score":0.21199536},"labels":[],"label_agreement":null},{"id":"W4389457455","doi":"10.2196/49185","title":"Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review","year":2023,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Public Health Agency; Public Health Agency of Canada; McMaster University","keywords":"Public health; Public health surveillance; Pandemic; Government (linguistics); Contact tracing; Grey literature; Disease surveillance; Public relations; Environmental health; Medicine; Political science; Coronavirus disease 2019 (COVID-19); MEDLINE; Infectious disease (medical specialty)","score_opus":0.20626220339411325,"score_gpt":0.481375141677558,"score_spread":0.27511293828344474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389457455","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005383729,0.99516785,0.00039003728,0.0014521139,0.0006949329,0.00035861722,0.00028596653,0.000013601517,0.0010985519],"genre_scores_gemma":[0.0041659283,0.992665,0.0007871966,0.0010619891,0.0002776213,0.00057809235,0.00026073316,0.000011839532,0.00019171248],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.969713,0.00978682,0.011747752,0.0018504187,0.0061486927,0.0007533137],"domain_scores_gemma":[0.8023307,0.15032804,0.022178454,0.0032714298,0.02089597,0.0009954867],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.032908283,0.001607609,0.0041572764,0.021761784,0.0014790721,0.006748358,0.0029877182,0.0040757316,0.004699309],"category_scores_gemma":[0.18557067,0.0015466146,0.006659651,0.022301808,0.0020256052,0.0069044787,0.0031350502,0.0032039923,0.0009138654],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015660026,0.000033982586,0.0010314388,0.8012622,0.0021766762,0.00026887158,0.0014000289,0.0003371577,0.0002604878,0.0018750831,0.010556496,0.18064094],"study_design_scores_gemma":[0.000022633609,0.000055835564,0.0012175486,0.93341625,0.0036438971,0.00027591345,0.0005927602,0.000071539034,0.00017337619,0.0006360341,0.059865326,0.00002891464],"about_ca_topic_score_codex":0.010946998,"about_ca_topic_score_gemma":0.015112215,"teacher_disagreement_score":0.96709174,"about_ca_system_score_codex":0.008462339,"about_ca_system_score_gemma":0.029477574,"threshold_uncertainty_score":0.1740377},"labels":[],"label_agreement":null},{"id":"W4389476129","doi":"10.2196/preprints.55010","title":"Figure Correction: Using Social Media to Help Understand Patient-Reported Health Outcomes of Post–COVID-19 Condition: Natural Language Processing Approach (Preprint)","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Social media; Terminology; Normalization (sociology); Computer science; Natural language processing; Preprint; Artificial intelligence; Named-entity recognition; Sentiment analysis; F1 score; Language model; Information retrieval; Machine learning; Data science; Task (project management); World Wide Web","score_opus":0.07853407279349083,"score_gpt":0.37875964316138316,"score_spread":0.3002255703678923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389476129","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037669998,0.0009969653,0.009415106,0.12397732,0.80870545,0.00027414196,0.03138341,0.009288703,0.012191886],"genre_scores_gemma":[0.14546907,0.005379143,0.059760794,0.100656584,0.19935367,0.0015446377,0.03520043,0.018808102,0.43382767],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969121,0.00058070803,0.00060088583,0.000533987,0.0010590139,0.00031330614],"domain_scores_gemma":[0.95391226,0.020427093,0.0022003718,0.0036243037,0.018643415,0.001192576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026693172,0.0018011762,0.0011593098,0.003543741,0.0021878914,0.0029667036,0.0025537626,0.0046770964,0.18986617],"category_scores_gemma":[0.07920338,0.0006840865,0.0015540422,0.002315753,0.0018293434,0.0022952438,0.0020163974,0.005511853,0.05914529],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015222462,0.000007782293,0.00053921907,0.00035645778,0.000026367261,0.000802517,0.0001323113,0.00012367997,0.00037220752,0.00089918665,0.9865558,0.010032305],"study_design_scores_gemma":[0.00013321775,0.000049258666,0.0051427437,0.00072752085,0.000082271814,0.003237252,0.0005540274,0.002169171,0.0024810303,0.0037255103,0.9816065,0.00009143401],"about_ca_topic_score_codex":0.018146908,"about_ca_topic_score_gemma":0.0165071,"teacher_disagreement_score":0.18986617,"about_ca_system_score_codex":0.002433803,"about_ca_system_score_gemma":0.0034078425,"threshold_uncertainty_score":0.6351656},"labels":[],"label_agreement":null},{"id":"W4389509924","doi":"10.1016/j.ajur.2023.08.009","title":"Search trends in the treatment for benign prostatic hyperplasia: A twenty-year analysis","year":2023,"lang":"en","type":"article","venue":"Asian journal of urology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; University of Toronto","funders":"","keywords":"Medicine; Hyperplasia; Gynecology; Urology; General surgery; Internal medicine","score_opus":0.030560655276045207,"score_gpt":0.34056127419695553,"score_spread":0.31000061892091035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389509924","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":[0.9672039,0.012144441,0.00010086614,0.00055415364,0.00002543184,0.00004385495,0.017780967,0.00003494638,0.0021113637],"genre_scores_gemma":[0.9848946,0.0055149132,0.00017372196,0.00017234868,0.000051237006,0.000041513536,0.008668327,0.000015510906,0.00046784265],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987973,0.00020431966,0.00032722822,0.00015903238,0.00039664685,0.00011548719],"domain_scores_gemma":[0.99183977,0.0017055203,0.004831209,0.0001656466,0.0009646038,0.00049322663],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0009936511,0.00019019467,0.0004094878,0.0071186777,0.00032770488,0.0009826294,0.00039120272,0.00046575262,0.0016623653],"category_scores_gemma":[0.0052000037,0.00019606708,0.0012728561,0.011275904,0.00019889447,0.001273409,0.00058532984,0.00039516436,0.000389589],"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":[0.00011312385,0.000026556641,0.9923631,0.0002966007,0.0002593667,0.00012088792,0.00015097781,0.000073668685,0.00007945576,0.00003133515,0.00095415907,0.0055308114],"study_design_scores_gemma":[0.000006368171,0.000050114537,0.99733704,0.00009766504,0.00014358117,0.00036335792,0.0003328323,0.00026515315,0.000054971068,0.000013465087,0.0013284775,0.000006972696],"about_ca_topic_score_codex":0.01726232,"about_ca_topic_score_gemma":0.019658413,"teacher_disagreement_score":0.9928813,"about_ca_system_score_codex":0.0006397,"about_ca_system_score_gemma":0.0006323363,"threshold_uncertainty_score":0.034323692},"labels":[],"label_agreement":null},{"id":"W4389719908","doi":"10.1093/jrsssc/qlad104","title":"Spatial modelling of infectious diseases with covariate measurement error","year":2023,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary; University of Manitoba","funders":"Canadian Statistical Sciences Institute; Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Statistics; Inference; Observational error; Econometrics; Computer science; Population; Infectious disease (medical specialty); Spatial epidemiology; Mathematics; Medicine; Disease; Artificial intelligence; Environmental health; Epidemiology","score_opus":0.030876745663166616,"score_gpt":0.2571265089763711,"score_spread":0.2262497633132045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389719908","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":[0.034668077,0.0003745625,0.9633885,0.00050359365,0.00005886829,0.000055218265,0.0003433356,0.00022176311,0.00038605733],"genre_scores_gemma":[0.801855,0.00051509764,0.19387807,0.00021846953,0.00011543803,0.00027241025,0.0007119406,0.000075178876,0.0023582517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.991911,0.0057475213,0.0003434634,0.0011985599,0.0004962956,0.00030319774],"domain_scores_gemma":[0.9656546,0.027372716,0.0030789205,0.0018123063,0.0017435962,0.00033787245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014686753,0.0009483331,0.0017948458,0.0017840483,0.00060100364,0.00151337,0.003301895,0.0020864992,0.0013682441],"category_scores_gemma":[0.04083703,0.0009400733,0.002000855,0.0025654584,0.002022215,0.0015314493,0.002580093,0.0018845495,0.0002330203],"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":[0.000060151517,0.00002896361,0.011383612,0.00007926072,0.00014925413,0.00012283763,0.000114560076,0.94187486,0.00024948068,0.03538487,0.00038331343,0.010168814],"study_design_scores_gemma":[0.000012095314,0.000026228583,0.0009814642,0.000019388737,0.000019787896,0.000021045953,0.000014272165,0.9826303,0.0001133017,0.015615323,0.00053462386,0.000012083761],"about_ca_topic_score_codex":0.021271601,"about_ca_topic_score_gemma":0.011221898,"teacher_disagreement_score":0.021271601,"about_ca_system_score_codex":0.0018770117,"about_ca_system_score_gemma":0.0017351708,"threshold_uncertainty_score":0.077671885},"labels":[],"label_agreement":null},{"id":"W4390096977","doi":"10.14745/ccdr.v49i1112a06","title":"Syndromic surveillance performance in Canada throughout the COVID-19 pandemic, March 1, 2020 to March 4, 2023","year":2023,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Medicine; Coronavirus; Respiratory system; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Virology; Intensive care medicine; Disease; Infectious disease (medical specialty); Internal medicine","score_opus":0.04303009431873788,"score_gpt":0.3247735469365699,"score_spread":0.281743452617832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390096977","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":[0.94264793,0.0028470913,0.0009223292,0.0060483646,0.00014305735,0.0002579988,0.029409457,0.00048705854,0.017236646],"genre_scores_gemma":[0.9886064,0.0009837449,0.0012334011,0.0004438445,0.00001540345,0.00004008648,0.006592692,0.000025630794,0.002058735],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9970708,0.0002479964,0.00019510006,0.0003409199,0.0014145069,0.000730609],"domain_scores_gemma":[0.98685735,0.0006493466,0.0010690796,0.00024148954,0.008609717,0.0025729963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026749605,0.00052085874,0.00040387022,0.0018158875,0.0028131185,0.0020803707,0.0015632816,0.00040110288,0.0012302153],"category_scores_gemma":[0.012224952,0.00033417088,0.0005052805,0.0042930013,0.00085447583,0.00060382014,0.0017140892,0.0007195512,0.0002959221],"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":[0.00035260522,0.00008777574,0.90963167,0.00017189802,0.00020641224,0.0001313883,0.0026045972,0.004539299,0.00064001395,0.0006741675,0.01962956,0.061330654],"study_design_scores_gemma":[0.000025076359,0.0001160756,0.98220545,0.00014306519,0.000059781833,0.000069847934,0.0031621226,0.0057052416,0.0006384682,0.00008049367,0.007743386,0.000050993473],"about_ca_topic_score_codex":0.99803156,"about_ca_topic_score_gemma":0.9984346,"teacher_disagreement_score":0.07892041,"about_ca_system_score_codex":0.07892041,"about_ca_system_score_gemma":0.10288646,"threshold_uncertainty_score":0.5726103},"labels":[],"label_agreement":null},{"id":"W4390145975","doi":"10.3103/s106653072304004x","title":"Estimating Sample Skewness from Sample Data Summaries and Associated Evaluation of Normality","year":2023,"lang":"en","type":"article","venue":"Mathematical Methods of Statistics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Skewness; Estimator; Normality; Sample (material); Statistics; Econometrics; Mathematics; Parametric statistics; Nonparametric statistics; Computer science","score_opus":0.23016694684008743,"score_gpt":0.48754226420970703,"score_spread":0.2573753173696196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390145975","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":[0.012568684,0.00033178114,0.9855129,0.00017830053,0.000037214697,0.00008638593,0.0002660651,0.0006378718,0.00038069888],"genre_scores_gemma":[0.38661987,0.00078094786,0.6086111,0.00019161163,0.00028998885,0.0006690148,0.0018125887,0.00029496776,0.00072992477],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9929032,0.0037499955,0.0007447039,0.0007582604,0.0016153221,0.00022850263],"domain_scores_gemma":[0.9291917,0.055708833,0.0050219717,0.0051196394,0.0044762115,0.00048154034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0149550075,0.0009412205,0.0014583444,0.0048973444,0.00043594628,0.0023604275,0.0012658433,0.0010932335,0.0015132913],"category_scores_gemma":[0.121190466,0.0004871322,0.00097178447,0.0025244711,0.0011725405,0.002717631,0.002236082,0.0016900974,0.0004683091],"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":[0.00061684777,0.00018870691,0.05894633,0.0009346993,0.0006765057,0.00069159083,0.0008778486,0.25852066,0.012534363,0.113915585,0.0077785943,0.5443183],"study_design_scores_gemma":[0.000052317795,0.00017104422,0.00855109,0.0001751978,0.00007549318,0.00051068846,0.00018534368,0.8782799,0.006816893,0.10159713,0.0034778256,0.00010701806],"about_ca_topic_score_codex":0.0009437195,"about_ca_topic_score_gemma":0.0009057528,"teacher_disagreement_score":0.0149550075,"about_ca_system_score_codex":0.00072743313,"about_ca_system_score_gemma":0.0012310346,"threshold_uncertainty_score":0.079090595},"labels":[],"label_agreement":null},{"id":"W4390585354","doi":"10.3389/fpubh.2023.1349985","title":"Editorial: Applications of geospatial information technologies and spatial statistics in health services research","year":2024,"lang":"en","type":"editorial","venue":"Frontiers in Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"Sichuan Province Science and Technology Support Program; Health Commission of Sichuan Province; Sichuan University; National Natural Science Foundation of China","keywords":"Geospatial analysis; Data science; Spatial analysis; Computer science; Health informatics; Data mining; Information retrieval; Geography; Medicine; Remote sensing; Public health","score_opus":0.012862212362347969,"score_gpt":0.34553707689651847,"score_spread":0.3326748645341705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390585354","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013014119,0.0024349904,0.00014406697,0.017803373,0.9783825,0.000020847358,0.00010292154,0.000066020904,0.0010321337],"genre_scores_gemma":[0.00018452262,0.002031523,0.000109652756,0.007942912,0.9846869,0.000022354732,0.00004423233,0.00004001187,0.0049378206],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99144214,0.0016832089,0.0011096069,0.0010503785,0.0042799753,0.00043472525],"domain_scores_gemma":[0.95059323,0.023609377,0.0025146208,0.0011620487,0.016982613,0.0051379804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013379932,0.006666449,0.0058149267,0.008308251,0.004270732,0.011289255,0.0049883863,0.015405554,0.033771563],"category_scores_gemma":[0.048719823,0.0016200036,0.004290662,0.0031317095,0.002946761,0.0058600246,0.0020810894,0.01776311,0.023349296],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023743123,0.000007906056,0.000016128817,0.00014754795,0.00001474431,0.000044793167,0.0000065494273,0.00002108702,0.000018254132,0.00018677353,0.99693155,0.0025808827],"study_design_scores_gemma":[0.00012735557,0.00002842977,0.00027222745,0.0008043257,0.00009015046,0.00022599225,0.000042715845,0.00031634368,0.0001072827,0.0022824516,0.99567324,0.000029471896],"about_ca_topic_score_codex":0.002572539,"about_ca_topic_score_gemma":0.006642834,"teacher_disagreement_score":0.033771563,"about_ca_system_score_codex":0.003757216,"about_ca_system_score_gemma":0.0042053238,"threshold_uncertainty_score":0.11297715},"labels":[],"label_agreement":null},{"id":"W4390621394","doi":"10.1007/978-1-4614-8414-1_64-1","title":"On Scan Statistics Through the Finite Markov Chain Imbedding Approach","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","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 Manitoba; Public Health Ontario; University of Toronto","funders":"","keywords":"Markov chain; Statistics; Computer science; Markov process; Poisson distribution; Mathematics; Statistical physics; Algorithm; Applied mathematics; Physics","score_opus":0.03636964629872158,"score_gpt":0.2850515743646594,"score_spread":0.2486819280659378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390621394","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":[0.0009814828,0.0018952211,0.984396,0.0007754027,0.00017790425,0.000013276826,0.00013750837,0.0001523399,0.011470934],"genre_scores_gemma":[0.166638,0.013966584,0.7585386,0.0012363602,0.0016191932,0.00032066647,0.0007176066,0.0009460961,0.056016866],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987501,0.00067156024,0.00006092579,0.00016363765,0.00030669395,0.00004708052],"domain_scores_gemma":[0.9873188,0.011374035,0.00025305577,0.00047397756,0.00046163463,0.00011854853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036280819,0.0011391769,0.0012269428,0.0015272798,0.00041659237,0.0021126864,0.0018638133,0.0014398311,0.009735158],"category_scores_gemma":[0.016423695,0.00090424204,0.0009408762,0.0022722853,0.0025947245,0.0048220847,0.0016742838,0.00404667,0.0019343558],"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":[0.000009666227,0.0000105582985,0.00014691908,0.000052772564,0.000011749945,0.000045781137,0.000055565753,0.0174831,0.00015909331,0.956299,0.0031288965,0.022596817],"study_design_scores_gemma":[0.0000026502257,0.000007911508,0.000056784414,0.000029670207,0.0000041502326,0.000048671183,0.000009134306,0.07435411,0.00012556095,0.919218,0.0061341766,0.0000092241025],"about_ca_topic_score_codex":0.0023449857,"about_ca_topic_score_gemma":0.0018976416,"teacher_disagreement_score":0.009735158,"about_ca_system_score_codex":0.0016017937,"about_ca_system_score_gemma":0.0011769437,"threshold_uncertainty_score":0.03256738},"labels":[],"label_agreement":null},{"id":"W4390707187","doi":"10.2196/47673","title":"Combining Digital and Molecular Approaches Using Health and Alternate Data Sources in a Next-Generation Surveillance System for Anticipating Outbreaks of Pandemic Potential","year":2024,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Pan American Health Organization; Agência Nacional de Vigilância Sanitária; Universidade Federal do Rio de Janeiro; Fundação Oswaldo Cruz; Rockefeller Foundation","keywords":"Data science; Pandemic; Computer science; Identification (biology); Disease surveillance; Risk analysis (engineering); Outbreak; Infectious disease (medical specialty); Disparate system; Public health; Medicine; Disease; Coronavirus disease 2019 (COVID-19); Biology","score_opus":0.17576845748829611,"score_gpt":0.3561391086657109,"score_spread":0.1803706511774148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390707187","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":[0.059277773,0.0030986385,0.90407985,0.007895499,0.0005884583,0.0007776196,0.0030732816,0.008536958,0.0126718115],"genre_scores_gemma":[0.48962185,0.0022411651,0.49678063,0.0027264198,0.0004200031,0.0004458611,0.003669677,0.00027228703,0.0038221392],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99749017,0.00057470053,0.0002959699,0.0008190165,0.00064578356,0.00017436098],"domain_scores_gemma":[0.9949562,0.001661315,0.00056295143,0.0011057645,0.0014144939,0.0002992919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004582493,0.0010231901,0.0010841246,0.00410012,0.0010623413,0.0051527624,0.0021244213,0.001839305,0.0018418194],"category_scores_gemma":[0.008059757,0.00068894256,0.0009311461,0.0027005342,0.0012968138,0.006954944,0.004052463,0.0015307516,0.0011658125],"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":[0.0010625245,0.00093731337,0.08799088,0.0011717927,0.00095957774,0.0013319915,0.002529295,0.098847836,0.04674704,0.057294063,0.024710316,0.67641735],"study_design_scores_gemma":[0.00009576833,0.00037216724,0.01597859,0.0004249436,0.00047589207,0.00055165077,0.0014927989,0.7716124,0.032671433,0.100043215,0.07596837,0.0003128262],"about_ca_topic_score_codex":0.0067124516,"about_ca_topic_score_gemma":0.0069729155,"teacher_disagreement_score":0.0067124516,"about_ca_system_score_codex":0.0013287391,"about_ca_system_score_gemma":0.002117343,"threshold_uncertainty_score":0.024234831},"labels":[],"label_agreement":null},{"id":"W4390985279","doi":"10.2196/52306","title":"Web-Based Search Volume for HIV Tests and HIV-Testing Preferences During the COVID-19 Pandemic in Japan: Infodemiology Study","year":2024,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Health, Labour and Welfare","keywords":"Pandemic; Medicine; Test (biology); Population; Human immunodeficiency virus (HIV); Coronavirus disease 2019 (COVID-19); Family medicine; Demography; Environmental health; Internal medicine; Infectious disease (medical specialty); Disease; Biology","score_opus":0.15208696418672163,"score_gpt":0.4610823210724316,"score_spread":0.30899535688570995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390985279","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":[0.98016626,0.0026967425,0.000087831104,0.00030849164,0.000018487513,0.00005792087,0.015064442,0.00002482115,0.0015750638],"genre_scores_gemma":[0.9870805,0.0011589787,0.00016745515,0.00021265638,0.000041692067,0.00008649843,0.010524314,0.000023158149,0.00070473517],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977144,0.0004443317,0.0007402516,0.00042783518,0.00039592333,0.00027726684],"domain_scores_gemma":[0.9843062,0.005137364,0.007169941,0.000553945,0.0016060633,0.0012264516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017054586,0.00041004934,0.0008636,0.008063982,0.00058775244,0.0018834146,0.00066558656,0.00072639994,0.0037139603],"category_scores_gemma":[0.014263821,0.00028685958,0.0015402568,0.013551947,0.00040897232,0.0034530738,0.0011636468,0.00075445307,0.0008502963],"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":[0.00041223044,0.00009442998,0.98905563,0.00074166496,0.00045105393,0.00024822535,0.0011059385,0.000075746844,0.00016908621,0.000063267806,0.0019722846,0.0056104576],"study_design_scores_gemma":[0.000027774919,0.000100522506,0.99435294,0.00012581909,0.00043470328,0.0005204464,0.0024076216,0.0005272692,0.000101338555,0.00004754197,0.0013238074,0.000030252731],"about_ca_topic_score_codex":0.02906945,"about_ca_topic_score_gemma":0.037907492,"teacher_disagreement_score":0.02906945,"about_ca_system_score_codex":0.0011235164,"about_ca_system_score_gemma":0.0012517871,"threshold_uncertainty_score":0.05780047},"labels":[],"label_agreement":null},{"id":"W4391040681","doi":"10.15353/cjo.v47i3.4441","title":"SIDA - Précautions Recommandées pour les Soins Ophtalmiques","year":2021,"lang":"fr","type":"article","venue":"Canadian journal of optometry/CJO. Canadian journal of optometry","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Philosophy","score_opus":0.02651661175873844,"score_gpt":0.3405156585222121,"score_spread":0.3139990467634737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391040681","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1461379,0.28287068,0.017602999,0.17750438,0.018796325,0.0055096294,0.04973892,0.0023556727,0.29948348],"genre_scores_gemma":[0.59866756,0.2380725,0.036364388,0.04019321,0.0036921704,0.0027924941,0.029621523,0.00025629584,0.050339784],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9938969,0.0017026059,0.0013355479,0.0002483947,0.00213276,0.00068379845],"domain_scores_gemma":[0.9899052,0.0022276593,0.0019706157,0.0008393915,0.00393525,0.0011219612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074305246,0.00050349656,0.0005658134,0.0027304357,0.0011635814,0.0015048175,0.0011243889,0.0012117444,0.0058214115],"category_scores_gemma":[0.018534381,0.00022830805,0.0008851127,0.0017595885,0.0008162234,0.00059496856,0.0015631826,0.0025549498,0.0012950962],"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":[0.0004829825,0.0004606748,0.12930718,0.005993204,0.00017009943,0.0029149025,0.002810893,0.0006114351,0.006891863,0.015392895,0.17307936,0.6618846],"study_design_scores_gemma":[0.00009174423,0.000450561,0.16623497,0.010909428,0.00024864526,0.0026420779,0.0023471008,0.00047238613,0.00685099,0.0021911054,0.80749345,0.0000674933],"about_ca_topic_score_codex":0.11485945,"about_ca_topic_score_gemma":0.111964546,"teacher_disagreement_score":0.11485945,"about_ca_system_score_codex":0.0024797325,"about_ca_system_score_gemma":0.016647927,"threshold_uncertainty_score":0.22838181},"labels":[],"label_agreement":null},{"id":"W4391059465","doi":"10.1016/b978-0-443-21598-8.00005-1","title":"Big data and artificial intelligence for pandemic preparedness","year":2024,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Pandemic; Big data; Preparedness; Public health; Data science; Coronavirus disease 2019 (COVID-19); Psychological intervention; Analytics; Computer science; Medicine; Infectious disease (medical specialty); Medical emergency; Political science; Data mining; Disease; Nursing; Pathology","score_opus":0.10913544758389562,"score_gpt":0.3354094640280741,"score_spread":0.22627401644417847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391059465","genre_codex":"other","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":[0.0009087204,0.14605068,0.053762127,0.025481673,0.010638177,0.00010312467,0.0014927216,0.0007808192,0.7607819],"genre_scores_gemma":[0.014897707,0.152789,0.049520373,0.008197584,0.00788269,0.00015745508,0.0016350393,0.0006856491,0.76423454],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.999635,0.00007126619,0.000017853203,0.00004969805,0.00020828357,0.00001783377],"domain_scores_gemma":[0.9986671,0.0009838252,0.00003255097,0.00010054404,0.00015957266,0.000056468016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086013856,0.0009898773,0.00052018126,0.0018770277,0.0005093706,0.0046124808,0.0005943763,0.0014861957,0.040521584],"category_scores_gemma":[0.0018571032,0.00040409493,0.0003993393,0.0031366393,0.0010702978,0.0054939073,0.0013799967,0.0026104865,0.013370386],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000098948,0.000025905705,0.00012021146,0.00043967416,0.000014809709,0.000044499036,0.00011662711,0.0014728468,0.0004357229,0.21651535,0.48944467,0.29135978],"study_design_scores_gemma":[0.0000020783536,0.0000053555996,0.00012547789,0.00026723114,0.0000037790426,0.000052471394,0.000055091696,0.0015455623,0.00012575643,0.12476957,0.8730397,0.000007925562],"about_ca_topic_score_codex":0.0029199896,"about_ca_topic_score_gemma":0.0047743977,"teacher_disagreement_score":0.040521584,"about_ca_system_score_codex":0.0012670802,"about_ca_system_score_gemma":0.0012243493,"threshold_uncertainty_score":0.13555825},"labels":[],"label_agreement":null},{"id":"W4391387602","doi":"10.2196/52093","title":"Using EpiCore to Enable Rapid Verification of Potential Health Threats: Illustrated Use Cases and Summary Statistics","year":2024,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Computer science; Data science; Environmental health; Medicine; Mathematics","score_opus":0.10307471168089413,"score_gpt":0.37213347946714087,"score_spread":0.2690587677862467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391387602","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":[0.8821023,0.0022616633,0.036294132,0.002027305,0.00016913576,0.0025820753,0.045655865,0.0050913002,0.023816343],"genre_scores_gemma":[0.94199854,0.001245923,0.027247772,0.00033996845,0.0000933807,0.0020124367,0.023533644,0.00033457472,0.003193766],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.987672,0.004512618,0.0016095092,0.0010993273,0.0043996526,0.00070691184],"domain_scores_gemma":[0.9064212,0.06683032,0.007899833,0.007372504,0.010545548,0.0009306365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014370403,0.00088030385,0.0005095175,0.0049993093,0.00064855587,0.0017590909,0.0009456029,0.0011609425,0.002699738],"category_scores_gemma":[0.056178205,0.00030693202,0.0010121834,0.003879994,0.000690839,0.002629402,0.0017678958,0.00085413514,0.0012139659],"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":[0.0021229102,0.000830055,0.628844,0.0029163314,0.0007209215,0.0023684218,0.0040466907,0.027504303,0.0044299797,0.0059696087,0.072547905,0.24769886],"study_design_scores_gemma":[0.00037043288,0.0053960592,0.61053777,0.0013609172,0.0009545962,0.0059626973,0.009228291,0.18300757,0.022947557,0.00830155,0.15138672,0.0005458612],"about_ca_topic_score_codex":0.009749013,"about_ca_topic_score_gemma":0.009052372,"teacher_disagreement_score":0.014370403,"about_ca_system_score_codex":0.0011681619,"about_ca_system_score_gemma":0.0009314061,"threshold_uncertainty_score":0.07599884},"labels":[],"label_agreement":null},{"id":"W4391479799","doi":"10.2196/50653","title":"Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks","year":2024,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Communicable disease; Scan statistic; Outbreak; Computer science; Disease surveillance; Public health; Cluster (spacecraft); Geography; Population; Spatial epidemiology; Environmental health; Data mining; Medicine; Cartography; Statistics; Epidemiology; Pathology; Mathematics","score_opus":0.03804653447924851,"score_gpt":0.3306574563730415,"score_spread":0.292610921893793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391479799","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":[0.0047024777,0.0013123302,0.78278244,0.0023853348,0.0009105708,0.0025603627,0.023571132,0.16655444,0.015220869],"genre_scores_gemma":[0.020456959,0.0016028568,0.90643555,0.0026075132,0.00048594785,0.0041133547,0.016169734,0.018505707,0.029622298],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908125,0.00027008395,0.000104461826,0.00018582666,0.00029189143,0.00006643811],"domain_scores_gemma":[0.9929635,0.0046374598,0.0003293655,0.00033646438,0.0013255354,0.00040782779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002469808,0.0029003404,0.0013236388,0.0016290738,0.00070662476,0.001955655,0.002826962,0.0013494093,0.10806415],"category_scores_gemma":[0.012952,0.001736898,0.001883497,0.0012060117,0.00031224528,0.0026483347,0.0020266068,0.0018553998,0.035203654],"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":[0.00055010314,0.00021399648,0.003906873,0.0012508135,0.00016465162,0.0006953233,0.0006328591,0.010323188,0.0073069287,0.004749896,0.72607297,0.24413227],"study_design_scores_gemma":[0.0008070765,0.00031039937,0.012725585,0.0010030647,0.0001255181,0.0015305926,0.0005371773,0.1655718,0.011458231,0.025730742,0.779869,0.00033082606],"about_ca_topic_score_codex":0.0061925794,"about_ca_topic_score_gemma":0.00835598,"teacher_disagreement_score":0.10806415,"about_ca_system_score_codex":0.000991898,"about_ca_system_score_gemma":0.0013200678,"threshold_uncertainty_score":0.36151057},"labels":[],"label_agreement":null},{"id":"W4391512641","doi":"10.1016/j.lana.2024.100680","title":"Integrating the results of at-home diagnostic testing with public health surveillance to better track COVID-19 and future public health threats","year":2024,"lang":"en","type":"article","venue":"The Lancet Regional Health - Americas","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research; Canada Research Chairs","keywords":"Coronavirus disease 2019 (COVID-19); Public health; Public health surveillance; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Track (disk drive); Environmental health; Pandemic; Medicine; Business; Virology; Computer science; Outbreak; Nursing; Infectious disease (medical specialty)","score_opus":0.16724140495471315,"score_gpt":0.3955596701362548,"score_spread":0.22831826518154166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391512641","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":[0.23323725,0.04512435,0.2348486,0.15609537,0.01811911,0.002915152,0.16697934,0.0060366555,0.1366441],"genre_scores_gemma":[0.71685874,0.013041744,0.15644602,0.04056625,0.004097461,0.0012708586,0.060575772,0.0005912722,0.0065518683],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9831473,0.009057648,0.0018708399,0.0019143885,0.003078465,0.0009312796],"domain_scores_gemma":[0.9240046,0.02917375,0.013237697,0.010490852,0.019582333,0.003510803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022851722,0.0012216822,0.0016314767,0.00546747,0.00044588777,0.003890328,0.002940488,0.0019073308,0.0039584106],"category_scores_gemma":[0.053775065,0.00076294,0.0017319748,0.0036426608,0.00067281566,0.0030148246,0.003063769,0.0034044352,0.0017420638],"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":[0.00028414757,0.0005103306,0.5978913,0.0029385148,0.0012690068,0.0001897076,0.00083520793,0.005567803,0.00257134,0.0063795894,0.1396634,0.24189962],"study_design_scores_gemma":[0.00028043956,0.0011365253,0.6224202,0.007801401,0.0015952297,0.0010149437,0.0034221602,0.04156345,0.012770025,0.041628268,0.265968,0.00039931398],"about_ca_topic_score_codex":0.031077102,"about_ca_topic_score_gemma":0.03916758,"teacher_disagreement_score":0.031077102,"about_ca_system_score_codex":0.002348128,"about_ca_system_score_gemma":0.005175145,"threshold_uncertainty_score":0.12085289},"labels":[],"label_agreement":null},{"id":"W4391617778","doi":"10.32920/25169585","title":"Prospective Space-Time Cluster Detection of Emerging SARS-CoV-2 Outbreaks in the City of Toronto","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Public health; Outbreak; Cluster (spacecraft); Logistic regression; Geography; Coronavirus disease 2019 (COVID-19); Agency (philosophy); Distribution (mathematics); Marital status; Space (punctuation); Environmental health; Demography; Medicine; Disease; Infectious disease (medical specialty); Computer science; Sociology; Virology","score_opus":0.017681234564663095,"score_gpt":0.309964890568741,"score_spread":0.2922836560040779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391617778","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":[0.9962692,0.00007379246,0.0003772825,0.00009207482,0.0000048056313,0.000013243931,0.0025447342,0.000013693794,0.00061128713],"genre_scores_gemma":[0.9962591,0.000045498316,0.0003602154,0.0000060045204,0.000002385904,0.0000069331113,0.0027530359,0.0000023050466,0.0005646283],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99977154,0.000039625662,0.000013013083,0.000058043606,0.000050927345,0.00006697007],"domain_scores_gemma":[0.9988513,0.00020856594,0.00027932905,0.00008764368,0.00037312077,0.00020001126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041699465,0.00023134559,0.00015761591,0.0006477846,0.00045559704,0.00065106625,0.0003315194,0.00016823596,0.0011023848],"category_scores_gemma":[0.0024745339,0.0001321551,0.00025286354,0.00090991386,0.00023457108,0.00014744935,0.00048054452,0.00021993055,0.00019975807],"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":[0.00011118819,0.000012261258,0.9929126,0.000018014394,0.000031770393,0.00010221648,0.00035454825,0.0017934812,0.00051741966,0.00013079461,0.0010732529,0.0029425996],"study_design_scores_gemma":[0.0000035083572,0.000022682776,0.9911306,0.000010768415,0.000013993176,0.000030440251,0.0007540119,0.007203651,0.00027850404,0.000022563638,0.00052256504,0.0000066963757],"about_ca_topic_score_codex":0.78097063,"about_ca_topic_score_gemma":0.82677907,"teacher_disagreement_score":0.21902937,"about_ca_system_score_codex":0.002711868,"about_ca_system_score_gemma":0.002885742,"threshold_uncertainty_score":0.44063854},"labels":[],"label_agreement":null},{"id":"W4391618149","doi":"10.32920/25169585.v1","title":"Prospective Space-Time Cluster Detection of Emerging SARS-CoV-2 Outbreaks in the City of Toronto","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Public health; Outbreak; Cluster (spacecraft); Logistic regression; Coronavirus disease 2019 (COVID-19); Geography; Agency (philosophy); Environmental health; Marital status; Distribution (mathematics); Public health surveillance; Medicine; Demography; Disease; Infectious disease (medical specialty); Virology; Computer science; Sociology","score_opus":0.017681234564663095,"score_gpt":0.309964890568741,"score_spread":0.2922836560040779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391618149","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":[0.9962692,0.00007379246,0.0003772825,0.00009207482,0.0000048056313,0.000013243931,0.0025447342,0.000013693794,0.00061128713],"genre_scores_gemma":[0.9962591,0.000045498316,0.0003602154,0.0000060045204,0.000002385904,0.0000069331113,0.0027530359,0.0000023050466,0.0005646283],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99977154,0.000039625662,0.000013013083,0.000058043606,0.000050927345,0.00006697007],"domain_scores_gemma":[0.9988513,0.00020856594,0.00027932905,0.00008764368,0.00037312077,0.00020001126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041699465,0.00023134559,0.00015761591,0.0006477846,0.00045559704,0.00065106625,0.0003315194,0.00016823596,0.0011023848],"category_scores_gemma":[0.0024745339,0.0001321551,0.00025286354,0.00090991386,0.00023457108,0.00014744935,0.00048054452,0.00021993055,0.00019975807],"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":[0.00011118819,0.000012261258,0.9929126,0.000018014394,0.000031770393,0.00010221648,0.00035454825,0.0017934812,0.00051741966,0.00013079461,0.0010732529,0.0029425996],"study_design_scores_gemma":[0.0000035083572,0.000022682776,0.9911306,0.000010768415,0.000013993176,0.000030440251,0.0007540119,0.007203651,0.00027850404,0.000022563638,0.00052256504,0.0000066963757],"about_ca_topic_score_codex":0.78097063,"about_ca_topic_score_gemma":0.82677907,"teacher_disagreement_score":0.21902937,"about_ca_system_score_codex":0.002711868,"about_ca_system_score_gemma":0.002885742,"threshold_uncertainty_score":0.44063854},"labels":[],"label_agreement":null},{"id":"W4391792885","doi":"10.2196/54340","title":"Responding to the Return of Influenza in the United States by Applying Centers for Disease Control and Prevention Surveillance, Analysis, and Modeling to Inform Understanding of Seasonal Influenza","year":2024,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Seasonal influenza; Coronavirus disease 2019 (COVID-19); Pandemic; Preprint; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Pandemic influenza; Data science; Computer science; Environmental health; Risk analysis (engineering); Medicine; Virology; Infectious disease (medical specialty); Disease; Outbreak; World Wide Web","score_opus":0.05514442290467831,"score_gpt":0.3635147890382519,"score_spread":0.3083703661335736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391792885","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":[0.10349832,0.09542859,0.41626483,0.30827868,0.006991523,0.0018110494,0.010202118,0.0045822198,0.0529427],"genre_scores_gemma":[0.4963406,0.10145947,0.36215925,0.024701776,0.0037881813,0.00089480064,0.007358998,0.00027731268,0.0030195464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.987105,0.00825664,0.0010829997,0.0009965185,0.002166472,0.00039234277],"domain_scores_gemma":[0.9792317,0.010578701,0.002029122,0.0028305955,0.0046487576,0.00068108016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024851767,0.0010195115,0.00082499924,0.0038290133,0.0007929191,0.004350614,0.0014974019,0.0011664234,0.001251427],"category_scores_gemma":[0.037794296,0.0003737511,0.0014118736,0.0023407182,0.00084289693,0.0031202293,0.0035116938,0.0028721497,0.000584045],"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":[0.00007036131,0.00026672278,0.11310052,0.0024390772,0.001020495,0.00012420499,0.0019984418,0.02025447,0.0028554266,0.018365836,0.06832383,0.77118075],"study_design_scores_gemma":[0.00014577292,0.0010569146,0.0788939,0.013934207,0.0016221757,0.00064168876,0.0119759,0.17940868,0.010503814,0.13446225,0.56691957,0.0004350936],"about_ca_topic_score_codex":0.013754321,"about_ca_topic_score_gemma":0.022754142,"teacher_disagreement_score":0.024851767,"about_ca_system_score_codex":0.0013282557,"about_ca_system_score_gemma":0.006491821,"threshold_uncertainty_score":0.13143027},"labels":[],"label_agreement":null},{"id":"W4391885490","doi":"10.1186/s12913-024-10691-z","title":"Barriers and enablers for implementation of digital-linked diagnostics models at point-of-care in South Africa: stakeholder engagement","year":2024,"lang":"en","type":"article","venue":"BMC Health Services Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Impact","funders":"Universiteit Stellenbosch; University of Pretoria; McMaster University","keywords":"Stakeholder; Health informatics; Digital health; Process management; Stakeholder engagement; Nursing research; Medicine; Psychological intervention; Key (lock); Knowledge management; Health care; Business; Nursing; Computer science; Public health; Public relations; Political science","score_opus":0.13451665124681153,"score_gpt":0.42763365577498125,"score_spread":0.2931170045281697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391885490","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":[0.95858556,0.0009819537,0.004457566,0.025823234,0.00008045584,0.00075912435,0.00014491043,0.000019678304,0.009147571],"genre_scores_gemma":[0.9956892,0.00055136066,0.0020050243,0.00096262,0.000008762535,0.00032167637,0.000024590423,0.0000058977907,0.0004308214],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.97222805,0.02192863,0.0008917484,0.0006199164,0.0014374169,0.002894317],"domain_scores_gemma":[0.97113264,0.022443563,0.002161009,0.00066187186,0.0014085274,0.0021922772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030675322,0.00042059584,0.0004533367,0.0008205168,0.0044362266,0.004126574,0.001260469,0.0012204284,0.0036730168],"category_scores_gemma":[0.046212275,0.00040087735,0.0005411493,0.0009412595,0.0029430576,0.0039919843,0.01111306,0.0022101377,0.00018193465],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053150504,0.0005786766,0.10659717,0.0031409396,0.00014962965,0.0039666067,0.71837896,0.001260249,0.0042775213,0.0207928,0.0040804506,0.13624546],"study_design_scores_gemma":[0.000106001244,0.0007838566,0.04403922,0.0037591725,0.00012625422,0.0009184393,0.8865661,0.0023409238,0.0022308351,0.009671085,0.04935775,0.0001002577],"about_ca_topic_score_codex":0.009929934,"about_ca_topic_score_gemma":0.011241333,"teacher_disagreement_score":0.030675322,"about_ca_system_score_codex":0.0056668175,"about_ca_system_score_gemma":0.016787954,"threshold_uncertainty_score":0.16222852},"labels":[],"label_agreement":null},{"id":"W4391995172","doi":"10.3390/curroncol31030084","title":"Lung Cancer Prevalence in Virginia: A Spatial Zipcode-Level Analysis via INLA","year":2024,"lang":"en","type":"article","venue":"Current Oncology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Cancer Institute; National Institutes of Health","keywords":"Covariate; Demography; Poisson regression; Overdispersion; Population; Statistics; Medicine; Geography; Gerontology; Mathematics","score_opus":0.06959483743361028,"score_gpt":0.443818454746742,"score_spread":0.3742236173131317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391995172","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":[0.9703921,0.0003130997,0.0036083655,0.00018239602,0.000021413833,0.000066270215,0.023700833,0.00013146053,0.001584045],"genre_scores_gemma":[0.98423094,0.00010317522,0.0024388894,0.000043219035,0.000017958033,0.00010528464,0.012554771,0.000035536195,0.00047026816],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99820316,0.00068929035,0.00013640642,0.00046225224,0.00024882462,0.00026004005],"domain_scores_gemma":[0.9957944,0.0013953394,0.0012723425,0.0005142822,0.0007637361,0.0002598565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016387017,0.00024770925,0.000482455,0.002743439,0.0005704418,0.0012561465,0.001047399,0.00034121028,0.0021354987],"category_scores_gemma":[0.006375204,0.00021780509,0.0011948113,0.0042593125,0.00029214862,0.00039864075,0.0015412427,0.0005969415,0.0005388529],"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":[0.000063111416,0.000022686225,0.9948461,0.000028352104,0.0001980581,0.00003224615,0.000109148714,0.0007190486,0.00007436421,0.00016597813,0.0012406352,0.0025003424],"study_design_scores_gemma":[0.00001013051,0.000072865005,0.9841728,0.00005625875,0.00019398304,0.00017497854,0.00075697957,0.009672052,0.00016338345,0.0003014505,0.0044081043,0.000016930115],"about_ca_topic_score_codex":0.13455161,"about_ca_topic_score_gemma":0.12582871,"teacher_disagreement_score":0.13455161,"about_ca_system_score_codex":0.0014462535,"about_ca_system_score_gemma":0.0017658124,"threshold_uncertainty_score":0.26753688},"labels":[],"label_agreement":null},{"id":"W4392094056","doi":"10.1016/j.idm.2024.02.008","title":"Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report","year":2024,"lang":"en","type":"review","venue":"Infectious Disease Modelling","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada); Sanofi (Canada); University of Guelph; York University; BlueDot (Canada); University of Manitoba","funders":"Hospices Civils de Lyon","keywords":"Preparedness; Pandemic; Multidisciplinary approach; Excellence; Coronavirus disease 2019 (COVID-19); Political science; Public relations; Medicine; Data science; Infectious disease (medical specialty); Computer science; Disease","score_opus":0.07029143524030602,"score_gpt":0.34684228481068213,"score_spread":0.2765508495703761,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392094056","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":[0.00016612082,0.9919625,0.00037299312,0.0054339506,0.00093969516,0.000008626347,0.000022473048,0.0000061857204,0.0010874354],"genre_scores_gemma":[0.0026254903,0.99342537,0.00069921295,0.0022133656,0.00044532394,0.000021888018,0.000042948923,0.000007783415,0.0005185612],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99825245,0.0009314967,0.0001630971,0.00015576821,0.0003669188,0.00013019242],"domain_scores_gemma":[0.9951816,0.0036535673,0.00023720157,0.000080405545,0.0006032849,0.00024401801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067865397,0.0010206467,0.0012063369,0.0021143307,0.00059734157,0.0026634182,0.0010531573,0.0026592175,0.0022637404],"category_scores_gemma":[0.006748006,0.00046357207,0.001221617,0.0025452115,0.0010550247,0.0026047025,0.0020023428,0.004839922,0.00080789725],"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":[0.00015174877,0.00012323777,0.00023577912,0.044782847,0.0003004848,0.00045525306,0.0020132428,0.0020904907,0.00089608616,0.044996124,0.11295038,0.79100424],"study_design_scores_gemma":[0.000016663083,0.00007589393,0.00035133245,0.016462382,0.00011263645,0.00030991025,0.0005609283,0.00017470366,0.00027328523,0.0064727985,0.97515565,0.000033874858],"about_ca_topic_score_codex":0.0030142316,"about_ca_topic_score_gemma":0.006430381,"teacher_disagreement_score":0.0067865397,"about_ca_system_score_codex":0.0019606787,"about_ca_system_score_gemma":0.005040191,"threshold_uncertainty_score":0.035891056},"labels":[],"label_agreement":null},{"id":"W4392370559","doi":"10.1002/sam.11668","title":"Marginal clustered multistate models for longitudinal progressive processes with informative cluster size","year":2024,"lang":"en","type":"article","venue":"Statistical Analysis and Data Mining The ASA Data Science Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"","keywords":"Resampling; Inference; Cluster (spacecraft); Computer science; Statistics; Data mining; Process (computing); Econometrics; Artificial intelligence; Mathematics","score_opus":0.0727690246232361,"score_gpt":0.38694199091094134,"score_spread":0.31417296628770525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392370559","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":[0.062228292,0.000355017,0.9351175,0.00068505754,0.0000716671,0.00017669551,0.000461199,0.00022995361,0.00067469594],"genre_scores_gemma":[0.83237666,0.00047862402,0.15967183,0.0003428565,0.00013680145,0.00073583913,0.0011734591,0.00012391567,0.0049599702],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9942151,0.003345849,0.0002310043,0.001401948,0.00044289214,0.0003632725],"domain_scores_gemma":[0.9487565,0.040121846,0.003570903,0.0035851768,0.0030809145,0.0008847097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023020994,0.0010333379,0.0021872004,0.002085357,0.0009789724,0.0024998446,0.004688284,0.0020436468,0.004307344],"category_scores_gemma":[0.05482862,0.00093773665,0.0028157749,0.0019993351,0.00336074,0.003019294,0.0029391663,0.0036859787,0.00049658614],"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":[0.00030122916,0.0001332618,0.018704083,0.00019465349,0.00048030354,0.0002525447,0.00074638054,0.7340716,0.0006175365,0.2183296,0.002304114,0.023864713],"study_design_scores_gemma":[0.000021119515,0.00003006381,0.0014553306,0.000025141588,0.000043984746,0.000018834904,0.000056560002,0.9503825,0.00014383816,0.047328193,0.00047051912,0.000023860353],"about_ca_topic_score_codex":0.013356085,"about_ca_topic_score_gemma":0.011313359,"teacher_disagreement_score":0.023020994,"about_ca_system_score_codex":0.0023039829,"about_ca_system_score_gemma":0.0021247368,"threshold_uncertainty_score":0.12174815},"labels":[],"label_agreement":null},{"id":"W4392478835","doi":"10.2196/51880","title":"Now Is the Time to Strengthen Government-Academic Data Infrastructures to Jump-Start Future Public Health Crisis Response","year":2024,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Data sharing; Public relations; Context (archaeology); Scarcity; Public health; Health care; Political science; Business; Medicine; Economics","score_opus":0.04255418476019374,"score_gpt":0.3392807931401172,"score_spread":0.2967266083799235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392478835","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014805759,0.008842234,0.062489428,0.86632425,0.0055916463,0.00044157356,0.0027286236,0.0021757816,0.036600765],"genre_scores_gemma":[0.43817848,0.026538324,0.28749362,0.19545962,0.010406451,0.0021943108,0.013571451,0.002418491,0.023739286],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9383648,0.039345082,0.0033470374,0.0052161557,0.007268765,0.0064581516],"domain_scores_gemma":[0.689931,0.11438606,0.022352833,0.0770438,0.044074457,0.052211814],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.06271355,0.0011526346,0.0012858516,0.0048263124,0.01001005,0.030692281,0.008519879,0.010812475,0.031614546],"category_scores_gemma":[0.1449554,0.00091510307,0.0019881052,0.01242997,0.009507346,0.07384977,0.038987245,0.015948635,0.017166167],"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":[0.00025465694,0.000430659,0.016830835,0.0021541412,0.0002254928,0.00064129796,0.017683337,0.0028615175,0.0018237146,0.36837295,0.3388378,0.2498836],"study_design_scores_gemma":[0.000032410226,0.000065370565,0.0024672642,0.0013196274,0.000029082994,0.00019961296,0.023349082,0.0012241276,0.0005867866,0.14181045,0.8288185,0.0000976529],"about_ca_topic_score_codex":0.007018296,"about_ca_topic_score_gemma":0.0064998954,"teacher_disagreement_score":0.9693077,"about_ca_system_score_codex":0.012267374,"about_ca_system_score_gemma":0.056599773,"threshold_uncertainty_score":0.33166492},"labels":[],"label_agreement":null},{"id":"W4392796661","doi":"10.1038/s41598-024-56360-3","title":"Author Correction: Inferring disease subtypes from clusters in explanation space","year":2024,"lang":"en","type":"erratum","venue":"Scientific Reports","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute; Montreal Neurological Institute and Hospital","funders":"","keywords":"Disease; Space (punctuation); Computer science; Computational biology; Data science; Biology; Medicine; Pathology","score_opus":0.017452666499052333,"score_gpt":0.28713310935376696,"score_spread":0.2696804428547146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392796661","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00045226482,0.0008352725,0.008520278,0.1056217,0.8678783,0.00006500479,0.007827932,0.002151608,0.0066476595],"genre_scores_gemma":[0.0568764,0.008640378,0.05685732,0.15037063,0.25734368,0.00069833745,0.022989696,0.016449371,0.4297742],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9918795,0.0019888715,0.0012551504,0.0013458724,0.003121843,0.00040879592],"domain_scores_gemma":[0.9029747,0.03813004,0.0025383073,0.0072526196,0.04729215,0.0018121169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008262836,0.0023187029,0.001454364,0.00468521,0.003961141,0.0048226267,0.0034278575,0.0043439264,0.11978108],"category_scores_gemma":[0.17322499,0.0012162472,0.0018862294,0.004150112,0.0026814458,0.0029817796,0.003348779,0.009726437,0.042074766],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019901925,0.0000028875984,0.00007641144,0.00006209043,0.000011797764,0.00012482815,0.00005235194,0.000050191047,0.000028619275,0.0011964656,0.99418396,0.004190485],"study_design_scores_gemma":[0.000069332316,0.000013058987,0.0004947921,0.0005314241,0.00008057521,0.000915277,0.00020143794,0.0008353305,0.00058123475,0.0068487697,0.98936886,0.000060007842],"about_ca_topic_score_codex":0.031685617,"about_ca_topic_score_gemma":0.036894616,"teacher_disagreement_score":0.11978108,"about_ca_system_score_codex":0.0039981487,"about_ca_system_score_gemma":0.008398554,"threshold_uncertainty_score":0.4007076},"labels":[],"label_agreement":null},{"id":"W4392861584","doi":"10.22541/au.171052397.79869572/v1","title":"Development and validation of a geographic search filter for MEDLINE (PubMed) to identify studies conducted in Germany","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Health Services and Policy Research","funders":"Deutsche Forschungsgemeinschaft","keywords":"MEDLINE; Data science; Computer science; Medicine; Information retrieval; Political science","score_opus":0.11257872658561807,"score_gpt":0.3996769114974192,"score_spread":0.2870981849118011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392861584","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33435354,0.048785113,0.15404049,0.0068457057,0.0014594974,0.055927545,0.37477443,0.007745179,0.016068535],"genre_scores_gemma":[0.32796127,0.0090021305,0.4232786,0.0015844362,0.0004071579,0.043992225,0.19005091,0.0008321915,0.0028910765],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9746727,0.00545063,0.013315323,0.003182147,0.002828366,0.0005508173],"domain_scores_gemma":[0.82534146,0.12309562,0.023745231,0.007621964,0.017801123,0.0023945854],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.042342655,0.0018989579,0.004577385,0.04085272,0.0018671217,0.005127666,0.002872095,0.00207965,0.009363606],"category_scores_gemma":[0.15469627,0.0007580818,0.0049513723,0.019946361,0.00095098274,0.0032611147,0.004482861,0.00077517604,0.0019516625],"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":[0.0073914146,0.0007145833,0.1758523,0.22993968,0.01703292,0.003405931,0.013027065,0.004064786,0.030790212,0.010394826,0.06788069,0.43950567],"study_design_scores_gemma":[0.004636971,0.0022308948,0.54912895,0.048512843,0.04308417,0.0036412682,0.008080266,0.012432963,0.018815987,0.008910208,0.2997591,0.0007663953],"about_ca_topic_score_codex":0.011486564,"about_ca_topic_score_gemma":0.02682707,"teacher_disagreement_score":0.95765734,"about_ca_system_score_codex":0.0023714064,"about_ca_system_score_gemma":0.015311452,"threshold_uncertainty_score":0.22393203},"labels":[],"label_agreement":null},{"id":"W4393007443","doi":"10.2196/52191","title":"The Role of Trust as a Driver of Private-Provider Participation in Disease Surveillance: Cross-Sectional Survey From Nigeria","year":2024,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Public health surveillance; Public health; Medicine; Environmental health; Disease surveillance; Health facility; Health care; Family medicine; Medical emergency; Nursing; Population; Health services","score_opus":0.025500635691832505,"score_gpt":0.35123844063136805,"score_spread":0.3257378049395355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393007443","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":[0.9995634,0.00004789868,0.000026120586,0.00005480096,0.0000012616119,0.000011464305,0.000067308494,3.676791e-7,0.00022734239],"genre_scores_gemma":[0.9997787,0.00006900725,0.00003858631,0.00001987259,0.0000010608184,0.0000078621115,0.00003222906,3.436722e-7,0.000052350562],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99909747,0.00035936388,0.00013785345,0.00008049691,0.00014308187,0.00018185485],"domain_scores_gemma":[0.99587375,0.0011375891,0.0019498733,0.00016474437,0.00033939246,0.0005346796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00201349,0.00013556551,0.00023651954,0.00045091394,0.00079936534,0.00087525503,0.00029568438,0.00038691305,0.0014794933],"category_scores_gemma":[0.004489597,0.0003988816,0.00021285689,0.0005929156,0.00044225602,0.0006068511,0.0007175547,0.0007272291,0.00015279208],"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":[0.00001182498,0.000053826752,0.9966627,0.000017380618,0.000005330963,0.000066121276,0.0023225276,0.000006672308,0.0000666887,0.000021632322,0.000045924167,0.0007192937],"study_design_scores_gemma":[0.0000020535952,0.000094919116,0.98799545,0.000052450818,0.000010216929,0.00021002429,0.011186605,0.0001364716,0.000048349597,0.000017965365,0.00024179135,0.0000036365468],"about_ca_topic_score_codex":0.020903336,"about_ca_topic_score_gemma":0.028443096,"teacher_disagreement_score":0.020903336,"about_ca_system_score_codex":0.0007181071,"about_ca_system_score_gemma":0.0011635188,"threshold_uncertainty_score":0.041563332},"labels":[],"label_agreement":null},{"id":"W4393151768","doi":"10.31219/osf.io/8tyqn","title":"Analyzing differences between spatial exposure estimation methods: A case study of outdoor food and beverage advertising in London, Canada","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Wilfrid Laurier University; Athabasca University; Western University","funders":"Canadian Institutes of Health Research; Children's Health Foundation; Children's Health Research Institute","keywords":"Estimation; Advertising; Geography; Business; Economics","score_opus":0.029315347869841586,"score_gpt":0.33770953584989144,"score_spread":0.30839418798004986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393151768","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":[0.99562824,0.00046073523,0.0006770935,0.00040907573,0.000005317544,0.00013220806,0.00035849222,0.000009468415,0.0023194002],"genre_scores_gemma":[0.9955509,0.00060274027,0.0020313864,0.00012667266,0.0000041067738,0.00004928272,0.00022939948,0.00001718697,0.0013882951],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9943809,0.0021151607,0.0003213398,0.00052568305,0.0016303558,0.0010265707],"domain_scores_gemma":[0.9901097,0.0040185438,0.0010221144,0.00052801403,0.0037602314,0.0005614471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045374045,0.00053542445,0.0006844074,0.001528099,0.0043963315,0.002450959,0.0017287289,0.0009936005,0.0012405851],"category_scores_gemma":[0.015261315,0.0004885675,0.0005772141,0.00488981,0.00202636,0.00071044924,0.0016994502,0.0008766685,0.0001717393],"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":[0.00036779774,0.00032425227,0.8644741,0.00041173524,0.0002041813,0.005955972,0.080731384,0.0021866164,0.0008133053,0.0015342366,0.0017490732,0.04124733],"study_design_scores_gemma":[0.00006766188,0.00040659707,0.7993044,0.0004234673,0.00022555166,0.0016002764,0.17923573,0.0071236533,0.0013315044,0.00036541614,0.009767165,0.00014867546],"about_ca_topic_score_codex":0.99158704,"about_ca_topic_score_gemma":0.9956441,"teacher_disagreement_score":0.036260594,"about_ca_system_score_codex":0.036260594,"about_ca_system_score_gemma":0.031814676,"threshold_uncertainty_score":0.26309025},"labels":[],"label_agreement":null},{"id":"W4393152703","doi":"10.3329/dujs.v72i1.71247","title":"Proposing a New Estimator of Overdispersion for Multinomial Data","year":2024,"lang":"en","type":"article","venue":"Dhaka University Journal of Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Overdispersion; Multinomial distribution; Estimator; Mathematics; Statistics; Econometrics; Computer science; Count data; Poisson distribution","score_opus":0.04558003620295655,"score_gpt":0.3378817903689092,"score_spread":0.29230175416595267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393152703","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":[0.0036137544,0.00030075572,0.99544024,0.00013066556,0.0000880332,0.000038310423,0.00007746331,0.00015216345,0.00015849363],"genre_scores_gemma":[0.12816286,0.0008034599,0.86772686,0.00041477382,0.00048280766,0.0004030306,0.00085631345,0.00015426708,0.0009956427],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99015874,0.0049787825,0.0007033453,0.0021331988,0.0017373689,0.00028852944],"domain_scores_gemma":[0.9449125,0.040429913,0.0042470554,0.004059342,0.0056398767,0.0007113006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018676212,0.0010827581,0.0018835247,0.003042992,0.00051830395,0.0027014355,0.0032508052,0.0020730223,0.0012791163],"category_scores_gemma":[0.07763131,0.00063732336,0.00133837,0.0033354214,0.0018354729,0.0038707696,0.0025092938,0.00295535,0.00058089395],"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":[0.00030446902,0.0004177781,0.10489562,0.0012466569,0.0017222561,0.0006644427,0.00093683304,0.18717879,0.014369898,0.11643847,0.0075478186,0.56427705],"study_design_scores_gemma":[0.00010574282,0.00036699738,0.014445768,0.00025656878,0.00023026441,0.0009688699,0.0002505991,0.9049471,0.0048306384,0.06287083,0.010513594,0.00021298982],"about_ca_topic_score_codex":0.0016314533,"about_ca_topic_score_gemma":0.0016379341,"teacher_disagreement_score":0.018676212,"about_ca_system_score_codex":0.0008753756,"about_ca_system_score_gemma":0.0020771557,"threshold_uncertainty_score":0.09877038},"labels":[],"label_agreement":null},{"id":"W4393732063","doi":"10.5281/zenodo.8284426","title":"Supplementary Data for \"Identification of Neighborhood Hotspots via the Cumulative Hazard Index: Results from a Community-Partnered Low-cost Sensor Deployment\"","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Strathcona Community Hospital; University of British Columbia","funders":"","keywords":"Software deployment; Index (typography); Identification (biology); Hazard; Computer science; Statistics; Data mining; Environmental science; Geography; Mathematics; World Wide Web","score_opus":0.08784549061034376,"score_gpt":0.33072272211980447,"score_spread":0.24287723150946072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393732063","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00036757882,0.000016820433,0.0007495087,0.00015349475,0.00013091043,0.000042585798,0.9960311,0.0007050127,0.0018029169],"genre_scores_gemma":[0.006818423,0.00010661886,0.007501525,0.00045436237,0.000070714406,0.00056869985,0.97701275,0.0013434993,0.0061233277],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989945,0.00014307175,0.00014096638,0.00018216555,0.000411503,0.00012775211],"domain_scores_gemma":[0.98784304,0.0053510894,0.00083667523,0.0011684187,0.0043373182,0.00046342643],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018092041,0.001023879,0.0008059811,0.0019986085,0.0008699868,0.0018053795,0.0020278562,0.001048728,0.5813716],"category_scores_gemma":[0.016983893,0.000639479,0.0010357788,0.004394391,0.00031987138,0.0020833386,0.0018171907,0.0012895438,0.14048198],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005499947,0.00003392626,0.0015918016,0.00052910886,0.000024509194,0.00003309633,0.000041686693,0.0013066871,0.00013822356,0.00072224083,0.99088895,0.0046347873],"study_design_scores_gemma":[0.000738184,0.00007455828,0.028231433,0.0009687485,0.000090662725,0.0001705377,0.00069756445,0.0064532077,0.0012295562,0.008945591,0.9522701,0.00012985758],"about_ca_topic_score_codex":0.038778715,"about_ca_topic_score_gemma":0.0708828,"teacher_disagreement_score":0.5813716,"about_ca_system_score_codex":0.0013406408,"about_ca_system_score_gemma":0.0031515544,"threshold_uncertainty_score":0.59712255},"labels":[],"label_agreement":null},{"id":"W4393791066","doi":"10.5281/zenodo.4434971","title":"A Geo-Tagged COVID-19 Twitter Dataset for 10 North American Metropolitan Areas","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Metropolitan area; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Geography; Cartography; Virology; Archaeology; Medicine; Outbreak; Infectious disease (medical specialty)","score_opus":0.055115464838458895,"score_gpt":0.3239826475311255,"score_spread":0.2688671826926666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393791066","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020813965,0.000086259475,0.00021435866,0.00018549587,0.00006628051,0.00003592512,0.99574226,0.00061161735,0.0009763662],"genre_scores_gemma":[0.0014108439,0.000045167733,0.00038159522,0.00004439036,0.000009958892,0.00006589106,0.9974469,0.000025052917,0.0005703073],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994254,0.00009806518,0.000061109364,0.00014477097,0.00017847921,0.00009218628],"domain_scores_gemma":[0.99907553,0.00015984221,0.00008781464,0.00018174665,0.00034572574,0.00014935494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005607784,0.0016298665,0.0008217319,0.0024061003,0.0009094302,0.0009882405,0.0015609716,0.0013836187,0.01107686],"category_scores_gemma":[0.0019232185,0.00039660203,0.0007714825,0.003995091,0.00033873666,0.0009076072,0.0014300839,0.0011596306,0.016910838],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008251372,0.00006488465,0.0034298454,0.0003947579,0.00003791577,0.00007631301,0.000059583417,0.0012734815,0.0005911291,0.0005096948,0.98833746,0.0051424457],"study_design_scores_gemma":[0.0001981232,0.00006648401,0.038625263,0.00027322627,0.00006492033,0.00021495266,0.0005302985,0.010528288,0.0017960626,0.0017184671,0.94589335,0.00009057404],"about_ca_topic_score_codex":0.09605044,"about_ca_topic_score_gemma":0.17775074,"teacher_disagreement_score":0.09605044,"about_ca_system_score_codex":0.0016330532,"about_ca_system_score_gemma":0.0021172483,"threshold_uncertainty_score":0.19098276},"labels":[],"label_agreement":null},{"id":"W4394012209","doi":"10.1136/bmjopen-2023-079776","title":"Public health surveillance through community health workers: a scoping review of evidence from 25 low-income and middle-income countries","year":2024,"lang":"en","type":"review","venue":"BMJ Open","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Public health; Medicine; Global health; Disease surveillance; Public health surveillance; Environmental health; Health promotion; Pandemic; Community engagement; Disease; Public relations; Nursing; Infectious disease (medical specialty); Political science; Coronavirus disease 2019 (COVID-19); Pathology","score_opus":0.4772666812737599,"score_gpt":0.5289273700072792,"score_spread":0.05166068873351931,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394012209","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":[0.0013072036,0.9957902,0.0002206734,0.00056843174,0.00011833354,0.0005374797,0.000660719,0.000008899001,0.0007880815],"genre_scores_gemma":[0.012407147,0.9844599,0.0008521491,0.0006042313,0.000059237012,0.0010818637,0.00045725537,0.0000072684575,0.00007095052],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9743163,0.008639635,0.0107502965,0.0015631306,0.0040346165,0.0006960783],"domain_scores_gemma":[0.8786703,0.09397451,0.013396522,0.001413071,0.011555409,0.0009901498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029034717,0.0017218916,0.0062767034,0.028509792,0.001526228,0.005180546,0.0034940238,0.0030845995,0.0045833136],"category_scores_gemma":[0.109250836,0.0016172496,0.0068556955,0.02699397,0.001612143,0.004648138,0.0038983466,0.0018894054,0.0005241143],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011508303,0.000030045547,0.0012309701,0.9225356,0.0035172629,0.00014685468,0.00094062136,0.000091886075,0.00008365253,0.00031153837,0.0017840514,0.06921248],"study_design_scores_gemma":[0.000039633294,0.000080937236,0.002105587,0.97552186,0.009524576,0.00012121486,0.000708293,0.0000288573,0.00009272441,0.00010596916,0.011651651,0.000018742256],"about_ca_topic_score_codex":0.017974911,"about_ca_topic_score_gemma":0.04229061,"teacher_disagreement_score":0.029034717,"about_ca_system_score_codex":0.0072204825,"about_ca_system_score_gemma":0.033151038,"threshold_uncertainty_score":0.15355211},"labels":[],"label_agreement":null},{"id":"W4394074396","doi":"10.6084/m9.figshare.14880710","title":"Additional file 1 of Development and validation of an automated emergency department-based syndromic surveillance system to enhance public health surveillance in Yukon: a lower-resourced and remote setting","year":2021,"lang":"en","type":"dataset","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Medical emergency; Emergency department; Public health; Public health surveillance; Computer science; Data science; Database; Medicine; Nursing","score_opus":0.021128800150217116,"score_gpt":0.2977590843399773,"score_spread":0.27663028418976016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394074396","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000108461856,0.000004616736,0.00003232385,0.00001895685,0.0000044489884,0.000013594897,0.9996332,0.000039122006,0.00014532208],"genre_scores_gemma":[0.0021162848,0.000016225213,0.00040163205,0.000046016023,0.000005317899,0.00022030888,0.9963559,0.00005498192,0.0007833343],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993352,0.00011540094,0.00012954364,0.00021126024,0.00010591988,0.00010264599],"domain_scores_gemma":[0.99349385,0.0033877972,0.00039084294,0.000738261,0.0016876004,0.00030165847],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012142608,0.0009107271,0.0008614624,0.0017410467,0.00073330064,0.0013021636,0.0018668338,0.0013265446,0.34162092],"category_scores_gemma":[0.011173426,0.00049000425,0.0010718666,0.0034829692,0.0003325421,0.0008866747,0.00087369105,0.00083888735,0.054452483],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019494412,0.000052254574,0.0064906348,0.0014025386,0.0000837732,0.000050560244,0.00004405592,0.00084483414,0.000078496174,0.00047691338,0.9874241,0.0028568828],"study_design_scores_gemma":[0.0034244992,0.00016071556,0.07656016,0.0018314106,0.00039648524,0.00031241792,0.0005226633,0.0035422244,0.0011094694,0.004486661,0.9074882,0.00016511595],"about_ca_topic_score_codex":0.054349504,"about_ca_topic_score_gemma":0.0911384,"teacher_disagreement_score":0.9456505,"about_ca_system_score_codex":0.0016714464,"about_ca_system_score_gemma":0.003399537,"threshold_uncertainty_score":0.9390978},"labels":[],"label_agreement":null},{"id":"W4394123801","doi":"10.6084/m9.figshare.14036528.v1","title":"Data for Fisman et al., Test-Adjusted Incidence of COVID-19 in Ontario","year":2021,"lang":"en","type":"dataset","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Test (biology); Incidence (geometry); Geography; Statistics; Mathematics; Virology; Medicine; Biology; Outbreak; Internal medicine","score_opus":0.15811136471157428,"score_gpt":0.381256782239952,"score_spread":0.22314541752837774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394123801","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014325295,0.00004803776,0.0000222855,0.000049326118,0.000008700676,0.0000072024727,0.99912804,0.00004748589,0.0005456182],"genre_scores_gemma":[0.0012271524,0.000091384405,0.00012976964,0.000041121337,0.000007970526,0.000041153544,0.9972205,0.000032515356,0.0012084394],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989814,0.000072262,0.00012002325,0.0002283435,0.00038599392,0.0002118901],"domain_scores_gemma":[0.99485874,0.00065514864,0.0007175634,0.0005698988,0.0026379789,0.00056080765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007808904,0.0009758129,0.0009919438,0.0026077763,0.0010756765,0.0015918511,0.001875847,0.0010117327,0.05208314],"category_scores_gemma":[0.010623704,0.00063417834,0.001066568,0.007461124,0.00039417655,0.0007073059,0.0010151104,0.0009507486,0.022070909],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050370418,0.0000074016593,0.0038500568,0.0004207755,0.000034867786,0.00001422828,0.000037254405,0.00020999488,0.000038030583,0.0002941595,0.9933582,0.0016845152],"study_design_scores_gemma":[0.0003166185,0.000015811764,0.06801841,0.00070967386,0.00008977696,0.00005928967,0.00014717937,0.0006702296,0.0001917581,0.000551472,0.92918503,0.0000447155],"about_ca_topic_score_codex":0.90527326,"about_ca_topic_score_gemma":0.9516502,"teacher_disagreement_score":0.09472674,"about_ca_system_score_codex":0.010807534,"about_ca_system_score_gemma":0.02000984,"threshold_uncertainty_score":0.19056916},"labels":[],"label_agreement":null},{"id":"W4394184452","doi":"10.6084/m9.figshare.14331311","title":"Metadata record for: A sub-national real-time epidemiological and vaccination database for the COVID-19 pandemic in Canada","year":2021,"lang":"en","type":"dataset","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Metadata; Pandemic; Coronavirus disease 2019 (COVID-19); Database; 2019-20 coronavirus outbreak; Epidemiology; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); National database; Computer science; World Wide Web; Data science; Virology; Medicine; Outbreak","score_opus":0.1524274087039737,"score_gpt":0.37183122554926124,"score_spread":0.21940381684528754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394184452","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000075196316,0.000017160706,0.000035895868,0.00005132327,0.000009689006,0.000011482894,0.9989637,0.00014155303,0.00069406384],"genre_scores_gemma":[0.0007486623,0.000059241313,0.0002654219,0.000048080703,0.0000043440873,0.000036452675,0.9978472,0.00006260718,0.0009280153],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988681,0.00006722621,0.000107993794,0.0002196352,0.0004587686,0.0002782405],"domain_scores_gemma":[0.992732,0.0008033053,0.00035453445,0.0006795895,0.004492014,0.00093848974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000829426,0.0015148196,0.0012217241,0.0046884953,0.0024038649,0.003186195,0.0029429288,0.0012971233,0.08481249],"category_scores_gemma":[0.008584235,0.00071996235,0.0009908202,0.013252351,0.0007130291,0.0012841828,0.0015806389,0.0016608055,0.0477137],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005353735,0.000010916376,0.0017281303,0.00033043273,0.000019418923,0.000022335276,0.000030588795,0.00036847987,0.00007980915,0.00058309274,0.99441355,0.0023596704],"study_design_scores_gemma":[0.00017800738,0.000012725939,0.020613624,0.0005532603,0.000047988593,0.000054125412,0.00027923632,0.0010464116,0.0006862017,0.001025833,0.97541875,0.000083873536],"about_ca_topic_score_codex":0.9576264,"about_ca_topic_score_gemma":0.96712697,"teacher_disagreement_score":0.08481249,"about_ca_system_score_codex":0.022462644,"about_ca_system_score_gemma":0.049679197,"threshold_uncertainty_score":0.28372604},"labels":[],"label_agreement":null},{"id":"W4394223395","doi":"10.6084/m9.figshare.14036528.v3","title":"Data for Fisman et al., Test-Adjusted Incidence of COVID-19 in Ontario","year":2021,"lang":"en","type":"dataset","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Incidence (geometry); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Test (biology); Virology; Medicine; Mathematics; Geology; Internal medicine; Outbreak; Paleontology","score_opus":0.15811136471157428,"score_gpt":0.381256782239952,"score_spread":0.22314541752837774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394223395","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014325295,0.00004803776,0.0000222855,0.000049326118,0.000008700676,0.0000072024727,0.99912804,0.00004748589,0.0005456182],"genre_scores_gemma":[0.0012271524,0.000091384405,0.00012976964,0.000041121337,0.000007970526,0.000041153544,0.9972205,0.000032515356,0.0012084394],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989814,0.000072262,0.00012002325,0.0002283435,0.00038599392,0.0002118901],"domain_scores_gemma":[0.99485874,0.00065514864,0.0007175634,0.0005698988,0.0026379789,0.00056080765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007808904,0.0009758129,0.0009919438,0.0026077763,0.0010756765,0.0015918511,0.001875847,0.0010117327,0.05208314],"category_scores_gemma":[0.010623704,0.00063417834,0.001066568,0.007461124,0.00039417655,0.0007073059,0.0010151104,0.0009507486,0.022070909],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050370418,0.0000074016593,0.0038500568,0.0004207755,0.000034867786,0.00001422828,0.000037254405,0.00020999488,0.000038030583,0.0002941595,0.9933582,0.0016845152],"study_design_scores_gemma":[0.0003166185,0.000015811764,0.06801841,0.00070967386,0.00008977696,0.00005928967,0.00014717937,0.0006702296,0.0001917581,0.000551472,0.92918503,0.0000447155],"about_ca_topic_score_codex":0.90527326,"about_ca_topic_score_gemma":0.9516502,"teacher_disagreement_score":0.09472674,"about_ca_system_score_codex":0.010807534,"about_ca_system_score_gemma":0.02000984,"threshold_uncertainty_score":0.19056916},"labels":[],"label_agreement":null},{"id":"W4394272854","doi":"10.6084/m9.figshare.14207351","title":"TransAtlas Dataset (June 2019)","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Institute on Minority Health and Health Disparities","keywords":"Computer science","score_opus":0.033681769903411055,"score_gpt":0.3051715472187399,"score_spread":0.27148977731532886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394272854","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008545832,0.000038836337,0.000037991293,0.00007205596,0.000021173204,0.0000058640076,0.99920446,0.00012641597,0.00040767627],"genre_scores_gemma":[0.0005033675,0.000050624567,0.0002129458,0.00005283677,0.000008161653,0.00006311593,0.99855787,0.000041524338,0.0005095377],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993305,0.00015776945,0.00009792933,0.00016687869,0.0001403339,0.000106576575],"domain_scores_gemma":[0.99833494,0.00046287506,0.00015802511,0.00029622222,0.00057306694,0.00017485181],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088879064,0.0011491604,0.0008446802,0.0025559913,0.0007307641,0.0019433174,0.0021172322,0.0013659644,0.068295635],"category_scores_gemma":[0.005663575,0.000423246,0.0011833096,0.0049610916,0.0003094439,0.0009929305,0.001957668,0.0013751659,0.063180886],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003090114,0.0000094143,0.000935458,0.00042445314,0.000026050895,0.000019304367,0.00003494966,0.00020182002,0.00006068046,0.00046286362,0.9963671,0.0014269949],"study_design_scores_gemma":[0.00015376067,0.000017086311,0.0065360446,0.00040088978,0.000034939745,0.000052168758,0.0002099706,0.00053560484,0.00020158313,0.0010327119,0.9907912,0.0000340912],"about_ca_topic_score_codex":0.08287025,"about_ca_topic_score_gemma":0.122055374,"teacher_disagreement_score":0.08287025,"about_ca_system_score_codex":0.0014226184,"about_ca_system_score_gemma":0.0023580012,"threshold_uncertainty_score":0.22847164},"labels":[],"label_agreement":null},{"id":"W4394309049","doi":"10.6084/m9.figshare.14036528.v2","title":"Data for Fisman et al., Test-Adjusted Incidence of COVID-19 in Ontario","year":2021,"lang":"en","type":"dataset","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Test (biology); Incidence (geometry); Geography; Statistics; Mathematics; Virology; Medicine; Biology; Outbreak; Internal medicine","score_opus":0.15811136471157428,"score_gpt":0.381256782239952,"score_spread":0.22314541752837774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394309049","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014325295,0.00004803776,0.0000222855,0.000049326118,0.000008700676,0.0000072024727,0.99912804,0.00004748589,0.0005456182],"genre_scores_gemma":[0.0012271524,0.000091384405,0.00012976964,0.000041121337,0.000007970526,0.000041153544,0.9972205,0.000032515356,0.0012084394],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989814,0.000072262,0.00012002325,0.0002283435,0.00038599392,0.0002118901],"domain_scores_gemma":[0.99485874,0.00065514864,0.0007175634,0.0005698988,0.0026379789,0.00056080765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007808904,0.0009758129,0.0009919438,0.0026077763,0.0010756765,0.0015918511,0.001875847,0.0010117327,0.05208314],"category_scores_gemma":[0.010623704,0.00063417834,0.001066568,0.007461124,0.00039417655,0.0007073059,0.0010151104,0.0009507486,0.022070909],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050370418,0.0000074016593,0.0038500568,0.0004207755,0.000034867786,0.00001422828,0.000037254405,0.00020999488,0.000038030583,0.0002941595,0.9933582,0.0016845152],"study_design_scores_gemma":[0.0003166185,0.000015811764,0.06801841,0.00070967386,0.00008977696,0.00005928967,0.00014717937,0.0006702296,0.0001917581,0.000551472,0.92918503,0.0000447155],"about_ca_topic_score_codex":0.90527326,"about_ca_topic_score_gemma":0.9516502,"teacher_disagreement_score":0.09472674,"about_ca_system_score_codex":0.010807534,"about_ca_system_score_gemma":0.02000984,"threshold_uncertainty_score":0.19056916},"labels":[],"label_agreement":null},{"id":"W4394325863","doi":"10.6084/m9.figshare.16775881","title":"Analysis code and dataset to accompany Salmonella Heidelberg analysis from Ontario 2013","year":2021,"lang":"en","type":"dataset","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Salmonella; Code (set theory); Computer science; Programming language; Biology; Genetics","score_opus":0.037646909341560564,"score_gpt":0.30466787866115386,"score_spread":0.2670209693195933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394325863","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013330857,0.0000149401685,0.0000975643,0.000034806904,0.000006070179,0.000021597985,0.999161,0.000111675654,0.00041899522],"genre_scores_gemma":[0.0015320742,0.000048222966,0.00061924476,0.000048044585,0.0000069422035,0.00028324296,0.9957426,0.00016184027,0.0015576924],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9992494,0.000081723505,0.000107168315,0.00019777947,0.00020412385,0.0001596945],"domain_scores_gemma":[0.9952401,0.0010351863,0.0004736392,0.0006862196,0.002064498,0.00050042925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010411334,0.00083192444,0.0010508627,0.002587406,0.0010920423,0.001267466,0.0016551937,0.00070280704,0.121480025],"category_scores_gemma":[0.008959506,0.00060268317,0.0009670472,0.0055773514,0.000385033,0.0004854468,0.0014480802,0.0008321802,0.037867017],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000105322106,0.000009651245,0.004026552,0.0006466215,0.000049100647,0.000021814478,0.000051660012,0.00030925055,0.00009843509,0.00036356095,0.99156356,0.0027543914],"study_design_scores_gemma":[0.00036136596,0.000019762405,0.042148396,0.0006615614,0.0001130192,0.00006692445,0.00017954048,0.0006154005,0.00034267225,0.0011881391,0.9542548,0.00004832921],"about_ca_topic_score_codex":0.6024148,"about_ca_topic_score_gemma":0.77572733,"teacher_disagreement_score":0.3975852,"about_ca_system_score_codex":0.005762576,"about_ca_system_score_gemma":0.013636072,"threshold_uncertainty_score":0.7998533},"labels":[],"label_agreement":null},{"id":"W4394521151","doi":"10.6084/m9.figshare.14036528","title":"Data for Fisman et al., Test-Adjusted Incidence of COVID-19 in Ontario","year":2021,"lang":"en","type":"dataset","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Incidence (geometry); Test (biology); Geography; Virology; Statistics; Mathematics; Medicine; Biology; Internal medicine; Outbreak","score_opus":0.15811136471157428,"score_gpt":0.381256782239952,"score_spread":0.22314541752837774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394521151","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014325295,0.00004803776,0.0000222855,0.000049326118,0.000008700676,0.0000072024727,0.99912804,0.00004748589,0.0005456182],"genre_scores_gemma":[0.0012271524,0.000091384405,0.00012976964,0.000041121337,0.000007970526,0.000041153544,0.9972205,0.000032515356,0.0012084394],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989814,0.000072262,0.00012002325,0.0002283435,0.00038599392,0.0002118901],"domain_scores_gemma":[0.99485874,0.00065514864,0.0007175634,0.0005698988,0.0026379789,0.00056080765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007808904,0.0009758129,0.0009919438,0.0026077763,0.0010756765,0.0015918511,0.001875847,0.0010117327,0.05208314],"category_scores_gemma":[0.010623704,0.00063417834,0.001066568,0.007461124,0.00039417655,0.0007073059,0.0010151104,0.0009507486,0.022070909],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050370418,0.0000074016593,0.0038500568,0.0004207755,0.000034867786,0.00001422828,0.000037254405,0.00020999488,0.000038030583,0.0002941595,0.9933582,0.0016845152],"study_design_scores_gemma":[0.0003166185,0.000015811764,0.06801841,0.00070967386,0.00008977696,0.00005928967,0.00014717937,0.0006702296,0.0001917581,0.000551472,0.92918503,0.0000447155],"about_ca_topic_score_codex":0.90527326,"about_ca_topic_score_gemma":0.9516502,"teacher_disagreement_score":0.09472674,"about_ca_system_score_codex":0.010807534,"about_ca_system_score_gemma":0.02000984,"threshold_uncertainty_score":0.19056916},"labels":[],"label_agreement":null},{"id":"W4394707403","doi":"10.1016/j.puhe.2024.03.004","title":"Surveillance system integration: reporting the results of a global multicountry survey","year":2024,"lang":"en","type":"article","venue":"Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Agency of Canada","funders":"World Health Organization; Bill and Melinda Gates Foundation","keywords":"Business; Public health; Disease surveillance; Public health surveillance; Environmental health; Notifiable disease; Agency (philosophy); Medicine; Population","score_opus":0.08552649569330406,"score_gpt":0.37802858053607175,"score_spread":0.2925020848427677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394707403","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":[0.52937496,0.0029667243,0.032673918,0.0036592449,0.00065073714,0.015571172,0.3822765,0.002278038,0.030548654],"genre_scores_gemma":[0.8017678,0.0019777126,0.035202675,0.0012794988,0.00030025913,0.023065891,0.13394554,0.0002230663,0.0022375323],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9730358,0.011320981,0.008470097,0.0017172412,0.004249573,0.0012062638],"domain_scores_gemma":[0.9320607,0.009753008,0.030969113,0.00821822,0.017263666,0.001735328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02279554,0.00073861115,0.0008236227,0.0070393593,0.00047679918,0.001347205,0.0011714642,0.00076081924,0.0035550706],"category_scores_gemma":[0.04608993,0.00050829945,0.0012389962,0.009067616,0.00041044556,0.0020739054,0.002730689,0.00085952523,0.0013284083],"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":[0.00010330373,0.00009820913,0.9304678,0.0018729783,0.00046226507,0.000092607224,0.0011866335,0.0006391157,0.000676049,0.0003608892,0.030701805,0.033338364],"study_design_scores_gemma":[0.000020638008,0.00017184003,0.97903866,0.00043725682,0.000083880506,0.00021017288,0.0014157605,0.0010349137,0.000607295,0.00020776749,0.016728261,0.000043462303],"about_ca_topic_score_codex":0.0084551275,"about_ca_topic_score_gemma":0.005033083,"teacher_disagreement_score":0.02279554,"about_ca_system_score_codex":0.0012266805,"about_ca_system_score_gemma":0.003263681,"threshold_uncertainty_score":0.12055582},"labels":[],"label_agreement":null},{"id":"W4394748264","doi":"10.3233/sji-230021","title":"A simulation study of sampling in difficult settings: Statistical superiority of a little-used method","year":2024,"lang":"en","type":"article","venue":"Statistical Journal of the IAOS","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"McMaster University; Impact","funders":"Canadian Institutes of Health Research","keywords":"Cluster sampling; Statistics; Sampling (signal processing); Population; Sample (material); Cluster (spacecraft); Sample size determination; Simple random sample; Systematic sampling; Cluster analysis; Stratified sampling; Mean squared error; Grid; Sampling design; Multistage sampling; Econometrics; Mathematics; Demography; Computer science","score_opus":0.03368988599722543,"score_gpt":0.40606111217402063,"score_spread":0.3723712261767952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394748264","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.750529,0.001652126,0.2395069,0.0020889181,0.0002314549,0.0009913687,0.0006127347,0.00031233142,0.004075058],"genre_scores_gemma":[0.9286032,0.0002600864,0.06955571,0.00027776844,0.000043879285,0.0005318263,0.00028366863,0.000055808046,0.00038817362],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.94233686,0.052678276,0.0009167846,0.0016774114,0.0016595031,0.00073118723],"domain_scores_gemma":[0.3120833,0.6553682,0.008506605,0.014919608,0.007621651,0.0015006426],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.095645815,0.0009129163,0.0015123729,0.0015298292,0.0010344924,0.0013423325,0.0023105675,0.002108647,0.0015656457],"category_scores_gemma":[0.3080935,0.0005943627,0.0014975009,0.0015636248,0.0023680562,0.0027034432,0.0020189593,0.0026828756,0.00014522765],"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":[0.0031988008,0.0008197004,0.060431294,0.0005599208,0.000928002,0.00030120232,0.0010218419,0.8752015,0.0007889909,0.028395116,0.002080027,0.026273526],"study_design_scores_gemma":[0.00044215342,0.0008634846,0.0037991384,0.000084513784,0.00010167366,0.00010213108,0.00021556688,0.984021,0.0004910806,0.0092746625,0.00055812305,0.00004634556],"about_ca_topic_score_codex":0.0117967725,"about_ca_topic_score_gemma":0.006289586,"teacher_disagreement_score":0.9043542,"about_ca_system_score_codex":0.002052454,"about_ca_system_score_gemma":0.0021869682,"threshold_uncertainty_score":0.50582945},"labels":[],"label_agreement":null},{"id":"W4394853134","doi":"10.1162/2e3983f5.0d7d9a7b","title":"Review 3: \"From Exposure to Detection: Unraveling the Digital Signature of Infectious Diseases and Patient Behavior Using Smartwatches and Patient Self-Reporting\"","year":2024,"lang":"en","type":"peer-review","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network","funders":"","keywords":"Medicine; Medical diagnosis; Cohort; Pharyngitis; Smartwatch; Coronavirus disease 2019 (COVID-19); Wearable computer; Intensive care medicine; Infectious disease (medical specialty); Internal medicine; Disease; Computer science; Pathology","score_opus":0.020776588888611287,"score_gpt":0.2961949213300916,"score_spread":0.27541833244148034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394853134","genre_codex":"review","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00031432335,0.941915,0.0011559611,0.02644563,0.021790978,0.00047914687,0.0008896027,0.00009492534,0.0069145034],"genre_scores_gemma":[0.0027055605,0.9755633,0.0009552075,0.009878302,0.006556027,0.00029118435,0.0005635351,0.000054730444,0.0034320718],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98723906,0.0048494227,0.0021245207,0.00075471966,0.004715523,0.00031674816],"domain_scores_gemma":[0.92973405,0.034977414,0.0066054505,0.0018323002,0.025671044,0.0011798504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012917979,0.00095472805,0.0025915548,0.005577881,0.00088131765,0.0043901308,0.0027755746,0.0028922216,0.01879742],"category_scores_gemma":[0.085909754,0.000602865,0.0022680839,0.00420307,0.001846054,0.0036079476,0.002669862,0.0025074412,0.007862429],"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":[0.00013794299,0.000037214377,0.0004942395,0.16477783,0.00066041923,0.00011160517,0.00039275957,0.00011132603,0.00040672137,0.0032602334,0.38871193,0.44089785],"study_design_scores_gemma":[0.000058053865,0.0000677701,0.0015722535,0.11584334,0.0006864862,0.00035222448,0.00016768223,0.000048157955,0.00020440789,0.0011902835,0.87977576,0.000033674994],"about_ca_topic_score_codex":0.003589707,"about_ca_topic_score_gemma":0.0076894267,"teacher_disagreement_score":0.01879742,"about_ca_system_score_codex":0.001736721,"about_ca_system_score_gemma":0.013458238,"threshold_uncertainty_score":0.06831765},"labels":[],"label_agreement":null},{"id":"W4395070811","doi":"10.1038/s41598-024-60120-8","title":"Assessment of using Google Trends for real-time monitoring of infectious disease outbreaks: a measles case study","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Merck Canada Inc. (Canada)","funders":"","keywords":"Measles; Outbreak; Spearman's rank correlation coefficient; Rank correlation; Medicine; Pearson product-moment correlation coefficient; Environmental health; Pandemic; Disease; Routine immunization; Infectious disease (medical specialty); Coronavirus disease 2019 (COVID-19); Demography; Vaccination; Statistics; Virology; Pathology","score_opus":0.03603283045671161,"score_gpt":0.3771721774257667,"score_spread":0.34113934696905507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395070811","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":[0.98970515,0.00067966635,0.0032252604,0.00037983106,0.000038175738,0.00025805528,0.003215151,0.00015398784,0.0023446477],"genre_scores_gemma":[0.98840827,0.0005090202,0.0077624004,0.00005596859,0.000047291014,0.00011252946,0.0026921246,0.000015063713,0.00039738105],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975107,0.0009132073,0.00040815127,0.00041139344,0.0005021165,0.00025445467],"domain_scores_gemma":[0.99300283,0.0028449704,0.0014710231,0.0005725254,0.0017472496,0.0003614281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031201937,0.0007000683,0.0006426013,0.0058469055,0.0005213932,0.0016203508,0.0006908308,0.00090666936,0.00056063756],"category_scores_gemma":[0.009355152,0.000246852,0.00083079515,0.0044133407,0.0003959038,0.0020726023,0.0010859272,0.00036895525,0.00028608332],"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":[0.0003516476,0.00028948454,0.9388031,0.00078503636,0.00035517028,0.004281851,0.002122509,0.00602229,0.0023866696,0.0010214058,0.002949988,0.040630847],"study_design_scores_gemma":[0.00005923287,0.0008459957,0.8307701,0.00029925848,0.0006331388,0.005514028,0.0128593715,0.13342083,0.0047855247,0.0008955376,0.0097609805,0.00015605187],"about_ca_topic_score_codex":0.02019755,"about_ca_topic_score_gemma":0.019790728,"teacher_disagreement_score":0.02019755,"about_ca_system_score_codex":0.0007753223,"about_ca_system_score_gemma":0.0007696798,"threshold_uncertainty_score":0.04015994},"labels":[],"label_agreement":null},{"id":"W4395944149","doi":"10.15468/dl.buqtts","title":"Occurrence Download","year":2022,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Download; Computer science; World Wide Web","score_opus":0.014756793107581904,"score_gpt":0.2390371012776884,"score_spread":0.2242803081701065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395944149","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006555147,0.000032672575,0.000060802602,0.00005347237,0.000015198592,0.000007450028,0.9985089,0.0005710196,0.00068481226],"genre_scores_gemma":[0.00019970175,0.000032064458,0.00023460572,0.000048455153,0.0000036679487,0.00003754239,0.998938,0.00011662783,0.00038933102],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989998,0.0001479195,0.00012942497,0.00033633478,0.00023397224,0.00015255924],"domain_scores_gemma":[0.9978516,0.0005938205,0.00019967621,0.00058256835,0.0005124802,0.00025982058],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00093473366,0.002031747,0.0013397512,0.0040468816,0.00090709626,0.0023100686,0.0026367102,0.0020468254,0.10009652],"category_scores_gemma":[0.0055207578,0.00079847465,0.0011799318,0.0074102953,0.0004220735,0.0020371247,0.0022124567,0.0019823387,0.15600486],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003311956,0.0000144545465,0.00046111716,0.00041037786,0.000014707721,0.000018963721,0.000021237105,0.00017554012,0.00010746765,0.00036351918,0.9969446,0.0014347595],"study_design_scores_gemma":[0.00009188515,0.000011324319,0.0024151488,0.00018224692,0.000014873376,0.000056570265,0.000085533575,0.0003647183,0.00024220845,0.0009910063,0.9955244,0.000019969395],"about_ca_topic_score_codex":0.023459332,"about_ca_topic_score_gemma":0.039407995,"teacher_disagreement_score":0.8999035,"about_ca_system_score_codex":0.0015784565,"about_ca_system_score_gemma":0.0022053123,"threshold_uncertainty_score":0.3348562},"labels":[],"label_agreement":null},{"id":"W4396231590","doi":"10.4103/apjtm.apjtm_194_24","title":"Using X Social Networks (formerly Twitter) and web news mining to predict the measles outbreak","year":2024,"lang":"en","type":"article","venue":"Asian Pacific Journal of Tropical Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Measles; Outbreak; Rash; Medicine; Context (archaeology); Measles vaccine; Environmental health; Public health; Measles virus; Infectious disease (medical specialty); Disease; Vaccination; Virology; Geography","score_opus":0.035493628594373786,"score_gpt":0.3148827397781803,"score_spread":0.27938911118380655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396231590","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":[0.9332971,0.0044238074,0.02099246,0.002879265,0.0005786496,0.0003353828,0.02572849,0.0018990196,0.009865735],"genre_scores_gemma":[0.960397,0.0016485277,0.01516937,0.00029857713,0.00072478486,0.00013306527,0.01956811,0.000044924203,0.002015609],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995478,0.00009003236,0.00007083407,0.00013770348,0.00009824363,0.00005543016],"domain_scores_gemma":[0.998831,0.00052446505,0.00035430474,0.00006501338,0.000120150384,0.00010502454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057698804,0.0010609602,0.00052668137,0.0053109704,0.0003871667,0.0013461526,0.0005532617,0.0009493383,0.0012614478],"category_scores_gemma":[0.0027860235,0.00025888436,0.0007541174,0.0017838437,0.00018023685,0.001627363,0.0007343856,0.0006827234,0.0010574814],"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":[0.0010270436,0.0011151786,0.6687666,0.0007403346,0.00085827475,0.0015609624,0.00042356158,0.08648706,0.0039815493,0.0017603786,0.030586224,0.20269285],"study_design_scores_gemma":[0.000044496286,0.00021388385,0.09913962,0.00012750608,0.00019675061,0.00036774753,0.0005982202,0.88832545,0.0017539219,0.0025596386,0.00662696,0.000045716166],"about_ca_topic_score_codex":0.009688037,"about_ca_topic_score_gemma":0.0095536085,"teacher_disagreement_score":0.009688037,"about_ca_system_score_codex":0.00049517496,"about_ca_system_score_gemma":0.00046321185,"threshold_uncertainty_score":0.019263327},"labels":[],"label_agreement":null},{"id":"W4396502155","doi":"10.14745/ccdr.v50i34a02","title":"Innovations in public health surveillance: An overview of novel use of data and analytic methods","year":2024,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McGill University; Public Health Agency of Canada","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Public health; Disease surveillance; Public health surveillance; Data governance; Big data; Business; Computer science; Data science; Data quality; Medicine; Marketing","score_opus":0.4229131117574185,"score_gpt":0.48218989827995345,"score_spread":0.05927678652253493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396502155","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020662297,0.74570656,0.18543233,0.041653078,0.0022495491,0.0008173367,0.0004069592,0.0002715443,0.0213965],"genre_scores_gemma":[0.046141837,0.66352594,0.27410626,0.0071406676,0.0047724615,0.0020343517,0.00045446103,0.00018545473,0.0016386549],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.8862025,0.081329,0.0084743425,0.0057457355,0.016866282,0.0013820915],"domain_scores_gemma":[0.76482314,0.21226619,0.004239778,0.0077594537,0.009563406,0.001347971],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.10681183,0.0022667332,0.0025461647,0.020580057,0.002107625,0.012196321,0.0043900996,0.0056433827,0.0021212334],"category_scores_gemma":[0.096978515,0.0019319714,0.003351427,0.014767498,0.015732413,0.013071018,0.006381841,0.009328388,0.0007176593],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103956976,0.00027921755,0.006402466,0.015185937,0.00043694366,0.00029779086,0.0034994637,0.0033403505,0.0005447657,0.40293357,0.010475429,0.5565001],"study_design_scores_gemma":[0.00007952155,0.00036394654,0.005777955,0.033189807,0.0002527305,0.0013674514,0.0028755926,0.01068653,0.0020451886,0.42138967,0.5215823,0.0003893913],"about_ca_topic_score_codex":0.013124384,"about_ca_topic_score_gemma":0.006317872,"teacher_disagreement_score":0.98351514,"about_ca_system_score_codex":0.01648488,"about_ca_system_score_gemma":0.01589112,"threshold_uncertainty_score":0.5648817},"labels":[],"label_agreement":null},{"id":"W4396511904","doi":"10.23889/ijpds.v9i1.2358","title":"Trend control charts for multiple sclerosis case definitions","year":2024,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; University of Manitoba","funders":"","keywords":"Multiple sclerosis; Control (management); Computer science; Data science; Natural language processing; Psychology; Artificial intelligence; Psychiatry","score_opus":0.20951296168717207,"score_gpt":0.4038379860269997,"score_spread":0.19432502433982765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396511904","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":[0.114971995,0.00736361,0.7112612,0.0018731613,0.0022764292,0.017588701,0.086900294,0.023862937,0.033901688],"genre_scores_gemma":[0.3931461,0.0031483397,0.5054261,0.00042937577,0.00043842272,0.028651133,0.06133122,0.0018449925,0.0055843643],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9583776,0.018826425,0.0065471693,0.0040861648,0.01150002,0.0006626358],"domain_scores_gemma":[0.8316247,0.074478514,0.03813929,0.01239864,0.042481832,0.00087712036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04405642,0.001324183,0.0014458438,0.015616202,0.0009271327,0.0028071816,0.0030559113,0.0008923477,0.013218815],"category_scores_gemma":[0.18714073,0.0007697689,0.0019173018,0.011791854,0.00079216727,0.0020888925,0.0016883655,0.002578036,0.0016781363],"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":[0.0033180062,0.0006522367,0.19668478,0.0044949893,0.001555857,0.00071498164,0.00392517,0.040898737,0.0025847978,0.036653396,0.18295021,0.52556676],"study_design_scores_gemma":[0.0019634217,0.0019904478,0.35953563,0.0049067247,0.0013965877,0.002399708,0.0022982773,0.23566365,0.010654752,0.024675248,0.353612,0.0009034996],"about_ca_topic_score_codex":0.020407716,"about_ca_topic_score_gemma":0.013599666,"teacher_disagreement_score":0.04405642,"about_ca_system_score_codex":0.002478026,"about_ca_system_score_gemma":0.005400416,"threshold_uncertainty_score":0.23299539},"labels":[],"label_agreement":null},{"id":"W4396579226","doi":"10.2139/ssrn.4808948","title":"A Systematic Review of Existing Surveillance Systems Assessment Approaches","year":2024,"lang":"en","type":"review","venue":"SSRN Electronic Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Canadian Red Cross Society","funders":"","keywords":"Computer science; Systematic review; Risk analysis (engineering); Medicine; Political science; MEDLINE; Law","score_opus":0.08548048974679175,"score_gpt":0.384112981162733,"score_spread":0.2986324914159413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396579226","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006426596,0.9957711,0.0007356927,0.00037893967,0.00021039839,0.0011583086,0.0007506126,0.000017165543,0.0003350758],"genre_scores_gemma":[0.017489415,0.97368896,0.0050230757,0.000984629,0.00012439782,0.0019625172,0.0005440994,0.000015049601,0.00016773063],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9514205,0.018868515,0.019941712,0.0026610321,0.006544275,0.0005640084],"domain_scores_gemma":[0.8954765,0.08052974,0.014353812,0.0018498526,0.0071054725,0.0006846407],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.044855513,0.0028760103,0.011243809,0.01758273,0.0010373119,0.005074639,0.0038784496,0.002302345,0.007372721],"category_scores_gemma":[0.1519568,0.002138187,0.017503824,0.015184207,0.0015452896,0.004881587,0.0041106846,0.0026854235,0.00055981043],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002798404,0.000017959514,0.00085558306,0.925084,0.027974084,0.000043387627,0.00020284946,0.00014264973,0.00006500155,0.00037249923,0.0012229958,0.043739177],"study_design_scores_gemma":[0.0005426144,0.00020452602,0.0024523574,0.82410055,0.15808007,0.00012760739,0.00022028781,0.00015594474,0.0001305248,0.0006614111,0.013269665,0.000054399727],"about_ca_topic_score_codex":0.014556427,"about_ca_topic_score_gemma":0.051643264,"teacher_disagreement_score":0.95514446,"about_ca_system_score_codex":0.007927194,"about_ca_system_score_gemma":0.023331672,"threshold_uncertainty_score":0.23722148},"labels":[],"label_agreement":null},{"id":"W4396621042","doi":"10.21203/rs.3.rs-4244182/v1","title":"Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Data science; Artificial intelligence; Population; Machine learning; Intersection (aeronautics); Big data; Pandemic; Time series; Operations research; Data mining; Coronavirus disease 2019 (COVID-19); Geography; Disease; Infectious disease (medical specialty); Medicine; Engineering; Cartography","score_opus":0.17555565172002052,"score_gpt":0.4866044541106803,"score_spread":0.3110488023906598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396621042","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":[0.86238396,0.0017494143,0.10062246,0.015186469,0.00054060685,0.00033078023,0.0053365245,0.0022068277,0.011642932],"genre_scores_gemma":[0.9409381,0.00039348725,0.052630052,0.00051485293,0.00018091584,0.000078784855,0.0027912522,0.00014102753,0.0023315581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854773,0.0008746689,0.00006734644,0.00017700496,0.00021155506,0.00012164584],"domain_scores_gemma":[0.98786616,0.009826418,0.00024225104,0.0007769463,0.0009231029,0.00036503357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046182903,0.0008094824,0.0005740446,0.0007206835,0.0007010511,0.0015693099,0.0012505107,0.0019099914,0.001762795],"category_scores_gemma":[0.013970201,0.00030264287,0.000651253,0.0010899606,0.0006758593,0.0019009555,0.001034618,0.0022578032,0.0005651063],"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":[0.0016289547,0.0016876756,0.046960626,0.00047837038,0.00029423143,0.0031272513,0.0010848957,0.7692562,0.0050553456,0.016136134,0.042752724,0.11153755],"study_design_scores_gemma":[0.00011679687,0.00013874998,0.0020048094,0.000017149316,0.000031721156,0.00016510977,0.00041020833,0.9853461,0.0018007467,0.0057402877,0.0042024143,0.00002591762],"about_ca_topic_score_codex":0.029331109,"about_ca_topic_score_gemma":0.023605594,"teacher_disagreement_score":0.029331109,"about_ca_system_score_codex":0.0011640054,"about_ca_system_score_gemma":0.001327258,"threshold_uncertainty_score":0.05832076},"labels":[],"label_agreement":null},{"id":"W4396648165","doi":"10.23977/jeis.2024.090202","title":"Analysis of COVID-19 epidemic data in Chinese mainland","year":2024,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Virology; Mainland China; Pandemic; Geography; Medicine; China; Outbreak; Internal medicine","score_opus":0.020039192744669247,"score_gpt":0.36742135947186055,"score_spread":0.3473821667271913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396648165","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":[0.988934,0.00010707731,0.0007646857,0.0000950934,0.000018079758,0.00007590138,0.008572162,0.00008609624,0.0013469266],"genre_scores_gemma":[0.97180915,0.00017713264,0.0013787535,0.000037075304,0.000022273149,0.00012750224,0.025582707,0.000014692116,0.0008508226],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991517,0.00008377342,0.0001601874,0.00017502841,0.00027557902,0.00015361703],"domain_scores_gemma":[0.9983292,0.00035267082,0.00039305844,0.0001630933,0.0006178039,0.00014410981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013793297,0.00045964713,0.00041653437,0.004251711,0.0004281272,0.0005874637,0.00038149042,0.00029230354,0.00088069146],"category_scores_gemma":[0.0023174535,0.00016526043,0.00044174574,0.003963906,0.00029884407,0.0004783123,0.00048714774,0.00027624614,0.00022187732],"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":[0.00018356153,0.00010932928,0.96228963,0.0002149626,0.00012028446,0.000832055,0.0009511709,0.0050612004,0.0030734835,0.0004284633,0.0033816947,0.023354186],"study_design_scores_gemma":[0.0000076859415,0.00007162853,0.98539424,0.00001534989,0.000035514433,0.00015190957,0.00078590226,0.010614965,0.00074613234,0.00007071191,0.0020820706,0.000023873205],"about_ca_topic_score_codex":0.06141872,"about_ca_topic_score_gemma":0.03811459,"teacher_disagreement_score":0.06141872,"about_ca_system_score_codex":0.0011858931,"about_ca_system_score_gemma":0.0017387161,"threshold_uncertainty_score":0.12212241},"labels":[],"label_agreement":null},{"id":"W4396651776","doi":"10.2196/50125","title":"Collective Intelligence–Based Participatory COVID-19 Surveillance in Accra, Ghana: Pilot Mixed Methods Study","year":2024,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Allergy and Infectious Diseases; University of Ghana","keywords":"Coronavirus disease 2019 (COVID-19); Citizen journalism; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Collective intelligence; Geography; Computer science; Virology; Medicine; Artificial intelligence; Outbreak; World Wide Web","score_opus":0.15579260536142958,"score_gpt":0.4915230398766718,"score_spread":0.3357304345152422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396651776","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":[0.98996603,0.000485208,0.001430828,0.0006912958,0.00003335479,0.0050626076,0.00043893076,0.000017911567,0.0018736966],"genre_scores_gemma":[0.9766018,0.0006454238,0.006591109,0.0010718594,0.00006003639,0.013181759,0.0003113437,0.000012305687,0.0015243539],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9959054,0.00273212,0.00019298744,0.0002972787,0.00021700314,0.00065515214],"domain_scores_gemma":[0.99433774,0.0027091026,0.0009942806,0.00037218645,0.0008066845,0.00078007864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008862593,0.00066121964,0.00074884784,0.00085495884,0.0028912358,0.0011926634,0.0012280213,0.0009666686,0.005989705],"category_scores_gemma":[0.0069810324,0.00058627734,0.0006742936,0.0007633759,0.001356376,0.0013679152,0.0019259141,0.0010641316,0.0006692902],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00550811,0.02771956,0.4217536,0.0049106427,0.0003778431,0.007867334,0.3371509,0.0012396326,0.005340578,0.0037642266,0.009323827,0.17504372],"study_design_scores_gemma":[0.0024808072,0.023422379,0.40374428,0.003147924,0.00043235483,0.001591021,0.5151228,0.0035001659,0.001643209,0.0041759377,0.04054763,0.00019160031],"about_ca_topic_score_codex":0.01576965,"about_ca_topic_score_gemma":0.028885487,"teacher_disagreement_score":0.01576965,"about_ca_system_score_codex":0.0025398738,"about_ca_system_score_gemma":0.0046262494,"threshold_uncertainty_score":0.04687047},"labels":[],"label_agreement":null},{"id":"W4396859999","doi":"10.1007/978-3-031-49783-4_5","title":"What Data?","year":2024,"lang":"ca","type":"book-chapter","venue":"Springer Actuarial","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Geology","score_opus":0.05249692082699402,"score_gpt":0.31367725812291336,"score_spread":0.26118033729591933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396859999","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00045225167,0.03349198,0.020790022,0.09219588,0.016986722,0.00008697487,0.0048844186,0.0020515863,0.82906026],"genre_scores_gemma":[0.007126545,0.027435983,0.00872286,0.040870648,0.009413371,0.00016483491,0.0033901192,0.0018459439,0.9010297],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99874663,0.0003148447,0.000061244114,0.00018541381,0.0006226748,0.00006915185],"domain_scores_gemma":[0.9956963,0.0024955394,0.00016962933,0.000528318,0.00076880155,0.00034139754],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0027689475,0.00092084694,0.0007332157,0.0022660778,0.0009481785,0.0066574565,0.0009769617,0.0015702336,0.15771468],"category_scores_gemma":[0.013067119,0.0005761349,0.0004871085,0.0022894898,0.0015309994,0.009729486,0.002464676,0.004296303,0.114109546],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000063483517,0.0000068518034,0.00009625918,0.00005270324,0.000004302065,0.000012449315,0.00008285542,0.00007070413,0.00004265239,0.034606498,0.8354535,0.12956487],"study_design_scores_gemma":[0.0000013047601,0.0000022623578,0.000081891245,0.00010343704,0.0000020084155,0.0000290445,0.000055374046,0.000115897696,0.00002838021,0.021441966,0.9781342,0.0000042734337],"about_ca_topic_score_codex":0.004083225,"about_ca_topic_score_gemma":0.006418431,"teacher_disagreement_score":0.84228534,"about_ca_system_score_codex":0.0014386853,"about_ca_system_score_gemma":0.0021031878,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4396882671","doi":"10.48550/arxiv.2405.08841","title":"Best practices for estimating and reporting epidemiological delay distributions of infectious diseases using public health surveillance and healthcare data","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Epidemiology; Public health; Health care; Environmental health; Public health surveillance; Medicine; Data science; Computer science; Nursing; Political science; Pathology","score_opus":0.447815242296908,"score_gpt":0.38257038243107544,"score_spread":0.06524485986583256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396882671","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033447512,0.009220989,0.9558718,0.018506732,0.0011697061,0.0035673345,0.0034719363,0.0025682333,0.0022785317],"genre_scores_gemma":[0.012459932,0.004832111,0.97691387,0.0012942238,0.00025084542,0.002230055,0.0015915302,0.0002123384,0.00021514895],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.42333826,0.41467574,0.09353522,0.01420714,0.052053727,0.0021898623],"domain_scores_gemma":[0.21311845,0.43769744,0.059330758,0.11606297,0.17017742,0.0036130636],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.45210063,0.0060734735,0.005765462,0.028309884,0.0031184144,0.0147703905,0.0149114905,0.008006399,0.002054029],"category_scores_gemma":[0.7535397,0.0052389335,0.0062808185,0.020748712,0.007303106,0.014097838,0.011920664,0.013377988,0.0032410163],"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":[0.0005117847,0.00063696335,0.041234404,0.016540922,0.0031150589,0.000539207,0.007200557,0.034585346,0.0024617799,0.043976266,0.09101,0.7581877],"study_design_scores_gemma":[0.0013513202,0.0014116798,0.03188855,0.09327834,0.0035701485,0.0016615746,0.009313716,0.1509895,0.03129134,0.34619245,0.32638782,0.002663564],"about_ca_topic_score_codex":0.025347358,"about_ca_topic_score_gemma":0.023801895,"teacher_disagreement_score":0.54789937,"about_ca_system_score_codex":0.005640654,"about_ca_system_score_gemma":0.01872975,"threshold_uncertainty_score":0.6756575},"labels":[],"label_agreement":null},{"id":"W4396952976","doi":"10.2196/preprints.50088","title":"Public and Research Interest in Telemedicine From 2017 to 2022: Infodemiology Study of Google Trends Data and Bibliometric Analysis of Scientific Literature (Preprint)","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Preprint; Bibliometrics; Library science; Data science; Computer science; Political science; World Wide Web","score_opus":0.39573180625071047,"score_gpt":0.4752559717799997,"score_spread":0.0795241655292892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396952976","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61338735,0.043785475,0.00080136204,0.0047392654,0.0002459457,0.00044782864,0.32341573,0.00026757384,0.012909544],"genre_scores_gemma":[0.9124529,0.015654549,0.0014326154,0.00043125823,0.00034519387,0.0007222574,0.067501664,0.00007497004,0.0013846152],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99253684,0.0010833172,0.0023574973,0.000753414,0.0026505494,0.0006183237],"domain_scores_gemma":[0.9356944,0.03646478,0.01857307,0.0010659515,0.0070942803,0.0011074606],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0043374677,0.0004075832,0.0013675746,0.07976406,0.0005297233,0.0035298197,0.0007181249,0.00084352435,0.0057615275],"category_scores_gemma":[0.03333844,0.00020759153,0.0027683685,0.13749069,0.00067379995,0.0035398046,0.0019167536,0.00072374695,0.0014299545],"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":[0.00067845127,0.0001325456,0.8459507,0.029259508,0.0029680638,0.0010790232,0.0041853115,0.0011915665,0.0005011907,0.0044462914,0.036908466,0.07269896],"study_design_scores_gemma":[0.000049083956,0.0001610468,0.94801134,0.005006301,0.0015402342,0.0011163448,0.00967266,0.0023136223,0.0004647976,0.0010176967,0.030569782,0.000077132616],"about_ca_topic_score_codex":0.014891989,"about_ca_topic_score_gemma":0.012243062,"teacher_disagreement_score":0.9956625,"about_ca_system_score_codex":0.0022591024,"about_ca_system_score_gemma":0.0039561703,"threshold_uncertainty_score":0.029610574},"labels":[],"label_agreement":null},{"id":"W4396973891","doi":"10.2196/preprints.60264","title":"Mersivity: XRSC (eXtended Reality Spatial Computing) for Health, Well-Being, and Accessibility (Preprint)","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Preprint; Computer science; Computer graphics (images); Human–computer interaction; World Wide Web","score_opus":0.02547562122042466,"score_gpt":0.36925088829007613,"score_spread":0.34377526706965145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396973891","genre_codex":"other","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":[0.005482228,0.03742349,0.41611588,0.02849257,0.022160178,0.00044351214,0.00859863,0.037633013,0.44365057],"genre_scores_gemma":[0.10923423,0.04855977,0.2437846,0.0065734233,0.017389676,0.0011419497,0.019571513,0.015538302,0.5382066],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987858,0.00030257108,0.00006756123,0.00020794693,0.00056786323,0.000068355606],"domain_scores_gemma":[0.9983026,0.0005423955,0.00007178429,0.00032929704,0.00053707045,0.00021689326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013356622,0.0011953954,0.00079983857,0.0012834816,0.0007619007,0.004740862,0.0011690346,0.0017576647,0.12081854],"category_scores_gemma":[0.0039461697,0.00045047616,0.000967355,0.0020923344,0.0012038759,0.0034339125,0.003985403,0.002467754,0.04028052],"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":[0.000075135955,0.00003683272,0.00033342888,0.000717966,0.000035244844,0.00017301958,0.00068375375,0.0017349924,0.0013154077,0.21057074,0.57281524,0.21150821],"study_design_scores_gemma":[0.000016301723,0.000028245564,0.0006414,0.00026867917,0.000012559283,0.0002966478,0.00016709785,0.0031735606,0.00079734507,0.04021723,0.9543532,0.000027656853],"about_ca_topic_score_codex":0.0027699973,"about_ca_topic_score_gemma":0.0018865076,"teacher_disagreement_score":0.12081854,"about_ca_system_score_codex":0.0011656431,"about_ca_system_score_gemma":0.0011048906,"threshold_uncertainty_score":0.40417826},"labels":[],"label_agreement":null},{"id":"W4398217583","doi":"10.2196/56275","title":"Key Characteristics and Perception of Different Outbreak Surveillance Systems in Côte d’Ivoire: Cross-Sectional Survey Among Users","year":2024,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cross-sectional study; Cote d ivoire; Key (lock); Environmental health; Outbreak; Perception; Geography; Public health surveillance; Public health; Medicine; Computer science; Computer security; Psychology; Virology; Humanities; Nursing","score_opus":0.0316809377152264,"score_gpt":0.32302357105935475,"score_spread":0.29134263334412835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398217583","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":[0.9993961,0.000081404185,0.00008632915,0.000066168766,0.0000018788543,0.00002853452,0.00011611828,0.0000021280446,0.00022134284],"genre_scores_gemma":[0.9993605,0.00013188538,0.0001897339,0.00005579587,0.0000020170235,0.000033539436,0.000080348655,0.000002263857,0.00014389661],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99744475,0.0009856205,0.00038455662,0.00026855036,0.00051584316,0.00040069982],"domain_scores_gemma":[0.9927287,0.002779765,0.001779984,0.0002247835,0.0013403945,0.0011463924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003262127,0.0002645025,0.00038852642,0.0011509071,0.0007721919,0.0014252953,0.00040766364,0.00066393963,0.0015932157],"category_scores_gemma":[0.008851605,0.0005801539,0.0004463741,0.0011938136,0.0006760371,0.001409718,0.0010305635,0.0005864422,0.00025961155],"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":[0.00006738247,0.00014306231,0.95496756,0.00014175526,0.000038502236,0.0003148411,0.037791915,0.00006411977,0.0012875577,0.000048078753,0.00018698833,0.0049482],"study_design_scores_gemma":[0.00000439187,0.00035545055,0.94372594,0.000077471675,0.000013202521,0.00024673875,0.054275904,0.00025366328,0.00020593591,0.000017849796,0.0008035569,0.000019864292],"about_ca_topic_score_codex":0.024648456,"about_ca_topic_score_gemma":0.017491011,"teacher_disagreement_score":0.024648456,"about_ca_system_score_codex":0.0010242757,"about_ca_system_score_gemma":0.0007664772,"threshold_uncertainty_score":0.04900998},"labels":[],"label_agreement":null},{"id":"W4398769702","doi":"10.2196/56406","title":"Geographic Disparities in Online Searches for Psoriasis Biologics in the United States: Google Trends Analysis","year":2024,"lang":"en","type":"letter","venue":"JMIR Dermatology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Psoriasis; Geography; Medicine; Dermatology","score_opus":0.04564565887649012,"score_gpt":0.3458304822650946,"score_spread":0.30018482338860447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398769702","genre_codex":"commentary","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":[0.37191567,0.008560212,0.0002251005,0.5566403,0.0013269903,0.00007229254,0.041543756,0.00012362341,0.019592064],"genre_scores_gemma":[0.80462134,0.009746383,0.000902958,0.16263005,0.0035578709,0.00011718305,0.013120877,0.00007868236,0.0052246144],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977471,0.00056621706,0.0005383496,0.00022583643,0.0006280609,0.0002945148],"domain_scores_gemma":[0.98214877,0.0080470415,0.005233996,0.0006881792,0.0025350172,0.0013469938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00210509,0.00014659221,0.00037397543,0.0021597082,0.0006873927,0.0019072444,0.00058577664,0.0023549541,0.0038143978],"category_scores_gemma":[0.017264673,0.00025279,0.0008934716,0.006348425,0.00057341956,0.0017377671,0.0011626851,0.002314289,0.001113931],"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":[0.00030176243,0.00009530168,0.68610775,0.000315452,0.0002876266,0.00060107734,0.0009257349,0.00015198885,0.00015987859,0.0013249739,0.2725371,0.037191346],"study_design_scores_gemma":[0.00018569706,0.00014283299,0.87886167,0.0012218278,0.00060338323,0.0016832151,0.008987173,0.0019929486,0.00032361646,0.0020623843,0.10384796,0.00008728592],"about_ca_topic_score_codex":0.10840624,"about_ca_topic_score_gemma":0.1919029,"teacher_disagreement_score":0.10840624,"about_ca_system_score_codex":0.0018259012,"about_ca_system_score_gemma":0.0032192494,"threshold_uncertainty_score":0.21555048},"labels":[],"label_agreement":null},{"id":"W4398780344","doi":"10.2196/57349","title":"A Bayesian System to Detect and Track Outbreaks of Influenza-Like Illnesses Including Novel Diseases: Algorithm Development and Validation","year":2024,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Institute of General Medical Sciences","keywords":"Bayesian probability; Computer science; Outbreak; Track (disk drive); Algorithm; Bayes' theorem; Data mining; Virology; Medicine; Artificial intelligence","score_opus":0.033712168421963194,"score_gpt":0.3256431243058932,"score_spread":0.29193095588393003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398780344","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":[0.043850835,0.00023583893,0.9509148,0.00034014077,0.000054660148,0.00040836105,0.00023536688,0.0024197767,0.0015402702],"genre_scores_gemma":[0.38856846,0.0002051948,0.6076891,0.00029305724,0.000055924123,0.0007166467,0.00065117615,0.00016352817,0.0016569068],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99773204,0.0009175959,0.0002011188,0.00047453336,0.00053352636,0.00014110089],"domain_scores_gemma":[0.9878897,0.007879105,0.00062739156,0.00049293286,0.0028894711,0.00022149135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009461152,0.001010373,0.0010395967,0.0016113005,0.0009938759,0.0014069182,0.0018191559,0.0020081678,0.00247407],"category_scores_gemma":[0.026316209,0.0007524732,0.0009064228,0.0006516926,0.0006318595,0.001797456,0.0012958255,0.0018164304,0.00097177335],"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":[0.00039990264,0.0003941646,0.01349905,0.00017760615,0.00023138023,0.00012205539,0.00015670847,0.7487443,0.0038922676,0.0045397542,0.0020505656,0.22579224],"study_design_scores_gemma":[0.000024551617,0.000036336292,0.00043277966,0.000010169547,0.0000112437665,0.000016930608,0.000007148963,0.9978756,0.00060501404,0.0007372218,0.00023596981,0.0000070666924],"about_ca_topic_score_codex":0.022554265,"about_ca_topic_score_gemma":0.016251339,"teacher_disagreement_score":0.022554265,"about_ca_system_score_codex":0.0018689326,"about_ca_system_score_gemma":0.004258105,"threshold_uncertainty_score":0.050035954},"labels":[],"label_agreement":null},{"id":"W4399431451","doi":"10.2196/56229","title":"Spatiotemporal Analysis of HIV/AIDS Incidence in China From 2009 to 2019 and Its Association With Socioeconomic Factors: Geospatial Study","year":2024,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Environmental health; Socioeconomic status; China; Geospatial analysis; Population; Geography; Incidence (geometry); Demography; Public health; Medicine; Cartography; Sociology","score_opus":0.013769313172329226,"score_gpt":0.3030150395229444,"score_spread":0.2892457263506152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399431451","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":[0.991703,0.0003887568,0.00054245506,0.00017709318,0.000012137063,0.00004734126,0.005979855,0.000029805922,0.0011195912],"genre_scores_gemma":[0.99552274,0.00023416444,0.00047382477,0.000020576945,0.000009283854,0.000042822812,0.003410647,0.0000036999893,0.00028220203],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99948066,0.000078476456,0.00008101545,0.000127498,0.00012567105,0.00010670528],"domain_scores_gemma":[0.9991886,0.00011714159,0.00022256361,0.00009907699,0.00024431967,0.00012822483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009520539,0.0004302875,0.00036707285,0.0036874027,0.00066354935,0.00070971064,0.0005815543,0.00027866656,0.0011306383],"category_scores_gemma":[0.001588858,0.00025463276,0.0011145518,0.005721676,0.0004263806,0.0004904313,0.00093796535,0.00029355864,0.00015877165],"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":[0.000040622603,0.000021806967,0.9920364,0.000058398495,0.00010284082,0.00017967293,0.00028667328,0.0013253546,0.00018481855,0.0002620001,0.0005911458,0.004910292],"study_design_scores_gemma":[0.0000032276505,0.00002301623,0.99495536,0.000017868428,0.00006557758,0.000106314175,0.0005842314,0.0032887645,0.0000914781,0.00007639826,0.0007760222,0.000011717462],"about_ca_topic_score_codex":0.15527514,"about_ca_topic_score_gemma":0.14159337,"teacher_disagreement_score":0.15527514,"about_ca_system_score_codex":0.0020351154,"about_ca_system_score_gemma":0.002676709,"threshold_uncertainty_score":0.30874264},"labels":[],"label_agreement":null},{"id":"W4399543625","doi":"10.1177/14604582241260659","title":"Multi-criteria decision analysis method for differential diagnosis of tropical febrile diseases","year":2024,"lang":"en","type":"article","venue":"Health Informatics Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Royal University","funders":"","keywords":"Medical diagnosis; Analytic hierarchy process; Medicine; Dengue fever; Disease; Diagnostic test; Decision support system; Intensive care medicine; Computer science; Data mining; Emergency medicine; Operations research; Pathology","score_opus":0.04101187434695682,"score_gpt":0.4277534918561066,"score_spread":0.3867416175091498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399543625","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":[0.017108455,0.0006331155,0.97862816,0.00041466317,0.000101940124,0.00054696715,0.00025591304,0.0002600621,0.0020507064],"genre_scores_gemma":[0.3738702,0.00046516288,0.62330896,0.00015725283,0.000094313866,0.00077215617,0.000404314,0.000038215097,0.0008894565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9947642,0.002892511,0.0005204289,0.0005212141,0.0010893053,0.00021226186],"domain_scores_gemma":[0.9941041,0.004265237,0.00034469995,0.000109778586,0.0009516686,0.00022441993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074702217,0.0012509319,0.0017469614,0.0046058195,0.0012992505,0.0021839559,0.0012393638,0.00095261773,0.0039235726],"category_scores_gemma":[0.012071526,0.00047551625,0.001832519,0.0024286879,0.00050941546,0.0010422678,0.0014494323,0.0014116069,0.00039101965],"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":[0.00071540417,0.00054718624,0.011349412,0.0016418375,0.0007422521,0.0005809578,0.0007753439,0.51390684,0.0040110275,0.019876705,0.005888959,0.43996403],"study_design_scores_gemma":[0.000068528934,0.00017908332,0.0009784398,0.00009770393,0.000090224625,0.00009932918,0.00016231049,0.9837564,0.00083561166,0.011878085,0.0018222781,0.00003194416],"about_ca_topic_score_codex":0.0049067605,"about_ca_topic_score_gemma":0.005171569,"teacher_disagreement_score":0.0074702217,"about_ca_system_score_codex":0.0016522903,"about_ca_system_score_gemma":0.0039256713,"threshold_uncertainty_score":0.039506793},"labels":[],"label_agreement":null},{"id":"W4400195546","doi":"10.3390/risks12070108","title":"Unified Spatial Clustering of Territory Risk to Uncover Impact of COVID-19 Pandemic on Major Coverages of Auto Insurance","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cluster analysis; Computer science; Data mining; Data science; Credibility; Machine learning","score_opus":0.04616120389319194,"score_gpt":0.37652674146910914,"score_spread":0.3303655375759172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400195546","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":[0.9131227,0.0011957361,0.06982113,0.0013843096,0.00008005097,0.00015948119,0.00644049,0.000722401,0.0070736865],"genre_scores_gemma":[0.98568916,0.00021710931,0.009913486,0.00005103047,0.000025715097,0.00003489527,0.0033740196,0.000036080506,0.00065850397],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988618,0.00029209658,0.00008018909,0.00033926152,0.00023686711,0.00018993315],"domain_scores_gemma":[0.99661714,0.0010839247,0.0007776427,0.0005364358,0.000757682,0.00022725973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017421303,0.0006900218,0.0006344888,0.004908549,0.00067410024,0.0018862177,0.0010171826,0.0007921144,0.0015585752],"category_scores_gemma":[0.007070089,0.00028223204,0.0011149298,0.0040702554,0.0006741038,0.0014539957,0.0015925606,0.0010446227,0.00044327482],"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":[0.0002286729,0.00016706652,0.675837,0.00017857116,0.0004680017,0.00058522116,0.0011930071,0.23732874,0.0018562806,0.012939631,0.007865525,0.061352354],"study_design_scores_gemma":[0.000009620325,0.00005547293,0.23667017,0.00008463331,0.00011630259,0.00026500667,0.002240388,0.7436144,0.0010200198,0.011584393,0.004275843,0.000063834035],"about_ca_topic_score_codex":0.05809783,"about_ca_topic_score_gemma":0.04420792,"teacher_disagreement_score":0.05809783,"about_ca_system_score_codex":0.0016775955,"about_ca_system_score_gemma":0.0015840937,"threshold_uncertainty_score":0.115519345},"labels":[],"label_agreement":null},{"id":"W4400214517","doi":"10.2196/53711","title":"An Ontology to Bridge the Clinical Management of Patients and Public Health Responses for Strengthening Infectious Disease Surveillance: Design Science Study","year":2024,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Infectious disease (medical specialty); Public health; Disease surveillance; Ontology; Bridge (graph theory); Medicine; Public health surveillance; Disease; Environmental health; Computer science; Pathology","score_opus":0.1932543379450898,"score_gpt":0.5313005794270085,"score_spread":0.33804624148191864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400214517","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":[0.03787251,0.00067187945,0.92403144,0.008902655,0.00017240454,0.0013583299,0.0003371483,0.00022215223,0.02643142],"genre_scores_gemma":[0.19773719,0.0008079375,0.7960853,0.0008700486,0.000045342607,0.0012573196,0.00041502248,0.00006801035,0.0027138193],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9937469,0.003273301,0.00071236835,0.00069105654,0.0011681911,0.00040828795],"domain_scores_gemma":[0.9918968,0.004520715,0.0007221623,0.0010023572,0.0013335894,0.0005245142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011335638,0.00041497682,0.00034955022,0.002072903,0.0020537367,0.0041143233,0.0017172392,0.0018691587,0.0016000689],"category_scores_gemma":[0.0103855105,0.0004886616,0.0016390574,0.0019474799,0.0046300567,0.0073935767,0.0037447088,0.001888884,0.00024896272],"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":[0.000038484664,0.0002851828,0.0035607012,0.0005141556,0.00006503612,0.0003136323,0.004617378,0.011612168,0.0038893512,0.9277464,0.002506975,0.044850416],"study_design_scores_gemma":[0.00015074301,0.00026946835,0.0038584839,0.0017627856,0.0003467736,0.0012133839,0.010723722,0.15821843,0.008983534,0.4940377,0.32028183,0.00015318376],"about_ca_topic_score_codex":0.00837045,"about_ca_topic_score_gemma":0.008627746,"teacher_disagreement_score":0.011335638,"about_ca_system_score_codex":0.0044318545,"about_ca_system_score_gemma":0.011369215,"threshold_uncertainty_score":0.05994934},"labels":[],"label_agreement":null},{"id":"W4400223147","doi":"10.9778/cmajo.20240002","title":"Correction to “Validity of diagnoses of SARS-CoV-2 infection in Canadian administrative health data: a multiprovince, population-based cohort study”","year":2024,"lang":"en","type":"article","venue":"CMAJ Open","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Medical diagnosis; Cohort; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Cohort study; Population; Coronavirus disease 2019 (COVID-19); Medicine; Environmental health; Pathology; Disease; Infectious disease (medical specialty)","score_opus":0.1210950407706034,"score_gpt":0.4377783610442567,"score_spread":0.3166833202736533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400223147","genre_codex":"editorial","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001450922,0.0006526786,0.0041137473,0.11367272,0.8496983,0.0001317314,0.023195742,0.0028702775,0.0042139594],"genre_scores_gemma":[0.13297881,0.006072831,0.038470007,0.30818436,0.2518651,0.0016364626,0.05372795,0.015467845,0.1915966],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.98126155,0.0037679572,0.0046358844,0.0022455975,0.006117827,0.0019711454],"domain_scores_gemma":[0.82860965,0.07048041,0.008986686,0.013304551,0.07548046,0.0031381815],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.013079123,0.002208992,0.0029039849,0.0063269758,0.0048032,0.005027536,0.005030067,0.0076502236,0.09572504],"category_scores_gemma":[0.2516953,0.0022316903,0.0028237512,0.0070557464,0.003255552,0.001933534,0.0031473192,0.013647333,0.033154603],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007404854,0.00000847486,0.00027978697,0.00015654117,0.000030629068,0.0002458608,0.0001164756,0.00004689575,0.00006287332,0.0005163689,0.995417,0.0030449196],"study_design_scores_gemma":[0.00036997197,0.000036827292,0.005713197,0.0012073045,0.00017959892,0.0012799462,0.0003951152,0.0008315586,0.0008851702,0.003343842,0.9856023,0.00015517694],"about_ca_topic_score_codex":0.061196394,"about_ca_topic_score_gemma":0.055901885,"teacher_disagreement_score":0.99427503,"about_ca_system_score_codex":0.005724946,"about_ca_system_score_gemma":0.010071923,"threshold_uncertainty_score":0.32023215},"labels":[],"label_agreement":null},{"id":"W4400273636","doi":"10.2139/ssrn.4883670","title":"Avian Influenza: Lessons from the Past Outbreaks and Inventory of Data Sources, Mathematical and AI Models, and Early Warning Systems for Forecasting and Hotspot Detection to tackle the ongoing outbreak","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Canada; Artificial Intelligence in Medicine (Canada); University of Toronto; York University","funders":"","keywords":"Outbreak; Hotspot (geology); Warning system; Influenza A virus subtype H5N1; Early warning system; Geography; Computer science; Data science; Operations research; Virology; Engineering; Biology; Telecommunications","score_opus":0.06246051261340876,"score_gpt":0.31642912811459767,"score_spread":0.25396861550118893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400273636","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":[0.19960457,0.022079315,0.7019579,0.06178871,0.0011171576,0.0000749544,0.0039544175,0.0006028361,0.008820188],"genre_scores_gemma":[0.8476992,0.016874459,0.12725471,0.0011860396,0.001246797,0.00005751002,0.0025439875,0.00018214613,0.002955155],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995209,0.0002204307,0.00003861261,0.00010294094,0.0000916632,0.000025447787],"domain_scores_gemma":[0.98911417,0.008051972,0.0006095094,0.0008974665,0.0010174375,0.00030942593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036954905,0.00087583356,0.0009595325,0.0014118031,0.00035267725,0.002738503,0.0012175773,0.0011592027,0.0014720073],"category_scores_gemma":[0.02172525,0.0006399497,0.00075876043,0.0015169884,0.0011879541,0.005622483,0.0009784782,0.0024680991,0.00022881715],"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":[0.00039664662,0.0003157807,0.06293451,0.0007322109,0.0004283236,0.0003609274,0.0004399011,0.47693047,0.001489462,0.13655736,0.015770486,0.30364394],"study_design_scores_gemma":[0.0000344086,0.000062481886,0.007721895,0.0001827442,0.00006723315,0.000114650175,0.00018891318,0.7768615,0.00071330596,0.20778169,0.0062243906,0.000046769284],"about_ca_topic_score_codex":0.013359328,"about_ca_topic_score_gemma":0.010784603,"teacher_disagreement_score":0.013359328,"about_ca_system_score_codex":0.0011145646,"about_ca_system_score_gemma":0.0013931426,"threshold_uncertainty_score":0.026563168},"labels":[],"label_agreement":null},{"id":"W4400614530","doi":"10.2196/56510","title":"The Automatic Context Measurement Tool (ACMT) to Compile Participant-Specific Built and Social Environment Measures for Health Research: Development and Usability Study","year":2024,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Population; Usability; Context (archaeology); Computer science; Geographic information system; Geography; World Wide Web; Cartography; Human–computer interaction; Environmental health","score_opus":0.42422198874269257,"score_gpt":0.49077999780709003,"score_spread":0.06655800906439746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400614530","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":[0.09523606,0.00075487513,0.7154957,0.0010141355,0.0006140581,0.014978297,0.020070512,0.14673178,0.005104588],"genre_scores_gemma":[0.15457255,0.0003213099,0.79444134,0.0005173646,0.000097674136,0.028758273,0.010492566,0.008248644,0.0025503065],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9849261,0.0077091227,0.002357278,0.0018726784,0.0025881808,0.0005464971],"domain_scores_gemma":[0.8956258,0.08260445,0.002526509,0.00828021,0.00983009,0.0011328611],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027664691,0.0024184505,0.0011937761,0.0034008685,0.0007178269,0.0021220683,0.0021919205,0.0010019252,0.008663224],"category_scores_gemma":[0.09201732,0.0013781493,0.0023165133,0.0021680323,0.0007217712,0.002871702,0.0054134503,0.0015650905,0.002960742],"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":[0.0023382995,0.0015271581,0.05220144,0.008874837,0.000854231,0.001175999,0.014718043,0.006983193,0.023117915,0.0053857365,0.09960498,0.78321815],"study_design_scores_gemma":[0.003881338,0.007233105,0.18745422,0.0067074127,0.0017191882,0.0035467204,0.010061003,0.16322581,0.08258487,0.025580365,0.5061411,0.0018647771],"about_ca_topic_score_codex":0.0021174548,"about_ca_topic_score_gemma":0.0030007188,"teacher_disagreement_score":0.027664691,"about_ca_system_score_codex":0.0009508623,"about_ca_system_score_gemma":0.0025695954,"threshold_uncertainty_score":0.14630663},"labels":[],"label_agreement":null},{"id":"W4400872668","doi":"10.1016/s1473-3099(24)00435-3","title":"Artificial intelligence to transform public health in Africa","year":2024,"lang":"en","type":"letter","venue":"The Lancet Infectious Diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Mastercard Foundation; Bill and Melinda Gates Foundation","keywords":"Public health; Psychology; Political science; Medicine; Nursing","score_opus":0.0816459223406515,"score_gpt":0.3303875269335338,"score_spread":0.2487416045928823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400872668","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013877093,0.001677932,0.00015270147,0.98785853,0.007427059,0.0000104356495,0.00004322141,0.000009531863,0.0026818991],"genre_scores_gemma":[0.004932688,0.0023923418,0.00048464863,0.9699864,0.012667267,0.000059815164,0.000027670081,0.000018058541,0.00943113],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9897912,0.005346052,0.00052651163,0.00056902994,0.002155692,0.0016115867],"domain_scores_gemma":[0.97107613,0.02179724,0.0010873362,0.0006746761,0.0023626082,0.003002077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015812201,0.0012290716,0.0011950613,0.001111747,0.004213647,0.008076724,0.002131084,0.05934451,0.010487735],"category_scores_gemma":[0.051631246,0.0006853251,0.0015238379,0.00093548384,0.008564774,0.009952493,0.006493488,0.038718313,0.0038753538],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060180904,0.00004885637,0.0004546787,0.00016616799,0.00003579409,0.00047237639,0.00041225928,0.00021074063,0.00021882901,0.041530572,0.93534607,0.021043539],"study_design_scores_gemma":[0.00019525348,0.000061222,0.00094054936,0.00073253724,0.000031110038,0.0001804756,0.0008499862,0.0008102144,0.00021126274,0.069451414,0.92647773,0.000058290163],"about_ca_topic_score_codex":0.02740035,"about_ca_topic_score_gemma":0.03032995,"teacher_disagreement_score":0.05934451,"about_ca_system_score_codex":0.0070223548,"about_ca_system_score_gemma":0.015722286,"threshold_uncertainty_score":0.083623946},"labels":[],"label_agreement":null},{"id":"W4400921190","doi":"10.2196/49871","title":"Spatiotemporal Cluster Detection for COVID-19 Outbreak Surveillance: Descriptive Analysis Study","year":2024,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cluster (spacecraft); Coronavirus disease 2019 (COVID-19); Outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Pandemic; Disease surveillance; Environmental health; Geography; Public health; Virology; Medicine; Computer science; Infectious disease (medical specialty); Disease","score_opus":0.051797644770172645,"score_gpt":0.36863874096862126,"score_spread":0.31684109619844864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400921190","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":[0.97875834,0.00016678986,0.015822886,0.00012699561,0.000012698273,0.00042497882,0.0037870654,0.000076072094,0.00082424853],"genre_scores_gemma":[0.99168736,0.0000764719,0.0050611557,0.00002247672,0.0000062220893,0.00034885638,0.002591954,0.000016763057,0.00018870746],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99528235,0.0022134727,0.00058344105,0.00096032483,0.0006373048,0.00032322883],"domain_scores_gemma":[0.9859016,0.008137614,0.0024121644,0.0014358338,0.0017731567,0.0003396499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008103509,0.000345727,0.00042100882,0.0021862525,0.00048448134,0.0009931868,0.00083806046,0.0002892094,0.002215269],"category_scores_gemma":[0.021003798,0.0003234593,0.0013004744,0.0026807345,0.00040253435,0.00089463824,0.0008688784,0.00053238455,0.00027064813],"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":[0.0002254477,0.00015336431,0.9870183,0.00007953553,0.0002638331,0.0001182362,0.00061468413,0.0015900667,0.00019066357,0.0004307337,0.0013894843,0.00792573],"study_design_scores_gemma":[0.00004396521,0.0009656697,0.8778963,0.00012302124,0.00034180484,0.0007098339,0.00824905,0.10579681,0.0010827314,0.0009755862,0.0037583176,0.0000568843],"about_ca_topic_score_codex":0.016785828,"about_ca_topic_score_gemma":0.013986742,"teacher_disagreement_score":0.016785828,"about_ca_system_score_codex":0.0014233523,"about_ca_system_score_gemma":0.0023545104,"threshold_uncertainty_score":0.04285592},"labels":[],"label_agreement":null},{"id":"W4400934472","doi":"10.7554/elife.91849.2.sa2","title":"eLife Assessment: Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States","year":2024,"lang":"en","type":"peer-review","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Dynamics (music); Interference (communication); Virology; Biology; Computer science; Physics; Telecommunications","score_opus":0.03663216310828602,"score_gpt":0.367122645282699,"score_spread":0.33049048217441296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400934472","genre_codex":"empirical","genre_gemma":"review","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9520067,0.00095003156,0.019091751,0.0023072443,0.0001047346,0.000055470344,0.010116087,0.0012006983,0.014167308],"genre_scores_gemma":[0.9744013,0.00028442964,0.014246957,0.00045994474,0.000054232863,0.00003930941,0.007738866,0.00013514602,0.0026398518],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991848,0.00025906766,0.000040792318,0.00022980882,0.00018974034,0.00009586831],"domain_scores_gemma":[0.99790514,0.0009272628,0.00032776134,0.00014826642,0.00054914353,0.00014255094],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002334202,0.00031821852,0.00038607148,0.000765965,0.0005689098,0.001159798,0.0007028801,0.00043493492,0.0036886204],"category_scores_gemma":[0.008463087,0.00018131918,0.00053815614,0.0010231555,0.00025113026,0.0007365408,0.0010712056,0.00055738894,0.0008949409],"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":[0.00038776308,0.000062755855,0.85967016,0.00010140551,0.00046389762,0.00020367054,0.00030705566,0.027032595,0.0016989347,0.0013209272,0.022378687,0.08637208],"study_design_scores_gemma":[0.0001195907,0.00020164993,0.60224503,0.00015059963,0.0003623836,0.0002710013,0.0015865248,0.35574448,0.0037375395,0.005984976,0.029545192,0.00005097736],"about_ca_topic_score_codex":0.026684305,"about_ca_topic_score_gemma":0.040642977,"teacher_disagreement_score":0.9976658,"about_ca_system_score_codex":0.00069577596,"about_ca_system_score_gemma":0.0007153568,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4401118284","doi":"10.2196/48139","title":"A Geospatial Drug Abuse Risk Assessment and Monitoring Dashboard Tailored for School Students: Development Study With Requirement Analysis and Acceptance Evaluation","year":2024,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universiti Kebangsaan Malaysia; Pusat Pengurusan Penyelidikan dan Instrumentasi; Ministry of Higher Education, Malaysia","keywords":"Geospatial analysis; Dashboard; Substance abuse; Medicine; Environmental health; Computer security; Geography; Computer science; Data science; Psychiatry","score_opus":0.03544074699878294,"score_gpt":0.4064065192370368,"score_spread":0.3709657722382539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401118284","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":[0.9690694,0.0000317322,0.025024042,0.00018382478,0.00002146351,0.0033567583,0.00018606154,0.00033019646,0.0017964091],"genre_scores_gemma":[0.8658021,0.00017158531,0.123498216,0.0001404246,0.0000206152,0.006297189,0.0009107879,0.000159045,0.003000015],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99385,0.003201551,0.00049689243,0.00045623837,0.0014984211,0.00049697375],"domain_scores_gemma":[0.974343,0.014724953,0.0011389543,0.0016176556,0.006699205,0.0014762334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013226748,0.0008606821,0.0006056334,0.0014527342,0.00068925804,0.001584123,0.0012582324,0.0008216892,0.0016707114],"category_scores_gemma":[0.026901554,0.00048605032,0.0006914875,0.0006430844,0.00077025976,0.0018263757,0.0015991781,0.0009873473,0.00040208563],"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":[0.0022381574,0.03053716,0.16287276,0.003829628,0.00018749587,0.0056869923,0.10337147,0.01407911,0.14392842,0.0047162967,0.009094343,0.5194581],"study_design_scores_gemma":[0.0012705167,0.0429153,0.43296868,0.0017497201,0.00058846985,0.003816466,0.13137026,0.14498581,0.16250484,0.0027864152,0.07429356,0.0007500253],"about_ca_topic_score_codex":0.0016390356,"about_ca_topic_score_gemma":0.0017414056,"teacher_disagreement_score":0.013226748,"about_ca_system_score_codex":0.0011261667,"about_ca_system_score_gemma":0.002110493,"threshold_uncertainty_score":0.06995058},"labels":[],"label_agreement":null},{"id":"W4401392610","doi":"10.1080/00401706.2024.2374186","title":"Statistical Modeling of Occupant Behavior <b>Statistical Modeling of Occupant Behavior</b> , JanKloppenborg Møller, Marcel Schweiker, Rune Korsholm Andersen, Burak Gunay, Selin Yilmaz, Verena MarieBarthelmes, and Henrik Madsen, Boca Raton, FL: CRC Press, Taylor &amp; Francis Group, Chapman and Hall, 2024, xv + 366 pp., 35 b/w illustrations, $ 160.00 (hbk), ISBN 978-1-032-33460-8.","year":2024,"lang":"en","type":"article","venue":"Technometrics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Statistical analysis; Humanities; Mathematics; Statistics; Art","score_opus":0.04235644974148925,"score_gpt":0.30488625030733674,"score_spread":0.2625298005658475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401392610","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":[0.004861944,0.3399101,0.5890443,0.024111036,0.009390842,0.0001177297,0.0057275407,0.0024215488,0.024414992],"genre_scores_gemma":[0.12892449,0.54457825,0.189659,0.006396384,0.0152124185,0.0009637885,0.016523203,0.0021905445,0.09555195],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998102,0.0007539764,0.00011938676,0.00032846,0.00063414936,0.00006200423],"domain_scores_gemma":[0.99393445,0.004707715,0.00038949947,0.00031491814,0.0005510712,0.000102401136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003345523,0.0016192404,0.0012618846,0.0016664356,0.00032220758,0.002156804,0.0011476852,0.0012077251,0.0060868417],"category_scores_gemma":[0.009946162,0.0012386298,0.0012012327,0.0030220351,0.002200067,0.002256926,0.0008154647,0.0027019817,0.0045026205],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079860205,0.00007858431,0.0057776663,0.0016081646,0.000424535,0.00014095579,0.0003171191,0.045239307,0.0014751009,0.06809939,0.5232938,0.3534656],"study_design_scores_gemma":[0.00002503286,0.00017293656,0.02013548,0.0016949313,0.00023724325,0.00073460786,0.00033998388,0.14683864,0.0016856205,0.21612892,0.6118334,0.00017327812],"about_ca_topic_score_codex":0.0138863465,"about_ca_topic_score_gemma":0.009418322,"teacher_disagreement_score":0.0138863465,"about_ca_system_score_codex":0.0012025005,"about_ca_system_score_gemma":0.0019507377,"threshold_uncertainty_score":0.027611017},"labels":[],"label_agreement":null},{"id":"W4401476119","doi":"10.1038/s44276-024-00071-9","title":"Alternative cancer clinics’ use of Google listings and reviews to mislead potential patients","year":2024,"lang":"en","type":"article","venue":"BJC Reports","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Health Economics; University of Alberta; Simon Fraser University","funders":"Canadian Institutes of Health Research; Stem Cell Network; Public Health Agency; National Institute for Health and Care Research; Public Health Agency of Canada","keywords":"Medicine; Appeal; Family medicine; Alternative medicine; Cancer treatment; Listing (finance); Cancer; Business; Internal medicine; Finance; Pathology","score_opus":0.04331871642930883,"score_gpt":0.35794369682338334,"score_spread":0.3146249803940745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401476119","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":[0.8668226,0.018508047,0.0041820365,0.027658548,0.001097078,0.0010986502,0.014911775,0.00047580013,0.06524544],"genre_scores_gemma":[0.9798824,0.0034764833,0.004694342,0.005603102,0.00072140753,0.00036413484,0.0019513244,0.00008301272,0.003223757],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.97823596,0.012256307,0.002752149,0.0010220521,0.0051098345,0.0006237844],"domain_scores_gemma":[0.75688076,0.153573,0.06543847,0.0064836903,0.014074125,0.0035499614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013607274,0.00024679457,0.00036961888,0.00485553,0.0012051016,0.0033746937,0.00061971816,0.00066403556,0.0038152914],"category_scores_gemma":[0.10635784,0.00023559472,0.00031737864,0.0035846855,0.0010592787,0.0021229964,0.002040872,0.000643043,0.0007435009],"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":[0.0008512597,0.00014623653,0.56460685,0.008189659,0.00037689786,0.0024072523,0.03794362,0.00037732473,0.002614444,0.0062122177,0.12190742,0.25436676],"study_design_scores_gemma":[0.00016399391,0.0005042027,0.5439893,0.010599433,0.0006356722,0.0063872794,0.04014761,0.004080166,0.005524372,0.0037488837,0.38389596,0.00032315534],"about_ca_topic_score_codex":0.0064057056,"about_ca_topic_score_gemma":0.016164683,"teacher_disagreement_score":0.013607274,"about_ca_system_score_codex":0.0021299273,"about_ca_system_score_gemma":0.0026995556,"threshold_uncertainty_score":0.07196301},"labels":[],"label_agreement":null},{"id":"W4401510310","doi":"10.2196/49197","title":"Influence of Breast Cancer Awareness Month on Public Interest of Breast Cancer in High-Income Countries Between 2012 and 2022: Google Trends Analysis","year":2024,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Breast cancer; Context (archaeology); Medicine; Breast cancer awareness; Cancer; Demography; Geography; Internal medicine","score_opus":0.01521604914412556,"score_gpt":0.3303458023964562,"score_spread":0.31512975325233067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401510310","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":[0.96029884,0.0017435008,0.00012942014,0.0003057381,0.00002954904,0.00003656385,0.034968782,0.00005299102,0.002434707],"genre_scores_gemma":[0.9890486,0.0005118963,0.00016436866,0.000040505118,0.00001948276,0.000036992773,0.009851047,0.000012118115,0.00031494576],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990427,0.0001763636,0.00014814553,0.00015702732,0.00028758362,0.00018812],"domain_scores_gemma":[0.99355656,0.0017056854,0.002810827,0.00020600537,0.0012413404,0.00047953258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011271547,0.00030610472,0.0004890712,0.00515208,0.00028765487,0.001432158,0.00047552376,0.00036442015,0.0016988678],"category_scores_gemma":[0.005714457,0.00016444271,0.0014977476,0.010648747,0.00031703964,0.0010973958,0.00091020827,0.00065123005,0.00053339254],"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":[0.00009448122,0.000015619366,0.9945774,0.00017285164,0.00016515183,0.000087807464,0.0002933474,0.00022702529,0.000060411552,0.00006338966,0.0012153516,0.003027206],"study_design_scores_gemma":[0.000002161281,0.000034002387,0.99637115,0.000053620035,0.00008186496,0.00016449248,0.0010434731,0.0008688037,0.000057007943,0.000015659925,0.0012993739,0.0000083715295],"about_ca_topic_score_codex":0.05484606,"about_ca_topic_score_gemma":0.04777679,"teacher_disagreement_score":0.05484606,"about_ca_system_score_codex":0.00068271137,"about_ca_system_score_gemma":0.00076049444,"threshold_uncertainty_score":0.10905361},"labels":[],"label_agreement":null},{"id":"W4401536794","doi":"10.1109/tnb.2024.3442912","title":"Ontology-based Data Collection for a Hybrid Outbreak Detection Method Using Social Media","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on NanoBioscience","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Ontario Tech University; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Social media; Data science; Context (archaeology); Citizen science; Ontology; Infectious disease (medical specialty); Dissemination; Profiling (computer programming); Artificial intelligence; Data mining; Disease; World Wide Web; Medicine; Geography; Telecommunications","score_opus":0.0870826229065504,"score_gpt":0.37351877800238364,"score_spread":0.28643615509583326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401536794","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":[0.060747795,0.00039491165,0.91745895,0.0015289973,0.00019301528,0.0006757956,0.007366079,0.0073833447,0.0042511923],"genre_scores_gemma":[0.5201374,0.0004769147,0.45798126,0.00042531049,0.00015090268,0.0009904875,0.015702752,0.00024481685,0.0038901207],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993267,0.00009000464,0.000076885335,0.00020918067,0.0002285124,0.00006875316],"domain_scores_gemma":[0.99915683,0.0002715818,0.000106316875,0.00015870215,0.00025025257,0.000056307068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000885699,0.0009690366,0.00064688607,0.0030148649,0.0007526772,0.0012347861,0.0015355569,0.0010131901,0.0017476717],"category_scores_gemma":[0.003322158,0.0004062903,0.0012351142,0.001817623,0.0004060196,0.0026124415,0.0019895297,0.0013517876,0.0011524656],"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":[0.00044226827,0.0013344588,0.036841046,0.00082586694,0.0004026695,0.0013453735,0.0011339557,0.16667661,0.03745963,0.01722346,0.029654982,0.7066596],"study_design_scores_gemma":[0.000017897617,0.000046720903,0.0039325114,0.000042209867,0.000051252817,0.00010313521,0.0003279137,0.9687808,0.006761341,0.010533806,0.009368259,0.000034182787],"about_ca_topic_score_codex":0.013174882,"about_ca_topic_score_gemma":0.021618675,"teacher_disagreement_score":0.013174882,"about_ca_system_score_codex":0.00092711905,"about_ca_system_score_gemma":0.0018313559,"threshold_uncertainty_score":0.02619642},"labels":[],"label_agreement":null},{"id":"W4401569394","doi":"10.2139/ssrn.4924323","title":"Estimation of Background HIV Incidence Using the Recent Infection Testing Algorithm (Rita): Experiences from the Insight HIV Prevention Study in Six Sub-Saharan African Countries","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Child, Adolescent and Family Mental Health","funders":"","keywords":"Human immunodeficiency virus (HIV); Incidence (geometry); Medicine; Virology; Algorithm; Computer science; Mathematics","score_opus":0.034603877503096604,"score_gpt":0.3239754766943996,"score_spread":0.28937159919130295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401569394","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":[0.99358976,0.00029521968,0.0030555543,0.00021550774,0.000007568853,0.00010289925,0.0017918817,0.00003737299,0.0009041729],"genre_scores_gemma":[0.99149007,0.00017809396,0.0064787413,0.000050218285,0.0000061172263,0.00006259179,0.0015466476,0.000013416539,0.00017402865],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9955314,0.0037436688,0.00013405104,0.00020399835,0.00021692514,0.00016995582],"domain_scores_gemma":[0.99141496,0.005614934,0.0008301956,0.0008648457,0.0009308556,0.00034428688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009337673,0.00039191436,0.00053763366,0.0008009669,0.00029560545,0.0008609429,0.00081953837,0.0004373984,0.00039385183],"category_scores_gemma":[0.018164765,0.0002427711,0.00033889365,0.0014326526,0.0002885462,0.0006047578,0.0008527934,0.00054149394,0.00015649633],"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":[0.000559476,0.00018804133,0.9446157,0.00007966546,0.000116077055,0.00009858861,0.0018719261,0.0066671087,0.0003259573,0.00045794234,0.0009645293,0.04405499],"study_design_scores_gemma":[0.00014059852,0.00086272345,0.9360508,0.000116448056,0.00021201519,0.00028024305,0.0038055284,0.053001933,0.0016545248,0.0007513688,0.0030831485,0.00004081463],"about_ca_topic_score_codex":0.036596324,"about_ca_topic_score_gemma":0.02394623,"teacher_disagreement_score":0.036596324,"about_ca_system_score_codex":0.0004919553,"about_ca_system_score_gemma":0.0008395735,"threshold_uncertainty_score":0.0727666},"labels":[],"label_agreement":null},{"id":"W4401747741","doi":"10.1177/14604582241275844","title":"AI-based epidemic and pandemic early warning systems: A systematic scoping review","year":2024,"lang":"en","type":"article","venue":"Health Informatics Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; York University","funders":"","keywords":"Generalizability theory; Computer science; Warning system; Pandemic; Data science; Variety (cybernetics); Artificial intelligence; Data quality; Risk analysis (engineering); Machine learning; Coronavirus disease 2019 (COVID-19); Medicine; Engineering; Disease; Infectious disease (medical specialty); Psychology; Operations management","score_opus":0.047860214511885824,"score_gpt":0.40106392322349055,"score_spread":0.3532037087116047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401747741","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":[0.0005934462,0.99641114,0.00065252034,0.00088739337,0.00020040826,0.00039303122,0.00021302122,0.000014205539,0.00063487433],"genre_scores_gemma":[0.009626797,0.986888,0.0016668271,0.0006103414,0.00012454246,0.0007418421,0.00022201757,0.0000075981097,0.00011196574],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.98650056,0.0056729564,0.004447566,0.00070068694,0.0023637037,0.00031455734],"domain_scores_gemma":[0.89643294,0.08595445,0.00817296,0.0013330169,0.007565723,0.00054091105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020532947,0.001630506,0.0041387547,0.012596117,0.001035433,0.004120357,0.0025470692,0.0028121301,0.0045627556],"category_scores_gemma":[0.122227214,0.0011097768,0.0063342256,0.010827832,0.0012618791,0.004054461,0.0024536722,0.002181972,0.0005053867],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010566422,0.000038620572,0.00084936427,0.8586322,0.0026277048,0.00008094745,0.00056904403,0.0005677358,0.000105292434,0.001704665,0.0041231913,0.13059558],"study_design_scores_gemma":[0.000041407104,0.00007964199,0.0011197077,0.96350753,0.0068183998,0.00011410092,0.00034083755,0.00024199503,0.00011357591,0.0008646726,0.0267354,0.000022649052],"about_ca_topic_score_codex":0.011766781,"about_ca_topic_score_gemma":0.021018283,"teacher_disagreement_score":0.020532947,"about_ca_system_score_codex":0.005836089,"about_ca_system_score_gemma":0.02692146,"threshold_uncertainty_score":0.10858995},"labels":[],"label_agreement":null},{"id":"W4401970871","doi":"10.21203/rs.3.rs-4808089/v1","title":"Spatial and Temporal Variation of Malaria Incidence in Children Under 10 Years in a Pyrethroid-Resistant Vector Area in Southern Benin","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Data-Driven Disease Surveillance","field":"Medicine","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 Ottawa","funders":"","keywords":"Malaria; Incidence (geometry); Geography; Vector (molecular biology); Demography; Cartography; Transmission (telecommunications); Environmental health; Medicine; Biology; Immunology","score_opus":0.03212396611780387,"score_gpt":0.3509297692761892,"score_spread":0.3188058031583853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401970871","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":[0.99954283,0.00003091968,0.000023406224,0.000029100058,8.720218e-7,0.0000018673895,0.00024985053,0.0000016407519,0.00011961955],"genre_scores_gemma":[0.9995012,0.00003431083,0.000050904448,0.0000053054628,0.0000018863021,0.000004629299,0.0002365434,5.8844154e-7,0.00016467486],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99981624,0.00006012341,0.000009856581,0.000042540916,0.000017382386,0.000053872074],"domain_scores_gemma":[0.99956006,0.00011244679,0.00015390129,0.000012878122,0.000069876085,0.000090878784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022564514,0.00015811958,0.00015875258,0.00061195006,0.00026611076,0.0002831962,0.00029009473,0.00024976753,0.00086620095],"category_scores_gemma":[0.00057093735,0.00013219744,0.000115780706,0.00081553846,0.00027108457,0.00021020474,0.00037604483,0.00018798499,0.00009824797],"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":[0.00006663657,0.000021578157,0.9975775,0.000009398297,0.000016213655,0.000108013286,0.00048318994,0.00029168735,0.00034659595,0.00003731302,0.000149317,0.00089261937],"study_design_scores_gemma":[0.0000022618394,0.000017227814,0.9981938,0.000003798279,0.0000050825133,0.000052023992,0.0009328581,0.000639792,0.00003797404,0.000007915667,0.000105273415,0.0000019388115],"about_ca_topic_score_codex":0.13570179,"about_ca_topic_score_gemma":0.19194964,"teacher_disagreement_score":0.13570179,"about_ca_system_score_codex":0.0007857475,"about_ca_system_score_gemma":0.00049242965,"threshold_uncertainty_score":0.2698238},"labels":[],"label_agreement":null},{"id":"W4402088211","doi":"10.1101/2024.08.30.24312852","title":"Spatial and Temporal Hotspot Analysis of COVID-19 in Toronto","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada; McGill University; Public Health Ontario; University of Toronto","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Hotspot (geology); Geography; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Cartography; Virology; Geology; Medicine; Outbreak; Seismology; Infectious disease (medical specialty); Pathology","score_opus":0.024871565821200085,"score_gpt":0.34059453840809045,"score_spread":0.31572297258689036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402088211","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":[0.9738714,0.0004404113,0.0010286513,0.00026933692,0.000015040297,0.000047897393,0.02166899,0.00007051104,0.0025877545],"genre_scores_gemma":[0.9884355,0.00012568312,0.0009424608,0.000029709729,0.0000072994953,0.000021026304,0.009284742,0.00001000712,0.001143603],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99964297,0.000060652677,0.00002235347,0.00007125549,0.000080496706,0.00012230055],"domain_scores_gemma":[0.9988996,0.00018135282,0.00023785082,0.00008194654,0.00041455263,0.00018478493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004376764,0.00024352755,0.00023247815,0.0014507571,0.0007711007,0.00083224167,0.00044067935,0.00022679758,0.0020905263],"category_scores_gemma":[0.0019068498,0.00013123412,0.000327614,0.0025441356,0.00035514767,0.00023668828,0.0009179242,0.00019658935,0.0002695904],"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":[0.00017810454,0.000016965536,0.9761579,0.00011109619,0.00011399256,0.00034187647,0.0018156108,0.0045867013,0.0014501507,0.0008132821,0.0061842366,0.008230207],"study_design_scores_gemma":[0.00000534556,0.00002490048,0.9856396,0.000040779636,0.000032051925,0.00009665127,0.0032550413,0.0067413775,0.000399715,0.00011814257,0.003630276,0.000016103935],"about_ca_topic_score_codex":0.8963886,"about_ca_topic_score_gemma":0.9114298,"teacher_disagreement_score":0.10361141,"about_ca_system_score_codex":0.0052591804,"about_ca_system_score_gemma":0.0038801024,"threshold_uncertainty_score":0.20844316},"labels":[],"label_agreement":null},{"id":"W4402271121","doi":"10.4324/9781003335511","title":"Health Security Intelligence","year":2024,"lang":"en","type":"book","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Charles Sturt University; Foreign and Commonwealth Office","keywords":"Health security; Computer science; Computer security; Medicine; Nursing; Public health","score_opus":0.020519449173798988,"score_gpt":0.327147743091937,"score_spread":0.306628293918138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402271121","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002720133,0.0070914836,0.000869755,0.014720691,0.00422328,0.00008484654,0.00040527733,0.00020390443,0.97212887],"genre_scores_gemma":[0.006046672,0.008703857,0.0010221114,0.010755518,0.001722134,0.00007993386,0.00056161574,0.0001347392,0.9709735],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99859685,0.00029496977,0.00007302573,0.00023700175,0.00057913986,0.00021906666],"domain_scores_gemma":[0.99911064,0.00023723679,0.00006107061,0.00012380719,0.0002893314,0.00017786252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010309259,0.0009363542,0.00052021816,0.0014685588,0.002486422,0.009755137,0.0011332008,0.0041846256,0.21146904],"category_scores_gemma":[0.0030925043,0.00047173994,0.00068333204,0.0019019941,0.0021659415,0.0061557363,0.0036523861,0.0038327365,0.11437986],"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":[0.00001309686,0.00001391513,0.00014190833,0.00019832907,0.000003835897,0.00008294951,0.000664521,0.00007909212,0.00013838011,0.09435055,0.8316831,0.07263028],"study_design_scores_gemma":[0.0000011003309,0.000003569488,0.000088930254,0.00007479108,6.543612e-7,0.00004482291,0.00014438055,0.000014452858,0.000017529383,0.002203469,0.9974043,0.0000020304144],"about_ca_topic_score_codex":0.0058167875,"about_ca_topic_score_gemma":0.0073518194,"teacher_disagreement_score":0.21146904,"about_ca_system_score_codex":0.003280255,"about_ca_system_score_gemma":0.0043507167,"threshold_uncertainty_score":0.7074344},"labels":[],"label_agreement":null},{"id":"W4402443124","doi":"10.2196/56343","title":"Application of Spatial Analysis on Electronic Health Records to Characterize Patient Phenotypes: Systematic Review","year":2024,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Cancer Institute","keywords":"Geocoding; Medicine; MEDLINE; Medical diagnosis; Data science; Clinical decision support system; Data mining; Computer science; Cartography; Decision support system; Pathology; Geography","score_opus":0.019143011872597742,"score_gpt":0.35967826943969494,"score_spread":0.3405352575670972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402443124","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":[0.0024985732,0.99283034,0.0011841438,0.0006227782,0.0001501338,0.00048103763,0.0016491597,0.000022596707,0.0005612538],"genre_scores_gemma":[0.039997473,0.9535144,0.0038874631,0.0006119114,0.00011525844,0.000862228,0.00089435454,0.000014655896,0.000102143116],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.97836375,0.0082649905,0.0077646035,0.0017040367,0.0035941903,0.00030839807],"domain_scores_gemma":[0.8740769,0.101289675,0.01453923,0.0025622486,0.007187745,0.00034411415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016072804,0.0013134135,0.0048858416,0.016292257,0.000700055,0.0030678192,0.0022506027,0.0013527194,0.003362022],"category_scores_gemma":[0.1020574,0.0009031838,0.008000439,0.022332797,0.0011390623,0.003264078,0.0019489634,0.0010289183,0.0003134377],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011481004,0.000016555878,0.0054081716,0.90447533,0.014952562,0.00010581649,0.00041348545,0.00036507784,0.00008590456,0.00059069926,0.0024549505,0.071016654],"study_design_scores_gemma":[0.00013652176,0.000112455775,0.011816282,0.8994088,0.06554278,0.0003107559,0.000710876,0.00036089416,0.00018788758,0.00076844083,0.020585036,0.00005925506],"about_ca_topic_score_codex":0.013799089,"about_ca_topic_score_gemma":0.034849364,"teacher_disagreement_score":0.016292257,"about_ca_system_score_codex":0.0032684433,"about_ca_system_score_gemma":0.014735307,"threshold_uncertainty_score":0.085002184},"labels":[],"label_agreement":null},{"id":"W4402448853","doi":"10.1007/978-981-97-5116-7_8","title":"Health and Disease Mapping","year":2024,"lang":"en","type":"book-chapter","venue":"City development","topic":"Data-Driven Disease Surveillance","field":"Medicine","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 Ottawa","funders":"","keywords":"Disease; Medicine; Internal medicine","score_opus":0.03697786160030818,"score_gpt":0.2753048975142527,"score_spread":0.2383270359139445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402448853","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00044819212,0.009399927,0.02690753,0.0065382263,0.0019005894,0.00006217955,0.0014844448,0.0004929512,0.9527659],"genre_scores_gemma":[0.009973238,0.013846681,0.018892737,0.0026168812,0.00068057416,0.00008428614,0.0016000541,0.00043536603,0.95187014],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997136,0.00009495193,0.000010520715,0.000050818824,0.00010875543,0.00002127155],"domain_scores_gemma":[0.999689,0.00015634146,0.0000104059345,0.00005252338,0.00007092438,0.000020885485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006575841,0.00088727265,0.00046698094,0.0021292425,0.0009097951,0.0039558937,0.00059993594,0.00087401,0.081908636],"category_scores_gemma":[0.001581677,0.0004018988,0.00035061318,0.0031599698,0.001158531,0.00332197,0.0017992201,0.0013630781,0.025827348],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000047787503,0.000010282056,0.00021537227,0.00012431522,0.000007465225,0.000031797525,0.00034647726,0.000848298,0.00013541937,0.2947483,0.4751973,0.22833027],"study_design_scores_gemma":[7.862185e-7,0.0000023998455,0.0002250938,0.000072794435,0.0000023095909,0.00004290426,0.00020568236,0.00038240335,0.000062402534,0.06225706,0.93674284,0.0000032371895],"about_ca_topic_score_codex":0.013751876,"about_ca_topic_score_gemma":0.026019538,"teacher_disagreement_score":0.081908636,"about_ca_system_score_codex":0.0019436076,"about_ca_system_score_gemma":0.0017222785,"threshold_uncertainty_score":0.27401173},"labels":[],"label_agreement":null},{"id":"W4402724397","doi":"10.2196/52977","title":"Seasonal and Weekly Patterns of Korean Adolescents’ Web Search Activity on Insomnia: Retrospective Study","year":2024,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Trade, Industry and Energy; Institute for Information and Communications Technology Promotion; Korea Institute for Advancement of Technology; Pusan National University; National Research Foundation","keywords":"Insomnia; Retrospective cohort study; Psychology; Medicine; Psychiatry; Internal medicine","score_opus":0.04407601288114647,"score_gpt":0.4002006405804047,"score_spread":0.35612462769925823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402724397","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":[0.99793977,0.0005140881,0.0000914819,0.000018650038,0.0000060852876,0.00002779489,0.0010149628,0.0000034319833,0.00038368488],"genre_scores_gemma":[0.9979767,0.00039337706,0.00015839563,0.000028005643,0.000009558329,0.000037065234,0.001169959,0.000003688172,0.00022314582],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997507,0.000032527485,0.0000659301,0.0000698162,0.0000430515,0.000038050905],"domain_scores_gemma":[0.99947125,0.000047765086,0.00023679927,0.000038055183,0.00011962627,0.00008663769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035520623,0.00021282687,0.0003316572,0.0009522824,0.0003449341,0.0004348292,0.0002295332,0.00020617267,0.0009796671],"category_scores_gemma":[0.00073753396,0.00027882442,0.00049850997,0.0011377843,0.00018466127,0.000488851,0.00045149264,0.00032085666,0.00018956751],"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":[0.0000475351,0.000026813465,0.9979444,0.000045145553,0.00003542521,0.000057369885,0.0002109978,0.000015412179,0.00014601069,0.00001254247,0.00012794747,0.0013302389],"study_design_scores_gemma":[0.0000022290853,0.000044234304,0.99897885,0.000011274278,0.000039614864,0.00012248816,0.0004237478,0.00007053659,0.000043685493,0.000006833084,0.00025323778,0.00000336405],"about_ca_topic_score_codex":0.008624765,"about_ca_topic_score_gemma":0.01771672,"teacher_disagreement_score":0.008624765,"about_ca_system_score_codex":0.00030229622,"about_ca_system_score_gemma":0.0003162689,"threshold_uncertainty_score":0.01714915},"labels":[],"label_agreement":null},{"id":"W4402826670","doi":"10.7554/elife.91849.3.sa0","title":"eLife assessment: Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States","year":2024,"lang":"en","type":"peer-review","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"McGill University","funders":"","keywords":"Interference (communication); Dynamics (music); Evolutionary biology; Biology; Geography; Virology; Genealogy; Computer science; History; Physics; Telecommunications","score_opus":0.03663216310828602,"score_gpt":0.367122645282699,"score_spread":0.33049048217441296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402826670","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43471003,0.00618508,0.04171467,0.110063896,0.0077001406,0.0020428263,0.15860854,0.0060137617,0.23296094],"genre_scores_gemma":[0.7666092,0.0037195676,0.07986057,0.011412843,0.0019216306,0.001190727,0.072170004,0.0020318087,0.061083686],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9908087,0.0034822256,0.00055538287,0.0009412658,0.003447206,0.0007651481],"domain_scores_gemma":[0.9404994,0.022242041,0.0033817925,0.0023328483,0.029240116,0.0023037621],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.022639602,0.0004436809,0.0006536813,0.0023948571,0.0011404945,0.0037935453,0.0019869495,0.0009659869,0.014667678],"category_scores_gemma":[0.10179797,0.0003591197,0.000713909,0.001808073,0.00056561845,0.0018792679,0.003156973,0.0012503285,0.003964416],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045857663,0.00010388313,0.2728302,0.0007970866,0.00058355357,0.000216423,0.0012296744,0.0036386636,0.000638496,0.00444269,0.46887976,0.24618095],"study_design_scores_gemma":[0.00043722332,0.00022505534,0.26505998,0.0024541062,0.0007062128,0.00022548845,0.005394122,0.045631986,0.003906386,0.011771035,0.6640385,0.00014998081],"about_ca_topic_score_codex":0.039754175,"about_ca_topic_score_gemma":0.074258916,"teacher_disagreement_score":0.97736037,"about_ca_system_score_codex":0.0017365194,"about_ca_system_score_gemma":0.010049318,"threshold_uncertainty_score":0.11973107},"labels":[],"label_agreement":null},{"id":"W4402860563","doi":"10.3390/s24196246","title":"Comparative Assessment of Multimodal Sensor Data Quality Collected Using Android and iOS Smartphones in Real-World Settings","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Vector Institute; Centre for Addiction and Mental Health","funders":"Krembil Foundation; University of Washington","keywords":"Android (operating system); Computer science; Accelerometer; Global Positioning System; Data quality; Gyroscope; Correctness; Real-time computing; Data collection; Scalability; Embedded system; Database; Engineering; Operating system","score_opus":0.10534491367951483,"score_gpt":0.44393878070947873,"score_spread":0.3385938670299639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402860563","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":[0.98506826,0.0012364545,0.007824395,0.00028515607,0.00005919007,0.00013115734,0.0036879247,0.00015063098,0.0015569245],"genre_scores_gemma":[0.9926991,0.000357898,0.003858237,0.00010628766,0.00003857521,0.00009168312,0.0026166968,0.00003191313,0.00019961539],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9955996,0.0015077017,0.0006559943,0.00066310185,0.0013319608,0.00024154232],"domain_scores_gemma":[0.9817452,0.0071087508,0.0036321485,0.0013955106,0.0056716017,0.00044673312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004553303,0.00065994414,0.0007213985,0.0020912923,0.0002780471,0.0014957436,0.00057663646,0.00067203,0.000751614],"category_scores_gemma":[0.03074136,0.00025257052,0.0007426143,0.0017102072,0.0005255285,0.0011298497,0.0013860374,0.00041325917,0.00027821312],"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":[0.001109036,0.000099914665,0.9259335,0.0009224955,0.0009690274,0.00035216112,0.0025071984,0.0024449863,0.0075291446,0.000328019,0.0014007341,0.056403846],"study_design_scores_gemma":[0.000015841704,0.00030278802,0.9871661,0.00014426904,0.0002260281,0.0005123208,0.0015338137,0.006354638,0.0019363977,0.00029412674,0.0014606037,0.00005306147],"about_ca_topic_score_codex":0.0040231775,"about_ca_topic_score_gemma":0.0054350747,"teacher_disagreement_score":0.004553303,"about_ca_system_score_codex":0.00031563814,"about_ca_system_score_gemma":0.000355463,"threshold_uncertainty_score":0.024080455},"labels":[],"label_agreement":null},{"id":"W4402867153","doi":"10.1177/14604582241270759","title":"Public interest and seasonal peaks for wisdom teeth related web inquiries – A google trends analysis","year":2024,"lang":"en","type":"article","venue":"Health Informatics Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public interest; Statistical analysis; Geography; Web search query; Demography; Search engine; Information retrieval; Computer science; Statistics; Political science; Mathematics; Sociology","score_opus":0.08845456252441083,"score_gpt":0.3675896000649861,"score_spread":0.2791350375405752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402867153","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":[0.9761134,0.00093850895,0.0010115694,0.0002603691,0.000019324652,0.00011322333,0.018466834,0.000087837325,0.0029889352],"genre_scores_gemma":[0.98794705,0.0003533479,0.0015163536,0.000025801693,0.000023780221,0.000081472084,0.009507129,0.000016073802,0.00052890135],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987141,0.00021485024,0.00026876802,0.00019512216,0.00045126135,0.0001560559],"domain_scores_gemma":[0.9932807,0.002721399,0.0021645115,0.00026841013,0.0013664134,0.0001986181],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015890858,0.00019721454,0.00036362736,0.011352736,0.00027960466,0.001337187,0.00028425144,0.00024099303,0.0013092965],"category_scores_gemma":[0.006933232,0.0001294974,0.00081845874,0.0143282255,0.00022684927,0.0011822303,0.0006543202,0.00029987012,0.00046736165],"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":[0.00019616698,0.000050461786,0.96451366,0.00047967938,0.00017576297,0.00023359698,0.00097406603,0.00058107224,0.001006123,0.00045177306,0.001969418,0.029368097],"study_design_scores_gemma":[0.0000048255843,0.00006983896,0.9909417,0.000056849327,0.00008041498,0.0002658331,0.0018988573,0.0033339094,0.00047804235,0.00012575115,0.0027305966,0.00001344687],"about_ca_topic_score_codex":0.016897162,"about_ca_topic_score_gemma":0.020812064,"teacher_disagreement_score":0.016897162,"about_ca_system_score_codex":0.0006319751,"about_ca_system_score_gemma":0.000870654,"threshold_uncertainty_score":0.03359765},"labels":[],"label_agreement":null},{"id":"W4402903223","doi":"10.1007/978-3-031-66413-7_6","title":"Applying Spatial Analysis Functionality to Enhance Routine Cancer Surveillance Systems","year":2024,"lang":"en","type":"book-chapter","venue":"Global perspectives on health geography","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Provincial Health Services Authority","funders":"","keywords":"Computer science; Geography; Cartography","score_opus":0.015826413402380324,"score_gpt":0.3419955862120689,"score_spread":0.32616917280968855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402903223","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":[0.009246743,0.0002249479,0.8976786,0.0007937499,0.00016367526,0.00013456255,0.002574438,0.06980861,0.019374687],"genre_scores_gemma":[0.11774753,0.0005449156,0.8456122,0.0006681713,0.00010619367,0.00017220718,0.0061883237,0.008946165,0.020014245],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99928206,0.00018746615,0.00006204907,0.00012232533,0.0003010786,0.000045036908],"domain_scores_gemma":[0.9964301,0.0019770279,0.00008673413,0.00075805007,0.00066048536,0.000087702974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002012446,0.00097115966,0.00042610668,0.0016332989,0.0003703248,0.0026034713,0.001366114,0.00061872846,0.012581779],"category_scores_gemma":[0.0055967015,0.0006222795,0.00095090934,0.0012335076,0.00038670303,0.0025024298,0.0021663145,0.00086283876,0.005534015],"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":[0.00030919389,0.00016212741,0.010232174,0.0007225208,0.00017446029,0.0005999902,0.0018545724,0.028248267,0.042227004,0.037317187,0.123440236,0.7547122],"study_design_scores_gemma":[0.00006627298,0.00011090467,0.0060483622,0.00033971024,0.00018031348,0.0008948115,0.0004970156,0.4046593,0.070734896,0.050115034,0.46622384,0.00012944263],"about_ca_topic_score_codex":0.008862701,"about_ca_topic_score_gemma":0.010239987,"teacher_disagreement_score":0.012581779,"about_ca_system_score_codex":0.0006182789,"about_ca_system_score_gemma":0.00070914277,"threshold_uncertainty_score":0.042090178},"labels":[],"label_agreement":null},{"id":"W4402903257","doi":"10.1007/978-3-031-66413-7_4","title":"Geospatial Science and Health: Overview of Data and Methods","year":2024,"lang":"en","type":"book-chapter","venue":"Global perspectives on health geography","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Geospatial analysis; Data science; Computer science; Geography; Remote sensing","score_opus":0.10271110128250088,"score_gpt":0.46477567998161246,"score_spread":0.36206457869911157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402903257","genre_codex":"review","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":[0.0010795048,0.4662143,0.3887401,0.02649619,0.004253666,0.00041195692,0.0074311993,0.0013438371,0.104029246],"genre_scores_gemma":[0.015169585,0.53841615,0.39240974,0.008018425,0.0048861266,0.00092872384,0.0058587906,0.0009893004,0.033323076],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9965802,0.001566245,0.00028201728,0.00032837418,0.0011499738,0.00009322214],"domain_scores_gemma":[0.9900303,0.008225916,0.00024150175,0.00049842783,0.00086135353,0.0001425105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073579503,0.0014231658,0.001593249,0.006348789,0.00061617617,0.0071517727,0.0018525743,0.0019461123,0.011088577],"category_scores_gemma":[0.012340587,0.00111828,0.0010770803,0.015282606,0.0027772486,0.0058665066,0.0020418619,0.003109675,0.0059573934],"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":[0.00002726618,0.000058222515,0.0016329795,0.002621413,0.00010890653,0.00011633763,0.00036290684,0.0032404228,0.00037784487,0.354634,0.18894349,0.44787621],"study_design_scores_gemma":[0.000008121455,0.000019122559,0.0013709016,0.0016394475,0.00004205,0.00031530103,0.0002960536,0.0030321253,0.00025430284,0.25133762,0.74164265,0.000042274525],"about_ca_topic_score_codex":0.010282555,"about_ca_topic_score_gemma":0.011129404,"teacher_disagreement_score":0.011088577,"about_ca_system_score_codex":0.0021749847,"about_ca_system_score_gemma":0.004741803,"threshold_uncertainty_score":0.03891307},"labels":[],"label_agreement":null},{"id":"W4402947503","doi":"10.2196/60282","title":"US Public Interest in Merkel Cell Carcinoma Following Jimmy Buffett’s Death and Implications for Continued Health Advocacy: Infodemiology Study of Google Trends","year":2024,"lang":"en","type":"article","venue":"JMIR Dermatology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Child Health and Human Development","keywords":"Preprint; Merkel cell carcinoma; Public health; Political science; Medicine; Carcinoma; Pathology; Physics","score_opus":0.06504143664479756,"score_gpt":0.37352924349827815,"score_spread":0.3084878068534806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402947503","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":[0.71184903,0.026080184,0.00021355294,0.1273755,0.0013777164,0.00023763375,0.071037635,0.00018157478,0.061647177],"genre_scores_gemma":[0.9459206,0.015059463,0.00036137988,0.020181075,0.0018724662,0.00011397471,0.01069328,0.00007553426,0.0057221996],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99776006,0.0004510049,0.0003175453,0.00021750099,0.0008823209,0.00037156718],"domain_scores_gemma":[0.9667623,0.010094844,0.016274052,0.0010371581,0.003377907,0.0024538098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018169191,0.00019181446,0.00042208537,0.004701078,0.00145375,0.0035841954,0.0007698719,0.001584882,0.007827323],"category_scores_gemma":[0.023655515,0.00024273407,0.0008460609,0.009189634,0.00093082414,0.0043647853,0.0019684676,0.0020932427,0.0017071278],"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":[0.00040285083,0.00016884968,0.78676677,0.0017747441,0.00023925732,0.00053711,0.005728917,0.00006779707,0.00018239323,0.002731193,0.13954727,0.06185285],"study_design_scores_gemma":[0.000053279637,0.00013392021,0.8836001,0.002432874,0.00038676278,0.001456392,0.013988424,0.0005005032,0.00050849264,0.0010339639,0.09582464,0.000080679965],"about_ca_topic_score_codex":0.058875356,"about_ca_topic_score_gemma":0.06383601,"teacher_disagreement_score":0.058875356,"about_ca_system_score_codex":0.001682917,"about_ca_system_score_gemma":0.0027717391,"threshold_uncertainty_score":0.11706531},"labels":[],"label_agreement":null},{"id":"W4403035462","doi":"10.3390/healthcare12191959","title":"Avian Influenza: Lessons from Past Outbreaks and an Inventory of Data Sources, Mathematical and AI Models, and Early Warning Systems for Forecasting and Hotspot Detection to Tackle Ongoing Outbreaks","year":2024,"lang":"en","type":"review","venue":"Healthcare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Canada; University of Toronto; Artificial Intelligence in Medicine (Canada); Response Biomedical (Canada); York University","funders":"Natural Sciences and Engineering Research Council of Canada; International Development Research Centre","keywords":"Outbreak; Influenza A virus subtype H5N1; Biosecurity; Hotspot (geology); Warning system; Case fatality rate; Early warning system; Transmission (telecommunications); Geography; Environmental health; Environmental resource management; Environmental planning; Biology; Computer science; Ecology; Medicine; Virology; Environmental science; Population","score_opus":0.2505741555593702,"score_gpt":0.4359070391841005,"score_spread":0.18533288362473033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403035462","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":[0.015295503,0.6005736,0.31820348,0.04971644,0.0028581049,0.00018009498,0.0033764886,0.00094973977,0.008846514],"genre_scores_gemma":[0.10945893,0.67387,0.20633294,0.003515979,0.0018753451,0.00027150146,0.003208255,0.0001324023,0.0013346169],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9988508,0.000478741,0.00016091041,0.00017030815,0.00030272242,0.00003662524],"domain_scores_gemma":[0.9923671,0.0049208202,0.0006568218,0.00053745264,0.0013479858,0.00016976772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006620829,0.0012163735,0.0013137733,0.002432891,0.00033416483,0.0028271757,0.0016259088,0.0013423407,0.0011699961],"category_scores_gemma":[0.012669904,0.0005459368,0.0011839184,0.0023400043,0.0009287453,0.007854127,0.0014185004,0.0027389706,0.0003845638],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017516258,0.000117945994,0.015601991,0.014585055,0.0010356648,0.0003574015,0.0007226914,0.056315795,0.0023374085,0.0721805,0.037110694,0.7994597],"study_design_scores_gemma":[0.000076098106,0.00041109815,0.014475811,0.023583477,0.0014483238,0.0008906767,0.0016144644,0.14603288,0.003948174,0.36258957,0.44447115,0.00045827424],"about_ca_topic_score_codex":0.0052910172,"about_ca_topic_score_gemma":0.005735571,"teacher_disagreement_score":0.006620829,"about_ca_system_score_codex":0.00124987,"about_ca_system_score_gemma":0.0025001646,"threshold_uncertainty_score":0.03501469},"labels":[],"label_agreement":null},{"id":"W4403408225","doi":"10.1007/978-1-4614-8033-4_64","title":"On Scan Statistics Through the Finite Markov Chain Imbedding Approach","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","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 Manitoba; Public Health Ontario; University of Toronto","funders":"","keywords":"Markov chain; Statistics; Computer science; Statistical physics; Mathematics; Physics","score_opus":0.023391701160826055,"score_gpt":0.2792858575409446,"score_spread":0.25589415638011853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403408225","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":[0.0010017898,0.0019484278,0.9838749,0.0008020436,0.00018070787,0.000013676973,0.00014336662,0.00015775186,0.011877447],"genre_scores_gemma":[0.1653542,0.013757912,0.7599016,0.0012793081,0.0016027789,0.0003249361,0.0007243569,0.0009621329,0.05609284],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99874264,0.0006759041,0.00006076646,0.00016562486,0.00030722268,0.00004800691],"domain_scores_gemma":[0.98740035,0.0113137355,0.0002502442,0.00046920203,0.0004486749,0.00011777849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036395867,0.0011333233,0.0012198322,0.0015004192,0.00042068068,0.002133039,0.0018883123,0.0014726318,0.010008958],"category_scores_gemma":[0.016354023,0.0009045135,0.0009366393,0.0022534055,0.002617945,0.0049041538,0.0016565741,0.004089703,0.0019752684],"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":[0.000009653661,0.000010469531,0.00014178662,0.000051766558,0.000011368807,0.000044474822,0.000054714306,0.016311279,0.00015656257,0.95859814,0.003076342,0.021533428],"study_design_scores_gemma":[0.0000026624216,0.000007871263,0.000057068526,0.000029342285,0.000004107625,0.000047724483,0.000009111956,0.0700065,0.00012519021,0.9235661,0.006135179,0.000009111366],"about_ca_topic_score_codex":0.0023380793,"about_ca_topic_score_gemma":0.0019028373,"teacher_disagreement_score":0.010008958,"about_ca_system_score_codex":0.0016229006,"about_ca_system_score_gemma":0.0011726364,"threshold_uncertainty_score":0.033483326},"labels":[],"label_agreement":null},{"id":"W4403816209","doi":"10.1093/eurpub/ckae144.844","title":"9.Y.2. Leveraging GenAI and Deep Learning for the Analysis of Public Health Data: A Global Perspective","year":2024,"lang":"en","type":"article","venue":"European Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Perspective (graphical); Public health; Data science; Environmental health; Psychology; Computer science; Medicine; Artificial intelligence; Nursing","score_opus":0.15974847914738752,"score_gpt":0.39747876010907723,"score_spread":0.23773028096168972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403816209","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05189932,0.026019227,0.3525507,0.1708467,0.006266604,0.0005868362,0.009515836,0.015671989,0.3666428],"genre_scores_gemma":[0.4045786,0.03044461,0.2758132,0.02384447,0.005853222,0.0005477459,0.013943336,0.0029827,0.24199216],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99942976,0.00024179406,0.000015709196,0.000091238755,0.00013379127,0.00008766233],"domain_scores_gemma":[0.9984554,0.0006165802,0.000086883236,0.00021262688,0.0003926641,0.00023583732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003229045,0.0003823721,0.0003329212,0.0011811227,0.00053784845,0.0027547379,0.00072129775,0.001386454,0.018740615],"category_scores_gemma":[0.002610535,0.0002333291,0.0005255578,0.0013187081,0.0007352361,0.0027986849,0.0017778042,0.0021425355,0.005658312],"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":[0.00026437547,0.00019390575,0.0067915893,0.0007076168,0.00010514987,0.00044341115,0.0009447212,0.012999468,0.008864126,0.08362998,0.34361497,0.5414408],"study_design_scores_gemma":[0.000040458493,0.00018564869,0.012425921,0.00078992284,0.00004675041,0.0004407813,0.0011926296,0.07071073,0.008099188,0.094500676,0.81150115,0.00006612776],"about_ca_topic_score_codex":0.003980903,"about_ca_topic_score_gemma":0.005167351,"teacher_disagreement_score":0.018740615,"about_ca_system_score_codex":0.0009592012,"about_ca_system_score_gemma":0.0014286974,"threshold_uncertainty_score":0.062693655},"labels":[],"label_agreement":null},{"id":"W4403824920","doi":"10.1093/eurpub/ckae144.417","title":"Monitoring event data extracted from online news for outbreak detection","year":2024,"lang":"en","type":"article","venue":"European Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Outbreak; Event (particle physics); Computer science; Data mining; Medicine; Virology","score_opus":0.19208585046724258,"score_gpt":0.403372942756249,"score_spread":0.21128709228900644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403824920","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":[0.8989012,0.0012704191,0.030538391,0.0004692319,0.00022884387,0.0006349013,0.060498122,0.003168909,0.004289943],"genre_scores_gemma":[0.91225344,0.00036582327,0.036071666,0.00007505,0.00021464538,0.0002605043,0.049862042,0.000064206826,0.00083255663],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980609,0.0003315577,0.00035486685,0.0005115688,0.0006092746,0.00013176008],"domain_scores_gemma":[0.9889197,0.0047535,0.002610755,0.00096136675,0.0023376471,0.0004171316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018507731,0.0007412445,0.00057286344,0.0077018393,0.00032953167,0.0010914619,0.0005326566,0.0006841531,0.0010359293],"category_scores_gemma":[0.0107078925,0.00023618624,0.0005030447,0.004124879,0.00026770146,0.0010558481,0.00067749346,0.0005324155,0.0008239524],"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":[0.0009417393,0.0005393995,0.7859446,0.0012863448,0.0002994123,0.0010754071,0.00071569416,0.012661749,0.015902968,0.0008804028,0.012812334,0.1669399],"study_design_scores_gemma":[0.00006982222,0.0005075448,0.73225504,0.0001822849,0.0002451102,0.0010211191,0.00087419193,0.22801165,0.01882061,0.0016867056,0.01622677,0.00009916975],"about_ca_topic_score_codex":0.00397616,"about_ca_topic_score_gemma":0.004188904,"teacher_disagreement_score":0.0077018393,"about_ca_system_score_codex":0.00045482395,"about_ca_system_score_gemma":0.00047280124,"threshold_uncertainty_score":0.009787917},"labels":[],"label_agreement":null},{"id":"W4403853214","doi":"10.1093/pnasnexus/pgae430","title":"Internet-based surveillance to track trends in seasonal allergies across the United States","year":2024,"lang":"en","type":"article","venue":"PNAS Nexus","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of Colorado Boulder; Cooperative Institute for Research in Environmental Sciences; National Science Foundation","keywords":"Allergy; Proxy (statistics); Geography; Quarter (Canadian coin); Demography; The Internet; Population; Aeroallergen; Medicine; Environmental health; Computer science; Allergen","score_opus":0.026402653175020465,"score_gpt":0.3250069909501656,"score_spread":0.29860433777514517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403853214","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":[0.89097077,0.002281571,0.013171825,0.0025421835,0.0001289649,0.00023513248,0.08095555,0.0005238363,0.009190238],"genre_scores_gemma":[0.95057255,0.0012455592,0.015011127,0.0004218958,0.00012078137,0.0002160502,0.03143443,0.000024696623,0.00095289916],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99951684,0.00016977187,0.00006172254,0.0001250372,0.00008923429,0.000037377788],"domain_scores_gemma":[0.9982297,0.00061512244,0.0005137257,0.00020946437,0.00030362414,0.00012836751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089823623,0.00022290528,0.00023113922,0.0023415133,0.00018277745,0.0006158172,0.00040806198,0.00028950485,0.000790428],"category_scores_gemma":[0.0033994769,0.00013800002,0.00025919185,0.0027318266,0.000102638725,0.00051921105,0.00087280537,0.00037655514,0.00029751932],"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":[0.00005130551,0.000095663185,0.93367356,0.0001780599,0.00016572981,0.00006628544,0.00029429994,0.0038236512,0.0007149992,0.00081994064,0.011561886,0.04855459],"study_design_scores_gemma":[0.000023557956,0.000105576015,0.914312,0.00023342026,0.0001334079,0.00014494361,0.0012596595,0.06082568,0.0012442878,0.0018725516,0.019824134,0.000020744272],"about_ca_topic_score_codex":0.033125557,"about_ca_topic_score_gemma":0.050361995,"teacher_disagreement_score":0.033125557,"about_ca_system_score_codex":0.00041691898,"about_ca_system_score_gemma":0.00053345406,"threshold_uncertainty_score":0.06586546},"labels":[],"label_agreement":null},{"id":"W4403890742","doi":"10.2196/57748","title":"The Complex Interaction Between Sleep-Related Information, Misinformation, and Sleep Health: Call for Comprehensive Research on Sleep Infodemiology and Infoveillance","year":2024,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Misinformation; Sleep (system call); Preprint; Psychology; Computer science; World Wide Web; Computer security","score_opus":0.12695687360333605,"score_gpt":0.45312505596540475,"score_spread":0.3261681823620687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403890742","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038615167,0.46865034,0.0021672552,0.5163715,0.0029772834,0.00007746819,0.00017850815,0.000077234494,0.0056388616],"genre_scores_gemma":[0.08998091,0.7788765,0.010832552,0.10663342,0.010357873,0.00039242912,0.000264968,0.00004944002,0.0026118716],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9929355,0.0042337407,0.00047008245,0.0005638206,0.0013559222,0.00044095726],"domain_scores_gemma":[0.8358007,0.12744474,0.0068428987,0.0036979362,0.016635833,0.00957795],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.017910305,0.00065672514,0.0025508567,0.004220597,0.002884472,0.011654131,0.0022889366,0.0078910915,0.0133906435],"category_scores_gemma":[0.042481385,0.0005921823,0.002376492,0.0030122215,0.010621313,0.019242473,0.0043099914,0.012060515,0.0015579959],"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":[0.000180308,0.0004670963,0.018515572,0.013641725,0.0005475566,0.00041589636,0.0063693947,0.00040377773,0.00061657187,0.047128055,0.12978992,0.7819241],"study_design_scores_gemma":[0.0000985773,0.00060283096,0.0642106,0.07866122,0.0009158574,0.0028328707,0.044743333,0.0014470328,0.00067872216,0.17949755,0.62585205,0.0004593636],"about_ca_topic_score_codex":0.013876249,"about_ca_topic_score_gemma":0.020880086,"teacher_disagreement_score":0.9820897,"about_ca_system_score_codex":0.005859158,"about_ca_system_score_gemma":0.022871844,"threshold_uncertainty_score":0.09471989},"labels":[],"label_agreement":null},{"id":"W4403936360","doi":"10.1371/journal.pcbi.1012464","title":"Enhancing insights in sexually transmitted infection mapping: Syphilis in Forsyth County, North Carolina, a case study","year":2024,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases; National Institute on Minority Health and Health Disparities; National Institute of Allergy and Infectious Diseases; National Institutes of Health","keywords":"Outbreak; Syphilis; Geolocation; Geography; Demography; Congenital syphilis; Computer science; Medicine; Virology","score_opus":0.026429579091691004,"score_gpt":0.3002190553907791,"score_spread":0.27378947629908806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403936360","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":[0.99543047,0.00008984965,0.00125876,0.00067119533,0.0000058755736,0.00007396599,0.00072093186,0.000023388844,0.0017254942],"genre_scores_gemma":[0.9933901,0.00013792774,0.0048614987,0.00009236507,0.000008198735,0.000063586886,0.00058657286,0.000008676156,0.0008511119],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99921536,0.0004265569,0.000025540965,0.0000918901,0.00011804302,0.00012262282],"domain_scores_gemma":[0.99790454,0.0011796717,0.0002740164,0.00013133301,0.00038875913,0.00012172014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009544861,0.00032977463,0.00016114042,0.0012829925,0.0013171824,0.0008187379,0.0010092676,0.0005492153,0.0012255849],"category_scores_gemma":[0.0061213505,0.00019716132,0.00026500836,0.0020275782,0.00048070005,0.0004022762,0.0010481612,0.00065756997,0.000102859274],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014158108,0.00043660565,0.8542869,0.0003211129,0.00013830703,0.009263362,0.024486823,0.0260068,0.0021700675,0.0025783612,0.009244661,0.070925385],"study_design_scores_gemma":[0.00004117283,0.00023259596,0.78439164,0.0002770551,0.00012848688,0.0016759462,0.08808047,0.1093346,0.0020542906,0.0016918636,0.012011071,0.00008083497],"about_ca_topic_score_codex":0.6741758,"about_ca_topic_score_gemma":0.8157739,"teacher_disagreement_score":0.6741758,"about_ca_system_score_codex":0.0039150673,"about_ca_system_score_gemma":0.0038512782,"threshold_uncertainty_score":0.6554861},"labels":[],"label_agreement":null},{"id":"W4404007994","doi":"10.34172/hpp.43117","title":"Temporal trends in online searches related to COVID-19 vaccine safety: A digital infodemiology study","year":2024,"lang":"en","type":"article","venue":"Health Promotion Perspectives","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Global Health Research; University of Toronto","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Medicine; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Vaccine safety; 2019-20 coronavirus outbreak; Vaccination; Public health; Environmental health; Demography; Virology; Internal medicine; Immunization; Immunology; Outbreak; Pathology","score_opus":0.0771233517664646,"score_gpt":0.45266402652376125,"score_spread":0.37554067475729663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404007994","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":[0.98371524,0.0008935528,0.00014172118,0.0006522019,0.000013603448,0.0000392863,0.011155646,0.00001733721,0.0033714403],"genre_scores_gemma":[0.99171174,0.00071875425,0.00023067594,0.00017551644,0.00003843382,0.00005243685,0.00607936,0.000013006303,0.0009800307],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988814,0.0002280856,0.00023177578,0.00020844159,0.00028814963,0.00016224515],"domain_scores_gemma":[0.9855903,0.0034902655,0.008016207,0.00043378313,0.0016944007,0.000775183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001285674,0.0001595324,0.00029277406,0.0048878565,0.0003599804,0.001711656,0.00049640913,0.000494698,0.0037449738],"category_scores_gemma":[0.009544742,0.00018090472,0.00059038086,0.009713636,0.0002882058,0.0025068317,0.0009687066,0.0007677093,0.00088314473],"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":[0.000103531784,0.00007352201,0.9900143,0.00011447208,0.00007549782,0.00008501491,0.0007268129,0.00007376614,0.00008466086,0.00020257107,0.0014240617,0.007021879],"study_design_scores_gemma":[0.0000041770927,0.000060305247,0.9944364,0.0000710905,0.000041359493,0.00018746096,0.0020118945,0.00051517313,0.0001028355,0.000049051017,0.002512483,0.0000078453195],"about_ca_topic_score_codex":0.024898086,"about_ca_topic_score_gemma":0.032028116,"teacher_disagreement_score":0.024898086,"about_ca_system_score_codex":0.0008054318,"about_ca_system_score_gemma":0.00076726277,"threshold_uncertainty_score":0.049506307},"labels":[],"label_agreement":null},{"id":"W4404158575","doi":"10.1162/2e3983f5.6bd4d05a","title":"Review 1: \"Diagnostic Accuracy of Chest X-Ray Computer Aided Detection Software and Blood Biomarkers for Detection of Prevalent and Incident Tuberculosis in Household Contacts Followed up for 5 Years\"","year":2024,"lang":"en","type":"peer-review","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","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 British Columbia","funders":"","keywords":"Tuberculosis; Medicine; Software; Radiology; Pathology; Computer science; Operating system","score_opus":0.030115944067392496,"score_gpt":0.3080677168446976,"score_spread":0.2779517727773051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404158575","genre_codex":"review","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007602781,0.9559618,0.00083545176,0.018105749,0.01907736,0.0003999152,0.0023184652,0.00006473686,0.002476246],"genre_scores_gemma":[0.009562194,0.96124506,0.0018564012,0.013070699,0.009090636,0.00029993258,0.0023493625,0.00006248697,0.0024632087],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9866397,0.00461119,0.0028746095,0.001165779,0.0044628824,0.00024581086],"domain_scores_gemma":[0.86250967,0.07093922,0.009539024,0.0018940009,0.054259434,0.00085865497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014342625,0.0012341382,0.003747225,0.0063448716,0.0005460253,0.0029123928,0.0028387832,0.0031807898,0.005225658],"category_scores_gemma":[0.13797219,0.0009143837,0.002507248,0.0041911285,0.0014525371,0.0021866902,0.001707847,0.001807597,0.0027022029],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034952455,0.000041021634,0.0028157297,0.16171607,0.002293899,0.00008014018,0.00020878151,0.00018409369,0.00064265396,0.0017018521,0.44857705,0.3813892],"study_design_scores_gemma":[0.00040399935,0.00040956071,0.021401966,0.26069477,0.009031103,0.0008809968,0.00034933453,0.00041232147,0.0009351463,0.001691985,0.703638,0.00015078661],"about_ca_topic_score_codex":0.010216656,"about_ca_topic_score_gemma":0.016768105,"teacher_disagreement_score":0.014342625,"about_ca_system_score_codex":0.0026784344,"about_ca_system_score_gemma":0.010421913,"threshold_uncertainty_score":0.07585192},"labels":[],"label_agreement":null},{"id":"W4404257818","doi":"10.29173/cjen500","title":"Fisting—What is it and why should I have a high index of suspicion?","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Emergency Nursing","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Office of the Chief Medical Examiner","funders":"","keywords":"Index (typography); Psychology; Computer science; World Wide Web","score_opus":0.05005194235623689,"score_gpt":0.32786080751458574,"score_spread":0.27780886515834885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404257818","genre_codex":"commentary","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":[0.2554753,0.042493846,0.018613616,0.58880424,0.0061372565,0.0004684276,0.004078605,0.0012104831,0.08271821],"genre_scores_gemma":[0.8647804,0.028686348,0.022339186,0.06899811,0.008090945,0.00021537427,0.0021232814,0.00027111362,0.004495235],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99701285,0.00068012957,0.0007138833,0.00034118711,0.0008525872,0.00039944376],"domain_scores_gemma":[0.98081183,0.0062514846,0.004301435,0.0006923486,0.0040114718,0.0039314213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023891826,0.00049685244,0.0010920948,0.0024342893,0.0013547745,0.0039793244,0.0012959642,0.0027309654,0.005158933],"category_scores_gemma":[0.045880184,0.00042829337,0.0006274409,0.0013606359,0.0009816908,0.0046704994,0.0013390962,0.0050667753,0.0026995195],"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":[0.00017619318,0.00026852058,0.777795,0.00064170343,0.00011228223,0.0047314847,0.0015892009,0.00031783502,0.0009272114,0.001981042,0.10113748,0.11032201],"study_design_scores_gemma":[0.000112311856,0.0006172889,0.65580577,0.012153059,0.0005691707,0.09025315,0.026195316,0.006035576,0.0033652247,0.032242864,0.17213894,0.0005112339],"about_ca_topic_score_codex":0.0072659487,"about_ca_topic_score_gemma":0.0097447,"teacher_disagreement_score":0.0072659487,"about_ca_system_score_codex":0.001495429,"about_ca_system_score_gemma":0.0024650816,"threshold_uncertainty_score":0.017258346},"labels":[],"label_agreement":null},{"id":"W4404720494","doi":"10.2196/64969","title":"Public Health Data Exchange Through Health Information Exchange Organizations: National Survey Study","year":2024,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Health information exchange; Public health; Descriptive statistics; Survey data collection; American Community Survey; Information exchange; Business; Demographics; Census; Health care; Medicine; Environmental health; Computer science; Population; Demography; Statistics; Economics; Nursing; Health information; Economic growth","score_opus":0.16220252087149736,"score_gpt":0.3943952833184859,"score_spread":0.23219276244698853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404720494","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":[0.98512554,0.00035591994,0.0005462289,0.0013573166,0.000021399559,0.0004441826,0.009003736,0.000016201586,0.0031294608],"genre_scores_gemma":[0.9907018,0.0005801362,0.00091254787,0.0009550732,0.000025239591,0.00071404193,0.0055769845,0.000013574922,0.0005205368],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9896917,0.005476352,0.0015133934,0.00079079595,0.0014624364,0.0010653178],"domain_scores_gemma":[0.973525,0.007499322,0.0107306605,0.0020369051,0.0036825,0.0025256092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011080204,0.00033517147,0.00038035872,0.0028492038,0.0013621924,0.0015533622,0.0014657017,0.0009898952,0.0030959682],"category_scores_gemma":[0.024005767,0.0007824397,0.0005649463,0.0069183228,0.0007170034,0.003771538,0.0028170578,0.0022757808,0.0009682808],"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":[0.000032890966,0.00018852756,0.992234,0.000063736974,0.000036630398,0.000038182945,0.0020764996,0.000035690282,0.000022930062,0.00012539147,0.002296532,0.0028491053],"study_design_scores_gemma":[0.000024840638,0.00015209045,0.98056626,0.0001656637,0.0000351081,0.00017098228,0.014995502,0.0005170279,0.00006066518,0.0000968041,0.0031976614,0.000017420893],"about_ca_topic_score_codex":0.032378167,"about_ca_topic_score_gemma":0.035653893,"teacher_disagreement_score":0.032378167,"about_ca_system_score_codex":0.0019302608,"about_ca_system_score_gemma":0.003086022,"threshold_uncertainty_score":0.064379394},"labels":[],"label_agreement":null},{"id":"W4404773505","doi":"10.3389/fdgth.2024.1399992","title":"It's late, but not too late to transform health systems: a global digital citizen science observatory for local solutions to global problems","year":2024,"lang":"en","type":"article","venue":"Frontiers in Digital Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Children’s Health Research Institute; Lawson Health Research Institute; Western University","funders":"Canada Research Chairs","keywords":"Observatory; Citizen science; Political science; Health science; Computer science; Meteorology; Environmental science; Regional science; Geography; Physics; Medicine; Astronomy","score_opus":0.028534415187737566,"score_gpt":0.3165752016743233,"score_spread":0.2880407864865857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404773505","genre_codex":"commentary","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":[0.009665194,0.005804398,0.011935286,0.9424264,0.00862708,0.00017645939,0.00021134212,0.0005331444,0.020620678],"genre_scores_gemma":[0.33917588,0.016755091,0.070099846,0.50811857,0.009603259,0.0006912474,0.0011980354,0.0013558448,0.053002175],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98338586,0.010105898,0.00049118244,0.0015146158,0.0022028065,0.0022995225],"domain_scores_gemma":[0.9609754,0.01236078,0.0016402904,0.00480624,0.0038963004,0.016320905],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.034347143,0.0009935668,0.0008021371,0.0016179696,0.010194604,0.021111185,0.0024394405,0.012230814,0.010262934],"category_scores_gemma":[0.026420208,0.00076583144,0.001281419,0.0019899937,0.017095467,0.035016038,0.024290327,0.02000643,0.0030252032],"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":[0.0002523132,0.00038740542,0.0062230434,0.00095365965,0.00009767541,0.0012562283,0.051593624,0.0007623815,0.0038051608,0.22576769,0.53646153,0.17243934],"study_design_scores_gemma":[0.0000483543,0.00015672084,0.0014928231,0.000619655,0.000025967736,0.00025478922,0.024464011,0.0005152561,0.00076272566,0.07379174,0.89779484,0.000073189476],"about_ca_topic_score_codex":0.003511093,"about_ca_topic_score_gemma":0.0044275033,"teacher_disagreement_score":0.99756056,"about_ca_system_score_codex":0.005958193,"about_ca_system_score_gemma":0.024760209,"threshold_uncertainty_score":0.18164724},"labels":[],"label_agreement":null},{"id":"W4404964388","doi":"10.2196/60754","title":"Promises and Pitfalls of Internet Search Data in Mental Health: Critical Review","year":2024,"lang":"en","type":"review","venue":"JMIR Mental Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Wellcome Trust","keywords":"Preprint; The Internet; Mental health; Psychology; Computer science; Data science; Medicine; Psychiatry; World Wide Web","score_opus":0.16621141944248552,"score_gpt":0.5233615580071613,"score_spread":0.3571501385646758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404964388","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":[0.000030934676,0.99792874,0.00006963017,0.0016501326,0.0001627444,0.000012066204,0.000015334994,0.0000022216238,0.00012820879],"genre_scores_gemma":[0.0006893003,0.99784744,0.00027003663,0.0008802113,0.00023277725,0.00003743119,0.000017045775,0.0000021317471,0.000023532075],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9904302,0.0049385363,0.002241934,0.00044809742,0.0017184261,0.00022293431],"domain_scores_gemma":[0.82298124,0.15705979,0.007542807,0.0015074108,0.009879532,0.0010292362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027270596,0.0010477473,0.0044103437,0.007864293,0.0007660953,0.0038915514,0.0029640982,0.0037381682,0.0040334854],"category_scores_gemma":[0.07821077,0.0010315841,0.002901974,0.008990071,0.0028114042,0.006897414,0.0028240532,0.0048993574,0.00083033304],"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":[0.00016537443,0.000041079373,0.0005088074,0.32215142,0.0015767103,0.00016034202,0.0004414146,0.00037421912,0.000098606346,0.009095759,0.02480381,0.64058244],"study_design_scores_gemma":[0.000054871252,0.00013966758,0.0012539251,0.68902594,0.0030984331,0.0005245685,0.00047654792,0.00026503668,0.00012417957,0.005863962,0.29910702,0.000065873515],"about_ca_topic_score_codex":0.0053366637,"about_ca_topic_score_gemma":0.011616284,"teacher_disagreement_score":0.027270596,"about_ca_system_score_codex":0.003613154,"about_ca_system_score_gemma":0.012114085,"threshold_uncertainty_score":0.14422244},"labels":[],"label_agreement":null},{"id":"W4405054246","doi":"10.32388/riq3nd","title":"Review of: \"Google Trends in the Diagnosis of Meteosensitivity\"","year":2024,"lang":"en","type":"peer-review","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario","funders":"","keywords":"Data science; Computer science; Information retrieval; Geography","score_opus":0.05647726232069589,"score_gpt":0.3803432022210868,"score_spread":0.3238659399003909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405054246","genre_codex":"commentary","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":[0.0019420581,0.07197955,0.001422773,0.57310617,0.23299216,0.0005131279,0.05957335,0.0011337188,0.057337146],"genre_scores_gemma":[0.022550857,0.19575393,0.004254958,0.2482236,0.30019197,0.0007086644,0.06314999,0.0021929357,0.16297315],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99357694,0.0015592587,0.0009762753,0.00049759867,0.0030817934,0.00030807633],"domain_scores_gemma":[0.90586764,0.028016139,0.0063722976,0.0032157793,0.05212842,0.0043997103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009732983,0.0006410185,0.0013958706,0.008257266,0.0011180297,0.0043456014,0.0019149755,0.002268426,0.07543066],"category_scores_gemma":[0.08977804,0.00043203696,0.0013981835,0.0059421137,0.0011106863,0.0038859346,0.002560699,0.0028761332,0.03721311],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015528545,0.000003206196,0.00029787893,0.0007160226,0.000014215157,0.000028609318,0.000026210759,0.000011550912,0.000030036572,0.0002549931,0.9837219,0.014879716],"study_design_scores_gemma":[0.000017275528,0.000016864673,0.0031005694,0.0036107886,0.000055185923,0.00013693223,0.00009597164,0.00004923002,0.000081258666,0.0003452026,0.9924729,0.000017855302],"about_ca_topic_score_codex":0.011162747,"about_ca_topic_score_gemma":0.027088495,"teacher_disagreement_score":0.07543066,"about_ca_system_score_codex":0.0020809893,"about_ca_system_score_gemma":0.007500837,"threshold_uncertainty_score":0.25234067},"labels":[],"label_agreement":null},{"id":"W4405093471","doi":"10.17269/s41997-024-00949-8","title":"Innovation through collaboration: Identifying opportunities to improve congenital anomalies surveillance in Canada","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Newfoundland and Labrador Centre for Applied Health Research; Newborn Screening Ontario; Manitoba Health; Government of Northwest Territories; Health PEI; Cancer Care Nova Scotia; Alberta Health Services; Government of New Brunswick; University of British Columbia; Ministry of Health","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Agency (philosophy); Standardization; Government (linguistics); Public health; Business; Medicine; Health care; Public relations; Environmental health; Family medicine; Political science; Nursing","score_opus":0.09750624823148737,"score_gpt":0.3321370394444834,"score_spread":0.23463079121299604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405093471","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":[0.6532856,0.007934101,0.021677583,0.1842761,0.0009687007,0.0058595273,0.011192754,0.0009228164,0.11388287],"genre_scores_gemma":[0.9276201,0.003773056,0.051477123,0.007607491,0.00010439193,0.0014453185,0.0033253343,0.00011270969,0.0045344387],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.96388894,0.011776904,0.0018407862,0.00241925,0.011434118,0.008639955],"domain_scores_gemma":[0.8845407,0.017461894,0.009378124,0.004552825,0.04583886,0.038227607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033526603,0.00054472336,0.00058172975,0.0063874647,0.011133304,0.007856041,0.0040455903,0.0014239236,0.003142513],"category_scores_gemma":[0.09202612,0.00040101228,0.0009888677,0.01077389,0.0033552214,0.0039043557,0.016362768,0.0024033699,0.00030530526],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014533916,0.00028443953,0.58318007,0.0016920639,0.00018113384,0.0007657766,0.046620328,0.0011882294,0.0007211442,0.016836654,0.063737415,0.28464743],"study_design_scores_gemma":[0.00014948379,0.00034288524,0.61107177,0.005029104,0.00033968972,0.00046307922,0.1266472,0.007104717,0.0014694307,0.011211131,0.23591727,0.0002543004],"about_ca_topic_score_codex":0.9594154,"about_ca_topic_score_gemma":0.9723026,"teacher_disagreement_score":0.88442683,"about_ca_system_score_codex":0.1155732,"about_ca_system_score_gemma":0.55474794,"threshold_uncertainty_score":0.83854616},"labels":[],"label_agreement":null},{"id":"W4405231484","doi":"10.2196/62993","title":"Patterns of Public Interest in Lipomas and Lipoma-Removal Procedures: Google Trends Analysis","year":2024,"lang":"en","type":"article","venue":"JMIR Dermatology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprint; Lipoma; Computer science; Medicine; Radiology; World Wide Web","score_opus":0.03369964061972908,"score_gpt":0.320301878686029,"score_spread":0.2866022380662999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405231484","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":[0.633038,0.017001214,0.0009005077,0.0036520194,0.00009117605,0.00022622153,0.33023223,0.0003349717,0.014523654],"genre_scores_gemma":[0.8826927,0.00978516,0.0019888245,0.00057188905,0.00010142536,0.00019761865,0.10325963,0.00007477103,0.0013280128],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983531,0.00023203193,0.00037511304,0.00026558232,0.00056659075,0.00020756526],"domain_scores_gemma":[0.98924273,0.0039312444,0.004552536,0.00030122112,0.0016247203,0.00034759825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010470394,0.00027250408,0.0004837181,0.012583557,0.0002898874,0.0015825342,0.00050632405,0.00042490344,0.0022712639],"category_scores_gemma":[0.008793336,0.0001378585,0.0011628346,0.018585654,0.00027579218,0.0016752451,0.0009917445,0.00062671484,0.00078934],"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":[0.0003665104,0.00005235047,0.90011555,0.0048445207,0.0005602191,0.0005439869,0.0015096554,0.0009143158,0.00077989587,0.0019844207,0.034997825,0.053330746],"study_design_scores_gemma":[0.00002408327,0.000079764126,0.9396291,0.0015953861,0.00038883742,0.0017792269,0.003706912,0.0037622093,0.0009942647,0.0006837644,0.04730627,0.000050235365],"about_ca_topic_score_codex":0.031113487,"about_ca_topic_score_gemma":0.035150044,"teacher_disagreement_score":0.031113487,"about_ca_system_score_codex":0.0010153488,"about_ca_system_score_gemma":0.0012059641,"threshold_uncertainty_score":0.061864734},"labels":[],"label_agreement":null},{"id":"W4405234630","doi":"10.1016/j.idm.2024.12.005","title":"State-space modelling for infectious disease surveillance data: Dynamic regression and covariance analysis","year":2024,"lang":"en","type":"article","venue":"Infectious Disease Modelling","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Sanofi","keywords":"Covariance; Statistics; Infectious disease (medical specialty); Regression analysis; Geography; State space; Computer science; Mathematics; Disease; Medicine","score_opus":0.02944584107628936,"score_gpt":0.31035192253439536,"score_spread":0.280906081458106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405234630","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":[0.20356436,0.0008114976,0.78535795,0.0015537781,0.00006684979,0.00018700951,0.005137022,0.0005131205,0.0028084207],"genre_scores_gemma":[0.8937999,0.0006528109,0.09762807,0.00012324171,0.000046778438,0.00020367934,0.004772354,0.000090725356,0.002682403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817395,0.00088750967,0.000083754436,0.00041067475,0.0002844335,0.00015969797],"domain_scores_gemma":[0.9934722,0.0043081883,0.0009076632,0.000513826,0.00068691315,0.00011128035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004237306,0.0007056843,0.000868041,0.0014837402,0.0005808884,0.0013826236,0.0012267838,0.0005982048,0.0014342777],"category_scores_gemma":[0.01861797,0.00034556436,0.0012517316,0.0020950204,0.000770171,0.00083211676,0.0011036893,0.0012625734,0.00024357189],"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":[0.000064511274,0.00008915708,0.09065143,0.00013693894,0.00032748192,0.00013415985,0.00040758267,0.79084164,0.00074642483,0.059360936,0.0031071184,0.054132648],"study_design_scores_gemma":[0.0000060024804,0.000017805824,0.007983508,0.000020926915,0.000023454764,0.000019695872,0.000058081885,0.9788817,0.00014450467,0.01110448,0.0017201015,0.000019759576],"about_ca_topic_score_codex":0.45745996,"about_ca_topic_score_gemma":0.40600282,"teacher_disagreement_score":0.45745996,"about_ca_system_score_codex":0.0036970696,"about_ca_system_score_gemma":0.0055733067,"threshold_uncertainty_score":0.9095945},"labels":[],"label_agreement":null},{"id":"W4405311192","doi":"10.1016/j.cageo.2024.105817","title":"ETASbootstrap 0.2.0: A flexible R package for computing bootstrap confidence intervals for parameters in the space–time epidemic-type aftershock sequence model, with four case studies","year":2024,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Data-Driven Disease Surveillance","field":"Medicine","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","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Aftershock; Sequence (biology); Confidence interval; R package; Computer science; Interval (graph theory); Space (punctuation); Statistics; Data mining; Programming language; Mathematics; Seismology; Geology; Operating system","score_opus":0.1835238911152382,"score_gpt":0.4040004433317287,"score_spread":0.22047655221649048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405311192","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024727616,0.0008369107,0.82477415,0.00039160997,0.0002716525,0.0004841947,0.052458957,0.0914409,0.0046140733],"genre_scores_gemma":[0.14456941,0.0007107958,0.7283515,0.00040252795,0.0001620069,0.0033798122,0.059862476,0.058685724,0.0038757725],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963845,0.0017592547,0.00036025562,0.0005899248,0.00071206,0.00019395062],"domain_scores_gemma":[0.9633565,0.029478021,0.0023578834,0.0023720495,0.002093606,0.00034199771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008734155,0.0025658193,0.0020436875,0.0042932685,0.00062321156,0.00272735,0.0033854335,0.0014344307,0.0305552],"category_scores_gemma":[0.06671259,0.0013212721,0.002582811,0.0038313293,0.0007351096,0.0022761424,0.0025855077,0.0027656462,0.013766978],"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":[0.0015108613,0.00037504052,0.044570457,0.00435364,0.00211074,0.0023179092,0.0017481411,0.1793669,0.007130351,0.0362144,0.4180079,0.30229354],"study_design_scores_gemma":[0.0006148881,0.0003547042,0.02289094,0.00076598494,0.0005688708,0.0020622618,0.00043190536,0.65516025,0.012527354,0.05563678,0.24849379,0.0004924035],"about_ca_topic_score_codex":0.0049584582,"about_ca_topic_score_gemma":0.0040375656,"teacher_disagreement_score":0.0305552,"about_ca_system_score_codex":0.00058299367,"about_ca_system_score_gemma":0.0019212307,"threshold_uncertainty_score":0.10221732},"labels":[],"label_agreement":null},{"id":"W4405370858","doi":"10.1145/3708549","title":"Disease Outbreak Detection and Forecasting: A Review of Methods and Data Sources","year":2024,"lang":"en","type":"review","venue":"ACM Transactions on Computing for Healthcare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Harm; Social media; Outbreak; Infectious disease (medical specialty); Public health; Leverage (statistics); Disease surveillance; Disease; The Internet; Internet privacy; Data science; Health care; Business; Public relations; Environmental health; Computer science; Medicine; Political science; World Wide Web; Pathology","score_opus":0.2436126184403192,"score_gpt":0.5038544175407229,"score_spread":0.2602417991004037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405370858","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":[0.00027592466,0.99062514,0.005755243,0.0011764126,0.0003666059,0.00008662339,0.000585629,0.00008306152,0.0010453211],"genre_scores_gemma":[0.0023221779,0.98833805,0.0074903183,0.00036580904,0.00047158156,0.00011214586,0.00069964916,0.000025240943,0.00017506507],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9954814,0.0013092973,0.0010469285,0.0007008759,0.0013390573,0.00012251425],"domain_scores_gemma":[0.95648885,0.03578878,0.0019232449,0.0010031005,0.004462481,0.00033357873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011196604,0.002563553,0.003708745,0.010914819,0.0005712234,0.003065599,0.0040185484,0.0019022078,0.0029789135],"category_scores_gemma":[0.032586634,0.0014303031,0.0027969969,0.01330124,0.0011055572,0.0045215306,0.0015392056,0.0026644748,0.0018387643],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009392499,0.00010262439,0.0020220676,0.04097786,0.0005440432,0.00006804543,0.00016233457,0.0028560304,0.0002761183,0.005072949,0.025940908,0.9218831],"study_design_scores_gemma":[0.0000715009,0.00024310689,0.00674864,0.07500814,0.0021240944,0.0006600281,0.00041144123,0.007258506,0.0013517645,0.019294,0.8865339,0.00029496866],"about_ca_topic_score_codex":0.008954121,"about_ca_topic_score_gemma":0.0072768973,"teacher_disagreement_score":0.011196604,"about_ca_system_score_codex":0.0018470557,"about_ca_system_score_gemma":0.0056039933,"threshold_uncertainty_score":0.059213996},"labels":[],"label_agreement":null},{"id":"W4405459928","doi":"10.2196/58391","title":"Effect of Health Literacy on Antiviral Treatment of Hepatitis B: Instrumental Variable Analysis","year":2024,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"World Health Organization","keywords":"Instrumental variable; Hepatitis B; Health literacy; Literacy; Affect (linguistics); Medicine; China; Environmental health; Virology; Psychology; Political science; Health care; Statistics; Mathematics","score_opus":0.01403687210822591,"score_gpt":0.34024444824060285,"score_spread":0.32620757613237694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405459928","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":[0.9179668,0.0039617317,0.06209849,0.0066684335,0.00034437783,0.0004771174,0.0037720767,0.00054233836,0.00416861],"genre_scores_gemma":[0.98873156,0.0006560364,0.005571355,0.00045380258,0.00012807672,0.00023336775,0.0011033035,0.000024624758,0.0030978995],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98757,0.008995082,0.00040303645,0.0012573334,0.0005606795,0.0012139244],"domain_scores_gemma":[0.9386943,0.05138086,0.006235282,0.0014465279,0.0012313422,0.0010116951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013383843,0.0011395382,0.0021720363,0.0014505348,0.000582343,0.0016375802,0.0017942062,0.0015887568,0.007627044],"category_scores_gemma":[0.036955845,0.0005801061,0.004205088,0.0015647351,0.0016316709,0.0012207627,0.0022866717,0.0032806965,0.00059766055],"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":[0.0015221664,0.0011706376,0.8144219,0.00063969096,0.0065108677,0.00079436693,0.00045136336,0.120447904,0.0005232634,0.013524503,0.0052452176,0.034748107],"study_design_scores_gemma":[0.0008488817,0.0012440545,0.3561372,0.00031122798,0.004220622,0.00017301695,0.0006875756,0.62018377,0.0010295223,0.009740756,0.005267428,0.00015601618],"about_ca_topic_score_codex":0.03480995,"about_ca_topic_score_gemma":0.010192493,"teacher_disagreement_score":0.03480995,"about_ca_system_score_codex":0.0016027403,"about_ca_system_score_gemma":0.0034629316,"threshold_uncertainty_score":0.07078141},"labels":[],"label_agreement":null},{"id":"W4405596178","doi":"10.1016/j.jclinepi.2024.111648","title":"Meta-epidemiology and reporting characteristics of mapping reviews: a scoping review","year":2024,"lang":"en","type":"review","venue":"Journal of Clinical Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University Medical Centre; Bruyère","funders":"National Office for Philosophy and Social Sciences","keywords":"Epidemiology; MEDLINE; Systematic review; Meta-analysis; Medicine; Data science; Environmental health; Computer science; Political science; Pathology","score_opus":0.8119543235295741,"score_gpt":0.6447508381367261,"score_spread":0.16720348539284802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405596178","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00094930583,0.9964185,0.001093216,0.00032535358,0.000115687886,0.00032537096,0.00053363526,0.000019291521,0.00021978316],"genre_scores_gemma":[0.03417908,0.9572816,0.0041998387,0.0012413599,0.00027479054,0.0014921057,0.0010803846,0.00006885479,0.00018204648],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.891501,0.041168593,0.047905758,0.006939203,0.011327197,0.0011582125],"domain_scores_gemma":[0.5958525,0.33452728,0.04604494,0.0078124623,0.014822515,0.0009404003],"candidate_categories":["metaresearch","metaepi_narrow","metaepi_broad"],"consensus_categories":[],"category_scores_codex":[0.10266451,0.0026037795,0.017455483,0.018342761,0.0009545635,0.008741024,0.003841591,0.003783942,0.005108979],"category_scores_gemma":[0.38830978,0.0029168634,0.024564961,0.018836,0.0022047404,0.0064791283,0.004473223,0.0032877298,0.0006366206],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066869886,0.000028123353,0.006946373,0.7626815,0.16055046,0.00010869856,0.00043019123,0.00036139952,0.00015983597,0.0008228401,0.0022784958,0.06496347],"study_design_scores_gemma":[0.0003222161,0.00014830762,0.007955372,0.5187978,0.4585471,0.00038019288,0.00027200027,0.00025902363,0.00023901706,0.001687539,0.011316721,0.0000747405],"about_ca_topic_score_codex":0.0073409188,"about_ca_topic_score_gemma":0.014894737,"teacher_disagreement_score":0.99739623,"about_ca_system_score_codex":0.0060117966,"about_ca_system_score_gemma":0.012347353,"threshold_uncertainty_score":0.5429483},"labels":[{"model":"gemma","categories":["metaresearch","metaepi_broad"],"domain":"reporting","study_design":"systematic_review","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":["metaresearch","metaepi_broad"],"domain":"reporting","study_design":"design_other","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"split"},{"id":"W4405654482","doi":"10.2196/67050","title":"Impact of Primary Health Care Data Quality on Infectious Disease Surveillance in Brazil: Case Study","year":2024,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundação Oswaldo Cruz; Universidade Federal do Rio de Janeiro; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Preprint; Infectious disease (medical specialty); Environmental health; Medicine; Public health surveillance; Quality (philosophy); Public health; Disease surveillance; Primary care; Data quality; Disease; Family medicine; Computer science; World Wide Web; Nursing; Business; Pathology","score_opus":0.06219398795865693,"score_gpt":0.43408551225494074,"score_spread":0.3718915242962838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405654482","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":[0.98409295,0.0021709702,0.0034702145,0.0039946744,0.00002531769,0.00040065512,0.0015387738,0.00005539995,0.0042509567],"genre_scores_gemma":[0.9975821,0.00041404375,0.0014107721,0.0001691712,0.000011161796,0.00006192957,0.0002902272,0.000005739247,0.000054870958],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98286855,0.009997745,0.00171271,0.0011854892,0.002895868,0.0013396303],"domain_scores_gemma":[0.93989605,0.035421524,0.0117212515,0.0039727446,0.0071080853,0.0018803279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016879275,0.00043565297,0.00047690282,0.001540105,0.0010273928,0.0018567472,0.0010497535,0.00083958753,0.0007232243],"category_scores_gemma":[0.07636748,0.0003087581,0.0008434895,0.0037026433,0.0013809792,0.0010958585,0.0022396785,0.0008346253,0.0000708303],"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":[0.00010539461,0.00015398955,0.9635154,0.00043029938,0.00015005172,0.0021365646,0.0037986885,0.002241161,0.00036305198,0.0013289405,0.0017114234,0.02406503],"study_design_scores_gemma":[0.00006346252,0.00051205774,0.9437151,0.0011056196,0.00028569016,0.0056999875,0.012301675,0.024794742,0.0013152681,0.001541876,0.008589935,0.000074544856],"about_ca_topic_score_codex":0.1740101,"about_ca_topic_score_gemma":0.08418724,"teacher_disagreement_score":0.1740101,"about_ca_system_score_codex":0.0070742345,"about_ca_system_score_gemma":0.0062477007,"threshold_uncertainty_score":0.34599447},"labels":[],"label_agreement":null},{"id":"W4405757776","doi":"10.1101/2024.12.20.24319425","title":"A century of weekly notifiable disease incidence data by province in Canada","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University; McMaster University","funders":"","keywords":"Communicable disease; Disease surveillance; Notifiable disease; Incidence (geometry); Context (archaeology); Geography; Government (linguistics); Public health; Infectious disease (medical specialty); Public health surveillance; Disease; Medicine; Pathology","score_opus":0.018718613073779033,"score_gpt":0.2686166860727717,"score_spread":0.2498980729989927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405757776","genre_codex":"dataset","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":[0.13068801,0.0075254226,0.008861793,0.0060641947,0.0009351559,0.0003514781,0.81458986,0.0013434445,0.029640716],"genre_scores_gemma":[0.3998653,0.010419493,0.024439782,0.0015648337,0.0003172589,0.00061865617,0.54133344,0.0009501208,0.020490987],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99325037,0.00034044607,0.00068562635,0.0009146559,0.0040570428,0.0007519149],"domain_scores_gemma":[0.9688417,0.0022860833,0.0018049879,0.0017102765,0.024004806,0.0013521533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032682428,0.0006002677,0.00066470954,0.013205264,0.0024272224,0.0032101704,0.0016529968,0.00037802928,0.004751658],"category_scores_gemma":[0.020313874,0.0005023887,0.0006461691,0.04210959,0.00095576013,0.00087851146,0.0015383946,0.0011116321,0.0011946532],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003328993,0.000053548058,0.2814039,0.0017637254,0.00026455845,0.00044079663,0.006674952,0.004974036,0.0015976568,0.007814297,0.40708795,0.2875917],"study_design_scores_gemma":[0.000023918235,0.000021402293,0.53023255,0.0008972839,0.000107401414,0.00011934725,0.0020841933,0.0023485117,0.0010713396,0.00083052565,0.4621195,0.0001440024],"about_ca_topic_score_codex":0.99492407,"about_ca_topic_score_gemma":0.9953904,"teacher_disagreement_score":0.049256947,"about_ca_system_score_codex":0.049256947,"about_ca_system_score_gemma":0.076137125,"threshold_uncertainty_score":0.35738587},"labels":[],"label_agreement":null},{"id":"W4405765420","doi":"10.48550/arxiv.2412.16209","title":"Challenges in the calibration of tree-based models for imbalanced classification","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperparameter; Tree (set theory); Machine learning; Artificial intelligence; Statistics; Econometrics; Random forest; Computer science; Mathematics; Psychology","score_opus":0.22072643659830626,"score_gpt":0.25181084766784667,"score_spread":0.031084411069540407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405765420","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0158191,0.0017579611,0.97123116,0.0068528233,0.0002527173,0.00013886392,0.00033614016,0.0005707338,0.0030404855],"genre_scores_gemma":[0.6192422,0.0030546568,0.36753845,0.0042462363,0.0009099778,0.0010466286,0.0010422609,0.00067661057,0.0022429281],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97069484,0.021093005,0.0011741885,0.0030052946,0.0034479932,0.0005847085],"domain_scores_gemma":[0.86273605,0.11252388,0.00620074,0.01098364,0.006841681,0.00071409025],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.061692975,0.0011412579,0.0022930761,0.0019347204,0.0014391238,0.0050785984,0.0046654306,0.003487152,0.0018237334],"category_scores_gemma":[0.25714675,0.001643217,0.0015320902,0.002285021,0.0044391123,0.007007959,0.0039779074,0.009170295,0.0011326746],"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":[0.00017279784,0.00010186452,0.010122664,0.0005005778,0.000317487,0.00023410737,0.0010929849,0.59058726,0.00075533934,0.28161523,0.008146008,0.106353655],"study_design_scores_gemma":[0.000029016615,0.000032467084,0.0011826593,0.00023458182,0.00002285251,0.00014275956,0.00015139913,0.5149422,0.0006287684,0.47791433,0.00466979,0.000049084225],"about_ca_topic_score_codex":0.0049249213,"about_ca_topic_score_gemma":0.0032268278,"teacher_disagreement_score":0.93830705,"about_ca_system_score_codex":0.0035446417,"about_ca_system_score_gemma":0.0026918151,"threshold_uncertainty_score":0.32626748},"labels":[],"label_agreement":null},{"id":"W4405799500","doi":"10.2196/63149","title":"Harnessing Internet Search Data as a Potential Tool for Medical Diagnosis: Literature Review","year":2024,"lang":"en","type":"review","venue":"JMIR Mental Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprint; The Internet; Computer science; Data science; World Wide Web; Internet privacy; Medicine","score_opus":0.06708307283095832,"score_gpt":0.4848174856317804,"score_spread":0.4177344128008221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405799500","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":[0.0006117647,0.99756634,0.00016995992,0.00070697226,0.00010374382,0.00007495233,0.00017328233,0.000006002236,0.00058707735],"genre_scores_gemma":[0.0051832595,0.9934274,0.00049691787,0.000450793,0.00011486465,0.00012738984,0.00013664289,0.0000037806394,0.00005896997],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.9926676,0.002631795,0.002470861,0.00053712784,0.0014646146,0.00022795398],"domain_scores_gemma":[0.8892147,0.09534114,0.007978245,0.0008905962,0.005912796,0.0006624101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009315863,0.0009726602,0.0031636367,0.026650764,0.0008446247,0.003957061,0.0017603993,0.0025073718,0.00485544],"category_scores_gemma":[0.054627787,0.0008439847,0.0027708746,0.026522908,0.0016609977,0.005303341,0.0022472173,0.0012980853,0.000724965],"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":[0.00014658319,0.00004912502,0.0020140556,0.708726,0.0013930202,0.0002633948,0.0008409111,0.00017795424,0.00018838461,0.0020301987,0.006360516,0.27780986],"study_design_scores_gemma":[0.000049471568,0.00010892255,0.004669377,0.90230685,0.006674304,0.0008652769,0.0010864063,0.00017336707,0.00023578729,0.0013259932,0.0824537,0.00005045433],"about_ca_topic_score_codex":0.005653225,"about_ca_topic_score_gemma":0.0112087,"teacher_disagreement_score":0.026650764,"about_ca_system_score_codex":0.003121097,"about_ca_system_score_gemma":0.013486014,"threshold_uncertainty_score":0.04926759},"labels":[],"label_agreement":null},{"id":"W4405905688","doi":"10.2196/57414","title":"Analyzing Online Search Trends for Kidney, Prostate, and Bladder Cancers in China: Infodemiology Study Using Baidu Search Data (2011-2023)","year":2024,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprint; China; Prostate cancer; Kidney cancer; Medicine; Urology; Computer science; Cancer; Geography; Internal medicine; World Wide Web; Archaeology","score_opus":0.0868996060981746,"score_gpt":0.4435907796276943,"score_spread":0.3566911735295197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405905688","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":[0.98276216,0.0006211996,0.000111669084,0.00014280813,0.0000068540053,0.000030082823,0.015581684,0.000024510751,0.0007190417],"genre_scores_gemma":[0.9880135,0.00028345484,0.00019898836,0.00005952408,0.0000107100495,0.0000420455,0.0109845195,0.0000058544524,0.00040143848],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992355,0.00008791006,0.00014444727,0.00015634538,0.0002296792,0.00014606009],"domain_scores_gemma":[0.99721247,0.00053173874,0.001126564,0.00017002606,0.00058469624,0.00037455905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001131163,0.00036492615,0.0005119267,0.0048617134,0.00042955927,0.00073934434,0.00052713865,0.0003264697,0.001477808],"category_scores_gemma":[0.0028096428,0.00020739113,0.0008627389,0.008865924,0.0002595999,0.0007765386,0.0008276102,0.0003744196,0.00031340183],"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":[0.000046971538,0.000023771487,0.99518365,0.000101566366,0.00007757899,0.00007781988,0.00023759644,0.00019968452,0.00014506481,0.000056550743,0.00070429867,0.0031453806],"study_design_scores_gemma":[0.0000034969128,0.000024222973,0.99728394,0.000017190278,0.000041122774,0.0000588609,0.0003198947,0.0014621353,0.00009530143,0.00001469817,0.00067224365,0.0000068532004],"about_ca_topic_score_codex":0.17196552,"about_ca_topic_score_gemma":0.21151793,"teacher_disagreement_score":0.17196552,"about_ca_system_score_codex":0.0019363324,"about_ca_system_score_gemma":0.002113676,"threshold_uncertainty_score":0.3419292},"labels":[],"label_agreement":null},{"id":"W4405967102","doi":"10.1101/2024.12.27.24319518","title":"Challenges in Estimating Time-Varying Epidemic Severity Rates from Aggregate Data","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Aggregate (composite); Econometrics; Aggregate data; Statistics; Economics; Mathematics; Materials science","score_opus":0.12226295429110917,"score_gpt":0.36420638072100664,"score_spread":0.24194342642989747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405967102","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":[0.1606429,0.00286242,0.81896,0.008475208,0.0003367911,0.00023215757,0.00539102,0.0007165733,0.002382998],"genre_scores_gemma":[0.8127807,0.001275021,0.1779478,0.0010948575,0.00042778128,0.00034186098,0.0048813573,0.00014170399,0.001108916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9907831,0.0069717034,0.0004809202,0.0009235685,0.0006719293,0.00016874354],"domain_scores_gemma":[0.8835133,0.10191116,0.0049849087,0.0064521474,0.0026891627,0.00044927074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029382985,0.0007627051,0.0012224038,0.0015565116,0.0004944755,0.0025233235,0.0021838716,0.002012994,0.00081243744],"category_scores_gemma":[0.14531387,0.00090709364,0.0008775573,0.0018197232,0.0011284293,0.002271614,0.0016843227,0.0024509788,0.0004233765],"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":[0.00018547477,0.00012555788,0.12884137,0.0008411879,0.0006312974,0.0003360087,0.0007317408,0.72380865,0.002371547,0.05197639,0.007187431,0.08296327],"study_design_scores_gemma":[0.000038606206,0.00005579913,0.01663006,0.00020917303,0.0000489111,0.00018568589,0.0002999157,0.8643049,0.0013597201,0.11265731,0.00414131,0.00006860256],"about_ca_topic_score_codex":0.009438616,"about_ca_topic_score_gemma":0.0051946514,"teacher_disagreement_score":0.029382985,"about_ca_system_score_codex":0.00084729923,"about_ca_system_score_gemma":0.0010605898,"threshold_uncertainty_score":0.15539396},"labels":[],"label_agreement":null},{"id":"W4406018067","doi":"10.2139/ssrn.5078277","title":"Analyzing Reddit Social Media Content in the United States Related to H5N1: A Sentiment and Topic Modeling Study","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Health Canada; Artificial Intelligence in Medicine (Canada); York University; University of Toronto","funders":"","keywords":"Download; Computer science; Social media; World Wide Web; Internet privacy","score_opus":0.034576237898412035,"score_gpt":0.3174738620034144,"score_spread":0.28289762410500235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406018067","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":[0.9955872,0.00018175297,0.00064682437,0.00046868518,0.000028669787,0.00001520998,0.0021097702,0.000012501788,0.00094933505],"genre_scores_gemma":[0.99498606,0.00014749517,0.0006184029,0.000092538794,0.00008869815,0.000016383148,0.0034539935,0.000009611574,0.00058679533],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965215,0.00016298098,0.000020903406,0.00006883092,0.000052229443,0.00004301656],"domain_scores_gemma":[0.9947955,0.0037271203,0.0006267941,0.00016307151,0.00050277344,0.00018480308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012315572,0.00027043506,0.00021621272,0.0012618683,0.00035922445,0.0008295722,0.00027680214,0.0004968875,0.00074214605],"category_scores_gemma":[0.0056189517,0.00014429081,0.00041229924,0.0014117003,0.00017702226,0.0007424389,0.00040848777,0.0005790587,0.0003037937],"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":[0.00026313117,0.0003164695,0.96225125,0.00008540321,0.00021286466,0.00018964199,0.0013698046,0.0037272812,0.002722269,0.0007083849,0.0062400093,0.021913612],"study_design_scores_gemma":[0.000012144241,0.000095359836,0.9123805,0.00003812373,0.00015893404,0.00010918805,0.0029492895,0.07919193,0.000987948,0.00064316316,0.0034152456,0.000018334667],"about_ca_topic_score_codex":0.021186082,"about_ca_topic_score_gemma":0.022987736,"teacher_disagreement_score":0.021186082,"about_ca_system_score_codex":0.0005495279,"about_ca_system_score_gemma":0.00028304395,"threshold_uncertainty_score":0.042125523},"labels":[],"label_agreement":null},{"id":"W4406525832","doi":"10.3390/covid5010012","title":"The Disappearance of COVID-19 Data Dashboards: The Case of Ephemeral Data","year":2025,"lang":"en","type":"article","venue":"COVID","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Institute of Advanced Industrial Science and Technology; National Aeronautics and Space Administration","keywords":"Ephemeral key; Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Computer science; Pandemic; Data science; Geography; Biology; Computer security; Virology; Medicine; Outbreak","score_opus":0.08474136929157326,"score_gpt":0.41936001447980403,"score_spread":0.33461864518823076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406525832","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17021018,0.16149157,0.24593833,0.20071596,0.011295056,0.0015364937,0.022339521,0.008457888,0.1780151],"genre_scores_gemma":[0.63863087,0.085061714,0.2123838,0.0290896,0.004183638,0.0012481427,0.013103897,0.003576142,0.012722224],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9554304,0.0205237,0.0063806274,0.0039253524,0.012455818,0.0012841604],"domain_scores_gemma":[0.6996744,0.20588987,0.019528154,0.031920377,0.04002382,0.0029633602],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04830445,0.0008186089,0.0009975871,0.00872044,0.0016955473,0.014373963,0.0024877423,0.0021354193,0.0042810347],"category_scores_gemma":[0.18398656,0.0010043087,0.0018731715,0.013335334,0.00443773,0.022007547,0.0049590203,0.005811413,0.0014068834],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034441636,0.000109428096,0.046292443,0.011159591,0.00044359275,0.0011752899,0.01634384,0.003720488,0.002203593,0.14346634,0.14005858,0.6346824],"study_design_scores_gemma":[0.00003173452,0.0001237876,0.017926686,0.008371412,0.00016499906,0.0011273682,0.009035518,0.003792341,0.002998138,0.023946244,0.9323027,0.00017914065],"about_ca_topic_score_codex":0.009587702,"about_ca_topic_score_gemma":0.010981591,"teacher_disagreement_score":0.95169556,"about_ca_system_score_codex":0.0033590675,"about_ca_system_score_gemma":0.0043344377,"threshold_uncertainty_score":0.2554614},"labels":[],"label_agreement":null},{"id":"W4406774434","doi":"10.3389/fneph.2025.1519481","title":"Global and national public awareness and interest in glomerular diseases from 2004 to 2024","year":2025,"lang":"en","type":"article","venue":"Frontiers in Nephrology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Lupus nephritis; Focal segmental glomerulosclerosis; Nephropathy; Public health; Medicine; Glomerulonephritis; Diabetic nephropathy; Membranous nephropathy; Glomerulosclerosis; Demography; Political science; Geography; Internal medicine; Diabetes mellitus; Proteinuria; Endocrinology; Pathology; Kidney; Disease; Sociology","score_opus":0.016405670491274015,"score_gpt":0.2916509807618373,"score_spread":0.2752453102705633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406774434","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":[0.8683751,0.015339589,0.0003212977,0.005381912,0.00018542034,0.00006849603,0.09187593,0.0001292199,0.018323058],"genre_scores_gemma":[0.9791667,0.0038947978,0.00030494676,0.0005343965,0.000120462726,0.000050616807,0.01472965,0.000012705764,0.0011857258],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999102,0.00019203301,0.00016159468,0.00013152824,0.00022669611,0.0001862201],"domain_scores_gemma":[0.9942053,0.0012306105,0.0028450615,0.00013396775,0.00089103694,0.0006940846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010494371,0.00021368414,0.00026534183,0.0048754015,0.00022478022,0.0013625986,0.0003598725,0.00043629654,0.0039324793],"category_scores_gemma":[0.0060184277,0.0000943841,0.0008814768,0.0057102186,0.00028358173,0.0017233454,0.0012485036,0.000553938,0.0008325839],"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":[0.00013734109,0.000034922836,0.95576185,0.0010072031,0.00020019156,0.00015668295,0.0011143901,0.000322622,0.0001469475,0.00041688545,0.010997513,0.0297034],"study_design_scores_gemma":[0.000007279835,0.00008305476,0.98000485,0.00057395495,0.00014702608,0.00040940993,0.0025309073,0.00038714366,0.00013695804,0.00015102616,0.01555099,0.000017468596],"about_ca_topic_score_codex":0.022876099,"about_ca_topic_score_gemma":0.02458143,"teacher_disagreement_score":0.022876099,"about_ca_system_score_codex":0.000879276,"about_ca_system_score_gemma":0.000977695,"threshold_uncertainty_score":0.045485854},"labels":[],"label_agreement":null},{"id":"W4406945804","doi":"10.2196/58767","title":"Monitoring Public Health Through a Comprehensive Primary Care Database in the Netherlands: Overview of the Nivel Syndromic Surveillance System","year":2025,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprint; Public health surveillance; Public health; Primary care; Medicine; Database; Data science; Computer science; Environmental health; World Wide Web; Family medicine; Nursing","score_opus":0.0735716439734698,"score_gpt":0.3528006809434492,"score_spread":0.27922903696997936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406945804","genre_codex":"dataset","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":[0.2719373,0.02817693,0.0934819,0.0070895874,0.00042795864,0.013350953,0.5555943,0.0037305139,0.026210496],"genre_scores_gemma":[0.42907146,0.013457715,0.23555459,0.0014246166,0.00029175,0.014102644,0.30222312,0.0006448057,0.0032292474],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.979731,0.008325477,0.005479741,0.002823047,0.0032345243,0.000406312],"domain_scores_gemma":[0.9750406,0.0095057255,0.0048364676,0.0021317923,0.0071521453,0.0013332766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01850992,0.0006191538,0.0015585872,0.008136961,0.00046909766,0.0043291477,0.0025205573,0.0009938973,0.0025654593],"category_scores_gemma":[0.045596275,0.00093991845,0.0011884747,0.015119768,0.00051077595,0.0028584744,0.0025210488,0.0006165627,0.0007731771],"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":[0.0005665937,0.00034989638,0.58954436,0.007295746,0.0016051653,0.00081484014,0.0026595127,0.008510286,0.001679203,0.0049849106,0.058583498,0.32340604],"study_design_scores_gemma":[0.0005723083,0.00032571677,0.7715697,0.0035909114,0.0007752637,0.0014565177,0.0020152142,0.04165894,0.0018695692,0.002426053,0.17347388,0.00026583063],"about_ca_topic_score_codex":0.12683871,"about_ca_topic_score_gemma":0.070565395,"teacher_disagreement_score":0.12683871,"about_ca_system_score_codex":0.0052371966,"about_ca_system_score_gemma":0.009656001,"threshold_uncertainty_score":0.25220084},"labels":[],"label_agreement":null},{"id":"W4406971157","doi":"10.2196/53328","title":"Assessing Public Interest in Mammography, Computed Tomography Lung Cancer Screening, and Computed Tomography Colonography Screening Examinations Using Internet Search Data: Cross-Sectional Study","year":2025,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Mammography; Lung cancer screening; Lung cancer; Preprint; Cross-sectional study; Cancer screening; Radiology; Medical physics; Breast cancer; Cancer; Oncology; Internal medicine; Pathology; Computer science; World Wide Web","score_opus":0.14568159859585117,"score_gpt":0.44031057256456335,"score_spread":0.2946289739687122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406971157","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":[0.9951095,0.0006713796,0.00011992085,0.00008954075,0.000006214322,0.00006606617,0.0030780283,0.0000070788224,0.0008523655],"genre_scores_gemma":[0.9970697,0.00040399111,0.00020883426,0.00006858021,0.000013496353,0.00007850158,0.0019908224,0.0000070333303,0.00015904797],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9966718,0.0009709036,0.0007694813,0.0005487516,0.000750594,0.00028850927],"domain_scores_gemma":[0.9816113,0.006422965,0.007976531,0.0006834456,0.0023654234,0.00094042724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004714204,0.00022852192,0.00052788155,0.0059191156,0.0004689975,0.0013860745,0.0005527378,0.00067834754,0.0018086047],"category_scores_gemma":[0.015250412,0.00046140482,0.00090934493,0.007980302,0.00052015175,0.0025412997,0.0012576442,0.00082307827,0.00055524916],"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":[0.000039538463,0.000040652223,0.99843484,0.00006604249,0.00007634099,0.000035031844,0.00029188092,0.000027155684,0.000036267313,0.000024944802,0.00015283635,0.00077451323],"study_design_scores_gemma":[0.0000049414716,0.0000997775,0.99703336,0.000047503752,0.0000708832,0.0002825125,0.0015364137,0.00032820783,0.00004874124,0.000017876775,0.00052070327,0.00000904221],"about_ca_topic_score_codex":0.010151123,"about_ca_topic_score_gemma":0.009066794,"teacher_disagreement_score":0.010151123,"about_ca_system_score_codex":0.0007328661,"about_ca_system_score_gemma":0.0006302146,"threshold_uncertainty_score":0.02493143},"labels":[],"label_agreement":null},{"id":"W4407056447","doi":"10.1016/j.artmed.2025.103076","title":"Leveraging deep-learning and unconventional data for real-time surveillance, forecasting, and early warning of respiratory pathogens outbreak","year":2025,"lang":"en","type":"article","venue":"Artificial Intelligence in Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University; Artificial Intelligence in Medicine (Canada)","funders":"Foreign, Commonwealth and Development Office; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; International Development Research Centre","keywords":"Computer science; Warning system; Coronavirus disease 2019 (COVID-19); Deep learning; Outbreak; Artificial intelligence; Data science; Machine learning; Data mining; Infectious disease (medical specialty); Virology; Medicine; Telecommunications","score_opus":0.07791149259808346,"score_gpt":0.3554139716428498,"score_spread":0.2775024790447664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407056447","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":[0.463603,0.0026552875,0.50834614,0.004202261,0.0005381428,0.00021735077,0.005471323,0.007949079,0.0070174136],"genre_scores_gemma":[0.94756734,0.00039366822,0.04799153,0.0002770736,0.00007233944,0.000058804144,0.0024110998,0.000044527696,0.001183651],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999566,0.00007173791,0.00003212733,0.00014105174,0.00009576743,0.00009322408],"domain_scores_gemma":[0.99891627,0.0003446913,0.00016020464,0.00014963734,0.00035579002,0.00007344145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009964487,0.0011369127,0.00045389688,0.00083600427,0.00035194572,0.00094948174,0.0010497941,0.000828063,0.00079033803],"category_scores_gemma":[0.0033920102,0.000344235,0.0005612567,0.0007181903,0.00042465806,0.0014378327,0.001089619,0.0014602824,0.00043934237],"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":[0.0002735934,0.00044018897,0.04617086,0.00022616178,0.00019486084,0.00028783325,0.00017050744,0.73993725,0.010215345,0.0019836817,0.008637403,0.19146235],"study_design_scores_gemma":[0.000004879989,0.00002932839,0.0022775198,0.000015372598,0.000015440215,0.000013447558,0.000025362731,0.9936365,0.002163062,0.0011368907,0.0006737989,0.0000084761905],"about_ca_topic_score_codex":0.055538535,"about_ca_topic_score_gemma":0.066583306,"teacher_disagreement_score":0.055538535,"about_ca_system_score_codex":0.0013437299,"about_ca_system_score_gemma":0.0018864145,"threshold_uncertainty_score":0.11043054},"labels":[],"label_agreement":null},{"id":"W4407219777","doi":"10.3138/9781487552718-007","title":"Chapter Five. Monitoring Uncertainty in Alberta","year":2024,"lang":"en","type":"book-chapter","venue":"University of Toronto Press eBooks","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Geography; Environmental science","score_opus":0.01983193602857846,"score_gpt":0.23369653214625044,"score_spread":0.21386459611767197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407219777","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019457424,0.120935604,0.013815469,0.01300803,0.003221404,0.00006544389,0.0011468942,0.00044080717,0.8454206],"genre_scores_gemma":[0.054753374,0.121251605,0.016061736,0.002522272,0.0008636451,0.000053084386,0.0009952378,0.00024135204,0.80325776],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995103,0.000068836125,0.000018748455,0.000051867257,0.00028378348,0.00006646245],"domain_scores_gemma":[0.99931085,0.00025953882,0.000025856485,0.000032526943,0.00029876045,0.00007242342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008795831,0.0007866683,0.00041896358,0.0023712262,0.0014105183,0.0048934817,0.0012206627,0.0010801979,0.034415923],"category_scores_gemma":[0.0020733508,0.00036962275,0.00038138786,0.0057073683,0.0013626951,0.0014832478,0.0011124054,0.0013591341,0.0035187106],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034516765,0.000029108358,0.0010115508,0.00038909726,0.000023585892,0.0001801773,0.0010472566,0.0073398678,0.0002554735,0.13830058,0.43938783,0.41200092],"study_design_scores_gemma":[0.0000034005634,0.0000075476246,0.0014910295,0.00040122654,0.000013032261,0.000058478556,0.0006438604,0.0014909066,0.00018616606,0.034600914,0.96108234,0.000021124217],"about_ca_topic_score_codex":0.7431323,"about_ca_topic_score_gemma":0.86886436,"teacher_disagreement_score":0.2568677,"about_ca_system_score_codex":0.02138408,"about_ca_system_score_gemma":0.016264295,"threshold_uncertainty_score":0.5167609},"labels":[],"label_agreement":null},{"id":"W4407568760","doi":"10.1101/2025.02.13.638012","title":"Developing Foundation Models for Predicting Viral Animal Host Range in Intelligent Surveillance","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Key Research and Development Program of China; Peking University; National Natural Science Foundation of China","keywords":"Host (biology); Foundation (evidence); Range (aeronautics); Computer science; Virology; Biology; Engineering; Political science; Ecology; Aerospace engineering","score_opus":0.03026296275175833,"score_gpt":0.2780579926039816,"score_spread":0.2477950298522233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407568760","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":[0.2913818,0.0010726748,0.6992992,0.0012296895,0.00009461759,0.00008275273,0.0014667608,0.0027228072,0.0026496497],"genre_scores_gemma":[0.92966086,0.00022765319,0.06701985,0.00020306914,0.000059966194,0.00009493029,0.0015319914,0.00006511392,0.0011365375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995578,0.00016452609,0.000025025833,0.0001358622,0.000057533267,0.00005922657],"domain_scores_gemma":[0.9974177,0.00187645,0.00024115865,0.00013327561,0.00024562562,0.00008575346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018076331,0.0009111354,0.0007347209,0.0012386764,0.00028772757,0.00077884964,0.0009468672,0.0009170378,0.0010280482],"category_scores_gemma":[0.004674542,0.00045905903,0.0008390572,0.0005453928,0.00051038875,0.0011375475,0.00090934924,0.0010546749,0.00027293726],"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":[0.00009818421,0.00005300496,0.010169817,0.00004217103,0.00006991432,0.0000663589,0.000035061614,0.9481863,0.0010163268,0.0018228826,0.0011930866,0.037246972],"study_design_scores_gemma":[0.000002098133,0.000008987265,0.00020145309,0.0000034240063,0.0000032194596,0.000005578224,0.0000027279718,0.99861765,0.00010309588,0.0009717171,0.00007848628,0.0000015176718],"about_ca_topic_score_codex":0.0080990875,"about_ca_topic_score_gemma":0.009198934,"teacher_disagreement_score":0.0080990875,"about_ca_system_score_codex":0.0008266014,"about_ca_system_score_gemma":0.00086493324,"threshold_uncertainty_score":0.016103864},"labels":[],"label_agreement":null},{"id":"W4407573348","doi":"10.52041/iase2023.607","title":"Engaging students by working with real-life survey data concerning COVID-19 pandemic","year":2024,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Pandemic; Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Computer science; Data science; Virology; Medicine; Outbreak","score_opus":0.24468053085755453,"score_gpt":0.4228710408567952,"score_spread":0.1781905099992407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407573348","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":[0.803899,0.0007321331,0.060770653,0.08070191,0.0023352038,0.0014893044,0.001714923,0.0025827477,0.045774035],"genre_scores_gemma":[0.911426,0.0006890641,0.056580875,0.01366334,0.00048167453,0.0011070134,0.0013269524,0.00029756041,0.014427469],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98644686,0.009697401,0.000399144,0.0012391642,0.00096398307,0.0012534531],"domain_scores_gemma":[0.9286451,0.03428324,0.005081351,0.006012726,0.008248531,0.017729077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013756888,0.0009696822,0.00060531625,0.0010660711,0.002660461,0.008444984,0.0020579875,0.0026306142,0.0169823],"category_scores_gemma":[0.075881146,0.0005549021,0.00066149316,0.00063136464,0.0013922607,0.004926665,0.008721932,0.004140219,0.009174055],"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":[0.0008513429,0.010915438,0.27890965,0.001193603,0.00025238917,0.0010799777,0.15069687,0.0036667015,0.010266286,0.005304703,0.12474086,0.41212216],"study_design_scores_gemma":[0.00035323494,0.00392804,0.05927065,0.0012481635,0.00024305999,0.001127826,0.30173573,0.017715814,0.010863986,0.02857733,0.5744834,0.00045274562],"about_ca_topic_score_codex":0.0012458764,"about_ca_topic_score_gemma":0.0037112245,"teacher_disagreement_score":0.0169823,"about_ca_system_score_codex":0.0013122619,"about_ca_system_score_gemma":0.004454639,"threshold_uncertainty_score":0.072754264},"labels":[],"label_agreement":null},{"id":"W4408084830","doi":"10.1162/2e3983f5.ab25bc3c","title":"Review 1: \"Inconsistent Rebound of Invasive Pneumococcal Disease in Connecticut following the Initial Phase of the SARS-CoV-2 Pandemic\"","year":2025,"lang":"en","type":"peer-review","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"NOSM University","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Pneumococcal disease; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Virology; Medicine; Phase (matter); Disease; Streptococcus pneumoniae; Infectious disease (medical specialty); Biology; Microbiology; Internal medicine; Outbreak; Chemistry","score_opus":0.10105869387192806,"score_gpt":0.4215100761431911,"score_spread":0.320451382271263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408084830","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007581444,0.16060662,0.0020038676,0.34778395,0.46524814,0.0037697097,0.0093859425,0.0005404857,0.0099031925],"genre_scores_gemma":[0.016987773,0.36036742,0.0053063626,0.3468477,0.22622502,0.00538101,0.008453117,0.0008707066,0.02956096],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9459723,0.018531617,0.012883994,0.002770988,0.01852544,0.0013156583],"domain_scores_gemma":[0.6174986,0.10739241,0.02900834,0.008244875,0.2325096,0.005346173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.059510794,0.0019325479,0.0056762076,0.012290039,0.0026459682,0.0071347146,0.0051570856,0.0077631325,0.018056147],"category_scores_gemma":[0.34156623,0.0014625598,0.004113948,0.008127376,0.0031814836,0.004223225,0.0035444633,0.004035909,0.008155701],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008612357,0.000009042942,0.0003125894,0.028182589,0.00029478438,0.000064630476,0.00010602739,0.00003753593,0.000074374046,0.00041757137,0.9392809,0.031133795],"study_design_scores_gemma":[0.0003506364,0.000091534755,0.0038788766,0.124184914,0.0013279775,0.00035526865,0.00025910238,0.00020366564,0.00020383915,0.0011155535,0.8679275,0.000101133235],"about_ca_topic_score_codex":0.017656485,"about_ca_topic_score_gemma":0.037254434,"teacher_disagreement_score":0.059510794,"about_ca_system_score_codex":0.009800715,"about_ca_system_score_gemma":0.026825495,"threshold_uncertainty_score":0.31472695},"labels":[],"label_agreement":null},{"id":"W4408084856","doi":"10.1162/2e3983f5.14b67a8e","title":"Review 3: \"Inconsistent Rebound of Invasive Pneumococcal Disease in Connecticut following the Initial Phase of the SARS-CoV-2 Pandemic\"","year":2025,"lang":"en","type":"peer-review","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Pneumococcal disease; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Virology; 2019-20 coronavirus outbreak; Medicine; Disease; Streptococcus pneumoniae; Infectious disease (medical specialty); Biology; Microbiology; Internal medicine; Outbreak","score_opus":0.09969584190604527,"score_gpt":0.422566336509469,"score_spread":0.32287049460342376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408084856","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007631996,0.1623988,0.0020074376,0.34364992,0.46478537,0.0045142267,0.010038754,0.0006177782,0.011224464],"genre_scores_gemma":[0.015397916,0.33713278,0.005167007,0.38579175,0.20995495,0.006434429,0.008124847,0.0009655772,0.031030733],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9464353,0.018610287,0.0122112,0.002609412,0.018781621,0.0013523282],"domain_scores_gemma":[0.65767884,0.10016421,0.028720627,0.008812984,0.19949283,0.0051305406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06346495,0.0018562063,0.005676105,0.011170333,0.0026167398,0.0074241194,0.0056056436,0.008579935,0.020149432],"category_scores_gemma":[0.32865658,0.0015782492,0.00476973,0.0073858476,0.003195483,0.004065787,0.0036389178,0.004201857,0.008721252],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010567478,0.000009908977,0.00027569712,0.029638564,0.00034901424,0.0000651064,0.00010263755,0.00004007567,0.00008606478,0.00042496,0.9400534,0.028848989],"study_design_scores_gemma":[0.00053106976,0.00010026238,0.0036473135,0.13439108,0.0016570935,0.00033932985,0.00021743635,0.0002229796,0.00021549173,0.0012114656,0.857369,0.000097453456],"about_ca_topic_score_codex":0.02050038,"about_ca_topic_score_gemma":0.042762965,"teacher_disagreement_score":0.06346495,"about_ca_system_score_codex":0.01074711,"about_ca_system_score_gemma":0.027221452,"threshold_uncertainty_score":0.33563876},"labels":[],"label_agreement":null},{"id":"W4408100928","doi":"10.1371/journal.pone.0296206","title":"Use of a single case-finding questionnaire to simultaneously target multiple related diseases allows enhanced disease detection","year":2025,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","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; Ottawa Hospital; University of Ottawa","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Disease; Pathology","score_opus":0.04616708323457396,"score_gpt":0.2585276891649817,"score_spread":0.21236060593040773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408100928","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":[0.6360513,0.00065190735,0.35140145,0.0005594443,0.000100943435,0.0077672345,0.0006774631,0.00048639264,0.0023038643],"genre_scores_gemma":[0.68078524,0.0002841744,0.312854,0.00025964706,0.00008786452,0.0049529895,0.00029584143,0.00003002626,0.00045015218],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.97513247,0.016163459,0.0015641327,0.0027876012,0.0040098815,0.00034249548],"domain_scores_gemma":[0.9419005,0.037512448,0.010186363,0.005269633,0.0043763937,0.00075474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.034962025,0.0011060486,0.0011881002,0.0027040965,0.00036938023,0.0008958461,0.0016072401,0.0011251812,0.001656231],"category_scores_gemma":[0.06156663,0.00046007626,0.0011025995,0.001362633,0.0008666022,0.0014528448,0.0017086286,0.0005261889,0.00056591287],"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":[0.0017272992,0.0042518047,0.668989,0.001174984,0.00071182265,0.00053888146,0.0012846987,0.004378335,0.03176639,0.0013461743,0.00094016653,0.2828904],"study_design_scores_gemma":[0.0012973637,0.02943041,0.85116756,0.000409154,0.0009664524,0.00577236,0.0009808057,0.05473844,0.045346823,0.00427613,0.0053259516,0.00028868736],"about_ca_topic_score_codex":0.0005440613,"about_ca_topic_score_gemma":0.0007286425,"teacher_disagreement_score":0.034962025,"about_ca_system_score_codex":0.00047300477,"about_ca_system_score_gemma":0.0016195579,"threshold_uncertainty_score":0.18489909},"labels":[],"label_agreement":null},{"id":"W4408144022","doi":"10.2196/64020","title":"Breast Cancer Screening Participation and Internet Search Activity in a Japanese Population: Decade-Long Time-Series Study","year":2025,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"The Internet; Breast cancer; Cancer; Series (stratigraphy); Population; Medicine; Oncology; Demography; Computer science; World Wide Web; Internal medicine; Environmental health; Biology; Sociology","score_opus":0.024068240406359104,"score_gpt":0.38085706134193503,"score_spread":0.35678882093557596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408144022","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":[0.9978479,0.00047730142,0.00012079578,0.00006439745,0.000010415914,0.000033694083,0.0011224343,0.0000056168046,0.0003174936],"genre_scores_gemma":[0.9968951,0.0003071361,0.0001553613,0.000077189965,0.000026239957,0.0000705774,0.0021666812,0.000004884855,0.00029686012],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99914324,0.00015941991,0.00018412646,0.00023505415,0.0001486565,0.0001294801],"domain_scores_gemma":[0.9977785,0.00031418967,0.0008302447,0.00029021568,0.00040832354,0.0003785684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011665701,0.00040646864,0.0006491195,0.0014677829,0.00062134454,0.0007192224,0.0005621189,0.00058247155,0.0012811951],"category_scores_gemma":[0.0027679354,0.00040552064,0.001251138,0.0029443733,0.0003015346,0.0007552055,0.0007609737,0.00075814314,0.00039697043],"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":[0.000067228364,0.000053935546,0.9984409,0.000031910713,0.00013376345,0.0000676977,0.00021014766,0.000032964603,0.00006419865,0.00001050638,0.00015936865,0.0007273089],"study_design_scores_gemma":[0.0000070919377,0.00010230234,0.998467,0.0000120851655,0.00015005525,0.00011044086,0.0005234928,0.0003363042,0.000021351825,0.000013282493,0.00024960612,0.0000069183247],"about_ca_topic_score_codex":0.026373671,"about_ca_topic_score_gemma":0.025815839,"teacher_disagreement_score":0.026373671,"about_ca_system_score_codex":0.00069849024,"about_ca_system_score_gemma":0.0006820783,"threshold_uncertainty_score":0.052440345},"labels":[],"label_agreement":null},{"id":"W4408426859","doi":"10.5194/egusphere-egu25-10685","title":"Leveraging modern geospatial data science techniques for multi-hazard exposure analysis in British Columbia, Canada","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Future Earth; BGC Engineering (Canada)","funders":"","keywords":"Geospatial analysis; Hazard; Data science; Geography; Geomatics; Computer science; Remote sensing","score_opus":0.04213762643359533,"score_gpt":0.3212531562661186,"score_spread":0.2791155298325233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408426859","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":[0.4837833,0.008030467,0.17753465,0.022600966,0.0004199223,0.002574271,0.08892554,0.009058928,0.20707196],"genre_scores_gemma":[0.726644,0.0053422693,0.19668712,0.0010747941,0.000030562125,0.00043916743,0.022254936,0.0010079531,0.0465192],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9982052,0.00019695086,0.00014354917,0.00030011864,0.00089007994,0.0002641651],"domain_scores_gemma":[0.99471164,0.0006743053,0.00017252794,0.00036912435,0.0036020256,0.00047035553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018468711,0.0005359269,0.00036345268,0.0043267603,0.0033559594,0.00500204,0.0013637735,0.0003754123,0.0044760867],"category_scores_gemma":[0.006942286,0.0005750993,0.00054904545,0.009315142,0.0012307749,0.0010995237,0.0021615801,0.0008498269,0.0007729922],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021594616,0.00021649899,0.19825661,0.0008375226,0.0002609614,0.0011716064,0.006278413,0.065931275,0.0051128613,0.02361449,0.1057111,0.5923927],"study_design_scores_gemma":[0.0000776215,0.00006246439,0.3901429,0.0010309363,0.0001912453,0.0002233197,0.018869352,0.19281358,0.005539202,0.013807896,0.37685168,0.0003898581],"about_ca_topic_score_codex":0.9980844,"about_ca_topic_score_gemma":0.9987878,"teacher_disagreement_score":0.049782258,"about_ca_system_score_codex":0.049782258,"about_ca_system_score_gemma":0.100826435,"threshold_uncertainty_score":0.3611973},"labels":[],"label_agreement":null},{"id":"W4408733262","doi":"10.1016/j.focus.2025.100334","title":"Spatially Continuous Maps of Disease Risk: An Analysis of Mortality Disparities in the United States","year":2025,"lang":"en","type":"article","venue":"AJPM Focus","topic":"Data-Driven Disease Surveillance","field":"Medicine","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; University of Toronto","keywords":"Medicine; Disease; Geography; Environmental health; Internal medicine","score_opus":0.012804688982106377,"score_gpt":0.2987701560365936,"score_spread":0.2859654670544872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408733262","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":[0.9606601,0.0019162984,0.014078881,0.0021480937,0.00004174295,0.0001472903,0.015060987,0.00030634995,0.0056402455],"genre_scores_gemma":[0.9867713,0.00047783367,0.00962588,0.000044394917,0.000013646594,0.000085021515,0.0027321926,0.000015255703,0.0002343884],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99894017,0.00058925233,0.00006065306,0.00016390935,0.00018142194,0.000064663196],"domain_scores_gemma":[0.9969207,0.0016592818,0.0005537631,0.00025376177,0.0005069921,0.00010544813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002040607,0.00029221686,0.00034107437,0.0046282243,0.0005000093,0.001442517,0.0005968394,0.00034680747,0.0017112055],"category_scores_gemma":[0.0118268225,0.00017510344,0.00089198264,0.006871756,0.0005540845,0.0008330838,0.002156253,0.0004296127,0.00013720123],"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":[0.00012828049,0.0001146067,0.8580013,0.00037787925,0.0006075984,0.0002784442,0.0033206902,0.04804541,0.0002461136,0.013376257,0.007023261,0.06848019],"study_design_scores_gemma":[0.00003701024,0.00010312782,0.87368184,0.00029573918,0.00021646688,0.0001789136,0.0072505292,0.08056705,0.00020600099,0.018956069,0.018436547,0.00007073421],"about_ca_topic_score_codex":0.09223517,"about_ca_topic_score_gemma":0.0746672,"teacher_disagreement_score":0.09223517,"about_ca_system_score_codex":0.0014939351,"about_ca_system_score_gemma":0.0013173636,"threshold_uncertainty_score":0.18339664},"labels":[],"label_agreement":null},{"id":"W4408889478","doi":"10.1109/ic2ai62984.2025.10932178","title":"Enhancing Epidemic Early Warning Systems with Social Media Data","year":2025,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Social media; Warning system; Computer science; Internet privacy; Data science; Computer security; Telecommunications; World Wide Web","score_opus":0.035162329387536294,"score_gpt":0.3126290845105228,"score_spread":0.2774667551229865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408889478","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":[0.10442441,0.33573377,0.4009949,0.08314475,0.0042414125,0.0043349117,0.015729263,0.0034979759,0.047898572],"genre_scores_gemma":[0.5814433,0.14848165,0.25046125,0.00637352,0.0017609117,0.0022014861,0.006571647,0.00016819706,0.0025380016],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9900531,0.006918377,0.00086847704,0.00064923655,0.0013432738,0.0001675013],"domain_scores_gemma":[0.91296726,0.07338635,0.0039616,0.0021290428,0.0071010366,0.00045480125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019735582,0.0012824276,0.0011702932,0.0069440487,0.00057588564,0.004665827,0.0015280661,0.0016723569,0.0021850865],"category_scores_gemma":[0.07076178,0.0005584019,0.0022150162,0.0034318538,0.0006769555,0.0065470543,0.0026621316,0.001899766,0.00055062],"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":[0.00029385407,0.00037765593,0.026788035,0.035616472,0.002993986,0.00022537989,0.0021407623,0.024617078,0.0037748741,0.02552771,0.021124277,0.85651994],"study_design_scores_gemma":[0.00052844424,0.0017878751,0.05197121,0.07434998,0.008672578,0.00062571414,0.0070521818,0.2186,0.020349845,0.14075914,0.47457594,0.00072714355],"about_ca_topic_score_codex":0.004283235,"about_ca_topic_score_gemma":0.0067459387,"teacher_disagreement_score":0.019735582,"about_ca_system_score_codex":0.0013466624,"about_ca_system_score_gemma":0.0031821025,"threshold_uncertainty_score":0.10437304},"labels":[],"label_agreement":null},{"id":"W4408906477","doi":"10.2196/preprints.75025","title":"Analyzing Public Google Search Interest on Measles in Canada: Identifying Key Moments for Targeted Risk Communication (Preprint)","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Preprint; Key (lock); Internet privacy; Computer science; Data science; Computer security; World Wide Web","score_opus":0.0904232382622655,"score_gpt":0.3446667577445044,"score_spread":0.2542435194822389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408906477","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":[0.6726164,0.001374011,0.00064025266,0.0025436848,0.000101074555,0.000095389405,0.30309063,0.0005452163,0.018993214],"genre_scores_gemma":[0.8848336,0.0010670524,0.0014480521,0.0002623173,0.000052289677,0.0000531348,0.10208984,0.00011325213,0.010080498],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993166,0.000032578668,0.000045517634,0.000088338515,0.0002927805,0.00022418714],"domain_scores_gemma":[0.99551815,0.0007495293,0.00096554676,0.00014703641,0.002151606,0.00046823226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004776184,0.0003313541,0.00031869762,0.0049831565,0.00094078336,0.0027004492,0.00049380027,0.00039302142,0.004534797],"category_scores_gemma":[0.0049199886,0.00013620975,0.0004087556,0.011071465,0.0004666534,0.00065656117,0.0009385965,0.0004645707,0.0015578846],"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":[0.00021032692,0.000043313645,0.8613472,0.00040373893,0.00011749125,0.00030910748,0.0024680758,0.0018594284,0.00084550556,0.0023116819,0.10126738,0.028816743],"study_design_scores_gemma":[0.000007665356,0.000017487322,0.96238285,0.00011934631,0.00004789742,0.00009291296,0.005353206,0.0042041223,0.00064388086,0.00027772915,0.026814023,0.000038936392],"about_ca_topic_score_codex":0.9658386,"about_ca_topic_score_gemma":0.9771713,"teacher_disagreement_score":0.03416139,"about_ca_system_score_codex":0.007941648,"about_ca_system_score_gemma":0.015546421,"threshold_uncertainty_score":0.06872511},"labels":[],"label_agreement":null},{"id":"W4409239746","doi":"10.24057/2071-9388-2025-3568","title":"Exploring Geospatial Clusters of Five Primary Respiratory Diseases in South Punjab, Pakistan: An Epidemiological Examination","year":2025,"lang":"en","type":"article","venue":"GEOGRAPHY ENVIRONMENT SUSTAINABILITY","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Geospatial analysis; Epidemiology; Geography; Geomatics; Environmental health; Cartography; Medicine; Pathology","score_opus":0.026361066730395,"score_gpt":0.2839505439108565,"score_spread":0.2575894771804615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409239746","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":[0.99697006,0.00010373393,0.00041868573,0.00013664442,0.0000034921634,0.000051423078,0.0013095282,0.0000074882205,0.0009989786],"genre_scores_gemma":[0.99821234,0.0001199026,0.0008399427,0.000014791382,0.0000030011304,0.000030602332,0.00065722194,0.000001076472,0.00012109866],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99955994,0.000107944674,0.00004145603,0.00010515252,0.00009844826,0.000087078035],"domain_scores_gemma":[0.9991498,0.00023122971,0.00032721262,0.000061608385,0.00014156617,0.000088551664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005969426,0.00019613813,0.00018086367,0.0026911357,0.00080482644,0.0008594172,0.00034976823,0.00019800062,0.0011118525],"category_scores_gemma":[0.0016500062,0.00023345795,0.00025508035,0.0043090847,0.0005353785,0.0004927891,0.001007235,0.00026254705,0.00010282651],"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":[0.000018335164,0.000025700174,0.9895502,0.000056163684,0.00003055023,0.00027854275,0.0018740022,0.00042010154,0.00022687214,0.0002583835,0.00034395108,0.006917138],"study_design_scores_gemma":[0.0000019869956,0.000028508786,0.9895281,0.000031367443,0.000018118455,0.00030724634,0.008078619,0.00095025124,0.00007635388,0.0001019392,0.00086962694,0.000007779313],"about_ca_topic_score_codex":0.08082291,"about_ca_topic_score_gemma":0.12185444,"teacher_disagreement_score":0.08082291,"about_ca_system_score_codex":0.00080994726,"about_ca_system_score_gemma":0.0020302488,"threshold_uncertainty_score":0.16070497},"labels":[],"label_agreement":null},{"id":"W4409322903","doi":"10.1016/j.forsciint.2025.112470","title":"Development of a methamphetamine chemical profiling program in Quebec, Canada for use in an intelligence perspective","year":2025,"lang":"en","type":"article","venue":"Forensic Science International","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Profiling (computer programming); Methamphetamine; Perspective (graphical); Data science; Medicine; Computer science; Psychiatry; Artificial intelligence","score_opus":0.033547637989990574,"score_gpt":0.3629578450700147,"score_spread":0.32941020708002416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409322903","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":[0.71871895,0.002004879,0.08255057,0.009484397,0.00020784981,0.009298581,0.026443847,0.004624493,0.14666642],"genre_scores_gemma":[0.7736778,0.0015138019,0.1317907,0.0016510232,0.00004257056,0.0012540311,0.015138668,0.00027902736,0.0746523],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99883217,0.00016791443,0.000035290886,0.00018693856,0.0005382196,0.00023955751],"domain_scores_gemma":[0.9955128,0.00018817121,0.00016750618,0.00014376233,0.0034357568,0.0005520205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017337729,0.00051644334,0.00019279911,0.0025834586,0.003022231,0.0014641916,0.0015338456,0.00042762634,0.005298287],"category_scores_gemma":[0.0026288908,0.00024099393,0.00020994795,0.0028097802,0.0005954488,0.00095196225,0.000726697,0.0004625531,0.00084889086],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006171162,0.0009871515,0.20413885,0.0003636544,0.00008229754,0.0016253359,0.0069361385,0.007784875,0.031599328,0.006884964,0.060501512,0.67847884],"study_design_scores_gemma":[0.00023457455,0.0010987675,0.5667218,0.00035781754,0.00012381516,0.00065410323,0.0141749885,0.041887708,0.02383013,0.0009428838,0.34973982,0.00023358206],"about_ca_topic_score_codex":0.98952544,"about_ca_topic_score_gemma":0.9901259,"teacher_disagreement_score":0.028094547,"about_ca_system_score_codex":0.028094547,"about_ca_system_score_gemma":0.07531941,"threshold_uncertainty_score":0.20384115},"labels":[],"label_agreement":null},{"id":"W4409323587","doi":"10.2196/68724","title":"Assessing the Dissemination of Federal Risk Communication by News Media Outlets During Enteric Illness Outbreaks: Canadian Content Analysis","year":2025,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Guelph; Public Health Agency of Canada","funders":"","keywords":"Outbreak; News media; Public health; Medicine; Mass media; Government (linguistics); Health communication; Environmental health; Public relations; Advertising; Political science; Business; Nursing; Pathology","score_opus":0.02592377432936143,"score_gpt":0.33930734118802314,"score_spread":0.3133835668586617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409323587","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":[0.7964034,0.020005463,0.0031217656,0.0045457864,0.00017214881,0.008389755,0.14734285,0.0001662242,0.019852666],"genre_scores_gemma":[0.90964794,0.018715847,0.01334964,0.0009328786,0.000106968764,0.006176865,0.04852754,0.000077559,0.0024648423],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9840768,0.0025530008,0.0034080306,0.0012575272,0.0075333356,0.0011713195],"domain_scores_gemma":[0.870722,0.04514057,0.022493146,0.0028408407,0.056039598,0.002763858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021391366,0.0007585074,0.0011574902,0.052460466,0.0043066326,0.004656076,0.0022005683,0.00075015984,0.0032989932],"category_scores_gemma":[0.114783816,0.0006945384,0.001863981,0.06447111,0.0024139588,0.0023745787,0.0038246815,0.0010028315,0.0003102314],"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":[0.00069026253,0.00021744799,0.71997845,0.04001514,0.0012904721,0.00069868966,0.08492468,0.0006826643,0.0013367211,0.0024876518,0.017078336,0.13059948],"study_design_scores_gemma":[0.00013215603,0.00013690318,0.9120381,0.0094018765,0.0016407948,0.00019598135,0.036149766,0.0012345635,0.00081960484,0.0002923987,0.037802707,0.00015521879],"about_ca_topic_score_codex":0.95544016,"about_ca_topic_score_gemma":0.96293986,"teacher_disagreement_score":0.05920466,"about_ca_system_score_codex":0.05920466,"about_ca_system_score_gemma":0.1492232,"threshold_uncertainty_score":0.4295619},"labels":[],"label_agreement":null},{"id":"W4409529414","doi":"10.2196/66223","title":"Leveraging Electronic Health Records in International Humanitarian Clinics for Population Health Research: Cross-Sectional Study","year":2025,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"St. Clair College","funders":"","keywords":"Concordance; Public health; Medical classification; Documentation; Family medicine; Cross-sectional study; Population; Medicine; Medical record; Test (biology); Diagnosis code; Terminology; Medical emergency; Psychology; Nursing; Computer science; Environmental health; Pathology","score_opus":0.1431953505405493,"score_gpt":0.4806827429693594,"score_spread":0.3374873924288101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409529414","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":[0.9953027,0.0005194785,0.0007045428,0.00019321233,0.000020969028,0.00038045418,0.0017728682,0.000009243816,0.0010965156],"genre_scores_gemma":[0.99617106,0.000310236,0.0011647445,0.00033493334,0.000045720073,0.0005119602,0.0013122929,0.000007812769,0.0001411981],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9850867,0.007416829,0.0023296236,0.0017783268,0.00251201,0.0008766142],"domain_scores_gemma":[0.960224,0.012807631,0.01956939,0.0032190995,0.0030228014,0.0011571364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012164989,0.00038509668,0.00047192976,0.0033112392,0.0009795929,0.0019830565,0.0010213874,0.0009203421,0.0014690008],"category_scores_gemma":[0.03236568,0.0008387751,0.00063520094,0.005206018,0.0006656208,0.0022198616,0.0021639029,0.0012806845,0.00045744234],"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":[0.000022856677,0.00009144162,0.9982304,0.000030941253,0.0000306882,0.000027109658,0.00043047775,0.000010607753,0.000028609376,0.000024936317,0.00015747588,0.00091455627],"study_design_scores_gemma":[0.00001764816,0.00020523688,0.9956956,0.00009954552,0.00005402094,0.00025832804,0.0025002018,0.00029072506,0.00007613227,0.00002704172,0.0007654557,0.000010110276],"about_ca_topic_score_codex":0.006800433,"about_ca_topic_score_gemma":0.010211979,"teacher_disagreement_score":0.012164989,"about_ca_system_score_codex":0.001009448,"about_ca_system_score_gemma":0.001524761,"threshold_uncertainty_score":0.064335406},"labels":[],"label_agreement":null},{"id":"W4409689323","doi":"10.1111/tmi.14115","title":"Modelling of potential risk areas of pertussis cases in the Philippines using bioclimatic envelopes","year":2025,"lang":"en","type":"article","venue":"Tropical Medicine & International Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Outbreak; Quarter (Canadian coin); Geography; Epidemiology; Environmental health; Public health; Socioeconomics; Medicine","score_opus":0.06330572130008576,"score_gpt":0.3753636992794056,"score_spread":0.31205797797931983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409689323","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":[0.9609177,0.00021867867,0.035373155,0.00024770678,0.000017971923,0.000048486494,0.0007343017,0.00021557168,0.0022264463],"genre_scores_gemma":[0.9967276,0.00006683203,0.0024097501,0.000009284263,0.0000054660622,0.000030966705,0.00018831588,0.0000084183575,0.0005534001],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998419,0.000056675068,0.000007794498,0.000050965813,0.000016103833,0.000026521027],"domain_scores_gemma":[0.9996138,0.00019664131,0.00007581544,0.00001828219,0.00006417485,0.000031274445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044054224,0.00064541085,0.0002826365,0.0006879351,0.00030925867,0.000882383,0.0007844567,0.0005567998,0.0012763147],"category_scores_gemma":[0.0011617878,0.000292986,0.00066980225,0.00046249558,0.00046613324,0.00046770638,0.0005957891,0.00038455136,0.00017361135],"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":[0.00004182776,0.000036304868,0.029271394,0.000028081615,0.00003603262,0.000091797076,0.00007264877,0.9654609,0.00047338763,0.0003549221,0.00014905693,0.0039836066],"study_design_scores_gemma":[0.0000034289974,0.000015257462,0.004366349,0.0000036110102,0.0000060580755,0.000012042293,0.000029361354,0.99517643,0.00009707129,0.00019569075,0.00009066516,0.000004008939],"about_ca_topic_score_codex":0.041358452,"about_ca_topic_score_gemma":0.015723297,"teacher_disagreement_score":0.041358452,"about_ca_system_score_codex":0.00097468717,"about_ca_system_score_gemma":0.00092225353,"threshold_uncertainty_score":0.082235396},"labels":[],"label_agreement":null},{"id":"W4410019259","doi":"10.2196/76462","title":"Correction: Assessing and Improving Data Integrity in Web-Based Surveys: Comparison of Fraud Detection Systems in a COVID-19 Study","year":2025,"lang":"en","type":"erratum","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Computer science; Data science; Medicine; Virology","score_opus":0.15041980846989303,"score_gpt":0.4972091895050547,"score_spread":0.34678938103516166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410019259","genre_codex":"editorial","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015901349,0.00057038077,0.00103154,0.09973232,0.8943982,0.00008011539,0.0023164873,0.00056807685,0.0011439176],"genre_scores_gemma":[0.019419473,0.0090300655,0.013782397,0.35152337,0.49834278,0.0011357154,0.0052667134,0.0032675194,0.09823193],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.96773404,0.0072565405,0.0080697425,0.0026273015,0.012550104,0.0017622601],"domain_scores_gemma":[0.7552759,0.08668013,0.009473172,0.011658537,0.13187264,0.0050396686],"candidate_categories":["metaresearch","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.024729652,0.0037023283,0.0033937276,0.006662424,0.0055112448,0.0075719613,0.0056667123,0.013728704,0.046822477],"category_scores_gemma":[0.32748762,0.002503562,0.0037622792,0.0052665374,0.004775469,0.0034705538,0.003137716,0.020070115,0.024352366],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043409644,0.000009241116,0.000099776924,0.00020384193,0.000027492482,0.00014704283,0.000064407,0.00003306175,0.000024044266,0.00048243816,0.99500716,0.0038581141],"study_design_scores_gemma":[0.00023684479,0.00007959512,0.0019186249,0.002264002,0.00021149492,0.00084937905,0.00039400865,0.00085133425,0.0005838142,0.003168912,0.9892737,0.00016824836],"about_ca_topic_score_codex":0.043148078,"about_ca_topic_score_gemma":0.040579706,"teacher_disagreement_score":0.9862713,"about_ca_system_score_codex":0.0076250304,"about_ca_system_score_gemma":0.017886996,"threshold_uncertainty_score":0.15663683},"labels":[],"label_agreement":null},{"id":"W4410233943","doi":"10.1186/s12942-025-00399-6","title":"MaskMyPy: python tools for performing and analyzing geographic masks","year":2025,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Simon Fraser University","funders":"Social Sciences and Humanities Research Council; Social Sciences and Humanities Research Council of Canada","keywords":"Geospatial analysis; Python (programming language); Computer science; Masking (illustration); Geographic information system; Data science; Set (abstract data type); Geographic coordinate system; Data mining; Cartography; Geography","score_opus":0.026077988083018356,"score_gpt":0.37249954654071576,"score_spread":0.3464215584576974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410233943","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004812841,0.00020458191,0.3278976,0.0007688749,0.00017858302,0.00040566482,0.0214631,0.6358012,0.008467704],"genre_scores_gemma":[0.08385804,0.0007289266,0.62377375,0.002013541,0.00014793946,0.002730911,0.056759093,0.21736114,0.012626678],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979572,0.00039559673,0.0002454329,0.00039518948,0.0007649913,0.00024162667],"domain_scores_gemma":[0.99432397,0.0025278444,0.0005778506,0.0014256157,0.00082964456,0.00031512722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003122584,0.0025232201,0.0010521159,0.0016032486,0.001024485,0.0026703519,0.0040757935,0.001104283,0.036145773],"category_scores_gemma":[0.01614079,0.0018134828,0.002609161,0.0014297741,0.0019710024,0.005442629,0.005793228,0.003633067,0.023858204],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008667943,0.00026056534,0.011080629,0.002815872,0.00030270143,0.00070694106,0.0020659126,0.028033424,0.011679026,0.03887199,0.71367955,0.1896366],"study_design_scores_gemma":[0.00036325897,0.00015622581,0.009053543,0.0005290137,0.00011051225,0.00081334333,0.0004913317,0.2533637,0.03680212,0.11536497,0.58257097,0.000381027],"about_ca_topic_score_codex":0.0065529933,"about_ca_topic_score_gemma":0.008121696,"teacher_disagreement_score":0.036145773,"about_ca_system_score_codex":0.0013466874,"about_ca_system_score_gemma":0.0039504864,"threshold_uncertainty_score":0.120919645},"labels":[],"label_agreement":null},{"id":"W4410447130","doi":"10.3233/shti250581","title":"Practical Approach for Evaluating Machine Learning Anomaly Detection Algorithms for Epidemic Early Warning Systems","year":2025,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Anomaly detection; Machine learning; Warning system; Artificial intelligence; Data mining; Acinetobacter baumannii; Isolation (microbiology); Gold standard (test); Algorithm; Bioinformatics; Statistics","score_opus":0.12594027904276797,"score_gpt":0.46700666389853657,"score_spread":0.34106638485576857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410447130","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":[0.17270969,0.001000286,0.8161875,0.0008109243,0.00022399682,0.001085913,0.000502726,0.0015126499,0.0059662513],"genre_scores_gemma":[0.5286307,0.00021132079,0.46873355,0.00010338502,0.000051660794,0.0009952103,0.0004903834,0.000073834664,0.0007099002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9867077,0.008563652,0.0010781825,0.00084154,0.002545173,0.0002638019],"domain_scores_gemma":[0.97156054,0.018717537,0.0011121932,0.002146173,0.0061724694,0.00029105216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01748254,0.001282116,0.000812446,0.0024339072,0.00063996465,0.0016007044,0.0015055228,0.0019393131,0.001968124],"category_scores_gemma":[0.06447925,0.00035211418,0.0005138891,0.0013795182,0.0006407681,0.0015603453,0.0018061685,0.001200189,0.00043496772],"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":[0.0010673148,0.0010389438,0.020205004,0.0008182855,0.00033717268,0.00022173276,0.0003329173,0.6090533,0.013824467,0.018917276,0.0050955005,0.32908806],"study_design_scores_gemma":[0.00009069252,0.0009505695,0.0027468589,0.000060764174,0.000038019116,0.00009064401,0.00016552232,0.9819902,0.0061586597,0.006003589,0.0016796169,0.000024797006],"about_ca_topic_score_codex":0.0019173438,"about_ca_topic_score_gemma":0.0015999835,"teacher_disagreement_score":0.01748254,"about_ca_system_score_codex":0.0010997698,"about_ca_system_score_gemma":0.0013509863,"threshold_uncertainty_score":0.09245759},"labels":[],"label_agreement":null},{"id":"W4410447594","doi":"10.3233/shti250502","title":"Automatic Annotation of Disposition Counts in News Articles","year":2025,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Disposition; Trustworthiness; Annotation; Computer science; Aggregate (composite); Downstream (manufacturing); Natural language processing; Artificial intelligence; Internet privacy; Psychology; Business; Social psychology; Marketing","score_opus":0.02501833108721605,"score_gpt":0.38201416649642,"score_spread":0.35699583540920393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410447594","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":[0.40382457,0.0057334118,0.1917292,0.0040240595,0.0029867801,0.0017903779,0.32667404,0.028896375,0.034341235],"genre_scores_gemma":[0.37312543,0.001879716,0.29017344,0.00044392273,0.0015674654,0.0013941965,0.31990534,0.0011663716,0.010344218],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976553,0.00048070148,0.00044886497,0.0006118995,0.0006733623,0.0001298807],"domain_scores_gemma":[0.9762415,0.01231153,0.0033497964,0.0014427259,0.00600395,0.0006505675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019570114,0.0009896717,0.0006241005,0.016512264,0.0013124326,0.002703997,0.0007281392,0.0009404764,0.0030858784],"category_scores_gemma":[0.01604698,0.00038062932,0.0005340386,0.0077558085,0.0005213485,0.0024239453,0.0018224377,0.0011294634,0.003124048],"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":[0.0013920176,0.00068226986,0.12596543,0.0062537147,0.0002906635,0.003202155,0.0064037875,0.006069487,0.051024735,0.008673129,0.20251517,0.5875276],"study_design_scores_gemma":[0.0002133443,0.0003559635,0.27859077,0.0014890928,0.00059781584,0.0022399984,0.008371288,0.16879408,0.059077784,0.019002387,0.4608908,0.00037670168],"about_ca_topic_score_codex":0.005925688,"about_ca_topic_score_gemma":0.012334717,"teacher_disagreement_score":0.016512264,"about_ca_system_score_codex":0.0007180079,"about_ca_system_score_gemma":0.0017081251,"threshold_uncertainty_score":0.011782408},"labels":[],"label_agreement":null},{"id":"W4410548799","doi":"10.5867/medwave.2025.04.3031","title":"Event-based surveillance in middle- and low-income countries: An evidence map","year":2025,"lang":"en","type":"review","venue":"Medwave","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Medicine; Psychological intervention; China; Outbreak; Environmental health; Public health; Low and middle income countries; Grey literature; Developing country; Economic growth; MEDLINE; Geography; Nursing","score_opus":0.040933357153919846,"score_gpt":0.3544817792633432,"score_spread":0.31354842210942335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410548799","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":[0.0009189648,0.994783,0.0007006174,0.0014161622,0.00019221225,0.0006982154,0.00023098201,0.00001691617,0.0010428103],"genre_scores_gemma":[0.020579021,0.97311056,0.0040312517,0.0008569813,0.00010519103,0.0009792347,0.00024916208,0.0000051479005,0.00008344309],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.978215,0.009465283,0.0071349647,0.0008354791,0.0038984076,0.0004510443],"domain_scores_gemma":[0.8999679,0.07617933,0.011797856,0.0011613601,0.010233155,0.0006604697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027864257,0.0018684062,0.0062427525,0.028066644,0.0010041498,0.0061431755,0.002192588,0.0026228738,0.0037557313],"category_scores_gemma":[0.09251601,0.0013056552,0.0068279286,0.02202035,0.0011514439,0.0070160264,0.0030659835,0.002218789,0.00045877253],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021604197,0.000053923,0.0029030633,0.78430134,0.004916033,0.00018084185,0.0005668262,0.0005518448,0.00019250698,0.0034949754,0.0030143184,0.19960825],"study_design_scores_gemma":[0.00013036987,0.00022871418,0.004757054,0.9454656,0.014889076,0.00029314906,0.0007281503,0.000346362,0.00016421275,0.0023108677,0.030646084,0.000040383504],"about_ca_topic_score_codex":0.008444485,"about_ca_topic_score_gemma":0.01658858,"teacher_disagreement_score":0.028066644,"about_ca_system_score_codex":0.007242337,"about_ca_system_score_gemma":0.016890928,"threshold_uncertainty_score":0.14736205},"labels":[],"label_agreement":null},{"id":"W4410733140","doi":"10.2196/69040","title":"Tracking Public Interest in Rare Diseases and Eosinophilic Disorders in Germany: Web Search Analysis","year":2025,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Eosinophilia; Hypereosinophilic syndrome; Medicine; Eosinophilic; Pathology","score_opus":0.03913491688666641,"score_gpt":0.35277361139739083,"score_spread":0.3136386945107244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410733140","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":[0.943796,0.004781392,0.00022007956,0.0002955693,0.000010813754,0.000064149746,0.048805308,0.000060638933,0.00196614],"genre_scores_gemma":[0.9764999,0.0022551657,0.00056745677,0.00006806348,0.000019975447,0.000072916446,0.020049252,0.000014175741,0.00045298325],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986953,0.00023539687,0.00038251618,0.00020659981,0.0003123398,0.00016776595],"domain_scores_gemma":[0.99195576,0.0033158925,0.0035503483,0.00018099039,0.000654221,0.00034273128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012841264,0.00029386356,0.00051994855,0.017405357,0.00020255889,0.0014853637,0.00032554442,0.00038570567,0.0018676646],"category_scores_gemma":[0.0073664114,0.00015132852,0.00073853764,0.021097256,0.00021884803,0.0015088501,0.0007165587,0.00024595927,0.0005628201],"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":[0.00028143567,0.00004380019,0.96661645,0.0019040349,0.0003678975,0.00057073234,0.00088149257,0.0006142397,0.0005534353,0.0003242745,0.0040672803,0.023774955],"study_design_scores_gemma":[0.000010011962,0.00005672894,0.9924379,0.00024317543,0.00019836104,0.0007360363,0.0011216782,0.0013582582,0.00026773955,0.000087106775,0.0034674995,0.000015596457],"about_ca_topic_score_codex":0.01981525,"about_ca_topic_score_gemma":0.020270115,"teacher_disagreement_score":0.01981525,"about_ca_system_score_codex":0.001124923,"about_ca_system_score_gemma":0.001000462,"threshold_uncertainty_score":0.039399803},"labels":[],"label_agreement":null},{"id":"W4411108659","doi":"10.21203/rs.3.rs-6648511/v1","title":"Mapping Neighborhood-Level Drivers of Type 2 Diabetes: A Predictive-Causal Approach for Precision Public Health","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research; University of Toronto; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Type 2 diabetes; Public health; Computer science; Medicine; Diabetes mellitus; Nursing","score_opus":0.1928271858314638,"score_gpt":0.4247510763758341,"score_spread":0.2319238905443703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411108659","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":[0.05585537,0.0014107293,0.9313248,0.0036775267,0.00019239243,0.0001224753,0.004393918,0.00050889695,0.0025138548],"genre_scores_gemma":[0.7307948,0.0015328196,0.26066378,0.00040893065,0.00028499542,0.00026822367,0.0038822317,0.00009372365,0.0020704425],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963207,0.0022721807,0.00015594777,0.0008105933,0.00032295595,0.00011763889],"domain_scores_gemma":[0.9706497,0.02423378,0.0013557546,0.0026460548,0.0008244716,0.00029028638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009159179,0.00093551865,0.0014163723,0.0031294208,0.00074804615,0.0027804214,0.0017271463,0.0014643869,0.0031397748],"category_scores_gemma":[0.060249705,0.00084321987,0.0018494406,0.003855512,0.0011408443,0.0024747835,0.002347936,0.0019022903,0.0003636459],"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":[0.0002693576,0.00033820348,0.13074513,0.00084545586,0.00161912,0.00049985974,0.0007411978,0.42086115,0.0009785607,0.25142598,0.008598902,0.18307705],"study_design_scores_gemma":[0.00003914865,0.000037233305,0.008243595,0.00010155201,0.00020268364,0.00007880961,0.00023572828,0.61074305,0.00037228994,0.3766946,0.003225687,0.000025652187],"about_ca_topic_score_codex":0.01598422,"about_ca_topic_score_gemma":0.010916842,"teacher_disagreement_score":0.01598422,"about_ca_system_score_codex":0.0010432046,"about_ca_system_score_gemma":0.0027458065,"threshold_uncertainty_score":0.048438966},"labels":[],"label_agreement":null},{"id":"W4411125596","doi":"10.1016/j.hlpt.2025.101055","title":"How has Aggregated Mobility Data-informed public health research?","year":2025,"lang":"en","type":"article","venue":"Health Policy and Technology","topic":"Data-Driven Disease Surveillance","field":"Medicine","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; Queen's University","funders":"Canadian Institutes of Health Research","keywords":"Public health; Data science; Computer science; Medicine; Nursing","score_opus":0.30493836868462687,"score_gpt":0.4966828332330223,"score_spread":0.19174446454839544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411125596","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003657507,0.76940846,0.01760127,0.19169307,0.0069658034,0.00057055475,0.0025650137,0.0000942164,0.0074440525],"genre_scores_gemma":[0.12542284,0.7655337,0.031672776,0.061235297,0.010036508,0.0033551787,0.0017057352,0.00017045505,0.00086756615],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.70181006,0.24052295,0.0248552,0.011264767,0.019181909,0.0023651],"domain_scores_gemma":[0.31303445,0.6190129,0.023686437,0.018546924,0.023871968,0.0018474114],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.23479918,0.001571811,0.0053660385,0.011401627,0.0023089733,0.01740494,0.0045675575,0.007906532,0.0069093327],"category_scores_gemma":[0.52742213,0.0018155038,0.005457721,0.014786562,0.01470259,0.029901484,0.010191674,0.009540979,0.0012383767],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005643971,0.000103240185,0.009854452,0.2669064,0.005107442,0.0005429327,0.014749002,0.0037952606,0.00032823352,0.22081442,0.042531464,0.43470287],"study_design_scores_gemma":[0.00014039452,0.00027330738,0.00495868,0.5278209,0.0029744122,0.0004788727,0.008817874,0.0010764017,0.0005720053,0.16528828,0.28736344,0.00023544149],"about_ca_topic_score_codex":0.011292929,"about_ca_topic_score_gemma":0.008101176,"teacher_disagreement_score":0.76520085,"about_ca_system_score_codex":0.009976861,"about_ca_system_score_gemma":0.032161787,"threshold_uncertainty_score":0.94362885},"labels":[],"label_agreement":null},{"id":"W4411140207","doi":"10.1016/j.eswa.2025.128482","title":"Leveraging social media and google trends to identify waves of avian influenza outbreaks in USA and Canada","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Canadian Food Inspection Agency; University of Guelph","funders":"Ontario Ministry of Agriculture, Food and Rural Affairs; University of Guelph","keywords":"Outbreak; Social media; Influenza A virus subtype H5N1; Computer science; Data science; Virology; World Wide Web; Biology; Virus","score_opus":0.01766921158636678,"score_gpt":0.31357207274542853,"score_spread":0.2959028611590617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411140207","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":[0.90114903,0.0017931769,0.002680622,0.0019114866,0.00017367747,0.0001462709,0.07754442,0.0005074624,0.014093873],"genre_scores_gemma":[0.9580295,0.0010041344,0.0043475064,0.0002451547,0.000081029146,0.000036353398,0.032146383,0.00004818965,0.0040618996],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99948114,0.000042010328,0.000039514915,0.00008922198,0.00023771437,0.00011040521],"domain_scores_gemma":[0.9967776,0.0004982618,0.0002938579,0.000113405156,0.0020225265,0.00029433187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070328277,0.00035687452,0.00025756648,0.004430773,0.00067516114,0.001707683,0.0006808985,0.0003760445,0.0008564415],"category_scores_gemma":[0.003890548,0.00018575758,0.00046864714,0.0049544815,0.00022837598,0.00062211254,0.000651692,0.00045792077,0.00033118832],"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":[0.00019352009,0.000107931846,0.89650345,0.00020462692,0.00035292422,0.00034499788,0.0008677114,0.0060863667,0.0015755497,0.00066844036,0.026995124,0.066099375],"study_design_scores_gemma":[0.000018629413,0.000043144457,0.9052757,0.000112273156,0.00020044194,0.00011125769,0.0030707184,0.061965592,0.0015409753,0.00048137843,0.02711592,0.00006399035],"about_ca_topic_score_codex":0.9772309,"about_ca_topic_score_gemma":0.98796576,"teacher_disagreement_score":0.022769094,"about_ca_system_score_codex":0.0058984677,"about_ca_system_score_gemma":0.009231582,"threshold_uncertainty_score":0.04580629},"labels":[],"label_agreement":null},{"id":"W4411201195","doi":"10.14745/ccdr.v51i05a06","title":"Proposed approaches for public health surveillance: A literature review for the Canadian National HIV Surveillance Program","year":2025,"lang":"en","type":"review","venue":"Canada Communicable Disease Report","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"McGill University Health Centre; Public Health Agency of Canada","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Human immunodeficiency virus (HIV); Public health; Public health surveillance; Environmental health; Medicine; Political science; Virology; Nursing","score_opus":0.12473347687708486,"score_gpt":0.3776812844362158,"score_spread":0.25294780755913093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411201195","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":[0.00026689173,0.98996454,0.0003081003,0.0071226773,0.0002791182,0.00012681342,0.00012440106,0.000008742568,0.0017986491],"genre_scores_gemma":[0.0077015213,0.9873732,0.0023617626,0.001945323,0.00009567775,0.00021461325,0.00013300704,0.0000061521187,0.00016876172],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.96764195,0.012251425,0.005708746,0.0015473707,0.011020001,0.0018305029],"domain_scores_gemma":[0.82811445,0.10288817,0.0077424143,0.0021913317,0.055707466,0.0033561843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.055655304,0.0018902357,0.0033277378,0.04027725,0.0069368845,0.01386239,0.004916829,0.004371201,0.0038121426],"category_scores_gemma":[0.11090029,0.0015399202,0.0039223707,0.05649509,0.0073372326,0.006806413,0.004305009,0.0053248736,0.0004945884],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.0001153462,0.00007485548,0.003039772,0.36026922,0.0006654746,0.0004256295,0.011927655,0.0010828264,0.00019488698,0.032702163,0.045141675,0.54436046],"study_design_scores_gemma":[0.000031265772,0.000039147348,0.005519074,0.76331455,0.0013440306,0.00026625398,0.008464901,0.0003354083,0.00012990757,0.0034043558,0.21705829,0.0000927842],"about_ca_topic_score_codex":0.7612889,"about_ca_topic_score_gemma":0.8636756,"teacher_disagreement_score":0.8495989,"about_ca_system_score_codex":0.15040113,"about_ca_system_score_gemma":0.35000086,"threshold_uncertainty_score":0.98541427},"labels":[],"label_agreement":null},{"id":"W4411277337","doi":"10.3389/frai.2025.1636444","title":"Editorial: Outbreak oracles: how AI's journey through COVID-19 shapes future epidemic strategy","year":2025,"lang":"en","type":"editorial","venue":"Frontiers in Artificial Intelligence","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Outbreak; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Virology; Geography; History; Biology; Medicine; Infectious disease (medical specialty); Disease; Pathology","score_opus":0.03730720062742551,"score_gpt":0.3618857507731086,"score_spread":0.32457855014568304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411277337","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000056501318,0.0028233426,0.00036225322,0.0890803,0.9055153,0.000018155437,0.00007613938,0.00007469962,0.0019933134],"genre_scores_gemma":[0.0012392015,0.004273386,0.00026501674,0.01961288,0.96622777,0.000022201266,0.000041418687,0.00007144165,0.008246664],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99592835,0.0010541211,0.00046470674,0.00045628668,0.0018452728,0.00025123067],"domain_scores_gemma":[0.966406,0.020128123,0.0011073807,0.0006716533,0.008473806,0.003213159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008008019,0.002514387,0.0016766238,0.002920098,0.0036174925,0.007950061,0.0028854813,0.012363534,0.016049465],"category_scores_gemma":[0.047537886,0.00072715245,0.0015564122,0.0011343062,0.0027155962,0.0046637477,0.0015786783,0.01614453,0.0074293003],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013364747,0.0000048812954,0.000023075481,0.000082047685,0.000006396681,0.000094556155,0.00003231462,0.000042142186,0.000018193849,0.00065186905,0.9961003,0.0029308957],"study_design_scores_gemma":[0.000028578525,0.00001811776,0.00014964113,0.00045658925,0.00003710365,0.000257967,0.00012773655,0.0003028596,0.0000795321,0.0036593822,0.99486285,0.000019649657],"about_ca_topic_score_codex":0.0026241315,"about_ca_topic_score_gemma":0.0067989742,"teacher_disagreement_score":0.016049465,"about_ca_system_score_codex":0.0028706614,"about_ca_system_score_gemma":0.0044022026,"threshold_uncertainty_score":0.05369085},"labels":[],"label_agreement":null},{"id":"W4411303057","doi":"10.2196/75025","title":"Analyzing Public Google Search Interest in Measles Within Canada: Identifying Key Moments for Targeted Risk Communication","year":2025,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Preprint; Key (lock); World Wide Web; Internet privacy; Computer science; Public health; Data science; Computer security; Medicine; Nursing","score_opus":0.15610658823050966,"score_gpt":0.44491834681211695,"score_spread":0.2888117585816073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411303057","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":[0.9611932,0.0017503941,0.0003297994,0.0018568909,0.00003364176,0.00010138376,0.024960475,0.00009195012,0.009682229],"genre_scores_gemma":[0.99332595,0.00074485794,0.00044060344,0.00015488068,0.000017982953,0.000027894279,0.0040849447,0.000012783934,0.0011901075],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990854,0.000074020456,0.000081031234,0.00008691568,0.0003370633,0.00033561894],"domain_scores_gemma":[0.994162,0.00095967663,0.002011115,0.00013823595,0.002097072,0.0006317689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073932915,0.00024377124,0.00030257332,0.0046625594,0.0014009217,0.0023381494,0.00069893035,0.00037125038,0.0023230575],"category_scores_gemma":[0.0070244027,0.00013368516,0.0003416302,0.009948284,0.0005804819,0.0009235141,0.0012034602,0.00065952376,0.00041346624],"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":[0.00015481246,0.000036892718,0.9690635,0.000337436,0.000058477926,0.00020823098,0.0040906407,0.00035202992,0.0003105631,0.00057148654,0.007061855,0.017754145],"study_design_scores_gemma":[0.000004098761,0.000016111257,0.9833512,0.00014587598,0.00003409976,0.0001061975,0.009843596,0.0010575135,0.00024306528,0.00009749079,0.0050836,0.000017214432],"about_ca_topic_score_codex":0.95170474,"about_ca_topic_score_gemma":0.9713788,"teacher_disagreement_score":0.04829526,"about_ca_system_score_codex":0.011723622,"about_ca_system_score_gemma":0.019286113,"threshold_uncertainty_score":0.097159326},"labels":[],"label_agreement":null},{"id":"W4411565490","doi":"10.3390/info16070520","title":"Predicting Predisposition to Tropical Diseases in Female Adults Using Risk Factors: An Explainable-Machine Learning Approach","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Royal University","funders":"","keywords":"Genetic predisposition; Artificial intelligence; Machine learning; Computer science; Medicine; Disease; Pathology","score_opus":0.01084278504879178,"score_gpt":0.26936436875101133,"score_spread":0.25852158370221956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411565490","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":[0.6648719,0.0006588197,0.32902595,0.0023566613,0.000048857382,0.00014755088,0.0012918175,0.00034000762,0.0012585875],"genre_scores_gemma":[0.9753032,0.00012444526,0.023673786,0.00009113896,0.000037446185,0.000042604664,0.00042411152,0.000007593671,0.00029571523],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995696,0.00026470664,0.000019776653,0.000083443745,0.000029889692,0.00003255815],"domain_scores_gemma":[0.99642533,0.002947637,0.00028446564,0.00014404405,0.00012955665,0.00006884496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018830613,0.0005512389,0.00038869958,0.0010404885,0.00018902044,0.00063928345,0.0005301862,0.0004949249,0.000983865],"category_scores_gemma":[0.0064850086,0.00018183806,0.0006933345,0.00044883642,0.0002061367,0.0004007188,0.0005336084,0.00086590427,0.0000925754],"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":[0.00030219718,0.00040745427,0.2964759,0.00014000727,0.00047009913,0.0004549436,0.00045665234,0.56625056,0.0014070913,0.007941663,0.0018074322,0.12388602],"study_design_scores_gemma":[0.0000098564815,0.00004989801,0.012255815,0.000015651396,0.0000357491,0.000033664106,0.000051965155,0.98190355,0.00017985543,0.0052061314,0.00024888455,0.000009004975],"about_ca_topic_score_codex":0.006754062,"about_ca_topic_score_gemma":0.007253786,"teacher_disagreement_score":0.006754062,"about_ca_system_score_codex":0.00044907889,"about_ca_system_score_gemma":0.00062661653,"threshold_uncertainty_score":0.0134295225},"labels":[],"label_agreement":null},{"id":"W4411642237","doi":"10.1371/journal.pone.0326139","title":"Cross sectional analysis of clinical trials search results for cancer patients using a navigator-assisted clinical trials search using five different search engines","year":2025,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Windsor; Western University; University of Toronto","funders":"TD Bank","keywords":"Clinical trial; Medicine; MEDLINE; Health care; Alternative medicine; Internal medicine; Pathology","score_opus":0.5850392335894545,"score_gpt":0.5836365293384033,"score_spread":0.0014027042510512677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411642237","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98416466,0.0024907293,0.0013501395,0.0003963485,0.000028457393,0.0010752267,0.009068045,0.00011982656,0.0013066167],"genre_scores_gemma":[0.9891327,0.00064188126,0.0026418227,0.00021715816,0.000022124985,0.0008426225,0.006241356,0.000035991285,0.00022427787],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9463767,0.019765107,0.022881977,0.004231384,0.005733187,0.0010115812],"domain_scores_gemma":[0.5914296,0.26785475,0.08987628,0.010712346,0.036065996,0.0040610866],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.043470904,0.00045453967,0.00120529,0.015942087,0.0010868381,0.0029851866,0.0008923103,0.0010570075,0.0022187815],"category_scores_gemma":[0.19367017,0.00052838394,0.0020791772,0.015197804,0.0008664502,0.0036996696,0.0018367087,0.0011100332,0.0006120169],"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":[0.0009697077,0.00030290624,0.978084,0.0016813047,0.0014615866,0.00017778568,0.0024931175,0.00034396324,0.0003924439,0.00017808824,0.001793253,0.012121867],"study_design_scores_gemma":[0.00014942396,0.0011612894,0.9850286,0.0004808001,0.0017240625,0.0009683777,0.0036033385,0.0025094629,0.0008952327,0.00018360576,0.0031765676,0.00011921346],"about_ca_topic_score_codex":0.0054697674,"about_ca_topic_score_gemma":0.008918976,"teacher_disagreement_score":0.9565291,"about_ca_system_score_codex":0.0020167266,"about_ca_system_score_gemma":0.0036580402,"threshold_uncertainty_score":0.22989887},"labels":[],"label_agreement":null},{"id":"W4411795973","doi":"10.2196/73631","title":"Probability-Based Early Warning for Seasonal Influenza in China: Model Development Study","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprint; Warning system; China; Coronavirus disease 2019 (COVID-19); Seasonal influenza; Early warning system; Computer science; Environmental health; Medicine; Geography; Telecommunications; World Wide Web; Internal medicine","score_opus":0.030487659413121544,"score_gpt":0.3404966830842518,"score_spread":0.31000902367113026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411795973","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":[0.9268697,0.0015876896,0.061851215,0.0017345415,0.00010807408,0.00016159496,0.0017301568,0.0005178244,0.0054393183],"genre_scores_gemma":[0.9901586,0.00033597855,0.0062852255,0.00009568768,0.000028418392,0.00012944877,0.00083203235,0.000022157143,0.0021124347],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996984,0.00011285504,0.000014369174,0.00007475977,0.00003113522,0.000068475245],"domain_scores_gemma":[0.9960957,0.0028725702,0.00021726704,0.000072508694,0.00063529616,0.00010662347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024136512,0.0011865919,0.0010034852,0.0009955429,0.00051958486,0.0006789885,0.0016246414,0.0012880776,0.002453999],"category_scores_gemma":[0.0044862204,0.00049722963,0.0011665056,0.0005937002,0.0005573366,0.00096511294,0.001020282,0.0015175149,0.00024504826],"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":[0.000038664493,0.000037776183,0.0041721016,0.000027693157,0.000027674912,0.000048191527,0.000015659589,0.9925241,0.000053177122,0.0004241091,0.00034561477,0.00228536],"study_design_scores_gemma":[0.0000053573804,0.000008754366,0.00041756363,0.0000025464171,0.0000065649447,0.0000029671514,0.000005227735,0.99931836,0.000021045984,0.00017631338,0.00003273095,0.0000026218152],"about_ca_topic_score_codex":0.14248212,"about_ca_topic_score_gemma":0.07322108,"teacher_disagreement_score":0.14248212,"about_ca_system_score_codex":0.0021457726,"about_ca_system_score_gemma":0.0027554098,"threshold_uncertainty_score":0.28330553},"labels":[],"label_agreement":null},{"id":"W4411904655","doi":"10.2196/73953","title":"A “Pandemic-Proof” Methodology for Outbreak Detection Adapted From COVID-19’s Impact on Notifications of Infectious Diseases in the Netherlands: Surveillance Study","year":2025,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprint; Coronavirus disease 2019 (COVID-19); Pandemic; Outbreak; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Virology; Infectious disease (medical specialty); Geography; Computer science; Biology; Medicine; Disease; World Wide Web","score_opus":0.09125934212832319,"score_gpt":0.4220310390624018,"score_spread":0.3307716969340786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411904655","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":[0.32207677,0.00087050564,0.66484356,0.00057725015,0.0003104696,0.0038338008,0.0040826695,0.0006405947,0.002764334],"genre_scores_gemma":[0.49502626,0.00030461908,0.49321303,0.00024206715,0.00011604556,0.0048569487,0.00490657,0.00014184097,0.0011926797],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9667899,0.025629962,0.00253692,0.0030277036,0.0016341235,0.00038134903],"domain_scores_gemma":[0.9196682,0.05598933,0.0091088265,0.009121166,0.0057215835,0.00039081028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040910553,0.00070838287,0.0006454736,0.0021316488,0.00052573043,0.0011105404,0.0017048272,0.0010398548,0.0015541516],"category_scores_gemma":[0.104762614,0.00069548795,0.0019419416,0.0019884545,0.00066780054,0.0011034228,0.0015544107,0.0011741179,0.00021256121],"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":[0.0014005791,0.0009517414,0.53210443,0.0017878966,0.0027206354,0.0012740518,0.005265475,0.09644461,0.005064734,0.027785275,0.013223843,0.31197676],"study_design_scores_gemma":[0.0008963357,0.0023292433,0.26776972,0.00053062144,0.0009804601,0.0022638778,0.002512218,0.6530545,0.009881419,0.014810907,0.044630475,0.00034029473],"about_ca_topic_score_codex":0.022747317,"about_ca_topic_score_gemma":0.010043077,"teacher_disagreement_score":0.040910553,"about_ca_system_score_codex":0.0014109496,"about_ca_system_score_gemma":0.0029175568,"threshold_uncertainty_score":0.2163583},"labels":[],"label_agreement":null},{"id":"W4412009847","doi":"10.2196/73916","title":"Comparing Multiple Imputation Methods to Address Missing Patient Demographics in Immunization Information Systems: Retrospective Cohort Study","year":2025,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprint; Demographics; Imputation (statistics); Missing data; Medicine; Cohort; Statistics; Computer science; Data science; Demography; Mathematics; World Wide Web; Internal medicine","score_opus":0.026860805791502556,"score_gpt":0.360973797100699,"score_spread":0.3341129913091964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412009847","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":[0.9170299,0.0029378368,0.068893656,0.0010924861,0.0002836252,0.0009432856,0.007548006,0.00017813097,0.0010930791],"genre_scores_gemma":[0.96017104,0.000782807,0.031396322,0.00055660936,0.0001392081,0.0014451152,0.004961928,0.00010104509,0.00044599257],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9592542,0.030221354,0.0026450756,0.0038980443,0.002756267,0.0012250519],"domain_scores_gemma":[0.9132964,0.050630372,0.012273007,0.015846126,0.006642243,0.0013119271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06454482,0.00080248003,0.0009929262,0.0017577911,0.00093364285,0.0014553422,0.0025577194,0.0013631061,0.0021378705],"category_scores_gemma":[0.12831971,0.0007959654,0.0046948413,0.0027143478,0.00050193944,0.001434008,0.0016763526,0.0023175594,0.00051820395],"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":[0.0022266803,0.0004195065,0.9596037,0.00030145017,0.0058879624,0.00008982665,0.0006711428,0.0034016827,0.00012351602,0.0012858347,0.0035523758,0.022436285],"study_design_scores_gemma":[0.0017148829,0.0055819657,0.8603584,0.0009769989,0.0087471455,0.0011107556,0.0024953824,0.10196834,0.0012851323,0.004034463,0.011493098,0.00023354754],"about_ca_topic_score_codex":0.011218862,"about_ca_topic_score_gemma":0.010749681,"teacher_disagreement_score":0.06454482,"about_ca_system_score_codex":0.0011984234,"about_ca_system_score_gemma":0.0027695613,"threshold_uncertainty_score":0.34134972},"labels":[],"label_agreement":null},{"id":"W4412176837","doi":"10.1007/978-3-031-94845-9_13","title":"Simulation of Acute Upper Respiratory Tract Infections in Kharkiv Oblast (Ukraine): The ARIMA Model Approach","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Balsillie School of International Affairs","funders":"","keywords":"Autoregressive integrated moving average; Respiratory tract; Medicine; Respiratory tract infections; Respiratory system; Internal medicine; Mathematics; Statistics; Time series","score_opus":0.020494446989939408,"score_gpt":0.2711549311698091,"score_spread":0.2506604841798697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412176837","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":[0.82382697,0.0016098956,0.148969,0.0022534635,0.0002943313,0.00011455671,0.0031817046,0.000673767,0.019076291],"genre_scores_gemma":[0.9812463,0.0004372298,0.011308829,0.000099122146,0.000047021917,0.00010124328,0.0007795943,0.00004599793,0.005934618],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968493,0.00015165507,0.00001693816,0.00005395127,0.00002931227,0.00006325554],"domain_scores_gemma":[0.9990404,0.00071057223,0.00007474791,0.000029248375,0.0000937683,0.00005115451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005590693,0.0005036043,0.00091227464,0.0003032595,0.00042269702,0.00096725364,0.0012302991,0.0014900572,0.0019359096],"category_scores_gemma":[0.001946798,0.00046928218,0.0009935721,0.0003890781,0.0003301697,0.00063176523,0.00084360316,0.00097318063,0.00024059285],"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":[0.000024796947,0.00002019703,0.0015691454,0.000014548069,0.000025806048,0.000040807296,0.000021450167,0.9951479,0.0001383601,0.001275548,0.00032057866,0.0014009294],"study_design_scores_gemma":[0.0000044602116,0.000012842452,0.00043902465,0.0000033988438,0.000007945259,0.000007716755,0.00002153725,0.9987478,0.000060550825,0.0005117107,0.00017960732,0.0000032506],"about_ca_topic_score_codex":0.070351906,"about_ca_topic_score_gemma":0.036955602,"teacher_disagreement_score":0.070351906,"about_ca_system_score_codex":0.0009704049,"about_ca_system_score_gemma":0.0015378373,"threshold_uncertainty_score":0.13988483},"labels":[],"label_agreement":null},{"id":"W4412399890","doi":"10.2196/74317","title":"Public Interest in Dry Eye Disease and Its Association With Environmental Parameters in Taiwan: Google Trends Infodemiology Study","year":2025,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprint; Association (psychology); Geography; Psychology; Computer science; World Wide Web","score_opus":0.040035710421553815,"score_gpt":0.3178528453633487,"score_spread":0.2778171349417949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412399890","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":[0.9966042,0.00030815654,0.000064013846,0.0001201098,0.0000045163483,0.000036445097,0.0023178654,0.000006378055,0.0005382865],"genre_scores_gemma":[0.9975247,0.0001770043,0.000102628226,0.000045616194,0.000011048399,0.00005035874,0.0018976231,0.0000031805218,0.00018783039],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99951816,0.00006923842,0.00011302295,0.000095592666,0.00009510183,0.00010890623],"domain_scores_gemma":[0.9982375,0.00028771846,0.00079629995,0.00013499531,0.00029412133,0.00024928257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070762075,0.00024781367,0.00034792017,0.0028250916,0.00042544736,0.00085958873,0.00033177424,0.00041541556,0.0014168412],"category_scores_gemma":[0.0023736435,0.00022630702,0.0009481745,0.004807001,0.00028022702,0.001050642,0.0009819878,0.0005758519,0.00029031385],"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":[0.000037909813,0.000022753058,0.9974667,0.00006365147,0.000044317003,0.00012464967,0.0003928507,0.00003106269,0.000068633824,0.000032125594,0.0002586306,0.0014566847],"study_design_scores_gemma":[0.000004344186,0.000039568808,0.99756914,0.000026655915,0.000047919144,0.0001545062,0.0013317527,0.0003127171,0.00004031754,0.00001876589,0.00044847836,0.0000057020748],"about_ca_topic_score_codex":0.04679366,"about_ca_topic_score_gemma":0.054185964,"teacher_disagreement_score":0.04679366,"about_ca_system_score_codex":0.00085072656,"about_ca_system_score_gemma":0.0010204959,"threshold_uncertainty_score":0.09304261},"labels":[],"label_agreement":null},{"id":"W4412765880","doi":"10.3389/frai.2025.1645467","title":"AI-driven epidemic intelligence: the future of outbreak detection and response","year":2025,"lang":"en","type":"article","venue":"Frontiers in Artificial Intelligence","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Waterloo","funders":"Canada Research Chairs","keywords":"Computer science; Preparedness; Transformative learning; Pandemic; Data science; Process (computing); Risk analysis (engineering); Computer security; Infectious disease (medical specialty); Coronavirus disease 2019 (COVID-19); Business; Medicine; Political science; Psychology; Disease","score_opus":0.015647127194612063,"score_gpt":0.3100486027799237,"score_spread":0.2944014755853116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412765880","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":[0.012847724,0.02537943,0.85866153,0.07995144,0.0016510087,0.00015204592,0.00060454285,0.0026244998,0.01812769],"genre_scores_gemma":[0.41889384,0.024335207,0.5387702,0.009241008,0.0021044968,0.00030147147,0.0010961018,0.00034448612,0.004913208],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977648,0.0011137767,0.00015600573,0.00033202823,0.0005001657,0.00013312964],"domain_scores_gemma":[0.9906318,0.006560229,0.00048140017,0.0008003478,0.0010441969,0.00048208563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071543604,0.00083410053,0.0009663601,0.0014915499,0.00072748587,0.0058836257,0.0021992663,0.002689052,0.0020407496],"category_scores_gemma":[0.011702221,0.00044684132,0.00088575005,0.0012769147,0.00323183,0.008009208,0.0027145531,0.004196553,0.0008269386],"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":[0.00025924656,0.00028169408,0.00533918,0.0013275899,0.0003495024,0.00029030055,0.000990739,0.13394298,0.0056317924,0.38817543,0.035123806,0.42828777],"study_design_scores_gemma":[0.000035654062,0.00010907219,0.00092801324,0.00031222345,0.0000683924,0.00013672136,0.0005509766,0.47378924,0.0019678744,0.4491075,0.07288355,0.00011080389],"about_ca_topic_score_codex":0.00331888,"about_ca_topic_score_gemma":0.002161821,"teacher_disagreement_score":0.0071543604,"about_ca_system_score_codex":0.0017127402,"about_ca_system_score_gemma":0.0027951647,"threshold_uncertainty_score":0.037836313},"labels":[],"label_agreement":null},{"id":"W4413368484","doi":"10.2196/75395","title":"Public Information Needs and Interest in Specific Food and Drug Allergy Disorders in Germany (2022–2024): Google Search Engine Analysis","year":2025,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprint; Allergy; Medicine; Population; Environmental health; World Wide Web; Computer science","score_opus":0.04112038943264835,"score_gpt":0.34674174250882644,"score_spread":0.3056213530761781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413368484","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":[0.89387095,0.005468755,0.0001310446,0.00076879025,0.000017897852,0.00010907924,0.09648697,0.00005121357,0.0030952834],"genre_scores_gemma":[0.9632122,0.00271706,0.00038497848,0.000119907054,0.000024197796,0.00010492786,0.032881632,0.000013627004,0.00054140465],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99890924,0.00015837183,0.00027447884,0.00013486708,0.00032185053,0.00020120888],"domain_scores_gemma":[0.9935772,0.0030378168,0.0022928445,0.00010681495,0.00065436144,0.00033094952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013897212,0.0003526658,0.0005170977,0.015523457,0.00025602573,0.0016496849,0.0003811022,0.0005343676,0.0023129564],"category_scores_gemma":[0.008012585,0.00016702568,0.0011227294,0.016718438,0.0002899048,0.0019757617,0.0010810678,0.00034151017,0.0005381277],"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":[0.0008208044,0.00008253094,0.9466102,0.004857096,0.0004914849,0.0011851785,0.002691147,0.0007955083,0.0007455648,0.0008255098,0.011212001,0.029683003],"study_design_scores_gemma":[0.000017103099,0.00006762666,0.9850101,0.00057481776,0.00031513642,0.0008415584,0.00345695,0.0013461955,0.00029834555,0.0001369438,0.007910653,0.000024668474],"about_ca_topic_score_codex":0.04476826,"about_ca_topic_score_gemma":0.037566483,"teacher_disagreement_score":0.04476826,"about_ca_system_score_codex":0.0014365995,"about_ca_system_score_gemma":0.0014215336,"threshold_uncertainty_score":0.089015365},"labels":[],"label_agreement":null},{"id":"W4413385328","doi":"10.1101/2025.08.19.25333998","title":"<i>Nowcast-It:</i> A Practical Toolbox for Real-Time Adjustment of Reporting Delays in Epidemic Surveillance","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Science Foundation","keywords":"Toolbox; Computer science; Real-time data; Real-time computing; Econometrics; Economics; Operating system; Programming language","score_opus":0.0602521578613127,"score_gpt":0.386775333927648,"score_spread":0.3265231760663353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413385328","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":[0.0022879418,0.0002792826,0.71156615,0.0007625214,0.00035590318,0.00019694804,0.009369313,0.26934567,0.005836346],"genre_scores_gemma":[0.07101017,0.0008663072,0.80862814,0.001415343,0.00041743982,0.0015936989,0.014286451,0.086390875,0.015391569],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988733,0.00037347147,0.00013243347,0.0002644082,0.0002637894,0.000092617425],"domain_scores_gemma":[0.9888997,0.0070783175,0.00079418375,0.0012502345,0.0016148722,0.00036258993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00355563,0.0019816717,0.0008284387,0.0015062353,0.00038034582,0.0020294508,0.00264255,0.0013742377,0.08904182],"category_scores_gemma":[0.028543504,0.0010115664,0.001223405,0.0009114371,0.00053414755,0.0021471544,0.002453873,0.0024140244,0.042243343],"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":[0.00070712186,0.00018108769,0.0059739365,0.0015656231,0.0002363337,0.00063271093,0.0006008928,0.091523975,0.007903388,0.02895457,0.5273397,0.3343807],"study_design_scores_gemma":[0.00054686127,0.00014970671,0.0033432017,0.0006190164,0.0000926624,0.00060395536,0.000110714485,0.6369038,0.022036217,0.04958873,0.2857213,0.00028390953],"about_ca_topic_score_codex":0.003009967,"about_ca_topic_score_gemma":0.0029842844,"teacher_disagreement_score":0.08904182,"about_ca_system_score_codex":0.00073004473,"about_ca_system_score_gemma":0.0015363811,"threshold_uncertainty_score":0.29787457},"labels":[],"label_agreement":null},{"id":"W4413755049","doi":"10.2196/71992","title":"Association Between Alcohol Consumption and Psoriasis: Exploratory Analysis of Crowdsourced Web Search Data in Sweden","year":2025,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Association (psychology); Alcohol consumption; Psoriasis; Consumption (sociology); Data science; Psychology; Alcohol; World Wide Web; Computer science; Medicine; Sociology; Biology; Dermatology; Social science","score_opus":0.14497184714936703,"score_gpt":0.4648497523693611,"score_spread":0.31987790521999404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413755049","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":[0.99111944,0.00038020557,0.00042413603,0.00013289672,0.000007725166,0.00006234937,0.007343042,0.000037359867,0.00049284135],"genre_scores_gemma":[0.9845471,0.00029339228,0.0016387767,0.000048321792,0.000020782309,0.00018172356,0.012909921,0.000019580304,0.00034036473],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99724245,0.0013433272,0.0003212433,0.00038297332,0.00047201096,0.00023807527],"domain_scores_gemma":[0.9837488,0.011876695,0.00227044,0.00083003915,0.0009032022,0.00037079665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028924828,0.00042501505,0.0006935516,0.007641429,0.00048736957,0.0014809811,0.00045152495,0.00059406646,0.0009110729],"category_scores_gemma":[0.011882505,0.00017614955,0.0014946231,0.0066367825,0.000532649,0.0007868831,0.0014993165,0.0003840934,0.0004968766],"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":[0.0009095838,0.00046867624,0.95190406,0.0014451934,0.00085025246,0.0015804026,0.0059568854,0.0060371184,0.0014609208,0.00045755418,0.0037027835,0.02522648],"study_design_scores_gemma":[0.000046562316,0.00022734347,0.95976204,0.00037450515,0.00027352181,0.000836391,0.013986638,0.017518908,0.0008405675,0.0006377228,0.005416432,0.00007953579],"about_ca_topic_score_codex":0.019134657,"about_ca_topic_score_gemma":0.01571403,"teacher_disagreement_score":0.019134657,"about_ca_system_score_codex":0.000775371,"about_ca_system_score_gemma":0.0011246187,"threshold_uncertainty_score":0.0380466},"labels":[],"label_agreement":null},{"id":"W4413848731","doi":"10.1177/1179173x241275887","title":"Cross-Country Comparison of Bans on Internet Tobacco Advertising, and Search Interest in Vaping Products","year":2025,"lang":"en","type":"article","venue":"Tobacco Use Insights","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Advertising; The Internet; Business; Tobacco industry; Online advertising; Political science; Law","score_opus":0.0599777886757897,"score_gpt":0.356119944611595,"score_spread":0.29614215593580534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413848731","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":[0.9963426,0.0007482648,0.00008835956,0.00003681696,0.000009287051,0.000010255143,0.0015454355,0.0000030790495,0.0012158459],"genre_scores_gemma":[0.9987583,0.00018283493,0.000052195584,0.00001278659,0.000005278756,0.0000074812406,0.00083748373,0.0000016853488,0.00014204506],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99923277,0.00021016154,0.00013453251,0.00015709539,0.00014377439,0.000121774574],"domain_scores_gemma":[0.99448556,0.0015287667,0.00277101,0.00026942627,0.00058284524,0.00036245486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011831755,0.00015880314,0.00027194608,0.0020774652,0.00020272081,0.000870418,0.00018831581,0.00026224807,0.001985065],"category_scores_gemma":[0.004358598,0.00017129071,0.0006090968,0.0023743096,0.0003587149,0.0005254105,0.0007301936,0.00034063956,0.00024619952],"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":[0.000114745635,0.00002032445,0.9972385,0.00006532114,0.00019637433,0.0000428877,0.00019215803,0.00006729202,0.00015062258,0.000053991564,0.000101571684,0.0017562647],"study_design_scores_gemma":[0.0000013279325,0.000040784267,0.9990638,0.000015186749,0.000048717084,0.00006194825,0.00046009372,0.00005075655,0.00005459672,0.000008557468,0.00019225177,0.0000019991153],"about_ca_topic_score_codex":0.0057278997,"about_ca_topic_score_gemma":0.0055290577,"teacher_disagreement_score":0.0057278997,"about_ca_system_score_codex":0.00019103618,"about_ca_system_score_gemma":0.00021410751,"threshold_uncertainty_score":0.011389077},"labels":[],"label_agreement":null},{"id":"W4413895164","doi":"10.1007/978-3-031-90154-6_20","title":"Disease Prevention, Including Early Detection of Illnesses (EPHO5)","year":2025,"lang":"en","type":"book-chapter","venue":"Springer series on epidemiology and public health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"BC Centre for Disease Control; University of British Columbia","funders":"","keywords":"Disease; Medicine; Pathology","score_opus":0.09105890334772657,"score_gpt":0.35953704226905675,"score_spread":0.2684781389213302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413895164","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010634713,0.061712004,0.013703382,0.026170852,0.008896048,0.0002183059,0.0007388589,0.0005165057,0.88698053],"genre_scores_gemma":[0.011426536,0.055357616,0.013126363,0.01464895,0.0023211595,0.00015634876,0.0005871066,0.00022821403,0.90214765],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995696,0.00011528181,0.000019194531,0.00005470381,0.00019837751,0.00004281068],"domain_scores_gemma":[0.99952483,0.00023675416,0.000026146568,0.000030177403,0.000112064285,0.00007004182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000809209,0.0006209553,0.00025778782,0.0007931749,0.0006196963,0.0023979007,0.00068246806,0.0013918662,0.045977753],"category_scores_gemma":[0.0013256888,0.00020337966,0.00032662816,0.0007611332,0.0012404657,0.0017347289,0.0017076016,0.0017536309,0.015948836],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013193058,0.000033516313,0.00021390578,0.0005081139,0.000004250046,0.000056597983,0.00048176464,0.00030046797,0.00029711355,0.063512966,0.5637121,0.37086606],"study_design_scores_gemma":[0.000002066221,0.000009706018,0.00038628612,0.00041548227,0.0000016550559,0.00009311271,0.00008240138,0.00010449592,0.000114704606,0.0065109367,0.99227506,0.000003998856],"about_ca_topic_score_codex":0.011100799,"about_ca_topic_score_gemma":0.032072563,"teacher_disagreement_score":0.045977753,"about_ca_system_score_codex":0.0022484108,"about_ca_system_score_gemma":0.0028191132,"threshold_uncertainty_score":0.15381086},"labels":[],"label_agreement":null},{"id":"W4413932916","doi":"10.3390/jrfm18090488","title":"Risk Prediction of International Stock Markets with Complex Spatio-Temporal Correlations: A Spatio-Temporal Graph Convolutional Regression Model Integrating Uncertainty Quantification","year":2025,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Regression; Stock (firearms); Econometrics; Graph; Artificial intelligence; Data mining; Theoretical computer science; Mathematics; Geography; Statistics","score_opus":0.01647359962724146,"score_gpt":0.2661785216987703,"score_spread":0.24970492207152886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413932916","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":[0.3233805,0.0014055693,0.6660079,0.001718497,0.00011261761,0.000057078574,0.0007944602,0.00065779075,0.0058656046],"genre_scores_gemma":[0.9804326,0.0004322355,0.015197289,0.00012713257,0.000031607284,0.000038611,0.00042238226,0.000037905025,0.0032802876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971944,0.00007262403,0.000012793915,0.00010236797,0.00004475765,0.000047931295],"domain_scores_gemma":[0.9992924,0.00035788657,0.00012494905,0.000044152668,0.0001417174,0.000038829272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009296108,0.0008329343,0.00053775415,0.00055773545,0.00026520502,0.0009837394,0.0012215995,0.0010391324,0.0013092352],"category_scores_gemma":[0.0026306466,0.0003876642,0.0009191026,0.00055851345,0.00056868943,0.0012471718,0.00085187034,0.0014035974,0.00019794097],"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":[0.00003834429,0.000018487597,0.0026415254,0.000021537235,0.000041135456,0.00007291118,0.000025064197,0.9830482,0.0005406135,0.0045547825,0.0005142944,0.008483016],"study_design_scores_gemma":[9.723245e-7,0.0000022480979,0.00016472959,0.0000013746278,0.0000037296052,0.000003064138,0.0000013746632,0.99901855,0.00005086599,0.000709177,0.00004220117,0.0000017364896],"about_ca_topic_score_codex":0.026867054,"about_ca_topic_score_gemma":0.017103136,"teacher_disagreement_score":0.026867054,"about_ca_system_score_codex":0.0010614439,"about_ca_system_score_gemma":0.00087755476,"threshold_uncertainty_score":0.053421378},"labels":[],"label_agreement":null},{"id":"W4413986064","doi":"10.31718/mep.2025.29.2.01","title":"INFODEMIOLOGY OF RUSSO-UKRAINIAN WAR: USING GOOGLE TRENDS TO STUDY THE GLOBAL SPREAD OF UKRAINIAN REFUGEES WITH ALLERGIC DISEASES","year":2025,"lang":"en","type":"article","venue":"The Medical and Ecological Problems","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Ukrainian; Refugee; Political science; Development economics; Medicine; Law; Economics","score_opus":0.023526471925890996,"score_gpt":0.3267694510833438,"score_spread":0.3032429791574528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413986064","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":[0.9909021,0.0014078661,0.00032280188,0.00027641348,0.000017431494,0.000036769226,0.004319727,0.000025117057,0.0026916596],"genre_scores_gemma":[0.99674976,0.0007188714,0.00037616648,0.000050226034,0.00001588362,0.0000227039,0.0017818534,0.0000074701766,0.00027698342],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99958366,0.00012427862,0.000057567795,0.00008477103,0.00007904483,0.000070770446],"domain_scores_gemma":[0.99884146,0.0002652651,0.00057263114,0.00007122742,0.0001781704,0.00007132095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006693581,0.0002485254,0.00029826,0.0029073115,0.00021358574,0.0012337556,0.00021613459,0.00022605815,0.0011383573],"category_scores_gemma":[0.0028205414,0.00010629479,0.00050300534,0.0030948,0.0002870683,0.0013374469,0.00077231403,0.0003474016,0.00025874694],"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":[0.00013080557,0.00003423118,0.9731871,0.00031665838,0.0001305262,0.00038153023,0.0024231737,0.0006890594,0.00038298292,0.00042139817,0.0010996975,0.020802831],"study_design_scores_gemma":[0.0000025440493,0.00005460769,0.9897556,0.000120299905,0.00005632023,0.00040563563,0.004715314,0.0015162708,0.00023624304,0.00011205999,0.0030129333,0.000012195016],"about_ca_topic_score_codex":0.027497392,"about_ca_topic_score_gemma":0.02641715,"teacher_disagreement_score":0.027497392,"about_ca_system_score_codex":0.0006666318,"about_ca_system_score_gemma":0.000674071,"threshold_uncertainty_score":0.054674685},"labels":[],"label_agreement":null},{"id":"W4414083506","doi":"10.2196/70746","title":"Analyzing Reddit Social Media Content in the United States Related to H5N1: Sentiment and Topic Modeling Study","year":2025,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"York University; Artificial Intelligence in Medicine (Canada); University of Toronto","funders":"Foreign, Commonwealth and Development Office; Natural Sciences and Engineering Research Council of Canada; International Development Research Centre; University of Toronto; World Health Organization","keywords":"Social media; Topic model; Public health; Content analysis; Public health interventions; Health communication; Psychological intervention; Content (measure theory)","score_opus":0.12005992596272065,"score_gpt":0.45331092240425863,"score_spread":0.333250996441538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414083506","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":[0.99376726,0.0003016871,0.002981897,0.00026693274,0.00003463364,0.00003969906,0.0016213738,0.00004761889,0.00093900715],"genre_scores_gemma":[0.99236804,0.0002437899,0.003121396,0.000053402466,0.000091251524,0.00005512629,0.0034153112,0.000014513159,0.000637117],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995209,0.00019303765,0.000036668236,0.00011986313,0.000074182295,0.000055254917],"domain_scores_gemma":[0.9962327,0.0027405517,0.00043080255,0.000113964445,0.0003772273,0.00010490009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014549168,0.00042239795,0.00024174,0.0018462314,0.0003463221,0.0006461152,0.00021904612,0.00036153302,0.0005493326],"category_scores_gemma":[0.003985526,0.00012740552,0.0006308859,0.001438506,0.00019527062,0.0005955682,0.0004338074,0.0004322753,0.00024498705],"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":[0.00060813344,0.0005196809,0.8554999,0.00033001977,0.0003482783,0.00065533136,0.0050846697,0.018852914,0.010690883,0.0009771708,0.008343376,0.09808957],"study_design_scores_gemma":[0.00001562907,0.0001407808,0.64503235,0.00006882932,0.00014670388,0.0002614554,0.004016114,0.34265968,0.0021849822,0.00061956735,0.004814414,0.00003954111],"about_ca_topic_score_codex":0.012666303,"about_ca_topic_score_gemma":0.017622245,"teacher_disagreement_score":0.012666303,"about_ca_system_score_codex":0.0005660202,"about_ca_system_score_gemma":0.0003173354,"threshold_uncertainty_score":0.025185168},"labels":[],"label_agreement":null},{"id":"W4414113051","doi":"10.1177/17103568251378517","title":"Results of Photopatch Testing in Israel: A Retrospective Cohort Study","year":2025,"lang":"en","type":"article","venue":"Dermatitis","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Retrospective cohort study; Allergic contact dermatitis; Single Center; Contact dermatitis; Atopic dermatitis; Cohort; Patch testing","score_opus":0.01273671873469743,"score_gpt":0.28852096823615453,"score_spread":0.2757842495014571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414113051","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":[0.99942786,0.00010461114,0.00004892488,0.000011206767,0.0000022925153,0.000011027858,0.00017865386,0.0000016849739,0.00021371202],"genre_scores_gemma":[0.99952865,0.0000730375,0.00004132018,0.000020528805,0.0000066299176,0.000010015431,0.00026555063,0.0000016191287,0.000052656982],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993088,0.0001329201,0.00009015818,0.00019801506,0.00014218611,0.00012779256],"domain_scores_gemma":[0.9986707,0.00025454685,0.00058216136,0.00011478721,0.00016763178,0.00021025837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073722657,0.0002767496,0.0003844227,0.00084766676,0.00042128848,0.00058164715,0.0003322221,0.0004132783,0.0017309891],"category_scores_gemma":[0.0015793488,0.00035381533,0.00035481263,0.0008816377,0.00031040033,0.00055579987,0.0003533035,0.00042402197,0.00033905523],"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":[0.000034162902,0.000020227728,0.9993243,0.0000030254603,0.000012337587,0.000102823775,0.000033315075,0.0000064987794,0.00007724181,0.0000032488988,0.000031056563,0.0003516123],"study_design_scores_gemma":[0.0000056188114,0.00013151902,0.9981567,0.0000067247975,0.000023186232,0.0011318726,0.00024057373,0.000084382176,0.00006706017,0.000008229367,0.00014007991,0.000003953682],"about_ca_topic_score_codex":0.002536887,"about_ca_topic_score_gemma":0.0021091267,"teacher_disagreement_score":0.002536887,"about_ca_system_score_codex":0.00034746603,"about_ca_system_score_gemma":0.00038712888,"threshold_uncertainty_score":0.0057907104},"labels":[],"label_agreement":null},{"id":"W4414227719","doi":"10.2196/75291","title":"Identifying the Factors Associated With Spatial Clustering of Incident HIV Infection Cases in High-Prevalence Regions: Quantitative Geospatial Study","year":2025,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Geospatial analysis; Cluster analysis; Human immunodeficiency virus (HIV); Transmission (telecommunications); Spatial epidemiology; Psychological intervention; Health geography","score_opus":0.06300668031302144,"score_gpt":0.3585971697772855,"score_spread":0.2955904894642641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414227719","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":[0.99840957,0.000049643942,0.00072222215,0.000044305496,0.000002839837,0.000023546747,0.0005606918,0.00000675949,0.00018038008],"genre_scores_gemma":[0.999097,0.000023914925,0.000413165,0.0000063586963,0.0000021450091,0.0000214445,0.00038682003,0.0000016108964,0.000047564274],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989008,0.000417004,0.000095903866,0.00025736925,0.00016800509,0.00016094069],"domain_scores_gemma":[0.9971264,0.0008141435,0.0011066007,0.00032486426,0.00035060637,0.00027734082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016016694,0.000336467,0.00023489067,0.0016315007,0.0005751462,0.000637935,0.0006196963,0.0003643616,0.00095989136],"category_scores_gemma":[0.004961054,0.0002815784,0.00074947753,0.00317975,0.00057157356,0.00063680176,0.00082917867,0.00040789554,0.00013040558],"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":[0.00001902441,0.000014500073,0.9985197,0.000008775335,0.000039806375,0.000036304344,0.00018633713,0.0002589345,0.0000567447,0.00006636215,0.0000815621,0.0007120305],"study_design_scores_gemma":[0.0000036713307,0.00004205496,0.9944623,0.000009565486,0.00003726833,0.00011503092,0.0012988112,0.0035808953,0.000058717767,0.00014583698,0.00023624206,0.000009518222],"about_ca_topic_score_codex":0.032345757,"about_ca_topic_score_gemma":0.03311982,"teacher_disagreement_score":0.032345757,"about_ca_system_score_codex":0.0007656876,"about_ca_system_score_gemma":0.0012161754,"threshold_uncertainty_score":0.06431502},"labels":[],"label_agreement":null},{"id":"W4414238952","doi":"10.2196/70756","title":"Quality Assessment of Health Information on Social Media During a Public Health Crisis: Infodemiology Study","year":2025,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Transparency (behavior); Public health; Social media; Health information; Quality (philosophy); HRHIS; Health policy; Quality assessment","score_opus":0.13293092432790599,"score_gpt":0.4838560295350464,"score_spread":0.35092510520714043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414238952","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":[0.99567384,0.00064874865,0.00040855916,0.00044226108,0.000024148072,0.00021255079,0.0011862132,0.0000114507975,0.0013923061],"genre_scores_gemma":[0.99812084,0.00029733573,0.0004921987,0.00012332105,0.00004058866,0.000109920045,0.00061935134,0.000007606213,0.00018878316],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9893662,0.0043220986,0.0022089006,0.000771388,0.00277909,0.00055235054],"domain_scores_gemma":[0.90722543,0.03549132,0.039421123,0.0042531984,0.011135366,0.002473563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013038567,0.00027083114,0.00055126305,0.005513296,0.00079647644,0.0023531287,0.0006602039,0.00089736073,0.0014801373],"category_scores_gemma":[0.063657,0.00034420745,0.0010806056,0.006015413,0.00087891583,0.0027073014,0.0023041482,0.0010264864,0.0002753781],"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":[0.00016576883,0.00018622611,0.98603815,0.0002207842,0.0001972056,0.00006958662,0.0028630653,0.00005908827,0.000092712944,0.00011456865,0.00045466123,0.009538215],"study_design_scores_gemma":[0.000013101373,0.00025538093,0.99233776,0.00014349353,0.00010153665,0.00022771551,0.0049324394,0.0005606256,0.00019961278,0.000091556445,0.0011162824,0.000020588996],"about_ca_topic_score_codex":0.0040074335,"about_ca_topic_score_gemma":0.0039151344,"teacher_disagreement_score":0.013038567,"about_ca_system_score_codex":0.0016030311,"about_ca_system_score_gemma":0.0012851935,"threshold_uncertainty_score":0.0689553},"labels":[],"label_agreement":null},{"id":"W4414418015","doi":"10.2196/77274","title":"Associations Between Online Search Trends and Outpatient Visits for Common Medical Symptoms in the United States from 2004 to 2019: Time Series Ecological Study","year":2025,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"The Internet; Population; Health care; Primary care; Population health; Health information; MEDLINE; Information seeking behavior","score_opus":0.04523886838216693,"score_gpt":0.44175362896945625,"score_spread":0.3965147605872893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414418015","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":[0.99355364,0.00033674107,0.000109894034,0.00021183398,0.000014466895,0.000029116689,0.0050706076,0.000015314077,0.0006584368],"genre_scores_gemma":[0.9960349,0.00017227145,0.0001323321,0.00007054536,0.0000131354445,0.000040919465,0.0033373584,0.0000048548845,0.00019351496],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985668,0.00042164695,0.00023078249,0.00029261332,0.0002585312,0.00022957231],"domain_scores_gemma":[0.9942386,0.0014528736,0.002651549,0.00036458226,0.0006628708,0.0006294102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015380372,0.0002624777,0.00048035174,0.0023697128,0.00054562755,0.0011066024,0.0008355197,0.00073816825,0.0017989255],"category_scores_gemma":[0.0060376385,0.0003112413,0.0012581485,0.004797805,0.00048270109,0.0010163593,0.0012878659,0.0011557263,0.00035162864],"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":[0.00005527343,0.00007289597,0.9983217,0.00002578302,0.000118287084,0.00003471997,0.00018136081,0.000099460965,0.000032867945,0.000037265647,0.00030771238,0.00071258936],"study_design_scores_gemma":[0.0000039635156,0.000075392876,0.9979995,0.000015815416,0.000044965294,0.000050714665,0.0007432492,0.0007497176,0.000018098759,0.000023109007,0.00027007548,0.000005443928],"about_ca_topic_score_codex":0.08086355,"about_ca_topic_score_gemma":0.09682543,"teacher_disagreement_score":0.08086355,"about_ca_system_score_codex":0.0011536458,"about_ca_system_score_gemma":0.0011207183,"threshold_uncertainty_score":0.16078573},"labels":[],"label_agreement":null},{"id":"W4414542067","doi":"10.2196/72888","title":"Identifying Gaps and Challenges in Acute Hepatitis B Surveillance in the Country of Georgia: Comprehensive Surveillance System Evaluation","year":2025,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Acute hepatitis B; Notification system; Hepatitis B; Public health surveillance; Disease surveillance; Epidemiological surveillance; Reliability (semiconductor); Risk assessment","score_opus":0.06252834837951858,"score_gpt":0.3498043339707245,"score_spread":0.28727598559120593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414542067","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":[0.97472614,0.003120096,0.0060995137,0.0053078085,0.0000847347,0.0044819373,0.003448242,0.00012010906,0.002611555],"genre_scores_gemma":[0.97741824,0.0008474452,0.016526405,0.0007101052,0.000029178489,0.0020352977,0.0023474596,0.000011460619,0.00007450185],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.94100016,0.044213835,0.005707312,0.0017458154,0.005223731,0.0021092007],"domain_scores_gemma":[0.9200073,0.038041648,0.012098942,0.0034547185,0.02182574,0.004571589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13657326,0.00083477807,0.0012260403,0.0031161858,0.000850701,0.0022751247,0.0013287036,0.0010969454,0.00050510373],"category_scores_gemma":[0.11911278,0.0004846208,0.0025178206,0.003534425,0.00081277743,0.0032041583,0.0036672733,0.0007451941,0.00006336954],"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":[0.0007152741,0.00072857825,0.8890554,0.0026651644,0.00203392,0.00014472606,0.004747324,0.008514135,0.00022553142,0.0011657607,0.004535957,0.08546824],"study_design_scores_gemma":[0.00029825658,0.004045893,0.93812317,0.0037348014,0.0015360219,0.00014510934,0.0090359235,0.035207488,0.00043239674,0.0014804717,0.0058718026,0.00008878032],"about_ca_topic_score_codex":0.0389478,"about_ca_topic_score_gemma":0.06253169,"teacher_disagreement_score":0.13657326,"about_ca_system_score_codex":0.0070360135,"about_ca_system_score_gemma":0.022745091,"threshold_uncertainty_score":0.72227705},"labels":[],"label_agreement":null},{"id":"W4414594140","doi":"10.2196/82057","title":"Author’s Reply: \"Data Contamination in AI Evaluation\"","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Contamination; Context (archaeology); Process (computing); MEDLINE","score_opus":0.038663800948801806,"score_gpt":0.40989969781291385,"score_spread":0.37123589686411207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414594140","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015209426,0.00035505992,0.00026079698,0.97182107,0.026882801,0.00001001236,0.00007339733,0.000028313065,0.00041656027],"genre_scores_gemma":[0.0016413365,0.000271943,0.0004654768,0.983136,0.013239308,0.000039715305,0.000028196688,0.00002799661,0.0011500552],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9476401,0.021918043,0.009340547,0.004696767,0.012781293,0.003623323],"domain_scores_gemma":[0.69955444,0.20857981,0.0114774825,0.0069197514,0.06587007,0.007598328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039128013,0.0009872627,0.0019261929,0.0023558617,0.005769066,0.009259405,0.003970012,0.05502641,0.011171118],"category_scores_gemma":[0.3306311,0.0015139738,0.0023226298,0.0021343029,0.007914047,0.0051142313,0.005786207,0.05079997,0.007843163],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004270726,0.000016043841,0.00071595184,0.00015709603,0.000057572033,0.00018765962,0.00041221819,0.00006704393,0.000072123614,0.0021353369,0.9917,0.004436256],"study_design_scores_gemma":[0.00021986627,0.00006406142,0.002552753,0.0025471584,0.00026697936,0.0010010939,0.002442778,0.0007437785,0.00087639067,0.015056267,0.9740128,0.00021613206],"about_ca_topic_score_codex":0.009618024,"about_ca_topic_score_gemma":0.010137639,"teacher_disagreement_score":0.05502641,"about_ca_system_score_codex":0.0059355046,"about_ca_system_score_gemma":0.017564027,"threshold_uncertainty_score":0.20693123},"labels":[],"label_agreement":null},{"id":"W4414600508","doi":"10.2196/69655","title":"Comparison of Virus Watch COVID-19 Positivity, Incidence, and Hospitalization Rates With Other Surveillance Systems: Surveillance Study","year":2025,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Wellcome Trust","keywords":"Public health surveillance; Public health; Epidemiological surveillance; Incidence (geometry); Disease surveillance; Epidemiology; Coronavirus disease 2019 (COVID-19); MEDLINE","score_opus":0.03256367335960373,"score_gpt":0.38389172617872147,"score_spread":0.3513280528191177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414600508","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":[0.9955552,0.00064374483,0.0010713274,0.000060601185,0.00002666728,0.00013414095,0.0016304274,0.000014262767,0.00086365954],"genre_scores_gemma":[0.99708265,0.0001692362,0.0005631682,0.000036670153,0.000023448114,0.00012929838,0.0018738538,0.00000524313,0.000116483505],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9861308,0.008083314,0.0015130867,0.0020449494,0.00158417,0.00064379483],"domain_scores_gemma":[0.97214305,0.012859257,0.0081698615,0.0025106948,0.0033734832,0.0009435497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0153250145,0.00060406537,0.0007528942,0.0021058826,0.00025077496,0.0012496224,0.0009418443,0.00065375544,0.00091592956],"category_scores_gemma":[0.032260936,0.00041776572,0.0019273079,0.0014538683,0.00050410646,0.0017202016,0.0016733077,0.00059912255,0.00023695742],"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":[0.0014755069,0.00008806244,0.9902215,0.00017002154,0.0012741404,0.000025413286,0.0003415651,0.00048547928,0.00019709079,0.00012661934,0.00028484737,0.0053097466],"study_design_scores_gemma":[0.00012185751,0.002911887,0.99134785,0.00008809832,0.0005971838,0.00019089309,0.0006109864,0.0030028722,0.00034046752,0.000070224036,0.0006935006,0.000024315204],"about_ca_topic_score_codex":0.0098667415,"about_ca_topic_score_gemma":0.008358379,"teacher_disagreement_score":0.0153250145,"about_ca_system_score_codex":0.0012227433,"about_ca_system_score_gemma":0.00085285475,"threshold_uncertainty_score":0.081047356},"labels":[],"label_agreement":null},{"id":"W4415012779","doi":"10.14745/ccdr.v51i09a03","title":"Minimum data cleaning recommendations for infection prevention and control acute care surveillance reporting: A solution for \"garbage in, garbage out\"","year":2025,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Sunnybrook Hospital; SGS (Canada); Université de Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre Hospitalier Universitaire Sainte-Justine; Alberta Health Services; Université de Sherbrooke; Public Health Agency of Canada; Interior Health","funders":"","keywords":"Quality (philosophy); Data quality; Garbage; Control (management); Infection control; Quality management","score_opus":0.048540958355638666,"score_gpt":0.36699238304784454,"score_spread":0.3184514246922059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415012779","genre_codex":"commentary","genre_gemma":"methods","domain_codex":null,"domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026615374,0.013433686,0.33212903,0.5130447,0.008010171,0.019606864,0.028407149,0.009162034,0.049590986],"genre_scores_gemma":[0.057069845,0.005229227,0.87743413,0.03230368,0.0008863223,0.0055240626,0.016084561,0.00058833155,0.004879838],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.7254096,0.10767311,0.059341967,0.006982755,0.0936474,0.0069450918],"domain_scores_gemma":[0.36771256,0.10784407,0.05195814,0.042339113,0.41407803,0.016068017],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.23522201,0.0019222568,0.0021190555,0.013476851,0.0067084534,0.008246726,0.014136821,0.0044883015,0.0038909581],"category_scores_gemma":[0.3833024,0.0023429873,0.0032287533,0.009682695,0.0028834615,0.0053312443,0.007843085,0.010357061,0.0016971714],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020748135,0.00049108564,0.050466973,0.0061087287,0.00034122818,0.00026616044,0.0060872436,0.004291194,0.0026183631,0.014940447,0.6154648,0.29871634],"study_design_scores_gemma":[0.00060584326,0.00083296135,0.16605668,0.03486036,0.00089599594,0.0007512502,0.00861184,0.014827962,0.009397692,0.020715265,0.74141574,0.0010284033],"about_ca_topic_score_codex":0.46234354,"about_ca_topic_score_gemma":0.54156834,"teacher_disagreement_score":0.9654713,"about_ca_system_score_codex":0.034528658,"about_ca_system_score_gemma":0.23767479,"threshold_uncertainty_score":0.9431074},"labels":[],"label_agreement":null},{"id":"W4415052138","doi":"10.2196/85512","title":"Correction: Comparison of Virus Watch COVID-19 Positivity, Incidence, and Hospitalization Rates With Other Surveillance Systems: Surveillance Study","year":2025,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Public health surveillance; Public health; Virus; Epidemiology; Epidemiological surveillance; Outbreak","score_opus":0.02645221079665163,"score_gpt":0.36370153072160805,"score_spread":0.33724931992495644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415052138","genre_codex":"editorial","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00040203534,0.001214542,0.00074046245,0.04250724,0.94219553,0.000082211096,0.011454194,0.00035641665,0.0010474325],"genre_scores_gemma":[0.06298544,0.010357937,0.009168449,0.15030599,0.63590574,0.0017089267,0.022392515,0.003371805,0.10380319],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.98490715,0.00451014,0.0033161123,0.0020121008,0.0041896226,0.001064815],"domain_scores_gemma":[0.8705365,0.041194785,0.00796968,0.00819153,0.06768788,0.004419631],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.014680869,0.0030142758,0.0027455273,0.0064324834,0.0016764099,0.005114161,0.005041296,0.006386774,0.055507436],"category_scores_gemma":[0.23318017,0.0021958598,0.002899614,0.0070734574,0.0020488328,0.002519082,0.002092013,0.01023818,0.022520801],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087308,0.000007311415,0.0005926165,0.0005382184,0.00009707232,0.000088287634,0.000044002318,0.00006125195,0.000025123656,0.00025034268,0.99523634,0.0029721148],"study_design_scores_gemma":[0.00070730527,0.00009983914,0.018919455,0.004216783,0.0005770534,0.00085390965,0.0005238059,0.0015904384,0.0005567776,0.0014053812,0.9703062,0.00024300917],"about_ca_topic_score_codex":0.06341417,"about_ca_topic_score_gemma":0.043875474,"teacher_disagreement_score":0.98531914,"about_ca_system_score_codex":0.0059027644,"about_ca_system_score_gemma":0.011711297,"threshold_uncertainty_score":0.18569088},"labels":[],"label_agreement":null},{"id":"W4415323123","doi":"10.34105/j.kmel.2025.17.031","title":"From data to insights: Machine learning in thematic analysis of complex health conditions on social media","year":2025,"lang":"en","type":"article","venue":"Knowledge Management & E-Learning An International Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Thompson Rivers University","funders":"Thompson Rivers University","keywords":"Thematic analysis; Social media; Thematic map; Big data; Filter (signal processing); Process (computing); Class (philosophy); Focus (optics)","score_opus":0.0916651111064284,"score_gpt":0.4145368601336094,"score_spread":0.322871749027181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415323123","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":[0.07297285,0.0011390614,0.91227055,0.006807912,0.00022306027,0.0012387682,0.0013608496,0.0009741283,0.0030128562],"genre_scores_gemma":[0.28404242,0.0005262143,0.71156144,0.00051000563,0.00018424734,0.0012405998,0.0012075974,0.00010637001,0.0006211366],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9797323,0.015962869,0.0009223805,0.001779891,0.0012691758,0.00033333883],"domain_scores_gemma":[0.8913638,0.095659554,0.0044501647,0.0039402805,0.0039424696,0.00064368255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029057376,0.0012895918,0.0010965556,0.008208365,0.002171926,0.0067532524,0.0024126961,0.0016531708,0.0019675966],"category_scores_gemma":[0.08577071,0.0007395289,0.0025262896,0.0062621916,0.0030532381,0.0069390195,0.0050431527,0.004310306,0.0007150358],"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":[0.0005568479,0.00082151085,0.04163567,0.0022448453,0.000664082,0.00066625944,0.02893021,0.072961554,0.0046157995,0.041157175,0.013096603,0.7926495],"study_design_scores_gemma":[0.000054286716,0.000108749446,0.0078591285,0.0005207274,0.00010012679,0.00014113383,0.011247702,0.7794494,0.0025422086,0.18705405,0.010800276,0.00012213533],"about_ca_topic_score_codex":0.0048990482,"about_ca_topic_score_gemma":0.0066070845,"teacher_disagreement_score":0.029057376,"about_ca_system_score_codex":0.00323927,"about_ca_system_score_gemma":0.0026333805,"threshold_uncertainty_score":0.15367186},"labels":[],"label_agreement":null},{"id":"W4415403594","doi":"10.2196/86209","title":"Multimodal Data Approaches for Examining the 2024-2025 Highly Pathogenic Avian Influenza Outbreak in the United States: Descriptive Study","year":2025,"lang":"en","type":"preprint","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Outbreak; Influenza A virus subtype H5N1; Highly pathogenic; Public health; Public health surveillance; Disease surveillance; One Health; Pandemic","score_opus":0.19946910389953973,"score_gpt":0.3780268757312629,"score_spread":0.1785577718317232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415403594","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":[0.96456814,0.0014793745,0.0042633926,0.0011095781,0.000041576328,0.0010469331,0.022308355,0.00007615864,0.0051063667],"genre_scores_gemma":[0.96989685,0.0011931828,0.009363952,0.00046030147,0.000038998718,0.0019063195,0.016107148,0.00003220245,0.0010010806],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987306,0.00046606988,0.00021812614,0.00016545963,0.00026890222,0.0001508022],"domain_scores_gemma":[0.99334866,0.0028484974,0.0014480514,0.00041326572,0.0016291846,0.00031234015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027140637,0.00033042007,0.0002393005,0.003817806,0.0008410933,0.001243683,0.0004950846,0.00039322063,0.0021913054],"category_scores_gemma":[0.011475031,0.00020745223,0.0007869239,0.0058148107,0.0003592243,0.0012633867,0.0017388345,0.000650881,0.00038408002],"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":[0.00016545889,0.0003284199,0.9394376,0.00041400184,0.0001679581,0.00034225322,0.006062324,0.0005939956,0.000297324,0.00068076054,0.008770235,0.042739622],"study_design_scores_gemma":[0.00001653048,0.00025104263,0.93250686,0.0005589904,0.00018978902,0.0004499361,0.045837376,0.0039459406,0.0004590587,0.00056752417,0.015161641,0.000055367345],"about_ca_topic_score_codex":0.045961108,"about_ca_topic_score_gemma":0.06982245,"teacher_disagreement_score":0.045961108,"about_ca_system_score_codex":0.0018608229,"about_ca_system_score_gemma":0.0021639038,"threshold_uncertainty_score":0.09138715},"labels":[],"label_agreement":null},{"id":"W4415442946","doi":"10.1093/oxfordhb/9780197783160.013.0052","title":"Early Warning for Environmental and Disaster Intelligence","year":2025,"lang":"en","type":"book-chapter","venue":"Oxford University Press eBooks","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Warning system; Environmental security; Pace; Natural disaster; Environmental disaster; Disaster research; Core (optical fiber); Politics","score_opus":0.017416822862667183,"score_gpt":0.21657043300322978,"score_spread":0.1991536101405626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415442946","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010310228,0.08640299,0.025358234,0.01967055,0.008554186,0.00009129674,0.0003668434,0.00038351462,0.85814124],"genre_scores_gemma":[0.017355338,0.047378942,0.018499257,0.0046148924,0.0021328456,0.00006579435,0.00037347345,0.00016736187,0.9094121],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99969363,0.00007161028,0.000013789159,0.000038462378,0.00016086076,0.00002163259],"domain_scores_gemma":[0.9994752,0.00028930863,0.000026283094,0.000029094814,0.000142327,0.000037667716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053942075,0.00054462656,0.00024952783,0.0018336375,0.00066728937,0.00346863,0.00058374763,0.0017533094,0.03907171],"category_scores_gemma":[0.0012801454,0.00026193945,0.00022638342,0.0010942629,0.001081349,0.0031913908,0.0014294405,0.0024480426,0.013070687],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000171426,0.000029223354,0.00013424229,0.0003562266,0.0000048741554,0.00014144421,0.000697255,0.00091359566,0.00084839045,0.3386334,0.4018722,0.25635198],"study_design_scores_gemma":[0.0000015676992,0.000005437128,0.00009376958,0.00027018556,0.0000016107809,0.000102623075,0.0000947391,0.00030759643,0.0001422262,0.018885504,0.9800894,0.0000051558463],"about_ca_topic_score_codex":0.0020847525,"about_ca_topic_score_gemma":0.0034329516,"teacher_disagreement_score":0.03907171,"about_ca_system_score_codex":0.0013392562,"about_ca_system_score_gemma":0.0010300928,"threshold_uncertainty_score":0.13070786},"labels":[],"label_agreement":null},{"id":"W4415782980","doi":"10.1038/s41598-025-13675-z","title":"Establishing and validating syndromic surveillance of gastrointestinal infections using routine emergency department data, Germany, 2019–2023","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Robert Koch Institut; Bundesministerium für Gesundheit; Bundesministerium für Bildung und Forschung; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology","keywords":"Emergency department; Medical diagnosis; Outbreak; Medical record; Public health surveillance; Outpatient visits; Electronic surveillance; MEDLINE","score_opus":0.026459790673961182,"score_gpt":0.3194934248347711,"score_spread":0.2930336341608099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415782980","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":[0.98454523,0.00089249667,0.004908652,0.00072836573,0.00009363182,0.00023459256,0.006716505,0.00007783544,0.0018026435],"genre_scores_gemma":[0.9824746,0.00038567913,0.0072783,0.0001982383,0.000044477354,0.0001800778,0.009233055,0.0000127664025,0.0001928345],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99256295,0.0032953545,0.0012453937,0.0010018981,0.0012050285,0.00068950094],"domain_scores_gemma":[0.9856776,0.0036910966,0.0040460685,0.0014654183,0.0043463567,0.00077348883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013553993,0.00048373608,0.00037737325,0.0026413437,0.0003016082,0.0015770025,0.0006977877,0.00032952038,0.00037357386],"category_scores_gemma":[0.024374317,0.00029234617,0.00040039097,0.002519511,0.00043413363,0.00078450376,0.001702721,0.00036261947,0.00018616914],"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":[0.00008943389,0.00004224973,0.9677316,0.00015786222,0.00014453354,0.00010719361,0.00041224752,0.0016324857,0.00088621664,0.0004059952,0.0020060535,0.026384225],"study_design_scores_gemma":[0.00001359501,0.0001075956,0.98551476,0.00016906933,0.00006763189,0.00013873483,0.0005313923,0.009103796,0.0012680751,0.00012294673,0.0029448809,0.000017502798],"about_ca_topic_score_codex":0.058545407,"about_ca_topic_score_gemma":0.06193979,"teacher_disagreement_score":0.058545407,"about_ca_system_score_codex":0.0023736376,"about_ca_system_score_gemma":0.003811395,"threshold_uncertainty_score":0.1164093},"labels":[],"label_agreement":null},{"id":"W4416102265","doi":"10.1017/gmh.2025.10088","title":"The use of geographic information systems (GIS) in studying mental health service delivery: A scoping review","year":2025,"lang":"en","type":"review","venue":"Cambridge Prisms Global Mental Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Addiction and Mental Health","funders":"","keywords":"Geographic information system; GIS and public health; Mental health; Information system; Service delivery framework; Service (business); Scope (computer science); Local information systems","score_opus":0.06389162842073727,"score_gpt":0.3828523893096117,"score_spread":0.31896076088887443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416102265","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":[0.00043949872,0.9974806,0.00041594077,0.00047787002,0.00013752212,0.00026239984,0.00016050038,0.0000086790415,0.00061697804],"genre_scores_gemma":[0.0034945677,0.99475443,0.0009457567,0.00015176438,0.000049561215,0.00042546002,0.000113460934,0.0000042369743,0.000060731778],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9799227,0.008912083,0.0067287656,0.0009966858,0.0030786502,0.00036104643],"domain_scores_gemma":[0.8821501,0.10148105,0.007540016,0.0013392984,0.0070649977,0.00042452166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02108795,0.0016477165,0.005539387,0.037696164,0.001519138,0.0067992,0.0028587624,0.0033648931,0.0039522788],"category_scores_gemma":[0.09299476,0.0016509781,0.0059756944,0.045107108,0.002293853,0.007297187,0.0033681088,0.0018352169,0.0006599252],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006202944,0.00003296659,0.0012034259,0.72774315,0.0017306154,0.00014018305,0.0014032784,0.00046050467,0.00013753789,0.0024339545,0.0045296396,0.2601227],"study_design_scores_gemma":[0.00002214541,0.000067135596,0.0018505593,0.950619,0.0037399996,0.0001793119,0.0010684156,0.00019698839,0.00011635557,0.0008770972,0.041230958,0.000032124637],"about_ca_topic_score_codex":0.015391103,"about_ca_topic_score_gemma":0.025485372,"teacher_disagreement_score":0.037696164,"about_ca_system_score_codex":0.0065004895,"about_ca_system_score_gemma":0.020107567,"threshold_uncertainty_score":0.11152506},"labels":[],"label_agreement":null},{"id":"W4416186906","doi":"10.1186/s12992-025-01125-4","title":"Do alcohol industry-funded organisations act to correct misinformation? A qualitative study of pregnancy and infant health content following independent analysis","year":2025,"lang":"en","type":"article","venue":"Globalization and Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Misinformation; Public health; Qualitative research; Pregnancy; Content analysis; Health services research; Social policy; Alcohol; Health policy","score_opus":0.07304392898863654,"score_gpt":0.43309903898032703,"score_spread":0.3600551099916905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416186906","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":[0.9780553,0.0009653665,0.0019583679,0.008455482,0.00008194182,0.00026210028,0.0001225942,0.000029607196,0.010069178],"genre_scores_gemma":[0.9948566,0.00055423635,0.00076537114,0.0011369003,0.000017199949,0.0002854766,0.00004080819,0.000026725631,0.002316647],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9511751,0.041248247,0.0011236363,0.0010727401,0.0024057247,0.002974435],"domain_scores_gemma":[0.88482976,0.094509,0.007389525,0.002235276,0.0076077585,0.0034287258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04010575,0.00044717177,0.0006985918,0.0031591272,0.011188366,0.006050532,0.0021157507,0.0025837866,0.003341892],"category_scores_gemma":[0.09282553,0.0008526662,0.00039315087,0.0027808049,0.011713775,0.008091521,0.008951143,0.003429024,0.00045086085],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011745198,0.000014094385,0.0018889002,0.0000703926,0.000001398344,0.00017208967,0.99504846,0.0000071188715,0.00010753611,0.0008452943,0.00023358528,0.0015993865],"study_design_scores_gemma":[0.0000015285948,0.00001158355,0.0009727968,0.00014485205,0.0000017777099,0.000051839117,0.99470913,0.00004475119,0.00007694157,0.00016526146,0.0038142202,0.00000525036],"about_ca_topic_score_codex":0.017461855,"about_ca_topic_score_gemma":0.01845211,"teacher_disagreement_score":0.04010575,"about_ca_system_score_codex":0.010594933,"about_ca_system_score_gemma":0.009786477,"threshold_uncertainty_score":0.212102},"labels":[],"label_agreement":null},{"id":"W4416435722","doi":"10.2196/80331","title":"Digital Survey–Based Tracing of COVID-19 Over the Early Pandemic: Comprehensive Geospatial and Symptomatic Analysis in Lebanon","year":2025,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Geospatial analysis; Public health; Public health surveillance; Digital health; Pandemic; Outbreak; Disease surveillance; Geographic information system; Tracing","score_opus":0.032519249194292825,"score_gpt":0.34035937102614544,"score_spread":0.3078401218318526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416435722","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":[0.99235415,0.00032166744,0.0007289609,0.00021709841,0.000009172939,0.0001576487,0.004808812,0.00003652686,0.0013659053],"genre_scores_gemma":[0.9910623,0.00040347292,0.0026532835,0.000092147675,0.0000163191,0.00024358244,0.0049651456,0.0000063295965,0.00055750465],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994522,0.00024991579,0.000038753904,0.00008260768,0.00008202777,0.00009458308],"domain_scores_gemma":[0.9987908,0.00025922872,0.0003546813,0.000073522126,0.00039287505,0.00012884893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014886559,0.00034444628,0.00019252098,0.0034995703,0.00029163325,0.0006770226,0.00046867508,0.00028811902,0.001109158],"category_scores_gemma":[0.0020425743,0.00013581963,0.0002134471,0.0025587038,0.00025898337,0.00044864282,0.0009168648,0.0002188232,0.00019089744],"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":[0.00012325324,0.00016103331,0.9606208,0.00015725348,0.00006171434,0.0004089691,0.0021623468,0.001483171,0.00053615763,0.00025043817,0.0024473749,0.031587392],"study_design_scores_gemma":[0.000015602034,0.0001331527,0.9779277,0.00015534263,0.000031800962,0.00017660184,0.010644002,0.0062613804,0.00029612865,0.00020577516,0.004136046,0.00001637535],"about_ca_topic_score_codex":0.07125091,"about_ca_topic_score_gemma":0.08481464,"teacher_disagreement_score":0.07125091,"about_ca_system_score_codex":0.0015943152,"about_ca_system_score_gemma":0.001011433,"threshold_uncertainty_score":0},"labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"grok","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"opus","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W4416597810","doi":"10.6000/1929-6029.2025.14.64","title":"Machine Learning-Based Prediction of Seasonal Influenza Trends in Saudi Arabia: A Tool for Regional Public Health Planning","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"World Health Organization","keywords":"Preparedness; Seasonal influenza; Random forest; Generalization; Public health; Population; Support vector machine","score_opus":0.11869238465763388,"score_gpt":0.48425609025335664,"score_spread":0.36556370559572277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416597810","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":[0.9515851,0.0010529398,0.04117676,0.001360914,0.00004194545,0.00005860258,0.002634796,0.00047285898,0.0016160745],"genre_scores_gemma":[0.9902526,0.00020410425,0.008266152,0.0000295616,0.000012932772,0.000011786569,0.0010492869,0.000005938245,0.00016746542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981683,0.00009254149,0.000017852399,0.000030302399,0.000023168317,0.00001933551],"domain_scores_gemma":[0.9989863,0.00049193116,0.00016817143,0.0000668366,0.00023325927,0.000053411222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00104698,0.00041125488,0.000281969,0.0010019682,0.00013318549,0.00054701424,0.00032365703,0.00030851626,0.000633506],"category_scores_gemma":[0.0036124708,0.000126266,0.00032872966,0.00047513068,0.000096394,0.00046795903,0.0003185369,0.00037616282,0.0002092028],"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":[0.00028986484,0.00017993734,0.3634592,0.00014939281,0.0001848025,0.00013909268,0.0001709199,0.5020338,0.0022224635,0.0014940081,0.0028431462,0.12683322],"study_design_scores_gemma":[0.00000822591,0.00006218675,0.028214855,0.000024663275,0.000026246162,0.000018260342,0.00021082597,0.96943665,0.00073707453,0.0006417733,0.00061030465,0.0000089285795],"about_ca_topic_score_codex":0.030632751,"about_ca_topic_score_gemma":0.019556616,"teacher_disagreement_score":0.030632751,"about_ca_system_score_codex":0.00048640004,"about_ca_system_score_gemma":0.00081473996,"threshold_uncertainty_score":0.060908914},"labels":[],"label_agreement":null},{"id":"W4416609359","doi":"10.5256/f1000research.50696.r78936","title":"Referee report. For: Using DHS and MICS data to complement or replace NGO baseline health data: an exploratory study [version 1; peer review: 2 approved]","year":2021,"lang":"en","type":"article","venue":"Faculty of 1000 Research Ltd","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"UNICEF; Global Affairs Canada; United States Agency for International Development","keywords":"Exploratory research; Complement (music); Baseline (sea); Public health; Qualitative research; Peer review","score_opus":0.41572687075969034,"score_gpt":0.5272678369254988,"score_spread":0.1115409661658085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416609359","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018063649,0.0016514016,0.006119485,0.47659785,0.14909278,0.0064622993,0.26924875,0.0049947924,0.08402637],"genre_scores_gemma":[0.031516112,0.00313025,0.01709565,0.2888244,0.033520736,0.017054856,0.085800014,0.005404496,0.5176534],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98998696,0.0030438055,0.0014345172,0.0008887723,0.0038958387,0.00075016485],"domain_scores_gemma":[0.7508342,0.073562756,0.0052572787,0.008303711,0.15647416,0.005567935],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01626466,0.00093861663,0.0012284489,0.0044015567,0.0040925764,0.0040029217,0.002576663,0.004793884,0.48274574],"category_scores_gemma":[0.263288,0.0006081061,0.001372155,0.0058729146,0.0010262135,0.0036851522,0.0040396564,0.003905352,0.16192535],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008923808,0.0000064494557,0.00012266207,0.00006477617,0.000002510605,0.00002321916,0.000048756672,0.000010116852,0.000021257718,0.00020573438,0.99756587,0.0019198119],"study_design_scores_gemma":[0.00017195559,0.000028818527,0.006628502,0.0007722288,0.000034931298,0.00017121628,0.0016006919,0.00028428956,0.00037493778,0.0021221393,0.9877001,0.00011029827],"about_ca_topic_score_codex":0.107856706,"about_ca_topic_score_gemma":0.112520345,"teacher_disagreement_score":0.9837353,"about_ca_system_score_codex":0.0050247386,"about_ca_system_score_gemma":0.011685876,"threshold_uncertainty_score":0.7378004},"labels":[],"label_agreement":null},{"id":"W4416614470","doi":"10.5256/f1000research.50696.r78933","title":"Referee report. For: Using DHS and MICS data to complement or replace NGO baseline health data: an exploratory study [version 1; peer review: 1 approved]","year":2021,"lang":"en","type":"article","venue":"Faculty of 1000 Research Ltd","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"UNICEF; Global Affairs Canada; United States Agency for International Development","keywords":"Exploratory research; Complement (music); Baseline (sea); Public health; Qualitative research; Data collection","score_opus":0.42328285600240745,"score_gpt":0.5278967028890743,"score_spread":0.10461384688666686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416614470","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017353852,0.0016722691,0.0060238335,0.4769828,0.1468531,0.006342154,0.27265975,0.004965046,0.082765654],"genre_scores_gemma":[0.030982021,0.003229877,0.017322585,0.2955072,0.033512127,0.017127821,0.08760051,0.0055405754,0.5091774],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98983616,0.0031068104,0.0014602295,0.0008854008,0.003951875,0.0007595508],"domain_scores_gemma":[0.7483984,0.075126424,0.005267051,0.008162889,0.15748365,0.005561597],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01648345,0.0009425782,0.0012360399,0.00447601,0.0040781787,0.004024085,0.0026028114,0.004800183,0.48341635],"category_scores_gemma":[0.26616,0.0006076368,0.0013899714,0.0059729675,0.0010337352,0.0037262554,0.0040815943,0.0039501903,0.16319832],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008639464,0.000006242197,0.00012135294,0.000066465676,0.0000024707952,0.000022603524,0.000048329133,0.000009946428,0.000020158976,0.0002046883,0.99756753,0.0019216589],"study_design_scores_gemma":[0.00016916808,0.00002836697,0.0065474724,0.0008049716,0.000034909845,0.00016883286,0.0016321449,0.00027592937,0.0003613241,0.0021316395,0.9877357,0.000109592176],"about_ca_topic_score_codex":0.10963156,"about_ca_topic_score_gemma":0.11426234,"teacher_disagreement_score":0.48341635,"about_ca_system_score_codex":0.0050427457,"about_ca_system_score_gemma":0.011868249,"threshold_uncertainty_score":0.7368439},"labels":[],"label_agreement":null},{"id":"W4416734108","doi":"10.1155/adph/2190224","title":"Advancing Public Health Surveillance With Artificial Intelligence: A Systematic Review of Real‐Time Data Analytics and Disease Prediction","year":2025,"lang":"en","type":"article","venue":"Advances in Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wycliffe College","funders":"","keywords":"Predictive analytics; Public health surveillance; Disease surveillance; Analytics; Public health; Big data; Data analysis; Inclusion (mineral); Random forest","score_opus":0.0490463310442098,"score_gpt":0.3731109096799164,"score_spread":0.3240645786357066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416734108","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":[0.00026154905,0.99859077,0.00020843757,0.00038239223,0.00007527707,0.00008678992,0.0001500083,0.0000059739355,0.0002388222],"genre_scores_gemma":[0.0030306592,0.9955903,0.0007124508,0.00031424986,0.000055139448,0.00015965418,0.000100560384,0.0000035082055,0.000033569027],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99236333,0.002883886,0.0028606837,0.0005066209,0.0012026648,0.00018277991],"domain_scores_gemma":[0.911721,0.075968444,0.0064686704,0.0007989208,0.0045651495,0.0004779155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012023647,0.0013503883,0.004440126,0.013646441,0.00053170574,0.0034724474,0.0023974834,0.0019835776,0.0043236986],"category_scores_gemma":[0.057072256,0.00094158016,0.0054688985,0.015565108,0.0010754142,0.003522573,0.0018272123,0.0016973853,0.00045957966],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008537592,0.00003079912,0.0007421409,0.8594902,0.002391564,0.000081660815,0.0003188109,0.00028287873,0.0001091375,0.0007802313,0.0030146865,0.13267247],"study_design_scores_gemma":[0.000050497503,0.00013182945,0.0018752922,0.9464276,0.009575539,0.00021204882,0.00031485636,0.00020905935,0.00013673428,0.0007064712,0.04032363,0.00003645693],"about_ca_topic_score_codex":0.008314379,"about_ca_topic_score_gemma":0.0198886,"teacher_disagreement_score":0.013646441,"about_ca_system_score_codex":0.0030395223,"about_ca_system_score_gemma":0.016544066,"threshold_uncertainty_score":0.063587844},"labels":[],"label_agreement":null},{"id":"W4416909477","doi":"10.2196/72029","title":"Establishing One Health Surveillance Platform for Electronic Integrated Disease Surveillance and Response in Malawi: Action Design Research Study","year":2025,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Interoperability; Disease surveillance; Government (linguistics); Public health surveillance; Digital health; Function (biology); Key (lock); Reuse","score_opus":0.12620880320766212,"score_gpt":0.4245859513133613,"score_spread":0.29837714810569915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416909477","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":[0.8718642,0.0019535515,0.036434934,0.0047169756,0.00023105807,0.0699083,0.0007551765,0.000106161664,0.014029674],"genre_scores_gemma":[0.8354477,0.0012483095,0.08707341,0.0024557018,0.00008751325,0.07040109,0.00030265306,0.000029046423,0.0029545682],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.92110556,0.06635087,0.0026743417,0.0020609186,0.0029092382,0.0048989686],"domain_scores_gemma":[0.9200763,0.05064412,0.0064882697,0.0040265783,0.013600484,0.0051642763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.083137825,0.001016276,0.0008324815,0.0020350406,0.0053433347,0.004742181,0.0023466775,0.0022351928,0.0057012616],"category_scores_gemma":[0.056124713,0.0006812938,0.0013593568,0.0015220736,0.004010461,0.0035812417,0.0054808366,0.0033050429,0.00059926935],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0046331296,0.038993124,0.1333356,0.020921646,0.0008550492,0.00225211,0.2506187,0.010247342,0.005778001,0.06520995,0.009150113,0.45800525],"study_design_scores_gemma":[0.00711766,0.06943187,0.12186475,0.019161811,0.002442866,0.0007558404,0.5349093,0.019437678,0.015271537,0.031321242,0.17785057,0.00043481073],"about_ca_topic_score_codex":0.0089839,"about_ca_topic_score_gemma":0.019110352,"teacher_disagreement_score":0.083137825,"about_ca_system_score_codex":0.015008042,"about_ca_system_score_gemma":0.039403643,"threshold_uncertainty_score":0.4396801},"labels":[],"label_agreement":null},{"id":"W4417004106","doi":"10.1109/ghtc66843.2025.11266608","title":"A Data-Driven Mode for Public Health Interventions: A Case Study of the 2025 Measles Outbreak as Reflected in Social Media","year":2025,"lang":"","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Social media; Public health; Measles; Outbreak; Pandemic; Public health surveillance; Value (mathematics); Data collection","score_opus":0.23211147140669952,"score_gpt":0.4686238669369552,"score_spread":0.23651239553025566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417004106","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":[0.9851967,0.00012850192,0.0057727136,0.0036276707,0.000039899718,0.00031975392,0.0007932933,0.000067713045,0.0040537245],"genre_scores_gemma":[0.9858679,0.00014654783,0.011935555,0.0003711295,0.000045525125,0.00016839859,0.00038048535,0.000027873946,0.0010566675],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9952166,0.0032447185,0.0001995029,0.00028770693,0.0006962741,0.00035514496],"domain_scores_gemma":[0.9804519,0.014085281,0.0016926636,0.0011002893,0.0017237619,0.00094617676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051170224,0.0003674452,0.00028533206,0.0014332748,0.0020535218,0.0020338334,0.0007897151,0.0018311493,0.0011229459],"category_scores_gemma":[0.017505366,0.00026291827,0.00034901127,0.001619874,0.0011750148,0.0019922631,0.0015220953,0.0014274496,0.00031663576],"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":[0.0008462939,0.0023735622,0.65101177,0.00121775,0.00026278463,0.042729903,0.13896291,0.013091696,0.017625723,0.015535073,0.015858525,0.10048407],"study_design_scores_gemma":[0.00011649681,0.0017619719,0.36173517,0.0007887818,0.00015890066,0.011487587,0.44498274,0.08358912,0.014933228,0.011226711,0.068879776,0.00033959057],"about_ca_topic_score_codex":0.012362748,"about_ca_topic_score_gemma":0.021381013,"teacher_disagreement_score":0.012362748,"about_ca_system_score_codex":0.0015589471,"about_ca_system_score_gemma":0.0012604737,"threshold_uncertainty_score":0.02706176},"labels":[],"label_agreement":null},{"id":"W4417071687","doi":"10.1214/25-aoas2091","title":"Tomographic reconstruction of disease transmission landscapes from GPS-recorded random paths","year":2025,"lang":"","type":"article","venue":"The Annals of Applied Statistics","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"York University","funders":"","keywords":"Pattern recognition (psychology); Tomography; Transmission (telecommunications); Tomographic reconstruction; Iterative reconstruction","score_opus":0.02081823664402697,"score_gpt":0.2968701473228377,"score_spread":0.2760519106788107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417071687","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":[0.6303674,0.0010251293,0.35413936,0.0016932541,0.0000982134,0.00011062805,0.008173912,0.0014552111,0.0029369483],"genre_scores_gemma":[0.9534002,0.0003077986,0.043746293,0.000047651523,0.000026508236,0.000062569095,0.0017806771,0.000050747327,0.0005775548],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998274,0.00007148332,0.000010883924,0.000041882366,0.000027035048,0.000021268414],"domain_scores_gemma":[0.9985141,0.0010410422,0.00013010192,0.00013099394,0.00011927282,0.000064459564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043667122,0.0004254863,0.0004678018,0.0010832157,0.00024279588,0.0008845469,0.0010183079,0.0010175243,0.0019502948],"category_scores_gemma":[0.005180607,0.00065976614,0.00075600005,0.0015586235,0.00053656683,0.00060156523,0.0006900175,0.00065538334,0.0003819582],"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":[0.00009060128,0.000026431051,0.008967018,0.000059262085,0.000049732025,0.00016063587,0.00006962679,0.9788174,0.0007968424,0.0025932377,0.00080353953,0.0075655784],"study_design_scores_gemma":[0.000015008688,0.0000066574107,0.0015724599,0.000007294017,0.000007106417,0.00005488767,0.000023487606,0.99606615,0.000121873185,0.0018352721,0.00028223926,0.000007633559],"about_ca_topic_score_codex":0.020620935,"about_ca_topic_score_gemma":0.014742785,"teacher_disagreement_score":0.020620935,"about_ca_system_score_codex":0.0006158706,"about_ca_system_score_gemma":0.0008618105,"threshold_uncertainty_score":0.041001856},"labels":[],"label_agreement":null},{"id":"W4417278853","doi":"10.5194/ica-abs-10-66-2025","title":"Spatial Analysis of HPV Cancer Mortality in Persistent Poverty Counties in the US (1999-2020)","year":2025,"lang":"en","type":"article","venue":"Abstracts of the ICA","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Esri (Canada)","funders":"","keywords":"Poverty; Cervical cancer; Population; Cancer; Public health","score_opus":0.013826275303479605,"score_gpt":0.29931580992712264,"score_spread":0.285489534623643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417278853","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":[0.95981175,0.0005543746,0.0004042985,0.00045848623,0.000025101346,0.00002004107,0.03733221,0.000064225445,0.0013295738],"genre_scores_gemma":[0.9786742,0.00024505414,0.00041657058,0.00006704886,0.000013852169,0.000039261347,0.01990963,0.000011906089,0.00062243553],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996032,0.00009087663,0.000046927275,0.00008488068,0.00008475207,0.00008939318],"domain_scores_gemma":[0.99874985,0.0002210306,0.00033722463,0.00007696028,0.00046735897,0.00014763266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007119953,0.00022186905,0.0002373649,0.0023314918,0.0003320565,0.0006221383,0.00051741104,0.00029946756,0.0016500894],"category_scores_gemma":[0.002442348,0.0001599563,0.0007054731,0.0035276297,0.00017021013,0.00038573655,0.0010391314,0.0004010127,0.00036821558],"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":[0.000074512056,0.000033577497,0.9889234,0.00003246993,0.00014291295,0.000052201027,0.00014193635,0.0014843576,0.00007628379,0.00014672427,0.0041461415,0.0047455435],"study_design_scores_gemma":[0.000007988611,0.000030885512,0.9905483,0.000031840886,0.00005750841,0.000089951885,0.00111933,0.0049391873,0.00008294671,0.00009080415,0.0029924717,0.000008790274],"about_ca_topic_score_codex":0.2594213,"about_ca_topic_score_gemma":0.19332467,"teacher_disagreement_score":0.2594213,"about_ca_system_score_codex":0.0009156801,"about_ca_system_score_gemma":0.0011533321,"threshold_uncertainty_score":0.5158226},"labels":[],"label_agreement":null},{"id":"W4417415324","doi":"10.1017/ash.2025.10243","title":"Characterizing approaches used to display antimicrobial resistance data in veterinary and human medicine: a scoping review","year":2025,"lang":"en","type":"article","venue":"Antimicrobial Stewardship & Healthcare Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Visualization; Component (thermodynamics); Data visualization; Key (lock); Information visualization; Graphical display; Antibiotic resistance","score_opus":0.21744532214204856,"score_gpt":0.4421893578574599,"score_spread":0.22474403571541132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417415324","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009166784,0.9943878,0.0010160484,0.0011968564,0.00028287893,0.000596003,0.0004401173,0.000027973301,0.0011356074],"genre_scores_gemma":[0.00862734,0.98569435,0.003403789,0.00063678384,0.00012249779,0.0009482312,0.00036576696,0.000017438735,0.00018375789],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9730365,0.010645461,0.010104748,0.0012304374,0.0045364895,0.00044635966],"domain_scores_gemma":[0.7704612,0.1894175,0.018354474,0.0028204545,0.018183187,0.00076327455],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.037259374,0.0017298738,0.004100038,0.04119692,0.0013796292,0.0072912173,0.002527964,0.00347656,0.00543879],"category_scores_gemma":[0.16325788,0.0014759143,0.0063586514,0.037876338,0.001978577,0.00787497,0.0034920638,0.002144597,0.0009215333],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001157036,0.00004128152,0.0009850125,0.7721104,0.0017725779,0.0001432151,0.0012943017,0.00034846738,0.0003890051,0.0018657121,0.0042251726,0.21670906],"study_design_scores_gemma":[0.000023282719,0.000081709964,0.0013013115,0.9482936,0.0041446607,0.0001829043,0.0008439965,0.000106610954,0.00029033417,0.00074382697,0.043955203,0.00003254714],"about_ca_topic_score_codex":0.00784896,"about_ca_topic_score_gemma":0.0145517895,"teacher_disagreement_score":0.9627406,"about_ca_system_score_codex":0.0054723183,"about_ca_system_score_gemma":0.01912647,"threshold_uncertainty_score":0.19704872},"labels":[],"label_agreement":null},{"id":"W4417437964","doi":"10.2196/80922","title":"Using Artificial Intelligence Methods to Evaluate the Effect of the National Cytomegalovirus Awareness Month on the Content and Sentiment of Social Media Posts: Infodemiology Study","year":2025,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Social media; Conversation; Public health; Cytomegalovirus; Digital media; Health communication","score_opus":0.1764956833376086,"score_gpt":0.4866578831016268,"score_spread":0.31016219976401815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417437964","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":[0.9937443,0.00012487639,0.002768114,0.00023068297,0.00004443362,0.00025867193,0.001134293,0.00005046882,0.0016441689],"genre_scores_gemma":[0.98984456,0.000106692,0.0068187388,0.00013979225,0.000055639255,0.00058600464,0.0017459983,0.00001560984,0.0006869414],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9967179,0.0016470823,0.00033379096,0.00056007016,0.0005500853,0.00019110378],"domain_scores_gemma":[0.9778547,0.016279493,0.0033069022,0.0007831584,0.0011637127,0.000611964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055540055,0.00059498555,0.0005986134,0.0019722907,0.0005472682,0.0015285696,0.00043696814,0.000783217,0.0014516169],"category_scores_gemma":[0.018775463,0.00024902847,0.0013584527,0.0017492382,0.0005874425,0.0009481049,0.00069843436,0.0013504443,0.00050395704],"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":[0.0013692533,0.002323068,0.9312517,0.00027331617,0.00073974126,0.0001573877,0.00158647,0.0044081747,0.0021345885,0.00046903477,0.0016922046,0.053595092],"study_design_scores_gemma":[0.00007890212,0.0019646795,0.93279743,0.00005208978,0.00026139,0.00013155302,0.0017056281,0.05846502,0.0016008383,0.0009255902,0.0019705496,0.000046253408],"about_ca_topic_score_codex":0.0035906532,"about_ca_topic_score_gemma":0.0030854472,"teacher_disagreement_score":0.0055540055,"about_ca_system_score_codex":0.000756355,"about_ca_system_score_gemma":0.00049711304,"threshold_uncertainty_score":0.029372752},"labels":[],"label_agreement":null},{"id":"W4417439363","doi":"10.1371/journal.pgph.0005550","title":"A century of weekly notifiable disease incidence data by province in Canada","year":2025,"lang":"en","type":"article","venue":"PLOS Global Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University; Jewish General Hospital; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Incidence (geometry); Notifiable disease; Communicable disease; Disease surveillance; Public health surveillance; Public health; Government (linguistics); Infectious disease (medical specialty)","score_opus":0.03043836608600398,"score_gpt":0.30222438913503313,"score_spread":0.27178602304902916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417439363","genre_codex":"dataset","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":[0.118611224,0.0096508125,0.010745974,0.0071623465,0.0010733883,0.0003730597,0.81335884,0.0015699093,0.03745445],"genre_scores_gemma":[0.35103106,0.01452186,0.027325785,0.0020910164,0.00036141602,0.00068284426,0.571423,0.0011378562,0.031425085],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99394256,0.00026881244,0.0005890023,0.0008587122,0.003663646,0.0006772206],"domain_scores_gemma":[0.97738546,0.0014987389,0.001325064,0.0013352745,0.017243529,0.0012119454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029600707,0.0006311481,0.00073407707,0.01239682,0.0028047387,0.00334012,0.0017216105,0.000397566,0.0051187365],"category_scores_gemma":[0.018046152,0.00058682775,0.00066881225,0.04137422,0.001014597,0.0010542475,0.0015345183,0.0012295406,0.0012831533],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032663802,0.000045564044,0.1925552,0.0016303743,0.00023445359,0.00039973727,0.006626603,0.003907016,0.0014212662,0.008414494,0.4750807,0.30935803],"study_design_scores_gemma":[0.000024432213,0.00002049135,0.45723724,0.0008766463,0.000092139046,0.00012623808,0.0018861557,0.0018619443,0.0008757029,0.00090145314,0.53594786,0.0001497721],"about_ca_topic_score_codex":0.99553645,"about_ca_topic_score_gemma":0.9960335,"teacher_disagreement_score":0.049583334,"about_ca_system_score_codex":0.049583334,"about_ca_system_score_gemma":0.08662758,"threshold_uncertainty_score":0.3597539},"labels":[],"label_agreement":null},{"id":"W4417441746","doi":"10.2196/69143","title":"Application of Machine Learning to Auto-Code Injury Data in the e-CHIRPP System: Development and Evaluation Study","year":2025,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada","funders":"","keywords":"Adaptability; Development (topology); Support vector machine; Training set; Active learning (machine learning)","score_opus":0.08985256827463871,"score_gpt":0.42563057037265556,"score_spread":0.33577800209801684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417441746","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":[0.96996236,0.00036489242,0.018572839,0.0003381206,0.00008023726,0.005474188,0.0008209207,0.0012128245,0.0031734833],"genre_scores_gemma":[0.88376915,0.00052399316,0.105446875,0.00025705277,0.000030942778,0.0032149837,0.0039162636,0.00015172927,0.002688908],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99320436,0.0035071338,0.0005359409,0.0005953607,0.0017841788,0.00037304594],"domain_scores_gemma":[0.98192465,0.0072948234,0.00072284177,0.0010061696,0.008202878,0.0008487109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012060488,0.00078795425,0.00042506933,0.001134149,0.00045812552,0.0010206009,0.0016605494,0.0007276436,0.0013640994],"category_scores_gemma":[0.027504764,0.0003382497,0.0005684713,0.0007313919,0.0007164747,0.0014626983,0.0012406405,0.0009917945,0.00066193397],"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":[0.0046741623,0.020606296,0.1438752,0.0020623968,0.0006162464,0.00074676983,0.0035502429,0.03098715,0.014859548,0.0010241118,0.012612447,0.7643854],"study_design_scores_gemma":[0.0024627636,0.031305667,0.22782087,0.0010305283,0.00080289895,0.0012784352,0.004177334,0.646215,0.06106692,0.0009738603,0.02259973,0.00026589996],"about_ca_topic_score_codex":0.020226507,"about_ca_topic_score_gemma":0.01760919,"teacher_disagreement_score":0.020226507,"about_ca_system_score_codex":0.0020545921,"about_ca_system_score_gemma":0.0033834602,"threshold_uncertainty_score":0.06378275},"labels":[],"label_agreement":null},{"id":"W66041707","doi":"10.4018/jagr.2013040103","title":"Geographical Distribution and Surveillance of Tuberculosis (TB) Using Spatial Statistics","year":2013,"lang":"en","type":"article","venue":"International Journal of Applied Geospatial Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Spatial analysis; Geographic information system; Geography; Tuberculosis; Outlier; Quarter (Canadian coin); Distribution (mathematics); Environmental health; Disease surveillance; Population; Disease; Cartography; Statistics; Medicine; Mathematics; Remote sensing","score_opus":0.027100928137126747,"score_gpt":0.35455450705645974,"score_spread":0.327453578919333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W66041707","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":[0.7642194,0.0036589154,0.16044441,0.0028910632,0.00010455266,0.00037301306,0.040133484,0.0013481105,0.026826967],"genre_scores_gemma":[0.9497321,0.0013567854,0.04001418,0.000056305074,0.000034537432,0.00009843172,0.0078835515,0.000019037292,0.00080497115],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982528,0.0008529632,0.00020476895,0.00019210066,0.00043702542,0.000060364197],"domain_scores_gemma":[0.996609,0.0012392538,0.0010078792,0.00052731315,0.00054236664,0.00007432077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013641157,0.00024237768,0.00027742935,0.0049012224,0.00026076756,0.0010457236,0.0004320098,0.0001855986,0.0006472496],"category_scores_gemma":[0.0054697855,0.00016693253,0.00034457416,0.008337667,0.0004219788,0.00076993246,0.00073072617,0.0002223324,0.00030918643],"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":[0.000051129642,0.00009344159,0.6976356,0.00041193477,0.00023906335,0.00021433955,0.0015482141,0.026977282,0.002612645,0.013462365,0.0082265865,0.24852744],"study_design_scores_gemma":[0.00002353826,0.00032268997,0.83256114,0.00029812823,0.00018372473,0.0009937305,0.0058014607,0.09538707,0.0041681184,0.012599135,0.047584064,0.00007716007],"about_ca_topic_score_codex":0.028933093,"about_ca_topic_score_gemma":0.029589051,"teacher_disagreement_score":0.028933093,"about_ca_system_score_codex":0.00079199934,"about_ca_system_score_gemma":0.001247328,"threshold_uncertainty_score":0.05752939},"labels":[],"label_agreement":null},{"id":"W6885914021","doi":"10.1371/journal.pone.0298382.s003","title":"Thematic summary of focus group findings.","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Outreach; Focus group; Legislation; Health care; Thematic analysis; Public health; Confidentiality","score_opus":0.030350074532574805,"score_gpt":0.2878489529708913,"score_spread":0.2574988784383165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6885914021","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":[0.37385383,0.012475711,0.071943484,0.07249031,0.012304709,0.098061144,0.08201615,0.0018208235,0.27503377],"genre_scores_gemma":[0.5176131,0.015552923,0.057292163,0.03163203,0.0010649295,0.26586437,0.020639554,0.0015255016,0.088815354],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9835666,0.01054599,0.0011012969,0.0012272622,0.001736088,0.0018227096],"domain_scores_gemma":[0.9599114,0.025684398,0.0012833135,0.00094341504,0.010529987,0.0016474695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032903317,0.0015931338,0.0015647425,0.0077655823,0.010948563,0.0065109255,0.0041249576,0.0033994818,0.05836307],"category_scores_gemma":[0.072871305,0.0009084317,0.000991863,0.010016132,0.004334242,0.009695946,0.009951558,0.0043322467,0.012864431],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013916106,0.00011926209,0.0019538905,0.0051141353,0.000009899842,0.00053393067,0.8787961,0.000068840396,0.0008574809,0.0073121376,0.0667625,0.038332634],"study_design_scores_gemma":[0.00004483961,0.000058357415,0.0032347932,0.0030591171,0.000009846913,0.0001925544,0.89740026,0.00010426059,0.0003341172,0.003128492,0.09240142,0.00003198235],"about_ca_topic_score_codex":0.01714388,"about_ca_topic_score_gemma":0.016123194,"teacher_disagreement_score":0.05836307,"about_ca_system_score_codex":0.012651684,"about_ca_system_score_gemma":0.014987572,"threshold_uncertainty_score":0.1952439},"labels":[],"label_agreement":null},{"id":"W6889048475","doi":"10.25316/ir-19263","title":"Evaluating a custom address locator built from public data to improve geocoding results","year":2023,"lang":"en","type":"dissertation","venue":"VIUSpace (Vancouver Island University Library)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geocoding; Geographic information system; Point (geometry); Spatial analysis; Government (linguistics); Quality (philosophy)","score_opus":0.0570979112702762,"score_gpt":0.3047749442529514,"score_spread":0.24767703298267518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6889048475","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":[0.7863182,0.0005010428,0.15138432,0.001763161,0.00035628505,0.0031087135,0.018883312,0.015960008,0.021725006],"genre_scores_gemma":[0.7224479,0.00031479506,0.24564582,0.0004325783,0.000043644966,0.00092721893,0.023029918,0.00088150834,0.006276618],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9891451,0.0039050393,0.0010073985,0.0011286672,0.0041814027,0.0006324345],"domain_scores_gemma":[0.94933975,0.01389795,0.0027568613,0.0066264602,0.02628732,0.0010918113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016194254,0.0009139659,0.0006720463,0.004088188,0.0012090747,0.0033561941,0.0019760656,0.00077236927,0.0028153611],"category_scores_gemma":[0.06330083,0.00039070705,0.00061780243,0.00580056,0.0009160915,0.0032888972,0.0038356562,0.00082281156,0.0011637554],"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":[0.0015059721,0.0011024207,0.21006005,0.0019134744,0.00047108714,0.00070658093,0.0077690883,0.079878554,0.014798061,0.008697822,0.047421042,0.62567586],"study_design_scores_gemma":[0.0005603343,0.0019949179,0.19628824,0.0011423654,0.0005845054,0.00066030567,0.015141772,0.55102706,0.07927773,0.006137303,0.14650226,0.00068326417],"about_ca_topic_score_codex":0.41772532,"about_ca_topic_score_gemma":0.44138646,"teacher_disagreement_score":0.41772532,"about_ca_system_score_codex":0.008119219,"about_ca_system_score_gemma":0.012018477,"threshold_uncertainty_score":0.8305878},"labels":[],"label_agreement":null},{"id":"W6893343448","doi":"10.5281/zenodo.16792362","title":"Analyzing Google Trends Data on AI-Curated YouTube Content, Childhood Obesity, and Sleep Disorders","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Popularity; Childhood obesity; Limiting; Quarter (Canadian coin); Obesity; Public interest","score_opus":0.04126474975432118,"score_gpt":0.2866503576653466,"score_spread":0.2453856079110254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6893343448","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":[0.5615854,0.0010693097,0.001151778,0.00052129175,0.00008064663,0.00024696492,0.42182606,0.0006617992,0.012856692],"genre_scores_gemma":[0.5690993,0.001106608,0.004934488,0.00011521374,0.000113802744,0.00044102976,0.41935614,0.00015910143,0.00467437],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99929106,0.00007188677,0.00009892041,0.00012107471,0.0002917679,0.00012531107],"domain_scores_gemma":[0.99702615,0.0006590728,0.0007899752,0.0001319334,0.0011910504,0.00020182277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005875315,0.00048196956,0.00036407966,0.0123446435,0.0003784809,0.0010181604,0.00029906668,0.00026086604,0.0025192962],"category_scores_gemma":[0.004463072,0.00011948505,0.0006507492,0.015661994,0.00018827667,0.0012621599,0.00075995986,0.00033077717,0.0014684183],"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":[0.0003178277,0.00011768388,0.8634582,0.0013820231,0.00024698942,0.00043108247,0.0013696069,0.0022274377,0.0019369661,0.0017463454,0.058173668,0.06859209],"study_design_scores_gemma":[0.000012778191,0.00009516031,0.9564992,0.00018506832,0.000075437616,0.0002582375,0.0023409792,0.009148053,0.00094560534,0.00042109727,0.02998095,0.000037423262],"about_ca_topic_score_codex":0.10175948,"about_ca_topic_score_gemma":0.13488048,"teacher_disagreement_score":0.10175948,"about_ca_system_score_codex":0.0008868158,"about_ca_system_score_gemma":0.0009630995,"threshold_uncertainty_score":0.2023344},"labels":[],"label_agreement":null},{"id":"W6901510896","doi":"10.60692/x1cp2-3qd44","title":"Will Participatory Syndromic Surveillance Work in Latin America? Piloting a Mobile Approach to Crowdsource Influenza-Like Illness Data in Guatemala","year":2017,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mobile phone; mHealth; Latin Americans; Quarter (Canadian coin); Citizen journalism; Public health; Phone; Data collection; Health care; Quality (philosophy)","score_opus":0.09031881515905539,"score_gpt":0.2973676462563435,"score_spread":0.2070488310972881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901510896","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":[0.96210545,0.0007202287,0.0074781636,0.018980578,0.00014399945,0.0027227274,0.0005905053,0.0002745112,0.0069838758],"genre_scores_gemma":[0.97499824,0.00048862776,0.0141456025,0.0041423133,0.00005641145,0.0050065885,0.0002453081,0.000038897528,0.0008780558],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98659223,0.011206699,0.00025893727,0.0006058622,0.0003721265,0.00096407643],"domain_scores_gemma":[0.9889036,0.006050493,0.0010636154,0.00085529056,0.001962453,0.0011646546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020509342,0.0006299543,0.00046834574,0.0008121568,0.0028958884,0.0021145812,0.0014256068,0.0010633908,0.003008133],"category_scores_gemma":[0.026977552,0.0004207934,0.0006410662,0.0009210409,0.0021956384,0.0021333294,0.0045783864,0.0009798668,0.00034904605],"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":[0.0029873853,0.003651783,0.42787468,0.0025836385,0.00034276108,0.0028468582,0.22014266,0.0024159325,0.014165145,0.0049248654,0.016391376,0.30167294],"study_design_scores_gemma":[0.002668623,0.009235347,0.43083388,0.004764059,0.0006250984,0.00094743306,0.397089,0.016725695,0.0068348153,0.01086067,0.119009785,0.0004055508],"about_ca_topic_score_codex":0.06201218,"about_ca_topic_score_gemma":0.07935379,"teacher_disagreement_score":0.06201218,"about_ca_system_score_codex":0.003509083,"about_ca_system_score_gemma":0.009741554,"threshold_uncertainty_score":0.12330246},"labels":[],"label_agreement":null},{"id":"W6901634265","doi":"10.60692/thrzh-zhh56","title":"The state of integrated disease surveillance in seven countries: a synthesis report","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University; Public Health Agency of Canada","funders":"","keywords":"Conceptualization; CLARITY; Workforce; Disease surveillance; Focus group; Public health surveillance; Data sharing; Public health; Qualitative research","score_opus":0.019384456146401413,"score_gpt":0.23682841882154218,"score_spread":0.21744396267514077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901634265","genre_codex":"review","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":[0.16251302,0.7533992,0.016119273,0.026488658,0.0015767874,0.004376533,0.008245725,0.00013236656,0.02714838],"genre_scores_gemma":[0.66352403,0.29833987,0.025691438,0.0028676235,0.00014326772,0.005505068,0.0031456861,0.00007113971,0.00071189523],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.95375097,0.03019305,0.008424314,0.0022236202,0.0034936497,0.0019143749],"domain_scores_gemma":[0.8980072,0.07702896,0.0070761056,0.00356597,0.013059161,0.0012626267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.071142234,0.00089410477,0.0018170088,0.011074338,0.0022440804,0.010473686,0.0014656222,0.0013798604,0.0024435867],"category_scores_gemma":[0.08940585,0.0011428059,0.001676694,0.02673575,0.003768282,0.009824539,0.008113561,0.0026961365,0.00018316421],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028840633,0.00008470409,0.042168945,0.17445676,0.0014345588,0.000901657,0.3859125,0.0015249188,0.00073482544,0.05613348,0.011507667,0.3248515],"study_design_scores_gemma":[0.00007412644,0.0002575044,0.04608019,0.24796766,0.0017480532,0.00067771884,0.51438624,0.0006987673,0.0011072634,0.0072084307,0.17968191,0.00011215581],"about_ca_topic_score_codex":0.021525992,"about_ca_topic_score_gemma":0.018014763,"teacher_disagreement_score":0.071142234,"about_ca_system_score_codex":0.023635607,"about_ca_system_score_gemma":0.034161765,"threshold_uncertainty_score":0.3762406},"labels":[],"label_agreement":null},{"id":"W6901661290","doi":"10.60692/mnzk4-e2y69","title":"Will Participatory Syndromic Surveillance Work in Latin America? Piloting a Mobile Approach to Crowdsource Influenza-Like Illness Data in Guatemala","year":2017,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mobile phone; mHealth; Latin Americans; Quarter (Canadian coin); Citizen journalism; Public health; Phone; Data collection; Health care; Quality (philosophy)","score_opus":0.09031881515905539,"score_gpt":0.2973676462563435,"score_spread":0.2070488310972881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901661290","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":[0.96210545,0.0007202287,0.0074781636,0.018980578,0.00014399945,0.0027227274,0.0005905053,0.0002745112,0.0069838758],"genre_scores_gemma":[0.97499824,0.00048862776,0.0141456025,0.0041423133,0.00005641145,0.0050065885,0.0002453081,0.000038897528,0.0008780558],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98659223,0.011206699,0.00025893727,0.0006058622,0.0003721265,0.00096407643],"domain_scores_gemma":[0.9889036,0.006050493,0.0010636154,0.00085529056,0.001962453,0.0011646546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020509342,0.0006299543,0.00046834574,0.0008121568,0.0028958884,0.0021145812,0.0014256068,0.0010633908,0.003008133],"category_scores_gemma":[0.026977552,0.0004207934,0.0006410662,0.0009210409,0.0021956384,0.0021333294,0.0045783864,0.0009798668,0.00034904605],"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":[0.0029873853,0.003651783,0.42787468,0.0025836385,0.00034276108,0.0028468582,0.22014266,0.0024159325,0.014165145,0.0049248654,0.016391376,0.30167294],"study_design_scores_gemma":[0.002668623,0.009235347,0.43083388,0.004764059,0.0006250984,0.00094743306,0.397089,0.016725695,0.0068348153,0.01086067,0.119009785,0.0004055508],"about_ca_topic_score_codex":0.06201218,"about_ca_topic_score_gemma":0.07935379,"teacher_disagreement_score":0.06201218,"about_ca_system_score_codex":0.003509083,"about_ca_system_score_gemma":0.009741554,"threshold_uncertainty_score":0.12330246},"labels":[],"label_agreement":null},{"id":"W6901721867","doi":"10.60692/z7k0t-7k928","title":"Geographic differences in overweight and obesity prevalence in Peruvian children, 2010–2015","year":2018,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Overweight; Obesity; Body mass index; Population; Public health; Childhood obesity; Prevalence","score_opus":0.017361745297550298,"score_gpt":0.21715426286767234,"score_spread":0.19979251757012204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901721867","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":[0.9957373,0.0011083599,0.00011749625,0.00012776352,0.000006869105,0.000017436932,0.0016617252,0.000015865458,0.0012071618],"genre_scores_gemma":[0.99625325,0.0011259903,0.00021485641,0.000040196945,0.000013762176,0.00005114108,0.001989787,0.0000050305466,0.00030606787],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99961686,0.0001090961,0.000055158132,0.00008325262,0.000065503395,0.000070060014],"domain_scores_gemma":[0.9993048,0.00008964049,0.00037791813,0.000032100437,0.00013012995,0.000065556625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006329185,0.00027157582,0.00023297565,0.0014985537,0.00035120265,0.00055690494,0.00031560927,0.00026909122,0.0015374018],"category_scores_gemma":[0.0019294398,0.00029178173,0.000554189,0.0018688634,0.00035158722,0.00047334642,0.00081819156,0.00033311264,0.0002297219],"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":[0.00005348273,0.000017732898,0.9948514,0.00008141171,0.000091206355,0.00010922084,0.0011543696,0.000048634527,0.00022364267,0.00004907926,0.00031719354,0.0030025209],"study_design_scores_gemma":[0.0000015093851,0.00002141895,0.99868625,0.000012716984,0.000021055992,0.000107523236,0.00071292976,0.000032224347,0.000018223885,0.000007816267,0.0003759616,0.00000239088],"about_ca_topic_score_codex":0.029634366,"about_ca_topic_score_gemma":0.041007634,"teacher_disagreement_score":0.029634366,"about_ca_system_score_codex":0.00045240836,"about_ca_system_score_gemma":0.00036060569,"threshold_uncertainty_score":0.05892372},"labels":[],"label_agreement":null},{"id":"W6901740701","doi":"10.60692/ryxm3-yyq78","title":"SCM: a practical tool to implement hospital-based syndromic surveillance","year":2016,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Context (archaeology); Electronic surveillance; Data collection; Warning system; Exposition (narrative); Real world data; Patient data","score_opus":0.025906682786794002,"score_gpt":0.26459888950991534,"score_spread":0.23869220672312133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901740701","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.065001465,0.00021738965,0.7064816,0.0012396245,0.00031585427,0.0044809454,0.0057197553,0.20879301,0.007750391],"genre_scores_gemma":[0.12999594,0.00011217664,0.86150527,0.00022439362,0.000090008136,0.0017765927,0.0027675838,0.0017881333,0.001739899],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99593973,0.001598609,0.00055836444,0.0006107878,0.0011234718,0.00016902416],"domain_scores_gemma":[0.9791508,0.012007821,0.0018115074,0.0029148066,0.0031309952,0.0009840869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010892367,0.0013908241,0.0007839862,0.004717196,0.00057405146,0.0014788265,0.0017206069,0.00087388343,0.009852056],"category_scores_gemma":[0.03219853,0.000795294,0.00061235117,0.002038489,0.00043126257,0.0022831475,0.0027642986,0.0008868005,0.0032371785],"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":[0.001436689,0.0009486444,0.04269395,0.0010048188,0.00017894366,0.00083851453,0.0028359916,0.010800096,0.02103698,0.003731972,0.06389104,0.85060245],"study_design_scores_gemma":[0.0012033457,0.0032754655,0.0670547,0.0011796366,0.00029069342,0.0016779453,0.0025819265,0.6936728,0.054707926,0.01035986,0.16341597,0.0005797452],"about_ca_topic_score_codex":0.0016535675,"about_ca_topic_score_gemma":0.00130155,"teacher_disagreement_score":0.010892367,"about_ca_system_score_codex":0.000596594,"about_ca_system_score_gemma":0.0015677983,"threshold_uncertainty_score":0.05760503},"labels":[],"label_agreement":null},{"id":"W6901886177","doi":"10.60692/z8q8m-t0586","title":"SCM: a practical tool to implement hospital-based syndromic surveillance","year":2016,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Context (archaeology); Electronic surveillance; Data collection; Warning system; Exposition (narrative); Real world data; Patient data","score_opus":0.025906682786794002,"score_gpt":0.26459888950991534,"score_spread":0.23869220672312133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901886177","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":[0.065001465,0.00021738965,0.7064816,0.0012396245,0.00031585427,0.0044809454,0.0057197553,0.20879301,0.007750391],"genre_scores_gemma":[0.12999594,0.00011217664,0.86150527,0.00022439362,0.000090008136,0.0017765927,0.0027675838,0.0017881333,0.001739899],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99593973,0.001598609,0.00055836444,0.0006107878,0.0011234718,0.00016902416],"domain_scores_gemma":[0.9791508,0.012007821,0.0018115074,0.0029148066,0.0031309952,0.0009840869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010892367,0.0013908241,0.0007839862,0.004717196,0.00057405146,0.0014788265,0.0017206069,0.00087388343,0.009852056],"category_scores_gemma":[0.03219853,0.000795294,0.00061235117,0.002038489,0.00043126257,0.0022831475,0.0027642986,0.0008868005,0.0032371785],"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":[0.001436689,0.0009486444,0.04269395,0.0010048188,0.00017894366,0.00083851453,0.0028359916,0.010800096,0.02103698,0.003731972,0.06389104,0.85060245],"study_design_scores_gemma":[0.0012033457,0.0032754655,0.0670547,0.0011796366,0.00029069342,0.0016779453,0.0025819265,0.6936728,0.054707926,0.01035986,0.16341597,0.0005797452],"about_ca_topic_score_codex":0.0016535675,"about_ca_topic_score_gemma":0.00130155,"teacher_disagreement_score":0.010892367,"about_ca_system_score_codex":0.000596594,"about_ca_system_score_gemma":0.0015677983,"threshold_uncertainty_score":0.05760503},"labels":[],"label_agreement":null},{"id":"W6901892056","doi":"10.6084/m9.figshare.12806110","title":"Additional file 1 of A multimethod approach for county-scale geospatial analysis of emerging infectious diseases: a cross-sectional case study of COVID-19 incidence in Germany","year":2020,"lang":"en","type":"article","venue":"Open MIND","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Geospatial analysis; Incidence (geometry); Data collection; Statistical analysis; Infectious disease (medical specialty)","score_opus":0.05345765178044899,"score_gpt":0.39189945388779,"score_spread":0.33844180210734104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901892056","genre_codex":"dataset","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":[0.00047568607,0.000011946276,0.0007592249,0.000056642566,0.000008961147,0.00012461087,0.9976774,0.00019041267,0.0006951104],"genre_scores_gemma":[0.028245347,0.00014598973,0.013278079,0.0002757477,0.000058975278,0.0058219815,0.9437231,0.0008207585,0.007630099],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9987109,0.00038807103,0.00028474207,0.00027781862,0.00021118543,0.00012728044],"domain_scores_gemma":[0.9764539,0.018220967,0.001637772,0.001350637,0.0018638896,0.00047283352],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0023967822,0.0005966697,0.0007623517,0.002776389,0.0005875455,0.0012599108,0.0012862313,0.00067574147,0.6916023],"category_scores_gemma":[0.037300363,0.00047379156,0.0007204672,0.0054289256,0.00021633376,0.0010740029,0.0010721888,0.00058648724,0.060730908],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026161002,0.00009099371,0.012622502,0.0023983426,0.00012134444,0.00011397364,0.00025329384,0.0010298301,0.00010556723,0.0015719328,0.963554,0.017876668],"study_design_scores_gemma":[0.0033292708,0.00033461332,0.118649565,0.003721917,0.00055029476,0.0007654311,0.0019294178,0.005333749,0.0011898219,0.01596722,0.84803766,0.00019107467],"about_ca_topic_score_codex":0.0107127875,"about_ca_topic_score_gemma":0.017274821,"teacher_disagreement_score":0.6916023,"about_ca_system_score_codex":0.0007314288,"about_ca_system_score_gemma":0.0019079363,"threshold_uncertainty_score":0.43989187},"labels":[],"label_agreement":null},{"id":"W6901951261","doi":"10.6084/m9.figshare.14093323","title":"Vision 2035 Public Health Surveillance in India - A White Paper","year":2021,"lang":"en","type":"book","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public health; Public health surveillance; Health surveillance; White paper; White (mutation); Disease surveillance","score_opus":0.03861380801700679,"score_gpt":0.3032943121589334,"score_spread":0.26468050414192656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901951261","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007403724,0.015824532,0.008057165,0.106925674,0.039335493,0.0003506974,0.013437019,0.0030736644,0.81225544],"genre_scores_gemma":[0.010448689,0.014383781,0.015435998,0.03693326,0.0038408365,0.0003406044,0.0112138735,0.0010044294,0.90639865],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99800867,0.00032082738,0.000086911976,0.0001544022,0.0011711754,0.00025803037],"domain_scores_gemma":[0.9972083,0.0005598991,0.00011202036,0.0002969551,0.0012121968,0.00061067817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038077668,0.001029668,0.00040043675,0.0018725581,0.0014682092,0.0087742675,0.002032392,0.0029847014,0.024279632],"category_scores_gemma":[0.0044349227,0.0005635375,0.000751922,0.002180577,0.0015428711,0.0029302181,0.0023712227,0.0039398978,0.021054378],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048117154,0.0000070102783,0.000059062335,0.000060633243,0.0000021492333,0.000018654664,0.000052804317,0.00010487991,0.000046974274,0.024094881,0.9555822,0.019966008],"study_design_scores_gemma":[0.0000021645922,0.0000053595177,0.00027032648,0.00009069105,0.0000019843005,0.000025293317,0.000049940936,0.000049048747,0.00008079456,0.0032611329,0.9961577,0.00000557315],"about_ca_topic_score_codex":0.066384,"about_ca_topic_score_gemma":0.08809893,"teacher_disagreement_score":0.066384,"about_ca_system_score_codex":0.005799018,"about_ca_system_score_gemma":0.020898711,"threshold_uncertainty_score":0.13199526},"labels":[],"label_agreement":null},{"id":"W6902061839","doi":"10.6084/m9.figshare.23916780","title":"Additional file 2 of A social network analysis model approach to understand tuberculosis transmission in remote rural Madagascar","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Social network analysis; Transmission (telecommunications); Network analysis; Social network (sociolinguistics); Tuberculosis; Rural area","score_opus":0.033915783535409576,"score_gpt":0.2725420130127633,"score_spread":0.23862622947735374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6902061839","genre_codex":"dataset","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":[0.00047473886,0.000010927785,0.0005237068,0.00012406308,0.0000130748185,0.000069109876,0.99718314,0.00022250171,0.001378582],"genre_scores_gemma":[0.04736431,0.00014779581,0.010126187,0.00048270234,0.00008386033,0.0024408135,0.92266977,0.0010237064,0.015660757],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996458,0.00011889599,0.00003831907,0.00008192896,0.0000634072,0.000051572944],"domain_scores_gemma":[0.9881793,0.009928873,0.000402254,0.00042773839,0.00082597195,0.00023572154],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012719186,0.00051027717,0.0004821943,0.0012512334,0.00051368517,0.0010771792,0.0012735607,0.00070802314,0.7398304],"category_scores_gemma":[0.018903341,0.00031749727,0.0006878469,0.0022922005,0.00016368667,0.0010059725,0.0006603814,0.0007709952,0.06811937],"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":[0.0001520619,0.00007437704,0.007823654,0.0010837232,0.00006594923,0.00007720554,0.00014169712,0.0033415994,0.00006130775,0.0019669407,0.9746629,0.010548526],"study_design_scores_gemma":[0.0028863656,0.00028627063,0.05374872,0.0024204287,0.00035369094,0.00043789818,0.0013798297,0.026302049,0.0007006341,0.024189139,0.8871467,0.00014833237],"about_ca_topic_score_codex":0.019973272,"about_ca_topic_score_gemma":0.031566642,"teacher_disagreement_score":0.7398304,"about_ca_system_score_codex":0.00070160185,"about_ca_system_score_gemma":0.00123846,"threshold_uncertainty_score":0.37110037},"labels":[],"label_agreement":null},{"id":"W6902062760","doi":"10.6084/m9.figshare.20438274.v1","title":"Additional file 2 of The role of geographic information system and global positioning system in dementia care and research: a scoping review","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Geographic information system; Information system; Dementia; Global Positioning System; Interoperability; Positioning system; Key (lock); File system","score_opus":0.023349061979809627,"score_gpt":0.28790174969555815,"score_spread":0.2645526877157485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6902062760","genre_codex":"dataset","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":[0.00015584778,0.00066932297,0.0002444345,0.00029390215,0.00004474509,0.0011416322,0.99584633,0.00010758508,0.0014961744],"genre_scores_gemma":[0.017289737,0.008353106,0.014427107,0.0031884303,0.0004075918,0.08083533,0.84329325,0.0008667589,0.031338703],"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","domain_scores_codex":[0.99740607,0.0006055295,0.0010712155,0.00038806666,0.0003222181,0.00020694874],"domain_scores_gemma":[0.9331175,0.055488076,0.0047749616,0.0009410222,0.005128097,0.0005503865],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00475208,0.0015426736,0.003759417,0.013137892,0.0009359992,0.0025212825,0.0021590341,0.0020266753,0.7658713],"category_scores_gemma":[0.06622297,0.0009253866,0.0035720833,0.01888952,0.00046847542,0.003109137,0.0019618631,0.0011014767,0.038383044],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069584604,0.00005229048,0.0016749327,0.39126268,0.0006657811,0.0001597931,0.0003432348,0.00058169087,0.00017161191,0.0032048475,0.5855721,0.015615205],"study_design_scores_gemma":[0.009324437,0.00033483692,0.018406937,0.21130341,0.0040052556,0.00054498465,0.00096373376,0.0012747378,0.00050185656,0.010746214,0.7423326,0.0002609541],"about_ca_topic_score_codex":0.017687948,"about_ca_topic_score_gemma":0.030010238,"teacher_disagreement_score":0.7658713,"about_ca_system_score_codex":0.0033360748,"about_ca_system_score_gemma":0.0063355793,"threshold_uncertainty_score":0.33395618},"labels":[],"label_agreement":null},{"id":"W6902071052","doi":"10.6084/m9.figshare.15001326","title":"Additional file 2 of Need of care in interpreting Google Trends-based COVID-19 infodemiological study results: potential risk of false-positivity","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Rank (graph theory); Value (mathematics); Correlation; Causality (physics); Lag; Relation (database); Rank correlation","score_opus":0.024664893497189578,"score_gpt":0.3120191615719172,"score_spread":0.28735426807472764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6902071052","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027065523,0.000023934415,0.0004426016,0.00028954388,0.000039627488,0.00017485769,0.9968539,0.0007290898,0.0011757171],"genre_scores_gemma":[0.029368924,0.00023532374,0.015209919,0.0025675416,0.00033054352,0.008815755,0.92221826,0.0034604345,0.017793264],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9975504,0.0007139413,0.000655777,0.0004192044,0.0004141542,0.00024643194],"domain_scores_gemma":[0.87650055,0.10562818,0.005162887,0.0050769188,0.006420024,0.0012114262],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005885216,0.00082168536,0.0012475864,0.00484828,0.00081096706,0.0026152285,0.0019073979,0.001740756,0.80781907],"category_scores_gemma":[0.11724697,0.00055460836,0.0013926538,0.005322987,0.00035424024,0.0026883676,0.0017332927,0.00086972374,0.10164922],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040273575,0.00004195102,0.0042813146,0.0053155343,0.00009296489,0.00011516464,0.00010578639,0.0002953742,0.00006079287,0.0009821372,0.9778495,0.010456711],"study_design_scores_gemma":[0.0052017956,0.00029036726,0.05265934,0.012742613,0.00056927104,0.0011367917,0.0011624929,0.0042033163,0.0010835874,0.01972751,0.9009515,0.00027135012],"about_ca_topic_score_codex":0.0069329464,"about_ca_topic_score_gemma":0.014173352,"teacher_disagreement_score":0.80781907,"about_ca_system_score_codex":0.0014910585,"about_ca_system_score_gemma":0.003002734,"threshold_uncertainty_score":0.27412277},"labels":[],"label_agreement":null},{"id":"W6902090732","doi":"10.6084/m9.figshare.12806110.v1","title":"Additional file 1 of A multimethod approach for county-scale geospatial analysis of emerging infectious diseases: a cross-sectional case study of COVID-19 incidence in Germany","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Geospatial analysis; Incidence (geometry); Data collection; Statistical analysis; Infectious disease (medical specialty)","score_opus":0.05125608779321001,"score_gpt":0.3616069710021718,"score_spread":0.3103508832089618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6902090732","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00036836203,0.000010887099,0.00055040617,0.00004677174,0.000008853681,0.00009522399,0.9980362,0.00020762548,0.00067567633],"genre_scores_gemma":[0.02409427,0.00013164824,0.010164962,0.00027482002,0.00005563004,0.004186756,0.9526393,0.0009936616,0.0074590286],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99877995,0.00036152595,0.00024329872,0.00029082684,0.00018651264,0.00013798587],"domain_scores_gemma":[0.9786173,0.016575497,0.0013561231,0.0013726715,0.001631072,0.0004473346],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0021795304,0.00066524505,0.00080967834,0.002953718,0.0005932647,0.0013131897,0.0013858282,0.0006939195,0.74733895],"category_scores_gemma":[0.03332063,0.0005338699,0.0008421427,0.005808552,0.00022783554,0.0011865343,0.0010848116,0.0006361973,0.085088134],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022632083,0.00006640856,0.009716627,0.0019561097,0.00011141192,0.00008344766,0.00017941669,0.00089946546,0.00008329619,0.0012984836,0.9720081,0.013370898],"study_design_scores_gemma":[0.003227529,0.00030446638,0.10823573,0.0032282555,0.00050149794,0.0006420786,0.0014213634,0.004603744,0.0009795032,0.014538918,0.86213845,0.0001784051],"about_ca_topic_score_codex":0.01107673,"about_ca_topic_score_gemma":0.01726774,"teacher_disagreement_score":0.74733895,"about_ca_system_score_codex":0.00071127305,"about_ca_system_score_gemma":0.0016101771,"threshold_uncertainty_score":0.3603903},"labels":[],"label_agreement":null},{"id":"W6902120864","doi":"10.6084/m9.figshare.20438271","title":"Additional file 1 of The role of geographic information system and global positioning system in dementia care and research: a scoping review","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Geographic information system; Information system; Global Positioning System; Dementia; Interoperability; Positioning system; Key (lock)","score_opus":0.024248610389905684,"score_gpt":0.28853215041192604,"score_spread":0.26428354002202037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6902120864","genre_codex":"dataset","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":[0.000113962364,0.00029562606,0.00046201024,0.0004419595,0.00007317467,0.0021706633,0.9948502,0.00019596328,0.001396392],"genre_scores_gemma":[0.0077145863,0.0035471497,0.026760893,0.0029920572,0.00035962943,0.13147071,0.80750126,0.0010533186,0.018600363],"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","domain_scores_codex":[0.99481493,0.0015456714,0.0020738486,0.00053521426,0.00076891255,0.0002614457],"domain_scores_gemma":[0.84016836,0.13091604,0.008999968,0.0029016212,0.016004402,0.0010095773],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.011348064,0.0016814859,0.0026440008,0.01020651,0.0011927673,0.0027125734,0.0028866713,0.002249379,0.79481983],"category_scores_gemma":[0.1253447,0.0013016912,0.0037842204,0.014360775,0.00060052064,0.0035418624,0.0022915637,0.0016389661,0.067032814],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037238045,0.00004195021,0.00082014885,0.12063188,0.00022349144,0.0000628961,0.00025586184,0.0003269565,0.00007053722,0.00207872,0.8605804,0.0145347975],"study_design_scores_gemma":[0.007018419,0.00017774508,0.0109761935,0.1013677,0.0012952137,0.0003303494,0.0008300146,0.00072408345,0.00036290352,0.011368787,0.86533326,0.0002153852],"about_ca_topic_score_codex":0.013974734,"about_ca_topic_score_gemma":0.024660874,"teacher_disagreement_score":0.79481983,"about_ca_system_score_codex":0.0035019608,"about_ca_system_score_gemma":0.0111665735,"threshold_uncertainty_score":0.2926646},"labels":[],"label_agreement":null},{"id":"W6902198065","doi":"10.6084/m9.figshare.20438280","title":"Additional file 4 of The role of geographic information system and global positioning system in dementia care and research: a scoping review","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Geographic information system; Information system; Global Positioning System; Dementia; Interoperability; Positioning system; Key (lock); File system","score_opus":0.023978310899694183,"score_gpt":0.28851836288752764,"score_spread":0.2645400519878335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6902198065","genre_codex":"dataset","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":[0.00009282034,0.0001771035,0.00025836396,0.00028239965,0.000041546158,0.0004212131,0.99727875,0.00015181933,0.0012960563],"genre_scores_gemma":[0.009851043,0.0025949276,0.01377586,0.0019140949,0.00030089964,0.026549285,0.915616,0.000997003,0.028400978],"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","domain_scores_codex":[0.99703443,0.0008092526,0.0010895148,0.00039192155,0.00047118103,0.00020365388],"domain_scores_gemma":[0.8862913,0.09619458,0.0052353903,0.0020997224,0.0093930885,0.0007859964],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0063361195,0.0011701399,0.0019959442,0.008238661,0.0008200501,0.0024445099,0.0022273404,0.0017371692,0.8682777],"category_scores_gemma":[0.10081769,0.00081831246,0.0028048134,0.01314172,0.0003459357,0.003161439,0.0017607581,0.0010899013,0.08243338],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043697498,0.000042572043,0.0013146991,0.07760887,0.0002479678,0.000078591125,0.00020275016,0.00040004065,0.00007709022,0.0023045605,0.90052265,0.016763093],"study_design_scores_gemma":[0.0048554116,0.0001701807,0.014107602,0.053439308,0.0011967635,0.00031288006,0.00066866603,0.0008457439,0.00041314116,0.011059922,0.9127658,0.00016464712],"about_ca_topic_score_codex":0.012272911,"about_ca_topic_score_gemma":0.01730912,"teacher_disagreement_score":0.8682777,"about_ca_system_score_codex":0.0024995487,"about_ca_system_score_gemma":0.005518295,"threshold_uncertainty_score":0.18788582},"labels":[],"label_agreement":null},{"id":"W6904738573","doi":"10.1371/journal.pone.0283711.s001","title":"Residuals regression by triage score (1–2).","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Triage; Psychological intervention; Emergency department; Pandemic; Proxy (statistics); Population; Control (management); Regression analysis","score_opus":0.06574771680084823,"score_gpt":0.33422137142624336,"score_spread":0.26847365462539513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6904738573","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":[0.21966238,0.004740661,0.32974756,0.009535743,0.011453029,0.0066917404,0.3240261,0.030256376,0.06388632],"genre_scores_gemma":[0.62566966,0.0015742822,0.10594047,0.0019406531,0.00080697425,0.007305839,0.09030888,0.011956001,0.15449727],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.98600835,0.0066413586,0.0010190421,0.0035796228,0.0017835607,0.0009679597],"domain_scores_gemma":[0.98106134,0.008931496,0.0021904155,0.003977326,0.0033088832,0.00053060154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0149027435,0.0026587625,0.0018756605,0.002907885,0.00053591176,0.0017207616,0.0031660302,0.0015290638,0.109060705],"category_scores_gemma":[0.049533408,0.0011078322,0.0048075756,0.004791239,0.0008641149,0.0016449476,0.0022051926,0.0057107843,0.058990728],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022437873,0.0007243611,0.32146826,0.0014490434,0.003904893,0.00029863755,0.0011579988,0.012519907,0.0010697127,0.010224544,0.37766367,0.26727524],"study_design_scores_gemma":[0.00075118267,0.0016881732,0.47263348,0.0014057796,0.0020545782,0.0010447326,0.0021613678,0.1283338,0.0024842222,0.01554791,0.3715135,0.00038132182],"about_ca_topic_score_codex":0.059825547,"about_ca_topic_score_gemma":0.040753495,"teacher_disagreement_score":0.109060705,"about_ca_system_score_codex":0.0015773838,"about_ca_system_score_gemma":0.0032034363,"threshold_uncertainty_score":0.36484438},"labels":[],"label_agreement":null},{"id":"W6904787061","doi":"10.14288/1.0435279","title":"Health equity analysis of awareness of GetCheckedOnline in communities outside Vancouver, British Columbia","year":2025,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public health; Health equity; Equity (law); Government (linguistics)","score_opus":0.016189597074819675,"score_gpt":0.26316605523525116,"score_spread":0.24697645816043148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6904787061","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":[0.9945432,0.00014676234,0.00012220029,0.00014871893,0.0000046702175,0.00011188561,0.0030729382,0.000008183981,0.001841417],"genre_scores_gemma":[0.99755055,0.00016156564,0.0002222922,0.00005675567,0.0000022499273,0.00009401957,0.0011282887,0.0000046683467,0.0007795934],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99837613,0.00025850153,0.000119641154,0.0002596747,0.00042964626,0.0005564337],"domain_scores_gemma":[0.9926016,0.0009408096,0.0009513006,0.0002950372,0.003816709,0.0013944202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013521268,0.00039060778,0.0004909568,0.0017870391,0.0020279537,0.0016407938,0.0013369266,0.00041555933,0.002796888],"category_scores_gemma":[0.0059648394,0.0003533414,0.0005100734,0.0043377704,0.00076471857,0.00037921476,0.0016488652,0.0008466052,0.00024437023],"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":[0.00010076458,0.0000953958,0.98293996,0.00013496539,0.000070342525,0.00012604562,0.002854871,0.00031012762,0.00012987766,0.00012238449,0.0009926034,0.012122613],"study_design_scores_gemma":[0.000008568231,0.000059319085,0.99157363,0.00010082523,0.000033570657,0.000036915015,0.0067014596,0.00062626833,0.00006533082,0.000046836107,0.00073558954,0.000011677603],"about_ca_topic_score_codex":0.9796799,"about_ca_topic_score_gemma":0.9841828,"teacher_disagreement_score":0.020320117,"about_ca_system_score_codex":0.01788032,"about_ca_system_score_gemma":0.022813233,"threshold_uncertainty_score":0.12973142},"labels":[],"label_agreement":null},{"id":"W6911845715","doi":"10.5281/zenodo.16792363","title":"Analyzing Google Trends Data on AI-Curated YouTube Content, Childhood Obesity, and Sleep Disorders","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Popularity; Childhood obesity; Limiting; Quarter (Canadian coin); Obesity; Public interest","score_opus":0.04126474975432118,"score_gpt":0.2866503576653466,"score_spread":0.2453856079110254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6911845715","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":[0.5615854,0.0010693097,0.001151778,0.00052129175,0.00008064663,0.00024696492,0.42182606,0.0006617992,0.012856692],"genre_scores_gemma":[0.5690993,0.001106608,0.004934488,0.00011521374,0.000113802744,0.00044102976,0.41935614,0.00015910143,0.00467437],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99929106,0.00007188677,0.00009892041,0.00012107471,0.0002917679,0.00012531107],"domain_scores_gemma":[0.99702615,0.0006590728,0.0007899752,0.0001319334,0.0011910504,0.00020182277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005875315,0.00048196956,0.00036407966,0.0123446435,0.0003784809,0.0010181604,0.00029906668,0.00026086604,0.0025192962],"category_scores_gemma":[0.004463072,0.00011948505,0.0006507492,0.015661994,0.00018827667,0.0012621599,0.00075995986,0.00033077717,0.0014684183],"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":[0.0003178277,0.00011768388,0.8634582,0.0013820231,0.00024698942,0.00043108247,0.0013696069,0.0022274377,0.0019369661,0.0017463454,0.058173668,0.06859209],"study_design_scores_gemma":[0.000012778191,0.00009516031,0.9564992,0.00018506832,0.000075437616,0.0002582375,0.0023409792,0.009148053,0.00094560534,0.00042109727,0.02998095,0.000037423262],"about_ca_topic_score_codex":0.10175948,"about_ca_topic_score_gemma":0.13488048,"teacher_disagreement_score":0.10175948,"about_ca_system_score_codex":0.0008868158,"about_ca_system_score_gemma":0.0009630995,"threshold_uncertainty_score":0.2023344},"labels":[],"label_agreement":null},{"id":"W6920357447","doi":"10.60692/5h838-eq544","title":"Guidelines for creating framework data for GIS analysis in low‐ and middle‐income countries","year":2016,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"","keywords":"Key (lock); Mandate; Open data; Data sharing; Geographic information system; Interoperability; AM/FM/GIS; Data integration; Data mapping","score_opus":0.08675251130464066,"score_gpt":0.315394129702123,"score_spread":0.22864161839748232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920357447","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":[0.0048363125,0.003603982,0.6836959,0.026725203,0.0031940565,0.053347014,0.07761143,0.0132977,0.13368846],"genre_scores_gemma":[0.0056304494,0.0017852826,0.93328726,0.0014748732,0.0001478059,0.029675335,0.019478494,0.001874946,0.006645616],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.92674106,0.043568883,0.017577108,0.0016258629,0.008878651,0.0016085003],"domain_scores_gemma":[0.789637,0.09529162,0.010683451,0.025703374,0.07342702,0.005257478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.088785395,0.0022494798,0.0019258498,0.015297187,0.0025170338,0.00937851,0.009067621,0.0041253082,0.04121887],"category_scores_gemma":[0.20647788,0.003022784,0.004399215,0.016268248,0.0031374383,0.007838363,0.007413417,0.0071797874,0.02407336],"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":[0.00023362815,0.00031392893,0.0055591073,0.006107748,0.00015443012,0.001049606,0.008570865,0.0052701035,0.0015433736,0.06769035,0.6850875,0.21841931],"study_design_scores_gemma":[0.00016208451,0.000055342945,0.0036943252,0.0100140525,0.00006253034,0.00043167625,0.0040125083,0.0029498227,0.0014065222,0.033512708,0.9435456,0.00015289684],"about_ca_topic_score_codex":0.019369118,"about_ca_topic_score_gemma":0.029312808,"teacher_disagreement_score":0.088785395,"about_ca_system_score_codex":0.004295458,"about_ca_system_score_gemma":0.020756086,"threshold_uncertainty_score":0.4695477},"labels":[],"label_agreement":null},{"id":"W6920596308","doi":"10.6084/m9.figshare.20438271.v1","title":"Additional file 1 of The role of geographic information system and global positioning system in dementia care and research: a scoping review","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Geographic information system; Information system; Global Positioning System; Dementia; Interoperability; Positioning system; Key (lock)","score_opus":0.024248610389905684,"score_gpt":0.28853215041192604,"score_spread":0.26428354002202037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920596308","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000113962364,0.00029562606,0.00046201024,0.0004419595,0.00007317467,0.0021706633,0.9948502,0.00019596328,0.001396392],"genre_scores_gemma":[0.0077145863,0.0035471497,0.026760893,0.0029920572,0.00035962943,0.13147071,0.80750126,0.0010533186,0.018600363],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99481493,0.0015456714,0.0020738486,0.00053521426,0.00076891255,0.0002614457],"domain_scores_gemma":[0.84016836,0.13091604,0.008999968,0.0029016212,0.016004402,0.0010095773],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.011348064,0.0016814859,0.0026440008,0.01020651,0.0011927673,0.0027125734,0.0028866713,0.002249379,0.79481983],"category_scores_gemma":[0.1253447,0.0013016912,0.0037842204,0.014360775,0.00060052064,0.0035418624,0.0022915637,0.0016389661,0.067032814],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037238045,0.00004195021,0.00082014885,0.12063188,0.00022349144,0.0000628961,0.00025586184,0.0003269565,0.00007053722,0.00207872,0.8605804,0.0145347975],"study_design_scores_gemma":[0.007018419,0.00017774508,0.0109761935,0.1013677,0.0012952137,0.0003303494,0.0008300146,0.00072408345,0.00036290352,0.011368787,0.86533326,0.0002153852],"about_ca_topic_score_codex":0.013974734,"about_ca_topic_score_gemma":0.024660874,"teacher_disagreement_score":0.79481983,"about_ca_system_score_codex":0.0035019608,"about_ca_system_score_gemma":0.0111665735,"threshold_uncertainty_score":0.2926646},"labels":[],"label_agreement":null},{"id":"W6920790833","doi":"10.6084/m9.figshare.14093323.v2","title":"Vision 2035 Public Health Surveillance in India - A White Paper","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public health; Public health surveillance; Health surveillance; White paper; White (mutation); Disease surveillance","score_opus":0.03859671617043582,"score_gpt":0.3140568465513314,"score_spread":0.2754601303808956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920790833","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052545713,0.014836976,0.018170848,0.68393767,0.057195675,0.0010157737,0.032955796,0.0041993116,0.18243338],"genre_scores_gemma":[0.14645828,0.026388615,0.11198993,0.4269905,0.010358142,0.0028509388,0.07088008,0.0017164961,0.20236711],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98830146,0.0027710418,0.0007845035,0.00070178864,0.0049527567,0.0024883605],"domain_scores_gemma":[0.977358,0.003769833,0.0012131077,0.0017658349,0.007841152,0.008052141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026310481,0.0017786295,0.0007916654,0.0027683778,0.0024007496,0.012044863,0.004739582,0.006523056,0.009343475],"category_scores_gemma":[0.019623812,0.0008970624,0.0021153097,0.0024006686,0.0028007259,0.00471222,0.0073868856,0.008063791,0.005086442],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000061619176,0.0000730557,0.0015556475,0.00040073178,0.00004664163,0.00012367699,0.0003666959,0.0005517511,0.00030167238,0.04468875,0.9128995,0.038930215],"study_design_scores_gemma":[0.000029518354,0.00006920746,0.0061824317,0.0006979748,0.00003937194,0.000086105036,0.0006116786,0.00046126675,0.00054129784,0.009611475,0.9816228,0.00004696994],"about_ca_topic_score_codex":0.1235821,"about_ca_topic_score_gemma":0.09983438,"teacher_disagreement_score":0.1235821,"about_ca_system_score_codex":0.009783737,"about_ca_system_score_gemma":0.08886791,"threshold_uncertainty_score":0.24572551},"labels":[],"label_agreement":null},{"id":"W6920800126","doi":"10.6084/m9.figshare.17273274","title":"Additional file 1 of Relationship between internet research data of oral neoplasms and public health programs in the European Union","year":2021,"lang":"en","type":"article","venue":"Open MIND","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"European union; The Internet; Public health; Descriptive research; Public access; Descriptive statistics; MEDLINE","score_opus":0.4760503654018063,"score_gpt":0.4438884546693633,"score_spread":0.03216191073244301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920800126","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023324808,0.000040588915,0.00008478994,0.00008920067,0.000013935632,0.00013894342,0.99846506,0.000038845774,0.0008954399],"genre_scores_gemma":[0.01457211,0.0005976211,0.0026288861,0.0007109743,0.00013715889,0.0068403934,0.9603237,0.00028621336,0.013902961],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99873585,0.0002670964,0.00041243862,0.00021628743,0.00022796974,0.00014034123],"domain_scores_gemma":[0.9612288,0.030409237,0.0036353394,0.00079031195,0.0033906295,0.00054576236],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018783675,0.00073295966,0.0013012977,0.0049297544,0.00067265116,0.0011837848,0.0010855626,0.0010478337,0.7808863],"category_scores_gemma":[0.03850095,0.00043844117,0.00090197474,0.008236257,0.00021552734,0.0016179404,0.0010606095,0.0007699409,0.06674689],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037491223,0.000110121866,0.0050910003,0.011895914,0.00010715745,0.00006903183,0.00014140912,0.00032302356,0.000053034946,0.001491761,0.9694304,0.010912307],"study_design_scores_gemma":[0.0060472433,0.0004082991,0.09916534,0.023501195,0.000629616,0.00072777184,0.0017660385,0.001877583,0.0005434236,0.009565989,0.8555845,0.00018301321],"about_ca_topic_score_codex":0.0139426,"about_ca_topic_score_gemma":0.019188043,"teacher_disagreement_score":0.7808863,"about_ca_system_score_codex":0.0017009849,"about_ca_system_score_gemma":0.0026468502,"threshold_uncertainty_score":0.3125391},"labels":[],"label_agreement":null},{"id":"W6920842423","doi":"10.6084/m9.figshare.20438274","title":"Additional file 2 of The role of geographic information system and global positioning system in dementia care and research: a scoping review","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Geographic information system; Information system; Dementia; Global Positioning System; Interoperability; Positioning system; Key (lock); File system","score_opus":0.023349061979809627,"score_gpt":0.28790174969555815,"score_spread":0.2645526877157485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920842423","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015584778,0.00066932297,0.0002444345,0.00029390215,0.00004474509,0.0011416322,0.99584633,0.00010758508,0.0014961744],"genre_scores_gemma":[0.017289737,0.008353106,0.014427107,0.0031884303,0.0004075918,0.08083533,0.84329325,0.0008667589,0.031338703],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99740607,0.0006055295,0.0010712155,0.00038806666,0.0003222181,0.00020694874],"domain_scores_gemma":[0.9331175,0.055488076,0.0047749616,0.0009410222,0.005128097,0.0005503865],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00475208,0.0015426736,0.003759417,0.013137892,0.0009359992,0.0025212825,0.0021590341,0.0020266753,0.7658713],"category_scores_gemma":[0.06622297,0.0009253866,0.0035720833,0.01888952,0.00046847542,0.003109137,0.0019618631,0.0011014767,0.038383044],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069584604,0.00005229048,0.0016749327,0.39126268,0.0006657811,0.0001597931,0.0003432348,0.00058169087,0.00017161191,0.0032048475,0.5855721,0.015615205],"study_design_scores_gemma":[0.009324437,0.00033483692,0.018406937,0.21130341,0.0040052556,0.00054498465,0.00096373376,0.0012747378,0.00050185656,0.010746214,0.7423326,0.0002609541],"about_ca_topic_score_codex":0.017687948,"about_ca_topic_score_gemma":0.030010238,"teacher_disagreement_score":0.7658713,"about_ca_system_score_codex":0.0033360748,"about_ca_system_score_gemma":0.0063355793,"threshold_uncertainty_score":0.33395618},"labels":[],"label_agreement":null},{"id":"W6921015555","doi":"10.6084/m9.figshare.23916780.v1","title":"Additional file 2 of A social network analysis model approach to understand tuberculosis transmission in remote rural Madagascar","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Social network analysis; Transmission (telecommunications); Network analysis; Social network (sociolinguistics); Tuberculosis; Rural area","score_opus":0.033915783535409576,"score_gpt":0.2725420130127633,"score_spread":0.23862622947735374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6921015555","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00047473886,0.000010927785,0.0005237068,0.00012406308,0.0000130748185,0.000069109876,0.99718314,0.00022250171,0.001378582],"genre_scores_gemma":[0.04736431,0.00014779581,0.010126187,0.00048270234,0.00008386033,0.0024408135,0.92266977,0.0010237064,0.015660757],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996458,0.00011889599,0.00003831907,0.00008192896,0.0000634072,0.000051572944],"domain_scores_gemma":[0.9881793,0.009928873,0.000402254,0.00042773839,0.00082597195,0.00023572154],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012719186,0.00051027717,0.0004821943,0.0012512334,0.00051368517,0.0010771792,0.0012735607,0.00070802314,0.7398304],"category_scores_gemma":[0.018903341,0.00031749727,0.0006878469,0.0022922005,0.00016368667,0.0010059725,0.0006603814,0.0007709952,0.06811937],"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":[0.0001520619,0.00007437704,0.007823654,0.0010837232,0.00006594923,0.00007720554,0.00014169712,0.0033415994,0.00006130775,0.0019669407,0.9746629,0.010548526],"study_design_scores_gemma":[0.0028863656,0.00028627063,0.05374872,0.0024204287,0.00035369094,0.00043789818,0.0013798297,0.026302049,0.0007006341,0.024189139,0.8871467,0.00014833237],"about_ca_topic_score_codex":0.019973272,"about_ca_topic_score_gemma":0.031566642,"teacher_disagreement_score":0.7398304,"about_ca_system_score_codex":0.00070160185,"about_ca_system_score_gemma":0.00123846,"threshold_uncertainty_score":0.37110037},"labels":[],"label_agreement":null},{"id":"W6921079871","doi":"10.6084/m9.figshare.13347093","title":"The association between COVID-19 cases and deaths and web-based public inquiries","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Intraclass correlation; Public interest; Public health; Reliability (semiconductor); Disease; Association (psychology)","score_opus":0.11323212828060686,"score_gpt":0.3244028874560292,"score_spread":0.21117075917542233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6921079871","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":[0.9809987,0.0032370144,0.00033933535,0.00057828653,0.000050334867,0.000055423934,0.010256617,0.00003664199,0.004447685],"genre_scores_gemma":[0.99371076,0.0009381809,0.00026095385,0.00009113775,0.00005120997,0.00003363618,0.0042427154,0.00001247281,0.0006589574],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978218,0.00061289873,0.00048915925,0.00034017765,0.00047322092,0.00026283407],"domain_scores_gemma":[0.98032206,0.009320018,0.007613262,0.00057631935,0.0014324527,0.00073587557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018186806,0.00019902764,0.00034108345,0.0026514134,0.00020733595,0.0010844861,0.0004209292,0.00042373184,0.00432645],"category_scores_gemma":[0.018962028,0.00017260975,0.0007285706,0.00272735,0.00033168946,0.0013934658,0.0009129551,0.000776492,0.0006968596],"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":[0.00022187049,0.000025736506,0.9924567,0.0002713573,0.00013859403,0.00011651449,0.00023906719,0.00013382449,0.000108026325,0.00010645828,0.0009496426,0.005232163],"study_design_scores_gemma":[0.0000070401857,0.00006419158,0.99615496,0.00014809392,0.00008268996,0.00046077877,0.0006196218,0.000674194,0.00012377782,0.00009202221,0.0015612217,0.000011423295],"about_ca_topic_score_codex":0.006546729,"about_ca_topic_score_gemma":0.008357173,"teacher_disagreement_score":0.006546729,"about_ca_system_score_codex":0.00036052577,"about_ca_system_score_gemma":0.00040465523,"threshold_uncertainty_score":0.014473438},"labels":[],"label_agreement":null},{"id":"W6921086643","doi":"10.6084/m9.figshare.23916789","title":"Additional file 5 of A social network analysis model approach to understand tuberculosis transmission in remote rural Madagascar","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Social network analysis; Transmission (telecommunications); Network analysis; Social network (sociolinguistics); Tuberculosis; Qualitative analysis","score_opus":0.03450871794189434,"score_gpt":0.27285874202388455,"score_spread":0.2383500240819902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6921086643","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000478159,0.000012661743,0.0004994436,0.00013165736,0.000013950052,0.00007009354,0.99712664,0.00024437442,0.0014228978],"genre_scores_gemma":[0.049584445,0.00016866701,0.009978082,0.0005047684,0.00008658536,0.0022701228,0.920228,0.0010415756,0.016137693],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996086,0.00013703897,0.00004412997,0.00008604844,0.000067775705,0.00005643335],"domain_scores_gemma":[0.9865545,0.011325479,0.00046409442,0.00047315084,0.00092868204,0.00025400304],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013628236,0.0005381474,0.00051642314,0.0013260273,0.0005345099,0.0010981687,0.0012670212,0.0007410326,0.7576079],"category_scores_gemma":[0.02041128,0.0003370558,0.00074910047,0.0024274958,0.00016378111,0.0010748529,0.00068003114,0.00078036863,0.06853772],"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":[0.00018476338,0.00008101684,0.008235374,0.0013158867,0.00008018843,0.00008628212,0.00015442919,0.0036238052,0.000065172964,0.0019426313,0.9727624,0.0114679765],"study_design_scores_gemma":[0.0032157337,0.00031808967,0.058724087,0.002916348,0.00039752357,0.00043928198,0.0014751514,0.025823813,0.00074166927,0.025411217,0.8803708,0.00016632339],"about_ca_topic_score_codex":0.021576388,"about_ca_topic_score_gemma":0.03360004,"teacher_disagreement_score":0.7576079,"about_ca_system_score_codex":0.000764673,"about_ca_system_score_gemma":0.0012307363,"threshold_uncertainty_score":0.34574294},"labels":[],"label_agreement":null},{"id":"W6921124421","doi":"10.6084/m9.figshare.23916789.v1","title":"Additional file 5 of A social network analysis model approach to understand tuberculosis transmission in remote rural Madagascar","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Social network analysis; Transmission (telecommunications); Network analysis; Social network (sociolinguistics); Tuberculosis; Qualitative analysis","score_opus":0.03450871794189434,"score_gpt":0.27285874202388455,"score_spread":0.2383500240819902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6921124421","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000478159,0.000012661743,0.0004994436,0.00013165736,0.000013950052,0.00007009354,0.99712664,0.00024437442,0.0014228978],"genre_scores_gemma":[0.049584445,0.00016866701,0.009978082,0.0005047684,0.00008658536,0.0022701228,0.920228,0.0010415756,0.016137693],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996086,0.00013703897,0.00004412997,0.00008604844,0.000067775705,0.00005643335],"domain_scores_gemma":[0.9865545,0.011325479,0.00046409442,0.00047315084,0.00092868204,0.00025400304],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013628236,0.0005381474,0.00051642314,0.0013260273,0.0005345099,0.0010981687,0.0012670212,0.0007410326,0.7576079],"category_scores_gemma":[0.02041128,0.0003370558,0.00074910047,0.0024274958,0.00016378111,0.0010748529,0.00068003114,0.00078036863,0.06853772],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018476338,0.00008101684,0.008235374,0.0013158867,0.00008018843,0.00008628212,0.00015442919,0.0036238052,0.000065172964,0.0019426313,0.9727624,0.0114679765],"study_design_scores_gemma":[0.0032157337,0.00031808967,0.058724087,0.002916348,0.00039752357,0.00043928198,0.0014751514,0.025823813,0.00074166927,0.025411217,0.8803708,0.00016632339],"about_ca_topic_score_codex":0.021576388,"about_ca_topic_score_gemma":0.03360004,"teacher_disagreement_score":0.7576079,"about_ca_system_score_codex":0.000764673,"about_ca_system_score_gemma":0.0012307363,"threshold_uncertainty_score":0.34574294},"labels":[],"label_agreement":null},{"id":"W6921161528","doi":"10.6084/m9.figshare.20438277","title":"Additional file 3 of The role of geographic information system and global positioning system in dementia care and research: a scoping review","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Geographic information system; Information system; Dementia; Global Positioning System; Positioning system; Interoperability; Key (lock)","score_opus":0.02377782592312441,"score_gpt":0.2880602375878256,"score_spread":0.26428241166470123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6921161528","genre_codex":"dataset","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":[0.00007222398,0.0001456347,0.00018258549,0.00017009718,0.000031459145,0.00034275485,0.9980484,0.00010570987,0.0009010902],"genre_scores_gemma":[0.0065660123,0.0021744538,0.010583862,0.001554774,0.00023040573,0.023004496,0.9350949,0.00080192083,0.019989347],"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","domain_scores_codex":[0.9974076,0.0005309484,0.0010821562,0.00036256597,0.00042579407,0.00019097814],"domain_scores_gemma":[0.9144702,0.069913544,0.004967243,0.001635732,0.008422509,0.0005908898],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0050059003,0.0015729878,0.002439132,0.009585574,0.0009111272,0.002912455,0.0021349275,0.0018474453,0.82232606],"category_scores_gemma":[0.07595003,0.00089536863,0.0031111608,0.015768323,0.0003568789,0.003323933,0.0017413127,0.0011479427,0.0769712],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045512526,0.00003980236,0.0014052247,0.11416473,0.00031160875,0.00011279319,0.00020679434,0.00037728745,0.00013477748,0.0022343076,0.86681545,0.01374221],"study_design_scores_gemma":[0.004589812,0.00014003797,0.013304754,0.06284934,0.0015317564,0.00034175595,0.00065956084,0.0007269284,0.00060884154,0.010638897,0.9044344,0.00017406329],"about_ca_topic_score_codex":0.01333512,"about_ca_topic_score_gemma":0.019216573,"teacher_disagreement_score":0.82232606,"about_ca_system_score_codex":0.002804376,"about_ca_system_score_gemma":0.005991971,"threshold_uncertainty_score":0.25343025},"labels":[],"label_agreement":null},{"id":"W6921173356","doi":"10.6084/m9.figshare.23916777.v1","title":"Additional file 1 of A social network analysis model approach to understand tuberculosis transmission in remote rural Madagascar","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Social network analysis; Transmission (telecommunications); Network analysis; Social network (sociolinguistics); Tuberculosis; Rural area","score_opus":0.03515419398891206,"score_gpt":0.27312017207682726,"score_spread":0.2379659780879152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6921173356","genre_codex":"dataset","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":[0.0005237719,0.000011823806,0.0005677373,0.00012824754,0.000012535478,0.000069961236,0.9971199,0.00020587613,0.0013601535],"genre_scores_gemma":[0.05178431,0.0001712896,0.011228745,0.00048403672,0.000082177496,0.002590428,0.9176084,0.00096706214,0.015083587],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996531,0.00012656649,0.0000354294,0.00007896729,0.000059038553,0.000047017464],"domain_scores_gemma":[0.9874085,0.010709164,0.00041410595,0.00043029542,0.0008091205,0.00022877938],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013065869,0.0004909339,0.00045024112,0.0012392146,0.00049746665,0.001001242,0.0012368673,0.0006688582,0.72043604],"category_scores_gemma":[0.019326355,0.00031236347,0.0006221348,0.002343867,0.00015919824,0.000983566,0.0006501411,0.00077557575,0.066988274],"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":[0.00014139355,0.000077252174,0.008105805,0.0010369543,0.00006449418,0.00006970512,0.00014550894,0.0035208012,0.00005932005,0.0019789282,0.9737525,0.011047306],"study_design_scores_gemma":[0.0027910115,0.0003367527,0.059825346,0.0025644207,0.00037005232,0.00046960494,0.0014944538,0.030899009,0.00071779935,0.025544418,0.8748308,0.00015633112],"about_ca_topic_score_codex":0.018934553,"about_ca_topic_score_gemma":0.030454168,"teacher_disagreement_score":0.72043604,"about_ca_system_score_codex":0.0007038926,"about_ca_system_score_gemma":0.0012326814,"threshold_uncertainty_score":0.398764},"labels":[],"label_agreement":null},{"id":"W6922463062","doi":"10.1184/r1/11786922","title":"Lifestyle Disease Surveillance Using Population Search Behavior: Feasibility Study","year":2020,"lang":"en","type":"article","venue":"KiltHub Repository","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Generalizability theory; Multivariate statistics; Proxy (statistics); Regression analysis; Population; Disease; Regression; Model selection; Multivariate analysis","score_opus":0.06367894319392689,"score_gpt":0.3449601524450451,"score_spread":0.2812812092511182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6922463062","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":[0.983466,0.00011493057,0.005033151,0.00047434212,0.000026022843,0.0073024924,0.001500942,0.000057596273,0.0020246394],"genre_scores_gemma":[0.9743215,0.00019751818,0.012638173,0.0003445902,0.000065679065,0.009936755,0.001974922,0.000014153644,0.00050671236],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9829752,0.01399577,0.00066448044,0.00075068144,0.0009155063,0.00069830014],"domain_scores_gemma":[0.9674311,0.02034543,0.0029586777,0.0033088345,0.0040488834,0.0019069494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027962875,0.0009202462,0.00065876526,0.0021074424,0.0010210177,0.0010649884,0.0015780771,0.0014394966,0.002722272],"category_scores_gemma":[0.04881742,0.00080609246,0.0014731915,0.0017151852,0.00089327287,0.0031652628,0.0018591089,0.0012716395,0.0014097159],"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":[0.0049977354,0.031630058,0.8808746,0.00096946524,0.0004967862,0.000749269,0.004369628,0.0043171393,0.0009009029,0.0013018486,0.0026192653,0.066773444],"study_design_scores_gemma":[0.0035213723,0.09254389,0.82368004,0.00038891134,0.0006701757,0.0012033334,0.010454252,0.056326304,0.0019013634,0.0018832162,0.007169565,0.00025757172],"about_ca_topic_score_codex":0.02023534,"about_ca_topic_score_gemma":0.014743824,"teacher_disagreement_score":0.027962875,"about_ca_system_score_codex":0.0016975114,"about_ca_system_score_gemma":0.0046988586,"threshold_uncertainty_score":0.1478836},"labels":[],"label_agreement":null},{"id":"W6924905789","doi":"10.1594/pangaea.965756","title":"Multibeam bathymetry raw data (Kongsberg EM 122 entire dataset) of RV MARIA S. MERIAN during cruise MSM123","year":2024,"lang":"en","type":"dataset","venue":"Publishing Network for Geoscientific and Environmental Data (PANGAEA) (Alfred Wegener Institute for Polar and Marine Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nucleofection; TSG101; Gestational period; Diafiltration; Articular cartilage damage; Proteogenomics; Hyporeflexia; Liquation","score_opus":0.06581176515587656,"score_gpt":0.3245452440877929,"score_spread":0.25873347893191634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6924905789","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012155572,0.000046304634,0.000113270304,0.000027809487,0.000011656151,0.000011213098,0.9975483,0.00021890656,0.00080696936],"genre_scores_gemma":[0.0018996403,0.000028863711,0.00036276664,0.000010111343,0.0000029777855,0.00004029426,0.9970511,0.000044484477,0.0005597949],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994155,0.00007914309,0.000062621075,0.00016250726,0.00017422235,0.000105994266],"domain_scores_gemma":[0.99898297,0.00013675066,0.00014123038,0.0002465809,0.0003979859,0.000094400115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007625486,0.0009864344,0.00072949356,0.0016914658,0.0003249296,0.00096327375,0.0012132099,0.0007047138,0.015504617],"category_scores_gemma":[0.002573672,0.00043575952,0.0006509431,0.0025510727,0.00022584121,0.00050223974,0.0010712445,0.0006685842,0.025902968],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020427696,0.000037148417,0.0134187015,0.0007997019,0.00011308271,0.00010982923,0.00014392144,0.0015687266,0.0007818815,0.00062098505,0.9731482,0.009053415],"study_design_scores_gemma":[0.00014088339,0.000026199708,0.07267632,0.00034069488,0.00003899429,0.000101371006,0.0002940101,0.00111112,0.0011489497,0.00044521224,0.92363477,0.00004147866],"about_ca_topic_score_codex":0.078032374,"about_ca_topic_score_gemma":0.121879354,"teacher_disagreement_score":0.078032374,"about_ca_system_score_codex":0.0010245295,"about_ca_system_score_gemma":0.0016654251,"threshold_uncertainty_score":0.15515637},"labels":[],"label_agreement":null},{"id":"W6925338600","doi":"10.17632/vb4xvxk86v","title":"Sponge Assemblages and Predicted Archetypes in the Eastern Canadian Arctic","year":2019,"lang":"en","type":"dataset","venue":"Mendeley Data","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Arctic; Sponge; Bay; Fauna; Taxon; Cape","score_opus":0.07680203725846485,"score_gpt":0.32875260949085594,"score_spread":0.2519505722323911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6925338600","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9957749,0.0006416688,0.00010939825,0.000056405843,0.0000052882647,0.000010044209,0.0016278335,0.000010468788,0.0017638423],"genre_scores_gemma":[0.9981304,0.00042466715,0.00022632061,0.000019995794,0.0000023221721,0.000008017964,0.0009010211,0.0000046052783,0.00028254377],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994831,0.000027171027,0.000041480285,0.000117778196,0.00015572677,0.00017473417],"domain_scores_gemma":[0.9987143,0.00008641122,0.00029221756,0.000054116448,0.0005878866,0.00026513572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004556174,0.00045523993,0.00030246546,0.0050755227,0.0024330681,0.0012065547,0.00069567724,0.00031249842,0.0012854168],"category_scores_gemma":[0.001509857,0.0003694827,0.00038986318,0.003864428,0.0009297219,0.0003347164,0.0010331091,0.00028177552,0.00016091701],"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":[0.000085257154,0.0000063371303,0.9927152,0.000038772865,0.0000658624,0.000096075484,0.0009672882,0.00016720266,0.0014857872,0.00013525576,0.00027019143,0.003966782],"study_design_scores_gemma":[0.0000018632587,0.0000050365684,0.9985623,0.000011101082,0.000009978019,0.000051374955,0.00067295774,0.00017386473,0.000051503877,0.000025331945,0.0004296997,0.0000050963217],"about_ca_topic_score_codex":0.96907336,"about_ca_topic_score_gemma":0.99087137,"teacher_disagreement_score":0.030926645,"about_ca_system_score_codex":0.009403511,"about_ca_system_score_gemma":0.008783724,"threshold_uncertainty_score":0.06822759},"labels":[],"label_agreement":null},{"id":"W6926325237","doi":"10.25316/ir-14013","title":"Nanaimo Free Press [Wednesday, April 3, 1878]","year":2020,"lang":"en","type":"other","venue":"VIUSpace (Vancouver Island University Library)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Arc (geometry); Criminal law; Free press","score_opus":0.00902866903954923,"score_gpt":0.1966141040624115,"score_spread":0.18758543502286226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6926325237","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005208997,0.007285483,0.000108763525,0.003301412,0.0029566952,0.000021445012,0.004222709,0.00017723936,0.98140556],"genre_scores_gemma":[0.001004417,0.0016772044,0.000043000233,0.00034003123,0.00012581251,0.000008805031,0.0005120558,0.0000561207,0.99623257],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99984396,0.000011095472,0.000010452317,0.000034686433,0.000070568654,0.000029219465],"domain_scores_gemma":[0.99983203,0.000026204563,0.000014678765,0.000016572038,0.000070055125,0.000040459665],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00020471253,0.000674843,0.00032914235,0.00089021825,0.0016814043,0.0031961268,0.00042419398,0.0010712047,0.45756307],"category_scores_gemma":[0.00091574853,0.00034735046,0.00023092623,0.0015710643,0.00040471193,0.0018349218,0.001044375,0.0018713474,0.2849211],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024844543,0.000006037472,0.0002242839,0.0000627293,0.000002246701,0.0000614392,0.000068807196,0.000032549364,0.00008337192,0.006411812,0.96382403,0.029197874],"study_design_scores_gemma":[0.0000011170945,0.0000019056789,0.00046238274,0.000044809516,4.4481416e-7,0.000014034754,0.000031200507,0.0000072452194,0.000016311853,0.00016896476,0.99925,0.0000014639634],"about_ca_topic_score_codex":0.025456795,"about_ca_topic_score_gemma":0.12873426,"teacher_disagreement_score":0.54243696,"about_ca_system_score_codex":0.0012075854,"about_ca_system_score_gemma":0.0010982146,"threshold_uncertainty_score":0.77372044},"labels":[],"label_agreement":null},{"id":"W6929127842","doi":"10.4224/21274554","title":"Acoustics summary: sound insulation in mid-rise wood building: report to Research Consortium for Wood and Wood-Hybrid Mid-Rise Buildings","year":2014,"lang":"en","type":"report","venue":"NPARC","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"Natural Resources Canada; FPInnovations","keywords":"Soundproofing; Sound (geography); Sound production; Noise (video); Sound wave","score_opus":0.06375248552901613,"score_gpt":0.38135545764929807,"score_spread":0.31760297212028193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929127842","genre_codex":"other","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":[0.2218367,0.0048722457,0.033934586,0.020827092,0.009378204,0.005754365,0.34554404,0.003243584,0.3546092],"genre_scores_gemma":[0.40120307,0.0058290525,0.022889603,0.0021754175,0.0030545793,0.002338308,0.22135602,0.0007192223,0.34043464],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9949825,0.0004894271,0.00033566437,0.00033538198,0.0034493366,0.000407625],"domain_scores_gemma":[0.9875365,0.0010621669,0.00057852006,0.0004712665,0.009430595,0.00092095614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037705956,0.00076235493,0.00042294912,0.0022473563,0.00085654645,0.0013223214,0.0014056554,0.0012166423,0.014710114],"category_scores_gemma":[0.0055737244,0.00028799844,0.00044116544,0.0013165998,0.0003356436,0.00088423054,0.0013709265,0.0009873883,0.0068498026],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009016505,0.0011586988,0.11291533,0.0012170107,0.00012859424,0.0009294668,0.0009982147,0.0077173393,0.016153026,0.0013281279,0.6795993,0.17695335],"study_design_scores_gemma":[0.00013247838,0.0013897857,0.44624385,0.0005291803,0.00022298718,0.00063633913,0.0035316085,0.005124916,0.032710124,0.0007613752,0.5085718,0.00014551388],"about_ca_topic_score_codex":0.066492535,"about_ca_topic_score_gemma":0.067278914,"teacher_disagreement_score":0.066492535,"about_ca_system_score_codex":0.0015166793,"about_ca_system_score_gemma":0.0067052287,"threshold_uncertainty_score":0.13221097},"labels":[],"label_agreement":null},{"id":"W6929479967","doi":"10.5061/dryad.cg5vp","title":"Data from: Does white tail patch size indicate quality in male Cerulean Warblers (Setophaga cerulea)","year":2015,"lang":"en","type":"dataset","venue":"Data Archiving and Networked Services (DANS)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"","keywords":"Plumage; White (mutation); Feather; Wing; Large white; Quality (philosophy)","score_opus":0.04984235150577585,"score_gpt":0.32471242176928367,"score_spread":0.2748700702635078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929479967","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9711183,0.000060733644,0.00014655784,0.00021169013,0.00003107588,0.00005734325,0.022333862,0.000030383233,0.0060101235],"genre_scores_gemma":[0.97454304,0.00008493226,0.0003067326,0.000119680626,0.000016762828,0.00007532184,0.015016283,0.00002140954,0.009815889],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99980885,0.000022543147,0.000019340076,0.000056067922,0.00006477913,0.000028393471],"domain_scores_gemma":[0.99826247,0.00023664735,0.00061720965,0.00016537009,0.00041732175,0.0003009025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028885502,0.00013871348,0.00019945166,0.0003354515,0.00032619748,0.00041370932,0.00027620344,0.00024459005,0.015234485],"category_scores_gemma":[0.0012678952,0.00015285853,0.00013114241,0.00032521313,0.0002633995,0.00020910209,0.0002879471,0.00017585253,0.0040371],"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":[0.0002687332,0.00003538499,0.9891879,0.000044186756,0.000029845267,0.00005357965,0.00039570202,0.00005290339,0.0015696522,0.000018410157,0.0038499215,0.004493785],"study_design_scores_gemma":[0.0000069441326,0.000020260246,0.9983845,0.000006381745,0.000006482169,0.000016544085,0.00010269376,0.00004102956,0.000111746354,0.0000030488907,0.001298325,0.0000020469909],"about_ca_topic_score_codex":0.13045129,"about_ca_topic_score_gemma":0.34776732,"teacher_disagreement_score":0.13045129,"about_ca_system_score_codex":0.00057608413,"about_ca_system_score_gemma":0.00043951374,"threshold_uncertainty_score":0.25938398},"labels":[],"label_agreement":null},{"id":"W6930197401","doi":"10.5281/zenodo.12903282","title":"Wellness Farms CBD Gummies [Price 2024] Ingredients, Working &amp; Benefits?","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Variety (cybernetics); Quality (philosophy); Counterfeit; Scope (computer science); Broad spectrum; Signage; Work (physics); Bitter taste; Carbon footprint","score_opus":0.04113679479415183,"score_gpt":0.2709345291954972,"score_spread":0.22979773440134538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930197401","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010175932,0.0017334672,0.0007604588,0.0032170815,0.0048256703,0.00021974918,0.0031288809,0.0022830307,0.9828141],"genre_scores_gemma":[0.0020246173,0.0009983593,0.0006565933,0.0012385279,0.00040732275,0.000043024666,0.0015232215,0.000582021,0.9925263],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997168,0.000016542232,0.000010873063,0.000046560624,0.00017255986,0.00003663211],"domain_scores_gemma":[0.99937063,0.000066616245,0.000030050156,0.000060319802,0.00028488127,0.00018753571],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000351479,0.00086520816,0.0004360429,0.0011398917,0.0017105646,0.0033869091,0.0008310961,0.002080442,0.82827806],"category_scores_gemma":[0.0013152844,0.0004669645,0.00075235637,0.00065249583,0.0004740737,0.0026445037,0.0014362815,0.0021072659,0.6685774],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007521716,0.0000551083,0.00008056828,0.00013258695,0.0000024998467,0.000059135826,0.000020184701,0.000025583711,0.0008354052,0.001799012,0.89535654,0.10155814],"study_design_scores_gemma":[0.000009168442,0.000019806668,0.00025879903,0.000049329108,0.0000012640395,0.000050807204,0.000026677886,0.00002076741,0.00014033243,0.00023746197,0.99918,0.000005510577],"about_ca_topic_score_codex":0.0049303556,"about_ca_topic_score_gemma":0.011551663,"teacher_disagreement_score":0.17172194,"about_ca_system_score_codex":0.00083484285,"about_ca_system_score_gemma":0.0009941459,"threshold_uncertainty_score":0.2449404},"labels":[],"label_agreement":null},{"id":"W6930390737","doi":"10.5281/zenodo.11342129","title":"Sorex (Sorex) tundrensis Merriam 1900","year":2005,"lang":"it","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sorex; Subgenus; Shrew; Araneus; Taxonomy (biology)","score_opus":0.030558507514497367,"score_gpt":0.26262068691891716,"score_spread":0.23206217940441978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930390737","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":[0.5401736,0.02246221,0.003192201,0.00091466814,0.00064287434,0.0002249577,0.006035129,0.00070181553,0.42565253],"genre_scores_gemma":[0.94016385,0.0034762977,0.0023575933,0.00048883713,0.00013986265,0.00005573787,0.0024770498,0.000049837567,0.050790925],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9998758,0.0000121083,0.000010678467,0.000044420693,0.000034134842,0.000022889639],"domain_scores_gemma":[0.9998708,0.000006913926,0.00006997524,0.000009954534,0.00003109708,0.000011267559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014072518,0.0006219237,0.00023829642,0.00089654245,0.0010527701,0.00045027435,0.0004244792,0.00034046033,0.010558397],"category_scores_gemma":[0.0002632712,0.0001993701,0.00017755017,0.00052262394,0.00036573867,0.0006914348,0.0005786485,0.0003444323,0.0038252738],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005801377,0.0001031902,0.1464486,0.0008274096,0.00015517847,0.0028339229,0.0034390714,0.00068602525,0.036045097,0.0043041934,0.03971841,0.7648587],"study_design_scores_gemma":[0.00003600616,0.00012786358,0.51273614,0.00045198595,0.000056841483,0.00307935,0.0014945937,0.00019226952,0.0022887695,0.00043140753,0.47908866,0.000016128739],"about_ca_topic_score_codex":0.021449303,"about_ca_topic_score_gemma":0.05176337,"teacher_disagreement_score":0.021449303,"about_ca_system_score_codex":0.0010103495,"about_ca_system_score_gemma":0.00026808484,"threshold_uncertainty_score":0.04264891},"labels":[],"label_agreement":null},{"id":"W6930792339","doi":"10.5281/zenodo.15021745","title":"MNBC-ME identifies mobile elements and putative host species from metagenomic sequences","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Public Health Agency of Canada; University of Manitoba; Agriculture and Agri-Food Canada; Canadian Food Inspection Agency","funders":"Public Health Agency of Canada; Agriculture and Agri-Food Canada; Canadian Food Inspection Agency; University of Lethbridge","keywords":"Nanopore sequencing; Sequence (biology); Metagenomics; Host (biology); Test (biology); Plasmid; Genome","score_opus":0.02647726404180046,"score_gpt":0.2820661137010654,"score_spread":0.2555888496592649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930792339","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.077229366,0.001805709,0.07107955,0.0006513892,0.0007229507,0.00060737575,0.8019025,0.031233408,0.014767768],"genre_scores_gemma":[0.09239415,0.0009149073,0.19661175,0.00050786097,0.00011001175,0.001088987,0.68568933,0.013519321,0.009163748],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992193,0.00007199673,0.00006189412,0.00033344328,0.0001820655,0.0001313193],"domain_scores_gemma":[0.99906296,0.0004325086,0.00010849047,0.00016189193,0.00012875308,0.00010535895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014172035,0.0015445909,0.0020940485,0.002548521,0.0013560583,0.0026016023,0.001460694,0.0015106559,0.051662892],"category_scores_gemma":[0.0048517366,0.0011454086,0.001659221,0.0023868992,0.00041241897,0.0017250823,0.0017940378,0.0021331466,0.03175085],"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":[0.0052431445,0.000423902,0.025225978,0.011850366,0.0008402006,0.0008429076,0.0016957715,0.004815817,0.62481034,0.008913124,0.21560211,0.099736355],"study_design_scores_gemma":[0.001012152,0.0008714481,0.05038571,0.002368165,0.00082837825,0.0018088381,0.0014349852,0.04822699,0.35876694,0.008206727,0.5256282,0.0004614299],"about_ca_topic_score_codex":0.0024753956,"about_ca_topic_score_gemma":0.00604696,"teacher_disagreement_score":0.051662892,"about_ca_system_score_codex":0.0011021874,"about_ca_system_score_gemma":0.0012587173,"threshold_uncertainty_score":0.17282957},"labels":[],"label_agreement":null},{"id":"W6931877156","doi":"10.5683/sp2/lliojw","title":"Detection of HCOOH, CH3OH, CO, HCN and C2H6 in Wildfire Plumes Transported over Toronto Using Ground-based FTIR Measurements from 2002-2018","year":2020,"lang":"en","type":"dataset","venue":"Borealis","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Plume; Fourier transform infrared spectroscopy; Atmosphere (unit); Panache","score_opus":0.039334589489666186,"score_gpt":0.2931841688641394,"score_spread":0.2538495793744732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931877156","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005981147,0.00006993673,0.000044410586,0.000036179972,0.000014290751,0.000006693917,0.9988193,0.00013097937,0.00028007766],"genre_scores_gemma":[0.0019334012,0.000059231872,0.00023833415,0.00002590049,0.0000056084987,0.0000240141,0.99737334,0.00001659936,0.00032344644],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.999597,0.000034719134,0.00003703665,0.00012043717,0.00011989432,0.00009096156],"domain_scores_gemma":[0.9990226,0.00015499892,0.00012473187,0.00015086356,0.0004192525,0.00012765256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005460706,0.0019234774,0.0007817291,0.0019881963,0.00069266575,0.0009943576,0.0015798993,0.0011925305,0.014503688],"category_scores_gemma":[0.0019293184,0.00043080444,0.0009860802,0.002682122,0.00041168105,0.00049658,0.0011127669,0.0007978239,0.010141411],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017419581,0.000035329864,0.015479903,0.002035312,0.00019022344,0.00012862547,0.00013406528,0.0014158617,0.00134182,0.00048404455,0.9742433,0.004337415],"study_design_scores_gemma":[0.000371454,0.000057967496,0.17757626,0.00089295517,0.00030624546,0.00027403605,0.000704631,0.0034472817,0.0032883522,0.0012093008,0.81174505,0.00012649836],"about_ca_topic_score_codex":0.45771328,"about_ca_topic_score_gemma":0.6592893,"teacher_disagreement_score":0.54228675,"about_ca_system_score_codex":0.0038006774,"about_ca_system_score_gemma":0.0035836909,"threshold_uncertainty_score":0.9100982},"labels":[],"label_agreement":null},{"id":"W6932167067","doi":"10.5683/sp3/uticpy","title":"Characteristics of Natural Organic Matter in Forested Canadian Water Sources by LC-OCD","year":2023,"lang":"en","type":"dataset","venue":"Borealis","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"STREAMS; Total organic carbon; Hydrology (agriculture); Water source; Carbon fibers; Natural (archaeology); Organic matter","score_opus":0.007194196770495527,"score_gpt":0.23598637111035842,"score_spread":0.2287921743398629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6932167067","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042752186,0.00019342416,0.00009289731,0.00007216107,0.000009264626,0.000014296185,0.994518,0.00010193272,0.00072277634],"genre_scores_gemma":[0.0060642976,0.00013294307,0.0003604318,0.000041170417,0.0000037025607,0.000029374103,0.992874,0.000013537283,0.00048046687],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991418,0.00003680193,0.000056432822,0.00020832638,0.0003442252,0.00021238645],"domain_scores_gemma":[0.9976064,0.00018057339,0.00021366517,0.0002480884,0.0014994899,0.00025175864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006329236,0.001502915,0.00080633984,0.003571427,0.0014275836,0.0014536427,0.0022187287,0.0008825273,0.0043888157],"category_scores_gemma":[0.0026998639,0.0003738751,0.00089880044,0.010132649,0.00051637244,0.0003952412,0.0009396587,0.0008790306,0.0031395527],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032009612,0.00011424302,0.11811035,0.0013103974,0.0003531385,0.00024090565,0.00023704144,0.0035064423,0.0013340779,0.0012633321,0.8550886,0.018121403],"study_design_scores_gemma":[0.00020736238,0.00004031165,0.38894925,0.00059409783,0.00020746731,0.00022752039,0.000579865,0.0050664567,0.002334861,0.0009797231,0.6006557,0.0001574996],"about_ca_topic_score_codex":0.9609186,"about_ca_topic_score_gemma":0.97981185,"teacher_disagreement_score":0.039081395,"about_ca_system_score_codex":0.011549867,"about_ca_system_score_gemma":0.017460333,"threshold_uncertainty_score":0.083800495},"labels":[],"label_agreement":null},{"id":"W6939089964","doi":"10.6084/m9.figshare.14093323.v3","title":"Vision 2035 Public Health Surveillance in India - A White Paper","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public health; Public health surveillance; Health surveillance; White paper; White (mutation); Disease surveillance","score_opus":0.03859671617043582,"score_gpt":0.3140568465513314,"score_spread":0.2754601303808956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939089964","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052545713,0.014836976,0.018170848,0.68393767,0.057195675,0.0010157737,0.032955796,0.0041993116,0.18243338],"genre_scores_gemma":[0.14645828,0.026388615,0.11198993,0.4269905,0.010358142,0.0028509388,0.07088008,0.0017164961,0.20236711],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98830146,0.0027710418,0.0007845035,0.00070178864,0.0049527567,0.0024883605],"domain_scores_gemma":[0.977358,0.003769833,0.0012131077,0.0017658349,0.007841152,0.008052141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026310481,0.0017786295,0.0007916654,0.0027683778,0.0024007496,0.012044863,0.004739582,0.006523056,0.009343475],"category_scores_gemma":[0.019623812,0.0008970624,0.0021153097,0.0024006686,0.0028007259,0.00471222,0.0073868856,0.008063791,0.005086442],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000061619176,0.0000730557,0.0015556475,0.00040073178,0.00004664163,0.00012367699,0.0003666959,0.0005517511,0.00030167238,0.04468875,0.9128995,0.038930215],"study_design_scores_gemma":[0.000029518354,0.00006920746,0.0061824317,0.0006979748,0.00003937194,0.000086105036,0.0006116786,0.00046126675,0.00054129784,0.009611475,0.9816228,0.00004696994],"about_ca_topic_score_codex":0.1235821,"about_ca_topic_score_gemma":0.09983438,"teacher_disagreement_score":0.1235821,"about_ca_system_score_codex":0.009783737,"about_ca_system_score_gemma":0.08886791,"threshold_uncertainty_score":0.24572551},"labels":[],"label_agreement":null},{"id":"W6939292948","doi":"10.60692/j5j46-wgw27","title":"Food allergy: What are people looking for? An infodemiology study","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Relation (database); Food allergens; Value (mathematics); Service (business); Public access","score_opus":0.06321903444212162,"score_gpt":0.27835945141248136,"score_spread":0.21514041697035974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939292948","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":[0.9835381,0.009151086,0.000067521025,0.0014407687,0.000039152816,0.00015267964,0.0024404384,0.0000044421563,0.0031659119],"genre_scores_gemma":[0.99090767,0.0053643,0.00014960658,0.0013454157,0.00008559656,0.0002563447,0.0011256093,0.000006555644,0.0007589813],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986994,0.00032115783,0.00032023725,0.0001796889,0.00023778774,0.0002417206],"domain_scores_gemma":[0.99630547,0.0011445328,0.0015148662,0.00010894583,0.00041692884,0.00050925644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016006457,0.0002530864,0.00095211505,0.0029951192,0.0014374857,0.0013771076,0.00052639603,0.001101301,0.004858856],"category_scores_gemma":[0.0059131877,0.00047043993,0.0012546082,0.006533917,0.0005953756,0.003019548,0.0011112886,0.0011800363,0.00092270924],"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":[0.00046304608,0.0006090147,0.9649657,0.0026370944,0.00029362348,0.00077329425,0.012249489,0.00002429858,0.00012868237,0.0003486988,0.0042912886,0.013215648],"study_design_scores_gemma":[0.00008015358,0.00059484877,0.962625,0.0014400915,0.00042381758,0.0010563245,0.027683623,0.00011601941,0.00004874355,0.00021982564,0.0056729475,0.000038513164],"about_ca_topic_score_codex":0.021401938,"about_ca_topic_score_gemma":0.021210685,"teacher_disagreement_score":0.021401938,"about_ca_system_score_codex":0.0013263639,"about_ca_system_score_gemma":0.0014714411,"threshold_uncertainty_score":0.042554736},"labels":[],"label_agreement":null},{"id":"W6939504160","doi":"10.6084/m9.figshare.17273274.v1","title":"Additional file 1 of Relationship between internet research data of oral neoplasms and public health programs in the European Union","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"European union; The Internet; Public health; Descriptive research; Public access; Descriptive statistics; MEDLINE","score_opus":0.4964001349320217,"score_gpt":0.4118059046256584,"score_spread":0.08459423030636326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939504160","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016594294,0.000034775672,0.00007830954,0.000069717724,0.000011399539,0.000105080755,0.9987072,0.00004659316,0.00078088976],"genre_scores_gemma":[0.011592318,0.00047424814,0.0023789622,0.000584504,0.00011070991,0.0048460835,0.96806526,0.0003434949,0.011604402],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99869233,0.0002876221,0.0004023033,0.00024007833,0.00023383355,0.00014384033],"domain_scores_gemma":[0.9585802,0.033082422,0.0035311403,0.0008834521,0.0033404883,0.0005823513],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018933979,0.0007518489,0.0013020452,0.0053100944,0.0006332331,0.0012538831,0.0011178295,0.0010212113,0.8004426],"category_scores_gemma":[0.038673606,0.00046678956,0.0009802618,0.008608566,0.00022666182,0.0016589061,0.0011017979,0.0007574947,0.084304534],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000312673,0.00008052828,0.004125804,0.009597983,0.00010214886,0.000052901367,0.000111895584,0.00029479183,0.00004488636,0.0012535339,0.9745413,0.009481596],"study_design_scores_gemma":[0.0052670916,0.00034084613,0.086286925,0.018572494,0.00058332784,0.0006264318,0.0013565832,0.00164631,0.0004881711,0.0089259,0.87573785,0.0001679928],"about_ca_topic_score_codex":0.012975125,"about_ca_topic_score_gemma":0.017662093,"teacher_disagreement_score":0.8004426,"about_ca_system_score_codex":0.0016225019,"about_ca_system_score_gemma":0.0024106542,"threshold_uncertainty_score":0.28464437},"labels":[],"label_agreement":null},{"id":"W6939512343","doi":"10.6084/m9.figshare.20438277.v1","title":"Additional file 3 of The role of geographic information system and global positioning system in dementia care and research: a scoping review","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Geographic information system; Information system; Dementia; Global Positioning System; Positioning system; Interoperability; Key (lock)","score_opus":0.02377782592312441,"score_gpt":0.2880602375878256,"score_spread":0.26428241166470123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939512343","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007222398,0.0001456347,0.00018258549,0.00017009718,0.000031459145,0.00034275485,0.9980484,0.00010570987,0.0009010902],"genre_scores_gemma":[0.0065660123,0.0021744538,0.010583862,0.001554774,0.00023040573,0.023004496,0.9350949,0.00080192083,0.019989347],"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","domain_scores_codex":[0.9974076,0.0005309484,0.0010821562,0.00036256597,0.00042579407,0.00019097814],"domain_scores_gemma":[0.9144702,0.069913544,0.004967243,0.001635732,0.008422509,0.0005908898],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0050059003,0.0015729878,0.002439132,0.009585574,0.0009111272,0.002912455,0.0021349275,0.0018474453,0.82232606],"category_scores_gemma":[0.07595003,0.00089536863,0.0031111608,0.015768323,0.0003568789,0.003323933,0.0017413127,0.0011479427,0.0769712],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045512526,0.00003980236,0.0014052247,0.11416473,0.00031160875,0.00011279319,0.00020679434,0.00037728745,0.00013477748,0.0022343076,0.86681545,0.01374221],"study_design_scores_gemma":[0.004589812,0.00014003797,0.013304754,0.06284934,0.0015317564,0.00034175595,0.00065956084,0.0007269284,0.00060884154,0.010638897,0.9044344,0.00017406329],"about_ca_topic_score_codex":0.01333512,"about_ca_topic_score_gemma":0.019216573,"teacher_disagreement_score":0.82232606,"about_ca_system_score_codex":0.002804376,"about_ca_system_score_gemma":0.005991971,"threshold_uncertainty_score":0.25343025},"labels":[],"label_agreement":null},{"id":"W6939614275","doi":"10.6084/m9.figshare.13347093.v1","title":"The association between COVID-19 cases and deaths and web-based public inquiries","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Intraclass correlation; Public interest; Public health; Reliability (semiconductor); Disease; Association (psychology)","score_opus":0.11323212828060686,"score_gpt":0.3244028874560292,"score_spread":0.21117075917542233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939614275","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":[0.9809987,0.0032370144,0.00033933535,0.00057828653,0.000050334867,0.000055423934,0.010256617,0.00003664199,0.004447685],"genre_scores_gemma":[0.99371076,0.0009381809,0.00026095385,0.00009113775,0.00005120997,0.00003363618,0.0042427154,0.00001247281,0.0006589574],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978218,0.00061289873,0.00048915925,0.00034017765,0.00047322092,0.00026283407],"domain_scores_gemma":[0.98032206,0.009320018,0.007613262,0.00057631935,0.0014324527,0.00073587557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018186806,0.00019902764,0.00034108345,0.0026514134,0.00020733595,0.0010844861,0.0004209292,0.00042373184,0.00432645],"category_scores_gemma":[0.018962028,0.00017260975,0.0007285706,0.00272735,0.00033168946,0.0013934658,0.0009129551,0.000776492,0.0006968596],"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":[0.00022187049,0.000025736506,0.9924567,0.0002713573,0.00013859403,0.00011651449,0.00023906719,0.00013382449,0.000108026325,0.00010645828,0.0009496426,0.005232163],"study_design_scores_gemma":[0.0000070401857,0.00006419158,0.99615496,0.00014809392,0.00008268996,0.00046077877,0.0006196218,0.000674194,0.00012377782,0.00009202221,0.0015612217,0.000011423295],"about_ca_topic_score_codex":0.006546729,"about_ca_topic_score_gemma":0.008357173,"teacher_disagreement_score":0.006546729,"about_ca_system_score_codex":0.00036052577,"about_ca_system_score_gemma":0.00040465523,"threshold_uncertainty_score":0.014473438},"labels":[],"label_agreement":null},{"id":"W6944704363","doi":"10.20381/ruor-27230","title":"The Association Between Measles Cases and Migration/Settlement Patterns in Ontario","year":2021,"lang":"en","type":"article","venue":"uO Research (University of Ottawa)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Population; Transmission (telecommunications); Vaccination schedule; Measles","score_opus":0.06308036015128733,"score_gpt":0.3104462081764031,"score_spread":0.2473658480251158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6944704363","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":[0.9914858,0.00018795578,0.00011023154,0.00023648817,0.000008123379,0.000031299267,0.0051549883,0.000011254258,0.0027739136],"genre_scores_gemma":[0.99697495,0.00016919387,0.00015826484,0.000016718612,0.0000032544121,0.000015412219,0.0013542075,0.0000052741466,0.00130283],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99940395,0.000051344712,0.000048777612,0.000118425705,0.00019418693,0.0001833963],"domain_scores_gemma":[0.99833375,0.00013511394,0.0004977212,0.00008181955,0.00062175415,0.00032996843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035218216,0.00021249797,0.00025748936,0.0013925398,0.0017879299,0.0008670915,0.0007375143,0.00020566612,0.0028180052],"category_scores_gemma":[0.0021072659,0.00022784031,0.00042522865,0.003714648,0.00065400754,0.00027754708,0.0009497874,0.00024666262,0.00021451576],"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":[0.00008202155,0.000009995893,0.9925283,0.00005178361,0.000044353525,0.000107973254,0.0017542384,0.00020781012,0.00033701042,0.00016577075,0.0014089708,0.003301896],"study_design_scores_gemma":[0.0000034994316,0.0000075491835,0.9972459,0.000017268392,0.000012487661,0.00003125882,0.0014814214,0.00020572137,0.000057978967,0.0000284728,0.0009033355,0.0000051089246],"about_ca_topic_score_codex":0.9848567,"about_ca_topic_score_gemma":0.9943046,"teacher_disagreement_score":0.019919546,"about_ca_system_score_codex":0.019919546,"about_ca_system_score_gemma":0.017966092,"threshold_uncertainty_score":0.14452708},"labels":[],"label_agreement":null},{"id":"W6945280278","doi":"10.24397/pangloss-0007455","title":"Liste de mots en rục lue par une locutrice née à Mò O Ồ Ồ en 1975","year":2021,"lang":"fr","type":"article","venue":"Langues et civilisations à tradition orale","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Voice; Frame (networking); Southeast asia; Sound (geography); Feature (linguistics)","score_opus":0.022505066601887065,"score_gpt":0.2916372764282135,"score_spread":0.26913220982632646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6945280278","genre_codex":"dataset","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":[0.069720715,0.008717047,0.0043128836,0.0036527852,0.0046684607,0.00078441267,0.6267835,0.001907424,0.2794527],"genre_scores_gemma":[0.22064884,0.0050569233,0.014216821,0.0013127861,0.0011489297,0.0011251022,0.49048644,0.0012862893,0.26471788],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997205,0.000055056058,0.000041229345,0.000066274886,0.00007944594,0.00003743674],"domain_scores_gemma":[0.99817467,0.0007849573,0.0001405098,0.000060025384,0.0007038896,0.00013599338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037231937,0.00092345907,0.0004383806,0.0024361694,0.0014489499,0.0009553477,0.00045663526,0.0004720616,0.22309743],"category_scores_gemma":[0.0025293494,0.0002243248,0.00019961965,0.0016832872,0.00033117473,0.0008025978,0.0006802068,0.00056547666,0.054936174],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007026155,0.00008380924,0.009167133,0.0026194637,0.000032183958,0.0009136485,0.004058825,0.00012767263,0.012934234,0.005706112,0.84174037,0.121913865],"study_design_scores_gemma":[0.000029592078,0.00010991111,0.04385313,0.00045609835,0.000019804964,0.00076532783,0.0034009428,0.00019778396,0.0026141105,0.000375345,0.9481476,0.000030352972],"about_ca_topic_score_codex":0.030888941,"about_ca_topic_score_gemma":0.038452253,"teacher_disagreement_score":0.22309743,"about_ca_system_score_codex":0.0009798014,"about_ca_system_score_gemma":0.0013058201,"threshold_uncertainty_score":0.74633527},"labels":[],"label_agreement":null},{"id":"W6945389341","doi":"10.24385/lincoln.25162067.v2","title":"The Typographic Dante","year":2020,"lang":"en","type":"other","venue":"Lincoln repository","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Craft; Divine comedy; Purgatory; Poetry; Cabinet (room)","score_opus":0.005531394829693773,"score_gpt":0.23488438593083652,"score_spread":0.22935299110114274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6945389341","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059447,0.0023229634,0.0073643895,0.008908265,0.004722122,0.00022635984,0.0036671348,0.00047075332,0.9663734],"genre_scores_gemma":[0.11181184,0.0063093984,0.016475808,0.005703919,0.0012764415,0.001433097,0.006750485,0.002991075,0.8472479],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969093,0.0014281365,0.00018527985,0.00037311026,0.00088657846,0.00021762165],"domain_scores_gemma":[0.99507797,0.001979695,0.00025682477,0.0011107186,0.0011971291,0.00037757834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025228122,0.00077584625,0.00049205736,0.006357227,0.0070336154,0.010690666,0.0011759794,0.0013991619,0.0923534],"category_scores_gemma":[0.009767513,0.00032663377,0.00028756546,0.009250049,0.0049935034,0.0054528965,0.0051195864,0.0023780514,0.021537414],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070911745,0.00002240927,0.0013103175,0.00046694977,0.000004607866,0.00036109914,0.049503084,0.00013551819,0.0005537059,0.24380258,0.6292687,0.074500166],"study_design_scores_gemma":[9.779985e-7,0.0000017051749,0.000108468485,0.00014336423,5.4957246e-7,0.00007544159,0.0039783367,0.000022517877,0.00006475078,0.0016533423,0.99394774,0.0000029131752],"about_ca_topic_score_codex":0.014338756,"about_ca_topic_score_gemma":0.031886574,"teacher_disagreement_score":0.0923534,"about_ca_system_score_codex":0.004729103,"about_ca_system_score_gemma":0.0039283815,"threshold_uncertainty_score":0.30895293},"labels":[],"label_agreement":null},{"id":"W6949618176","doi":"10.5281/zenodo.15176332","title":"Enhancing Early Detection of Chronic Diseases in Underserved Canadian Populations","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Seneca Polytechnic","funders":"","keywords":"Population; Epidemiology; Disease; MEDLINE; Chronic disease","score_opus":0.029444427166920174,"score_gpt":0.2661574857916258,"score_spread":0.23671305862470562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6949618176","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":[0.71168643,0.016271634,0.016835336,0.06802036,0.0013973084,0.002527288,0.09407152,0.0011879625,0.08800222],"genre_scores_gemma":[0.95092666,0.006054674,0.01957015,0.0062320866,0.00024251538,0.000497745,0.01051241,0.00008033197,0.005883465],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976413,0.00043936394,0.00013190947,0.00024889884,0.0007824121,0.00075606455],"domain_scores_gemma":[0.98769975,0.0016458724,0.0007678627,0.00028383487,0.0072119203,0.0023906992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031696784,0.0005786276,0.00047115775,0.0032519735,0.002148753,0.002048697,0.0019264366,0.00065512385,0.004785776],"category_scores_gemma":[0.019156076,0.0002923562,0.0008852392,0.0028400982,0.00035958065,0.00077338197,0.0025054275,0.0011435002,0.0005388203],"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":[0.00022471287,0.0003785593,0.7157217,0.00089074136,0.00021377488,0.00015765431,0.0021117816,0.0014480591,0.0008430639,0.0018111479,0.06748942,0.20870936],"study_design_scores_gemma":[0.00010278367,0.0002051276,0.9302015,0.0014701685,0.00043947538,0.00015854962,0.004718682,0.0070322263,0.0015092633,0.0015627409,0.05247424,0.00012529074],"about_ca_topic_score_codex":0.9823085,"about_ca_topic_score_gemma":0.9867337,"teacher_disagreement_score":0.019974303,"about_ca_system_score_codex":0.019974303,"about_ca_system_score_gemma":0.07924212,"threshold_uncertainty_score":0.1449244},"labels":[],"label_agreement":null},{"id":"W6957422181","doi":"10.60692/7tzhk-yyz43","title":"A Smartphone-Based Application Improves the Accuracy, Completeness, and Timeliness of Cattle Disease Reporting and Surveillance in Ethiopia","year":2018,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Disease; Outbreak; Disease surveillance; Disease monitoring; Disease transmission; Cattle Diseases","score_opus":0.029976870554627076,"score_gpt":0.2632110410479762,"score_spread":0.23323417049334913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6957422181","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":[0.99117893,0.0004659989,0.00438016,0.00032004245,0.000058889495,0.00012273157,0.00041453302,0.0002349623,0.0028237307],"genre_scores_gemma":[0.9904204,0.00020363543,0.008611069,0.000075767355,0.000016221897,0.000022498518,0.00015562918,0.000006023784,0.00048876903],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978536,0.0012806833,0.00020503481,0.00021049666,0.0003008412,0.0001493596],"domain_scores_gemma":[0.99506676,0.0028696628,0.0005473067,0.0002830375,0.0010304829,0.0002027173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031468542,0.00038012708,0.00029244012,0.00072164705,0.00022795546,0.0010776175,0.00040111147,0.000372591,0.0011354003],"category_scores_gemma":[0.008078084,0.00016020527,0.0002761858,0.0004908735,0.00016378953,0.0008158156,0.0006017023,0.00024426426,0.00031146395],"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":[0.0029230902,0.0010983971,0.5504401,0.0010322812,0.00034229032,0.0006485125,0.0019799764,0.010027408,0.022634838,0.0005665537,0.004122912,0.4041837],"study_design_scores_gemma":[0.00026423603,0.0096452,0.7843203,0.00072768104,0.00076789997,0.0019240858,0.0061303102,0.13513875,0.03896341,0.000810761,0.021089345,0.00021799037],"about_ca_topic_score_codex":0.0057562334,"about_ca_topic_score_gemma":0.006975439,"teacher_disagreement_score":0.0057562334,"about_ca_system_score_codex":0.0004389564,"about_ca_system_score_gemma":0.0006493652,"threshold_uncertainty_score":0.016642392},"labels":[],"label_agreement":null},{"id":"W6958117113","doi":"10.60692/7m7bw-xc257","title":"Guidelines for creating framework data for GIS analysis in low‐ and middle‐income countries","year":2016,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"","keywords":"Key (lock); Mandate; Open data; Data sharing; Geographic information system; Interoperability; AM/FM/GIS; Data integration; Data mapping","score_opus":0.08675251130464066,"score_gpt":0.315394129702123,"score_spread":0.22864161839748232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958117113","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":[0.0048363125,0.003603982,0.6836959,0.026725203,0.0031940565,0.053347014,0.07761143,0.0132977,0.13368846],"genre_scores_gemma":[0.0056304494,0.0017852826,0.93328726,0.0014748732,0.0001478059,0.029675335,0.019478494,0.001874946,0.006645616],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.92674106,0.043568883,0.017577108,0.0016258629,0.008878651,0.0016085003],"domain_scores_gemma":[0.789637,0.09529162,0.010683451,0.025703374,0.07342702,0.005257478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.088785395,0.0022494798,0.0019258498,0.015297187,0.0025170338,0.00937851,0.009067621,0.0041253082,0.04121887],"category_scores_gemma":[0.20647788,0.003022784,0.004399215,0.016268248,0.0031374383,0.007838363,0.007413417,0.0071797874,0.02407336],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023362815,0.00031392893,0.0055591073,0.006107748,0.00015443012,0.001049606,0.008570865,0.0052701035,0.0015433736,0.06769035,0.6850875,0.21841931],"study_design_scores_gemma":[0.00016208451,0.000055342945,0.0036943252,0.0100140525,0.00006253034,0.00043167625,0.0040125083,0.0029498227,0.0014065222,0.033512708,0.9435456,0.00015289684],"about_ca_topic_score_codex":0.019369118,"about_ca_topic_score_gemma":0.029312808,"teacher_disagreement_score":0.088785395,"about_ca_system_score_codex":0.004295458,"about_ca_system_score_gemma":0.020756086,"threshold_uncertainty_score":0.4695477},"labels":[],"label_agreement":null},{"id":"W6958240696","doi":"10.6084/m9.figshare.26588502","title":"Additional file 1 of ATQ: alert time quality, an evaluation metric for assessing timely epidemic detection models within a school absenteeism-based surveillance system","year":2024,"lang":"en","type":"article","venue":"Open MIND","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Metric (unit); Data collection; Metric system; Key (lock); Scheme (mathematics)","score_opus":0.10706480099469536,"score_gpt":0.3986724691417562,"score_spread":0.29160766814706085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958240696","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005488686,0.000010778385,0.00085263967,0.00015368465,0.000015917925,0.00014372838,0.9967319,0.00037264914,0.0011698883],"genre_scores_gemma":[0.043604452,0.00010462428,0.012180158,0.0005819296,0.00013181324,0.0031018003,0.9303171,0.0011393738,0.008838719],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99815565,0.00055171794,0.00035589244,0.00029345142,0.0005121162,0.00013115277],"domain_scores_gemma":[0.89353526,0.09182127,0.003715244,0.0027760821,0.007247546,0.00090462196],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004109793,0.0008134863,0.0006603185,0.0021094268,0.00061620405,0.0014835374,0.0014676376,0.00096621516,0.7063742],"category_scores_gemma":[0.08392128,0.00040287658,0.00071923,0.0033371989,0.00025270687,0.0017116707,0.0008682139,0.00077893544,0.0794698],"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":[0.00042445943,0.00016021979,0.007897064,0.0018557978,0.00006613241,0.000037974907,0.000097483135,0.0021904712,0.000115029936,0.0012398207,0.97110975,0.014805924],"study_design_scores_gemma":[0.010286714,0.0011791027,0.09223397,0.0037973723,0.00051951833,0.0007035952,0.0015384471,0.036785677,0.0033254614,0.02395347,0.8252904,0.00038630457],"about_ca_topic_score_codex":0.009429179,"about_ca_topic_score_gemma":0.012089156,"teacher_disagreement_score":0.9958902,"about_ca_system_score_codex":0.0011718392,"about_ca_system_score_gemma":0.0019482716,"threshold_uncertainty_score":0.4188215},"labels":[],"label_agreement":null},{"id":"W6958287144","doi":"10.60692/vf975-avv65","title":"The state of integrated disease surveillance in seven countries: a synthesis report","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University; Public Health Agency of Canada","funders":"","keywords":"Conceptualization; CLARITY; Workforce; Disease surveillance; Focus group; Public health surveillance; Data sharing; Public health; Qualitative research","score_opus":0.019384456146401413,"score_gpt":0.23682841882154218,"score_spread":0.21744396267514077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958287144","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":[0.16251302,0.7533992,0.016119273,0.026488658,0.0015767874,0.004376533,0.008245725,0.00013236656,0.02714838],"genre_scores_gemma":[0.66352403,0.29833987,0.025691438,0.0028676235,0.00014326772,0.005505068,0.0031456861,0.00007113971,0.00071189523],"study_design_codex":"qualitative","study_design_gemma":"systematic_review","domain_scores_codex":[0.95375097,0.03019305,0.008424314,0.0022236202,0.0034936497,0.0019143749],"domain_scores_gemma":[0.8980072,0.07702896,0.0070761056,0.00356597,0.013059161,0.0012626267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.071142234,0.00089410477,0.0018170088,0.011074338,0.0022440804,0.010473686,0.0014656222,0.0013798604,0.0024435867],"category_scores_gemma":[0.08940585,0.0011428059,0.001676694,0.02673575,0.003768282,0.009824539,0.008113561,0.0026961365,0.00018316421],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028840633,0.00008470409,0.042168945,0.17445676,0.0014345588,0.000901657,0.3859125,0.0015249188,0.00073482544,0.05613348,0.011507667,0.3248515],"study_design_scores_gemma":[0.00007412644,0.0002575044,0.04608019,0.24796766,0.0017480532,0.00067771884,0.51438624,0.0006987673,0.0011072634,0.0072084307,0.17968191,0.00011215581],"about_ca_topic_score_codex":0.021525992,"about_ca_topic_score_gemma":0.018014763,"teacher_disagreement_score":0.071142234,"about_ca_system_score_codex":0.023635607,"about_ca_system_score_gemma":0.034161765,"threshold_uncertainty_score":0.3762406},"labels":[],"label_agreement":null},{"id":"W6958332922","doi":"10.6084/m9.figshare.20356491","title":"Additional file 1 of Geospatial analysis of cesarean section in Iran (2016–2020): exploring clustered patterns and measuring spatial interactions of available health services","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Table (database); Geospatial analysis; Section (typography); Poisson distribution; Spatial analysis; Geocoding; Poisson regression","score_opus":0.0698070389670261,"score_gpt":0.265266270469816,"score_spread":0.19545923150278988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958332922","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041604435,0.0000091039765,0.0000878219,0.00003770601,0.000006422605,0.000030304247,0.99895334,0.00006035606,0.00039902757],"genre_scores_gemma":[0.01701698,0.00009907749,0.002024222,0.00011213992,0.00003362905,0.0009383824,0.9768659,0.0001383133,0.0027713093],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9995004,0.0000892237,0.00011218366,0.000108301196,0.00010827868,0.000081565464],"domain_scores_gemma":[0.9955407,0.0021617927,0.0006617874,0.00035644532,0.0011027928,0.00017656166],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008458791,0.00068394875,0.00060298137,0.0023025658,0.0004330775,0.00084920524,0.0011580278,0.00048802645,0.45795754],"category_scores_gemma":[0.012698276,0.00027446015,0.0006932122,0.005354167,0.00013164735,0.0009263579,0.00083506684,0.00050818094,0.03664145],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015001056,0.000049685907,0.020542761,0.0015063862,0.00008287073,0.00006842969,0.0001067932,0.0009733225,0.000057046775,0.00066218886,0.96620154,0.009599078],"study_design_scores_gemma":[0.0021992354,0.0002381081,0.2715028,0.0034428264,0.0004710891,0.00053033326,0.0021845591,0.0065940516,0.00087763945,0.005428942,0.7063613,0.00016914128],"about_ca_topic_score_codex":0.03588883,"about_ca_topic_score_gemma":0.041440412,"teacher_disagreement_score":0.45795754,"about_ca_system_score_codex":0.0008195536,"about_ca_system_score_gemma":0.0017104205,"threshold_uncertainty_score":0.7731578},"labels":[],"label_agreement":null},{"id":"W6958429429","doi":"10.6084/m9.figshare.c.3610424_d2.v1","title":"Additional file 1: of Small-area spatio-temporal analyses of bladder and kidney cancer risk in Nova Scotia, Canada","year":2016,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Kidney cancer; Bladder cancer; Nova (rocket); Kidney disease; Kidney; Cancer; Nephrectomy","score_opus":0.046302940712154315,"score_gpt":0.2833655153377067,"score_spread":0.23706257462555239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958429429","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00032987614,0.000016635207,0.00008193377,0.00003603544,0.000010564516,0.000040873012,0.99872106,0.00004540807,0.0007176503],"genre_scores_gemma":[0.028419372,0.00026792593,0.0035065298,0.00023821306,0.000031950218,0.001140081,0.950679,0.00036630075,0.015350577],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99915695,0.00008149468,0.00011945187,0.00016376164,0.00025344745,0.00022481671],"domain_scores_gemma":[0.98909307,0.0037032277,0.0005558784,0.00086518336,0.0052466644,0.0005360314],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012052923,0.0008385554,0.0009840693,0.0040780357,0.0019025111,0.0015305054,0.0019926955,0.0005150744,0.40141416],"category_scores_gemma":[0.017381852,0.0006040602,0.0016170201,0.0094356965,0.00037991896,0.0006382439,0.0010192086,0.00067081803,0.019364312],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014558675,0.000020791695,0.028321458,0.001632801,0.00017216212,0.000087741566,0.0002518365,0.0009768831,0.00009531645,0.0007407974,0.9607843,0.0067702513],"study_design_scores_gemma":[0.0012485903,0.00006839385,0.4444378,0.004375433,0.000634395,0.00034382194,0.0020138528,0.0028223067,0.00079806015,0.0019331889,0.54111636,0.00020775475],"about_ca_topic_score_codex":0.9718007,"about_ca_topic_score_gemma":0.9729205,"teacher_disagreement_score":0.40141416,"about_ca_system_score_codex":0.010206911,"about_ca_system_score_gemma":0.028834311,"threshold_uncertainty_score":0.8538101},"labels":[],"label_agreement":null},{"id":"W6958566725","doi":"10.6084/m9.figshare.c.3637823_d1","title":"Additional file 1: of Evaluation of self-swabbing coupled with a telephone health helpline as an adjunct tool for surveillance of influenza viruses in Ontario","year":2016,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Adjunct; Helpline; Hotline; Masking (illustration); Influenza A virus subtype H5N1; Influenzavirus B; MEDLINE; Epidemiological surveillance","score_opus":0.056953523073824834,"score_gpt":0.3219852341828974,"score_spread":0.26503171110907253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958566725","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011106452,0.00002005088,0.00017812403,0.00013919795,0.00001985398,0.00072415464,0.9935592,0.00011017115,0.00413858],"genre_scores_gemma":[0.09410247,0.00038239945,0.0074835517,0.0008159858,0.00010840494,0.016108004,0.8463499,0.0007306958,0.033918574],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99763286,0.00044190531,0.00035843486,0.0002619411,0.00095888204,0.00034589137],"domain_scores_gemma":[0.9650344,0.014804758,0.0025229377,0.0016639445,0.014571584,0.0014023521],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0037052347,0.0009054482,0.0009303218,0.0021257922,0.002149561,0.001293416,0.0019122771,0.0005631963,0.5382605],"category_scores_gemma":[0.049320426,0.000615488,0.0023268946,0.0044030678,0.00044323684,0.0012252278,0.001006509,0.00063363736,0.032791488],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006443774,0.000106029074,0.018061897,0.0030631344,0.000114290306,0.000038679642,0.00033245547,0.0004353857,0.00006994693,0.00026661757,0.96382916,0.013038069],"study_design_scores_gemma":[0.0067372075,0.0005568595,0.5121699,0.007114189,0.00076899026,0.00019585824,0.002452893,0.0027821623,0.0009822178,0.0009210958,0.46511477,0.00020394703],"about_ca_topic_score_codex":0.8391467,"about_ca_topic_score_gemma":0.86776394,"teacher_disagreement_score":0.5382605,"about_ca_system_score_codex":0.0146829095,"about_ca_system_score_gemma":0.018215712,"threshold_uncertainty_score":0.65861535},"labels":[],"label_agreement":null},{"id":"W6958634902","doi":"10.6084/m9.figshare.c.3637823_d1.v1","title":"Additional file 1: of Evaluation of self-swabbing coupled with a telephone health helpline as an adjunct tool for surveillance of influenza viruses in Ontario","year":2016,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Adjunct; Helpline; Hotline; Masking (illustration); Influenza A virus subtype H5N1; Influenzavirus B; MEDLINE; Epidemiological surveillance","score_opus":0.056953523073824834,"score_gpt":0.3219852341828974,"score_spread":0.26503171110907253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958634902","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011106452,0.00002005088,0.00017812403,0.00013919795,0.00001985398,0.00072415464,0.9935592,0.00011017115,0.00413858],"genre_scores_gemma":[0.09410247,0.00038239945,0.0074835517,0.0008159858,0.00010840494,0.016108004,0.8463499,0.0007306958,0.033918574],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99763286,0.00044190531,0.00035843486,0.0002619411,0.00095888204,0.00034589137],"domain_scores_gemma":[0.9650344,0.014804758,0.0025229377,0.0016639445,0.014571584,0.0014023521],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0037052347,0.0009054482,0.0009303218,0.0021257922,0.002149561,0.001293416,0.0019122771,0.0005631963,0.5382605],"category_scores_gemma":[0.049320426,0.000615488,0.0023268946,0.0044030678,0.00044323684,0.0012252278,0.001006509,0.00063363736,0.032791488],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006443774,0.000106029074,0.018061897,0.0030631344,0.000114290306,0.000038679642,0.00033245547,0.0004353857,0.00006994693,0.00026661757,0.96382916,0.013038069],"study_design_scores_gemma":[0.0067372075,0.0005568595,0.5121699,0.007114189,0.00076899026,0.00019585824,0.002452893,0.0027821623,0.0009822178,0.0009210958,0.46511477,0.00020394703],"about_ca_topic_score_codex":0.8391467,"about_ca_topic_score_gemma":0.86776394,"teacher_disagreement_score":0.5382605,"about_ca_system_score_codex":0.0146829095,"about_ca_system_score_gemma":0.018215712,"threshold_uncertainty_score":0.65861535},"labels":[],"label_agreement":null},{"id":"W6958726710","doi":"10.6084/m9.figshare.20438280.v1","title":"Additional file 4 of The role of geographic information system and global positioning system in dementia care and research: a scoping review","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Geographic information system; Information system; Global Positioning System; Dementia; Interoperability; Positioning system; Key (lock); File system","score_opus":0.023978310899694183,"score_gpt":0.28851836288752764,"score_spread":0.2645400519878335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958726710","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009282034,0.0001771035,0.00025836396,0.00028239965,0.000041546158,0.0004212131,0.99727875,0.00015181933,0.0012960563],"genre_scores_gemma":[0.009851043,0.0025949276,0.01377586,0.0019140949,0.00030089964,0.026549285,0.915616,0.000997003,0.028400978],"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","domain_scores_codex":[0.99703443,0.0008092526,0.0010895148,0.00039192155,0.00047118103,0.00020365388],"domain_scores_gemma":[0.8862913,0.09619458,0.0052353903,0.0020997224,0.0093930885,0.0007859964],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0063361195,0.0011701399,0.0019959442,0.008238661,0.0008200501,0.0024445099,0.0022273404,0.0017371692,0.8682777],"category_scores_gemma":[0.10081769,0.00081831246,0.0028048134,0.01314172,0.0003459357,0.003161439,0.0017607581,0.0010899013,0.08243338],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043697498,0.000042572043,0.0013146991,0.07760887,0.0002479678,0.000078591125,0.00020275016,0.00040004065,0.00007709022,0.0023045605,0.90052265,0.016763093],"study_design_scores_gemma":[0.0048554116,0.0001701807,0.014107602,0.053439308,0.0011967635,0.00031288006,0.00066866603,0.0008457439,0.00041314116,0.011059922,0.9127658,0.00016464712],"about_ca_topic_score_codex":0.012272911,"about_ca_topic_score_gemma":0.01730912,"teacher_disagreement_score":0.8682777,"about_ca_system_score_codex":0.0024995487,"about_ca_system_score_gemma":0.005518295,"threshold_uncertainty_score":0.18788582},"labels":[],"label_agreement":null},{"id":"W6959261658","doi":"10.7939/r38912667","title":"Integrating Approaches to Geographic Variation in Methodologies for Public Health Surveillance","year":2018,"lang":"en","type":"dissertation","venue":"University of Alberta Library","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public health; Public health surveillance; Aggregate (composite); Plot (graphics); Aggregate data; Disease surveillance; Strengths and weaknesses; Data aggregator; Statistic; Spatial analysis","score_opus":0.10908383160631907,"score_gpt":0.2833601260173382,"score_spread":0.1742762944110191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6959261658","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":[0.0024480533,0.0015724798,0.9911471,0.0014319887,0.00014284471,0.00027780072,0.0002555362,0.00015480338,0.002569343],"genre_scores_gemma":[0.12028887,0.0027521893,0.87265027,0.0007682148,0.00027985612,0.0019529676,0.0003864824,0.00014841589,0.00077279116],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.86137825,0.11719518,0.004556768,0.0063463543,0.009732561,0.00079092797],"domain_scores_gemma":[0.7230621,0.23579623,0.010098177,0.021326466,0.0090393955,0.00067760365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.105342604,0.0013214892,0.0021588092,0.011301072,0.0013261813,0.007106276,0.003987022,0.0020255079,0.0032118505],"category_scores_gemma":[0.2564813,0.0011643077,0.0033699311,0.01583072,0.008137126,0.004964209,0.007015831,0.003911253,0.00043925655],"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":[0.000053737454,0.000082406266,0.020441398,0.0012065652,0.0012198043,0.00015695575,0.004411973,0.03693861,0.0002760192,0.72196317,0.0030210952,0.21022822],"study_design_scores_gemma":[0.00006065919,0.00012482963,0.005611477,0.000767311,0.00019593917,0.00016652566,0.001992561,0.060118973,0.00047001438,0.90822446,0.022168305,0.00009889983],"about_ca_topic_score_codex":0.014026518,"about_ca_topic_score_gemma":0.010752775,"teacher_disagreement_score":0.105342604,"about_ca_system_score_codex":0.0049097417,"about_ca_system_score_gemma":0.007793538,"threshold_uncertainty_score":0.5571116},"labels":[],"label_agreement":null},{"id":"W6961654960","doi":"10.1371/journal.pone.0288240.s004","title":"Manipulation guides for online database searches.","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pregnancy; Observational study; Confidence interval; Birth weight; Cochrane Library; Low birth weight; Relative risk; Meta-analysis; Systematic review; Epidemiology","score_opus":0.24982552034204855,"score_gpt":0.402314833913427,"score_spread":0.15248931357137843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6961654960","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00080936163,0.006955343,0.004060896,0.0023726223,0.0006361009,0.051349323,0.8978794,0.0059336433,0.030003283],"genre_scores_gemma":[0.007866372,0.025173608,0.1002033,0.00572124,0.00062293495,0.5341813,0.25993854,0.005089062,0.06120369],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9884278,0.0028274104,0.0060388306,0.0012339809,0.0009542478,0.00051775185],"domain_scores_gemma":[0.9452196,0.036564115,0.0072399746,0.0021058328,0.0076850047,0.0011854832],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.018019693,0.0031665766,0.009619191,0.036862127,0.0012115281,0.004366572,0.0055200746,0.0026735463,0.7015532],"category_scores_gemma":[0.071162574,0.0021506597,0.0039237123,0.04661468,0.0014399259,0.004361442,0.005383855,0.0022215026,0.1203935],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008962623,0.000089946174,0.0004004965,0.43302885,0.00044472437,0.00023725859,0.0007360054,0.0003563882,0.00054544845,0.0041809594,0.49208578,0.066997804],"study_design_scores_gemma":[0.004305387,0.00032085425,0.0049207485,0.17849173,0.0011592689,0.0004098102,0.0016088324,0.0009629103,0.00059499935,0.013123578,0.7938173,0.00028461035],"about_ca_topic_score_codex":0.008123744,"about_ca_topic_score_gemma":0.013293119,"teacher_disagreement_score":0.9819803,"about_ca_system_score_codex":0.004786507,"about_ca_system_score_gemma":0.016176708,"threshold_uncertainty_score":0.4256981},"labels":[],"label_agreement":null},{"id":"W6967866835","doi":"10.5281/zenodo.11695083","title":"Ul 845 pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quality (philosophy); Download; Control (management); Product (mathematics); Overlay; Component (thermodynamics)","score_opus":0.025430318990868027,"score_gpt":0.2666360971866372,"score_spread":0.2412057781957692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6967866835","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00039685096,0.0012018569,0.0026998206,0.0010279958,0.0025845575,0.0002158415,0.014205702,0.0064599426,0.9712073],"genre_scores_gemma":[0.0013667364,0.00085331337,0.0010434131,0.00057185255,0.0003502482,0.00006845494,0.007908597,0.0019167608,0.9859206],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99903774,0.00006125636,0.000062670755,0.0001446588,0.0005827315,0.000111050496],"domain_scores_gemma":[0.99731135,0.00027437974,0.00009200313,0.0003414706,0.0016216119,0.0003591202],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00074865733,0.0015394497,0.0011497248,0.0032752967,0.0019269473,0.008186938,0.0025542437,0.0021196543,0.9021758],"category_scores_gemma":[0.004373873,0.00094683433,0.0011379828,0.0030307898,0.000794462,0.0056072283,0.0031342218,0.0023880021,0.86719483],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006102347,0.00004002002,0.00005548324,0.00032530664,0.0000041832704,0.00008962713,0.000052143754,0.00010002978,0.0011928695,0.002631931,0.921385,0.07406235],"study_design_scores_gemma":[0.0000075479993,0.000012682783,0.0001383499,0.000067018445,0.0000024377173,0.000079397745,0.000040900602,0.00003994967,0.0006118957,0.0005912637,0.9984006,0.000007917148],"about_ca_topic_score_codex":0.0043703713,"about_ca_topic_score_gemma":0.0049388963,"teacher_disagreement_score":0.097824216,"about_ca_system_score_codex":0.001985169,"about_ca_system_score_gemma":0.0017239797,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W6976543735","doi":"10.60692/qg05f-5zj33","title":"Leveraging machine learning approaches for predicting potential Lyme disease cases and incidence rates in the United States using Twitter","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université de Montréal","funders":"","keywords":"Lyme disease; Word2vec; Disease; Incidence (geometry); LYME; Set (abstract data type); Spamming","score_opus":0.09808327153389258,"score_gpt":0.2726396494698408,"score_spread":0.17455637793594825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976543735","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":[0.94150627,0.0011286286,0.03464914,0.0018760125,0.00023115867,0.00019271234,0.012746932,0.0015050342,0.00616414],"genre_scores_gemma":[0.9726451,0.00030821163,0.016888317,0.0001322802,0.00016443411,0.00008601475,0.008491983,0.000029248915,0.0012544292],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999463,0.00017672643,0.00006702293,0.00014178404,0.000096315234,0.000055168424],"domain_scores_gemma":[0.9973878,0.0016537245,0.00033793226,0.00011929996,0.0003929249,0.00010825942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010784777,0.0006466179,0.00040993758,0.0040862532,0.00034009066,0.0009355837,0.00042109514,0.0006297787,0.0010713501],"category_scores_gemma":[0.003928225,0.00017757015,0.00050947524,0.0016527206,0.00016866144,0.001296047,0.0006317415,0.0006342137,0.0012116682],"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":[0.00068695634,0.0009076986,0.5446966,0.00044285983,0.00035550733,0.00046871215,0.00044707538,0.12594132,0.0067313984,0.000952689,0.014977759,0.3033915],"study_design_scores_gemma":[0.00001212149,0.00012596526,0.05763402,0.00006345866,0.000045341785,0.000093038405,0.00041809052,0.93485624,0.0025264341,0.0014648929,0.002732067,0.000028379225],"about_ca_topic_score_codex":0.0069195842,"about_ca_topic_score_gemma":0.01147362,"teacher_disagreement_score":0.0069195842,"about_ca_system_score_codex":0.0005584669,"about_ca_system_score_gemma":0.00038887878,"threshold_uncertainty_score":0.013758659},"labels":[],"label_agreement":null},{"id":"W6976544899","doi":"10.60692/takgx-m4256","title":"Did Advances in Global Surveillance and Notification Systems Make a Difference in the 2009 H1N1 Pandemic?–A Retrospective Analysis","year":2013,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Timeline; Pandemic; Outbreak; Disease surveillance; Identification (biology); Notification system; Public health; Public health surveillance; Strengths and weaknesses","score_opus":0.016078543308318354,"score_gpt":0.23698342360839741,"score_spread":0.22090488030007907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976544899","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":[0.9549513,0.025933415,0.0028079099,0.00092508853,0.000079766556,0.00051337696,0.0123425685,0.00003276577,0.0024138615],"genre_scores_gemma":[0.987837,0.005865357,0.00158284,0.00027377126,0.00004650546,0.00028532802,0.0037738879,0.000022607917,0.000312608],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98971343,0.0033268395,0.0035229693,0.0011960551,0.0016951459,0.00054554176],"domain_scores_gemma":[0.9450202,0.019879248,0.020652015,0.003865412,0.010005703,0.00057742547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016060034,0.00034709094,0.00066497346,0.0055726115,0.00045383914,0.0014001383,0.00045240208,0.00051558873,0.0009835234],"category_scores_gemma":[0.036878925,0.00070811016,0.0023163115,0.0074472907,0.0005575662,0.0018268775,0.00093973393,0.00071312767,0.0002494869],"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":[0.00041881175,0.000032742926,0.9820046,0.0011272171,0.001054618,0.0001306193,0.00075932534,0.00017295337,0.00016071506,0.0002641745,0.00085091754,0.013023472],"study_design_scores_gemma":[0.000039062474,0.00043410176,0.97933245,0.0015968175,0.002258403,0.0006743088,0.003686026,0.0007773232,0.000612104,0.00023459512,0.010290845,0.00006402938],"about_ca_topic_score_codex":0.013687405,"about_ca_topic_score_gemma":0.0132542355,"teacher_disagreement_score":0.016060034,"about_ca_system_score_codex":0.0014430837,"about_ca_system_score_gemma":0.0029675697,"threshold_uncertainty_score":0.08493459},"labels":[],"label_agreement":null},{"id":"W6976737392","doi":"10.6084/m9.figshare.14093323.v4","title":"Vision 2035 Public Health Surveillance in India - A White Paper","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public health; Public health surveillance; Health surveillance; White paper; White (mutation); Disease surveillance","score_opus":0.03859671617043582,"score_gpt":0.3140568465513314,"score_spread":0.2754601303808956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976737392","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052545713,0.014836976,0.018170848,0.68393767,0.057195675,0.0010157737,0.032955796,0.0041993116,0.18243338],"genre_scores_gemma":[0.14645828,0.026388615,0.11198993,0.4269905,0.010358142,0.0028509388,0.07088008,0.0017164961,0.20236711],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98830146,0.0027710418,0.0007845035,0.00070178864,0.0049527567,0.0024883605],"domain_scores_gemma":[0.977358,0.003769833,0.0012131077,0.0017658349,0.007841152,0.008052141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026310481,0.0017786295,0.0007916654,0.0027683778,0.0024007496,0.012044863,0.004739582,0.006523056,0.009343475],"category_scores_gemma":[0.019623812,0.0008970624,0.0021153097,0.0024006686,0.0028007259,0.00471222,0.0073868856,0.008063791,0.005086442],"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":[0.000061619176,0.0000730557,0.0015556475,0.00040073178,0.00004664163,0.00012367699,0.0003666959,0.0005517511,0.00030167238,0.04468875,0.9128995,0.038930215],"study_design_scores_gemma":[0.000029518354,0.00006920746,0.0061824317,0.0006979748,0.00003937194,0.000086105036,0.0006116786,0.00046126675,0.00054129784,0.009611475,0.9816228,0.00004696994],"about_ca_topic_score_codex":0.1235821,"about_ca_topic_score_gemma":0.09983438,"teacher_disagreement_score":0.1235821,"about_ca_system_score_codex":0.009783737,"about_ca_system_score_gemma":0.08886791,"threshold_uncertainty_score":0.24572551},"labels":[],"label_agreement":null},{"id":"W6976789600","doi":"10.60692/09rm5-q4s23","title":"Food allergy: What are people looking for? An infodemiology study","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Relation (database); Food allergens; Value (mathematics); Service (business); Public access","score_opus":0.06321903444212162,"score_gpt":0.27835945141248136,"score_spread":0.21514041697035974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976789600","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":[0.9835381,0.009151086,0.000067521025,0.0014407687,0.000039152816,0.00015267964,0.0024404384,0.0000044421563,0.0031659119],"genre_scores_gemma":[0.99090767,0.0053643,0.00014960658,0.0013454157,0.00008559656,0.0002563447,0.0011256093,0.000006555644,0.0007589813],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986994,0.00032115783,0.00032023725,0.0001796889,0.00023778774,0.0002417206],"domain_scores_gemma":[0.99630547,0.0011445328,0.0015148662,0.00010894583,0.00041692884,0.00050925644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016006457,0.0002530864,0.00095211505,0.0029951192,0.0014374857,0.0013771076,0.00052639603,0.001101301,0.004858856],"category_scores_gemma":[0.0059131877,0.00047043993,0.0012546082,0.006533917,0.0005953756,0.003019548,0.0011112886,0.0011800363,0.00092270924],"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":[0.00046304608,0.0006090147,0.9649657,0.0026370944,0.00029362348,0.00077329425,0.012249489,0.00002429858,0.00012868237,0.0003486988,0.0042912886,0.013215648],"study_design_scores_gemma":[0.00008015358,0.00059484877,0.962625,0.0014400915,0.00042381758,0.0010563245,0.027683623,0.00011601941,0.00004874355,0.00021982564,0.0056729475,0.000038513164],"about_ca_topic_score_codex":0.021401938,"about_ca_topic_score_gemma":0.021210685,"teacher_disagreement_score":0.021401938,"about_ca_system_score_codex":0.0013263639,"about_ca_system_score_gemma":0.0014714411,"threshold_uncertainty_score":0.042554736},"labels":[],"label_agreement":null},{"id":"W6976825505","doi":"10.6084/m9.figshare.14093323.v1","title":"Vision 2035 Public Health Surveillance in India - A White Paper","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public health; Public health surveillance; Health surveillance; White paper; White (mutation); Disease surveillance","score_opus":0.03859671617043582,"score_gpt":0.3140568465513314,"score_spread":0.2754601303808956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976825505","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052545713,0.014836976,0.018170848,0.68393767,0.057195675,0.0010157737,0.032955796,0.0041993116,0.18243338],"genre_scores_gemma":[0.14645828,0.026388615,0.11198993,0.4269905,0.010358142,0.0028509388,0.07088008,0.0017164961,0.20236711],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98830146,0.0027710418,0.0007845035,0.00070178864,0.0049527567,0.0024883605],"domain_scores_gemma":[0.977358,0.003769833,0.0012131077,0.0017658349,0.007841152,0.008052141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026310481,0.0017786295,0.0007916654,0.0027683778,0.0024007496,0.012044863,0.004739582,0.006523056,0.009343475],"category_scores_gemma":[0.019623812,0.0008970624,0.0021153097,0.0024006686,0.0028007259,0.00471222,0.0073868856,0.008063791,0.005086442],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000061619176,0.0000730557,0.0015556475,0.00040073178,0.00004664163,0.00012367699,0.0003666959,0.0005517511,0.00030167238,0.04468875,0.9128995,0.038930215],"study_design_scores_gemma":[0.000029518354,0.00006920746,0.0061824317,0.0006979748,0.00003937194,0.000086105036,0.0006116786,0.00046126675,0.00054129784,0.009611475,0.9816228,0.00004696994],"about_ca_topic_score_codex":0.1235821,"about_ca_topic_score_gemma":0.09983438,"teacher_disagreement_score":0.1235821,"about_ca_system_score_codex":0.009783737,"about_ca_system_score_gemma":0.08886791,"threshold_uncertainty_score":0.24572551},"labels":[],"label_agreement":null},{"id":"W6976853247","doi":"10.6084/m9.figshare.26617876","title":"Additional file 1 of Empowering health geography research with location-based social media data: innovative food word expansion and energy density prediction via word embedding and machine learning","year":2024,"lang":"en","type":"article","venue":"Open MIND","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Word (group theory); Calibration; Mixture model; Social media; Embedding; Vertical axis; Value (mathematics); Gaussian","score_opus":0.07095707022725643,"score_gpt":0.3595208128628305,"score_spread":0.28856374263557405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976853247","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001974275,0.0000068568474,0.0004916426,0.00008704453,0.000016226744,0.0000551314,0.99822193,0.0002810226,0.00064269744],"genre_scores_gemma":[0.013447087,0.0000941045,0.009800646,0.00033646967,0.0000882305,0.0024764393,0.96498346,0.0010574568,0.007716153],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948454,0.000129174,0.00006794111,0.00013945799,0.00011306939,0.00006583659],"domain_scores_gemma":[0.98368615,0.013146576,0.0005360409,0.00096454576,0.0012853694,0.0003813948],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016680245,0.00086731015,0.00078732456,0.0014540325,0.0007435873,0.0015427183,0.0017033987,0.0010849987,0.8279667],"category_scores_gemma":[0.03437937,0.0004529881,0.00060383655,0.0029584654,0.00026077477,0.0013116504,0.0011367183,0.0010492462,0.15965937],"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":[0.00014294639,0.000058175858,0.0024034807,0.0009010969,0.00002378154,0.000048199596,0.00006627123,0.0008948894,0.000058711983,0.00086253183,0.9848541,0.009685843],"study_design_scores_gemma":[0.005739201,0.0003546296,0.04071187,0.0027072136,0.0002259867,0.0004932379,0.0012810003,0.015082557,0.001832605,0.025686773,0.9056628,0.00022229433],"about_ca_topic_score_codex":0.012986531,"about_ca_topic_score_gemma":0.02862999,"teacher_disagreement_score":0.8279667,"about_ca_system_score_codex":0.0009517799,"about_ca_system_score_gemma":0.0018407413,"threshold_uncertainty_score":0.24538457},"labels":[],"label_agreement":null},{"id":"W6976930112","doi":"10.60692/y06qb-f5140","title":"Feature Selection for an Explainability Analysis in Detection of COVID-19 Active Cases from Facebook User-Based Online Surveys","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Feature selection; Boosting (machine learning); Gradient boosting; Random forest; Binary classification; Ranking (information retrieval); Receiver operating characteristic; Pattern recognition (psychology); Feature (linguistics); Binary number","score_opus":0.0659768184388181,"score_gpt":0.30876247770798454,"score_spread":0.24278565926916645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976930112","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":[0.8517034,0.00071373815,0.14183745,0.00067678746,0.000078470664,0.00030997262,0.001923962,0.00132263,0.0014336107],"genre_scores_gemma":[0.98042023,0.00004328356,0.018389482,0.000031333504,0.000031227573,0.00008090336,0.00085076777,0.000009932506,0.00014285557],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742115,0.0011597822,0.0002457884,0.00040138414,0.00054078316,0.0002311513],"domain_scores_gemma":[0.98838496,0.008361194,0.0013280024,0.0005634147,0.0011335881,0.00022885845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005401264,0.00058221346,0.0009293637,0.005325351,0.0003860645,0.00092451065,0.00066426053,0.0006156526,0.0011527565],"category_scores_gemma":[0.015662847,0.00014855406,0.0008987414,0.0018647859,0.00030120448,0.0008214082,0.0007836807,0.00063873286,0.0003652298],"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":[0.00057008304,0.00059865357,0.6690937,0.00027711055,0.00043402793,0.00035095093,0.00039966305,0.01389051,0.0047640787,0.0014273677,0.004933467,0.3032604],"study_design_scores_gemma":[0.000056626053,0.0003424506,0.35911673,0.00010603431,0.00027173082,0.00046460092,0.0005085426,0.6274842,0.0056026774,0.0035107112,0.0024774673,0.000058249923],"about_ca_topic_score_codex":0.001723508,"about_ca_topic_score_gemma":0.0017314506,"teacher_disagreement_score":0.005401264,"about_ca_system_score_codex":0.0003855586,"about_ca_system_score_gemma":0.00048673488,"threshold_uncertainty_score":0.02856499},"labels":[],"label_agreement":null},{"id":"W6977011172","doi":"10.60692/nfp8e-6h453","title":"Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Economic evaluation; Checklist; Context (archaeology); Stakeholder; Public health; Psychological intervention; Guideline; Intervention (counseling)","score_opus":0.0750384105516992,"score_gpt":0.380131821268476,"score_spread":0.3050934107167768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977011172","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020185818,0.04920251,0.23116048,0.09324731,0.033443965,0.14092167,0.37555367,0.012448933,0.062002867],"genre_scores_gemma":[0.015652997,0.04929525,0.28254017,0.02380359,0.0059986417,0.38652384,0.21211195,0.005222639,0.018850902],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.49656355,0.30599147,0.11824327,0.0077390736,0.06581686,0.0056457864],"domain_scores_gemma":[0.26279807,0.39656517,0.09142125,0.037762117,0.20775948,0.0036939406],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3614558,0.005063961,0.011702238,0.022137921,0.0024043052,0.019106539,0.011015701,0.014775776,0.066612385],"category_scores_gemma":[0.73744327,0.0056607965,0.017515505,0.038342822,0.0049793036,0.010107085,0.008166326,0.02244304,0.03697915],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012371184,0.00013505925,0.0008336203,0.048478376,0.0011580446,0.00013677044,0.0005377947,0.0023701624,0.00019739772,0.017252648,0.8284053,0.09925765],"study_design_scores_gemma":[0.0025953886,0.00023293942,0.003797317,0.112483814,0.0014404772,0.000285319,0.0004038229,0.0041186535,0.0010688024,0.03336323,0.83973444,0.00047574754],"about_ca_topic_score_codex":0.010037584,"about_ca_topic_score_gemma":0.004758516,"teacher_disagreement_score":0.6385442,"about_ca_system_score_codex":0.0142474165,"about_ca_system_score_gemma":0.043797005,"threshold_uncertainty_score":0.7874387},"labels":[],"label_agreement":null},{"id":"W6977131271","doi":"10.6084/m9.figshare.13101246","title":"Additional file 1 of Geospatial patterns of comorbidity prevalence among people with osteoarthritis in Alberta Canada","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Alberta Health Services; University of Calgary","funders":"","keywords":"Comorbidity; Contiguity; Geospatial analysis; Epidemiology; Multimorbidity; Typology","score_opus":0.012096504409537854,"score_gpt":0.20267670899081336,"score_spread":0.1905802045812755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977131271","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004021801,0.000016674463,0.000056101362,0.000046648613,0.000011018122,0.00004234321,0.99781346,0.000038611513,0.0015729163],"genre_scores_gemma":[0.017257178,0.00021365649,0.0017637322,0.00016394503,0.000034206816,0.0005375761,0.9677844,0.00014053902,0.0121047525],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99944025,0.000043605458,0.000057102778,0.00010346647,0.00021073528,0.00014479259],"domain_scores_gemma":[0.9943539,0.0012960342,0.000353001,0.0003442095,0.003248264,0.00040457855],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006448384,0.00066054217,0.0008337843,0.0032073122,0.0018633807,0.0013939602,0.0017700326,0.00049258623,0.5477436],"category_scores_gemma":[0.009219992,0.00040852983,0.0006969888,0.010398368,0.00027428856,0.0006802843,0.0009161168,0.00058470864,0.035318546],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004997573,0.000018082706,0.009316102,0.0004323519,0.000025423627,0.000034657245,0.00012098712,0.00031742564,0.00002558657,0.0005830998,0.9839451,0.0051313546],"study_design_scores_gemma":[0.0009969726,0.00005862965,0.2529578,0.002597406,0.00016706334,0.0003605599,0.0017833213,0.002684334,0.00030790042,0.0030683214,0.7348759,0.00014175102],"about_ca_topic_score_codex":0.93678015,"about_ca_topic_score_gemma":0.9593829,"teacher_disagreement_score":0.5477436,"about_ca_system_score_codex":0.008447484,"about_ca_system_score_gemma":0.020259215,"threshold_uncertainty_score":0.6450888},"labels":[],"label_agreement":null},{"id":"W6977186955","doi":"10.60692/w04d5-xe375","title":"Feature Selection for an Explainability Analysis in Detection of COVID-19 Active Cases from Facebook User-Based Online Surveys","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Feature selection; Boosting (machine learning); Gradient boosting; Random forest; Binary classification; Ranking (information retrieval); Receiver operating characteristic; Pattern recognition (psychology); Feature (linguistics); Binary number","score_opus":0.0659768184388181,"score_gpt":0.30876247770798454,"score_spread":0.24278565926916645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977186955","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":[0.8517034,0.00071373815,0.14183745,0.00067678746,0.000078470664,0.00030997262,0.001923962,0.00132263,0.0014336107],"genre_scores_gemma":[0.98042023,0.00004328356,0.018389482,0.000031333504,0.000031227573,0.00008090336,0.00085076777,0.000009932506,0.00014285557],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742115,0.0011597822,0.0002457884,0.00040138414,0.00054078316,0.0002311513],"domain_scores_gemma":[0.98838496,0.008361194,0.0013280024,0.0005634147,0.0011335881,0.00022885845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005401264,0.00058221346,0.0009293637,0.005325351,0.0003860645,0.00092451065,0.00066426053,0.0006156526,0.0011527565],"category_scores_gemma":[0.015662847,0.00014855406,0.0008987414,0.0018647859,0.00030120448,0.0008214082,0.0007836807,0.00063873286,0.0003652298],"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":[0.00057008304,0.00059865357,0.6690937,0.00027711055,0.00043402793,0.00035095093,0.00039966305,0.01389051,0.0047640787,0.0014273677,0.004933467,0.3032604],"study_design_scores_gemma":[0.000056626053,0.0003424506,0.35911673,0.00010603431,0.00027173082,0.00046460092,0.0005085426,0.6274842,0.0056026774,0.0035107112,0.0024774673,0.000058249923],"about_ca_topic_score_codex":0.001723508,"about_ca_topic_score_gemma":0.0017314506,"teacher_disagreement_score":0.005401264,"about_ca_system_score_codex":0.0003855586,"about_ca_system_score_gemma":0.00048673488,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W6977206918","doi":"10.6084/m9.figshare.15001326.v1","title":"Additional file 2 of Need of care in interpreting Google Trends-based COVID-19 infodemiological study results: potential risk of false-positivity","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Rank (graph theory); Value (mathematics); Correlation; Causality (physics); Lag; Relation (database); Rank correlation","score_opus":0.024664893497189578,"score_gpt":0.3120191615719172,"score_spread":0.28735426807472764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977206918","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027065523,0.000023934415,0.0004426016,0.00028954388,0.000039627488,0.00017485769,0.9968539,0.0007290898,0.0011757171],"genre_scores_gemma":[0.029368924,0.00023532374,0.015209919,0.0025675416,0.00033054352,0.008815755,0.92221826,0.0034604345,0.017793264],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975504,0.0007139413,0.000655777,0.0004192044,0.0004141542,0.00024643194],"domain_scores_gemma":[0.87650055,0.10562818,0.005162887,0.0050769188,0.006420024,0.0012114262],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005885216,0.00082168536,0.0012475864,0.00484828,0.00081096706,0.0026152285,0.0019073979,0.001740756,0.80781907],"category_scores_gemma":[0.11724697,0.00055460836,0.0013926538,0.005322987,0.00035424024,0.0026883676,0.0017332927,0.00086972374,0.10164922],"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":[0.00040273575,0.00004195102,0.0042813146,0.0053155343,0.00009296489,0.00011516464,0.00010578639,0.0002953742,0.00006079287,0.0009821372,0.9778495,0.010456711],"study_design_scores_gemma":[0.0052017956,0.00029036726,0.05265934,0.012742613,0.00056927104,0.0011367917,0.0011624929,0.0042033163,0.0010835874,0.01972751,0.9009515,0.00027135012],"about_ca_topic_score_codex":0.0069329464,"about_ca_topic_score_gemma":0.014173352,"teacher_disagreement_score":0.99411476,"about_ca_system_score_codex":0.0014910585,"about_ca_system_score_gemma":0.003002734,"threshold_uncertainty_score":0.27412277},"labels":[],"label_agreement":null},{"id":"W6977515585","doi":"10.6084/m9.figshare.c.3610424_d2","title":"Additional file 1: of Small-area spatio-temporal analyses of bladder and kidney cancer risk in Nova Scotia, Canada","year":2016,"lang":"en","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Kidney cancer; Bladder cancer; Nova (rocket); Kidney disease; Kidney; Cancer; Nephrectomy","score_opus":0.046302940712154315,"score_gpt":0.2833655153377067,"score_spread":0.23706257462555239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977515585","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00032987614,0.000016635207,0.00008193377,0.00003603544,0.000010564516,0.000040873012,0.99872106,0.00004540807,0.0007176503],"genre_scores_gemma":[0.028419372,0.00026792593,0.0035065298,0.00023821306,0.000031950218,0.001140081,0.950679,0.00036630075,0.015350577],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99915695,0.00008149468,0.00011945187,0.00016376164,0.00025344745,0.00022481671],"domain_scores_gemma":[0.98909307,0.0037032277,0.0005558784,0.00086518336,0.0052466644,0.0005360314],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012052923,0.0008385554,0.0009840693,0.0040780357,0.0019025111,0.0015305054,0.0019926955,0.0005150744,0.40141416],"category_scores_gemma":[0.017381852,0.0006040602,0.0016170201,0.0094356965,0.00037991896,0.0006382439,0.0010192086,0.00067081803,0.019364312],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014558675,0.000020791695,0.028321458,0.001632801,0.00017216212,0.000087741566,0.0002518365,0.0009768831,0.00009531645,0.0007407974,0.9607843,0.0067702513],"study_design_scores_gemma":[0.0012485903,0.00006839385,0.4444378,0.004375433,0.000634395,0.00034382194,0.0020138528,0.0028223067,0.00079806015,0.0019331889,0.54111636,0.00020775475],"about_ca_topic_score_codex":0.9718007,"about_ca_topic_score_gemma":0.9729205,"teacher_disagreement_score":0.40141416,"about_ca_system_score_codex":0.010206911,"about_ca_system_score_gemma":0.028834311,"threshold_uncertainty_score":0.8538101},"labels":[],"label_agreement":null},{"id":"W6980408809","doi":"","title":"Canadian Solar Announces Investment in eNow Solar-Based Power Management for Commercial Transportation","year":2017,"lang":"en","type":"other","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Investment (military); Power (physics); Electricity; Investment management; Solar power","score_opus":0.015192720693001595,"score_gpt":0.2772723471782956,"score_spread":0.262079626485294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6980408809","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01056474,0.0026649532,0.0027914476,0.071236104,0.006535739,0.0004568969,0.06705761,0.0023674008,0.83632505],"genre_scores_gemma":[0.03812799,0.0025882146,0.0022172108,0.008198866,0.0010722292,0.00007760073,0.022501677,0.00040243985,0.9248138],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981896,0.000055115583,0.000024228537,0.0000641114,0.0013085882,0.0003582961],"domain_scores_gemma":[0.993871,0.00033316656,0.000111376416,0.00015831893,0.0041454197,0.00138076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014963022,0.0006393544,0.0002871378,0.0014283729,0.0029890223,0.0030827716,0.0012101798,0.002098709,0.08157194],"category_scores_gemma":[0.0049984623,0.0002823203,0.00082364347,0.0016700713,0.0007339522,0.0007679442,0.0010705333,0.0020099815,0.010759436],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006448606,0.000035111658,0.0027541763,0.000038168815,0.000009400925,0.000049770366,0.00003224683,0.00022498063,0.00017225121,0.0034293246,0.9692534,0.023936687],"study_design_scores_gemma":[0.000029267596,0.000020009615,0.009859969,0.00004357101,0.000009980834,0.000031382813,0.00013665082,0.00063767354,0.00027583432,0.00048120838,0.988462,0.000012406893],"about_ca_topic_score_codex":0.9561062,"about_ca_topic_score_gemma":0.9807561,"teacher_disagreement_score":0.08157194,"about_ca_system_score_codex":0.019730022,"about_ca_system_score_gemma":0.06843809,"threshold_uncertainty_score":0.27288532},"labels":[],"label_agreement":null},{"id":"W6989384543","doi":"","title":"Assessing and improving the accuracy of surveillance case definitions using administrative data","year":2011,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Canadian Institutes of Health Research; McGill University Health Centre; McGill University","keywords":"Medical diagnosis; Pace; Medical record; MEDLINE; Health care; Predictive value; Public health","score_opus":0.18148564881060222,"score_gpt":0.3665097845350928,"score_spread":0.18502413572449058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6989384543","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95933473,0.0014182177,0.033450186,0.00081384904,0.00007972588,0.00031631527,0.0013277153,0.0001869028,0.0030723587],"genre_scores_gemma":[0.99000657,0.00013183146,0.009012087,0.00006757266,0.000057604815,0.000060797283,0.00059545465,0.000010173606,0.000057910464],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.88701224,0.0845165,0.00984831,0.0053957417,0.0114431465,0.0017839585],"domain_scores_gemma":[0.4958081,0.42371812,0.04164743,0.01596569,0.021184485,0.0016760881],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08736909,0.0008181175,0.00081159093,0.004970148,0.0006303597,0.0032091574,0.0020436514,0.0010810384,0.0007943075],"category_scores_gemma":[0.32960778,0.00051409,0.0011885739,0.0028448692,0.0011312697,0.0024915778,0.0021358635,0.0010190518,0.00026797826],"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":[0.00012727694,0.000043765474,0.9844765,0.00007719097,0.00023533285,0.000026806854,0.00026589888,0.0014832467,0.00015650301,0.00015490732,0.0002560995,0.012696472],"study_design_scores_gemma":[0.000052729156,0.0006094577,0.9359629,0.00020034611,0.00043514417,0.00038757746,0.00042602952,0.057235938,0.002320107,0.0009549665,0.0013690749,0.000045700264],"about_ca_topic_score_codex":0.0055767037,"about_ca_topic_score_gemma":0.004069185,"teacher_disagreement_score":0.9126309,"about_ca_system_score_codex":0.0012138155,"about_ca_system_score_gemma":0.0012634919,"threshold_uncertainty_score":0.46205747},"labels":[],"label_agreement":null},{"id":"W6991572017","doi":"","title":"How Municipal and Regional Police Align Their Practices with Canadian Security Intelligence Agencies: A Study in Nodal Governance","year":2022,"lang":"en","type":"dissertation","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nucleofection; TSG101; Gestational period; Hyporeflexia; Pretext; Proteogenomics","score_opus":0.03416488771319383,"score_gpt":0.277209307527143,"score_spread":0.2430444198139492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6991572017","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":[0.96084726,0.00040955792,0.0006163592,0.005307367,0.000029121698,0.00011592173,0.0001608826,0.000012328128,0.032501202],"genre_scores_gemma":[0.9949378,0.00039611926,0.00036692526,0.0002706972,0.0000038073965,0.00003203171,0.00007612448,0.000008595333,0.0039079],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99309665,0.0016559266,0.00017671708,0.00062346744,0.0019518655,0.0024954102],"domain_scores_gemma":[0.9925634,0.0013798353,0.0010113096,0.00039885906,0.0027759753,0.001870625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062522674,0.0002815886,0.00035751777,0.002554567,0.028317064,0.008335108,0.0019466524,0.00092840166,0.0027291025],"category_scores_gemma":[0.014996197,0.00045371673,0.00026309732,0.0071856366,0.01012414,0.0035500745,0.0067164297,0.0020342807,0.00023346752],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028076038,0.000048763606,0.07758629,0.00005397403,0.000011532706,0.00031624516,0.87936634,0.00023140303,0.0003185508,0.018778618,0.0030842836,0.020175887],"study_design_scores_gemma":[0.0000036423114,0.000017675671,0.06486774,0.00008418835,0.000008265023,0.00004592987,0.90770996,0.0002152502,0.00007771491,0.00049792335,0.026448771,0.00002303259],"about_ca_topic_score_codex":0.992046,"about_ca_topic_score_gemma":0.99577326,"teacher_disagreement_score":0.13736524,"about_ca_system_score_codex":0.13736524,"about_ca_system_score_gemma":0.18675998,"threshold_uncertainty_score":0.9966592},"labels":[],"label_agreement":null},{"id":"W6995661419","doi":"","title":"Perceived stress among Alzheimer support group members : a broader perspective / Kerry Kuluski. --","year":2004,"lang":"en","type":"dissertation","venue":"Knowledge Commons (Lakehead University)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Perspective (graphical); Stress (linguistics); Group (periodic table); Social support; Support group","score_opus":0.022609819027874213,"score_gpt":0.27988765868066723,"score_spread":0.257277839652793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6995661419","genre_codex":"commentary","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":[0.20415546,0.2545809,0.00043877034,0.50281847,0.0027232117,0.0002534627,0.0019437979,0.000033749224,0.033052247],"genre_scores_gemma":[0.61716753,0.28749764,0.0010375032,0.035425182,0.0025747912,0.0003754341,0.0008023403,0.000044122866,0.05507541],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9997327,0.00010550261,0.000013101985,0.00003139043,0.0000674728,0.00004989135],"domain_scores_gemma":[0.9993944,0.00014537993,0.00007314291,0.000011642629,0.00014894197,0.00022642965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015130399,0.0002942746,0.00036155505,0.0007212194,0.0016235481,0.0016379951,0.00039093,0.00070100767,0.004677596],"category_scores_gemma":[0.0013341728,0.00028992412,0.00018110474,0.0008791358,0.0011810136,0.0010338736,0.00058769615,0.0012287444,0.00067956856],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010982822,0.0003131219,0.14189874,0.0021253764,0.000051384075,0.0014303369,0.14399953,0.00009135722,0.0013347128,0.003462439,0.5262631,0.17892],"study_design_scores_gemma":[0.000044151417,0.00017616696,0.6387274,0.0028199404,0.0000424918,0.0011739823,0.14490914,0.000095149575,0.00027825419,0.0011639956,0.21052335,0.000046029323],"about_ca_topic_score_codex":0.27207327,"about_ca_topic_score_gemma":0.4834188,"teacher_disagreement_score":0.27207327,"about_ca_system_score_codex":0.0034406271,"about_ca_system_score_gemma":0.0054053897,"threshold_uncertainty_score":0.54097927},"labels":[],"label_agreement":null},{"id":"W6995920688","doi":"","title":"Prévision de l'état actuel du PIB québécois","year":2025,"lang":"fr","type":"other","venue":"Archipelago (University of Quebec in Montreal)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Microinsurance; Statistical analysis; Maximum likelihood","score_opus":0.005159494366728111,"score_gpt":0.20658012536371254,"score_spread":0.20142063099698443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6995920688","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":[0.8348764,0.008344887,0.046663016,0.006434131,0.0003345903,0.00018125969,0.06721837,0.0010169594,0.03493028],"genre_scores_gemma":[0.9383052,0.0023723322,0.01356978,0.00042182373,0.000082549035,0.00007948428,0.022550091,0.00016154934,0.022457223],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986499,0.00022543411,0.000074828225,0.00038358482,0.0004933626,0.00017294213],"domain_scores_gemma":[0.99442685,0.0014636556,0.0005206482,0.0003638459,0.0030334273,0.00019150205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039037496,0.0006734591,0.00072147924,0.002359876,0.0009260851,0.0034204458,0.0010608111,0.00081729935,0.0058610025],"category_scores_gemma":[0.010899546,0.00042925696,0.00080625224,0.0037236062,0.0005457121,0.0014664766,0.00078662485,0.0012742461,0.0011317742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050062715,0.00008293963,0.74179554,0.00071464526,0.000817906,0.00043432525,0.0023725876,0.08808585,0.002480903,0.013445355,0.029114425,0.120154925],"study_design_scores_gemma":[0.00003822639,0.0001358644,0.6195538,0.0007020808,0.00041656895,0.00018107593,0.0031608343,0.24168147,0.0036061164,0.004822867,0.12545046,0.00025059903],"about_ca_topic_score_codex":0.9134854,"about_ca_topic_score_gemma":0.9159744,"teacher_disagreement_score":0.08651459,"about_ca_system_score_codex":0.010057946,"about_ca_system_score_gemma":0.008752758,"threshold_uncertainty_score":0.17404813},"labels":[],"label_agreement":null},{"id":"W6997243743","doi":"","title":"Updating the Canadian Obesity Maps: an epidemic in progress","year":2014,"lang":"en","type":"article","venue":"The Sydney eScholarship Repository (The University of Sydney)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Obesity; Population; Public health; Population health; Health care; Portrait; Survey data collection; Data collection; Global health","score_opus":0.013836851318930749,"score_gpt":0.23054032479664455,"score_spread":0.2167034734777138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6997243743","genre_codex":"commentary","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":[0.085540056,0.07863972,0.018756418,0.3324651,0.022898128,0.00078672526,0.17104925,0.009112231,0.28075245],"genre_scores_gemma":[0.43888798,0.13227944,0.11840353,0.037805256,0.0043087136,0.00068956346,0.13058099,0.004152237,0.13289227],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99234164,0.00064401765,0.00034758353,0.00044038077,0.005363015,0.00086343946],"domain_scores_gemma":[0.96875876,0.0018349757,0.0010883195,0.0013212934,0.02408651,0.0029101197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006932244,0.0013329326,0.00075817964,0.012824322,0.010231919,0.010535014,0.0038922892,0.0015521663,0.018665684],"category_scores_gemma":[0.025209734,0.000652272,0.0011137855,0.029796608,0.0030508733,0.0045492095,0.00464299,0.0026947036,0.0035618441],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066893765,0.000025543111,0.018507244,0.00083699304,0.00005152199,0.00020358094,0.0036992144,0.00058575923,0.00028860304,0.0074988836,0.73403376,0.23420201],"study_design_scores_gemma":[0.00001320909,0.000013826013,0.054746177,0.0011860494,0.00006624043,0.0001580441,0.008222748,0.0006816025,0.0002472722,0.0024588923,0.932071,0.00013507468],"about_ca_topic_score_codex":0.9915999,"about_ca_topic_score_gemma":0.99369353,"teacher_disagreement_score":0.053779792,"about_ca_system_score_codex":0.053779792,"about_ca_system_score_gemma":0.1542036,"threshold_uncertainty_score":0.39020157},"labels":[],"label_agreement":null},{"id":"W6997255159","doi":"","title":"Valuing health surveillance as an information system: Interdisciplinary insights","year":2019,"lang":"en","type":"article","venue":"Prodinra (INRA Bordeaux-Aquitaine)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; McGill University","funders":"","keywords":"Information system; Population; Work (physics); Matching (statistics); Value of information; Identification (biology)","score_opus":0.008754110479032205,"score_gpt":0.2937762090586953,"score_spread":0.2850220985796631,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6997255159","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":[0.0802057,0.07792761,0.35959005,0.23359786,0.0011705036,0.0002837112,0.0003211572,0.000076563134,0.24682686],"genre_scores_gemma":[0.93222696,0.021884413,0.038062666,0.003212936,0.0012626429,0.0002458633,0.000072931936,0.000047818896,0.0029837075],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97179955,0.021164436,0.0007975273,0.0011628403,0.0040673623,0.001008294],"domain_scores_gemma":[0.94122475,0.052501887,0.0015686785,0.0019316663,0.0020252785,0.0007478007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024442684,0.0009931003,0.0014593697,0.008439619,0.0028547512,0.021571642,0.0019897372,0.00612382,0.002344439],"category_scores_gemma":[0.02393329,0.0006608889,0.0010530845,0.0061101452,0.031184113,0.022348894,0.0065990277,0.0044686175,0.00023152874],"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":[0.000008744041,0.000024330526,0.00031822405,0.00014044861,0.000020860498,0.00006494541,0.001723099,0.0019590273,0.00005502822,0.98856515,0.0005282531,0.0065919636],"study_design_scores_gemma":[0.00000536672,0.000018417857,0.00027060765,0.00035149555,0.000010613528,0.000054411965,0.0022433104,0.0043615676,0.0000877836,0.9841186,0.0084617855,0.00001602795],"about_ca_topic_score_codex":0.0019515603,"about_ca_topic_score_gemma":0.0010518213,"teacher_disagreement_score":0.024442684,"about_ca_system_score_codex":0.01229266,"about_ca_system_score_gemma":0.0046255738,"threshold_uncertainty_score":0.12926686},"labels":[],"label_agreement":null},{"id":"W6997422836","doi":"","title":"Wipro Records 24% YoY Growth in Net Income in the quarter | PRNewswire","year":2012,"lang":"en","type":"other","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Net income; Work (physics); Data collection","score_opus":0.0133146493533239,"score_gpt":0.26388478974210783,"score_spread":0.25057014038878395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6997422836","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074523455,0.0014118835,0.00087985705,0.014021419,0.0022735577,0.00012501003,0.7210588,0.004873958,0.24790317],"genre_scores_gemma":[0.019426294,0.0021837018,0.0014236755,0.0034146234,0.001842935,0.00020340229,0.44901884,0.0017097893,0.52077675],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993845,0.000052416628,0.000054999986,0.00009693221,0.00031310073,0.00009800273],"domain_scores_gemma":[0.9967372,0.0005428385,0.00060977647,0.00032303273,0.0011463137,0.0006408461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080116006,0.0007365043,0.0004198718,0.0028627045,0.000598977,0.0024659997,0.000579472,0.0010251796,0.12920508],"category_scores_gemma":[0.005907276,0.00045890638,0.00059737405,0.003102039,0.00019307193,0.0016326086,0.001156007,0.0015485237,0.10375957],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052741794,0.000015738187,0.0019262336,0.00007350172,0.000005943833,0.000016435977,0.000009647168,0.000032980144,0.00006664142,0.000306545,0.98133665,0.01615703],"study_design_scores_gemma":[0.000041738836,0.000026380323,0.036748227,0.00011945104,0.000015893107,0.000058470847,0.000064349406,0.0003879404,0.0005447819,0.00025681723,0.96171665,0.000019242621],"about_ca_topic_score_codex":0.03073945,"about_ca_topic_score_gemma":0.040765714,"teacher_disagreement_score":0.12920508,"about_ca_system_score_codex":0.0009412397,"about_ca_system_score_gemma":0.001573871,"threshold_uncertainty_score":0.43223405},"labels":[],"label_agreement":null},{"id":"W7009696632","doi":"","title":"Flotation Process of Porphyry Copper Ore to Prevent Acid Mine Drainage from Tailings and Waste Rock: A Cleaner Production Approach","year":2023,"lang":"fr","type":"other","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Agencia Nacional de Investigación y Desarrollo; Universidad de Antofagasta","keywords":"Covellite; Tailings; Chalcocite; Pyrite; Acid mine drainage; Copper extraction techniques","score_opus":0.008512323958822534,"score_gpt":0.2390391737849446,"score_spread":0.23052684982612207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7009696632","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":[0.98305315,0.0023145322,0.010468187,0.00016178985,0.000051015682,0.00011763707,0.0000931926,0.00008186438,0.0036586886],"genre_scores_gemma":[0.973822,0.0023230484,0.014540731,0.00008002956,0.000020280488,0.00005826207,0.00013199111,0.000020037065,0.009003653],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99990666,0.000008317369,0.000006136348,0.000022329064,0.00003437493,0.000022325956],"domain_scores_gemma":[0.99995625,0.0000061588435,0.00001359238,0.0000034621864,0.000013700083,0.0000067898764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016691933,0.00031363618,0.00031232357,0.00038547005,0.00031035504,0.00029809002,0.00027913347,0.00037775384,0.001301509],"category_scores_gemma":[0.000114373615,0.00012283998,0.00038685475,0.00018279957,0.00015959523,0.00026155042,0.00021458493,0.00030980018,0.00027353436],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018401272,0.00009991097,0.000959164,0.0004143144,0.000018794515,0.00022814822,0.00009971583,0.0014574474,0.97646457,0.00025920817,0.00017694052,0.019637741],"study_design_scores_gemma":[0.00003077546,0.00090713747,0.004958462,0.000048960457,0.00004794653,0.00022383741,0.0001384899,0.006222176,0.98000485,0.000098981625,0.007301748,0.000016704076],"about_ca_topic_score_codex":0.00213682,"about_ca_topic_score_gemma":0.0060089733,"teacher_disagreement_score":0.00213682,"about_ca_system_score_codex":0.00028008944,"about_ca_system_score_gemma":0.0003636055,"threshold_uncertainty_score":0.0043539405},"labels":[],"label_agreement":null},{"id":"W7010154513","doi":"","title":"GlobeNewswire: CREDIT AGRICOLE SA : First Quarter 2019","year":2019,"lang":"en","type":"other","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Period (music); Debt; Government (linguistics)","score_opus":0.006688531165654149,"score_gpt":0.23869544603261153,"score_spread":0.23200691486695738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7010154513","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004028668,0.0010532129,0.00053655746,0.028966645,0.011890562,0.000110523906,0.023104442,0.0033475205,0.9305877],"genre_scores_gemma":[0.0013333617,0.000526326,0.0002452459,0.005178001,0.0012917204,0.000051703886,0.0066850935,0.00069466187,0.98399377],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99932575,0.000086535656,0.000028697392,0.000074817624,0.0003720922,0.000112130896],"domain_scores_gemma":[0.994069,0.00068832614,0.0002596981,0.0003756277,0.002289306,0.0023180395],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0015453014,0.0010120893,0.00057793304,0.0024190177,0.0015136747,0.0060813627,0.0011769813,0.003517674,0.761505],"category_scores_gemma":[0.009539847,0.00038762554,0.0006466019,0.002115664,0.0007544003,0.0029403009,0.0024427802,0.002769809,0.665535],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008952746,0.0000063267494,0.000052980653,0.00001465414,4.8899653e-7,0.0000046163964,0.0000027015533,0.000008187103,0.0000128997135,0.00034395795,0.9924604,0.0070836917],"study_design_scores_gemma":[0.000011820616,0.000009058317,0.00041599965,0.000060937364,0.0000011574405,0.000008077536,0.000028187109,0.00006874705,0.000045228695,0.00087530364,0.99847025,0.0000052170303],"about_ca_topic_score_codex":0.017102873,"about_ca_topic_score_gemma":0.03659856,"teacher_disagreement_score":0.23849499,"about_ca_system_score_codex":0.002347642,"about_ca_system_score_gemma":0.004040915,"threshold_uncertainty_score":0.34018415},"labels":[],"label_agreement":null},{"id":"W7014384399","doi":"","title":"Payment Data Systems Announces Results for the Second Quarter of 2018","year":2018,"lang":"en","type":"other","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Payment; Data system; Payment system; Data collection","score_opus":0.04419925869048082,"score_gpt":0.31287412498955475,"score_spread":0.26867486629907394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7014384399","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011500553,0.002029945,0.005135955,0.06870408,0.0121471,0.0006466297,0.5606104,0.0068700584,0.33235526],"genre_scores_gemma":[0.045355085,0.0023102195,0.0054183016,0.016681282,0.0055569718,0.0008509094,0.36965266,0.0019973808,0.55217713],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99615186,0.00052187,0.00034573264,0.00034659888,0.0021526422,0.00048133067],"domain_scores_gemma":[0.9850503,0.0028497323,0.0018053743,0.00079012645,0.006860873,0.0026435035],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004676368,0.0007865349,0.0005720924,0.002758231,0.0011570151,0.0043404917,0.001180788,0.0018168783,0.087887295],"category_scores_gemma":[0.025182188,0.00040756725,0.0005773011,0.002518733,0.00039707322,0.0021588698,0.0019653994,0.0025085623,0.056668125],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008499743,0.00003457163,0.0027948928,0.00003601088,0.000005097215,0.000013191522,0.000013110677,0.00007670797,0.000049101203,0.0017141092,0.9830657,0.012112492],"study_design_scores_gemma":[0.00009114088,0.0000395552,0.017490264,0.00010006651,0.000011545787,0.00004572169,0.000077087505,0.0010750573,0.00054300117,0.0016298062,0.9788795,0.000017211672],"about_ca_topic_score_codex":0.027365949,"about_ca_topic_score_gemma":0.03182717,"teacher_disagreement_score":0.9121127,"about_ca_system_score_codex":0.0034963838,"about_ca_system_score_gemma":0.007428998,"threshold_uncertainty_score":0.29401225},"labels":[],"label_agreement":null},{"id":"W7015464723","doi":"","title":"Spatial modeling of repeated events","year":2021,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Spatial epidemiology; Spatial analysis; Poisson regression; Overdispersion; Covariate; Poisson distribution; Smoothing; Spatial correlation; Random effects model; Spatial variability","score_opus":0.01637078869146968,"score_gpt":0.23216711009354554,"score_spread":0.21579632140207586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7015464723","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":[0.014810094,0.0019397014,0.9677932,0.0014956037,0.00046681624,0.00020781427,0.0040947488,0.00089321996,0.008298777],"genre_scores_gemma":[0.61161596,0.0082426015,0.3078366,0.0010954025,0.0013201399,0.002066117,0.012012729,0.0008284412,0.05498196],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99400514,0.0026615937,0.00042098432,0.0016712105,0.0008211865,0.0004198813],"domain_scores_gemma":[0.9847319,0.010097139,0.0019228229,0.0014665577,0.0014741741,0.0003074144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006253672,0.0014381785,0.0022197457,0.0033052596,0.0011153478,0.003731426,0.0046351487,0.002569361,0.01669523],"category_scores_gemma":[0.023551468,0.0012198002,0.003921633,0.0049166093,0.0019237385,0.003228531,0.0034304305,0.0025980761,0.0029752036],"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":[0.000099528115,0.000086458334,0.011551769,0.00040747956,0.00044149815,0.00071329315,0.0006455579,0.46327883,0.0005486177,0.4671227,0.00885072,0.046253555],"study_design_scores_gemma":[0.000028916777,0.00006688568,0.0019168901,0.00009734241,0.00010241823,0.00023116055,0.00013542989,0.8160754,0.00017422243,0.165253,0.01586663,0.000051707142],"about_ca_topic_score_codex":0.03172982,"about_ca_topic_score_gemma":0.017051335,"teacher_disagreement_score":0.03172982,"about_ca_system_score_codex":0.002142352,"about_ca_system_score_gemma":0.0026041262,"threshold_uncertainty_score":0.063090265},"labels":[],"label_agreement":null},{"id":"W7018718375","doi":"","title":"Elon Stumbles on DOGE Transparency & Fighting Misinformation With Science","year":2025,"lang":"en","type":"other","venue":"Internet Archive (Internet Archive)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Joins; Misinformation; Transparency (behavior); Newspaper; Skepticism; Cover (algebra)","score_opus":0.011412223083008044,"score_gpt":0.26155749317973875,"score_spread":0.2501452700967307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7018718375","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00048045325,0.0070635127,0.0009552403,0.23988725,0.029909376,0.00003565639,0.0007041197,0.00078074256,0.7201836],"genre_scores_gemma":[0.0040974175,0.0042407606,0.0005962202,0.044937342,0.0055048536,0.000022307213,0.0002443711,0.00048509237,0.9398715],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981743,0.0003540637,0.000076153345,0.00017821345,0.0009868881,0.00023031238],"domain_scores_gemma":[0.994611,0.0017103974,0.00022680314,0.00046323513,0.0013495132,0.0016389706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020542957,0.0006606823,0.00046934796,0.0018882683,0.0047670645,0.012048681,0.00055429444,0.0035371715,0.25323665],"category_scores_gemma":[0.009691441,0.00035449027,0.0005159449,0.0014491659,0.0028992184,0.008886541,0.0033262102,0.0037600405,0.12537156],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003750583,0.0000038544463,0.000064381646,0.00001249696,6.719956e-7,0.000012274113,0.00006814957,0.0000062189724,0.000023126302,0.0040567745,0.9853998,0.010348461],"study_design_scores_gemma":[0.0000014882102,0.0000028281854,0.00013224366,0.00003134491,5.8123067e-7,0.000016071626,0.00017762942,0.000015589103,0.000034497192,0.0016618159,0.9979231,0.0000028695247],"about_ca_topic_score_codex":0.012302733,"about_ca_topic_score_gemma":0.03441871,"teacher_disagreement_score":0.25323665,"about_ca_system_score_codex":0.0023720835,"about_ca_system_score_gemma":0.003650176,"threshold_uncertainty_score":0.847161},"labels":[],"label_agreement":null},{"id":"W7024896438","doi":"","title":"Stronghold Digital Mining Reports Third Quarter 2021 Results and Provides Operational Update","year":2021,"lang":"en","type":"other","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Identification (biology); Payment; Key (lock); Work (physics)","score_opus":0.011659744126849355,"score_gpt":0.2595808854274773,"score_spread":0.24792114130062795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024896438","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043590055,0.0017613737,0.0076725446,0.05060737,0.008847289,0.00082818134,0.42747265,0.013824489,0.48462716],"genre_scores_gemma":[0.0226157,0.0028283687,0.013353325,0.015577347,0.0035416333,0.0010783534,0.44914925,0.004495274,0.48736075],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99297214,0.00077848893,0.00059697893,0.0003594076,0.004740614,0.00055227696],"domain_scores_gemma":[0.93725336,0.013484043,0.0052039986,0.0051687406,0.03183503,0.007054802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010647065,0.0013912434,0.00081259693,0.006938589,0.0012363754,0.007981959,0.0023450605,0.002313107,0.15627708],"category_scores_gemma":[0.057465583,0.00061929424,0.0010979803,0.005733532,0.0007237183,0.004826597,0.003367008,0.0025829612,0.15378031],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005947461,0.000055618064,0.0019002211,0.00008975184,0.000007504122,0.000012126161,0.000011830268,0.0001468574,0.000071920644,0.0011164024,0.96110696,0.03542144],"study_design_scores_gemma":[0.00006474702,0.000039437124,0.005703288,0.00023893823,0.000018181752,0.000031266525,0.000060513146,0.0013176516,0.0006330819,0.0021101492,0.9897593,0.000023441007],"about_ca_topic_score_codex":0.0343354,"about_ca_topic_score_gemma":0.027766768,"teacher_disagreement_score":0.15627708,"about_ca_system_score_codex":0.0035403783,"about_ca_system_score_gemma":0.012834962,"threshold_uncertainty_score":0.5227989},"labels":[],"label_agreement":null},{"id":"W7026613444","doi":"","title":"ARCHIVEIT-1830-NONE-BLTYBR-20120119190146-00000-crawling114.us.archive.org-6680.warc.gz","year":2012,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Work (physics); Process (computing)","score_opus":0.0077611976997870495,"score_gpt":0.2091681167687607,"score_spread":0.20140691906897365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7026613444","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006676603,0.00021998747,0.0032006295,0.00047647278,0.000176042,0.0001762098,0.72010124,0.12714945,0.14783223],"genre_scores_gemma":[0.0026475347,0.0003035768,0.0049716397,0.00031844355,0.00007646516,0.0001920734,0.7796187,0.04919072,0.16268091],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988269,0.00011073794,0.00008827999,0.00025975853,0.0005430229,0.00017126193],"domain_scores_gemma":[0.9921875,0.0011692301,0.00031465618,0.0024459048,0.0029078154,0.0009748334],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0015119626,0.0021075658,0.001207976,0.0077259005,0.0014876717,0.004999127,0.0021747488,0.0014682994,0.62557125],"category_scores_gemma":[0.011478629,0.0016574685,0.00084079127,0.010309337,0.0005934912,0.0027094753,0.0031636758,0.0011846647,0.79533964],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027242462,0.000012955066,0.00029044139,0.00010462871,0.000005803398,0.000007552473,0.000039188162,0.00004830166,0.00011820694,0.00033574202,0.98551756,0.013492386],"study_design_scores_gemma":[0.00003631042,0.0000083246305,0.0021048307,0.000074259624,0.000005984786,0.000030831303,0.00004007841,0.00031082705,0.00080031966,0.00043522235,0.99613285,0.000020075384],"about_ca_topic_score_codex":0.0817723,"about_ca_topic_score_gemma":0.08508199,"teacher_disagreement_score":0.37442875,"about_ca_system_score_codex":0.002298253,"about_ca_system_score_gemma":0.002772124,"threshold_uncertainty_score":0.53407717},"labels":[],"label_agreement":null},{"id":"W7027455946","doi":"","title":"1950 Chap. 0554. An Act Relative To Membership In The Town Of Brook-Line Retirement System.","year":2010,"lang":"en","type":"other","venue":"State Library's electronic repository (State Library of Massachusetts)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Work (physics); Quarter (Canadian coin); Payment","score_opus":0.011357418446758314,"score_gpt":0.2428641994898154,"score_spread":0.2315067810430571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7027455946","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004947635,0.012087831,0.0004304305,0.026346622,0.00787653,0.0001227313,0.009760216,0.00044912216,0.9424318],"genre_scores_gemma":[0.0016418649,0.0018780832,0.00020881943,0.011999369,0.000754362,0.00014864818,0.0008260729,0.000099997924,0.98244286],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989857,0.00015129945,0.00010956172,0.00014687062,0.00042751513,0.0001789702],"domain_scores_gemma":[0.9981762,0.00058044546,0.00014595647,0.000113752125,0.00073750084,0.00024612696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014821494,0.0010808269,0.00054845965,0.0019141264,0.0041412143,0.003050615,0.0012100281,0.006835538,0.22388478],"category_scores_gemma":[0.006595618,0.0008609384,0.00041981536,0.0026802414,0.0009995527,0.0018462499,0.0018647807,0.004763426,0.18488212],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064596065,0.0000044610383,0.000079680154,0.000021824804,5.074007e-7,0.000012633204,0.00006914945,0.000009219338,0.00001673551,0.00411157,0.9892083,0.006459484],"study_design_scores_gemma":[0.000005425554,0.000005430316,0.0017911938,0.00016505948,0.0000018462052,0.0000233389,0.000113294955,0.00001512652,0.000027470167,0.0006375586,0.9972078,0.0000064714113],"about_ca_topic_score_codex":0.21224405,"about_ca_topic_score_gemma":0.43127245,"teacher_disagreement_score":0.22388478,"about_ca_system_score_codex":0.0031676318,"about_ca_system_score_gemma":0.005511273,"threshold_uncertainty_score":0.7489692},"labels":[],"label_agreement":null},{"id":"W7029127520","doi":"","title":"Incidence of epithelial lesions of the conjunctiva in a review of 12,102 specimens in Canada (Quebec)","year":2011,"lang":"en","type":"article","venue":"LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Conjunctiva; Actinic keratosis; Incidence (geometry); Atypia; Mucoepidermoid carcinoma; Basal cell; Carcinoma in situ; Squamous metaplasia; Basal cell carcinoma","score_opus":0.026044041727834817,"score_gpt":0.24371864824288664,"score_spread":0.21767460651505183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7029127520","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":[0.9869098,0.006906278,0.000182726,0.00013181522,0.000020779225,0.000075474745,0.0034830314,0.000022894803,0.002267193],"genre_scores_gemma":[0.9958897,0.0020362241,0.00026445297,0.00008403948,0.000006469579,0.000012121095,0.0012726984,0.000004831657,0.00042952856],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999087,0.00006252364,0.000095409654,0.00013287932,0.00044883788,0.0001732789],"domain_scores_gemma":[0.997232,0.0001905983,0.0006880972,0.00010833647,0.0015359147,0.00024503947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006806276,0.00029949725,0.00023602348,0.003121925,0.0014826751,0.00093604426,0.00067214103,0.00033549307,0.001116664],"category_scores_gemma":[0.0020817153,0.0002733034,0.00022042742,0.004210653,0.0006726105,0.00026425775,0.0004822274,0.0002161447,0.00019423816],"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":[0.000036786343,0.000005768439,0.9918684,0.000093408,0.000033114226,0.0008088011,0.00025773564,0.000038164035,0.0009916109,0.000014575924,0.0005371536,0.005314407],"study_design_scores_gemma":[0.0000019713495,0.00001942897,0.99629056,0.000054333545,0.00002294422,0.0012772062,0.00052200037,0.00007375147,0.00025098576,0.0000027408296,0.001480456,0.0000036858614],"about_ca_topic_score_codex":0.91775227,"about_ca_topic_score_gemma":0.9579419,"teacher_disagreement_score":0.082247734,"about_ca_system_score_codex":0.009657461,"about_ca_system_score_gemma":0.009281934,"threshold_uncertainty_score":0.16546416},"labels":[],"label_agreement":null},{"id":"W7030031261","doi":"","title":"Mapping Health on the Internet","year":2002,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Strong","keywords":"Situated; The Internet; Software; Geographic information system; Public health; GIS and public health; Focus (optics); Focus group","score_opus":0.023558498393037135,"score_gpt":0.23633874147038625,"score_spread":0.2127802430773491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7030031261","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06246611,0.009751333,0.044355724,0.032722723,0.0018191161,0.0008185959,0.03206541,0.0036091567,0.81239194],"genre_scores_gemma":[0.5490033,0.035837308,0.15598468,0.005504898,0.0020630988,0.0012889836,0.038744543,0.00096617185,0.21060698],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99846256,0.0007237654,0.0000662314,0.00023640784,0.00038854737,0.00012262045],"domain_scores_gemma":[0.9971105,0.0014155296,0.00022776419,0.00040331454,0.00065682596,0.00018608372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015050246,0.00044113075,0.00024787794,0.0059007164,0.001378356,0.006009113,0.0009017096,0.0010965274,0.052995343],"category_scores_gemma":[0.0058851764,0.00027987376,0.00046123026,0.007389302,0.0007062158,0.004683641,0.002669138,0.00074351515,0.009186352],"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":[0.00006923749,0.000087547516,0.021837471,0.0008053191,0.00004512912,0.00027469906,0.0064411713,0.0017007481,0.0006801507,0.04846843,0.23486504,0.6847251],"study_design_scores_gemma":[0.000021348093,0.00007687128,0.022379292,0.0010051725,0.000046693625,0.00041636353,0.011389788,0.0024733364,0.00065633684,0.017247299,0.9442454,0.00004217919],"about_ca_topic_score_codex":0.012353518,"about_ca_topic_score_gemma":0.014098965,"teacher_disagreement_score":0.052995343,"about_ca_system_score_codex":0.0013278631,"about_ca_system_score_gemma":0.001809229,"threshold_uncertainty_score":0.17728704},"labels":[],"label_agreement":null},{"id":"W7034473997","doi":"","title":"Upholding diverse knowledge systems to identify marine areas of high ecological, socio-economic, and cultural value in Howe Sound/Atl'ka7tsem, British Columbia","year":2022,"lang":"en","type":"article","venue":"Western CEDAR (Western Washington University)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Traditional knowledge; Stewardship (theology); Indigenous; General partnership; Citizen journalism; Value (mathematics); Knowledge sharing; Citizen science; Livelihood","score_opus":0.021752387806527062,"score_gpt":0.2614787095105191,"score_spread":0.23972632170399205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7034473997","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":[0.96573144,0.00032365488,0.0028789246,0.0021663466,0.000014356339,0.00032118364,0.000072572315,0.00002162958,0.028469708],"genre_scores_gemma":[0.99234563,0.00016591405,0.003713119,0.00023335525,0.0000017557137,0.00011973475,0.000065090906,0.000007444365,0.0033480101],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99611795,0.0018997585,0.00014716346,0.000390551,0.00066039996,0.0007842354],"domain_scores_gemma":[0.99421257,0.0026520726,0.00034972338,0.0003596188,0.0015401386,0.00088579895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005680305,0.00029826284,0.0002989233,0.0025102356,0.012625234,0.00732211,0.0012117658,0.00070720346,0.00245372],"category_scores_gemma":[0.008490123,0.00033503727,0.0001730499,0.0025141323,0.00540879,0.0020737485,0.0071753804,0.001106741,0.00014221946],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087464614,0.00012575749,0.08119042,0.0003490593,0.00003931376,0.0018554485,0.7706987,0.00065860106,0.0051118913,0.008032151,0.0026512293,0.12919994],"study_design_scores_gemma":[0.000014272052,0.000066265246,0.050870176,0.00034915036,0.00003424145,0.0001551456,0.909112,0.001186048,0.0009539775,0.0026755892,0.034540813,0.00004232777],"about_ca_topic_score_codex":0.80676365,"about_ca_topic_score_gemma":0.9305901,"teacher_disagreement_score":0.19323635,"about_ca_system_score_codex":0.023548547,"about_ca_system_score_gemma":0.046438325,"threshold_uncertainty_score":0.3887487},"labels":[],"label_agreement":null},{"id":"W7034840896","doi":"","title":"Women's Volleyball Team at Canadian Interuniversity Athletic Union Tournament, 1997","year":2010,"lang":"en","type":"other","venue":"Saint Mary's University Institutional Repository (Saint Mary's University)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Work (physics); Football","score_opus":0.005839089978764585,"score_gpt":0.17880355828924252,"score_spread":0.17296446831047793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7034840896","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019113852,0.00062941696,0.00035775237,0.0008325206,0.00009392682,0.00016676346,0.9475059,0.0003379159,0.030961951],"genre_scores_gemma":[0.114127085,0.0031745874,0.002519071,0.00087156496,0.00007698729,0.00052366086,0.6452043,0.00028347672,0.23321924],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986683,0.000051489063,0.00008282266,0.00014272622,0.00071912335,0.0003356006],"domain_scores_gemma":[0.99531174,0.0001681464,0.00032989198,0.00014444938,0.0032711427,0.0007745579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093967206,0.00076713227,0.00046089047,0.004594317,0.0023653598,0.0014414459,0.0022308212,0.00052662665,0.01692413],"category_scores_gemma":[0.005628677,0.0005817764,0.00046352082,0.008200439,0.00028995704,0.00054101466,0.0010261241,0.00095374935,0.0057116807],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001704224,0.00010349937,0.14458284,0.00027098146,0.000059554175,0.00007367029,0.0005200113,0.0007556633,0.0001325834,0.001080422,0.8043555,0.0478949],"study_design_scores_gemma":[0.000057149296,0.000023090315,0.79945976,0.00024405928,0.00006001579,0.00006229084,0.0011999318,0.00088747643,0.0005251626,0.00019532956,0.19725128,0.00003443703],"about_ca_topic_score_codex":0.99423176,"about_ca_topic_score_gemma":0.9974904,"teacher_disagreement_score":0.023074329,"about_ca_system_score_codex":0.023074329,"about_ca_system_score_gemma":0.06576713,"threshold_uncertainty_score":0.16741675},"labels":[],"label_agreement":null},{"id":"W7036623248","doi":"","title":"Case 8 : Is it too Late to Re-evaluate? Creating Client-centered Changes within Canada’s Medical Surveillance System","year":2020,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Officer; Public health; Unit (ring theory); Citizenship; Public health surveillance; Confidentiality; Interpretation (philosophy); Medical surveillance","score_opus":0.14424965004351364,"score_gpt":0.3382104366549584,"score_spread":0.19396078661144478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036623248","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4671844,0.001456516,0.012420444,0.4262767,0.002211371,0.0008845454,0.0005745758,0.00046528238,0.08852622],"genre_scores_gemma":[0.90609974,0.0014642015,0.01203844,0.052375223,0.00034467952,0.0002598933,0.00019481897,0.00021987593,0.027003169],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9911714,0.002196726,0.0004002212,0.00071522297,0.0021892104,0.0033271585],"domain_scores_gemma":[0.98446727,0.0036570972,0.0010897898,0.0005094865,0.0036962468,0.006580108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051931976,0.000715707,0.0005392796,0.0013911997,0.02136322,0.0038822023,0.0033853096,0.0056564715,0.005947803],"category_scores_gemma":[0.02151194,0.0006438031,0.0007406746,0.0014587194,0.0042541004,0.0032129472,0.0036072652,0.012196522,0.0006330847],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028183055,0.0011144702,0.105765685,0.0004560218,0.0000754459,0.23680171,0.31003216,0.0014462216,0.002710766,0.03867908,0.20766695,0.09496963],"study_design_scores_gemma":[0.000063763364,0.0004228144,0.044223145,0.0022187952,0.00013690775,0.14756563,0.5052056,0.0065765786,0.0039953077,0.0155028375,0.27373403,0.00035466207],"about_ca_topic_score_codex":0.56971204,"about_ca_topic_score_gemma":0.7333855,"teacher_disagreement_score":0.966143,"about_ca_system_score_codex":0.033856977,"about_ca_system_score_gemma":0.042902734,"threshold_uncertainty_score":0.865644},"labels":[],"label_agreement":null},{"id":"W7037914753","doi":"","title":"#enterrasse : des reseaux à l'apero, quelle sera la boisson de l'ete ?","year":2020,"lang":"fr","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"European union; Quarter (Canadian coin); Capital (architecture); Exhibition","score_opus":0.008154288830723926,"score_gpt":0.21753934804808214,"score_spread":0.20938505921735823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7037914753","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004621659,0.0031587614,0.0011246644,0.13126206,0.009031725,0.00029057154,0.09040819,0.001909862,0.75819254],"genre_scores_gemma":[0.015395858,0.002319833,0.0008075809,0.025699181,0.0015024692,0.0001838489,0.02310564,0.00061990425,0.9303657],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99939704,0.00007931623,0.000041131578,0.00008165977,0.00019136108,0.0002095366],"domain_scores_gemma":[0.99795145,0.00025114417,0.00021079413,0.00012809146,0.00067422027,0.0007843657],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007914496,0.00035357708,0.0003978357,0.0007147824,0.0014706791,0.0029888134,0.00074156985,0.0026244435,0.30980188],"category_scores_gemma":[0.0046609533,0.0002350641,0.0003901463,0.0007590833,0.0003653292,0.002085605,0.0021568378,0.0025973285,0.16005166],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023938226,0.000007867218,0.0008596671,0.000070869624,0.0000016223007,0.00004782865,0.000082686456,0.000011441629,0.0000720819,0.0016441977,0.9755706,0.021607092],"study_design_scores_gemma":[0.000006398737,0.00000919073,0.0036917182,0.000105130996,0.000002064601,0.000048901085,0.00014460932,0.000032153628,0.00009051992,0.00033920977,0.99552476,0.0000052749397],"about_ca_topic_score_codex":0.07344994,"about_ca_topic_score_gemma":0.105885975,"teacher_disagreement_score":0.30980188,"about_ca_system_score_codex":0.002053852,"about_ca_system_score_gemma":0.0041072406,"threshold_uncertainty_score":0.98448384},"labels":[],"label_agreement":null},{"id":"W7067120513","doi":"","title":"La relation de soins et de services : entre le lien social de don, le lien d'autorité et le lien de sacrifice","year":2003,"lang":"fr","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Lien; Social relationship; Social life","score_opus":0.002832948098436561,"score_gpt":0.1769723900611219,"score_spread":0.17413944196268533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7067120513","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3345021,0.01590046,0.0049051675,0.10924167,0.0014012655,0.0000646305,0.00032792788,0.00007572316,0.53358096],"genre_scores_gemma":[0.9659046,0.003762346,0.0006610479,0.0028318514,0.00018121731,0.000042472304,0.00006498736,0.000039840517,0.026511682],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9920013,0.004755313,0.00014741019,0.00059482956,0.0008522417,0.0016488036],"domain_scores_gemma":[0.9918407,0.002725349,0.0014520272,0.00024935868,0.0010458444,0.0026867413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003191973,0.00044297075,0.000379594,0.0022636445,0.012931333,0.011188477,0.0009982655,0.0026216316,0.026762731],"category_scores_gemma":[0.0068842634,0.00022795265,0.00044473654,0.003948408,0.018804427,0.008115791,0.009379395,0.0028512704,0.0011765055],"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":[0.00009562634,0.00009514729,0.029168807,0.00039586826,0.000045163702,0.0005942514,0.16921118,0.00023608457,0.00028874172,0.7434501,0.01730701,0.039112035],"study_design_scores_gemma":[0.000019615358,0.00007286954,0.021777453,0.0010166818,0.000034308036,0.0005867337,0.39598355,0.00033539333,0.00022511769,0.07823021,0.50165784,0.000060179755],"about_ca_topic_score_codex":0.12280956,"about_ca_topic_score_gemma":0.2051862,"teacher_disagreement_score":0.12280956,"about_ca_system_score_codex":0.01629439,"about_ca_system_score_gemma":0.013959709,"threshold_uncertainty_score":0.24418944},"labels":[],"label_agreement":null},{"id":"W7071700798","doi":"","title":"Spatial survival analysis: an application to lung cancer data in Manitoba","year":2023,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Lung cancer; Cluster (spacecraft); Merge (version control); Cancer registry; Population; Spatial analysis; Spatial epidemiology; Proportional hazards model","score_opus":0.0252520696788859,"score_gpt":0.2930839327764663,"score_spread":0.26783186309758045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7071700798","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":[0.96196365,0.001079983,0.02090731,0.002502686,0.0000471068,0.0002794236,0.00912917,0.0006780036,0.0034125845],"genre_scores_gemma":[0.94287777,0.0009756577,0.046277653,0.00012447374,0.00002207086,0.0001521928,0.0063416734,0.000043342487,0.003185338],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99960357,0.00020000104,0.000022021326,0.00006341467,0.00007224524,0.00003880345],"domain_scores_gemma":[0.9986903,0.00075686123,0.00009865478,0.00010258913,0.00028485814,0.00006664624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020785627,0.00029910402,0.00027982998,0.0009096616,0.0006278525,0.00056440756,0.00069272757,0.00036511128,0.0010794002],"category_scores_gemma":[0.004263792,0.0001642199,0.00060931395,0.0024274476,0.00022979158,0.00021239223,0.0007575116,0.00038679226,0.00017041183],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003397844,0.00040344318,0.40675274,0.00041530054,0.00043899467,0.001366456,0.0019325662,0.40126547,0.0023914105,0.0077824863,0.014687315,0.16222408],"study_design_scores_gemma":[0.00006999614,0.00014117119,0.17843977,0.000091457645,0.000100957295,0.00026860883,0.0024616304,0.7998058,0.0011644126,0.0032080938,0.014201591,0.000046533256],"about_ca_topic_score_codex":0.45435524,"about_ca_topic_score_gemma":0.50778395,"teacher_disagreement_score":0.54564476,"about_ca_system_score_codex":0.0033556262,"about_ca_system_score_gemma":0.003453243,"threshold_uncertainty_score":0.90342116},"labels":[],"label_agreement":null},{"id":"W7084133213","doi":"10.64628/aam.scgsjt9k9","title":"As Ottawa helps the news industry, latest research suggests journalists’ loyalties are tough to buy","year":2019,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Government (linguistics); The Internet; Work (physics); Key (lock)","score_opus":0.05091323612869977,"score_gpt":0.38272156944001345,"score_spread":0.3318083333113137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084133213","genre_codex":"commentary","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":[0.025906373,0.013483061,0.0015459512,0.5920809,0.015744675,0.00024994244,0.0065769316,0.0016030066,0.34280926],"genre_scores_gemma":[0.37694296,0.013263747,0.008404211,0.24587245,0.011272458,0.00019100425,0.006497015,0.001474541,0.3360816],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.97306776,0.004922966,0.0017098605,0.002479527,0.013360477,0.0044594407],"domain_scores_gemma":[0.7462904,0.08296655,0.018066643,0.013017221,0.078006074,0.061653152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019254355,0.0008566587,0.0013178217,0.008157151,0.012476973,0.048245713,0.0019286658,0.0069730408,0.06776849],"category_scores_gemma":[0.11605133,0.0015359144,0.0010671442,0.007367642,0.008567124,0.0118945325,0.0042286348,0.008799594,0.021599773],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042354845,0.00011993754,0.036879122,0.0005345053,0.00018838042,0.000451765,0.003133975,0.00016721412,0.0010274772,0.026675096,0.8772762,0.053122792],"study_design_scores_gemma":[0.00008579842,0.000114383445,0.03158986,0.00042379877,0.00012184755,0.00025704462,0.0058218376,0.00022196151,0.0008822168,0.003223702,0.9570586,0.0001989523],"about_ca_topic_score_codex":0.46201727,"about_ca_topic_score_gemma":0.64424247,"teacher_disagreement_score":0.5379827,"about_ca_system_score_codex":0.029167851,"about_ca_system_score_gemma":0.05162786,"threshold_uncertainty_score":0.91865605},"labels":[],"label_agreement":null},{"id":"W7088306985","doi":"10.5281/zenodo.17321473","title":"FIGURES 213–216 in Contribution to the knowledge of psammophylous representatives of Procloeon Bengtsson 1915-Psammonella Glazaczow 1987 and Pseudocentroptiloides Jacob 1987 (Ephemeroptera: Baetidae)","year":2025,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Section (typography); Balsam; Government (linguistics); George (robot)","score_opus":0.014283026864355059,"score_gpt":0.27486432646118364,"score_spread":0.2605812995968286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7088306985","genre_codex":"other","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00358247,0.002580697,0.004565277,0.0006358093,0.002528319,0.0001451269,0.032258976,0.000913268,0.9527901],"genre_scores_gemma":[0.04664028,0.00542357,0.01382676,0.0004222743,0.0013756657,0.00016780115,0.10318046,0.0011261711,0.827837],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998839,0.000010013375,0.000009087298,0.000037356014,0.000045273515,0.000014423962],"domain_scores_gemma":[0.99975365,0.000031700805,0.000028820756,0.0000338527,0.00009808953,0.000053846466],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00014088671,0.0005415125,0.0002578043,0.002577489,0.0006495812,0.0006264213,0.0008502849,0.000448169,0.41082615],"category_scores_gemma":[0.0005475964,0.00018929297,0.00025400767,0.0021066463,0.00048657303,0.0008775728,0.00057585904,0.00073579926,0.16012505],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001099789,0.000028632008,0.0017126591,0.00040025596,0.000014798367,0.0003456734,0.00018624267,0.00023183823,0.006016404,0.0062069823,0.86178017,0.12296635],"study_design_scores_gemma":[0.0000034993086,0.0000041801077,0.005962158,0.000035559457,0.000005512062,0.00026590156,0.0000553858,0.00006097932,0.0005244407,0.0004299975,0.99264985,0.0000025401796],"about_ca_topic_score_codex":0.016479477,"about_ca_topic_score_gemma":0.03388542,"teacher_disagreement_score":0.58917385,"about_ca_system_score_codex":0.0009361089,"about_ca_system_score_gemma":0.0004418942,"threshold_uncertainty_score":0.84038496},"labels":[],"label_agreement":null},{"id":"W7095461620","doi":"","title":"RESEARCH ARTICLE Open Access","year":2013,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Observational study; Pandemic; Research article; Open research; Empirical research; Influenza pandemic","score_opus":0.23308741869792307,"score_gpt":0.5357299387449818,"score_spread":0.3026425200470587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7095461620","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20912191,0.005738156,0.0019883635,0.0075834147,0.0025123276,0.001206967,0.1314615,0.00085138145,0.63953596],"genre_scores_gemma":[0.38562995,0.0045262086,0.0026309919,0.001650317,0.0008649971,0.00039296417,0.05371649,0.00032698674,0.5502611],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978789,0.0001726994,0.000117101794,0.0002879016,0.0010095225,0.0005339992],"domain_scores_gemma":[0.9911084,0.0008219581,0.000835843,0.0010391406,0.0045227455,0.0016718685],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014749982,0.00041997264,0.0005845226,0.0021263063,0.002371093,0.003570514,0.0018774243,0.0009164093,0.19633819],"category_scores_gemma":[0.011101359,0.00027216604,0.0007136851,0.0028217447,0.0010940932,0.0009937112,0.0014099113,0.000680294,0.043055892],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008614597,0.0006719105,0.31875315,0.00087951124,0.00015286106,0.0008191944,0.0017428879,0.00019030581,0.0014472388,0.0046131294,0.334933,0.33493543],"study_design_scores_gemma":[0.00015305536,0.0002045148,0.5185919,0.00043723086,0.00008245102,0.000571988,0.0014897285,0.00020706374,0.0005551697,0.00084358786,0.4768311,0.000032247513],"about_ca_topic_score_codex":0.57445407,"about_ca_topic_score_gemma":0.5909883,"teacher_disagreement_score":0.8036618,"about_ca_system_score_codex":0.005907086,"about_ca_system_score_gemma":0.015664399,"threshold_uncertainty_score":0.85610414},"labels":[],"label_agreement":null},{"id":"W7096897596","doi":"","title":"Real-Time","year":2013,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Triage; Electronic surveillance; Emergency department; Telehealth; Public health surveillance; Disease surveillance; Asthma; Pandemic","score_opus":0.008032016503788382,"score_gpt":0.25179269505076796,"score_spread":0.24376067854697958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096897596","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3294583,0.004650464,0.5274961,0.0038053438,0.0049913675,0.003226404,0.02566176,0.024435306,0.07627496],"genre_scores_gemma":[0.8240148,0.0006361273,0.14675507,0.001802334,0.00044347605,0.0016136739,0.0115796365,0.0005859279,0.012568929],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9954845,0.00096348225,0.00027059345,0.0014459527,0.0016040535,0.0002314365],"domain_scores_gemma":[0.9932064,0.0022746546,0.00062669377,0.0010918565,0.0024613265,0.00033909726],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0031822687,0.0007743384,0.0004686561,0.0010684584,0.00041464326,0.0023745233,0.0015900846,0.0010604964,0.014528661],"category_scores_gemma":[0.011055642,0.00021825205,0.0003359173,0.00096363924,0.00039253148,0.0014792179,0.0013199471,0.0007922764,0.0063025006],"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":[0.005625836,0.0019866235,0.08195136,0.0012117655,0.00027012627,0.00081804866,0.0009927696,0.02289549,0.07575742,0.00868108,0.08069952,0.71910995],"study_design_scores_gemma":[0.0012002052,0.004478257,0.053425368,0.00041881227,0.00036919682,0.002445927,0.0010318873,0.49706864,0.14685053,0.010695009,0.28163138,0.00038468017],"about_ca_topic_score_codex":0.0019711028,"about_ca_topic_score_gemma":0.0013243777,"teacher_disagreement_score":0.98547137,"about_ca_system_score_codex":0.0008435702,"about_ca_system_score_gemma":0.000811131,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W7096981449","doi":"","title":"Count","year":2015,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Replication (statistics); Population; Equine influenza; Pandemic; Human immunodeficiency virus (HIV); Statistical analysis","score_opus":0.048539786172806014,"score_gpt":0.3127487676749053,"score_spread":0.26420898150209926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096981449","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004759961,0.0006532042,0.0014172872,0.011128325,0.023773389,0.00046445642,0.06362132,0.006685833,0.8917803],"genre_scores_gemma":[0.0021827726,0.00072474324,0.0014774324,0.004394107,0.0038241947,0.0003179889,0.02760666,0.0027839127,0.9566882],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988914,0.00013500518,0.00012283525,0.0002077792,0.0004800992,0.00016278321],"domain_scores_gemma":[0.9937262,0.0006140356,0.00023770507,0.00066221145,0.0035277952,0.0012321038],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010335038,0.001131517,0.001010579,0.0027287158,0.0020724365,0.006670201,0.0020129853,0.0021071045,0.8792103],"category_scores_gemma":[0.00993936,0.0005576539,0.00081291364,0.0029654796,0.00044779963,0.003398574,0.0027429694,0.0022449177,0.82753426],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009479165,0.000004325618,0.00005610793,0.000038292143,7.04348e-7,0.000011122075,0.000008536688,0.0000069045104,0.000017683285,0.0004874814,0.9884736,0.0108859185],"study_design_scores_gemma":[0.0000097033135,0.0000055862442,0.0003123699,0.0000645731,0.0000014020898,0.000034560024,0.00004212998,0.000019951114,0.000044726,0.00045433277,0.9990049,0.000005755244],"about_ca_topic_score_codex":0.0051950677,"about_ca_topic_score_gemma":0.006892996,"teacher_disagreement_score":0.12078971,"about_ca_system_score_codex":0.0013180289,"about_ca_system_score_gemma":0.0027551833,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W7097145842","doi":"","title":"Guidelines for the Investigation of Clusters of Non-Communicable Health Events","year":2011,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public health; Population health; Population; Public health surveillance; Health data; MEDLINE","score_opus":0.19516293758556405,"score_gpt":0.3906732069710604,"score_spread":0.19551026938549634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097145842","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":[0.009899892,0.017808093,0.3500718,0.08258574,0.012717856,0.1789626,0.18960144,0.007759228,0.15059337],"genre_scores_gemma":[0.009922439,0.012935204,0.724626,0.0122086955,0.0016314356,0.10886981,0.07100801,0.0013085484,0.057489827],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.935211,0.02937709,0.019676281,0.0013648084,0.012774536,0.0015963815],"domain_scores_gemma":[0.759927,0.061237313,0.01699966,0.012709218,0.14308608,0.0060407743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.060305536,0.0021990917,0.0032447372,0.01588078,0.0018670646,0.0041170553,0.008560884,0.005328049,0.024829175],"category_scores_gemma":[0.14008844,0.0029603057,0.004266291,0.014749947,0.0014543515,0.0029029204,0.0038024327,0.004735522,0.024210608],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018555482,0.0003160183,0.008283451,0.0024066994,0.00010470068,0.00030691884,0.0012755624,0.0013163211,0.0008729341,0.0026898019,0.8117664,0.17047572],"study_design_scores_gemma":[0.00057260133,0.00040338936,0.063857295,0.012105606,0.00020173113,0.00079962244,0.00237981,0.0034139843,0.001871661,0.008088943,0.9059913,0.0003140313],"about_ca_topic_score_codex":0.08267718,"about_ca_topic_score_gemma":0.13005532,"teacher_disagreement_score":0.08267718,"about_ca_system_score_codex":0.0034204929,"about_ca_system_score_gemma":0.021869147,"threshold_uncertainty_score":0.31892997},"labels":[],"label_agreement":null},{"id":"W7097252114","doi":"","title":"© 2002 Kluwer Academic Publishers. Printed in the Netherlands. 135 Health profiles of Hamilton: Spatial characterisation of neighbourhoods for health investigations","year":2001,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Neighbourhood (mathematics); Principal component analysis; Public health; Socioeconomic status; Spatial analysis; Cluster analysis; Spatial ecology; Health geography","score_opus":0.04601938041707976,"score_gpt":0.34825771398607,"score_spread":0.30223833356899027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097252114","genre_codex":"other","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":[0.009630371,0.03716653,0.015088884,0.008850499,0.0019415471,0.00039302552,0.01951159,0.0014849564,0.9059326],"genre_scores_gemma":[0.034385085,0.02340761,0.015803933,0.0012289754,0.00030704128,0.00033682355,0.0119926175,0.0009954084,0.9115425],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9995018,0.00008242677,0.000051281422,0.0001479065,0.00015646343,0.000060136383],"domain_scores_gemma":[0.9988172,0.00041958987,0.00011068725,0.00016723368,0.00033468928,0.00015055317],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00070896297,0.0007780645,0.00090796786,0.0013761058,0.00060342404,0.005426091,0.0011433357,0.0014727567,0.52304715],"category_scores_gemma":[0.0025862185,0.0004455619,0.00036615974,0.0029840686,0.0011344596,0.0017887948,0.0014584791,0.00082062196,0.30393538],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009135012,0.000038268943,0.003637731,0.001073124,0.000025872267,0.0005499648,0.0007943515,0.00043255292,0.0024954807,0.014975859,0.38801873,0.58786684],"study_design_scores_gemma":[0.00001553416,0.000022333761,0.008589027,0.000603456,0.000013094541,0.00056725193,0.00068586186,0.0004622608,0.0001764213,0.0034296461,0.98542136,0.000013640082],"about_ca_topic_score_codex":0.028893474,"about_ca_topic_score_gemma":0.049780842,"teacher_disagreement_score":0.52304715,"about_ca_system_score_codex":0.0013287651,"about_ca_system_score_gemma":0.001631629,"threshold_uncertainty_score":0.6803154},"labels":[],"label_agreement":null},{"id":"W7097277213","doi":"","title":"Abstract","year":2002,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Dryness; Lightning (connector); Spatial ecology; Common spatial pattern; Spatial distribution; Statistical analysis","score_opus":0.031775561268405736,"score_gpt":0.2666145225019981,"score_spread":0.23483896123359232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097277213","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13101998,0.0047377576,0.026209952,0.0102888895,0.0013584094,0.00042379872,0.12958984,0.0018139548,0.6945575],"genre_scores_gemma":[0.40341297,0.003222417,0.008084223,0.0009510932,0.00030670044,0.00015153263,0.05730959,0.0002470707,0.52631444],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99963796,0.00002011452,0.000021154572,0.00009244062,0.00017822746,0.000050002745],"domain_scores_gemma":[0.99915755,0.00005731039,0.00007306464,0.000102780235,0.00050587935,0.000103501436],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004006386,0.0002368786,0.00024754446,0.00084009767,0.0008643759,0.0015252125,0.00047963977,0.00030668484,0.13435933],"category_scores_gemma":[0.0018706904,0.00007895668,0.00019266274,0.0013277315,0.00035215213,0.0003967312,0.0006689289,0.0002020013,0.04000128],"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":[0.00043773674,0.00009173011,0.10799629,0.0005594736,0.00005830715,0.00073463324,0.0011768678,0.0016799939,0.0063469703,0.027846321,0.34178483,0.5112869],"study_design_scores_gemma":[0.000026869562,0.000060515296,0.15462613,0.00016489798,0.00003159583,0.00049767393,0.00059993763,0.0009441112,0.0015923165,0.0044097756,0.83702564,0.000020559271],"about_ca_topic_score_codex":0.16708712,"about_ca_topic_score_gemma":0.22125828,"teacher_disagreement_score":0.86564064,"about_ca_system_score_codex":0.002343119,"about_ca_system_score_gemma":0.0030866957,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W7097378109","doi":"","title":"OPINION","year":2016,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Simvastatin; Pharmacy; Product (mathematics); Quality (philosophy); Drug; Drug industry","score_opus":0.017863074182665,"score_gpt":0.2956810884198894,"score_spread":0.2778180142372244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097378109","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022746834,0.0060742106,0.00026721339,0.5419904,0.43822372,0.000029057195,0.00024599157,0.00008693518,0.012854868],"genre_scores_gemma":[0.0023916955,0.010190266,0.00037797555,0.5601276,0.38381284,0.000059100916,0.0003309818,0.000064222855,0.042645287],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986307,0.000194673,0.00012685952,0.00020720322,0.00065153424,0.00018893526],"domain_scores_gemma":[0.9947495,0.0012223652,0.0003607906,0.00019038818,0.0024478766,0.0010290429],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020663058,0.0007723428,0.00086655177,0.00069228036,0.0017344739,0.003895942,0.0021926141,0.014284257,0.05959973],"category_scores_gemma":[0.012397281,0.00039446732,0.0011444924,0.00051972107,0.0012099947,0.0038827558,0.001403989,0.012699681,0.033300485],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010237312,0.000009232892,0.00006959321,0.00005480132,0.0000032486005,0.000063178944,0.000012652465,0.000009515089,0.00003958417,0.00038382306,0.9907864,0.008557756],"study_design_scores_gemma":[0.0000122058,0.000010708508,0.00021254645,0.00015158868,0.0000066807556,0.00017952586,0.000063365536,0.0000330961,0.00004362054,0.0007834195,0.9984957,0.000007511003],"about_ca_topic_score_codex":0.0015528115,"about_ca_topic_score_gemma":0.0031040893,"teacher_disagreement_score":0.94040024,"about_ca_system_score_codex":0.0017754094,"about_ca_system_score_gemma":0.0024467944,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W7097595689","doi":"","title":"spatio-temporal Bayesian approach","year":2016,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bayesian probability; Swamp; Bayesian hierarchical modeling; Food consumption; Spatial analysis; Bayesian inference; Consumption (sociology)","score_opus":0.015707197334568594,"score_gpt":0.25903233631522016,"score_spread":0.24332513898065156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097595689","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":[0.020078864,0.0046428023,0.95389956,0.0066549834,0.00039584358,0.00016994071,0.0022696392,0.0003732002,0.011515197],"genre_scores_gemma":[0.56667376,0.007991341,0.39245886,0.0020404882,0.0013858571,0.0007970111,0.0067151557,0.00055011356,0.021387419],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99720144,0.0015719784,0.00011689581,0.0006368062,0.00030579322,0.00016716246],"domain_scores_gemma":[0.9873346,0.009835368,0.0008556756,0.00046564513,0.0011478998,0.0003608166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059249215,0.0009863968,0.0016628054,0.0028842925,0.0012260758,0.003528138,0.0028365676,0.0022812951,0.009256523],"category_scores_gemma":[0.023567077,0.0014423996,0.0022926964,0.0033571448,0.0016975058,0.0028394,0.0025174685,0.0024519584,0.0013686878],"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":[0.00024584538,0.0001516698,0.021284584,0.00051966606,0.00075898174,0.0009183934,0.00067218754,0.5555648,0.0005243158,0.28715706,0.025957411,0.10624503],"study_design_scores_gemma":[0.000031898333,0.00002518478,0.0022500479,0.00011206454,0.00009454751,0.00016534903,0.00015070585,0.8283431,0.00009693624,0.15530461,0.013376985,0.000048564056],"about_ca_topic_score_codex":0.077443205,"about_ca_topic_score_gemma":0.05221959,"teacher_disagreement_score":0.077443205,"about_ca_system_score_codex":0.0033514823,"about_ca_system_score_gemma":0.002907914,"threshold_uncertainty_score":0.1539849},"labels":[],"label_agreement":null},{"id":"W7097690350","doi":"","title":"RESEARCH ARTICLE Accuracy of syndrome definitions based on diagnoses in physician claims","year":2013,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Medical diagnosis; Primary care; Chart; Diagnosis code; MEDLINE; Medical record; Predictive value","score_opus":0.0806597169310956,"score_gpt":0.3629845446064615,"score_spread":0.28232482767536593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097690350","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9808498,0.0033947497,0.008138476,0.0006266679,0.00011191509,0.00012255629,0.0026417128,0.00011145805,0.0040027555],"genre_scores_gemma":[0.997384,0.00017954191,0.0015286095,0.00006149498,0.000051195217,0.000011032996,0.00071279163,0.000004973976,0.00006623889],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9565795,0.024001965,0.0052851723,0.003786378,0.009326637,0.0010203308],"domain_scores_gemma":[0.72352535,0.18017237,0.055731256,0.014738856,0.024299558,0.0015326467],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.028479723,0.00045641357,0.000561842,0.0039705853,0.00042908083,0.0022148865,0.0011589339,0.00078896666,0.001168395],"category_scores_gemma":[0.18823871,0.0002789241,0.0006855782,0.0021080913,0.0011611134,0.0013341017,0.0012668624,0.0006880879,0.0003069247],"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":[0.00021834964,0.000024003359,0.987336,0.00009068558,0.00017632582,0.000030572468,0.0001931941,0.00067525,0.0001950318,0.00008641081,0.0004098279,0.010564466],"study_design_scores_gemma":[0.000042425385,0.00021077282,0.9827692,0.00022388622,0.00016011845,0.00064449356,0.00022176674,0.012872574,0.0012808639,0.00049724913,0.0010509796,0.000025632733],"about_ca_topic_score_codex":0.012568534,"about_ca_topic_score_gemma":0.010695287,"teacher_disagreement_score":0.9715203,"about_ca_system_score_codex":0.001267839,"about_ca_system_score_gemma":0.00094787276,"threshold_uncertainty_score":0.150617},"labels":[],"label_agreement":null},{"id":"W7097694621","doi":"","title":"ISSUES ACROSS THE INTERNATIONAL","year":2006,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Variety (cybernetics); Public health; Health care; Qualitative research; Public health surveillance; Communicable disease; Healthcare system; Government (linguistics)","score_opus":0.011008424307822425,"score_gpt":0.32370901176843137,"score_spread":0.3127005874606089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097694621","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2563826,0.01532322,0.014017455,0.28258216,0.0029834253,0.00019226068,0.00028380813,0.00013062838,0.42810434],"genre_scores_gemma":[0.9672279,0.0035137634,0.00291538,0.0112161115,0.0004681322,0.00013796934,0.00012462863,0.00004996966,0.0143462205],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9876758,0.0067119678,0.000744269,0.0013649545,0.0013636404,0.0021394072],"domain_scores_gemma":[0.9868814,0.005470817,0.0017959147,0.0012032274,0.0023713075,0.0022774374],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.012898774,0.0003874558,0.00032649832,0.0014027288,0.0107853,0.013038807,0.0016363077,0.0033716434,0.0095987115],"category_scores_gemma":[0.02122792,0.00035822843,0.00044802594,0.002738985,0.007111877,0.011070005,0.009948242,0.004767732,0.00067357207],"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":[0.000045694855,0.00013335279,0.037872586,0.00048315313,0.00004618052,0.0026418173,0.3868893,0.00037205018,0.0010530966,0.37369967,0.047711525,0.14905159],"study_design_scores_gemma":[0.000007792929,0.00004735668,0.014340939,0.0008206613,0.000022286453,0.0010315215,0.4900723,0.00016037082,0.0003425903,0.025278078,0.46785036,0.000025781117],"about_ca_topic_score_codex":0.011626204,"about_ca_topic_score_gemma":0.007661976,"teacher_disagreement_score":0.99040127,"about_ca_system_score_codex":0.005367095,"about_ca_system_score_gemma":0.007220503,"threshold_uncertainty_score":0.068216026},"labels":[],"label_agreement":null},{"id":"W7097866268","doi":"","title":"Disclaimer","year":2001,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Disclaimer; Subject (documents); Quarter (Canadian coin); Payment; Government (linguistics)","score_opus":0.015819011281752914,"score_gpt":0.29158048890433,"score_spread":0.27576147762257713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097866268","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013658778,0.005280031,0.003255481,0.38577628,0.3773366,0.0005948181,0.007836787,0.0021689425,0.2163852],"genre_scores_gemma":[0.020765424,0.0053639146,0.005456277,0.16515513,0.07176483,0.0009966466,0.0038765075,0.0014481238,0.7251732],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9805977,0.0025140801,0.0024473032,0.0026501762,0.01003554,0.001755141],"domain_scores_gemma":[0.805546,0.030249886,0.008638134,0.014750647,0.13300441,0.0078109438],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.010254286,0.0011199072,0.0021943883,0.0036004214,0.0031825667,0.008263384,0.0051716454,0.008007985,0.19058481],"category_scores_gemma":[0.13867939,0.00067616947,0.0013436679,0.0038966136,0.002526261,0.0039280476,0.0039596832,0.00796091,0.15893477],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023489229,0.0000071515915,0.00008185359,0.000097830736,0.000003837544,0.00013904551,0.00018495048,0.000017409928,0.0000747634,0.0029821596,0.9900378,0.0063496875],"study_design_scores_gemma":[0.000009346338,0.000009383562,0.00025951266,0.00036051715,0.0000071264817,0.00027920963,0.00030140107,0.00009209157,0.00017303255,0.0013057259,0.9971891,0.000013613282],"about_ca_topic_score_codex":0.0061534964,"about_ca_topic_score_gemma":0.008736064,"teacher_disagreement_score":0.8094152,"about_ca_system_score_codex":0.006799641,"about_ca_system_score_gemma":0.010012317,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W7098141376","doi":"","title":"Highlights Communicable Diseases Surveillance","year":2014,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Communicable disease; Non-communicable disease; Quarter (Canadian coin); Disease surveillance; Disease; Public health","score_opus":0.009050261383497427,"score_gpt":0.2532108611059971,"score_spread":0.24416059972249968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7098141376","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01601375,0.0064920452,0.0060089603,0.015144199,0.0044778986,0.0030910925,0.6039059,0.0030905704,0.34177554],"genre_scores_gemma":[0.0886586,0.0129212225,0.018421032,0.009134099,0.003664644,0.0057197763,0.7516154,0.0008338531,0.109031335],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9954183,0.0008989779,0.000635315,0.00068740273,0.0017885866,0.00057138724],"domain_scores_gemma":[0.9859606,0.0014798591,0.0014872736,0.0009810824,0.008488223,0.0016029371],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0036852432,0.0011634151,0.00082309433,0.005828088,0.00096078165,0.002736886,0.002135145,0.0010445735,0.06539153],"category_scores_gemma":[0.011699905,0.0004577492,0.0008370118,0.0067831534,0.0002472496,0.0015965451,0.0021762613,0.0015875847,0.024464302],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000077723984,0.000056879162,0.018820632,0.0011470085,0.00006199634,0.000102165664,0.00031506116,0.00015941387,0.00041142385,0.0024400721,0.87106377,0.105343886],"study_design_scores_gemma":[0.000034893255,0.000059307327,0.04801274,0.00075575535,0.00003523433,0.0002297451,0.00044351091,0.00028504626,0.0004144196,0.00056566333,0.9491396,0.000024082508],"about_ca_topic_score_codex":0.048746448,"about_ca_topic_score_gemma":0.027618663,"teacher_disagreement_score":0.93460846,"about_ca_system_score_codex":0.0026368531,"about_ca_system_score_gemma":0.012875578,"threshold_uncertainty_score":0.2187565},"labels":[],"label_agreement":null},{"id":"W7099391961","doi":"","title":"Influenza (laboratory confirmed)","year":2005,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Communicable disease; Quarter (Canadian coin); Non-communicable disease; Disease; Disease surveillance; Public health","score_opus":0.015467518141834623,"score_gpt":0.3045685702820593,"score_spread":0.2891010521402247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099391961","genre_codex":"other","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":[0.29338378,0.011076036,0.0033451326,0.0020542566,0.0017974152,0.003985227,0.27428696,0.00086520927,0.40920594],"genre_scores_gemma":[0.77628005,0.0067455242,0.0043707243,0.0062608034,0.0006286641,0.0011577269,0.13716629,0.00011479514,0.06727545],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992631,0.0000808599,0.000086766144,0.00015840346,0.0002383821,0.00017249667],"domain_scores_gemma":[0.9993622,0.000037089303,0.00021274996,0.000046598292,0.00022033979,0.00012094137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000460289,0.00066620373,0.00063116674,0.00096941757,0.00059763604,0.00047220313,0.0004320471,0.00046823255,0.025944183],"category_scores_gemma":[0.0011810997,0.000121212266,0.00046992628,0.0009734,0.00014501717,0.00042258625,0.0004893118,0.000606477,0.008268415],"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":[0.0013136101,0.00047541608,0.6343932,0.002854391,0.00040207396,0.0042653573,0.00059950736,0.00047173304,0.008297166,0.0013469377,0.2534918,0.0920889],"study_design_scores_gemma":[0.000087166576,0.0005832367,0.8331253,0.00084246835,0.00012147636,0.0051404038,0.0005007537,0.0003956608,0.0010483619,0.00028888782,0.15781857,0.000047651876],"about_ca_topic_score_codex":0.026373798,"about_ca_topic_score_gemma":0.030372892,"teacher_disagreement_score":0.026373798,"about_ca_system_score_codex":0.00064530165,"about_ca_system_score_gemma":0.0008535067,"threshold_uncertainty_score":0.08679193},"labels":[],"label_agreement":null},{"id":"W7099421763","doi":"","title":"5Infection Prevention and Control, Calgary and Area, Alberta Health Services","year":2016,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"MEDLINE; Health services; Intervention (counseling); Control (management); Health services research; Public health","score_opus":0.006237768022042803,"score_gpt":0.25991012705441235,"score_spread":0.25367235903236957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099421763","genre_codex":"review","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038743883,0.73923874,0.0019493698,0.034879506,0.006718605,0.009144315,0.10088058,0.00044949324,0.067995496],"genre_scores_gemma":[0.25586182,0.6123516,0.042626914,0.016212711,0.002780032,0.0064323605,0.02985723,0.00024302679,0.033634342],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98723537,0.005433397,0.0024429562,0.00067158684,0.0036546346,0.0005620689],"domain_scores_gemma":[0.9025073,0.056170277,0.008605753,0.0013529115,0.025312273,0.00605142],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.018634101,0.0010331302,0.0020202307,0.009166517,0.001614567,0.0035901638,0.0015676692,0.0017010707,0.030927932],"category_scores_gemma":[0.089795984,0.00069396774,0.0010909842,0.009366777,0.0007951061,0.0010522958,0.0016238875,0.0012497368,0.0010719503],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0047623557,0.0003180019,0.070948765,0.18732497,0.002868006,0.0006793317,0.0017973355,0.0012172518,0.0020063415,0.00381162,0.25932917,0.46493688],"study_design_scores_gemma":[0.0028630341,0.0011958736,0.4199944,0.18780313,0.008542245,0.00033175194,0.0021641713,0.0018345168,0.0025805521,0.0015205364,0.37091002,0.00025982107],"about_ca_topic_score_codex":0.5772458,"about_ca_topic_score_gemma":0.83507705,"teacher_disagreement_score":0.96907204,"about_ca_system_score_codex":0.017240467,"about_ca_system_score_gemma":0.07955694,"threshold_uncertainty_score":0.8504878},"labels":[],"label_agreement":null},{"id":"W7100158144","doi":"","title":"ral ssBioMed CentBMC Public Health Open AcceResearch article The perceived impact of location privacy: A web-based survey of public health perspectives and requirements in the UK and Canada","year":2016,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public health; Scale (ratio); Perception; Confidentiality; Identification (biology); Health professionals; Public participation GIS; Information privacy","score_opus":0.1716226005718176,"score_gpt":0.4184307822376011,"score_spread":0.2468081816657835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100158144","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":[0.8660907,0.0020801362,0.00032896237,0.017458487,0.00011756329,0.00023896154,0.008140615,0.00007293909,0.10547164],"genre_scores_gemma":[0.9731312,0.0020853437,0.00037679743,0.0018365994,0.000076888624,0.0000671257,0.001388798,0.000022946218,0.021014348],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99804026,0.00038862572,0.000108126784,0.00011095718,0.0009325758,0.00041947907],"domain_scores_gemma":[0.99424595,0.0012327454,0.00091308623,0.00024250512,0.001792277,0.0015734329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015439235,0.00007714118,0.00018412451,0.0012881083,0.0019117213,0.0027139839,0.0003962166,0.00045122442,0.032925528],"category_scores_gemma":[0.00989059,0.00013954582,0.00011163255,0.0018819666,0.001308822,0.0007438042,0.0015695692,0.00040332173,0.0016754271],"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":[0.00015626867,0.00013091911,0.7562428,0.00053151086,0.000028964527,0.00058086996,0.038807236,0.00009925949,0.00090352795,0.0027480738,0.08063843,0.11913221],"study_design_scores_gemma":[0.000011467307,0.000055944438,0.8626242,0.00037898746,0.000011609974,0.00033992925,0.04360832,0.00012002259,0.00021822836,0.00014567273,0.09246368,0.000021952914],"about_ca_topic_score_codex":0.80407554,"about_ca_topic_score_gemma":0.8336298,"teacher_disagreement_score":0.19592446,"about_ca_system_score_codex":0.008143903,"about_ca_system_score_gemma":0.022166336,"threshold_uncertainty_score":0.39415658},"labels":[],"label_agreement":null},{"id":"W7100507550","doi":"","title":"Algebras for Querying Text Regions","year":2007,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Markup language; Class (philosophy); String (physics); Expressive power; Natural (archaeology); Digital library; Power (physics)","score_opus":0.027540631812778875,"score_gpt":0.3245048358740506,"score_spread":0.2969642040612717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100507550","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":[0.007839092,0.000601175,0.96983206,0.0009333193,0.00009304732,0.00023170974,0.003127506,0.0076401643,0.009701812],"genre_scores_gemma":[0.1941872,0.00094194233,0.7855898,0.00064644235,0.00025746605,0.00041698903,0.0065468177,0.00089723617,0.010516092],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99753773,0.00063164084,0.00042647676,0.0004547387,0.0008208792,0.00012856977],"domain_scores_gemma":[0.996738,0.0014310613,0.00034766717,0.0007448179,0.00058387767,0.00015451579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002948661,0.00056388404,0.0006710901,0.0026410911,0.0010201329,0.0040653334,0.0017273732,0.0011472027,0.01550249],"category_scores_gemma":[0.009009238,0.0005974031,0.001866887,0.0030367132,0.0012479733,0.008345836,0.0026423954,0.00119402,0.006302811],"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":[0.00018384978,0.00010371102,0.0017922416,0.0004752742,0.00007085695,0.00034470917,0.00065113086,0.0151375085,0.0067036483,0.78799784,0.025455685,0.1610835],"study_design_scores_gemma":[0.000062813764,0.000058150876,0.00053748395,0.00012452732,0.00005874014,0.00064214214,0.00038648112,0.19132136,0.010578218,0.692375,0.103761934,0.00009310473],"about_ca_topic_score_codex":0.0056441505,"about_ca_topic_score_gemma":0.0078082937,"teacher_disagreement_score":0.01550249,"about_ca_system_score_codex":0.0016840445,"about_ca_system_score_gemma":0.0011941231,"threshold_uncertainty_score":0.05186099},"labels":[],"label_agreement":null},{"id":"W7104250713","doi":"10.71781/684","title":"Développement d'un modèle de détection précoce des menaces sanitaires pour le GPHIN et analyse de la corrélation entre l'intensité de sa couverture médiatique de la COVID-19 et l'évolution des cas de SRAS-CoV-2 au Canada","year":2024,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"French; Web site; Coronavirus disease 2019 (COVID-19)","score_opus":0.030893058377787403,"score_gpt":0.3516095033270768,"score_spread":0.3207164449492894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104250713","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":[0.41117334,0.0018569145,0.56176955,0.003520775,0.0004179024,0.0010679463,0.0038792768,0.00730871,0.009005626],"genre_scores_gemma":[0.7604167,0.00070934434,0.22246616,0.0004096295,0.00013783028,0.00097158126,0.0040492075,0.00019618389,0.010643404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990252,0.00017802313,0.000070448295,0.00039767532,0.00021242829,0.000116171985],"domain_scores_gemma":[0.99523795,0.0025962985,0.00037239236,0.00018831475,0.0014048527,0.0002001891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002790384,0.0015169899,0.0010639075,0.0027120793,0.0008536352,0.0036443712,0.0021670249,0.001815554,0.004056179],"category_scores_gemma":[0.008773023,0.0007589409,0.0015032466,0.001617348,0.000843344,0.0023838843,0.00090426026,0.0018767751,0.0010687644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007690684,0.0004723309,0.08002488,0.00043222104,0.00048743095,0.00024079836,0.00071497617,0.740739,0.0045424076,0.0045259953,0.0052693626,0.16178161],"study_design_scores_gemma":[0.000025494184,0.00007791145,0.005072431,0.000034987406,0.00006262195,0.000029019246,0.000080263446,0.99057335,0.0011208672,0.0016340677,0.0012684905,0.00002039237],"about_ca_topic_score_codex":0.15429626,"about_ca_topic_score_gemma":0.11082277,"teacher_disagreement_score":0.8457037,"about_ca_system_score_codex":0.0035643205,"about_ca_system_score_gemma":0.0052902973,"threshold_uncertainty_score":0.30679637},"labels":[],"label_agreement":null},{"id":"W7104915183","doi":"10.71781/32452","title":"Améliorer la surveillance passive des infections associées aux soins de santé au Canada","year":2023,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Epidemiological surveillance; Health surveillance; Ile de france; Disease prevention; Medical screening","score_opus":0.006103663527229419,"score_gpt":0.21734856900095406,"score_spread":0.21124490547372465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104915183","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":[0.65644866,0.070818804,0.021304946,0.057839952,0.0015421284,0.0034337481,0.093795516,0.00073651486,0.09407961],"genre_scores_gemma":[0.8964095,0.033056166,0.02937188,0.009403267,0.00030339658,0.0017001592,0.017726965,0.000172737,0.011855998],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.97290426,0.0069499095,0.0030772788,0.0020501167,0.0127942,0.0022242074],"domain_scores_gemma":[0.8723898,0.025245154,0.009204162,0.004002802,0.08564688,0.0035111918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026122961,0.00062230404,0.0010055993,0.004889327,0.0034253108,0.005664851,0.002500776,0.00080531515,0.003587081],"category_scores_gemma":[0.09199431,0.0005122972,0.0012845828,0.00986788,0.0014540013,0.0020794491,0.0024129348,0.0015336222,0.00041056433],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005762179,0.0001732173,0.58649457,0.009127,0.0011952643,0.0002660342,0.023968691,0.0014543334,0.00150334,0.006801927,0.046876825,0.3215626],"study_design_scores_gemma":[0.000120170305,0.00029990694,0.7353253,0.010540563,0.0015401307,0.00021880519,0.019667612,0.002158844,0.0037821168,0.0015213032,0.22455654,0.00026869655],"about_ca_topic_score_codex":0.9738535,"about_ca_topic_score_gemma":0.9763057,"teacher_disagreement_score":0.04440753,"about_ca_system_score_codex":0.04440753,"about_ca_system_score_gemma":0.18818925,"threshold_uncertainty_score":0.32220072},"labels":[],"label_agreement":null},{"id":"W7110882323","doi":"10.1371/journal.pgph.0005452.s001","title":"Description of linked data repositories.","year":2025,"lang":"","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Syphilis; Poisson regression; Receipt; Confidence interval; Medical diagnosis; Regimen; Outbreak; Sexually transmitted disease","score_opus":0.09488462820893918,"score_gpt":0.33277440511800815,"score_spread":0.23788977690906898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7110882323","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021312025,0.00038201667,0.0064535784,0.0009889802,0.00033580553,0.00035728965,0.9510183,0.025181558,0.015069344],"genre_scores_gemma":[0.002540481,0.0007488779,0.011806237,0.0011713868,0.00014092097,0.00093897636,0.96856254,0.006966819,0.007123807],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9933041,0.0011383557,0.0018245269,0.0013465913,0.0018217607,0.00056469644],"domain_scores_gemma":[0.9803577,0.006706614,0.0016984781,0.0045098485,0.004707907,0.0020195583],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.009204146,0.0025811263,0.0023480342,0.012558644,0.0017580622,0.010411772,0.0049184063,0.003920765,0.32207012],"category_scores_gemma":[0.04337484,0.0013763811,0.002060841,0.01684047,0.00079278956,0.008593555,0.008875636,0.0031938746,0.30417284],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022427808,0.00003282258,0.0014287914,0.0027187206,0.00007725921,0.0001323632,0.00021912719,0.00038793727,0.00048941537,0.007833631,0.95206636,0.03438933],"study_design_scores_gemma":[0.000080317666,0.000016653568,0.0010327083,0.00064913096,0.000020786883,0.0000915362,0.00012996414,0.0006541707,0.0003170778,0.006617371,0.99034214,0.00004820878],"about_ca_topic_score_codex":0.012851299,"about_ca_topic_score_gemma":0.010570724,"teacher_disagreement_score":0.6779299,"about_ca_system_score_codex":0.0028591647,"about_ca_system_score_gemma":0.0067480826,"threshold_uncertainty_score":0.9669847},"labels":[],"label_agreement":null},{"id":"W7111235540","doi":"10.1371/journal.pntd.0013393.s004","title":"Database search strings.","year":2025,"lang":"","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Diarrhea; MEDLINE; Systematic review; Developing country; Developed country; Burden of disease; Epidemiology","score_opus":0.045801741843422845,"score_gpt":0.34335486568383183,"score_spread":0.297553123840409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7111235540","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008778082,0.017911889,0.0014296015,0.0036779216,0.0005375227,0.015355086,0.9212478,0.0017795835,0.037182674],"genre_scores_gemma":[0.01658968,0.1158823,0.02953086,0.011657109,0.0012592427,0.18998931,0.5678286,0.0027571884,0.064505726],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.9919425,0.001591869,0.004095803,0.0009890099,0.00095791236,0.00042290674],"domain_scores_gemma":[0.9699848,0.019110216,0.004537209,0.00069320615,0.0047727656,0.0009017394],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0057157036,0.003514398,0.011646857,0.03475562,0.0013916304,0.0066172266,0.0052871313,0.0044273334,0.61990696],"category_scores_gemma":[0.04397228,0.0015438609,0.0050792033,0.044345796,0.0012112748,0.0064253104,0.0053049033,0.002909466,0.1053692],"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":[0.0007082082,0.000057573787,0.00091328524,0.657294,0.0008590028,0.00032646838,0.00032479144,0.00037571226,0.00024352332,0.0038529756,0.28965676,0.045387685],"study_design_scores_gemma":[0.003987794,0.0003481715,0.005015023,0.3824104,0.0029778883,0.00066855334,0.0008309691,0.0011074548,0.00050414645,0.012225492,0.589622,0.0003021573],"about_ca_topic_score_codex":0.012442908,"about_ca_topic_score_gemma":0.014062246,"teacher_disagreement_score":0.38009304,"about_ca_system_score_codex":0.0068427958,"about_ca_system_score_gemma":0.016196245,"threshold_uncertainty_score":0.5421566},"labels":[],"label_agreement":null},{"id":"W7111371754","doi":"10.1371/journal.pgph.0004814.s001","title":"This file is the data collection instrument.","year":2025,"lang":"","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Salience (neuroscience); Salient; Disease; Perception; Outbreak; Quarter (Canadian coin)","score_opus":0.06560115971702545,"score_gpt":0.31701985113512754,"score_spread":0.2514186914181021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7111371754","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031218533,0.000047174126,0.0018769603,0.00060524937,0.0003227884,0.016530951,0.95053303,0.0009663008,0.025995728],"genre_scores_gemma":[0.017135087,0.00041248547,0.021846324,0.001427814,0.00021635203,0.23661141,0.6665782,0.0011714053,0.054600954],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981128,0.00047501063,0.0004404329,0.00021325848,0.0005105584,0.0002478666],"domain_scores_gemma":[0.9757505,0.01198656,0.0010332189,0.001574981,0.008643696,0.0010109076],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0057353317,0.0007654837,0.0012046071,0.0030381875,0.0017330841,0.0015015246,0.0015776017,0.0009888771,0.5357292],"category_scores_gemma":[0.028129818,0.0007307356,0.00044102754,0.005147285,0.00042006315,0.0016278977,0.0018019661,0.0015464879,0.15146707],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014355489,0.00021576091,0.0023932068,0.0008004432,0.000004683234,0.00004292238,0.00088944787,0.000094415234,0.0001791208,0.0011312499,0.95949835,0.034606926],"study_design_scores_gemma":[0.0003213085,0.0002280312,0.023281198,0.0011136009,0.000010956212,0.000052822135,0.0049710525,0.00036260978,0.00050657487,0.0018983338,0.9671929,0.00006066742],"about_ca_topic_score_codex":0.007180342,"about_ca_topic_score_gemma":0.009031099,"teacher_disagreement_score":0.46427077,"about_ca_system_score_codex":0.0019688152,"about_ca_system_score_gemma":0.004666939,"threshold_uncertainty_score":0.6622259},"labels":[],"label_agreement":null},{"id":"W7112327456","doi":"","title":"Coupler les données d’enquête de santé aux données de l'assurance maladie : avantages et opportunités pour la recherche en santé publique","year":2024,"lang":"en","type":"dissertation","venue":"Open Repository and Bibliography (University of Liège)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Institut National d'assurance Maladie-Invalidité; Institut für Arbeitsmarkt- und Berufsforschung","keywords":"Context (archaeology); Transmission (telecommunications); Identification (biology)","score_opus":0.0897947730230464,"score_gpt":0.35277452682926913,"score_spread":0.26297975380622274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7112327456","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":[0.025094483,0.15082169,0.5022698,0.13693395,0.012924523,0.0042100814,0.09225813,0.0051511717,0.07033614],"genre_scores_gemma":[0.11406,0.084538564,0.68170977,0.01249175,0.0034415647,0.0059969,0.0632014,0.0034470074,0.031112976],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7889871,0.15032175,0.013502077,0.009564376,0.035796735,0.0018279218],"domain_scores_gemma":[0.5900221,0.28556764,0.015340347,0.04799767,0.057064574,0.004007591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.15861867,0.0014505977,0.0024964118,0.0138058,0.0027570718,0.016704101,0.004025828,0.0034635472,0.021081753],"category_scores_gemma":[0.3746417,0.0016906994,0.003460622,0.021509739,0.003516344,0.012060425,0.009470268,0.008371621,0.011583476],"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":[0.00048252338,0.00022990633,0.01578525,0.009314453,0.0010485118,0.0005554008,0.010630853,0.001956046,0.0024111737,0.05627031,0.2069231,0.69439256],"study_design_scores_gemma":[0.000101417696,0.00010697469,0.012664421,0.009555014,0.00031286073,0.00070812105,0.0056184237,0.003703376,0.0023400423,0.03260611,0.9320842,0.00019905422],"about_ca_topic_score_codex":0.049870323,"about_ca_topic_score_gemma":0.032516085,"teacher_disagreement_score":0.15861867,"about_ca_system_score_codex":0.0074372697,"about_ca_system_score_gemma":0.027985882,"threshold_uncertainty_score":0.83886576},"labels":[],"label_agreement":null},{"id":"W7116056766","doi":"10.1371/journal.pgph.0005550.s001","title":"Methodological details.","year":2025,"lang":"","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Consistency (knowledge bases); Notifiable disease; Poliomyelitis; Quality (philosophy); Disease control; Data quality; Infectious disease (medical specialty); Control (management)","score_opus":0.2309468405451433,"score_gpt":0.41738030825821915,"score_spread":0.18643346771307584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116056766","genre_codex":"dataset","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":[0.003772844,0.0038810326,0.0641005,0.0149062425,0.008041782,0.05318322,0.64245486,0.0042607696,0.20539865],"genre_scores_gemma":[0.024708444,0.004147166,0.26283902,0.016657095,0.002016207,0.23380437,0.24491674,0.0056765266,0.20523445],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9767944,0.00841951,0.0030272172,0.0027185301,0.007400122,0.0016403114],"domain_scores_gemma":[0.908473,0.029585196,0.0023658692,0.017530886,0.03943733,0.002607774],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.03289095,0.0016277232,0.0012999115,0.0065129367,0.0042676693,0.004644279,0.004275335,0.0026167561,0.49157965],"category_scores_gemma":[0.14484063,0.0011917333,0.0025070438,0.010547259,0.0013147327,0.0021641387,0.003146265,0.0032820937,0.116830245],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045682307,0.00014241609,0.0018259636,0.0037904964,0.00007009172,0.00010098331,0.001367703,0.00058116013,0.00032166534,0.023796327,0.8657404,0.10180602],"study_design_scores_gemma":[0.00015407249,0.00005523516,0.0023630545,0.0018938306,0.00006828168,0.000068718655,0.0005552014,0.00032716498,0.00034334318,0.0068902825,0.987245,0.000035741796],"about_ca_topic_score_codex":0.1289982,"about_ca_topic_score_gemma":0.19689065,"teacher_disagreement_score":0.50842035,"about_ca_system_score_codex":0.009229929,"about_ca_system_score_gemma":0.042147566,"threshold_uncertainty_score":0.72519994},"labels":[],"label_agreement":null},{"id":"W7116067759","doi":"10.24095/hpcdp.46.1.04f","title":"Corrélation entre les recherches Internet liées au suicide sur Google Trends et les hospitalisations en raison de blessures auto-infligées ainsi que la mortalité par suicide au Canada","year":2025,"lang":"fr","type":"article","venue":"Promotion de la santé et prévention des maladies chroniques au Canada","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Ottawa Public Health; Public Health Agency of Canada","funders":"","keywords":"Poison control; Copycat; Suicide prevention; Context (archaeology); Suicide methods","score_opus":0.02376582235593642,"score_gpt":0.3325864598313492,"score_spread":0.3088206374754128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116067759","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":[0.7078145,0.013783513,0.0049110814,0.010523577,0.00061423826,0.0003360664,0.22319846,0.00067856244,0.038139988],"genre_scores_gemma":[0.9083026,0.0070893187,0.0037397242,0.0011747723,0.00026498616,0.0002607434,0.05290946,0.00023045068,0.026028035],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9946654,0.00080580066,0.0005596248,0.0007281618,0.0026745482,0.00056641217],"domain_scores_gemma":[0.94276386,0.019579887,0.00857627,0.002003352,0.025254328,0.0018223625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004135894,0.00048230833,0.0009006281,0.0061372747,0.0010619567,0.0041842186,0.0009426038,0.00059225195,0.0086674765],"category_scores_gemma":[0.039001778,0.00038092153,0.0018577194,0.013142085,0.0008803025,0.001670177,0.0013531124,0.0013591213,0.0017985831],"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":[0.00016369979,0.00002194994,0.95328397,0.0012073844,0.0007330493,0.00019447286,0.0019397581,0.0010136825,0.00030835773,0.0012807586,0.019502033,0.020350913],"study_design_scores_gemma":[0.00001165746,0.00003102525,0.9665837,0.0005282455,0.0003691932,0.00015724686,0.0036612437,0.0016812009,0.0005175378,0.00034657627,0.026053019,0.0000592601],"about_ca_topic_score_codex":0.86506426,"about_ca_topic_score_gemma":0.8861567,"teacher_disagreement_score":0.13493574,"about_ca_system_score_codex":0.008747315,"about_ca_system_score_gemma":0.016992867,"threshold_uncertainty_score":0.27146077},"labels":[],"label_agreement":null},{"id":"W7116072527","doi":"10.24095/hpcdp.46.1.04","title":"The association between Google Trends suicide-related Internet searches and self-harm hospitalizations and suicide mortality in Canada","year":2025,"lang":"en","type":"article","venue":"Health Promotion and Chronic Disease Prevention in Canada","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Ottawa; Public Health Agency of Canada","funders":"","keywords":"Commit; The Internet; Poison control; Suicide prevention; Association (psychology); Injury prevention; Occupational safety and health; Human factors and ergonomics","score_opus":0.023560599818957292,"score_gpt":0.33563163773869364,"score_spread":0.31207103791973634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116072527","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":[0.9654915,0.0032632775,0.00039139818,0.0014898563,0.000047880963,0.000076994176,0.023412725,0.000050712628,0.0057756626],"genre_scores_gemma":[0.9927832,0.001048761,0.00045839083,0.00019507925,0.000013816887,0.000025067944,0.004268921,0.0000139213635,0.0011928647],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989722,0.00013911875,0.00011115848,0.00015055096,0.0003486984,0.00027833795],"domain_scores_gemma":[0.99452955,0.00059984595,0.0014593332,0.0002045109,0.0024069862,0.00079980347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086776225,0.00031741287,0.0004968766,0.0027511953,0.0014164634,0.0016677942,0.0008969974,0.00032058608,0.0028546005],"category_scores_gemma":[0.006609376,0.0002845885,0.0008786584,0.0068745385,0.0005915846,0.0005949501,0.0011699771,0.00078259746,0.00021393856],"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":[0.0000657353,0.000018362198,0.9919566,0.00011431566,0.00015202188,0.000053037857,0.00042632056,0.00016724177,0.00005483942,0.00020108269,0.001653975,0.0051364717],"study_design_scores_gemma":[0.000006951009,0.000015617115,0.99638784,0.00008315212,0.000088503664,0.000046023026,0.0009309262,0.00061568496,0.00006713332,0.00006265964,0.0016816222,0.000013863154],"about_ca_topic_score_codex":0.9947855,"about_ca_topic_score_gemma":0.9967422,"teacher_disagreement_score":0.017831601,"about_ca_system_score_codex":0.017831601,"about_ca_system_score_gemma":0.031725667,"threshold_uncertainty_score":0.1293779},"labels":[],"label_agreement":null},{"id":"W7117165757","doi":"10.33540/3286","title":"The proof is in the pandemic: real-world evidence on COVID-19","year":2025,"lang":"","type":"dissertation","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Global Health Research","funders":"","keywords":"Receipt; Wearable computer; Myocarditis; Context (archaeology); Pericarditis; Wearable technology; Health data; Disease","score_opus":0.09469616599870681,"score_gpt":0.42558760820231784,"score_spread":0.330891442203611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117165757","genre_codex":"review","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":[0.062188357,0.79300547,0.0046030656,0.093432456,0.0060656457,0.0013420882,0.0069931312,0.00006472839,0.032305054],"genre_scores_gemma":[0.49596646,0.4369148,0.0067724856,0.046349566,0.006804408,0.00092341844,0.0045732,0.00012514385,0.0015706132],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.923654,0.054974377,0.009086405,0.004251087,0.0067076236,0.0013264419],"domain_scores_gemma":[0.43676993,0.50138336,0.0308106,0.011042532,0.017015213,0.0029783545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07118101,0.00077769236,0.0023591388,0.002944201,0.0012023522,0.0057303607,0.0023787934,0.0037374347,0.017381372],"category_scores_gemma":[0.33034554,0.0008950317,0.00389711,0.0041814814,0.003712408,0.006570711,0.0034795746,0.007013273,0.0016249911],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.017246667,0.0006828529,0.09546051,0.18792242,0.028604954,0.00051477045,0.0052302075,0.0009991198,0.0003965195,0.025473623,0.050949827,0.5865186],"study_design_scores_gemma":[0.0052876347,0.00881336,0.1820586,0.41792,0.039633203,0.0012630738,0.009736672,0.0014957961,0.0011221569,0.026160952,0.30608433,0.00042425876],"about_ca_topic_score_codex":0.0054280628,"about_ca_topic_score_gemma":0.00591621,"teacher_disagreement_score":0.07118101,"about_ca_system_score_codex":0.0023646995,"about_ca_system_score_gemma":0.005852343,"threshold_uncertainty_score":0.37644565},"labels":[],"label_agreement":null},{"id":"W7117490536","doi":"10.2196/83767","title":"Awareness and Use of Home-Based Respiratory Pathogen Testing Services in the Internet Era: Postpandemic Questionnaire Study","year":2025,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"China-Japan Friendship Hospital; National Health Commission of the People's Republic of China; Chinese Academy of Medical Sciences","keywords":"Accountability; Digital health; The Internet; Test (biology); Health care; Disease; Service (business); Health services","score_opus":0.07914886843759257,"score_gpt":0.41839593432154376,"score_spread":0.3392470658839512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117490536","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":[0.9994056,0.000047162714,0.000023358885,0.000066373985,0.0000033422025,0.00002845015,0.00010819163,0.0000011119705,0.0003164047],"genre_scores_gemma":[0.99921954,0.0001197854,0.000061458224,0.00011357754,0.0000052242776,0.00004712071,0.00012180525,0.0000011175943,0.0003102579],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99947053,0.00011750398,0.00008866713,0.000066243534,0.00012567865,0.00013137815],"domain_scores_gemma":[0.9980579,0.00040792892,0.0007219275,0.0000695279,0.0003393773,0.0004033062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001309146,0.00018825513,0.00032022013,0.0011988385,0.00056379975,0.0006276171,0.00028590093,0.0005983667,0.0018059511],"category_scores_gemma":[0.0028673755,0.00030821346,0.0004914407,0.0012847749,0.0003557824,0.0010577041,0.0006375602,0.0008220067,0.0003847455],"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":[0.000028324543,0.00029539154,0.99028087,0.000054740933,0.000018094923,0.00020923416,0.005313274,0.000028615053,0.00018646178,0.000036851314,0.0002446746,0.003303463],"study_design_scores_gemma":[0.0000053807644,0.00028865418,0.9872558,0.000021029227,0.00001625853,0.00020199546,0.01135487,0.00014725674,0.00006842369,0.0000139433105,0.000618587,0.0000078446155],"about_ca_topic_score_codex":0.00443941,"about_ca_topic_score_gemma":0.0048637255,"teacher_disagreement_score":0.00443941,"about_ca_system_score_codex":0.00060463476,"about_ca_system_score_gemma":0.000530726,"threshold_uncertainty_score":0.00882715},"labels":[],"label_agreement":null},{"id":"W7117544360","doi":"10.1016/j.ssmhs.2025.100166","title":"From legacy systems to real-time response: VPD-SMART implementation and its impact on public health surveillance in Paraguay and the Americas","year":2025,"lang":"en","type":"article","venue":"SSM - Health Systems","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"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":"EduCanada; Centers for Disease Control and Prevention","keywords":"Public health surveillance; Public health; Disease surveillance; Information system; Data collection; Consistency (knowledge bases); Health informatics; Digital health","score_opus":0.029939058152954355,"score_gpt":0.410216804988093,"score_spread":0.38027774683513865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117544360","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":[0.97971606,0.0004737409,0.0025037026,0.0050853463,0.00004539378,0.00016553051,0.00043371037,0.00021337459,0.011363066],"genre_scores_gemma":[0.99684614,0.0001965717,0.0018563617,0.00031026307,0.000010050835,0.000034435292,0.00021495904,0.00001479358,0.00051646633],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99616784,0.002143325,0.00020997196,0.00046857583,0.00049203646,0.0005182696],"domain_scores_gemma":[0.9946674,0.0019128397,0.00081032084,0.00076918356,0.0011545292,0.00068575016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047880723,0.00023839962,0.0001823101,0.0006451246,0.0008735941,0.0025470012,0.00081423164,0.000376579,0.0017133215],"category_scores_gemma":[0.01199498,0.00019354174,0.00023685193,0.0012082142,0.0014000809,0.0021900504,0.0026465205,0.00092069036,0.00018171743],"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":[0.00080667663,0.0007341866,0.5873217,0.00079159444,0.00021193642,0.0009868192,0.026112594,0.011547787,0.009338134,0.01487028,0.007816872,0.33946145],"study_design_scores_gemma":[0.00012452858,0.0011881593,0.83822745,0.0005879462,0.000118760865,0.00034157638,0.05231468,0.020510433,0.0068866317,0.0035008155,0.07608445,0.00011459253],"about_ca_topic_score_codex":0.082587376,"about_ca_topic_score_gemma":0.061871752,"teacher_disagreement_score":0.082587376,"about_ca_system_score_codex":0.0049948497,"about_ca_system_score_gemma":0.004067232,"threshold_uncertainty_score":0.1642133},"labels":[],"label_agreement":null},{"id":"W7131222715","doi":"10.5281/zenodo.18761791","title":"Methodological Evaluation of Public Health Surveillance Systems in Senegal Using Time-Series Forecasting Models","year":2003,"lang":"en","type":"article","venue":"Open MIND","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Autoregressive integrated moving average; Public health; Public health interventions; Quarter (Canadian coin); Disease surveillance; Psychological intervention; Public health surveillance","score_opus":0.6565283341560828,"score_gpt":0.46767011741165154,"score_spread":0.18885821674443126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131222715","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":[0.79812074,0.0011317512,0.19143929,0.0012189777,0.00017997867,0.0037437084,0.0013223999,0.0002001059,0.002643117],"genre_scores_gemma":[0.9286121,0.00020326686,0.06733621,0.000101929625,0.000029779281,0.0028211945,0.0006391499,0.0000145095955,0.00024185635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8102977,0.1742485,0.0074503305,0.0040910933,0.002891333,0.0010209724],"domain_scores_gemma":[0.5815273,0.37146512,0.01703884,0.015576406,0.013500277,0.00089203176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.17167032,0.0010066566,0.0011416064,0.0022959535,0.0013458976,0.002071494,0.002371894,0.0013180602,0.0013913845],"category_scores_gemma":[0.36920533,0.0007929645,0.0027598094,0.0033811713,0.0011912164,0.0017058582,0.002844662,0.0013137244,0.000101368605],"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":[0.0048109223,0.0011269703,0.24389021,0.0014434115,0.0052147345,0.00040129517,0.0036399867,0.5811255,0.00075064675,0.033749804,0.0013697299,0.12247672],"study_design_scores_gemma":[0.00098615,0.0025496841,0.051049948,0.00034157644,0.0014102989,0.00010265569,0.0021203626,0.9241592,0.0020336076,0.012563865,0.002554552,0.0001281159],"about_ca_topic_score_codex":0.056645177,"about_ca_topic_score_gemma":0.022600506,"teacher_disagreement_score":0.17167032,"about_ca_system_score_codex":0.005007832,"about_ca_system_score_gemma":0.004371384,"threshold_uncertainty_score":0.90789026},"labels":[],"label_agreement":null},{"id":"W7131237326","doi":"10.5281/zenodo.18761790","title":"Methodological Evaluation of Public Health Surveillance Systems in Senegal Using Time-Series Forecasting Models","year":2003,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Autoregressive integrated moving average; Public health; Public health interventions; Quarter (Canadian coin); Disease surveillance; Psychological intervention; Public health surveillance","score_opus":0.4636927743555923,"score_gpt":0.3791099598868584,"score_spread":0.08458281446873389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131237326","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":[0.7896534,0.0011950029,0.20013343,0.0011401384,0.00016476256,0.0034393296,0.0016037121,0.0002238564,0.0024464696],"genre_scores_gemma":[0.9278039,0.00022087061,0.06796309,0.00009370481,0.000027180882,0.0027906753,0.00083267427,0.000016301088,0.0002516261],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.83730716,0.14919749,0.006230383,0.0038589025,0.0024229796,0.000983098],"domain_scores_gemma":[0.64849305,0.30962524,0.0149146905,0.01415284,0.011952183,0.00086195185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.14785172,0.0010373386,0.0011603893,0.0022411442,0.0012664003,0.0019002173,0.0023059214,0.0012647513,0.0013561958],"category_scores_gemma":[0.31769916,0.00079801556,0.0026644494,0.0034393996,0.001090136,0.001533306,0.002771622,0.0012249605,0.00010365338],"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":[0.0044397963,0.0009023124,0.23250942,0.0012596023,0.004794006,0.0003774154,0.002720265,0.61389357,0.00075939327,0.027177986,0.0012640856,0.10990221],"study_design_scores_gemma":[0.0008654133,0.0021727304,0.053120572,0.00027561293,0.0012886346,0.00009672469,0.001755529,0.9255194,0.0020548594,0.01025363,0.002481962,0.00011494724],"about_ca_topic_score_codex":0.06686331,"about_ca_topic_score_gemma":0.02543217,"teacher_disagreement_score":0.14785172,"about_ca_system_score_codex":0.004775304,"about_ca_system_score_gemma":0.00445759,"threshold_uncertainty_score":0.781924},"labels":[],"label_agreement":null},{"id":"W7132811001","doi":"","title":"Trouvez ma clinique!","year":2011,"lang":"en","type":"article","venue":"NPARC","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Code (set theory); Cover (algebra); Table (database); Key (lock)","score_opus":0.04445782018089766,"score_gpt":0.2886732886928971,"score_spread":0.24421546851199943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132811001","genre_codex":"other","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":[0.014899513,0.009946013,0.01999384,0.15297863,0.023724958,0.00074335956,0.045651525,0.05228695,0.67977524],"genre_scores_gemma":[0.10738199,0.009404173,0.0350482,0.03934964,0.0075207097,0.00088179135,0.014789865,0.010523109,0.77510047],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9983552,0.00046452775,0.000069199945,0.00026576073,0.0006566015,0.00018875295],"domain_scores_gemma":[0.9943821,0.0017424714,0.0004930786,0.00053540716,0.0015965725,0.0012502878],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0023729492,0.00089553674,0.0009488775,0.0017083846,0.0014514033,0.0039814985,0.0010282501,0.0021869608,0.3194658],"category_scores_gemma":[0.015034648,0.0004427189,0.0008933737,0.0013018785,0.0006879295,0.0023322473,0.0019899814,0.0026955723,0.17347758],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016277966,0.000039039896,0.001856006,0.0001396929,0.0000214662,0.0002745743,0.00020794458,0.0001042242,0.00044731126,0.0036632782,0.91372555,0.07935823],"study_design_scores_gemma":[0.000028857408,0.000018105686,0.0013551192,0.00010847096,0.0000047122708,0.00023775212,0.00013258515,0.00013859035,0.00021080028,0.00052793184,0.99721944,0.000017665212],"about_ca_topic_score_codex":0.03385826,"about_ca_topic_score_gemma":0.04379531,"teacher_disagreement_score":0.3194658,"about_ca_system_score_codex":0.002363006,"about_ca_system_score_gemma":0.0019615002,"threshold_uncertainty_score":0.9706995},"labels":[],"label_agreement":null},{"id":"W7132929095","doi":"","title":"Data spatial aggregation issues in public health analysis, case study for the Toronto&apos;s inner city (Ontario)","year":2001,"lang":"en","type":"dissertation","venue":"TSpace","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public health; Inner city; Data collection; Field (mathematics); Public participation GIS; Urban planning","score_opus":0.14223680693722363,"score_gpt":0.468761027425626,"score_spread":0.32652422048840235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132929095","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":[0.5965707,0.023076998,0.118199766,0.112617455,0.00086743943,0.0018842387,0.03655907,0.0006212236,0.10960324],"genre_scores_gemma":[0.8889989,0.008870594,0.06408263,0.0013749504,0.00020243756,0.000528645,0.0055659716,0.00020804041,0.030167857],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9960051,0.0021134638,0.00023840634,0.00029485812,0.001046936,0.00030113565],"domain_scores_gemma":[0.9716143,0.020279147,0.0013139151,0.0015876368,0.004661131,0.00054377574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008631345,0.00030344343,0.000457928,0.0016564094,0.004143079,0.0046779644,0.0009249609,0.0012428404,0.003995482],"category_scores_gemma":[0.03126733,0.00056349376,0.00062550965,0.0132817095,0.0030273583,0.0013572508,0.0011847505,0.00081770215,0.00033064757],"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":[0.00035358337,0.00014046626,0.28428695,0.0012140787,0.0002508773,0.002916796,0.037886634,0.06476104,0.00088756497,0.12717667,0.24690843,0.23321688],"study_design_scores_gemma":[0.0002221196,0.00013851258,0.3756185,0.0015073236,0.00059024576,0.0010584568,0.08391186,0.097850844,0.0019919495,0.04739695,0.3894958,0.00021749003],"about_ca_topic_score_codex":0.97280854,"about_ca_topic_score_gemma":0.9845456,"teacher_disagreement_score":0.029318606,"about_ca_system_score_codex":0.029318606,"about_ca_system_score_gemma":0.03724174,"threshold_uncertainty_score":0.21272236},"labels":[],"label_agreement":null},{"id":"W7132942316","doi":"","title":"Topic Models for Primary Care and Community Health Monitoring","year":2024,"lang":"","type":"dissertation","venue":"TSpace","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Latent class model; Descriptive statistics; Topic model; Primary care; Thematic analysis; Leverage (statistics); Multivariate statistics; Latent variable; Health care; Community health","score_opus":0.04470519860873265,"score_gpt":0.39536663034997205,"score_spread":0.3506614317412394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132942316","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":[0.016513558,0.0037705847,0.96947855,0.0018901748,0.00031542638,0.00029526127,0.0037997884,0.0010543453,0.0028822646],"genre_scores_gemma":[0.52020663,0.006864968,0.4433599,0.0008402845,0.0021849545,0.002329205,0.014820259,0.00057421846,0.008819529],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9908414,0.0050076405,0.0006216112,0.0023077033,0.00090513966,0.00031642424],"domain_scores_gemma":[0.9638355,0.029871706,0.002441482,0.0018941071,0.0016096524,0.00034766403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013187659,0.0016590055,0.0020025554,0.0048702406,0.0011835706,0.00413858,0.0029990247,0.0023211022,0.0056613954],"category_scores_gemma":[0.04353522,0.0011639944,0.0036224995,0.0058716396,0.0014353257,0.005394672,0.0021999248,0.0036519922,0.0021382775],"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":[0.0004827669,0.00035202163,0.028145608,0.0013495468,0.0012631007,0.00047318306,0.0028427467,0.40051937,0.0017684856,0.24497357,0.02474839,0.29308128],"study_design_scores_gemma":[0.000040047737,0.00006239974,0.003197031,0.00013082859,0.00010042549,0.00014616213,0.00027343867,0.846284,0.00034491203,0.13797061,0.011394154,0.00005603921],"about_ca_topic_score_codex":0.014307768,"about_ca_topic_score_gemma":0.0108765345,"teacher_disagreement_score":0.014307768,"about_ca_system_score_codex":0.0027931507,"about_ca_system_score_gemma":0.00200072,"threshold_uncertainty_score":0.06974387},"labels":[],"label_agreement":null},{"id":"W7133026886","doi":"","title":"Changes in the Public Health Agency of Canada Pandemic Preparedness: A Case Study","year":2025,"lang":"","type":"dissertation","venue":"TSpace","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Health Canada; University of Toronto; Public Health Agency; Public Health Agency of Canada","keywords":"Public health; Agency (philosophy); Pandemic; Preparedness; Health promotion; International health","score_opus":0.06306104907572141,"score_gpt":0.3935503871426083,"score_spread":0.3304893380668869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133026886","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":[0.89345556,0.002413922,0.00063543324,0.030084446,0.00018808036,0.00035782412,0.0012903691,0.00003680832,0.07153753],"genre_scores_gemma":[0.98183006,0.0021085967,0.00087152247,0.0025840367,0.000032996508,0.000063563384,0.0003491996,0.000016494783,0.012143456],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99571383,0.0007806392,0.00008106056,0.00023916336,0.0012931601,0.001892065],"domain_scores_gemma":[0.99347335,0.0011177533,0.00051308813,0.0002336385,0.0027139953,0.0019482237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002991159,0.0003989818,0.0002901022,0.0017593732,0.020324538,0.0047746217,0.0025447009,0.0023564803,0.0030267152],"category_scores_gemma":[0.0077527706,0.00041301834,0.00042265694,0.004981232,0.0043863435,0.0012115452,0.002772323,0.00441828,0.0002617268],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.00028167217,0.00091729575,0.3112029,0.00053282507,0.00010911002,0.03359852,0.3944708,0.0025199198,0.0019280014,0.05108698,0.10836371,0.09498827],"study_design_scores_gemma":[0.000038887385,0.00015394241,0.2079169,0.00062564114,0.00007159035,0.0030512302,0.6316641,0.0019680583,0.0010056254,0.001307671,0.15209082,0.000105534615],"about_ca_topic_score_codex":0.9930576,"about_ca_topic_score_gemma":0.9966941,"teacher_disagreement_score":0.8538985,"about_ca_system_score_codex":0.1461015,"about_ca_system_score_gemma":0.16259225,"threshold_uncertainty_score":0.9904012},"labels":[],"label_agreement":null},{"id":"W7133092016","doi":"","title":"Data spatial aggregation issues in public health analysis case study for the Toronto&apos;s inner city","year":2001,"lang":"","type":"dissertation","venue":"TSpace","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bivariate analysis; Spatial epidemiology; Univariate; Neighbourhood (mathematics); Public health; Spatial analysis; Socioeconomic status; Interdependence; Epidemiology; Statistical hypothesis testing","score_opus":0.1619554898719889,"score_gpt":0.48552462449566647,"score_spread":0.32356913462367753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133092016","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":[0.8990815,0.002557987,0.04402003,0.0112377945,0.0001524038,0.0009953333,0.0040587485,0.00017264558,0.037723396],"genre_scores_gemma":[0.95844567,0.0010458261,0.036036815,0.00024137771,0.00003721015,0.00031712806,0.0011522617,0.000022197295,0.0027014185],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9927044,0.004021685,0.00036838543,0.00038120965,0.0020958236,0.00042852463],"domain_scores_gemma":[0.98890835,0.0064684073,0.0009682326,0.0013093416,0.0020658444,0.00027982116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064465785,0.0002690925,0.00035525518,0.0016776563,0.0028797772,0.001962388,0.00084257167,0.0006029311,0.0012222228],"category_scores_gemma":[0.018707013,0.00022085618,0.0004970604,0.006646573,0.0021140138,0.0007543,0.0014531999,0.00056373526,0.00010111162],"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":[0.00039536896,0.0002553485,0.49446374,0.0009808234,0.00032397595,0.012944969,0.061314207,0.07438831,0.002411894,0.18204916,0.031761024,0.13871121],"study_design_scores_gemma":[0.00013807003,0.00046447432,0.55738497,0.0007347779,0.00039274208,0.003686131,0.11293085,0.094898924,0.005820754,0.032146078,0.1912045,0.00019768001],"about_ca_topic_score_codex":0.59769225,"about_ca_topic_score_gemma":0.6663412,"teacher_disagreement_score":0.40230775,"about_ca_system_score_codex":0.014793946,"about_ca_system_score_gemma":0.009574603,"threshold_uncertainty_score":0.80935407},"labels":[],"label_agreement":null},{"id":"W7133105680","doi":"","title":"Statistical Computing With Graphics Processing Units","year":2023,"lang":"","type":"dissertation","venue":"TSpace","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Random number generation; Graphics; Software; Generator (circuit theory); Computation; Set (abstract data type); Aggregate (composite); Statistical inference; Gaussian","score_opus":0.03613584704827043,"score_gpt":0.373597714126368,"score_spread":0.33746186707809755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133105680","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":[0.0006799107,0.0030522684,0.79552686,0.004581548,0.008066101,0.0011582657,0.017774425,0.12555633,0.04360432],"genre_scores_gemma":[0.023537988,0.005892111,0.8558824,0.0050391173,0.0028933187,0.010172105,0.025012683,0.02952653,0.042043857],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9881216,0.0039936355,0.001730032,0.0021822653,0.003461957,0.0005105649],"domain_scores_gemma":[0.9720229,0.011459432,0.0014262622,0.007936219,0.006053833,0.0011012753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076757153,0.0034469885,0.003773561,0.0032025138,0.0011795773,0.010932295,0.005820771,0.0029342556,0.22752193],"category_scores_gemma":[0.053898416,0.0023477382,0.0036647653,0.006563768,0.0019348678,0.005062916,0.005405759,0.0074683693,0.15867832],"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":[0.00036179987,0.00008052455,0.00063791266,0.0016687048,0.00032279533,0.0003746363,0.00037161467,0.00596646,0.0026422935,0.06688891,0.6237012,0.2969831],"study_design_scores_gemma":[0.00029836377,0.000109823995,0.00053039007,0.0007349446,0.00009343959,0.00032108836,0.000104153245,0.026149755,0.0026333674,0.12468637,0.8441984,0.00013999775],"about_ca_topic_score_codex":0.0019120023,"about_ca_topic_score_gemma":0.0010200974,"teacher_disagreement_score":0.22752193,"about_ca_system_score_codex":0.00181579,"about_ca_system_score_gemma":0.0048195636,"threshold_uncertainty_score":0.76113665},"labels":[],"label_agreement":null},{"id":"W7133490478","doi":"10.5281/zenodo.18860429","title":"Mobile Health Surveillance in Nigeria's Federal Capital Territory: A Five-Year Review","year":2007,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Public health surveillance; Public health; Relevance (law); Health care; Health surveillance; Health policy; Inclusion (mineral); Community engagement; Disease surveillance","score_opus":0.01989278057864114,"score_gpt":0.2877596649944557,"score_spread":0.2678668844158146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133490478","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":[0.0038851444,0.9949804,0.00012536653,0.0004310463,0.00010001775,0.00008356437,0.00007719027,0.0000027327703,0.00031451983],"genre_scores_gemma":[0.023924893,0.9749376,0.0006186388,0.00018203462,0.000047212787,0.000113143695,0.00009921924,0.0000010652702,0.00007610988],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962806,0.0014488318,0.0014108467,0.00017983999,0.00054906635,0.00013084873],"domain_scores_gemma":[0.97947454,0.015358847,0.002678393,0.00021642668,0.0020154945,0.00025628784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008643381,0.0005909948,0.002282129,0.0091877105,0.00061146385,0.002293566,0.0008103217,0.0014991545,0.0010868136],"category_scores_gemma":[0.018897304,0.0006325368,0.0015799474,0.010547572,0.0006627805,0.0022706264,0.000882199,0.00048564767,0.00011350204],"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":[0.0003920616,0.00012469303,0.012647446,0.39716616,0.001699275,0.0011767566,0.0013530216,0.00047241803,0.0004166256,0.0007496048,0.00288198,0.5809199],"study_design_scores_gemma":[0.00023468531,0.001709191,0.07135141,0.73922706,0.015801487,0.005119318,0.005536797,0.00094931555,0.0012736551,0.00055719266,0.1581267,0.00011316802],"about_ca_topic_score_codex":0.018988857,"about_ca_topic_score_gemma":0.05150955,"teacher_disagreement_score":0.018988857,"about_ca_system_score_codex":0.0029345027,"about_ca_system_score_gemma":0.012177584,"threshold_uncertainty_score":0.0457111},"labels":[],"label_agreement":null},{"id":"W7133553146","doi":"10.5281/zenodo.18860428","title":"Mobile Health Surveillance in Nigeria's Federal Capital Territory: A Five-Year Review","year":2007,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Public health surveillance; Public health; Relevance (law); Health care; Health surveillance; Health policy; Inclusion (mineral); Community engagement; Disease surveillance","score_opus":0.01989278057864114,"score_gpt":0.2877596649944557,"score_spread":0.2678668844158146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133553146","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":[0.0038851444,0.9949804,0.00012536653,0.0004310463,0.00010001775,0.00008356437,0.00007719027,0.0000027327703,0.00031451983],"genre_scores_gemma":[0.023924893,0.9749376,0.0006186388,0.00018203462,0.000047212787,0.000113143695,0.00009921924,0.0000010652702,0.00007610988],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962806,0.0014488318,0.0014108467,0.00017983999,0.00054906635,0.00013084873],"domain_scores_gemma":[0.97947454,0.015358847,0.002678393,0.00021642668,0.0020154945,0.00025628784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008643381,0.0005909948,0.002282129,0.0091877105,0.00061146385,0.002293566,0.0008103217,0.0014991545,0.0010868136],"category_scores_gemma":[0.018897304,0.0006325368,0.0015799474,0.010547572,0.0006627805,0.0022706264,0.000882199,0.00048564767,0.00011350204],"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":[0.0003920616,0.00012469303,0.012647446,0.39716616,0.001699275,0.0011767566,0.0013530216,0.00047241803,0.0004166256,0.0007496048,0.00288198,0.5809199],"study_design_scores_gemma":[0.00023468531,0.001709191,0.07135141,0.73922706,0.015801487,0.005119318,0.005536797,0.00094931555,0.0012736551,0.00055719266,0.1581267,0.00011316802],"about_ca_topic_score_codex":0.018988857,"about_ca_topic_score_gemma":0.05150955,"teacher_disagreement_score":0.018988857,"about_ca_system_score_codex":0.0029345027,"about_ca_system_score_gemma":0.012177584,"threshold_uncertainty_score":0.0457111},"labels":[],"label_agreement":null},{"id":"W7134817102","doi":"10.5281/zenodo.18943427","title":"Methodological Evaluation of Public Health Surveillance Systems in Rwanda Using Panel Data to Measure Cost-Effectiveness","year":2012,"lang":"en","type":"article","venue":"Open MIND","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public health; Panel data; Public health surveillance; Quarter (Canadian coin); Measure (data warehouse); Investment (military); Health care; Data collection; Healthcare system","score_opus":0.8349019111533567,"score_gpt":0.5462131474456376,"score_spread":0.288688763707719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7134817102","genre_codex":"methods","genre_gemma":"empirical","domain_codex":"methods","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":[0.4442277,0.018600976,0.49172723,0.0058619184,0.0009918154,0.02368571,0.005110177,0.00027864403,0.009515854],"genre_scores_gemma":[0.85278594,0.0015398932,0.12764551,0.0006644001,0.000120662895,0.015379202,0.0013508191,0.000038050257,0.00047547225],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.3859682,0.58649266,0.015688231,0.004198549,0.0066616125,0.0009907223],"domain_scores_gemma":[0.34859473,0.5827652,0.030823821,0.024755828,0.012428731,0.0006316253],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.38813734,0.0013390477,0.0021083702,0.0021982663,0.00095643237,0.003172479,0.002787489,0.0018175261,0.0018896321],"category_scores_gemma":[0.56006116,0.0011753456,0.007613611,0.0030318333,0.0016253308,0.0016532573,0.0033701535,0.002702344,0.0001638114],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.029486166,0.0023665486,0.19674988,0.019855246,0.07834443,0.00052172254,0.006011954,0.25413352,0.0017256476,0.077494405,0.006000092,0.3273104],"study_design_scores_gemma":[0.01754631,0.031517766,0.21759212,0.017306088,0.061062317,0.00075947697,0.0037523443,0.48106432,0.014268508,0.118542895,0.035612516,0.0009753664],"about_ca_topic_score_codex":0.0081516905,"about_ca_topic_score_gemma":0.0045314077,"teacher_disagreement_score":0.38813734,"about_ca_system_score_codex":0.0050008465,"about_ca_system_score_gemma":0.004514625,"threshold_uncertainty_score":0.7545356},"labels":[],"label_agreement":null},{"id":"W7134836561","doi":"10.5281/zenodo.18943426","title":"Methodological Evaluation of Public Health Surveillance Systems in Rwanda Using Panel Data to Measure Cost-Effectiveness","year":2012,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public health; Panel data; Public health surveillance; Quarter (Canadian coin); Measure (data warehouse); Investment (military); Health care; Data collection; Healthcare system","score_opus":0.6999936095203574,"score_gpt":0.4561946768440539,"score_spread":0.24379893267630348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7134836561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":"methods","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":[0.44151655,0.021012673,0.49153045,0.0051119947,0.00089166965,0.02464529,0.006029741,0.00028701147,0.0089746835],"genre_scores_gemma":[0.85345036,0.0016869581,0.12502405,0.00059935445,0.00010559105,0.016992364,0.0016050512,0.000041316227,0.0004949498],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.42390773,0.5514554,0.013845711,0.004097376,0.0057125203,0.0009813729],"domain_scores_gemma":[0.4067033,0.5293235,0.02952363,0.022543205,0.011257658,0.0006486776],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.34848598,0.0013725968,0.0021706335,0.002125843,0.00087186566,0.0028756792,0.0025710955,0.0016601029,0.0019081989],"category_scores_gemma":[0.50603896,0.0011296059,0.0075623286,0.003021481,0.0014746302,0.0014514764,0.003285515,0.0025010603,0.00016229229],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.03516617,0.0022076378,0.1978676,0.019548781,0.08214819,0.00049478223,0.0048312703,0.26175883,0.0018782045,0.063724995,0.0058604036,0.32451323],"study_design_scores_gemma":[0.01961491,0.03407843,0.24190901,0.015185644,0.06743641,0.0007684141,0.0032423781,0.46408135,0.014587822,0.10230665,0.03588953,0.0008994498],"about_ca_topic_score_codex":0.008333638,"about_ca_topic_score_gemma":0.004443114,"teacher_disagreement_score":0.34848598,"about_ca_system_score_codex":0.0049438626,"about_ca_system_score_gemma":0.0042165467,"threshold_uncertainty_score":0.8034328},"labels":[],"label_agreement":null},{"id":"W7134943453","doi":"10.5281/zenodo.18947668","title":"Methodological Evaluation and Panel-Data Estimation for Yield Improvement in South African Public Health Surveillance Systems, 2000–2026","year":2002,"lang":"en","type":"article","venue":"Open MIND","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public health; Estimation; Yield (engineering); Rigour; Outcome (game theory); Inference; Resource allocation; Health economics; Causal inference","score_opus":0.5882585726683993,"score_gpt":0.45431299375732825,"score_spread":0.13394557891107106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7134943453","genre_codex":"review","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":[0.04678797,0.63974184,0.25325412,0.0186009,0.0033195773,0.022351071,0.008716632,0.00049462926,0.006733283],"genre_scores_gemma":[0.7083714,0.08747233,0.1552412,0.0044425996,0.00066031807,0.037202995,0.005498952,0.0001995761,0.0009105396],"study_design_codex":"meta_analysis","study_design_gemma":"observational","domain_scores_codex":[0.49072075,0.45218995,0.036955837,0.0059910743,0.0128567135,0.0012856416],"domain_scores_gemma":[0.48944724,0.4215426,0.043467596,0.019448169,0.02510841,0.0009859365],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.4306373,0.0026083481,0.006137272,0.0075460514,0.0011789218,0.005698062,0.004145704,0.0030819348,0.0041261683],"category_scores_gemma":[0.6259399,0.0017617681,0.02859689,0.009413367,0.002150542,0.004438023,0.005125083,0.0039763562,0.00032721192],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008073891,0.00030243196,0.037745055,0.2446647,0.41532227,0.0001920969,0.0034295858,0.028929047,0.00030738872,0.022838186,0.011409052,0.22678632],"study_design_scores_gemma":[0.006720623,0.0028432547,0.053251687,0.26417983,0.53463376,0.00030122124,0.001987964,0.046167348,0.0024175884,0.04036133,0.04663968,0.00049565564],"about_ca_topic_score_codex":0.025978561,"about_ca_topic_score_gemma":0.018391563,"teacher_disagreement_score":0.4306373,"about_ca_system_score_codex":0.014086111,"about_ca_system_score_gemma":0.015797827,"threshold_uncertainty_score":0.70212555},"labels":[],"label_agreement":null},{"id":"W7134984409","doi":"10.5281/zenodo.18947669","title":"Methodological Evaluation and Panel-Data Estimation for Yield Improvement in South African Public Health Surveillance Systems, 2000–2026","year":2002,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public health; Estimation; Yield (engineering); Rigour; Outcome (game theory); Inference; Resource allocation; Health economics; Causal inference","score_opus":0.4170646859272934,"score_gpt":0.3720446442579742,"score_spread":0.04502004166931922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7134984409","genre_codex":"review","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":[0.04678797,0.63974184,0.25325412,0.0186009,0.0033195773,0.022351071,0.008716632,0.00049462926,0.006733283],"genre_scores_gemma":[0.7083714,0.08747233,0.1552412,0.0044425996,0.00066031807,0.037202995,0.005498952,0.0001995761,0.0009105396],"study_design_codex":"meta_analysis","study_design_gemma":"observational","domain_scores_codex":[0.49072075,0.45218995,0.036955837,0.0059910743,0.0128567135,0.0012856416],"domain_scores_gemma":[0.48944724,0.4215426,0.043467596,0.019448169,0.02510841,0.0009859365],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.4306373,0.0026083481,0.006137272,0.0075460514,0.0011789218,0.005698062,0.004145704,0.0030819348,0.0041261683],"category_scores_gemma":[0.6259399,0.0017617681,0.02859689,0.009413367,0.002150542,0.004438023,0.005125083,0.0039763562,0.00032721192],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008073891,0.00030243196,0.037745055,0.2446647,0.41532227,0.0001920969,0.0034295858,0.028929047,0.00030738872,0.022838186,0.011409052,0.22678632],"study_design_scores_gemma":[0.006720623,0.0028432547,0.053251687,0.26417983,0.53463376,0.00030122124,0.001987964,0.046167348,0.0024175884,0.04036133,0.04663968,0.00049565564],"about_ca_topic_score_codex":0.025978561,"about_ca_topic_score_gemma":0.018391563,"teacher_disagreement_score":0.4306373,"about_ca_system_score_codex":0.014086111,"about_ca_system_score_gemma":0.015797827,"threshold_uncertainty_score":0.70212555},"labels":[],"label_agreement":null},{"id":"W7137058736","doi":"10.6084/m9.figshare.4445825.v1","title":"Additional file 1: of Evaluation of self-swabbing coupled with a telephone health helpline as an adjunct tool for surveillance of influenza viruses in Ontario","year":2016,"lang":"","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Adjunct; Helpline; Hotline; Masking (illustration); Influenza A virus subtype H5N1; Influenzavirus B; MEDLINE; Epidemiological surveillance","score_opus":0.05927915430826595,"score_gpt":0.3224376179632764,"score_spread":0.26315846365501044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7137058736","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001123082,0.000020083846,0.00018160837,0.0001407666,0.000020631105,0.0007618041,0.99328023,0.000120304314,0.004351445],"genre_scores_gemma":[0.09737213,0.00037858932,0.007854674,0.0008323696,0.00011372603,0.016485503,0.8408144,0.00079161505,0.035356957],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99747497,0.00047555327,0.00037542306,0.000273695,0.0010364664,0.00036383883],"domain_scores_gemma":[0.96293974,0.015795534,0.002626051,0.0018120625,0.015329158,0.0014974829],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003932087,0.00091968506,0.00094117346,0.0021618272,0.002202173,0.0013341557,0.0019812558,0.00057031325,0.54376006],"category_scores_gemma":[0.05151068,0.0006264783,0.0024005028,0.0043948083,0.0004559848,0.0012597626,0.0010373697,0.000644424,0.034555916],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006679267,0.00010814331,0.01733235,0.0030691402,0.00011367007,0.000037493992,0.0003363067,0.00043602704,0.00007081276,0.00026477466,0.9641307,0.013432572],"study_design_scores_gemma":[0.0067778435,0.00058417325,0.5097158,0.0070752446,0.000794118,0.00019289507,0.0024692402,0.0028196706,0.0010101986,0.0009277006,0.467424,0.00020921035],"about_ca_topic_score_codex":0.8388941,"about_ca_topic_score_gemma":0.8661964,"teacher_disagreement_score":0.54376006,"about_ca_system_score_codex":0.0151863815,"about_ca_system_score_gemma":0.018614309,"threshold_uncertainty_score":0.6507709},"labels":[],"label_agreement":null},{"id":"W7137147290","doi":"10.6084/m9.figshare.4445825","title":"Additional file 1: of Evaluation of self-swabbing coupled with a telephone health helpline as an adjunct tool for surveillance of influenza viruses in Ontario","year":2016,"lang":"","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Adjunct; Helpline; Hotline; Masking (illustration); Influenza A virus subtype H5N1; Influenzavirus B; MEDLINE; Epidemiological surveillance","score_opus":0.05927915430826595,"score_gpt":0.3224376179632764,"score_spread":0.26315846365501044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7137147290","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001123082,0.000020083846,0.00018160837,0.0001407666,0.000020631105,0.0007618041,0.99328023,0.000120304314,0.004351445],"genre_scores_gemma":[0.09737213,0.00037858932,0.007854674,0.0008323696,0.00011372603,0.016485503,0.8408144,0.00079161505,0.035356957],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99747497,0.00047555327,0.00037542306,0.000273695,0.0010364664,0.00036383883],"domain_scores_gemma":[0.96293974,0.015795534,0.002626051,0.0018120625,0.015329158,0.0014974829],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003932087,0.00091968506,0.00094117346,0.0021618272,0.002202173,0.0013341557,0.0019812558,0.00057031325,0.54376006],"category_scores_gemma":[0.05151068,0.0006264783,0.0024005028,0.0043948083,0.0004559848,0.0012597626,0.0010373697,0.000644424,0.034555916],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006679267,0.00010814331,0.01733235,0.0030691402,0.00011367007,0.000037493992,0.0003363067,0.00043602704,0.00007081276,0.00026477466,0.9641307,0.013432572],"study_design_scores_gemma":[0.0067778435,0.00058417325,0.5097158,0.0070752446,0.000794118,0.00019289507,0.0024692402,0.0028196706,0.0010101986,0.0009277006,0.467424,0.00020921035],"about_ca_topic_score_codex":0.8388941,"about_ca_topic_score_gemma":0.8661964,"teacher_disagreement_score":0.54376006,"about_ca_system_score_codex":0.0151863815,"about_ca_system_score_gemma":0.018614309,"threshold_uncertainty_score":0.6507709},"labels":[],"label_agreement":null},{"id":"W7137918011","doi":"10.6084/m9.figshare.4360583.v1","title":"Additional file 1: of Small-area spatio-temporal analyses of bladder and kidney cancer risk in Nova Scotia, Canada","year":2016,"lang":"","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Kidney cancer; Bladder cancer; Nova (rocket); Kidney disease; Kidney; Cancer; Nephrectomy","score_opus":0.04813491706078759,"score_gpt":0.283633128408554,"score_spread":0.23549821134776638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7137918011","genre_codex":"dataset","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":[0.00029670936,0.000015729189,0.000079891375,0.000034852135,0.000010513643,0.0000385422,0.9987901,0.00004741151,0.0006862016],"genre_scores_gemma":[0.026380524,0.00025320853,0.003539949,0.00023428591,0.000032398137,0.0010943782,0.9533994,0.00038926926,0.014676593],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9991358,0.00008359897,0.00012129106,0.00016991541,0.0002597551,0.00022968843],"domain_scores_gemma":[0.98860615,0.003964661,0.000569651,0.0009278069,0.005366752,0.0005650277],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012595172,0.00084817695,0.0009930179,0.0041504554,0.0019328034,0.0015894203,0.0020676523,0.00052927306,0.41590685],"category_scores_gemma":[0.01798141,0.0006125847,0.0016581868,0.00946522,0.00038845307,0.0006611349,0.0010503753,0.0006956812,0.020619014],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014395615,0.000020025473,0.025833303,0.0016351314,0.00016781231,0.00008526476,0.00024360261,0.0009328171,0.00009314285,0.0007255201,0.9635053,0.0066140713],"study_design_scores_gemma":[0.0012773067,0.00006841424,0.42326897,0.004560784,0.000652671,0.0003504431,0.0019836798,0.0028263726,0.00080149894,0.0020628844,0.56193334,0.00021363604],"about_ca_topic_score_codex":0.9688973,"about_ca_topic_score_gemma":0.9703517,"teacher_disagreement_score":0.41590685,"about_ca_system_score_codex":0.00995912,"about_ca_system_score_gemma":0.02891947,"threshold_uncertainty_score":0.833138},"labels":[],"label_agreement":null},{"id":"W7138138905","doi":"10.6084/m9.figshare.4360583","title":"Additional file 1: of Small-area spatio-temporal analyses of bladder and kidney cancer risk in Nova Scotia, Canada","year":2016,"lang":"","type":"article","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Kidney cancer; Bladder cancer; Nova (rocket); Kidney disease; Kidney; Cancer; Nephrectomy","score_opus":0.04813491706078759,"score_gpt":0.283633128408554,"score_spread":0.23549821134776638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7138138905","genre_codex":"dataset","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":[0.00029670936,0.000015729189,0.000079891375,0.000034852135,0.000010513643,0.0000385422,0.9987901,0.00004741151,0.0006862016],"genre_scores_gemma":[0.026380524,0.00025320853,0.003539949,0.00023428591,0.000032398137,0.0010943782,0.9533994,0.00038926926,0.014676593],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9991358,0.00008359897,0.00012129106,0.00016991541,0.0002597551,0.00022968843],"domain_scores_gemma":[0.98860615,0.003964661,0.000569651,0.0009278069,0.005366752,0.0005650277],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012595172,0.00084817695,0.0009930179,0.0041504554,0.0019328034,0.0015894203,0.0020676523,0.00052927306,0.41590685],"category_scores_gemma":[0.01798141,0.0006125847,0.0016581868,0.00946522,0.00038845307,0.0006611349,0.0010503753,0.0006956812,0.020619014],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014395615,0.000020025473,0.025833303,0.0016351314,0.00016781231,0.00008526476,0.00024360261,0.0009328171,0.00009314285,0.0007255201,0.9635053,0.0066140713],"study_design_scores_gemma":[0.0012773067,0.00006841424,0.42326897,0.004560784,0.000652671,0.0003504431,0.0019836798,0.0028263726,0.00080149894,0.0020628844,0.56193334,0.00021363604],"about_ca_topic_score_codex":0.9688973,"about_ca_topic_score_gemma":0.9703517,"teacher_disagreement_score":0.41590685,"about_ca_system_score_codex":0.00995912,"about_ca_system_score_gemma":0.02891947,"threshold_uncertainty_score":0.833138},"labels":[],"label_agreement":null},{"id":"W7138316729","doi":"10.5281/zenodo.19059177","title":"GIS-Aided Surveillance in Northern Nigeria: Evaluating Livestock Disease Outbreak Detection and Prevention Efforts","year":2014,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Outbreak; Livestock; Estimation; Metric (unit); Verifiable secret sharing; Key (lock); Thematic map","score_opus":0.026905186374378758,"score_gpt":0.27872102930902154,"score_spread":0.25181584293464276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7138316729","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":[0.8626877,0.07862178,0.02448289,0.0029477447,0.00015693644,0.002443729,0.0015092812,0.00006367329,0.027086243],"genre_scores_gemma":[0.941339,0.032788545,0.023983078,0.00013227185,0.00002206729,0.00063659466,0.00039550327,0.0000047386666,0.00069817033],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9931161,0.0053102514,0.00045045643,0.00023451488,0.0007361986,0.00015260784],"domain_scores_gemma":[0.9873667,0.009898792,0.0011756384,0.00015942211,0.0012965754,0.00010288511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010113723,0.000603567,0.000675386,0.0037923618,0.00052236044,0.0020066986,0.00047776304,0.00055942236,0.0010533108],"category_scores_gemma":[0.021824881,0.0002748912,0.00046199586,0.0033282707,0.00054348237,0.0014056494,0.0009699926,0.00030087386,0.00009017198],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000953237,0.0006894977,0.23067215,0.04029748,0.0009392437,0.0011065148,0.012544801,0.033024553,0.0032837708,0.017410848,0.0021209363,0.6569569],"study_design_scores_gemma":[0.0006179561,0.009654022,0.5230079,0.080859356,0.0057948916,0.0020834869,0.15680249,0.10106683,0.0216715,0.024405928,0.07371443,0.00032127977],"about_ca_topic_score_codex":0.013273219,"about_ca_topic_score_gemma":0.028253507,"teacher_disagreement_score":0.013273219,"about_ca_system_score_codex":0.0030466574,"about_ca_system_score_gemma":0.006496999,"threshold_uncertainty_score":0.053487062},"labels":[],"label_agreement":null},{"id":"W7138421758","doi":"10.5281/zenodo.19059178","title":"GIS-Aided Surveillance in Northern Nigeria: Evaluating Livestock Disease Outbreak Detection and Prevention Efforts","year":2014,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Outbreak; Livestock; Estimation; Metric (unit); Verifiable secret sharing; Key (lock); Thematic map","score_opus":0.026905186374378758,"score_gpt":0.27872102930902154,"score_spread":0.25181584293464276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7138421758","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":[0.8626877,0.07862178,0.02448289,0.0029477447,0.00015693644,0.002443729,0.0015092812,0.00006367329,0.027086243],"genre_scores_gemma":[0.941339,0.032788545,0.023983078,0.00013227185,0.00002206729,0.00063659466,0.00039550327,0.0000047386666,0.00069817033],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9931161,0.0053102514,0.00045045643,0.00023451488,0.0007361986,0.00015260784],"domain_scores_gemma":[0.9873667,0.009898792,0.0011756384,0.00015942211,0.0012965754,0.00010288511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010113723,0.000603567,0.000675386,0.0037923618,0.00052236044,0.0020066986,0.00047776304,0.00055942236,0.0010533108],"category_scores_gemma":[0.021824881,0.0002748912,0.00046199586,0.0033282707,0.00054348237,0.0014056494,0.0009699926,0.00030087386,0.00009017198],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000953237,0.0006894977,0.23067215,0.04029748,0.0009392437,0.0011065148,0.012544801,0.033024553,0.0032837708,0.017410848,0.0021209363,0.6569569],"study_design_scores_gemma":[0.0006179561,0.009654022,0.5230079,0.080859356,0.0057948916,0.0020834869,0.15680249,0.10106683,0.0216715,0.024405928,0.07371443,0.00032127977],"about_ca_topic_score_codex":0.013273219,"about_ca_topic_score_gemma":0.028253507,"teacher_disagreement_score":0.013273219,"about_ca_system_score_codex":0.0030466574,"about_ca_system_score_gemma":0.006496999,"threshold_uncertainty_score":0.053487062},"labels":[],"label_agreement":null},{"id":"W7138908345","doi":"10.1093/phe/phag003","title":"Digital disease surveillance and neocolonial governance of the global south","year":2025,"lang":"en","type":"article","venue":"Public Health Ethics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Addiction and Mental Health; McMaster University","funders":"","keywords":"Corporate governance; Power (physics); Global South; Global governance; Data Protection Act 1998; Developing country","score_opus":0.043838746156176604,"score_gpt":0.3556505221873079,"score_spread":0.3118117760311313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7138908345","genre_codex":"commentary","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":[0.15821314,0.0050127604,0.020006584,0.5691302,0.0006958954,0.00009701303,0.00009967276,0.00004610617,0.24669868],"genre_scores_gemma":[0.9738304,0.0011698724,0.0020831139,0.017454904,0.00014952818,0.000049584123,0.000023437993,0.000013939892,0.0052252416],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99029034,0.0077338354,0.00023748621,0.00051357085,0.000729312,0.00049533497],"domain_scores_gemma":[0.98383266,0.011464692,0.0012870599,0.001500979,0.0011034016,0.0008111751],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.014640916,0.00013641603,0.00016751181,0.00065599545,0.002641977,0.006943884,0.00044950287,0.0014491729,0.0031395243],"category_scores_gemma":[0.017421626,0.00012905571,0.00013266494,0.0006769697,0.02023291,0.0032013312,0.007075102,0.003082404,0.0002652824],"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":[0.000036901652,0.000020163112,0.006335019,0.000104947234,0.00001215024,0.0005367083,0.05868994,0.0005232709,0.00049342983,0.8887887,0.01004606,0.034412686],"study_design_scores_gemma":[0.000027692575,0.000051705712,0.008807908,0.001051201,0.000009481379,0.0006976615,0.033224326,0.001916872,0.0009764222,0.48940927,0.463792,0.000035503857],"about_ca_topic_score_codex":0.0040361146,"about_ca_topic_score_gemma":0.004104702,"teacher_disagreement_score":0.997358,"about_ca_system_score_codex":0.0046363766,"about_ca_system_score_gemma":0.0052234917,"threshold_uncertainty_score":0.07742947},"labels":[],"label_agreement":null},{"id":"W7139611878","doi":"","title":"Essays on Nowcasting : Machine learning, high-frequency data and nowcasting","year":2024,"lang":"en","type":"article","venue":"theses.fr (ABES)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nowcasting; Statistical analysis; Context (archaeology); Quarter (Canadian coin)","score_opus":0.04543185138968277,"score_gpt":0.30908187459390757,"score_spread":0.2636500232042248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7139611878","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":[0.008183457,0.25812238,0.3518993,0.25491783,0.04334625,0.00011608683,0.0014687695,0.00071357423,0.08123229],"genre_scores_gemma":[0.22278672,0.27270538,0.14233004,0.026698511,0.16690512,0.00043468046,0.0025351,0.0014364738,0.16416806],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99778336,0.0009186147,0.0001981118,0.00048261954,0.0005186731,0.00009852055],"domain_scores_gemma":[0.98430353,0.012632061,0.0005130806,0.00094882,0.0013473509,0.0002550548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004213337,0.0011130536,0.0011045844,0.002299294,0.001450425,0.006334469,0.0014671393,0.004244293,0.009030891],"category_scores_gemma":[0.025546297,0.00071873935,0.0014223143,0.0050482443,0.0036984554,0.007818648,0.0015359774,0.0055643865,0.0035872345],"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":[0.00013217847,0.00007227292,0.0017219958,0.00060104387,0.00013155873,0.0001766006,0.0006186413,0.020253463,0.00039036182,0.40828758,0.30607507,0.26153925],"study_design_scores_gemma":[0.000042625485,0.000036684716,0.0020893665,0.00068587466,0.000047352325,0.00019917413,0.00039523732,0.041285604,0.00041058043,0.49149686,0.4632221,0.00008856963],"about_ca_topic_score_codex":0.0075500244,"about_ca_topic_score_gemma":0.0048473827,"teacher_disagreement_score":0.009030891,"about_ca_system_score_codex":0.0031522408,"about_ca_system_score_gemma":0.0018033114,"threshold_uncertainty_score":0.03021127},"labels":[],"label_agreement":null},{"id":"W7151955701","doi":"10.70675/761dae81zc780z4c9bz82b9zc30bf22898b9","title":"Essays on Nowcasting : Machine learning, high-frequency data and nowcasting","year":2024,"lang":"","type":"dissertation","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nowcasting; Statistical analysis; Context (archaeology); Quarter (Canadian coin)","score_opus":0.032763583304276915,"score_gpt":0.3150217295529518,"score_spread":0.2822581462486749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7151955701","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":[0.010128158,0.13719775,0.3595852,0.32422453,0.078028604,0.00014886391,0.0031191285,0.001176796,0.086390935],"genre_scores_gemma":[0.21770307,0.13224031,0.13639686,0.038427245,0.19667818,0.00047077902,0.004174347,0.0025569035,0.2713523],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99664944,0.0012260147,0.00029756324,0.00071270455,0.00097172067,0.00014249381],"domain_scores_gemma":[0.9709809,0.022938853,0.00096958585,0.0018875874,0.0027613237,0.000461714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00554923,0.0010230434,0.0010942974,0.0028115553,0.0019159073,0.006916938,0.0014423728,0.0036333392,0.011937093],"category_scores_gemma":[0.04778931,0.00069828,0.0014234959,0.0050552087,0.0035989797,0.010279696,0.001633589,0.0057684723,0.00531437],"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":[0.00016461499,0.0000606999,0.0015323428,0.00053180853,0.00010118643,0.00013187258,0.0006866026,0.010602389,0.00057924585,0.27483034,0.45567873,0.25510007],"study_design_scores_gemma":[0.000046845355,0.000042676733,0.0020780107,0.00067459006,0.000048845992,0.00020832062,0.0004883265,0.025201444,0.0006705428,0.32631597,0.64411455,0.00010978407],"about_ca_topic_score_codex":0.0069407877,"about_ca_topic_score_gemma":0.0046335673,"teacher_disagreement_score":0.011937093,"about_ca_system_score_codex":0.003434438,"about_ca_system_score_gemma":0.0022654105,"threshold_uncertainty_score":0.039933503},"labels":[],"label_agreement":null},{"id":"W7161490535","doi":"10.23889/ijpds.v9i5.1660","title":"Understanding health data social licence: An international comparison of community attitudes towards health data use across Canada and Australia","year":2024,"lang":"en","type":"article","venue":"PubMed Central","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Health data; Social determinants of health; Public health; Community health; Data collection","score_opus":0.5379058468548273,"score_gpt":0.4826653344159444,"score_spread":0.05524051243888295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7161490535","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":[0.97518945,0.0012145636,0.000549406,0.009209286,0.00005509835,0.00015144644,0.0007909937,0.000015491454,0.012824264],"genre_scores_gemma":[0.99642,0.00075496035,0.0004333777,0.00087783806,0.000012343485,0.000042415133,0.0003039487,0.000010662387,0.001144481],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9890164,0.0032880092,0.0010312363,0.0005569903,0.004409071,0.0016981923],"domain_scores_gemma":[0.9193115,0.027780842,0.01639891,0.0024677678,0.022330182,0.011710867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018930059,0.00020362607,0.00048332356,0.0042443476,0.004940488,0.006170904,0.0016077206,0.0008825828,0.0026865567],"category_scores_gemma":[0.065774195,0.0003662071,0.00068990403,0.005159792,0.0041611074,0.0030848722,0.0055098673,0.0024649068,0.00015147189],"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":[0.000105048464,0.00015738241,0.7492471,0.00026203852,0.00013446632,0.0001381716,0.20933834,0.00010605083,0.00021116002,0.002548765,0.0044683805,0.033283178],"study_design_scores_gemma":[0.000009662416,0.000047269466,0.8642594,0.00046636278,0.000055356224,0.00006929573,0.12323872,0.00037633476,0.00011077853,0.00036648702,0.010941799,0.000058681613],"about_ca_topic_score_codex":0.9286374,"about_ca_topic_score_gemma":0.9426741,"teacher_disagreement_score":0.071362615,"about_ca_system_score_codex":0.024650566,"about_ca_system_score_gemma":0.056693316,"threshold_uncertainty_score":0.17885321},"labels":[],"label_agreement":null},{"id":"W7161938396","doi":"10.82308/9574","title":"Utility of neuraminidase inhibitor dispensing data as a tool for influenza surveillance","year":2015,"lang":"en","type":"dissertation","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Neuraminidase; Seasonal influenza; Autoregressive integrated moving average; Predictive value; Neuraminidase inhibitor; Pharmacy; Emergency department","score_opus":0.07480776361373295,"score_gpt":0.39344437383239866,"score_spread":0.31863661021866574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7161938396","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":[0.91779584,0.002407624,0.029225841,0.0012915946,0.00016436922,0.0004813572,0.036163066,0.00073678733,0.011733671],"genre_scores_gemma":[0.9754038,0.00037821638,0.015544605,0.00015658734,0.00006515478,0.00010094094,0.0077259047,0.00002377067,0.000601027],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98667985,0.006580851,0.0011457577,0.0014193201,0.0037166052,0.00045767412],"domain_scores_gemma":[0.9540533,0.020513332,0.009758074,0.003791116,0.010532093,0.0013521264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010770892,0.000802548,0.000607638,0.00390435,0.00055277336,0.0021723362,0.0011634035,0.00051207165,0.0009771278],"category_scores_gemma":[0.044010226,0.00036717486,0.00070645974,0.0056650606,0.00031877146,0.0008740054,0.00090722204,0.00075748924,0.00032350866],"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":[0.00023107954,0.00009095259,0.9644992,0.000103417726,0.00033002428,0.000050739465,0.00018439033,0.004618392,0.0004792616,0.00025310673,0.0015461504,0.027613446],"study_design_scores_gemma":[0.00003000482,0.00031363693,0.9189282,0.00013288218,0.00013180973,0.00010444575,0.0002416126,0.07513698,0.0012237952,0.00029875778,0.0034048054,0.000053047817],"about_ca_topic_score_codex":0.33394018,"about_ca_topic_score_gemma":0.31505206,"teacher_disagreement_score":0.33394018,"about_ca_system_score_codex":0.0029815743,"about_ca_system_score_gemma":0.004958818,"threshold_uncertainty_score":0.6639929},"labels":[],"label_agreement":null},{"id":"W7162012997","doi":"10.82308/2252","title":"Assessing and improving the accuracy of surveillance case definitions using administrative data","year":2011,"lang":"en","type":"dissertation","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Epidemiological surveillance; Medical screening; Context (archaeology); Electronic surveillance","score_opus":0.299984746021386,"score_gpt":0.4295491211330016,"score_spread":0.12956437511161562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7162012997","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7854982,0.005468823,0.17457494,0.0050811833,0.00055408175,0.003160214,0.0113176415,0.0010288975,0.01331604],"genre_scores_gemma":[0.89078265,0.0007843863,0.102357574,0.00041882411,0.00016165714,0.0010909023,0.0038537406,0.0000769483,0.00047324464],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.75776166,0.18416658,0.023799226,0.009335936,0.022715477,0.002221111],"domain_scores_gemma":[0.36547858,0.4970509,0.057442896,0.036671363,0.041895293,0.001460931],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.14531896,0.000863886,0.0011765907,0.006661093,0.0010331396,0.0057632215,0.0037640757,0.0013595639,0.001599079],"category_scores_gemma":[0.5454478,0.0007535327,0.0017327985,0.006158949,0.0014818601,0.004447478,0.0040704063,0.0015609738,0.0006760938],"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":[0.0005111159,0.0001645503,0.89214754,0.0010381271,0.000632206,0.00007651272,0.0033608386,0.0054107294,0.0005761065,0.0026664413,0.0026862589,0.09072965],"study_design_scores_gemma":[0.0002319192,0.0013867564,0.83361155,0.0025405497,0.0010615669,0.000853618,0.006458289,0.11529628,0.008050734,0.007774293,0.022493968,0.0002404679],"about_ca_topic_score_codex":0.013386632,"about_ca_topic_score_gemma":0.008469423,"teacher_disagreement_score":0.854681,"about_ca_system_score_codex":0.0024851593,"about_ca_system_score_gemma":0.0045904526,"threshold_uncertainty_score":0.7685293},"labels":[],"label_agreement":null},{"id":"W7162018242","doi":"10.82308/47488","title":"Privacy considerations when seeking health information online","year":2025,"lang":"en","type":"dissertation","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Web site; Information privacy; Public information; Public access; Access to information","score_opus":0.025382022980034454,"score_gpt":0.33584640556082623,"score_spread":0.3104643825807918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7162018242","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":[0.4947238,0.008524366,0.17255849,0.09330751,0.00085609406,0.0025976123,0.006989969,0.0009109538,0.21953121],"genre_scores_gemma":[0.9419631,0.0032668938,0.030106181,0.008770134,0.00043772248,0.0008608764,0.0013841212,0.00011119642,0.013099804],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9730041,0.019241288,0.0012254114,0.0014518109,0.0039457656,0.0011315782],"domain_scores_gemma":[0.87279564,0.10584126,0.007094776,0.007887292,0.005285466,0.0010956139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023106365,0.0005091708,0.00075793365,0.0020621852,0.0030436143,0.006738872,0.0012607786,0.0030401843,0.010773026],"category_scores_gemma":[0.09086766,0.00061788305,0.0011341615,0.0025735875,0.0027040758,0.0073039844,0.0024288057,0.0019667933,0.002091753],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015733733,0.00056776265,0.34324923,0.002199004,0.0004936926,0.004185092,0.05312535,0.0069493083,0.0041130767,0.1352486,0.03628727,0.41200832],"study_design_scores_gemma":[0.0003113931,0.0011746587,0.20182234,0.0038363806,0.001023166,0.011946216,0.07089321,0.06942776,0.013055045,0.33640724,0.2896715,0.00043108108],"about_ca_topic_score_codex":0.011697409,"about_ca_topic_score_gemma":0.008166914,"teacher_disagreement_score":0.023106365,"about_ca_system_score_codex":0.002076303,"about_ca_system_score_gemma":0.0028720542,"threshold_uncertainty_score":0.122199595},"labels":[],"label_agreement":null},{"id":"W7162035379","doi":"10.82308/48853","title":"The utility of a Geographic Information System in the detection of Tuberculosis transmission","year":2008,"lang":"en","type":"dissertation","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Epidemiological surveillance; Active tuberculosis; Information transmission; Non human primate","score_opus":0.007721186440702742,"score_gpt":0.24245868880604474,"score_spread":0.234737502365342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7162035379","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":[0.8809381,0.008072651,0.07550148,0.0043014,0.00025901935,0.0007128422,0.009239249,0.001332258,0.019642994],"genre_scores_gemma":[0.9496898,0.0013189958,0.04612306,0.00015692342,0.000070442075,0.00016534205,0.0012840639,0.00002340462,0.0011679594],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99591464,0.00280608,0.00022463621,0.00037297746,0.000568377,0.00011336999],"domain_scores_gemma":[0.9815221,0.013352384,0.0016405202,0.0008009856,0.0023214705,0.00036247057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065492815,0.0004510055,0.00040018282,0.0072453097,0.0004362622,0.0022069425,0.000691944,0.0006830589,0.0021789912],"category_scores_gemma":[0.02488307,0.0002736914,0.000667532,0.005106001,0.00060119614,0.0018969317,0.0010954129,0.00035293822,0.0007071677],"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":[0.0006460263,0.000113338276,0.74484473,0.0005686723,0.00031961928,0.00019742487,0.000499,0.007161065,0.0010637844,0.0020710137,0.0030380066,0.23947726],"study_design_scores_gemma":[0.00016355858,0.0017751654,0.8121532,0.00074985303,0.0007292483,0.0012694291,0.002369463,0.14880145,0.0044018724,0.007672811,0.019733394,0.0001805523],"about_ca_topic_score_codex":0.028969783,"about_ca_topic_score_gemma":0.022986744,"teacher_disagreement_score":0.028969783,"about_ca_system_score_codex":0.0015810528,"about_ca_system_score_gemma":0.0019325239,"threshold_uncertainty_score":0.057602346},"labels":[],"label_agreement":null},{"id":"W7162125570","doi":"10.82308/39352","title":"Land-use regression and spatio-temporal hierarchical models for environmental processes","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Homogeneous; Limiting; Statistical analysis","score_opus":0.020985003556593,"score_gpt":0.2932221394179353,"score_spread":0.2722371358613423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7162125570","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":[0.08895851,0.0015785907,0.90285254,0.00079025974,0.00008797083,0.00008028887,0.0019149736,0.00092732394,0.0028096773],"genre_scores_gemma":[0.8491317,0.0016946715,0.12874538,0.00015859508,0.00012558018,0.00037131822,0.0022281453,0.00022621299,0.017318266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989882,0.0005021894,0.000044547145,0.0002733266,0.00010223644,0.000089514215],"domain_scores_gemma":[0.99791294,0.0013914199,0.00026985406,0.00015753062,0.00021016496,0.000058044316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020453942,0.00079478684,0.0008895861,0.0011882077,0.00045056423,0.0012352308,0.0019306468,0.0010859278,0.003612011],"category_scores_gemma":[0.0046235607,0.0007368877,0.0020386232,0.0018808705,0.0007444293,0.0012689027,0.000924084,0.0013317595,0.00083409424],"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":[0.000053612726,0.000035998688,0.0071377945,0.00008182983,0.00020182971,0.00007236507,0.00010987427,0.9487107,0.0005990393,0.023584047,0.00056389504,0.018849066],"study_design_scores_gemma":[0.0000047762856,0.000007992467,0.001330116,0.0000055443884,0.000014982874,0.000009492131,0.0000135579385,0.9924402,0.00006192859,0.005442668,0.00066132605,0.0000074010704],"about_ca_topic_score_codex":0.10093794,"about_ca_topic_score_gemma":0.060515106,"teacher_disagreement_score":0.10093794,"about_ca_system_score_codex":0.0018082656,"about_ca_system_score_gemma":0.001473701,"threshold_uncertainty_score":0.20070082},"labels":[],"label_agreement":null}]}